From f41db1984d43b11bfca790de79f3fecf80cfb04f Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Tue, 18 Aug 2020 02:36:56 +0000 Subject: [PATCH] [Bot] Combine APIs and create typings --- data/api.json | 443361 ++++++++------- data/typing/numpy.distutils.misc_util.py | 6 - data/typing/numpy.lib.index_tricks.py | 129 +- data/typing/numpy.lib.stride_tricks.py | 28 +- data/typing/numpy.linalg.py | 64 +- data/typing/numpy.ma.core.py | 197 +- data/typing/numpy.ma.extras.py | 15 +- data/typing/numpy.matrixlib.defmatrix.py | 2 - data/typing/numpy.py | 52820 +- data/typing/numpy.random.mtrand.py | 754 +- data/typing/numpy.testing._private.utils.py | 4284 +- .../pandas.core.arrays.sparse.accessor.py | 6 - data/typing/pandas.core.base.py | 1 - data/typing/pandas.core.frame.py | 2965 +- data/typing/pandas.core.indexes.base.py | 56 +- data/typing/pandas.core.indexes.range.py | 21 - data/typing/pandas.core.indexing.py | 359 +- data/typing/pandas.core.reshape.concat.py | 14 +- data/typing/pandas.core.series.py | 116 +- data/typing/pandas.core.strings.py | 37 +- 20 files changed, 289728 insertions(+), 215507 deletions(-) diff --git a/data/api.json b/data/api.json index 16582a1..c7eff07 100644 --- a/data/api.json +++ b/data/api.json @@ -28,7 +28,8 @@ }, "metadata": { "usage.skimage": 4, - "usage.matplotlib": 1 + "usage.matplotlib": 1, + "usage.sklearn": 1 } }, { @@ -87,7 +88,8 @@ }, "metadata": { "usage.skimage": 2, - "usage.matplotlib": 1 + "usage.matplotlib": 1, + "usage.sklearn": 1 } }, { @@ -170,7 +172,8 @@ "usage.skimage": 23, "usage.xarray": 193, "usage.matplotlib": 88, - "usage.sample-usage": 1 + "usage.sample-usage": 1, + "usage.sklearn": 44 } }, { @@ -194,7 +197,8 @@ "metadata": { "usage.skimage": 17, "usage.xarray": 3, - "usage.matplotlib": 17 + "usage.matplotlib": 17, + "usage.sklearn": 9 } }, { @@ -219,7 +223,8 @@ }, "metadata": { "usage.skimage": 4, - "usage.matplotlib": 8 + "usage.matplotlib": 8, + "usage.sklearn": 4 } }, { @@ -298,7 +303,8 @@ }, "metadata": { "usage.skimage": 2, - "usage.matplotlib": 2 + "usage.matplotlib": 2, + "usage.sklearn": 1 } }, { @@ -322,7 +328,8 @@ "metadata": { "usage.skimage": 4, "usage.xarray": 21, - "usage.matplotlib": 51 + "usage.matplotlib": 51, + "usage.sklearn": 27 } }, { @@ -350,7 +357,8 @@ }, "metadata": { "usage.skimage": 5, - "usage.xarray": 1 + "usage.xarray": 1, + "usage.sklearn": 1 } }, { @@ -378,7 +386,8 @@ }, "metadata": { "usage.skimage": 1, - "usage.matplotlib": 3 + "usage.matplotlib": 3, + "usage.sklearn": 2 } }, { @@ -401,7 +410,8 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 1, + "usage.sklearn": 1 } }, { @@ -841,85 +851,51 @@ { "pos_or_kw_required": { "start": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] + "type": { + "module": "numpy", + "name": "float64" + } }, "stop": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] + "type": { + "module": "numpy", + "name": "float64" + } }, "num": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "numpy", + "name": "int64" + } } }, - "pos_or_kw_optional": { - "endpoint": { + "metadata": { + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "start": { "type": { - "name": "bool" + "module": "numpy", + "name": "int64" + } + }, + "stop": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "num": { + "type": { + "module": "numpy", + "name": "int64" } } }, "metadata": { - "usage.sklearn": 95 + "usage.sklearn": 2 } } ], 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"usage.matplotlib": 23, + "usage.sklearn": 72 } }, { @@ -1149,7 +1132,8 @@ "usage.skimage": 101, "usage.xarray": 79, "usage.matplotlib": 41, - "usage.sample-usage": 1 + "usage.sample-usage": 1, + "usage.sklearn": 427 } }, { @@ -1178,7 +1162,8 @@ "metadata": { "usage.skimage": 12, "usage.xarray": 1, - "usage.matplotlib": 1 + "usage.matplotlib": 1, + "usage.sklearn": 2 } }, { @@ -1289,7 +1274,8 @@ }, "metadata": { "usage.skimage": 1, - "usage.matplotlib": 2 + "usage.matplotlib": 2, + "usage.sklearn": 2 } }, { @@ -1370,7 +1356,8 @@ "metadata": { "usage.skimage": 31, "usage.xarray": 9, - "usage.matplotlib": 33 + "usage.matplotlib": 33, + "usage.sklearn": 183 } }, { @@ -1540,7 +1527,8 @@ "metadata": { "usage.skimage": 153, "usage.xarray": 14, - "usage.matplotlib": 27 + "usage.matplotlib": 27, + "usage.sklearn": 422 } }, { @@ -1558,7 +1546,8 @@ "metadata": { "usage.skimage": 12, "usage.xarray": 8, - "usage.matplotlib": 32 + "usage.matplotlib": 32, + "usage.sklearn": 61 } }, { @@ -1582,7 +1571,8 @@ "metadata": { "usage.skimage": 31, "usage.xarray": 1, - "usage.matplotlib": 2 + "usage.matplotlib": 2, + "usage.sklearn": 7 } }, { @@ -1600,7 +1590,8 @@ "metadata": { "usage.skimage": 10, "usage.xarray": 4, - "usage.matplotlib": 3 + "usage.matplotlib": 3, + "usage.sklearn": 17 } }, { @@ -1622,7 +1613,8 @@ }, "metadata": { "usage.skimage": 7, - "usage.xarray": 1 + "usage.xarray": 1, + "usage.sklearn": 3 } }, { @@ -1644,7 +1636,8 @@ }, "metadata": { "usage.skimage": 1, - "usage.matplotlib": 2 + "usage.matplotlib": 2, + "usage.sklearn": 3 } }, { @@ -1725,7 +1718,8 @@ }, "metadata": { "usage.skimage": 8, - "usage.matplotlib": 3 + "usage.matplotlib": 3, + "usage.sklearn": 2 } }, { @@ -1770,7 +1764,8 @@ } }, "metadata": { - "usage.skimage": 6 + "usage.skimage": 6, + "usage.sklearn": 1 } }, { @@ -1795,7 +1790,8 @@ }, "metadata": { "usage.skimage": 2, - "usage.matplotlib": 1 + "usage.matplotlib": 1, + "usage.sklearn": 3 } }, { @@ -1859,7 +1855,8 @@ "metadata": { "usage.skimage": 11, "usage.xarray": 1, - "usage.matplotlib": 8 + "usage.matplotlib": 8, + "usage.sklearn": 13 } }, { @@ -1884,7 +1881,8 @@ } }, "metadata": { - "usage.skimage": 6 + "usage.skimage": 6, + "usage.sklearn": 8 } }, { @@ -1911,7 +1909,8 @@ "metadata": { "usage.skimage": 9, "usage.xarray": 3, - "usage.matplotlib": 14 + "usage.matplotlib": 14, + "usage.sklearn": 21 } }, { @@ -1992,7 +1991,8 @@ } }, "metadata": { - "usage.skimage": 11 + "usage.skimage": 11, + "usage.sklearn": 1 } }, { @@ -2022,7 +2022,8 @@ }, "metadata": { "usage.skimage": 1, - "usage.matplotlib": 1 + "usage.matplotlib": 1, + "usage.sklearn": 4 } }, { @@ -2081,7 +2082,8 @@ }, "metadata": { "usage.skimage": 3, - "usage.matplotlib": 2 + "usage.matplotlib": 2, + "usage.sklearn": 1 } }, { @@ -2211,7 +2213,8 @@ "metadata": { "usage.skimage": 11, "usage.xarray": 4, - "usage.matplotlib": 2 + "usage.matplotlib": 2, + "usage.sklearn": 5 } }, { @@ -2350,7 +2353,8 @@ } }, "metadata": { - "usage.skimage": 3 + "usage.skimage": 3, + 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"pos_only_optional": { - "_1": { - "type": "type" + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "kw_only_optional": { + "kw_only_required": { "dtype": { - "type": "union", - "options": [ - { - "type": "type" - }, - { - "type": { - "module": "numpy", - "name": "dtype" - } - }, - { - "type": "None" - }, - { - "type": "str" - } - ] + "type": "None" }, "order": { - "type": "union", + "type": "str", "options": [ - { - "type": "str", - "options": [ - "C", - "F" - ] - }, - { - "type": "None" - } + "C" ] - }, - "copy": { - "type": { - "name": "bool" - } - }, - "ndmin": { - "type": { - "name": "int" - } } }, "metadata": { - "usage.sklearn": 2254 + "usage.sklearn": 3 } - } - ], - "asarray": [ + }, { - "pos_or_kw_required": { - "a": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, + "kw_only_required": { + "dtype": { + "type": "None" + }, + "order": { + "type": "None" + } + }, "metadata": { - "usage.skimage": 12, - "usage.xarray": 42, - "usage.matplotlib": 42 + "usage.sklearn": 9 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": "list", "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "name": "int" + } } } }, - "metadata": { - "usage.skimage": 2, - "usage.xarray": 11, - "usage.matplotlib": 12 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { + "kw_only_required": { + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.skimage": 43, - "usage.xarray": 229, - "usage.matplotlib": 96 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "name": "float" + "name": "int64" } + }, + "order": { + "type": "str", + "options": [ + "C" + ] } }, "metadata": { - "usage.skimage": 2, - "usage.xarray": 5, - "usage.matplotlib": 8 + "usage.sklearn": 14 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": "list", "item": { - "type": { - "name": "float" - } + "type": "bottom" } } }, - "metadata": { - "usage.skimage": 6, - "usage.xarray": 17, - "usage.matplotlib": 30 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "tuple", - "items": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - } + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "int64" + } + }, + "order": { + "type": "str", + "options": [ + "C" ] } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 2, - "usage.matplotlib": 2 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": "tuple", - "items": [ - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "tuple", + 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} }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": { "module": "numpy", "name": "ndarray" } - }, + } + }, + "kw_only_required": { "dtype": { "type": "type", "name": { "module": "numpy", - "name": "float64" + "name": "float32" } + }, + "order": { + "type": "str", + "options": [ + "C" + ] } }, "metadata": { - "usage.skimage": 2, - "usage.xarray": 1, - "usage.matplotlib": 13 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.skimage": 3, - "usage.xarray": 6, - "usage.matplotlib": 4 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "tuple", - "items": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - } - ] - }, + "kw_only_required": { "dtype": { "type": { "module": "numpy", @@ -18652,282 +18611,285 @@ } }, "metadata": { - 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"name": "int" - } - } - ] + "kw_only_required": { + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } } }, "metadata": { - "usage.skimage": 16, - "usage.matplotlib": 6 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "a", + "b" + ] + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "bool" + } + } + ] } - ] + } } }, - "metadata": { - "usage.skimage": 14, - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "tuple", - "items": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } + "kw_only_required": { + "dtype": { + "type": "str", + "options": [ + "O" ] } }, "metadata": 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"type": "list", "item": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - } - ] + "type": { + "name": "float" + } } - }, + } + }, + "kw_only_required": { "dtype": { "type": "type", "name": { @@ -18936,171 +18898,144 @@ } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 3 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": "list", "item": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "two", + "one" + ] } - }, + } + }, + "kw_only_required": { "dtype": { - "type": "type", - "name": { - "name": "float" + "type": { + "module": "numpy", + "name": "dtype" } } }, "metadata": { - "usage.skimage": 4, - "usage.matplotlib": 5 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": "list", "item": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } + "middle", + "low", + "high" ] } - }, + } + }, + "kw_only_required": { "dtype": { - "type": "type", - "name": { - "name": "float" + "type": { + "module": "numpy", + "name": "dtype" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": "list", "item": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } + "type": "str", + "options": [ + "z" + ] + } + } + }, + "kw_only_required": { + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" } } }, "metadata": { - "usage.skimage": 7, - "usage.xarray": 21, - "usage.matplotlib": 5 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "str", + "options": [ + "x1_z", + "x0_b", + "x0_a" + ] + } + } + }, + "kw_only_required": { "dtype": { "type": "type", "name": { - "name": "float" + "name": "object" } } }, "metadata": { - "usage.skimage": 4, - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": "tuple", - "items": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "list", + "item": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } } - ] - }, - "dtype": { - "type": "type", - "name": { - "name": "float" } } }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": "tuple", "items": [ { "type": { - "module": "numpy", - "name": "float64" + "name": "float" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "float" } - } - ] - } - }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "tuple", - "items": [ + }, { "type": { "name": "float" @@ -19117,3035 +19052,3757 @@ } } ] - }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" - } } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 2 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float32" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + } + ] } } }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 1 + "usage.sklearn": 2 } }, { - "pos_or_kw_required": { - "a": { - "type": "list", + "pos_only_required": { + "_0": { + "type": "list", "item": { - "type": "list", - "item": { - "type": { - "name": "int" + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + } + ] } - } + ] } - }, + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "kw_only_required": { "dtype": { "type": "type", "name": { "module": "numpy", - "name": "uint8" + "name": "float64" } + }, + "order": { + "type": "None" } }, "metadata": { - "usage.skimage": 1 + 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+ "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "pandas.core.frame", + "name": "DataFrame" } }, - "axis": { - "type": { - "name": "int" - } + "dtype": { + "type": "None" }, - "out": { + "order": { "type": "None" } }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": "object" + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + } }, - "axis": { - "type": { - "name": "int" + "dtype": { + "type": "type", + "name": { + "name": "object" } + }, + "order": { + "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - }, - "out": { - "type": "union", - "options": [ - { - "type": { - "name": "object" - } - }, - { - "type": { - "name": "bool" - } - } - ] - }, - "keepdims": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.pandas": 46 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "axis": { "type": { - "name": "int" + "module": "pandas.core.series", + "name": "Series" } + }, + "dtype": { + "type": "None" + }, + "order": { + "type": "None" } }, "metadata": { - "usage.scipy": 206 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": { - "name": "bool" + "type": "list", + "item": { + "type": { + "name": "float" + } } + }, + "dtype": { + "type": "None" + }, + "order": { + "type": "None" } }, "metadata": { - "usage.matplotlib": 3 + "usage.sklearn": 6 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_only_optional": { - "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -41607,33 +40336,26 @@ }, { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "numpy", + "name": "ndarray" } - } - ] - } - }, - "pos_or_kw_optional": { - "a": { - "type": "union", - "options": [ + }, { "type": { - "module": "pandas.core.series", - "name": "Series" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "numpy", + "name": "ndarray" } }, { "type": { "module": "numpy", - "name": "bool_" + "name": "ndarray" } }, { @@ -41641,88 +40363,11 @@ "module": "numpy", "name": "ndarray" } - } - ] - }, - "axis": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - } }, { "type": { - "name": "int" - } - } - ] - }, - "keepdims": { - "type": { - "name": "bool" - } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "a", - "keepdims" - ], - [ - "axis", - "keepdims" - ], - [ - "a", - "axis" - ] - ], - "kw_only_optional": { - "computing_meta": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.dask": 81 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "bool" - } - }, - { - "type": { - "module": "numpy", - "name": "bool_" - } - } - ] + "module": "numpy", + "name": "ndarray" } }, { @@ -41734,54 +40379,50 @@ { "type": { "module": "numpy", - "name": "bool_" + "name": "ndarray" } }, { "type": { - "name": "bool" + "module": "numpy", + "name": "ndarray" } } ] } }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" - } - } - }, "metadata": { - "usage.sklearn": 83 + "usage.sklearn": 5 } - } - ], - "empty_like": [ + }, { - "pos_only_required": { - "_0": { - "type": { + "pos_or_kw_required": { + "a": { + "type": "list", + "item": { + "type": "str" + } + }, + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "ndarray" + "name": "float64" } } }, "metadata": { - "usage.skimage": 33, - "usage.matplotlib": 10 + "usage.sklearn": 1253 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "kw_only_required": { + "pos_or_kw_required": { + "a": { + "type": "str", + "options": [ + "24" + ] + }, "dtype": { "type": "type", "name": { @@ -41791,149 +40432,138 @@ } }, "metadata": { - "usage.skimage": 3 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "kw_only_required": { + "pos_or_kw_required": { + "a": { + "type": "str", + "options": [ + "21.6" + ] + }, "dtype": { "type": "type", "name": { "module": "numpy", - "name": "uint16" + "name": "float64" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "kw_only_required": { + "pos_or_kw_required": { + "a": { + "type": "str", + "options": [ + "34.7" + ] + }, "dtype": { "type": "type", "name": { - "name": "float" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "kw_only_required": { + "pos_or_kw_required": { + "a": { + "type": "str", + "options": [ + "33.4" + ] + }, "dtype": { "type": "type", "name": { "module": "numpy", - "name": "uint8" + "name": "float64" } } }, "metadata": { - "usage.skimage": 4 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "kw_only_required": { + "pos_or_kw_required": { + "a": { + "type": "str", + "options": [ + "36.2" + ] + }, "dtype": { "type": "type", "name": { "module": "numpy", "name": "float64" } - }, - "order": { - "type": "str", - "options": [ - "C" - ] - }, - "subok": { - "type": { - "name": "bool" - } } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - } - }, - "kw_only_required": { + "pos_or_kw_required": { + "a": { + "type": "str", + "options": [ + "28.7" + ] + }, "dtype": { "type": "type", "name": { "module": "numpy", "name": "float64" } - }, - "order": { + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { "type": "str", "options": [ - "C" + "22.9" ] }, - "subok": { - "type": { - "name": "bool" + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "pos_or_kw_required": { + "a": { + "type": "str", + "options": [ + "27.1" + ] }, - "_1": { + "dtype": { "type": "type", "name": { "module": "numpy", @@ -41942,470 +40572,335 @@ } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" + "pos_or_kw_required": { + "a": { + "type": "str", + "options": [ + "16.5" + ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "xarray.core.variable", - "name": "IndexVariable" + "pos_or_kw_required": { + "a": { + "type": "str", + "options": [ + "18.9" + ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": "union", + "pos_or_kw_required": { + "a": { + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "pandas.core.arrays.string_", - "name": "StringArray" - } - }, - { - "type": "list", - "item": { - "type": "None" - } - } + "15" ] - } - }, - "kw_only_optional": { + }, "dtype": { - "type": "union", - "options": [ - { - "type": "type" - }, - { - "type": "str", - "options": [ - "float", - "f8", - "i8", - "object" - ] - }, - { - "type": { - "module": "numpy", - "name": "dtype" - } - } - ] + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.pandas": 18 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": { + "pos_or_kw_required": { + "a": { + "type": "str", + "options": [ + "21.7" + ] + }, + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "ndarray" + "name": "float64" } } }, - "kw_only_optional": { - "dtype": { - "type": "type" - } - }, "metadata": { - "usage.scipy": 103 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "kw_only_optional": { - "dtype": { - "type": { - "module": "numpy", - "name": "dtype" - } - }, - "shape": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - }, - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - }, - "order": { + "pos_or_kw_required": { + "a": { "type": "str", "options": [ - "F", - "C" + "20.4" ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.dask": 12 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "kw_only_optional": { - "dtype": { - "type": "union", + "pos_or_kw_required": { + "a": { + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "dtype" - } - }, - { - "type": "type", - "name": { - "module": "numpy", - "name": "float32" - } - } + "18.2" ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.sklearn": 26 + "usage.sklearn": 1 } - } - ], - "ones": [ + }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "19.9" ] }, "dtype": { "type": "type", "name": { "module": "numpy", - "name": "bool_" + "name": "float64" } } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "23.1" ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.skimage": 76, - "usage.xarray": 21, - "usage.matplotlib": 4, - "usage.sample-usage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "17.5" ] }, "dtype": { "type": "type", "name": { - "name": "float" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 3, - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "a": { + "type": "str", + "options": [ + "20.2" + ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 3, - "usage.xarray": 8 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "13.6" ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.skimage": 17, - "usage.xarray": 6 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "19.6" ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.skimage": 8, - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "15.2" ] }, "dtype": { "type": "type", "name": { "module": "numpy", - "name": "uint8" + "name": "float64" } } }, "metadata": { - "usage.skimage": 41, - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": { - "name": "int" + "a": { + "type": "str", + "options": [ + "14.5" + ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 19, - "usage.xarray": 21, - "usage.matplotlib": 40 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "15.6" ] }, "dtype": { "type": "type", "name": { "module": "numpy", - "name": "float32" + "name": "float64" } } }, "metadata": { - "usage.skimage": 3 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "13.9" ] }, "dtype": { "type": "type", "name": { "module": "numpy", - "name": "int8" + "name": "float64" } } }, "metadata": { - "usage.skimage": 6 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "16.6" ] }, "dtype": { @@ -42417,187 +40912,156 @@ } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": { - "name": "int" - } + "a": { + "type": "str", + "options": [ + "14.8" + ] }, "dtype": { "type": "type", "name": { "module": "numpy", - "name": "int64" + "name": "float64" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "18.4" ] }, "dtype": { "type": "type", "name": { - "name": "bool" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 29, - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } + "a": { + "type": "str", + "options": [ + "21" ] }, "dtype": { "type": "type", "name": { "module": "numpy", - "name": "uint8" + "name": "float64" } } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } + "a": { + "type": "str", + "options": [ + "12.7" ] }, "dtype": { "type": "type", "name": { "module": "numpy", - "name": "uint16" + "name": "float64" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "13.2" ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.skimage": 5, - "usage.xarray": 4, - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": { - "name": "int" - } + "a": { + "type": "str", + "options": [ + "13.1" + ] }, "dtype": { - "type": { + "type": "type", + "name": { "module": "numpy", - "name": "dtype" + "name": "float64" } } }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 6 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "13.5" ] }, "dtype": { "type": "type", "name": { - "name": "float" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": { - "name": "int" - } + "a": { + "type": "str", + "options": [ + "20" + ] }, "dtype": { "type": "type", @@ -42608,370 +41072,235 @@ } }, "metadata": { - "usage.skimage": 1, - "usage.matplotlib": 4 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "24.7" ] }, "dtype": { "type": "type", "name": { "module": "numpy", - "name": "uint8" + "name": "float64" } } }, "metadata": { - "usage.skimage": 3 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "30.8" ] }, "dtype": { "type": "type", "name": { - "name": "bool" + "module": "numpy", + "name": "float64" } - }, - "order": { - "type": "str", - "options": [ - "F" - ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "34.9" ] }, "dtype": { "type": "type", "name": { - "name": "bool" + "module": "numpy", + "name": "float64" } - }, - "order": { - "type": "str", - "options": [ - "F" - ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "a": { + "type": "str", + "options": [ + "26.6" ] }, "dtype": { "type": "type", "name": { - "name": "bool" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 5 + "usage.sklearn": 1 } }, { 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}, "dtype": { @@ -42983,5214 +41312,3951 @@ } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": { + "a": { + "type": "str", + "options": [ + "23.4" + ] + }, + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "ndarray" + "name": "float64" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": { - "name": "int" - } + "a": { + "type": "str", + "options": [ + "35.4" + ] }, "dtype": { "type": "type", "name": { - "name": "bool" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - "type": { + "a": { + "type": "str", + "options": [ + "31.6" + ] + }, + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "int64" + "name": "float64" } } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "shape": { - 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12 } }, { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] - } - }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] - } - } - ] + "pos_or_kw_required": { + "a": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "name": "float" } } - ] - } - }, - "pos_only_optional": { - "_1": { - "type": { - "name": "int" } - } - }, - "kw_only_optional": { - "axis": { - "type": { - "name": "int" + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } + }, + "order": { + "type": "None" } }, "metadata": { - "usage.sklearn": 126 + "usage.sklearn": 10 } - } - ], - "ones_like": [ + }, { "pos_or_kw_required": { "a": { @@ -48496,11 +45443,19 @@ "module": "numpy", "name": "ndarray" } + }, + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } + }, + "order": { + "type": "None" } }, "metadata": { - "usage.skimage": 17, - "usage.matplotlib": 8 + "usage.sklearn": 8 } }, { @@ -48514,20 +45469,23 @@ "dtype": { "type": "type", "name": { - "name": "bool" + "module": "numpy", + "name": "uint32" } } }, "metadata": { - "usage.skimage": 3 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "list", + "item": { + "type": { + "name": "bool" + } } }, "dtype": { @@ -48539,56 +45497,78 @@ } }, "metadata": { - "usage.skimage": 6 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "object" - } - }, - "metadata": { - "usage.xarray": 11 + "usage.sklearn": 4 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" + "type": "list", + "item": { + "type": { + "module": "importlib._bootstrap", + "name": "MonotonicConstraint" + } + } + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "int8" } } }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "int8" + } + } + }, + "metadata": { + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { "type": { - "module": "xarray.core.variable", - "name": "Variable" + "module": "sklearn.ensemble._hist_gradient_boosting.histogram", + "name": "_memoryviewslice" + } + }, + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" } } }, "metadata": { - "usage.xarray": 4 + "usage.sklearn": 3 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -48596,48 +45576,24 @@ } } ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "int32" + } } }, "metadata": { - "usage.pandas": 6 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] - } - }, - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - }, - { - "type": { - "name": "float" - } - }, + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -48647,21 +45603,45 @@ ] } }, - "pos_or_kw_optional": { + "metadata": { + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "list", + "item": { + "type": "str", + "options": [ + "A", + "C", + "B" + ] + } + }, "dtype": { - "type": "type" + "type": "None" + }, + "order": { + "type": "None" } }, "metadata": { - "usage.scipy": 62 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } } }, "dtype": { @@ -48670,1228 +45650,1114 @@ "module": "numpy", "name": "float32" } + }, + "order": { + "type": "None" } }, "metadata": { - "usage.matplotlib": 6 + "usage.sklearn": 14 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "type": "list", + "item": { + "type": "list", + "item": { + "type": "str", + "options": [ + "blue", + "red", + "green", + "purple", + "yellow" + ] + } } + }, + "dtype": { + "type": "None" + }, + "order": { + "type": "None" } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ - { - 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"type": "type", + "name": { + "name": "bool" + } + }, + "order": { + "type": "None" } }, "metadata": { - "usage.skimage": 42, - "usage.dask": 4 + "usage.sklearn": 3 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "uint8" + "type": "list", + "item": { + "type": "str", + "options": [ + "c", + "a", + "b" + ] } } }, "metadata": { - "usage.skimage": 6 + "usage.sklearn": 2 } }, { @@ -51410,21 +47836,23 @@ "a": { "type": "list", "item": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "c", + "a", + "b" + ] } }, "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" - } + "type": "None" + }, + "order": { + "type": "None" } }, "metadata": { - "usage.skimage": 6 + "usage.sklearn": 1 } }, { @@ -51432,31 +47860,23 @@ "a": { "type": "list", "item": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } + "c", + "b", + "a" ] } }, "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" - } + "type": "None" + }, + "order": { + "type": "None" } }, "metadata": { - "usage.skimage": 4 + "usage.sklearn": 3 } }, { @@ -51464,80 +47884,95 @@ "a": { "type": "list", "item": { - "type": { - "name": "float" - } + "type": "str", + "options": [ + "0", + "1" + ] } }, "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" - } + "type": "None" + }, + "order": { + "type": "None" } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "range" } }, "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" - } + "type": "None" + }, + "order": { + "type": "None" } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "list", + "item": { + "type": "str", + "options": [ + "3", + "2", + "1" + ] } }, "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float32" - } + "type": "None" + }, + "order": { + "type": "None" } }, "metadata": { - "usage.skimage": 3 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "dtype": { - "type": { - "module": "numpy", - "name": "dtype" + "type": "list", + "item": { + "type": "str", + "options": [ + "3", + "2", + "1" + ] } } }, "metadata": { - "usage.skimage": 4 + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "str", + "options": [ + "wrong_type" + ] + } + }, + "metadata": { + "usage.sklearn": 1 } }, { @@ -51551,33 +47986,38 @@ "dtype": { "type": "type", "name": { - "module": "numpy", - "name": "int64" + "name": "object" } } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 3 } }, { 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} }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "name": "float" + } + } + } + }, + "dtype": { + "type": "type", + "name": { + "name": "float" } + }, + "order": { + "type": "None" } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 27, - "usage.matplotlib": 22, - "usage.dask": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { - "type": "tuple", - "items": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { + "type": "list", + "item": { + "type": "list", + "item": { "type": { - "module": "numpy", - "name": "float64" + "name": "float" } } + } + }, + "dtype": { + "type": "None" + }, + "order": { + "type": "str", + "options": [ + "F" ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { "type": { - "name": "float" + "module": "sklearn.utils.estimator_checks", + "name": "_NotAnArray" } + }, + "dtype": { + "type": "None" + }, + "order": { + "type": "str", + "options": [ + "F" + ] } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { "type": { + "module": "pandas.core.series", + "name": "Series" + } + }, + "dtype": { + "type": "type", + "name": { "module": "numpy", "name": "float64" } } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" - } - } - } - }, - "metadata": { - "usage.skimage": 1, - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { "type": { + "module": "sklearn.utils.estimator_checks", + "name": "_NotAnArray" + } + }, + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "matrix" + "name": "float64" } } }, "metadata": { - 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"usage.xarray": 1 + "usage.sklearn": 5 } }, { "pos_or_kw_required": { "a": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "dtype": { "type": { "module": "numpy", - "name": "int8" + "name": "dtype" } + }, + "order": { + "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "int64" + } + } + }, + "dtype": { "type": { "module": "numpy", - "name": "int16" + "name": "dtype" } + }, + "order": { + "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": { - "name": "bytes" + "type": "list", + "item": { + "type": "str", + "options": [ + "ham", + "eggs", + "spam" + ] } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 3 } }, { "pos_or_kw_required": { "a": { - "type": { - 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} } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" + "type": "list", + "item": { + "type": "list", + "item": { + "type": "str", + "options": [ + "d", + "a" + ] + } } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "module": "pandas.core.frame", - "name": "DataFrame" + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": "str", + "options": [ + "1" + ] } - } - ] + ] + } } }, "metadata": { - "usage.pandas": 2 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "matrix" - } - }, - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "float" - } - } - } - ] - } - }, - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, + "type": "list", + "item": { + "type": "list", + "item": { + "type": "union", + "options": [ { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "1" + ] }, { "type": { @@ -53230,11 +50170,11 @@ } ] } - ] + } } }, "metadata": { - "usage.scipy": 162 + "usage.sklearn": 2 } }, { @@ -53242,22 +50182,29 @@ "a": { "type": "list", "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "2", + "1" + ] + }, + { + "type": { + "name": "int" + } } - }, - { - "type": "None" - } - ] + ] + } } } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 2 } }, { @@ -53265,14 +50212,16 @@ "a": { "type": "list", "item": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "spam", + "egg" + ] } } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { @@ -53280,413 +50229,458 @@ "a": { "type": "list", "item": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } + "type": "str", + "options": [ + "col_2", + "col_1" + ] } } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "str", - "options": [ - "spam", - "egg" - ] - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - } - ] - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, + "type": "tuple", + "items": [ { "type": { - "name": "float" + "name": "bool" } }, { "type": { - "module": "numpy", - "name": "matrix" + "name": "bool" } }, { "type": { - "name": "int" + "name": "bool" } } ] } }, - "pos_or_kw_optional": { - "order": { - "type": "str", - "options": [ - "K" - ] - } - }, "metadata": { - "usage.sklearn": 80 + "usage.sklearn": 1 } - } - ], - "min_scalar_type": [ + }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "a": { "type": { - "name": "int" + "module": "numpy", + "name": "matrix" + } + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } + }, + "order": { + "type": "None" } }, "metadata": { - "usage.skimage": 3 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "a": { "type": { "module": "numpy", - "name": "int64" + "name": "ndarray" + } + }, + "dtype": { + "type": "type", + "name": { + "name": "float" } + }, + "order": { + "type": "str", + "options": [ + "C" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 } }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "a": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" + } + }, + "dtype": { + "type": "type", + "name": { + "name": "float" } + }, + "order": { + "type": "str", + "options": [ + "F" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 } }, { - "pos_only_required": { - "_0": { - "type": "object" + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "dtype": { + "type": "type", + "name": { + "name": "bool" + } + }, + "order": { + "type": "str", + "options": [ + "C" + ] } }, "metadata": { - "usage.pandas": 10, - "usage.matplotlib": 1 + "usage.sklearn": 2 } }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "a": { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "numpy", + "name": "ndarray" } + }, + "dtype": { + "type": "type", + "name": { + "name": "bool" + } + }, + "order": { + "type": "str", + "options": [ + "F" + ] } }, "metadata": { - "usage.matplotlib": 3 + "usage.sklearn": 2 } }, { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "int64" - } + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "dtype": { + "type": "type", + "name": { + "name": "object" } + }, + "order": { + "type": "str", + "options": [ + "C" + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 2 } }, { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "bool_" - } - } - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" - } + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" } - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": { - "name": "float" - } + }, + "dtype": { + "type": "type", + "name": { + "name": "object" } + }, + "order": { + "type": "str", + "options": [ + "F" + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 2 } }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "a": { "type": { "module": "numpy", "name": "ndarray" } + }, + "dtype": { + "type": "type", + "name": { + "name": "object" + } + }, + "order": { + "type": "None" } }, "metadata": { - "usage.matplotlib": 5 + "usage.sklearn": 2 } }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "a": { "type": "list", "item": { - "type": "union", - "options": [ - { + "type": "list", + "item": { + "type": "list", + "item": { "type": { "name": "int" } - }, - { - "type": { - "name": "float" - } } - ] - } - } - }, - "metadata": { - "usage.matplotlib": 2 - } - }, - { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": { - "name": "int" } } + }, + "dtype": { + "type": "None" + }, + "order": { + "type": "None" } }, "metadata": { - "usage.matplotlib": 4 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "a": { "type": "list", "item": { - "type": { - "module": "numpy", - "name": "float128" + "type": "list", + "item": { + "type": "str", + "options": [ + "12", + "11", + "xx", + "13" + ] } } + }, + "dtype": { + "type": "None" + }, + "order": { + "type": "None" } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "a": { "type": "list", "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } + "type": "list", + "item": { + "type": { + "name": "bytes" } - ] + } } + }, + "dtype": { + "type": "None" + }, + "order": { + "type": "None" } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": "None" + "pos_or_kw_required": { + "a": { + "type": { + "module": "sklearn.utils._mocking", + "name": "MockDataFrame" } - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float32" } + }, + "order": { + "type": "None" } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "a": { "type": "list", "item": { - "type": { - "module": "numpy", - "name": "uint16" + "type": "list", + "item": { + "type": { + "name": "int" + } } } + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "int64" + } } }, "metadata": { - "usage.matplotlib": 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"type": "None" } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "name": "float" - } + "pos_or_kw_required": { + "a": { + "type": "object" }, - "_1": { - "type": { - "name": "int" - } + "axis": { + "type": "None" }, - "_2": { - "type": { - "name": "int" - } - } - }, - "kw_only_required": { "dtype": { - "type": "type", - "name": { - "name": "int" - } + "type": "None" } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "int" - } + "pos_or_kw_required": { + "a": { + "type": "object" }, - "_2": { + "axis": { "type": "None" } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "int" - } - } - }, - "kw_only_required": { - "dtype": { - "type": "str", - "options": [ - ">i2" - ] + "pos_or_kw_required": { + "a": { + "type": "object" } }, "metadata": { @@ -54678,74 +51417,45 @@ } }, { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } + "pos_or_kw_required": { + "a": { + "type": "object" }, - "_1": { + "axis": { "type": { "name": "int" } } }, - "kw_only_required": { - "dtype": { - "type": "str", - "options": [ - "i8" + { + "type": { + "module": "numpy", + "name": "dtype" + } + }, + { + "type": "str", + "options": [ + "f8", + "i8", + "i4", + "f4", + "u4" + ] + } + ] + }, + "out": { + "type": "union", + "options": [ + { + "type": { + "module": "dask.dataframe.core", + "name": "Scalar" + } + }, + { + "type": { + "module": "dask.array.core", + "name": "Array" + } + }, + { + "type": { + "module": "dask.dataframe.core", + "name": "Series" + } + } ] } }, + "pos_or_kw_optional_ordering": [ + [ + "axis", + "keepdims" + ], + [ + "axis", + "out" + ], + [ + "axis", + "dtype" + ], + [ + "dtype", + "keepdims" + ] + ], "metadata": { - "usage.xarray": 1 + "usage.dask": 216 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "int64" - } - }, - "_1": { + "pos_or_kw_required": { + "a": { "type": { - "module": "numpy", - "name": "int64" + "name": "float" } - }, - "_2": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "name": "float" - } - }, - "_1": { + "pos_or_kw_required": { + "a": { "type": { - "name": "float" + "module": "numpy", + "name": "float64" } } }, - "kw_only_required": { - "dtype": { - "type": "str", - "options": [ - "float64" - ] - } - }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 2 } }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "a": { "type": { "name": "int" } } }, - "kw_only_required": { - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "int32" - } - } - }, "metadata": { - "usage.xarray": 1, - "usage.matplotlib": 15 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "a": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, - "_1": { - "type": { - "name": "int" - } - } - }, - "kw_only_required": { + "axis": { + "type": "None" + }, "dtype": { "type": "type", "name": { - "name": "int" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.xarray": 1, - "usage.matplotlib": 1 + "usage.sklearn": 3 } }, { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "kw_only_required": { - "dtype": { - "type": "str", - "options": [ - "uint8" + "pos_or_kw_required": { + "a": { + "type": "tuple", + "items": [ + { + "type": { + "name": "float" + } + } ] } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "a": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } - } - }, - "kw_only_required": { + }, "dtype": { - "type": "str", - "options": [ - "i8" - ] + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 2 } }, { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, + "pos_or_kw_required": { + "a": { + "type": "tuple", + "items": [ { "type": { "name": "float" } }, - { - "type": "str", - "options": [ - "2019-01-01" - ] - } - ] - } - }, - "pos_only_optional": { - "_1": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, { "type": { - "module": "numpy", - "name": "int64" + "name": "float" } }, { "type": { - "module": "numpy", - "name": "uint64" + "name": "float" } }, { @@ -55196,26 +51929,14 @@ "name": "float" } }, - { - "type": "str", - "options": [ - "2019-01-06" - ] - } - ] - }, - "_2": { - "type": "union", - "options": [ { "type": { - "name": "int" + "name": "float" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "float" } }, { @@ -55223,42 +51944,14 @@ "name": "float" } }, - { - "type": "None" - } - ] - } - }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ] - ], - "kw_only_optional": { - "dtype": { - "type": "union", - "options": [ - { - "type": "type" - }, { "type": { - "module": "numpy", - "name": "dtype" + "name": "float" } }, - { - "type": "str" - } - ] - }, - "step": { - "type": "union", - "options": [ { "type": { - "name": "int" + "name": "float" } }, { @@ -55270,28 +51963,22 @@ } }, "metadata": { - "usage.pandas": 894 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "pos_only_optional": { - "_1": { - "type": "union", - "options": [ + "pos_or_kw_required": { + "a": { + "type": "tuple", + "items": [ { "type": { - "name": "int" + "name": "float" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "float" } }, { @@ -55301,18 +51988,12 @@ }, { "type": { - "module": "numpy", - "name": "int64" + "name": "float" } - } - ] - }, - "_2": { - "type": "union", - "options": [ + }, { "type": { - "name": "int" + "name": "float" } }, { @@ -55323,562 +52004,508 @@ ] } }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ] - ], - "kw_only_optional": { - "dtype": { - "type": "union", - "options": [ + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "tuple", + "items": [ { - "type": "type" + "type": { + "name": "float" + } }, { - "type": "str", - "options": [ - "int32", - "float64", - ">f4", - "f8" + ] + }, + { + "type": "type" } ] - } - }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" - } }, - "out": { - "type": { - "module": "numpy", - "name": "ndarray" + "shape": { + "type": "tuple", + "items": { + "type": { + "name": "int" + } } + }, + "order": { + "type": "str", + "options": [ + "F", + "C" + ] } }, "metadata": { - "usage.scipy": 30 + "usage.dask": 52 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, + "kw_only_required": { + "dtype": { "type": { "module": "numpy", - "name": "flatiter" + "name": "dtype" } } }, "metadata": { - "usage.matplotlib": 4 + "usage.sklearn": 44 } }, { - "pos_or_kw_required": { - "a": { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] + "pos_only_required": { + "_0": { + "type": { + "name": "int" } } }, + "kw_only_required": { + "order": { + "type": "str", + "options": [ + "C" + ] + } + }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" } - ] - } + }, + { + "type": { + "name": "int" + } + } + ] } }, - "metadata": { - "usage.matplotlib": 9 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "int" - } - } - ] - } + "kw_only_required": { + "order": { + "type": "str", + "options": [ + "C" + ] } }, "metadata": { - "usage.matplotlib": 6 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "axis": { + "pos_only_required": { + "_0": { "type": { "name": "int" } - }, + } + }, + "kw_only_required": { "dtype": { - "type": { + "type": "type", + "name": { "module": "numpy", - "name": "dtype" + "name": "int32" } } }, "metadata": { - "usage.matplotlib": 2 + "usage.sklearn": 2 } }, { - "pos_or_kw_required": { - "a": { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "int" - } - } - ] + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.matplotlib": 2 + "usage.sklearn": 2 } }, { - "pos_or_kw_required": { - "a": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } }, - "out": { - "type": "union", - "options": [ + "shape": { + "type": "tuple", + "items": [ { "type": { - "module": "dask.dataframe.core", - "name": "DataFrame" + "name": "int" } }, { "type": { - "module": "dask.array.core", - "name": "Array" + "name": "int" } } ] } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "out" - ] - ], "metadata": { - "usage.dask": 41 + "usage.sklearn": 6 } }, { - "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ + "kw_only_required": { + "shape": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { - "type": "list", - "item": { - "type": { - "name": "int" - } + "type": { + "name": "int" } } ] } }, - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ + "metadata": { + "usage.sklearn": 11 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { "name": "int" } }, { - "type": "None" + "type": { + "name": "int" + } } ] - }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" - } - }, - "out": { - "type": { - "module": "numpy", - "name": "ndarray" - } } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "dtype" - ] - ], - "metadata": { - "usage.sklearn": 33 - } - } - ], - "take_along_axis": [ - { - "pos_or_kw_required": { - "arr": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "indices": { + "kw_only_required": { + "dtype": { "type": { "module": "numpy", - "name": "ndarray" + "name": "dtype" } }, - "axis": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.skimage": 1, - "usage.dask": 3 - } - } - ], - "mean": [ - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "order": { + "type": "str", + "options": [ + "F" + ] } }, "metadata": { - "usage.skimage": 35, - "usage.xarray": 1, - "usage.matplotlib": 7 + "usage.sklearn": 6 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "axis": { + "pos_only_required": { + "_0": { "type": { "name": "int" } } }, - "metadata": { - "usage.skimage": 13, - "usage.xarray": 6, - "usage.matplotlib": 5 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "dask.array.core", - "name": "Array" + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } + }, + "order": { + "type": "str", + "options": [ + "C" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 3 } }, { - "pos_or_kw_required": { - "a": { + "kw_only_required": { + "dtype": { "type": { "module": "numpy", - "name": "ndarray" + "name": "dtype" } }, - "axis": { + "shape": { "type": "tuple", "items": [ { @@ -65759,19 +61826,32 @@ } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 2 + "usage.sklearn": 4 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } - }, - "axis": { + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "name": "object" + } + } + }, + "metadata": { + "usage.sklearn": 3 + } + }, + { + "pos_only_required": { + "_0": { "type": "tuple", "items": [ { @@ -65785,28 +61865,30 @@ } } ] - }, + } + }, + "kw_only_required": { "dtype": { "type": "type", "name": { "module": "numpy", - "name": "uint8" + "name": "float64" } + }, + "order": { + "type": "str", + "options": [ + "C" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 3 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "axis": { + "pos_only_required": { + "_0": { "type": "tuple", "items": [ { @@ -65816,257 +61898,317 @@ }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } } ] - }, + } + }, + "kw_only_required": { "dtype": { "type": "type", "name": { "module": "numpy", - "name": "float16" + "name": "float64" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, + "kw_only_required": { "dtype": { "type": "type", "name": { "module": "numpy", "name": "float64" } - } - }, - "metadata": { - "usage.skimage": 4 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" - } + }, + "shape": { + "type": { + "name": "int" } } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } - }, - "axis": { - "type": "None" + } + }, + "kw_only_required": { + "order": { + "type": "str", + "options": [ + "f" + ] } }, "metadata": { - "usage.xarray": 3 + "usage.sklearn": 2 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "axis": { + "pos_only_required": { + "_0": { "type": "tuple", "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, { "type": { "name": "int" } } ] - } - }, - "metadata": { - "usage.xarray": 3 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" + }, + "_1": { + "type": "type", + "name": { + "name": "float" } } }, "metadata": { - "usage.xarray": 3 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" - } + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] }, - "keepdims": { - "type": { - "name": "bool" + "_1": { + "type": "type", + "name": { + "name": "float" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "ndarray" + "name": "float64" } }, - "axis": { - "type": "None" - }, - "dtype": { - "type": "None" + "order": { + "type": "str", + "options": [ + "F" + ] } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } }, - "axis": { - "type": { - "name": "int" - } + "order": { + "type": "str", + "options": [ + "C" + ] }, - "keepdims": { + "shape": { "type": { - "name": "bool" + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 2 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "kw_only_required": { + "dtype": { "type": { "module": "numpy", - "name": "ndarray" - } - }, - "axis": { - "type": { - "name": "int" + "name": "dtype" } }, - "dtype": { - "type": "None" + "order": { + "type": "str", + "options": [ + "C" + ] } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 4 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } - }, - "axis": { - "type": "None" - }, + } + }, + "kw_only_required": { "dtype": { "type": "type", "name": { - "name": "float" + "module": "numpy", + "name": "int32" } + }, + "order": { + "type": "str", + "options": [ + "C" + ] } }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] + } + }, + "kw_only_required": { + "dtype": { "type": { "module": "numpy", - "name": "ndarray" - } - }, - "axis": { - "type": { - "name": "int" + "name": "dtype" } }, - "dtype": { - "type": "type", - "name": { - "name": "float" - } + "order": { + "type": "str", + "options": [ + "F" + ] } }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 2 } }, { - "pos_or_kw_required": { - "a": { - "type": "object" + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float32" + } }, - "axis": { - "type": "None" + "order": { + "type": "str", + "options": [ + "F" + ] } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": "object" - }, - "axis": { + "pos_only_required": { + "_0": { "type": "tuple", "items": [ { @@ -66077,93 +62219,155 @@ ] } }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } + }, + "order": { + "type": "str", + "options": [ + "C" + ] + } + }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 2 } }, { - "pos_or_kw_required": { - "a": { - "type": "object" + "kw_only_required": { + "shape": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": "object" - }, - "axis": { - "type": "None" - }, + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + } + }, + "kw_only_required": { "dtype": { - "type": "None" + "type": "type", + "name": { + "name": "int" + } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": "object" - }, - "axis": { + "pos_only_required": { + "_0": { "type": { "name": "int" } } }, + "kw_only_required": { + "dtype": { + "type": "str", + "options": [ + "int" + ] + } + }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 2 } }, { - "pos_or_kw_required": { - "a": { + "kw_only_required": { + "dtype": { "type": { - "module": "xarray.core.dataset", - "name": "Dataset" + "module": "numpy", + "name": "dtype" + } + }, + "shape": { + "type": { + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 4 } }, { - "pos_or_kw_required": { - "a": { - "type": "object" - }, - "axis": { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] + } + }, + "kw_only_required": { + "dtype": { "type": { - "name": "int" + "module": "numpy", + "name": "dtype" } - }, - "dtype": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } - }, + } + ], + "reshape": [ { "pos_or_kw_required": { "a": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + "newshape": { + "type": "list", + "item": { + "type": { + "name": "int" + } } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 2 } }, { @@ -66174,14 +62378,17 @@ "name": "ndarray" } }, - "keepdims": { - "type": { - "name": "bool" + "newshape": { + "type": "list", + "item": { + "type": { + "name": "int" + } } } }, "metadata": { - "usage.xarray": 3 + "usage.skimage": 2 } }, { @@ -66192,19 +62399,51 @@ "name": "ndarray" } }, - "axis": { + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.skimage": 11, + "usage.xarray": 3, + "usage.matplotlib": 2, + "usage.sklearn": 50 + } + }, + { + "pos_or_kw_required": { + "a": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, - "keepdims": { - "type": { - "name": "bool" - } + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.xarray": 3 + "usage.skimage": 3, + "usage.sklearn": 1 } }, { @@ -66215,7 +62454,7 @@ "name": "ndarray" } }, - "axis": { + "newshape": { "type": "tuple", "items": [ { @@ -66223,63 +62462,95 @@ "name": "int" } }, + { + "type": { + "name": "int" + } + }, { "type": { "name": "int" } } ] - }, - "keepdims": { - "type": { - "name": "bool" - } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 5, + "usage.xarray": 1, + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "newshape": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { "type": { - "module": "pandas.core.arrays.sparse.array", - "name": "SparseArray" + "name": "int" } }, { "type": { - "module": "pandas.core.series", - "name": "Series" + "name": "int" } }, { - "type": "list", - "item": { - "type": { - "name": "float" - } + "type": { + "name": "int" } } ] } }, - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ + "metadata": { + "usage.skimage": 4, + "usage.xarray": 2, + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "newshape": { + "type": "tuple", + "items": [ { - "type": "None" + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } }, { "type": { @@ -66287,260 +62558,131 @@ } } ] - }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "int64" - } - }, - "out": { - "type": { - "module": "numpy", - "name": "float64" - } } }, "metadata": { - "usage.pandas": 26 + "usage.skimage": 2 } }, { "pos_or_kw_required": { "a": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "newshape": { + "type": "tuple", + "items": [ { - "type": "None" + "type": { + "module": "numpy", + "name": "int64" + } }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } } ] - }, - "keepdims": { - "type": { - "name": "bool" - } } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "keepdims" - ] - ], "metadata": { - "usage.scipy": 89 + "usage.skimage": 1 } }, { "pos_or_kw_required": { "a": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - "keepdims": { "type": { - "name": "bool" + "module": "numpy", + "name": "ndarray" } }, - "dtype": { - "type": "str", - "options": [ - "float32", - "i8", - "f8" - ] - }, - "out": { - "type": "union", - "options": [ + "newshape": { + "type": "tuple", + "items": [ { "type": { - "module": "dask.dataframe.core", - "name": "Scalar" + "name": "int" } }, { "type": { - "module": "dask.dataframe.core", - "name": "Series" + "module": "numpy", + "name": "int64" } } ] + }, + "order": { + "type": "str", + "options": [ + "F" + ] } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "keepdims" - ], - [ - "axis", - "out" - ], - [ - "axis", - "dtype" - ] - ], "metadata": { - "usage.dask": 78 + "usage.skimage": 3 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "int" - } - } - ] - } - }, + "type": "list", + "item": { + "type": { + "name": "float" + } + } + }, + "newshape": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] + "name": "int" } } ] } }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" - } - }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" - } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "dtype" - ] - ], "metadata": { - "usage.sklearn": 231 + "usage.xarray": 2, + "usage.sklearn": 3 } - } - ], - "allclose": [ + }, { "pos_or_kw_required": { "a": { "type": "list", "item": { "type": { - "name": "int" + "name": "float" } } }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 7 } }, { @@ -66549,121 +62691,122 @@ "type": "list", "item": { "type": { - "name": "int" + "name": "float" } } }, - "b": { - "type": { - "module": "dask.array.core", - "name": "Array" - } - } - }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "float64" - } - }, - "b": { - "type": { - "name": "int" + "newshape": { + "type": "tuple", + "items": { + "type": "None" } } }, "metadata": { - "usage.skimage": 3 + "usage.xarray": 1 } }, { "pos_or_kw_required": { "a": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { + "type": "str", + "options": [ + "A2", + "A0", + "A4", + "A3" + ] + } } - } + ] }, - "b": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } } - } - } - }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.skimage": 18, - "usage.xarray": 6, - "usage.matplotlib": 3 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "float64" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "float64" - } + ] } }, "metadata": { - "usage.skimage": 1 + "usage.pandas": 6 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + } + ] }, - "b": { - "type": { - "name": "int" - } + "newshape": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] + } + }, + { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + } + ] } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 4 + "usage.scipy": 50 } }, { @@ -66674,327 +62817,76 @@ "name": "ndarray" } }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "rtol": { + "newshape": { "type": { "name": "int" } - }, - "atol": { - "type": { - "name": "float" - } } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 3 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "b": { "type": "list", "item": { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } } - } - }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "rtol": { - "type": { - "name": "float" - } - }, - "atol": { - "type": { - "name": "float" - } - } - }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "atol": { - "type": { - "name": "float" - } - } - }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "float64" - } }, - "rtol": { - "type": { - "name": "float" - } + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" - } - }, - "b": { "type": "list", "item": { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" - } - }, - "b": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" - } - }, - "b": { - "type": { - "name": "float" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "rtol": { - "type": { - "name": "float" - } - } - }, - "metadata": { - "usage.xarray": 5, - "usage.matplotlib": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "rtol": { - "type": { - "name": "float" - } - }, - "equal_nan": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.xarray": 4 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" - } - }, - "b": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" - } - }, - "equal_nan": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "equal_nan": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.xarray": 15 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "name": "int" - } - }, - "b": { - "type": { - "name": "int" - } - }, - "equal_nan": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "name": "bool" - } - }, - "b": { - "type": { - "name": "bool" - } }, - "equal_nan": { - "type": { - "name": "bool" - } + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 1 } }, { @@ -67005,118 +62897,161 @@ "name": "ndarray" } }, - "b": { - "type": { - "module": "sparse._coo.core", - "name": "COO" - } - }, - "equal_nan": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" - } - }, - "b": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" - } - }, - "equal_nan": { - "type": { - "name": "bool" - } + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] } }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" - } - }, - "b": { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "equal_nan": { - "type": { - "name": "bool" - } + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 6 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "int64" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "int64" - } + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] }, - "equal_nan": { - "type": { - "name": "bool" - } + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" - } + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 3 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -67125,54 +63060,76 @@ }, { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "name": "complex" + "module": "numpy", + "name": "ndarray" } } ] }, - "b": { - "type": "union", - "options": [ + "newshape": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } }, { "type": { - "name": "float" + "name": "int" } }, { "type": { - "name": "complex" + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" } }, { "type": { "module": "numpy", - "name": "float32" + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } } ] - } - }, - "pos_or_kw_optional": { - "rtol": { - "type": "union", - "options": [ + }, + "newshape": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -67180,52 +63137,64 @@ }, { "type": { - "name": "float" + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] - }, - "atol": { - "type": { - "name": "float" - } } }, "metadata": { - "usage.pandas": 42 + "usage.sklearn": 3 } }, { "pos_or_kw_required": { "a": { - "type": "object" - }, - "b": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "rtol": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } } ] }, - "atol": { - "type": "union", - "options": [ + "newshape": { + "type": "tuple", + "items": [ { "type": { - "name": "float" + "name": "int" } }, { @@ -67235,104 +63204,26 @@ }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] } }, - "pos_or_kw_optional_ordering": [ - [ - "rtol", - "atol" - ] - ], "metadata": { - "usage.scipy": 139 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "float64" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "float64" - } - }, - "b": { - "type": { - "name": "float" - } - } - }, - "metadata": { - "usage.matplotlib": 3 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "float64" - } - } - }, - "metadata": { - "usage.matplotlib": 4 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "object" - }, - "b": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "equal_nan": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.dask": 84 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -67341,38 +63232,41 @@ }, { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } }, { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } } ] }, - "b": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "atol": { - "type": { - "name": "float" - } - }, - "rtol": { - "type": "union", - "options": [ + "newshape": { + "type": "tuple", + "items": [ { "type": { - "name": "float" + "name": "int" + } + }, + { + "type": { + "name": "int" } }, { @@ -67383,103 +63277,88 @@ ] } }, - "pos_or_kw_optional_ordering": [ - [ - "rtol", - "atol" - ] - ], - "metadata": { - "usage.sklearn": 63 - } - } - ], - "argsort": [ - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.skimage": 17, - "usage.matplotlib": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "flatiter" - } - } - }, - "metadata": { - "usage.skimage": 2 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - } - }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 3 } }, { "pos_or_kw_required": { "a": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "kind": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": "None" + "type": { + "module": "numpy", + "name": "ndarray" + } }, { - "type": "str", - "options": [ - "quicksort", - "mergesort" - ] + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } ] }, - "axis": { - "type": { - "name": "int" - } - }, - "order": { - "type": "str", - "options": [ - "C" + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } ] } }, "metadata": { - "usage.pandas": 62 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -67487,60 +63366,67 @@ } }, { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "int64" - } + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } } ] - } - }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" - } }, - "kind": { - "type": "str", - "options": [ - "quicksort", - "mergesort" + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } ] } }, "metadata": { - "usage.scipy": 96 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.dask": 12 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -67550,137 +63436,92 @@ { "type": { "module": "numpy", - "name": "matrix" + "name": "ndarray" } }, { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "float" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } } ] - } - }, - "pos_or_kw_optional": { - "kind": { - "type": "str", - "options": [ - "mergesort" - ] }, - "axis": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.sklearn": 57 - } - } - ], - "hstack": [ - { - "pos_or_kw_required": { - "tup": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - } - }, - "metadata": { - "usage.skimage": 24, - "usage.xarray": 1, - "usage.matplotlib": 18 - } - }, - { - "pos_or_kw_required": { - "tup": { + "newshape": { "type": "tuple", "items": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] } }, "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_or_kw_required": { - "tup": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "int64" - } - } - } - }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_or_kw_required": { - "tup": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float32" - } - } - } - }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_or_kw_required": { - "tup": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" - } - } - } - }, - "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "tup": { + "a": { "type": "tuple", "items": [ { @@ -67700,19 +63541,7 @@ "module": "numpy", "name": "ndarray" } - } - ] - } - }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_or_kw_required": { - "tup": { - "type": "tuple", - "items": [ + }, { "type": { "module": "numpy", @@ -67724,129 +63553,67 @@ "module": "numpy", "name": "ndarray" } - } - ] - } - }, - "metadata": { - "usage.skimage": 1, - "usage.matplotlib": 1 - } - }, - { - "pos_or_kw_required": { - "tup": { - "type": "union", - "options": [ + }, { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "bool" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "list", - "item": { - "type": "str", - "options": [ - "x", - "y", - "z" - ] - } - } - ] - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" } }, { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "float" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } } ] - } - }, - "metadata": { - "usage.pandas": 9 - } - }, - { - "pos_or_kw_required": { - "tup": { - "type": "union", - "options": [ + }, + "newshape": { + "type": "tuple", + "items": [ { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "type": { + "name": "int" } }, { - "type": "tuple", - "items": { - "type": "object" + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] } }, "metadata": { - "usage.scipy": 237 + "usage.sklearn": 3 } }, { "pos_or_kw_required": { - "tup": { + "a": { "type": "tuple", "items": [ { @@ -67864,19 +63631,80 @@ { "type": { "module": "numpy", - "name": "int64" + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "tup": { + "a": { "type": "tuple", "items": [ { @@ -67894,233 +63722,414 @@ { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "tup": { + "a": { "type": "list", "item": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "uint8" - } - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "type": { + "name": "int" + } } + }, + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 3 } }, { "pos_or_kw_required": { - "tup": { + "a": { "type": "list", "item": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "uint8" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "list", + "item": { + "type": { + "name": "int" } - ] + } } + }, + "newshape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 2 } - }, + } + ], + "moveaxis": [ { "pos_or_kw_required": { - "tup": { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "source": { + "type": { + "name": "int" + } + }, + "destination": { + "type": { + "name": "int" } } }, "metadata": { - "usage.matplotlib": 11 + "usage.skimage": 4, + "usage.xarray": 1, + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { - "tup": { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "source": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "destination": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.matplotlib": 2 + "usage.xarray": 12 } }, { "pos_or_kw_required": { - "tup": { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" - } - } - } - ] + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "source": { + "type": "tuple", + "items": { + "type": "None" + } + }, + "destination": { + "type": "tuple", + "items": { + "type": "None" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 3 } }, { "pos_or_kw_required": { - "tup": { - "type": "object" + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "source": { + "type": { + "name": "range" + } + }, + "destination": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } } }, "metadata": { - "usage.dask": 7 + "usage.xarray": 2 } }, { "pos_or_kw_required": { - "tup": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": "object" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] + "a": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "source": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "destination": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "name": "int" + } + }, + "source": { + "type": "tuple", + "items": { + "type": "None" + } + }, + "destination": { + "type": "tuple", + "items": { + "type": "None" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "object" + }, + "source": { + "type": { + "name": "int" + } + }, + "destination": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "object" + }, + "source": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "destination": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "source": { + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "destination": { + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.scipy": 27 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "union", + "options": [ + { + "type": { + "module": "dask.array.core", + "name": "Array" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } } ] + }, + "source": { + "type": { + "name": "int" + } + }, + "destination": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.sklearn": 179 + "usage.dask": 4 } } ], - "argmax": [ + "rollaxis": [ { "pos_or_kw_required": { "a": { @@ -68128,24 +64137,40 @@ "module": "numpy", "name": "ndarray" } + }, + "axis": { + "type": { + "name": "int" + } } }, "metadata": { "usage.skimage": 12, - "usage.xarray": 4 + "usage.sklearn": 4 } }, { "pos_or_kw_required": { "a": { "type": { - "module": "dask.array.core", - "name": "Array" + "module": "numpy", + "name": "ndarray" + } + }, + "axis": { + "type": { + "name": "int" + } + }, + "start": { + "type": { + "name": "int" } } }, "metadata": { - "usage.skimage": 3 + "usage.skimage": 2, + "usage.sklearn": 1 } }, { @@ -68162,9 +64187,100 @@ } } }, + "pos_or_kw_optional": { + "start": { + "type": { + "name": "int" + } + } + }, "metadata": { - "usage.skimage": 3, - "usage.xarray": 7 + "usage.scipy": 73 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "union", + "options": [ + { + "type": { + "module": "dask.array.core", + "name": "Array" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "axis": { + "type": { + "name": "int" + } + } + }, + "pos_or_kw_optional": { + "start": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.dask": 5 + } + } + ], + "any": [ + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "bool_" + } + } + }, + "metadata": { + "usage.skimage": 2, + "usage.matplotlib": 1, + "usage.sklearn": 4 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.skimage": 33, + "usage.xarray": 4, + "usage.matplotlib": 11, + "usage.sklearn": 61 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "list", + "item": { + "type": { + "name": "bool" + } + } + } + }, + "metadata": { + "usage.skimage": 1, + "usage.sklearn": 4 } }, { @@ -68176,20 +64292,28 @@ } }, "axis": { - "type": "None" + "type": { + "name": "int" + } } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 4, + "usage.sklearn": 5 } }, { "pos_or_kw_required": { "a": { - "type": "object" + "type": { + "module": "numpy", + "name": "bool_" + } }, "axis": { - "type": "None" + "type": { + "name": "int" + } } }, "metadata": { @@ -68199,13 +64323,26 @@ { "pos_or_kw_required": { "a": { - "type": "object" + "type": { + "module": "sparse._coo.core", + "name": "COO" + } } }, "metadata": { "usage.xarray": 1 } }, + { + "pos_or_kw_required": { + "a": { + "type": "object" + } + }, + "metadata": { + "usage.xarray": 2 + } + }, { "pos_or_kw_required": { "a": { @@ -68219,6 +64356,35 @@ "usage.xarray": 1 } }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "out": { + "type": "None" + } + }, + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "object" + }, + "out": { + "type": "None" + } + }, + "metadata": { + "usage.xarray": 1 + } + }, { "pos_or_kw_required": { "a": { @@ -68232,6 +64398,45 @@ "usage.xarray": 1 } }, + { + "pos_or_kw_required": { + "a": { + "type": "object" + }, + "axis": { + "type": { + "name": "int" + } + }, + "out": { + "type": "None" + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "axis": { + "type": { + "name": "int" + } + }, + "out": { + "type": "None" + } + }, + "metadata": { + "usage.xarray": 2 + } + }, { "pos_or_kw_required": { "a": { @@ -68272,29 +64477,30 @@ "options": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "object" } }, { "type": { - "name": "int" + "name": "bool" } } ] + }, + "keepdims": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.pandas": 23 + "usage.pandas": 46 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "object" } }, "pos_or_kw_optional": { @@ -68305,7 +64511,33 @@ } }, "metadata": { - "usage.scipy": 24 + "usage.scipy": 206 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.matplotlib": 3, + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + }, + "metadata": { + "usage.matplotlib": 1 } }, { @@ -68334,8 +64566,8 @@ "options": [ { "type": { - "module": "dask.array.core", - "name": "Array" + "module": "pandas.core.series", + "name": "Series" } }, { @@ -68344,6 +64576,12 @@ "name": "MaskedArray" } }, + { + "type": { + "module": "numpy", + "name": "bool_" + } + }, { "type": { "module": "numpy", @@ -68356,26 +64594,42 @@ "type": "union", "options": [ { - "type": { - "name": "int" + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "name": "int" + } + } + ] } }, { - "type": "None" + "type": { + "name": "int" + } } ] }, - "out": { + "keepdims": { "type": { - "module": "dask.array.core", - "name": "Array" + "name": "bool" } } }, "pos_or_kw_optional_ordering": [ + [ + "a", + "keepdims" + ], [ "axis", - "out" + "keepdims" ], [ "a", @@ -68383,66 +64637,138 @@ ] ], "kw_only_optional": { - "keepdims": { + "computing_meta": { "type": { "name": "bool" } } }, "metadata": { - "usage.dask": 36 + "usage.dask": 81 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "matrix" - } + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "bool_" } - ] + } } }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" + "metadata": { + "usage.sklearn": 6 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "bool" + } + }, + { + "type": { + "module": "numpy", + "name": "bool_" + } + } + ] } } }, "metadata": { - "usage.sklearn": 77 + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "bool_" + } + }, + { + "type": { + "name": "bool" + } + } + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 } } ], - "logspace": [ + "empty_like": [ { - "pos_or_kw_required": { - "start": { + "pos_only_required": { + "_0": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } - }, - "stop": { + } + }, + "metadata": { + "usage.skimage": 33, + "usage.matplotlib": 10, + "usage.sklearn": 19 + } + }, + { + "pos_only_required": { + "_0": { "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { "module": "numpy", "name": "float64" } - }, - "num": { + } + }, + "metadata": { + "usage.skimage": 3 + } + }, + { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" + } + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "uint16" } } }, @@ -68451,249 +64777,262 @@ } }, { - "pos_or_kw_required": { - "start": { + "pos_only_required": { + "_0": { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } - }, - "stop": { - "type": { + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { "name": "float" } - }, - "num": { + } + }, + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" + } + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "uint8" } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 4 } }, { - "pos_or_kw_required": { - "start": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } }, - "stop": { - "type": "union", + "order": { + "type": "str", "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "float" - } - } + "C" ] - } - }, - "pos_or_kw_optional": { - "num": { - "type": "object" }, - "base": { + "subok": { "type": { - "name": "float" + "name": "bool" } } }, "metadata": { - "usage.scipy": 51 + "usage.skimage": 2 } }, { - "pos_or_kw_required": { - "start": { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "numpy.ma.core", + "name": "MaskedArray" } - }, - "stop": { - "type": { - "name": "int" + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } }, - "num": { - "type": { - "name": "int" - } + "order": { + "type": "str", + "options": [ + "C" + ] }, - "base": { + "subok": { "type": { - "name": "float" + "name": "bool" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 1 } }, { - "pos_or_kw_required": { - "start": { - "type": { - "name": "int" - } - }, - "stop": { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, - "num": { - "type": { - "name": "int" + "_1": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 2 } }, { - "pos_or_kw_required": { - "start": { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "xarray.core.variable", + "name": "Variable" } - }, - "stop": { + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "xarray.core.variable", + "name": "IndexVariable" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 1 } }, { - "pos_or_kw_required": { - "start": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "module": "numpy", - "name": "float64" + "module": "pandas.core.arrays.string_", + "name": "StringArray" + } + }, + { + "type": "list", + "item": { + "type": "None" } } ] - }, - "stop": { + } + }, + "kw_only_optional": { + "dtype": { "type": "union", "options": [ { - "type": { - "name": "int" - } + "type": "type" + }, + { + "type": "str", + "options": [ + "float", + "f8", + "i8", + "object" + ] }, { "type": { "module": "numpy", - "name": "float64" + "name": "dtype" } } ] - }, - "num": { - "type": { - "name": "int" - } - } - }, - "pos_or_kw_optional": { - "base": { - "type": { - "name": "float" - } } }, "metadata": { - "usage.sklearn": 17 + "usage.pandas": 18 } - } - ], - "delete": [ + }, { - "pos_or_kw_required": { - "arr": { + "pos_only_required": { + "_0": { "type": { "module": "numpy", "name": "ndarray" } - }, - "obj": { - "type": "tuple", - "items": { - "type": "None" - } - }, - "axis": { - "type": { - "name": "int" - } + } + }, + "kw_only_optional": { + "dtype": { + "type": "type" } }, "metadata": { - "usage.skimage": 1 + "usage.scipy": 103 } }, { - "pos_or_kw_required": { - "arr": { + "pos_only_required": { + "_0": { "type": { "module": "numpy", "name": "ndarray" } + } + }, + "kw_only_optional": { + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } }, - "obj": { - "type": "tuple", - "items": [ + "shape": { + "type": "union", + "options": [ { - "type": { - "name": "int" + "type": "tuple", + "items": { + "type": { + "name": "int" + } } }, { - "type": { - "name": "int" - } + "type": "None" }, { "type": { @@ -68702,25 +65041,66 @@ } ] }, - "axis": { + "order": { + "type": "str", + "options": [ + "F", + "C" + ] + } + }, + "metadata": { + "usage.dask": 12 + } + }, + { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" + } + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float32" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 4 } }, { - "pos_or_kw_required": { - "arr": { + "pos_only_required": { + "_0": { "type": { "module": "numpy", "name": "ndarray" } - }, - "obj": { + } + }, + "kw_only_required": { + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } + } + }, + "metadata": { + "usage.sklearn": 3 + } + } + ], + "ones": [ + { + "pos_or_kw_required": { + "shape": { "type": "tuple", "items": [ { @@ -68732,7 +65112,26 @@ "type": { "name": "int" } - }, + } + ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "bool_" + } + } + }, + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -68742,7 +65141,23 @@ "type": { "name": "int" } - }, + } + ] + } + }, + "metadata": { + "usage.skimage": 76, + "usage.xarray": 21, + "usage.matplotlib": 4, + "usage.sample-usage": 1, + "usage.sklearn": 81 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -68752,12 +65167,44 @@ "type": { "name": "int" } - }, - { - "type": { - "name": "int" - } - }, + } + ] + }, + "dtype": { + "type": "type", + "name": { + "name": "float" + } + } + }, + "metadata": { + "usage.skimage": 1, + "usage.xarray": 3, + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + } + }, + "metadata": { + "usage.skimage": 3, + "usage.xarray": 8, + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -68774,26 +65221,17 @@ } } ] - }, - "axis": { - "type": { - "name": "int" - } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 17, + "usage.xarray": 6, + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "arr": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "obj": { + "shape": { "type": "tuple", "items": [ { @@ -68815,7 +65253,21 @@ "type": { "name": "int" } - }, + } + ] + } + }, + "metadata": { + "usage.skimage": 8, + "usage.xarray": 1, + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -68825,7 +65277,42 @@ "type": { "name": "int" } - }, + } + ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "uint8" + } + } + }, + "metadata": { + "usage.skimage": 41, + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 19, + "usage.xarray": 21, + "usage.matplotlib": 40, + "usage.sklearn": 214 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -68838,27 +65325,24 @@ } ] }, - "axis": { - "type": { - "name": "int" + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float32" } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 3, + "usage.sklearn": 5 } }, { "pos_or_kw_required": { - "arr": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "obj": { - "type": "union", - "options": [ + "shape": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -68866,85 +65350,28 @@ }, { "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "name": "int" } } ] - } - }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "int8" } } }, "metadata": { - "usage.pandas": 28 + "usage.skimage": 6 } }, { "pos_or_kw_required": { - "arr": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "obj": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, + "shape": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -68952,96 +65379,84 @@ }, { "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "name": "int" } - }, - { - "type": "tuple", - "items": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] } ] - } - }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.scipy": 145 + "usage.skimage": 2, + "usage.sklearn": 4 } }, { "pos_or_kw_required": { - "arr": { + "shape": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, - "obj": { - "type": { - "name": "int" + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "int64" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 1, + "usage.sklearn": 6 } }, { "pos_or_kw_required": { - "arr": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "obj": { - "type": "union", - "options": [ + "shape": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { "type": { "name": "int" } + } + ] + }, + "dtype": { + "type": "type", + "name": { + "name": "bool" + } + } + }, + "metadata": { + "usage.skimage": 29, + "usage.matplotlib": 1, + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "int64" + } }, { "type": { @@ -69050,25 +65465,43 @@ } } ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "uint8" + } } }, "metadata": { - "usage.sklearn": 7 + "usage.skimage": 2 } - } - ], - "split": [ + }, { "pos_or_kw_required": { - "ary": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "shape": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] }, - "indices_or_sections": { - "type": { - "name": "int" + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "uint16" } } }, @@ -69078,579 +65511,541 @@ }, { "pos_or_kw_required": { - "ary": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "indices_or_sections": { - "type": "list", - "item": { - "type": { - "name": "int" + "shape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } } - } - }, - "axis": { - "type": { - "name": "int" - } + ] } }, "metadata": { - "usage.pandas": 4, - "usage.scipy": 8 + "usage.skimage": 5, + "usage.xarray": 4, + "usage.matplotlib": 1, + "usage.sklearn": 21 } }, { "pos_or_kw_required": { - "ary": { + "shape": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, - "indices_or_sections": { + "dtype": { "type": { "module": "numpy", - "name": "ndarray" + "name": "dtype" } } }, "metadata": { - "usage.matplotlib": 2, - "usage.sklearn": 7 + "usage.skimage": 2, + "usage.matplotlib": 6, + "usage.sklearn": 13 } - } - ], - "column_stack": [ + }, { "pos_or_kw_required": { - "tup": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "ndarray" + "shape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } } + ] + }, + "dtype": { + "type": "type", + "name": { + "name": "float" } } }, "metadata": { - "usage.skimage": 5, - "usage.matplotlib": 17, - "usage.sample-usage": 1 + "usage.skimage": 1, + "usage.xarray": 1 } }, { "pos_or_kw_required": { - "tup": { + "shape": { + "type": { + "name": "int" + } + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } + } + }, + "metadata": { + "usage.skimage": 1, + "usage.matplotlib": 4, + "usage.sklearn": 11 + } + }, + { + "pos_or_kw_required": { + "shape": { "type": "tuple", "items": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "uint8" + } } }, "metadata": { - "usage.skimage": 4, - "usage.matplotlib": 16 + "usage.skimage": 3 } }, { "pos_or_kw_required": { - "tup": { - "type": "union", - "options": [ + "shape": { + "type": "tuple", + "items": [ { - "type": "tuple", - "items": { - "type": { - "module": "numpy", - "name": 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[ + "uint8" + ] } }, "metadata": { - "usage.matplotlib": 4 + "usage.skimage": 2 } }, { "pos_or_kw_required": { - "tup": { - "type": "list", - "item": { - "type": "list", - "item": { + "shape": { + "type": "tuple", + "items": [ + { "type": { - "module": "numpy", - "name": "float64" + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" } } - } + ] + }, + "dtype": { + "type": "str", + "options": [ + "uint8" + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 1 } }, { "pos_or_kw_required": { - "tup": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" + "shape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } } + ] + }, + "dtype": { + "type": "type", + "name": { + "name": "bool" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 3, + "usage.xarray": 1, + "usage.sklearn": 5 } }, { "pos_or_kw_required": { - "tup": { - "type": "union", - "options": [ + "shape": { + 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- "name": "ndarray" - } - }, - "b": { + "shape": { "type": { "module": "numpy", "name": "ndarray" } - }, - "rtol": { - "type": { - "name": "float" - } - }, - "atol": { - "type": { - "name": "float" - } - }, - "equal_nan": { - "type": { - "name": "bool" - } } }, "metadata": { - "usage.xarray": 11 + "usage.skimage": 1 } }, { "pos_or_kw_required": { - "a": { + "shape": { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } }, - "b": { - "type": { - "module": "numpy", - "name": "float64" + "dtype": { + "type": "type", + "name": { + "name": "bool" } } }, "metadata": { - "usage.xarray": 1, - "usage.matplotlib": 4 + "usage.skimage": 1, + "usage.sklearn": 13 } }, { "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "float64" - } - }, - "b": { + "shape": { "type": { "module": "numpy", - "name": "float64" - } - }, - "rtol": { - "type": { - "name": "float" + "name": "int64" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 2, + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ + "shape": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "bool_" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { "type": { - "name": "float" - } - } - ] - }, - "b": { - "type": "union", - "options": [ - { - "type": { - "name": "bool" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { @@ -69661,154 +66056,185 @@ ] } }, - "pos_or_kw_optional": { - "equal_nan": { - "type": { - "name": "bool" - } - } - }, "metadata": { - "usage.pandas": 4 + "usage.xarray": 1 } }, { "pos_or_kw_required": { - "a": { - "type": "object" - }, - "b": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "rtol": { - "type": "union", - "options": [ + "shape": { + "type": "tuple", + "items": [ { "type": { - "name": "float" + "name": "int" } - }, + } + ] + }, + "dtype": { + "type": "type", + "name": { + "name": "int" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": { + "name": "int" + } + }, + "dtype": { + "type": "str", + "options": [ + ">f4" + ] + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": "union", + "options": [ { "type": { "name": "int" } }, { - "type": { - "module": "numpy", - "name": "float64" + "type": "tuple", + "items": { + "type": { + "name": "int" + } } } ] - }, - "atol": { + } + }, + "pos_or_kw_optional": { + "dtype": { "type": "union", "options": [ { "type": { - "name": "float" + "module": "numpy", + "name": "dtype" } }, { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "int64", + "float64", + "bool" + ] + }, + { + "type": "type" + } + ] + } + }, + "metadata": { + "usage.pandas": 116 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": "object" + } + }, + "pos_or_kw_optional": { + "dtype": { + "type": "union", + "options": [ + { + "type": "type" }, { "type": { "module": "numpy", - "name": "float64" + "name": "dtype" } + }, + { + "type": "str" } ] + }, + "order": { + "type": "str", + "options": [ + "c" + ] } }, "pos_or_kw_optional_ordering": [ [ - "rtol", - "atol" + "dtype", + "order" ] ], "metadata": { - "usage.scipy": 66 + "usage.scipy": 934 } }, { "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "float64" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "rtol": { - "type": { - "name": "int" - } + "shape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] }, - "atol": { - "type": { + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "float64" + "name": "uint16" } } }, "metadata": { - "usage.matplotlib": 2 + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "float64" - } - }, - "b": { + "shape": { "type": "list", "item": { "type": { - "name": "float" + "name": "int" } } - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "int64" - } - }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "rtol": { - "type": { - "name": "int" - } }, - "atol": { - "type": { + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "float64" + "name": "int32" } } }, @@ -69818,44 +66244,49 @@ }, { "pos_or_kw_required": { - "a": { + "shape": { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } }, - "b": { - "type": { - "name": "float" + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "int32" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.matplotlib": 2 } }, { "pos_or_kw_required": { - "a": { + "shape": { "type": "union", "options": [ { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "name": "int" + } + } + ] } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } - } - ] - }, - "b": { - "type": "union", - "options": [ + }, { "type": { "module": "numpy", @@ -69863,380 +66294,407 @@ } }, { - "type": { - "name": "int" + "type": "list", + "item": { + "type": { + "name": "int" + } } } ] } }, "pos_or_kw_optional": { - "rtol": { - "type": { - "name": "float" - } - }, - "atol": { - "type": { - "name": "int" - } - }, - "equal_nan": { - "type": { - "name": "bool" - } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "rtol", - "atol" - ] - ], - "metadata": { - "usage.dask": 3 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] - }, - "b": { + "dtype": { "type": "union", "options": [ { "type": { "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "float32" + "name": "dtype" } }, { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "list", + "item": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "a", + "b", + "c", + "col1", + "col2" + ] + }, + { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "f8" + ] + }, + { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "f4" + ] + }, + { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + ] + } + ] + } + ] + } + ] } }, { - "type": { - "name": "float" - } - } - ] - } - }, - "pos_or_kw_optional": { - "rtol": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } + "type": "type" }, { - "type": { - "name": "float" - } + "type": "str", + "options": [ + "i4", + "float32", + "f8" + ] } ] }, - "atol": { - "type": "union", + "order": { + "type": "str", "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - } + "F", + "C" ] - }, - "equal_nan": { - "type": { - "name": "bool" - } } }, "pos_or_kw_optional_ordering": [ [ - "atol", - "equal_nan" - ], - [ - "rtol", - "atol" + "dtype", + "order" ] ], "metadata": { - "usage.sklearn": 11 + "usage.dask": 186 } - } - ], - "gradient": [ + }, { "pos_or_kw_required": { - "f": { + "shape": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" + } + }, + "dtype": { + "type": "type", + "name": { + "name": "int" } } }, "metadata": { - "usage.skimage": 7, - "usage.matplotlib": 1 + "usage.sklearn": 8 } }, { "pos_or_kw_required": { - "f": { + "shape": { "type": { "module": "numpy", - "name": "ndarray" + "name": "int64" } - } - }, - "kw_only_required": { - "axis": { - "type": { + }, + "dtype": { + "type": "type", + "name": { "name": "int" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "f": { + "shape": { "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" + "name": "int" } - } - }, - "var_pos": [ - "varargs", - { + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } + }, + "order": { "type": "str", "options": [ - "v", - "t" + "C" ] } - ], - "kw_only_required": { - "axis": { + }, + "metadata": { + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "shape": { "type": { "name": "int" } }, - "edge_order": { - "type": { - "name": "int" - } + "dtype": { + "type": "None" } }, "metadata": { - "usage.xarray": 4 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "f": { - "type": { + "shape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] + }, + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "ndarray" + "name": "float64" } - } - }, - "var_pos": [ - "varargs", - { + }, + "order": { "type": "str", "options": [ - "v", - "t" + "C" ] } - ], - "kw_only_required": { - "axis": { + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "shape": { "type": { "name": "int" } }, - "edge_order": { - "type": { - "name": "int" + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float32" } } }, "metadata": { - "usage.xarray": 3 + "usage.sklearn": 16 } }, { "pos_or_kw_required": { - "f": { - "type": { - "module": "sparse._coo.core", - "name": "COO" + "shape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float32" } } }, - "var_pos": [ - "varargs", - { - "type": "str", - "options": [ - "v", - "t" + "metadata": { + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } ] - } - ], - "kw_only_required": { - "axis": { - "type": { - "name": "int" - } }, - "edge_order": { + "dtype": { "type": { - "name": "int" + "module": "numpy", + "name": "dtype" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "f": { - "type": "object" - } - }, - "var_pos": [ - "varargs", - { - "type": "str", - "options": [ - "v", - "t" + "shape": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } ] + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "int16" + } } - ], - "kw_only_required": { - "axis": { + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "shape": { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } }, - "edge_order": { - "type": { - "name": "int" + "dtype": { + "type": "type", + "name": { + "name": "bool" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "f": { + "shape": { "type": { "module": "numpy", - "name": "ndarray" + "name": "int64" + } + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" } } }, - "var_pos": [ - "varargs", - { - "type": "str", - "options": [ - "v", - "t" - ] - } - ], "metadata": { - "usage.matplotlib": 3 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "f": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "shape": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + "dtype": { + "type": "type", + "name": { + "name": "int" } } }, - "var_pos": [ - "varargs", - { + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": { + "name": "int" + } + }, + "dtype": { "type": "str", "options": [ - "v", - "t" + "int" ] } - ], + }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "f": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, + "shape": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -70244,66 +66702,56 @@ }, { "type": { - "name": "float" + "name": "int" } } ] + }, + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } } }, - "var_pos": [ - "varargs", - { - "type": "str", - "options": [ - "v", - "t" - ] - } - ], - "kw_only_optional": { - "axis": { - "type": "union", - "options": [ + "metadata": { + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "shape": { + "type": "tuple", + "items": [ { "type": { "name": "int" } }, { - "type": "None" - }, - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "name": "int" + } } ] }, - "edge_order": { - "type": { - "name": "int" + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "int32" } } }, "metadata": { - "usage.dask": 17 + "usage.sklearn": 1 } } ], - "argmin": [ + "triu": [ { "pos_or_kw_required": { - "a": { + "m": { "type": { "module": "numpy", "name": "ndarray" @@ -70311,115 +66759,166 @@ } }, "metadata": { - "usage.skimage": 5, - "usage.matplotlib": 2 + "usage.skimage": 1 } }, { "pos_or_kw_required": { - "a": { + "m": { "type": { "module": "numpy", "name": "ndarray" } - }, - "axis": { + } + }, + "pos_or_kw_optional": { + "k": { "type": { "name": "int" } } }, "metadata": { - "usage.skimage": 3, - "usage.xarray": 5, - "usage.matplotlib": 4 + "usage.scipy": 205, + "usage.dask": 8 } - }, + } + ], + "seterr": [ { "pos_or_kw_required": { - "a": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" - } - } + "invalid": { + "type": "str", + "options": [ + "ignore" + ] } }, "metadata": { - "usage.skimage": 2 + "usage.skimage": 1 } }, { "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "divide": { + "type": "str", + "options": [ + "warn" + ] }, - "axis": { - "type": "None" + "over": { + "type": "str", + "options": [ + "warn" + ] + }, + "under": { + "type": "str", + "options": [ + "ignore" + ] + }, + "invalid": { + "type": "str", + "options": [ + "warn" + ] } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1, + "usage.sklearn": 3 } }, { - "pos_or_kw_required": { - "a": { - "type": "object" + "pos_or_kw_optional": { + "divide": { + "type": "str", + "options": [ + "warn" + ] }, - "axis": { - "type": "None" + "over": { + "type": "str", + "options": [ + "warn" + ] + }, + "under": { + "type": "str", + "options": [ + "ignore" + ] + }, + "invalid": { + "type": "str", + "options": [ + "warn" + ] + }, + "all": { + "type": "str", + "options": [ + "ignore" + ] } }, + "pos_or_kw_optional_ordering": [ + [ + "over", + "under" + ], + [ + "under", + "invalid" + ], + [ + "divide", + "over" + ] + ], "metadata": { - "usage.xarray": 1 + "usage.dask": 2 } }, { "pos_or_kw_required": { - "a": { - "type": "object" + "all": { + "type": "str", + "options": [ + "ignore" + ] } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" - } + "all": { + "type": "str", + "options": [ + "raise" + ] } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } - }, + } + ], + "stack": [ { "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.dataset", - "name": "Dataset" + "arrays": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" + } } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "object" }, "axis": { "type": { @@ -70428,32 +66927,22 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 12, + "usage.xarray": 24, + "usage.matplotlib": 8 } }, { "pos_or_kw_required": { - "a": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ + "arrays": { + "type": "tuple", + "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, - { - "type": "None" - } - ] - }, - "out": { - "type": "union", - "options": [ { "type": { "module": "numpy", @@ -70462,26 +66951,33 @@ }, { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } ] + }, + "axis": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.pandas": 23 + "usage.skimage": 6 } }, { "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" + "arrays": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "float64" + } } - } - }, - "pos_or_kw_optional": { + }, "axis": { "type": { "name": "int" @@ -70489,14 +66985,14 @@ } }, "metadata": { - "usage.scipy": 27 + "usage.skimage": 2 } }, { - "pos_only_optional": { - "_0": { - "type": "union", - "options": [ + "pos_or_kw_required": { + "arrays": { + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -70505,103 +67001,38 @@ }, { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "numpy", + "name": "float64" } - } - ] - } - }, - "pos_or_kw_optional": { - "a": { - "type": "union", - "options": [ + }, { "type": { "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } + "name": "float64" } } ] }, "axis": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - } - }, - "pos_or_kw_optional_ordering": [ - [ - "a", - "axis" - ] - ], - "kw_only_optional": { - "keepdims": { "type": { - "name": "bool" + "name": "int" } } }, "metadata": { - "usage.dask": 35 + "usage.skimage": 2 } }, { "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ + "arrays": { + "type": "tuple", + "items": [ { - "type": "tuple", - "items": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] + "type": { + "module": "numpy", + "name": "float64" + } }, { "type": { @@ -70610,63 +67041,12 @@ } }, { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] + "type": { + "module": "numpy", + "name": "float64" } } ] - } - }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.sklearn": 23 - } - } - ], - "sort": [ - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.skimage": 13, - "usage.xarray": 2, - "usage.pandas": 33, - "usage.matplotlib": 4 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } }, "axis": { "type": { @@ -70675,119 +67055,66 @@ } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 2 } }, { "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ + "arrays": { + "type": "tuple", + "items": [ { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "complex128" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "complex" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] + "type": { + "module": "numpy", + "name": "float64" } }, { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } }, { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "numpy", + "name": "ndarray" } } ] - } - }, - "pos_or_kw_optional": { + }, "axis": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] + "type": { + "name": "int" + } } }, "metadata": { - "usage.scipy": 217 + "usage.skimage": 2 } }, { "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "dask.array.core", - "name": "Array" - } + "arrays": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" } - ] - } - }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" } } }, "metadata": { - "usage.dask": 14 + "usage.skimage": 2, + "usage.xarray": 2 } }, { "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ + "arrays": { + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -70795,33 +67122,35 @@ } }, { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } } ] } }, - "pos_or_kw_optional": { + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "arrays": { + "type": "list", + "item": { + "type": { + "module": "xarray.core.dataarray", + "name": "DataArray" + } + } + }, "axis": { "type": { "name": "int" @@ -70829,22 +67158,17 @@ } }, "metadata": { - "usage.sklearn": 48 + "usage.xarray": 1 } - } - ], - "diff": [ + }, { "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "n": { - "type": { - "name": "int" + "arrays": { + "type": "list", + "item": { + "type": { + "name": "float" + } } }, "axis": { @@ -70854,32 +67178,36 @@ } }, "metadata": { - "usage.skimage": 2, - "usage.xarray": 2 + "usage.xarray": 1 } }, { "pos_or_kw_required": { - "a": { + "arrays": { + "type": "list", + "item": { + "type": { + "module": "sparse._coo.core", + "name": "COO" + } + } + }, + "axis": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } }, "metadata": { - "usage.skimage": 7, - "usage.xarray": 4, - "usage.pandas": 6, - "usage.matplotlib": 18 + "usage.xarray": 2 } }, { "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" + "arrays": { + "type": "list", + "item": { + "type": "object" } }, "axis": { @@ -70889,119 +67217,70 @@ } }, "metadata": { - "usage.skimage": 5, - "usage.xarray": 7, - "usage.matplotlib": 5 + "usage.xarray": 3 } }, { "pos_or_kw_required": { - "a": { + "arrays": { "type": "union", "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, { "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": "tuple", - "items": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - } - ] + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" } - ] - } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] }, { "type": "list", "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } } } ] } }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" - } - } - }, "metadata": { - "usage.scipy": 127 + "usage.scipy": 3 } }, { "pos_or_kw_required": { - "a": { + "arrays": { "type": "tuple", "items": [ { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } }, { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } } ] + }, + "axis": { + "type": { + "name": "int" + } } }, "metadata": { @@ -71010,39 +67289,22 @@ }, { "pos_or_kw_required": { - "a": { + "arrays": { "type": "union", "options": [ { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] - } - }, - { - "type": { - "module": "dask.array.core", - "name": "Array" + "type": "tuple", + "items": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "list", + "item": { + "type": "object" } } ] @@ -71053,59 +67315,102 @@ "type": { "name": "int" } - }, - "n": { + } + }, + "metadata": { + "usage.dask": 32 + } + } + ], + "choose": [ + { + "pos_or_kw_required": { + "a": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" + } + }, + "choices": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" + } } } }, - "pos_or_kw_optional_ordering": [ - [ - "n", - "axis" - ] - ], "metadata": { - "usage.dask": 12 + "usage.skimage": 1 } }, { "pos_or_kw_required": { "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "choices": { "type": "union", "options": [ { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } }, { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" + "type": "tuple", + "items": [ + { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } - } + ] } ] } }, - "pos_or_kw_optional": { - "n": { - "type": { - "name": "int" - } - } - }, "metadata": { - "usage.sklearn": 49 + "usage.dask": 4 } } ], - "flatnonzero": [ + "amin": [ { "pos_or_kw_required": { "a": { @@ -71116,79 +67421,94 @@ } }, "metadata": { - "usage.skimage": 6, - "usage.xarray": 2, - "usage.pandas": 2, - "usage.scipy": 5, - "usage.dask": 2, - "usage.sklearn": 35 + "usage.skimage": 49, + "usage.xarray": 10, + "usage.matplotlib": 31, + "usage.sklearn": 46 } - } - ], - "copy": [ + }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "list", + "item": { + "type": { + "name": "int" + } } } }, "metadata": { - "usage.skimage": 12, - "usage.pandas": 1, - "usage.matplotlib": 7 + "usage.skimage": 1, + "usage.matplotlib": 2, + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "float64" + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "int64" + } } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 4 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "axis": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { "type": { - "name": "float" + "name": "int" } - }, + } + ] + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } }, { - "type": "list", - "item": { - "type": { - "name": "float" - } + "type": { + "name": "int" } } ] } }, "metadata": { - "usage.scipy": 53 + "usage.skimage": 1, + "usage.sklearn": 1 } }, { @@ -71198,90 +67518,58 @@ "module": "numpy", "name": "ndarray" } + }, + "axis": { + "type": { + "name": "int" + } } }, - "pos_or_kw_optional": { - "order": { - "type": "str", - "options": [ - "C" - ] - } - }, - "metadata": { - "usage.sklearn": 41 - } - } - ], - "atleast_2d": [ - { - "var_pos": [ - "arys", - { - "type": "str", - "options": [ - "v", - "t" - ] - } - ], "metadata": { - "usage.skimage": 7, - "usage.xarray": 2, - "usage.pandas": 18, - "usage.scipy": 239, - "usage.matplotlib": 12, - "usage.dask": 3, - "usage.sklearn": 45 + "usage.xarray": 6, + "usage.matplotlib": 6, + "usage.sklearn": 6 } - } - ], - "rot90": [ + }, { "pos_or_kw_required": { - "m": { + "a": { "type": { "module": "numpy", "name": "ndarray" } }, - "k": { - "type": { - "name": "int" - } + "axis": { + "type": "None" } }, "metadata": { - "usage.skimage": 2 + "usage.xarray": 6, + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "m": { + "a": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "dask.array.core", + "name": "Array" } + }, + "axis": { + "type": "None" } }, "metadata": { - "usage.skimage": 5, - "usage.scipy": 5 + "usage.xarray": 1 } - } - ], - "roll": [ + }, { "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "shift": { - "type": { - "name": "int" + "module": "dask.array.core", + "name": "Array" } }, "axis": { @@ -71291,154 +67579,101 @@ } }, "metadata": { - "usage.skimage": 8, - "usage.xarray": 1, - "usage.matplotlib": 3 + "usage.xarray": 1 } }, { "pos_or_kw_required": { "a": { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] - } - }, - "shift": { "type": { - "name": "int" + "module": "numpy", + "name": "datetime64" } } }, "metadata": { - "usage.skimage": 2 + "usage.xarray": 1 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "shift": { "type": "tuple", "items": [ { "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - "axis": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } } ] } }, "metadata": { - "usage.skimage": 2 + "usage.xarray": 2, + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "object" }, - "shift": { - "type": { - "name": "int" - } + "axis": { + "type": "None" } }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 1 + "usage.xarray": 1 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "shift": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": "object" } }, - "pos_or_kw_optional": { - "axis": { + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "a": { "type": { - "name": "int" + "module": "xarray.core.dataarray", + "name": "DataArray" } } }, "metadata": { - "usage.pandas": 10 + "usage.xarray": 1 } }, { "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "shift": { - "type": { - "name": "int" + "module": "xarray.core.dataset", + "name": "Dataset" } } }, - "pos_or_kw_optional": { + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "object" + }, "axis": { "type": { "name": "int" @@ -71446,62 +67681,19 @@ } }, "metadata": { - "usage.scipy": 8 + "usage.xarray": 1 } }, { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "shift": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - { - "type": { - "name": "int" - } - } - ] + "type": "object" } }, "pos_or_kw_optional": { "axis": { "type": "union", "options": [ - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, { "type": "None" }, @@ -71511,121 +67703,81 @@ } } ] - } - }, - "metadata": { - "usage.dask": 5 - } - } - ], - "tri": [ - { - "pos_or_kw_required": { - "N": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.skimage": 2, - "usage.sklearn": 1 - } - }, - { - "pos_or_kw_required": { - "N": { - "type": { - "name": "int" - } }, - "M": { + "out": { "type": { "name": "int" } }, - "k": { + "keepdims": { "type": { "name": "int" } } }, "metadata": { - "usage.skimage": 6 + "usage.pandas": 53 } }, { "pos_or_kw_required": { - "N": { + "a": { + "type": "object" + } + }, + "pos_or_kw_optional": { + "axis": { "type": { "name": "int" } }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "int32" + "keepdims": { + "type": { + "name": "bool" } } }, + "pos_or_kw_optional_ordering": [ + [ + "axis", + "keepdims" + ] + ], "metadata": { - "usage.skimage": 1 + "usage.scipy": 98 } }, { "pos_or_kw_required": { - "N": { - "type": { - "name": "int" - } - }, - "M": { - "type": { - "name": "int" - } - }, - "k": { - "type": { - "name": "int" - } - }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "bool_" + "a": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "float64" + } } } }, "metadata": { - "usage.scipy": 1 + "usage.matplotlib": 20, + "usage.sklearn": 10 } - } - ], - "prod": [ + }, { "pos_or_kw_required": { "a": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } + "type": "list", + "item": { + "type": { + "name": "float" } - ] + } } }, "metadata": { - "usage.skimage": 2, - "usage.xarray": 4, - "usage.matplotlib": 2 + "usage.matplotlib": 1, + "usage.sklearn": 1 } }, { @@ -71635,12 +67787,187 @@ "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + } + ] + } + }, + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] + } + }, + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] + } + }, + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + } + ] + } + }, + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_only_optional": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + ] + } + }, + "pos_or_kw_optional": { + "a": { + "type": "object" + }, + "axis": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": "None" + } + ] } }, { @@ -71649,11 +67976,99 @@ } } ] + }, + "keepdims": { + "type": { + "name": "bool" + } + }, + "out": { + "type": "union", + "options": [ + { + "type": { + "module": "dask.dataframe.core", + "name": "Scalar" + } + }, + { + "type": { + "module": "dask.dataframe.core", + "name": "Series" + } + } + ] + } + }, + "pos_or_kw_optional_ordering": [ + [ + "a", + "keepdims" + ], + [ + "axis", + "keepdims" + ], + [ + "axis", + "out" + ], + [ + "a", + "axis" + ] + ], + "kw_only_optional": { + "computing_meta": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.skimage": 3, - "usage.xarray": 3 + "usage.dask": 149 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "name": "float" + } + } + }, + "metadata": { + "usage.sklearn": 3 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.sklearn": 2 + } + } + ], + "amax": [ + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.skimage": 70, + "usage.xarray": 9, + "usage.matplotlib": 35, + "usage.sklearn": 52 } }, { @@ -71668,8 +68083,25 @@ } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 1 + "usage.skimage": 2, + "usage.matplotlib": 2, + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "int64" + } + } + } + }, + "metadata": { + "usage.skimage": 4 } }, { @@ -71679,17 +68111,57 @@ "module": "numpy", "name": "ndarray" } + }, + "axis": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.skimage": 3 + "usage.skimage": 6, + "usage.xarray": 6, + "usage.matplotlib": 6, + "usage.sklearn": 14 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "axis": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 2 } }, { "pos_or_kw_required": { "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "axis": { "type": "tuple", "items": [ + { + "type": { + "name": "int" + } + }, { "type": { "name": "int" @@ -71699,34 +68171,93 @@ } }, "metadata": { - "usage.xarray": 3 + "usage.skimage": 1, + "usage.xarray": 1 } }, { "pos_or_kw_required": { "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "axis": { "type": "tuple", - "items": { - "type": "None" + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "keepdims": { + "type": { + "name": "bool" } } }, "metadata": { - "usage.xarray": 1, - "usage.matplotlib": 2 + "usage.skimage": 2 } }, { "pos_or_kw_required": { "a": { - "type": "list", - "item": { - "type": "bottom" + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "axis": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "keepdims": { + "type": { + "name": "bool" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 2 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + }, + "metadata": { + "usage.skimage": 2, + "usage.matplotlib": 1 } }, { @@ -71748,7 +68279,19 @@ "type": { "name": "int" } - }, + } + ] + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -71758,7 +68301,7 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1 } }, { @@ -71771,6 +68314,23 @@ "name": "int" } }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.skimage": 3 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -71795,7 +68355,7 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1 } }, { @@ -71811,13 +68371,17 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 7, + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "a": { - "type": "object" + "type": { + "module": "dask.array.core", + "name": "Array" + } }, "axis": { "type": "None" @@ -71830,7 +68394,15 @@ { "pos_or_kw_required": { "a": { - "type": "object" + "type": { + "module": "dask.array.core", + "name": "Array" + } + }, + "axis": { + "type": { + "name": "int" + } } }, "metadata": { @@ -71841,8 +68413,8 @@ "pos_or_kw_required": { "a": { "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" + "module": "numpy", + "name": "datetime64" } } }, @@ -71853,10 +68425,10 @@ { "pos_or_kw_required": { "a": { - "type": { - "module": "xarray.core.dataset", - "name": "Dataset" - } + "type": "object" + }, + "axis": { + "type": "None" } }, "metadata": { @@ -71867,11 +68439,6 @@ "pos_or_kw_required": { "a": { "type": "object" - }, - "axis": { - "type": { - "name": "int" - } } }, "metadata": { @@ -71882,13 +68449,8 @@ "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "axis": { - "type": { - "name": "int" + "module": "xarray.core.dataarray", + "name": "DataArray" } } }, @@ -71899,134 +68461,64 @@ { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - } - } - ] + "type": { + "module": "xarray.core.dataset", + "name": "Dataset" + } } }, - "pos_or_kw_optional": { - "dtype": { - "type": "str", - "options": [ - "i8" - ] + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "object" }, "axis": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "name": "int" + } } }, "metadata": { - "usage.pandas": 52 + "usage.xarray": 1 } }, { "pos_or_kw_required": { "a": { + "type": "object" + } + }, + "pos_or_kw_optional": { + "axis": { "type": "union", "options": [ { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": "None" - } - ] - } + "type": "None" }, { "type": { "name": "int" } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } } ] - } - }, - "pos_or_kw_optional": { - "axis": { + }, + "out": { + "type": { + "name": "int" + } + }, + "keepdims": { "type": { "name": "int" } } }, "metadata": { - "usage.scipy": 63 + "usage.pandas": 41 } }, { @@ -72039,29 +68531,28 @@ "axis": { "type": "union", "options": [ - { - "type": "None" - }, { "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } + "items": [ + { + "type": { + "name": "int" } - ] - } + }, + { + "type": { + "name": "int" + } + } + ] }, { "type": { "name": "int" } + }, + { + "type": "None" } ] }, @@ -72069,390 +68560,190 @@ "type": { "name": "bool" } - }, - "dtype": { - "type": "union", - "options": [ - { - "type": "type", - "name": { - "name": "int" - } - }, - { - "type": "str", - "options": [ - "i8", - "f8", - "i4", - "f4" - ] - } - ] - }, - "out": { - "type": "union", - "options": [ - { - "type": { - "module": "dask.dataframe.core", - "name": "Scalar" - } - }, - { - "type": { - "module": "dask.dataframe.core", - "name": "Series" - } - } - ] } }, "pos_or_kw_optional_ordering": [ [ "axis", "keepdims" - ], - [ - "axis", - "out" ] ], "metadata": { - "usage.dask": 100 + "usage.scipy": 188 } }, { "pos_or_kw_required": { "a": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "float64" } - ] - } - }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" - } - }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" } } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "dtype" - ] - ], "metadata": { - "usage.sklearn": 10 + "usage.matplotlib": 20 } - } - ], - "unravel_index": [ + }, { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "int64" - } - }, - "_1": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } + "pos_or_kw_required": { + "a": { + "type": "list", + "item": { + "type": { + "name": "float" } - ] + } } }, "metadata": { - "usage.skimage": 8, - "usage.sklearn": 1 + "usage.matplotlib": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "_1": { + "pos_or_kw_required": { + "a": { "type": "tuple", "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "int" - } - } - ] - } - }, - "metadata": { - "usage.skimage": 2 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "int64" - } - }, - "_1": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } - } - ] - } - }, - "metadata": { - "usage.skimage": 4 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "int64" - } - }, - "_1": { - "type": "tuple", - "items": [ + }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } - } - ] - } - }, - "metadata": { - "usage.skimage": 2 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": "tuple", - "items": [ + }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } } ] } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 1 } }, { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { + "pos_or_kw_required": { + "a": { "type": "tuple", "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" } } ] } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 1 } }, { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ + "pos_or_kw_required": { + "a": { + "type": "tuple", + "items": [ { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } }, - { - "type": { - "name": "int" - } - } - ] - }, - "_1": { - "type": "union", - "options": [ { "type": { "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } + "name": "float64" } } ] } }, - "pos_only_optional": { - "_2": { - "type": "str", - "options": [ - "F" - ] - } - }, "metadata": { - "usage.scipy": 4 + "usage.matplotlib": 1 } }, { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "int64" - } - } - }, - "_1": { + "pos_or_kw_required": { + "a": { "type": "tuple", "items": [ { "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" + "module": "numpy", + "name": "int64" } } ] @@ -72463,26 +68754,14 @@ } }, { - "pos_only_required": { - "_0": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - "_1": { + "pos_or_kw_required": { + "a": { "type": "tuple", "items": [ { "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" + "module": "numpy", + "name": "float64" } } ] @@ -72493,7 +68772,7 @@ } }, { - "pos_only_required": { + "pos_only_optional": { "_0": { "type": "union", "options": [ @@ -72505,321 +68784,414 @@ }, { "type": { - "module": "numpy", - "name": "int64" + "module": "numpy.ma.core", + "name": "MaskedArray" } } ] } }, - "pos_only_optional": { - "_2": { - "type": "str", + "pos_or_kw_optional": { + "axis": { + "type": "union", "options": [ - "F", - "C" - ] - }, - "_1": { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" + { + "type": "None" + }, + { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "name": "int" + } + } + ] } - ] + }, + { + "type": { + "name": "int" + } + } + ] + }, + "keepdims": { + "type": { + "name": "bool" } + }, + "a": { + "type": "object" + }, + "out": { + "type": "union", + "options": [ + { + "type": { + "module": "dask.dataframe.core", + "name": "Scalar" + } + }, + { + "type": { + "module": "dask.dataframe.core", + "name": "Series" + } + } + ] } }, - "pos_only_optional_ordering": [ + "pos_or_kw_optional_ordering": [ [ - "_1", - "_2" + "a", + "keepdims" + ], + [ + "axis", + "keepdims" + ], + [ + "axis", + "out" + ], + [ + "a", + "axis" ] ], "kw_only_optional": { - "order": { - "type": "str", - "options": [ - "F", - "C" - ] - }, - "shape": { - "type": "tuple", - "items": { - "type": { - "name": "int" - } + "computing_meta": { + "type": { + "name": "bool" } } }, "metadata": { - "usage.dask": 12 + "usage.dask": 148 } } ], - "apply_over_axes": [ + "clip": [ { "pos_or_kw_required": { - "func": { - "type": "function", - "name": { + "a": { + "type": { "module": "numpy", - "name": "sum" + "name": "ndarray" + } + }, + "a_min": { + "type": { + "name": "int" + } + }, + "a_max": { + "type": { + "name": "int" } }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.skimage": 13, + "usage.matplotlib": 6, + "usage.sklearn": 4 + } + }, + { + "pos_or_kw_required": { "a": { "type": { "module": "numpy", "name": "ndarray" } }, - "axes": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "a_min": { + "type": { + "name": "float" + } + }, + "a_max": { + "type": { + "name": "float" + } } }, "metadata": { - "usage.skimage": 10 + "usage.skimage": 8, + "usage.matplotlib": 2, + "usage.sklearn": 4 } }, { "pos_or_kw_required": { - "func": { - "type": "function", - "name": { + "a": { + "type": { "module": "numpy", - "name": "sum" + "name": "ndarray" + } + }, + "a_min": { + "type": { + "name": "int" + } + }, + "a_max": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 18, + "usage.matplotlib": 5, + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "a_min": { + "type": { + "name": "int" } }, + "a_max": { + "type": "None" + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_or_kw_required": { "a": { "type": { "module": "numpy", "name": "ndarray" } }, - "axes": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "a_min": { + "type": "None" + }, + "a_max": { + "type": { + "name": "int" + } + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.scipy": 7 + "usage.skimage": 1, + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "func": { - "type": "function" + "a": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "a_min": { + "type": { + "name": "int" + } }, + "a_max": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_or_kw_required": { "a": { "type": { "module": "numpy", "name": "ndarray" } }, - "axes": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - } - }, - { - "type": { - "name": "int" - } - } - ] + "a_min": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.dask": 5 + "usage.skimage": 1, + "usage.matplotlib": 2 } - } - ], - "tile": [ + }, { "pos_or_kw_required": { - "A": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "a": { + "type": { + "module": "numpy", + "name": "ndarray" } }, - "reps": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "a_min": { + "type": { + "module": "numpy", + "name": "float32" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "float32" + } + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.skimage": 2 + "usage.skimage": 1 } }, { "pos_or_kw_required": { - "A": { + "a": { "type": { "module": "numpy", "name": "ndarray" } }, - "reps": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "a_min": { + "type": { + "name": "int" + } + }, + "a_max": { + "type": "None" } }, "metadata": { - "usage.skimage": 8, - "usage.xarray": 1, - "usage.matplotlib": 15 + "usage.skimage": 1 } }, { "pos_or_kw_required": { - "A": { + "a": { "type": { "module": "numpy", "name": "ndarray" } }, - "reps": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "a_min": { + "type": { + "name": "float" + } + }, + "a_max": { + "type": { + "name": "float" + } + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { "usage.skimage": 2, - "usage.xarray": 5 + "usage.sklearn": 3 } }, { "pos_or_kw_required": { - "A": { + "a": { "type": { "module": "numpy", "name": "ndarray" } }, - "reps": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "a_min": { + "type": { + "name": "int" + } + }, + "a_max": { + "type": "None" + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 1, + "usage.sklearn": 13 } }, { "pos_or_kw_required": { - "A": { + "a": { "type": { "module": "numpy", "name": "ndarray" } }, - "reps": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "a_min": { + "type": { + "module": "numpy", + "name": "float16" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "float16" + } } }, "metadata": { @@ -72828,27 +69200,22 @@ }, { "pos_or_kw_required": { - "A": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, - "reps": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "a_min": { + "type": { + "module": "numpy", + "name": "float32" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "float32" } } }, @@ -72858,221 +69225,167 @@ }, { "pos_or_kw_required": { - "A": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "range" - } - }, - { - "type": { - "module": "pandas.core.indexes.base", - "name": "Index" - } - }, - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int8" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "str", - "options": [ - "c", - "b", - "a" - ] - } - ] - } - } - ] + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, - "reps": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - } - ] + "a_min": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.pandas": 49 + "usage.skimage": 3, + "usage.sklearn": 5 } }, { "pos_or_kw_required": { - "A": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, - "reps": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - { - "type": { - "name": "int" - } - } - ] + "a_min": { + "type": { + "module": "numpy", + "name": "int16" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "int16" + } } }, "metadata": { - "usage.scipy": 51 + "usage.skimage": 1 } }, { "pos_or_kw_required": { - "A": { + "a": { + "type": { + "name": "int" + } + }, + "a_min": { + "type": { + "name": "int" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_or_kw_required": { + "a": { "type": { "module": "numpy", "name": "ndarray" } }, - "reps": { + "a_min": { "type": { - "name": "int" + "module": "numpy", + "name": "uint8" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "uint8" } } }, "metadata": { - "usage.matplotlib": 9 + "usage.skimage": 1 } }, { "pos_or_kw_required": { - "A": { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] + "a": { + "type": { + "module": "numpy", + "name": "float64" } }, - "reps": { + "a_min": { "type": { - "name": "int" + "module": "numpy", + "name": "float64" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 1 } }, { "pos_or_kw_required": { - "A": { - "type": "tuple", - "items": [ + "a": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + }, + "a_min": { + "type": { + "name": "float" + } + }, + "a_max": { + "type": { + "name": "float" + } + } + }, + "metadata": { + "usage.pandas": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "union", + "options": [ { "type": { "name": "float" } }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, { "type": { "module": "numpy", @@ -73081,9 +69394,9 @@ } ] }, - "reps": { - "type": "tuple", - "items": [ + "a_min": { + "type": "union", + "options": [ { "type": { "name": "int" @@ -73091,21 +69404,9 @@ }, { "type": { - "name": "int" + "name": "float" } - } - ] - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_or_kw_required": { - "A": { - "type": "union", - "options": [ + }, { "type": { "module": "numpy", @@ -73113,82 +69414,43 @@ } }, { - "type": "list", - "item": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } + "type": { + "module": "numpy", + "name": "float64" } } ] }, - "reps": { + "a_max": { "type": "union", "options": [ - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - }, { "type": { "name": "int" } - } - ] - } - }, - "metadata": { - "usage.dask": 18 - } - }, - { - "pos_or_kw_required": { - "A": { - "type": "object" - }, - "reps": { - "type": "union", - "options": [ + }, { - "type": "tuple", - "items": { - "type": { - "name": "int" - } + "type": { + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { - "type": "list", - "item": { - "type": { - "name": "int" - } + "type": { + "name": "float" } } ] } }, - "metadata": { - "usage.sklearn": 31 - } - } - ], - "real": [ - { - "pos_or_kw_required": { - "val": { + "pos_or_kw_optional": { + "out": { "type": { "module": "numpy", "name": "ndarray" @@ -73196,151 +69458,208 @@ } }, "metadata": { - "usage.skimage": 7, - "usage.sklearn": 5 + "usage.scipy": 38 } }, { "pos_or_kw_required": { - "val": { + "a": { "type": { - "name": "float" + "name": "int" + } + }, + "a_min": { + "type": { + "name": "int" + } + }, + "a_max": { + "type": { + "name": "int" } } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 3, + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "val": { + "a": { "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" + "name": "float" + } + }, + "a_min": { + "type": { + "name": "int" + } + }, + "a_max": { + "type": { + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.matplotlib": 2 } }, { "pos_or_kw_required": { - "val": { + "a": { "type": { "module": "numpy", - "name": "complex128" + "name": "float64" + } + }, + "a_min": { + "type": { + "name": "float" + } + }, + "a_max": { + "type": { + "name": "float" } } }, "metadata": { - "usage.pandas": 1 + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { - "val": { - "type": "object" - } - }, - "metadata": { - "usage.scipy": 106, - "usage.dask": 32 - } - } - ], - "imag": [ - { - "pos_or_kw_required": { - "val": { + "a": { "type": { "module": "numpy", "name": "ndarray" } + }, + "a_min": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "a_max": { + "type": { + "name": "float" + } } }, "metadata": { - "usage.skimage": 1, - "usage.sklearn": 1 + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { - "val": { + "a": { "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + "a_min": { + "type": { + "name": "int" + } + }, + "a_max": { + "type": { + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.matplotlib": 8 } }, { "pos_or_kw_required": { - "val": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "complex128" - } - } - ] + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "a_min": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 2 + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { - "val": { - "type": "object" + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "a_min": { + "type": "None" + }, + "a_max": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.scipy": 51, - "usage.dask": 31 + "usage.matplotlib": 1 } - } - ], - "ix_": [ + }, { - "var_pos": [ - "args", - { - "type": "str", - "options": [ - "v", - "t" - ] + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "a_min": { + "type": { + "module": "numpy", + "name": "float128" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "float128" + } + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" + } } - ], + }, "metadata": { - "usage.skimage": 2, - "usage.xarray": 3, - "usage.scipy": 1, - "usage.matplotlib": 6, - "usage.sklearn": 4 + "usage.matplotlib": 1 } }, - { - "metadata": { - "usage.xarray": 1 - } - } - ], - "convolve": [ { "pos_or_kw_required": { "a": { @@ -73349,23 +69668,27 @@ "name": "ndarray" } }, - "v": { - "type": "list", - "item": { - "type": { - "name": "float" - } + "a_min": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedConstant" } }, - "mode": { - "type": "str", - "options": [ - "valid" - ] + "a_max": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedConstant" + } + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 1 } }, { @@ -73373,6 +69696,18 @@ "a": { "type": "union", "options": [ + { + "type": { + "module": "dask.dataframe.core", + "name": "DataFrame" + } + }, + { + "type": { + "module": "dask.dataframe.core", + "name": "Series" + } + }, { "type": { "module": "numpy", @@ -73380,69 +69715,79 @@ } }, { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "complex128" - } - } - ] + "type": { + "module": "pandas.core.series", + "name": "Series" + } + }, + { + "type": { + "module": "pandas.core.frame", + "name": "DataFrame" } } ] }, - "v": { + "a_min": { "type": "union", "options": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "float" } }, { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "complex128" - } - } - ] + "type": { + "name": "int" } + }, + { + "type": "None" } ] - } - }, - "pos_or_kw_optional": { - "mode": { - "type": "str", + }, + "a_max": { + "type": "union", "options": [ - "full", - "same", - "valid" + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": "None" + } ] } }, "metadata": { - "usage.scipy": 654 + "usage.dask": 23 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "a_min": { + "type": { + "name": "float" + } + }, + "a_max": { + "type": "None" + } + }, + "metadata": { + "usage.sklearn": 1 } }, { @@ -73453,55 +69798,113 @@ "name": "ndarray" } }, - "v": { + "a_min": { + "type": "None" + }, + "a_max": { + "type": { + "name": "float" + } + }, + "out": { "type": { "module": "numpy", "name": "ndarray" } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "float64" + } }, - "mode": { - "type": "str", - "options": [ - "same" - ] + "a_min": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.matplotlib": 2 + "usage.sklearn": 2 } - } - ], - "array_equal": [ + }, { "pos_or_kw_required": { - "a1": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "a": { + "type": { + "module": "numpy", + "name": "float64" } }, - "a2": { + "a_min": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "a_max": { + "type": "None" + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { "type": { "module": "numpy", "name": "ndarray" } + }, + "a_min": { + "type": { + "name": "float" + } + }, + "a_max": { + "type": "None" } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "a1": { + "a": { "type": { "module": "numpy", "name": "ndarray" } }, - "a2": { + "a_min": { + "type": { + "name": "int" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "out": { "type": { "module": "numpy", "name": "ndarray" @@ -73509,80 +69912,237 @@ } }, "metadata": { - "usage.skimage": 2, - "usage.xarray": 1, - "usage.matplotlib": 1, - "usage.dask": 4 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "a1": { - "type": "tuple", - "items": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - } - ] + "a": { + "type": { + "module": "numpy", + "name": "float64" + } }, - "a2": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "a_min": { + "type": { + "module": "numpy", + "name": "float32" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "a1": { + "a": { "type": { "module": "numpy", "name": "ndarray" } }, - "a2": { + "a_min": { + "type": { + "module": "numpy", + "name": "float32" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "float64" + } + } + }, + "metadata": { + "usage.sklearn": 3 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "a_min": { + "type": { + "name": "int" + } + }, + "a_max": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "a_min": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "a_max": { + "type": "None" + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "a_min": { + "type": { + "name": "float" + } + }, + "a_max": { + "type": "None" + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "a_min": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "int64" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "a_min": { + "type": { + "name": "int" + } + }, + "a_max": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + } + ], + "squeeze": [ + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.skimage": 11, + "usage.sklearn": 16 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "axis": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 7 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "axis": { "type": "tuple", "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, { "type": { "name": "int" @@ -73592,19 +70152,18 @@ } }, "metadata": { - "usage.skimage": 1, "usage.xarray": 1 } }, { "pos_or_kw_required": { - "a1": { + "a": { "type": { "module": "numpy", "name": "ndarray" } }, - "a2": { + "axis": { "type": "tuple", "items": [ { @@ -73626,60 +70185,15 @@ }, { "pos_or_kw_required": { - "a1": { + "a": { "type": "union", "options": [ { "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "pandas.core.series", - "name": "Series" + "module": "pandas.core.frame", + "name": "DataFrame" } }, - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "bool" - } - }, - { - "type": { - "module": "numpy", - "name": "int32" - } - }, - { - "type": { - "name": "int" - } - } - ] - } - } - ] - }, - "a2": { - "type": "union", - "options": [ { "type": { "module": "numpy", @@ -73696,455 +70210,311 @@ } }, "metadata": { - "usage.pandas": 142 + "usage.pandas": 5 } }, { "pos_or_kw_required": { - "a1": { + "a": { "type": "union", "options": [ { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "None" - }, - { - "type": "str", - "options": [ - "2020-02-29", - "" - ] - } - ] - } - }, - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - } - ] - } - }, - { - "type": "str", - "options": [ - "hi" - ] - } - ] + "type": { + "module": "numpy", + "name": "ndarray" } }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "float" } }, { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": "tuple", - "items": [ - { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - }, - { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": "None" - } - ] - } - ] - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "numpy", + "name": "float64" } }, { - "type": "str", - "options": [ - "hi", - "hello" - ] + "type": { + "name": "int" + } } ] - }, - "a2": { + } + }, + "pos_or_kw_optional": { + "axis": { "type": "union", "options": [ { - "type": "list", - "item": { - "type": "list", - "item": { - "type": "bottom" + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } } - } + ] }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } + }, + { + "type": "None" } ] } }, "metadata": { - "usage.scipy": 135 + "usage.scipy": 35 } }, { "pos_or_kw_required": { - "a1": { - "type": "None" + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, - "a2": { - "type": "None" - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_or_kw_required": { - "a1": { + "axis": { "type": "union", "options": [ { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "float" + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } } - } - } - ] - }, - "a2": { - "type": "union", - "options": [ + ] + }, { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float32" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - } - ] - } + "type": "None" }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } ] } }, "metadata": { - "usage.sklearn": 33 + "usage.dask": 3 } - } - ], - "indices": [ + }, { "pos_or_kw_required": { - "dimensions": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } + "a": { + "type": { + "module": "numpy", + "name": "float64" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "name": "float" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "list", + "item": { + "type": { + "name": "float" } - ] + } } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "dimensions": { + "a": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + } + }, + "metadata": { + "usage.sklearn": 2 + } + } + ], + "concatenate": [ + { + "pos_only_required": { + "_0": { "type": "tuple", "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } } ] } }, + "kw_only_required": { + "axis": { + "type": { + "name": "int" + } + } + }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 2 + "usage.skimage": 3, + "usage.sklearn": 2 } }, { - "pos_or_kw_required": { - "dimensions": { + "pos_only_required": { + "_0": { "type": "tuple", "items": [ { "type": { "module": "numpy", - "name": "int64" + "name": "ndarray" } }, { "type": { "module": "numpy", - "name": "int64" + "name": "ndarray" } } ] - }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" + } + }, + "kw_only_required": { + "axis": { + "type": { + "name": "int" } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 3, + "usage.xarray": 1, + "usage.sample-usage": 1, + "usage.sklearn": 8 } }, { - "pos_or_kw_required": { - "dimensions": { - "type": "union", - "options": [ + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { - "type": "list", - "item": { - "type": { - "name": "int" - } + "type": { + "module": "numpy", + "name": "ndarray" } }, { - "type": "tuple", - "items": { - "type": { - "name": "int" - } + "type": { + "module": "numpy", + "name": "ndarray" } - } - ] - } - }, - "pos_or_kw_optional": { - "dtype": { - "type": "union", - "options": [ + }, { "type": { "module": "numpy", - "name": "dtype" + "name": "ndarray" } }, { - "type": "type" + "type": { + "module": "numpy", + "name": "ndarray" + } } ] } }, - "metadata": { - "usage.scipy": 26 - } - }, - { - "pos_or_kw_required": { - "dimensions": { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - } - } - }, - "pos_or_kw_optional": { - "dtype": { - "type": "type", - "name": { - "name": "float" + "kw_only_required": { + "axis": { + "type": { + "name": "int" } } }, "metadata": { - "usage.dask": 8 + "usage.skimage": 2 } }, { - "pos_or_kw_required": { - "dimensions": { - "type": "union", - "options": [ - { - "type": { - "name": "generator" - } - }, - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" } - ] + } } }, - "metadata": { - "usage.sklearn": 3 - } - } - ], - "insert": [ - { - "pos_or_kw_required": { - "arr": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "obj": { - "type": { - "name": "int" - } - }, - "values": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, + "kw_only_required": { "axis": { "type": { "name": "int" @@ -74152,76 +70522,39 @@ } }, "metadata": { - "usage.skimage": 3 + "usage.skimage": 12, + "usage.xarray": 33, + "usage.sklearn": 15 } }, { - "pos_or_kw_required": { - "arr": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "obj": { - "type": "union", - "options": [ + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { "module": "numpy", "name": "ndarray" } - }, - { - "type": { - "name": "int" - } - } - ] - }, - "values": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } } ] } }, "metadata": { - "usage.pandas": 15 + "usage.skimage": 3, + "usage.sklearn": 7 } }, { - "pos_or_kw_required": { - "arr": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "obj": { - "type": "union", - "options": [ + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { @@ -74229,16 +70562,6 @@ "module": "numpy", "name": "ndarray" } - } - ] - }, - "values": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } }, { "type": { @@ -74249,256 +70572,154 @@ ] } }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.scipy": 145 - } - }, - { - "pos_or_kw_required": { - "arr": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "obj": { - "type": { - "name": "int" - } - }, - "values": { - "type": { - "name": "float" - } - }, - "axis": { - "type": { - "name": "int" - } - } - }, "metadata": { - "usage.matplotlib": 2 + "usage.skimage": 4, + "usage.sklearn": 3 } }, { - "pos_or_kw_required": { - "arr": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "obj": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { + "type": "list", + "item": { "type": { "name": "int" } - }, - "step": { - "type": "None" } } ] - }, - "values": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" + } + }, + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { + "type": { + "name": "int" + } + } } - } - ] - }, - "axis": { - "type": { - "name": "int" + ] } } }, "metadata": { - "usage.dask": 11 + "usage.skimage": 2, + "usage.matplotlib": 1, + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "arr": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "obj": { - "type": { - "name": "int" - } - }, - "values": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { "type": { "module": "numpy", - "name": "int64" + "name": "ndarray" } } ] } }, "metadata": { - "usage.sklearn": 4 - } - } - ], - "full_like": [ - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "fill_value": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.skimage": 1, - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" - } - }, - "fill_value": { - "type": { - "name": "int" - } - }, - "dtype": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 + "usage.skimage": 5, + "usage.matplotlib": 6, + "usage.sklearn": 14 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" - } - }, - "fill_value": { - "type": { - "name": "bool" - } - }, - "dtype": { - "type": "type", - "name": { - "name": "bool" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" + } } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 6, + "usage.xarray": 3, + "usage.matplotlib": 38, + "usage.sklearn": 68 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" - } - }, - "fill_value": { - "type": { - "name": "int" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } } - }, - "dtype": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 16, + "usage.matplotlib": 4, + "usage.sklearn": 2 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" - } - }, - "fill_value": { - "type": { - "name": "bool" - } - }, - "dtype": { - "type": "type", - "name": { - "name": "bool" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + } + ] + } } } }, @@ -74507,54 +70728,78 @@ } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.variable", - "name": "IndexVariable" - } - }, - "fill_value": { - "type": { - "name": "int" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } - }, - "dtype": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 3, + "usage.matplotlib": 2 } }, { - "pos_or_kw_required": { - "a": { - "type": "object" - }, - "fill_value": { - "type": { - "name": "float" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": { + "name": "float" + } + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1, + "usage.matplotlib": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeGregorian" + } + } } - }, - "fill_value": { - "type": "object" - }, - "dtype": { - "type": "None" } }, "metadata": { @@ -74562,21 +70807,15 @@ } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" - } - }, - "fill_value": { - "type": { - "module": "numpy", - "name": "ndarray" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "module": "xarray.core.dataarray", + "name": "DataArray" + } } - }, - "dtype": { - "type": "None" } }, "metadata": { @@ -74584,220 +70823,290 @@ } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "module": "sparse._coo.core", + "name": "COO" + } } - }, - "fill_value": { - "type": "object" - }, - "dtype": { - "type": "None" } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" - } - }, - "fill_value": { + "kw_only_required": { + "axis": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } - }, - "dtype": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 2 } }, { - "pos_or_kw_required": { - "a": { - "type": "object" - }, - "fill_value": { - "type": { - "name": "bool" - } - }, - "dtype": { - "type": "type", - "name": { - "name": "bool" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "object" } } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "fill_value": { + "kw_only_required": { + "axis": { "type": { - "name": "bool" - } - }, - "dtype": { - "type": "type", - "name": { - "name": "bool" + "name": "int" } } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 7 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" - } - }, - "fill_value": { - "type": { - "name": "int" - } - }, - "dtype": { - "type": "type", - "name": { - "name": "int" - } + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": { + "type": "object" + } + }, + { + "type": "list", + "item": { + "type": "object" + } + } + ] } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" - } - }, - "fill_value": { + "kw_only_optional": { + "axis": { "type": { - "name": "float" + "name": "int" } - }, - "dtype": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 312 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { - "type": { - "module": "numpy", - "name": "float64" + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + { + "type": { + "module": "numpy", + "name": "flatiter" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": "list", + "item": { + "type": "object" + } + } + ] } }, { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } }, { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + } + ] } } ] - }, - "fill_value": { + } + }, + "pos_only_optional": { + "_1": { + "type": { + "name": "int" + } + } + }, + "kw_only_optional": { + "axis": { "type": "union", "options": [ { "type": { - "name": "float" + "name": "int" } }, { - "type": { - "name": "int" - } + "type": "None" } ] } }, - "pos_or_kw_optional": { - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" + "metadata": { + "usage.scipy": 377 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "_1": { + "type": { + "name": "int" } } }, "metadata": { - "usage.scipy": 29 + "usage.matplotlib": 4, + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": "list", "item": { - "type": { - "name": "float" - } - } - }, - "fill_value": { - "type": { - "name": "float" + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "float64" + } + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } } }, "metadata": { - "usage.matplotlib": 3 + "usage.matplotlib": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "fill_value": { - "type": { - "name": "float" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } } }, @@ -74806,475 +71115,606 @@ } }, { - "pos_or_kw_required": { - "a": { + "pos_only_required": { + "_0": { "type": "list", "item": { - "type": { - "module": "numpy", - "name": "float64" - } - } - }, - "fill_value": { - "type": { - "name": "float" + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "uint8" + } + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } } }, "metadata": { - "usage.matplotlib": 1 + "usage.matplotlib": 4 } }, { - "pos_or_kw_required": { - "a": { - "type": "object" - }, - "fill_value": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": 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}, "metadata": { - "usage.xarray": 1 + "usage.matplotlib": 2 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "axis": { - "type": { - "name": "int" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "bottom" } } }, "metadata": { - "usage.xarray": 1 + "usage.matplotlib": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "axis": { + "pos_only_required": { + "_0": { "type": "tuple", "items": [ + { + "type": "tuple", + "items": [ + { + "type": { + "name": "float" + } + } + ] + }, { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } ] } }, "metadata": { - "usage.xarray": 1 + "usage.matplotlib": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -75282,38 +71722,55 @@ } }, { - "type": { - "module": "pandas.core.series", - "name": "Series" - } + "type": "tuple", + "items": [ + { + "type": { + "name": "float" + } + } + ] } ] } }, - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { - "type": "None" + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] }, { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } ] } }, "metadata": { - "usage.pandas": 17 + "usage.matplotlib": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -75321,126 +71778,227 @@ } }, { - "type": "list", - "item": { - "type": { - "name": "float" + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } } - } + ] } ] } }, - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { - "type": "None" + "type": { + "module": "numpy", + "name": "ndarray" + } }, { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } } ] - }, - "keepdims": { - "type": { - "name": "bool" - } } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "keepdims" - ] - ], "metadata": { - "usage.scipy": 28 + "usage.matplotlib": 1 } }, { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "object" } - }, - "axis": { + } + }, + "pos_only_optional": { + "_1": { "type": { "name": "int" } - }, - "overwrite_input": { + } + }, + "kw_only_optional": { + "axis": { "type": { - "name": "bool" + "name": "int" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.dask": 117 } }, { - "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, { "type": { "module": "numpy", "name": "ndarray" } - }, + } + ] + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { - "type": "tuple", - "items": { + "type": "list", + "item": { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } ] } }, - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, { "type": "list", "item": { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } } }, { - "type": { - "name": "int" + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "float64" + } + } + }, + { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "float64" + } } } ] - }, - "keepdims": { - "type": { - "name": "bool" - } } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "keepdims" - ] - ], "metadata": { - "usage.dask": 18 + "usage.sklearn": 1 } }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + } + }, + "metadata": { + "usage.sklearn": 1 + } + } + ], + "ones_like": [ { "pos_or_kw_required": { "a": { @@ -75450,19 +72008,32 @@ } } }, - "pos_or_kw_optional": { - "axis": { + "metadata": { + "usage.skimage": 17, + "usage.matplotlib": 8, + "usage.sklearn": 29 + } + }, + { + "pos_or_kw_required": { + "a": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" + } + }, + "dtype": { + "type": "type", + "name": { + "name": "bool" } } }, "metadata": { - "usage.sklearn": 38 + "usage.skimage": 3, + "usage.sklearn": 2 } - } - ], - "asfortranarray": [ + }, { "pos_or_kw_required": { "a": { @@ -75470,32 +72041,53 @@ "module": "numpy", "name": "ndarray" } + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "uint8" + } } }, "metadata": { - "usage.skimage": 1, - "usage.dask": 2 + "usage.skimage": 6 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "object" + } + }, + "metadata": { + "usage.xarray": 11 } }, { "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "xarray.core.dataarray", + "name": "DataArray" } } }, - "pos_or_kw_optional": { - "dtype": { - "type": "type", - "name": { - "name": "float" + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "xarray.core.variable", + "name": "Variable" } } }, "metadata": { - "usage.scipy": 26 + "usage.xarray": 4 } }, { @@ -75505,263 +72097,321 @@ "options": [ { "type": { - "module": "numpy", - "name": "ndarray" + "module": "pandas.core.series", + "name": "Series" } }, { - "type": "list", - "item": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } + "type": { + "module": "numpy", + "name": "ndarray" } } ] } }, - "pos_or_kw_optional": { - "dtype": { - "type": "union", - "options": [ - { - "type": "type" + "metadata": { + "usage.pandas": 6 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + { + "type": "tuple", + "items": { + "type": { + "name": "int" + } + } + }, + { + "type": { + "name": "float" + } }, { "type": { "module": "numpy", - "name": "dtype" + "name": "ndarray" } } ] } }, + "pos_or_kw_optional": { + "dtype": { + "type": "type" + } + }, "metadata": { - "usage.sklearn": 28 + "usage.scipy": 62 } - } - ], - "cross": [ + }, { "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" } }, - "b": { - "type": { + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "ndarray" + "name": "float32" } } }, "metadata": { - "usage.skimage": 5, - "usage.scipy": 8 + "usage.matplotlib": 6 } - } - ], - "einsum": [ + }, { - "var_pos": [ - "operands", - { - "type": "str", - "options": [ - "v", - "t" - ] + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } } - ], + }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 83, - "usage.scipy": 33, - "usage.sklearn": 15 + "usage.matplotlib": 1 } }, { - "var_pos": [ - "operands", - { - "type": "str", + "pos_or_kw_required": { + "a": { + "type": "union", "options": [ - "v", - "t" + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } ] } - ], - "kw_only_optional": { - "optimize": { + }, + "pos_or_kw_optional": { + "order": { + "type": "str", + "options": [ + "F", + "C" + ] + }, + "shape": { "type": "union", "options": [ { - "type": { - "name": "bool" + "type": "tuple", + "items": { + "type": { + "name": "int" + } } }, { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - { - "type": "str", - "options": [ - "einsum_path" - ] - } - ] + "type": { + "name": "int" } }, { - "type": "str", - "options": [ - "optimal", - "greedy" - ] + "type": "None" } ] } }, "metadata": { - "usage.dask": 139 + "usage.dask": 11 } - } - ], - "nan_to_num": [ + }, { "pos_or_kw_required": { - "x": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" - } + "a": { + "type": { + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "x": { + "a": { "type": { "module": "numpy", "name": "ndarray" } + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float32" + } } }, "metadata": { - "usage.scipy": 4, - "usage.sklearn": 7 + "usage.sklearn": 3 } }, { "pos_or_kw_required": { - "x": { - "type": "object" + "a": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } } }, "metadata": { - "usage.dask": 23 + "usage.sklearn": 2 } } ], - "frombuffer": [ + "can_cast": [ { "pos_only_required": { "_0": { "type": { - "name": "bytes" + "name": "int" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "dtype" + } + } + }, + "metadata": { + "usage.skimage": 4 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "float" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "dtype" + } + } + }, + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "dtype" + } + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "dtype" + } + }, + "_1": { + "type": "type", + "name": { + "name": "bool" } } }, "kw_only_required": { - "dtype": { + "casting": { "type": "str", "options": [ - "int8" + "safe" ] } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { + "type": "object" + }, + "_1": { "type": "union", "options": [ { - "type": { - "name": "bytes" + "type": "type", + "name": { + "name": "bool" } }, - { - "type": { - "module": "pyarrow.lib", - "name": "Buffer" - } - } - ] - } - }, - "kw_only_required": { - "dtype": { - "type": "union", - "options": [ { "type": { "module": "numpy", "name": "dtype" } - }, - { - "type": "str", - "options": [ - "q", - ">i", - ">b" - ] - } - }, - "kw_only_optional": { - "count": { - "type": { - "name": "int" - } }, - "dtype": { + "_1": { "type": "union", "options": [ { - "type": "str" + "type": "type" }, { "type": { @@ -75817,34 +72453,32 @@ } }, { - "type": "dict", - "key": { - "type": "str", - "options": [ - "formats", - "names" - ] - }, - "value": { - "type": "list", - "item": { - "type": "str" - } - } - }, - { - "type": "type" + "type": "str", + "options": [ + "intp" + ] } ] - }, - "offset": { - "type": { - "name": "int" - } + } + }, + "pos_only_optional": { + "_2": { + "type": "str", + "options": [ + "safe" + ] + } + }, + "kw_only_optional": { + "casting": { + "type": "str", + "options": [ + "same_kind" + ] } }, "metadata": { - "usage.scipy": 31 + "usage.scipy": 185 } }, { @@ -75852,37 +72486,47 @@ "_0": { "type": { "module": "numpy", - "name": "ndarray" + "name": "dtype" } }, "_1": { "type": "type", "name": { - "module": "numpy", - "name": "uint8" + "name": "float" } + }, + "_2": { + "type": "str", + "options": [ + "same_kind" + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.matplotlib": 20 } }, { "pos_only_required": { "_0": { - "type": { + "type": "type", + "name": { "module": "numpy", - "name": "ndarray" + "name": "float128" } - } - }, - "kw_only_required": { - "dtype": { + }, + "_1": { "type": "type", "name": { "module": "numpy", - "name": "uint8" + "name": "float64" } + }, + "_2": { + "type": "str", + "options": [ + "equiv" + ] } }, "metadata": { @@ -75892,253 +72536,401 @@ { "pos_only_required": { "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "dtype" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "_1": { "type": { - "name": "bytes" + "module": "numpy", + "name": "dtype" } } }, "kw_only_required": { - "dtype": { - 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+ } + }, + { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } - }, - { - "pos_only_required": { - "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" - } - }, + ] + } + }, + "pos_or_kw_optional": { + "axis": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": "None" + }, + { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + ] + } + }, + "metadata": { + "usage.skimage": 14, + "usage.xarray": 1, + "usage.pandas": 10, + "usage.scipy": 8, + "usage.matplotlib": 4, + "usage.dask": 5 + } + }, + "tri": { + "pos_or_kw_required": { + "N": { + "type": { + "name": "int" + } + } + }, + "pos_or_kw_optional": { + "M": { + "type": { + "name": "int" + } + }, + "k": { + "type": { + "name": "int" + } + }, + "dtype": { + "type": "type" + } + }, + "pos_or_kw_optional_ordering": [ + [ + "M", + "k" + ], + [ + "k", + "dtype" + ] + ], + "metadata": { + "usage.skimage": 9, + "usage.scipy": 1, + "usage.sklearn": 1 + } + }, + "prod": { + "pos_or_kw_required": { + "a": { + "type": "object" + } + }, + "pos_or_kw_optional": { + "axis": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": "None" + }, + { + "type": "tuple", + "items": { + "type": "union", + "options": [ { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } + "type": "None" }, { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "name": "int" } } ] - }, - "_1": { - "type": "tuple", - "items": [ + } + } + ] + }, + "dtype": { + "type": "union", + "options": [ + { + "type": "type" + }, + { + "type": "str", + "options": [ + "i8", + "f8", + "i4", + "f4" + ] + } + ] + }, + "keepdims": { + "type": { + "name": "bool" + } + }, + "out": { + "type": "union", + "options": [ + { + "type": { + "module": "dask.dataframe.core", + "name": "Scalar" + } + }, + { + "type": { + "module": "dask.dataframe.core", + "name": "Series" + } + } + ] + } + }, + "pos_or_kw_optional_ordering": [ + [ + "axis", + "keepdims" + ], + [ + "axis", + "out" + ], + [ + "axis", + "dtype" + ] + ], + "metadata": { + "usage.skimage": 9, + "usage.xarray": 22, + "usage.pandas": 52, + "usage.scipy": 63, + "usage.matplotlib": 4, + "usage.dask": 100, + "usage.sklearn": 10 + } + }, + "unravel_index": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { + "type": "union", + "options": [ { "type": { "name": "int" @@ -144912,8 +132497,34 @@ }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } + } + ] + } + } + ] + } + }, + "pos_only_optional": { + "_2": { + "type": "str", + "options": [ + "F", + "C" + ] + }, + "_1": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "None" }, { "type": { @@ -144923,62 +132534,249 @@ ] } }, - "metadata": { - "usage.skimage": 2 + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { + "type": { + "name": "int" + } + } } - }, - { - "pos_only_required": { - "_0": { - "type": "tuple", - "items": [ + ] + } + }, + "pos_only_optional_ordering": [ + [ + "_1", + "_2" + ] + ], + "kw_only_optional": { + "order": { + "type": "str", + "options": [ + "F", + "C" + ] + }, + "shape": { + "type": "tuple", + "items": { + "type": { + "name": "int" + } + } + } + }, + "metadata": { + "usage.skimage": 18, + "usage.scipy": 4, + "usage.matplotlib": 2, + "usage.dask": 12, 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"usage.skimage": 2, + "usage.xarray": 4, + "usage.scipy": 1, + "usage.matplotlib": 6, + "usage.sklearn": 4 + } + }, + "convolve": { + "pos_or_kw_required": { + "a": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "int" } }, { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" + "type": { + "module": "numpy", + "name": "complex128" } } ] - }, - "_1": { - "type": "tuple", - "items": [ + } + } + ] + }, + "v": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { + "type": "union", + "options": [ { "type": { "name": "int" @@ -144986,73 +132784,73 @@ }, { "type": { - "name": "int" + "module": "numpy", + "name": "complex128" } }, { "type": { - "name": "int" + "name": "float" } } ] } - 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"float64" + } }, "_1": { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 5, + "usage.matplotlib": 17, + "usage.sklearn": 6 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - } - ] + "type": { + "module": "numpy", + "name": "bool_" + } }, "_1": { "type": { "module": "numpy", - "name": "ndarray" + "name": "bool_" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - } - ] + "type": { + "module": "numpy", + "name": "float64" + } }, "_1": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 2, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" - } - }, - "_1": { "type": { "name": "float" } } }, "metadata": { - "usage.xarray": 2 + "usage.skimage": 58, + "usage.xarray": 7, + "usage.matplotlib": 102, + "usage.sklearn": 83 } }, { @@ -149583,29 +139633,42 @@ "type": "tuple", "items": [ { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" + "type": { + "name": "int" } }, { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" + "type": { + "name": "int" + } + } + ] + }, + "_1": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" } }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.skimage": 3 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -149613,7 +139676,7 @@ }, { "type": { - "name": "ellipsis" + "name": "int" } } ] @@ -149626,27 +139689,26 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 3 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, "_1": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "type": { + "name": "float" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 2, + "usage.sklearn": 1 } }, { @@ -149660,44 +139722,13 @@ } }, { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" + "type": { + "name": "int" } }, { "type": { - "name": "ellipsis" + "name": "int" } } ] @@ -149710,24 +139741,23 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 3 } }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } - }, - "_1": { "type": "tuple", "items": [ { - "type": "str", - "options": [ - "a" - ] + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } }, { "type": { @@ -149735,30 +139765,39 @@ } } ] - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": "tuple", "items": [ { "type": { - "module": "xarray.core.variable", - "name": "Variable" + "name": "int" } }, { "type": { - "module": "xarray.core.variable", - "name": "Variable" + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] + } + }, + "metadata": { + "usage.skimage": 3 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, "_1": { "type": { @@ -149766,8 +139805,22 @@ } } }, + "kw_only_required": { + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 8 } }, { @@ -149777,129 +139830,121 @@ "items": [ { "type": { - "module": "xarray.core.variable", - "name": "Variable" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "module": "xarray.core.variable", - "name": "Variable" + "module": "numpy", + "name": "ndarray" } } ] + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, "_1": { "type": { - "module": "xarray.core.variable", - "name": "Variable" + "name": "float" + } + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float32" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 6 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": { - "module": "xarray.core.variable", - "name": "Variable" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } }, "_1": { "type": { - "module": "xarray.core.variable", - "name": "Variable" + "module": "numpy", + "name": "ndarray" + } + } + }, + "kw_only_required": { + "out": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 2, + "usage.sklearn": 10 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": { - "module": "xarray.core.variable", - "name": "Variable" - } - }, - { - "type": { - "module": "xarray.core.variable", - "name": "Variable" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } }, "_1": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "type": { + "module": "numpy", + "name": "int64" + } + } + }, + "kw_only_required": { + "out": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 2 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } }, "_1": { + "type": { + "name": "int" + } + } + }, + "kw_only_required": { + "out": { "type": { "module": "numpy", "name": "ndarray" @@ -149907,7 +139952,8 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1, + "usage.sklearn": 2 } }, { @@ -149921,17 +139967,20 @@ } }, { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" + "type": { + "name": "int" } }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -149939,12 +139988,44 @@ }, { "type": { - "name": "ellipsis" + "name": "int" } } ] + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, "_1": { + "type": { + "name": "int" + } + } + }, + "kw_only_required": { + "casting": { + "type": "str", + "options": [ + "unsafe" + ] + }, + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } + }, + "out": { "type": { "module": "numpy", "name": "ndarray" @@ -149952,283 +140033,198 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 7 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": { - "type": "None" + "type": { + "name": "int" } }, "_1": { - "type": "object" + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "kw_only_required": { + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 11 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": { - "type": "None" - } - }, - "_1": { "type": "list", "item": { - "type": { - "name": "int" - } + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": { - "type": "None" - } - }, - "_1": { "type": { - "module": "pandas._libs.tslibs.period", - "name": "Period" + "module": "dask.array.core", + "name": "Array" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 2, + "usage.xarray": 3 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "numpy", + "name": "int64" + } }, "_1": { - "type": "object" + "type": { + "module": "numpy", + "name": "int64" + } } }, "metadata": { - "usage.pandas": 5607, - "usage.scipy": 3994, - "usage.dask": 134, - "usage.sklearn": 1434 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } }, "_1": { - "type": "tuple", - "items": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - } - ] + "type": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.matplotlib": 2 + "usage.skimage": 1, + "usage.xarray": 1, + "usage.sklearn": 14 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - "_1": { "type": { - "module": "matplotlib.axes._subplots", - "name": "AxesSubplot" + "module": "numpy", + "name": "complex128" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - "_1": { "type": { "module": "numpy", "name": "ndarray" } } }, + "kw_only_required": { + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } + } + }, "metadata": { - "usage.matplotlib": 17 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - "_1": { "type": { - "name": "float" + "module": "numpy", + "name": "float32" } } }, "metadata": { - "usage.matplotlib": 2 + "usage.skimage": 1, + "usage.sklearn": 11 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } }, "_1": { "type": "tuple", "items": [ { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } } ] } }, "metadata": { - "usage.matplotlib": 2 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, "_1": { @@ -150236,398 +140232,247 @@ "items": [ { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] } }, "metadata": { - "usage.matplotlib": 2 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - "_1": { "type": { - "module": "numpy", - "name": "uint8" + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 2, + "usage.matplotlib": 18, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "module": "numpy", - "name": "uint8" + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" + } } } }, "metadata": { - "usage.matplotlib": 14 + "usage.skimage": 2, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" - } - } - ] + "type": { + "name": "int" + } }, "_1": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 1, + "usage.xarray": 4, + "usage.matplotlib": 2, + "usage.sklearn": 3 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "name": "bool" } - ] + } } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { + "type": "list", + "item": { "type": { "name": "int" } - }, - "step": { - "type": "None" + } + } + }, + "metadata": { + "usage.skimage": 3, + "usage.matplotlib": 6 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" } }, "_1": { "type": { + "name": "int" + } + } + }, + "kw_only_required": { + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "ndarray" + "name": "float32" } } }, "metadata": { - "usage.matplotlib": 4 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "xarray.core.dataarray", + "name": "DataArray" } - }, - "_1": { - "type": "tuple", - "items": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - } - ] } }, "metadata": { - "usage.matplotlib": 3 + "usage.xarray": 49 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "xarray.core.variable", + "name": "Variable" + } }, "_1": { "type": { - "name": "int" + "module": "xarray.core.dataarray", + "name": "DataArray" } } }, "metadata": { - "usage.matplotlib": 5 + "usage.xarray": 3 } }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "module": "xarray.core.dataarray", + "name": "DataArray" } }, "_1": { "type": { - "module": "numpy", - "name": "uint8" + "module": "xarray.core.variable", + "name": "Variable" } } }, "metadata": { - 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"type": { + "module": "xarray.core.dataarray", + "name": "DataArray" + } }, "_1": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } }, "metadata": { - "usage.matplotlib": 12 + "usage.xarray": 5 } }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": "None" - }, - "step": { - "type": { - "name": "int" - } + "type": { + "module": "sparse._coo.core", + "name": "COO" } }, "_1": { "type": { - "module": "numpy", - "name": "uint8" + "name": "int" } } }, "metadata": { - "usage.matplotlib": 8 + "usage.xarray": 3 } }, { @@ -150638,142 +140483,76 @@ } }, "_1": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" - } + "type": { + "module": "sparse._coo.core", + "name": "COO" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": { - "name": "int" 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"slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "module": "dask.array.core", + "name": "Array" } }, "_1": { "type": { - "name": "bool" + "module": "xarray.core.dataarray", + "name": "DataArray" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "dask.array.core", + "name": "Array" + } }, "_1": { "type": { "module": "numpy", - "name": "uint8" + "name": "ndarray" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" - } - } - ] + "type": { + "module": "xarray.core.dataarray", + "name": "DataArray" + } }, "_1": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" + } + } + }, + "kw_only_required": { + "out": { + "type": { + "module": "xarray.core.dataarray", + "name": "DataArray" } } }, "metadata": { - "usage.matplotlib": 2 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": { - "module": "numpy", - "name": "int64" - } - }, - "stop": { - "type": { - "module": "numpy", - "name": "int64" - } - }, - "step": { - "type": "None" + "type": { + "module": "numpy", + "name": "ndarray" } }, "_1": { "type": { - "name": "int" + "module": "xarray.core.dataarray", + "name": "DataArray" + } + } + }, + "kw_only_required": { + "out": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - "_1": { - "type": "tuple", - "items": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": "object" } }, "metadata": { - "usage.matplotlib": 2 + "usage.xarray": 7 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - } - ] + "type": "object" }, "_1": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" - } + "type": { + "name": "int" } } }, "metadata": { - "usage.matplotlib": 5 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": { - "name": "int" - } - } - }, - "_1": { "type": { - "name": "float" + "name": "bool" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 1, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - "_1": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - } - ] + "type": { + "module": "xarray.core.variable", + "name": "IndexVariable" + } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 3 } }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" - } - }, + "type": "object" + } + }, + "pos_only_optional": { "_1": { - "type": { - "name": "int" - } + "type": "object" + } + }, + "kw_only_optional": { + "dtype": { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "float64", + "float32" + ] + }, + { + "type": "type" + } + ] } }, "metadata": { - "usage.matplotlib": 2 + "usage.pandas": 1228 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ + "type": "object" + } + }, + "pos_only_optional": { + "_1": { + "type": "object" + }, + "_2": { + "type": "object" + }, + "_3": { + "type": "object" + }, + "_4": { + "type": "union", + "options": [ { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" + "type": { + "name": "float" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } }, { @@ -152335,71 +141383,97 @@ } ] }, - "_1": { + "_5": { + "type": { + "name": "int" + } + }, + "_6": { "type": { "name": "int" } } }, + "pos_only_optional_ordering": [ + [ + "_5", + "_6" + ], + [ + "_1", + "_2" + ], + [ + "_4", + "_5" + ], + [ + "_3", + "_4" + ], + [ + "_2", + "_3" + ] + ], + "kw_only_optional": { + "dtype": { + "type": "type" + }, + "out": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "where": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "bool" + } + } + ] + }, + "casting": { + "type": "str", + "options": [ + "unsafe" + ] + }, + "sig": { + "type": "str" + } + }, "metadata": { - "usage.matplotlib": 3 + "usage.scipy": 8006 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" - } - }, - { - "type": "slice", - "start": { + "type": "list", + "item": { + "type": "union", + "options": [ + { "type": { - "name": "int" + "name": "float" } }, - "stop": { + { "type": { "name": "int" } - }, - "step": { - "type": "None" } - } - ] - }, - "_1": { - "type": { - "module": "numpy", - "name": "ndarray" + ] } } }, @@ -152407,47 +141481,35 @@ "usage.matplotlib": 3 } }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "matplotlib.transforms", + "name": "Bbox" + } + } + }, + "metadata": { + "usage.matplotlib": 2 + } + }, { "pos_only_required": { "_0": { "type": "tuple", "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, { "type": { "name": "int" } }, { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" + "type": { + "name": "int" } } ] - }, - "_1": { - "type": { - "module": "numpy", - "name": "ndarray" - } } }, "metadata": { @@ -152457,49 +141519,14 @@ { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": { - "name": "int" - } - } - } - ] - }, - "_1": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "range" } } }, "metadata": { - "usage.matplotlib": 3 + "usage.matplotlib": 2, + "usage.sklearn": 1 } }, { @@ -152509,23 +141536,15 @@ "items": [ { "type": { - "module": "numpy", - "name": "int32" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "int32" + "name": "float" } } ] - }, - "_1": { - "type": { - "module": "numpy", - "name": "float64" - } } }, "metadata": { @@ -152539,171 +141558,84 @@ "items": [ { "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "int" + "name": "float" } }, { "type": { - "name": "int" + "name": "float" } } ] - }, - "_1": { - "type": { - "module": "numpy", - "name": "ndarray" - } } }, "metadata": { - "usage.matplotlib": 8 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": "None" - }, - "step": { - "type": { - "name": "int" - } - } - } - ] + "type": { + "name": "float" + } }, "_1": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "float" } } }, "metadata": { - "usage.matplotlib": 3 + "usage.matplotlib": 2, + "usage.sklearn": 9 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { + "type": "list", + "item": { + "type": "union", + "options": [ + { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, - "stop": { - "type": "None" - }, - "step": { + { "type": { - "name": "int" + "name": "float" } } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - } - ] - }, - "_1": { - "type": { - "module": "numpy", - "name": "ndarray" + ] } } }, "metadata": { - "usage.matplotlib": 4 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - }, - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } }, - "step": { - "type": "None" + { + "type": { + "name": "int" + } } - } - ] - }, - "_1": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - }, - "_1": { - "type": { - "name": "int" + ] } } }, @@ -152714,68 +141646,34 @@ { "pos_only_required": { "_0": { - "type": "slice", - "start": { + "type": "list", + "item": { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - "_1": { - "type": { - "name": "bool" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.matplotlib": 3, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - "_1": { "type": "list", "item": { "type": { - "name": "float" + "module": "numpy", + "name": "int64" } } } }, "metadata": { - "usage.matplotlib": 2 + "usage.matplotlib": 1 } }, { @@ -152786,19 +141684,9 @@ } }, "_1": { - "type": "tuple", - "items": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "name": "float" + } } }, "metadata": { @@ -152810,51 +141698,16 @@ "_0": { "type": "tuple", "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, { "type": { "name": "int" } - } - ] - }, - "_1": { - "type": "tuple", - "items": [ + }, { "type": { "name": "int" } }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "tuple", - "items": [ { "type": { "name": "int" @@ -152866,20 +141719,12 @@ } } ] - }, - "_1": { - "type": { - "module": "matplotlib.axes._subplots", - "name": "Axes3DSubplot" - } } }, "metadata": { "usage.matplotlib": 1 } - } - ], - "reshape": [ + }, { "pos_only_required": { "_0": { @@ -152887,7 +141732,7 @@ "items": [ { "type": { - "name": "int" + "name": "float" } }, { @@ -152899,30 +141744,31 @@ } }, "metadata": { - "usage.skimage": 34, - "usage.xarray": 51, - "usage.matplotlib": 50, - "usage.sample-usage": 1 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, "_1": { "type": { - "name": "int" + "name": "float" + } + }, + "_2": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.skimage": 52, - "usage.xarray": 86, - "usage.matplotlib": 35, - "usage.sample-usage": 1 + "usage.matplotlib": 1 } }, { @@ -152930,97 +141776,80 @@ "_0": { "type": { "module": "numpy", - "name": "ndarray" + "name": "int16" } } }, "metadata": { - "usage.skimage": 2 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } } - } - ] + ] + } } }, "metadata": { - "usage.skimage": 2 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "_1": { + "type": { + "name": "int" + } + }, + "_2": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.skimage": 12, - "usage.xarray": 27, - "usage.matplotlib": 20 + "usage.matplotlib": 1, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "name": "float" + } + }, + "_1": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } } }, "metadata": { - "usage.skimage": 7, - "usage.xarray": 13 + "usage.matplotlib": 1 } }, { @@ -153030,39 +141859,29 @@ "items": [ { "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" + "name": "float" } }, { "type": { - "name": "int" + "name": "float" } }, { "type": { - "name": "int" + "name": "float" } }, { "type": { - "name": "int" + "name": "float" } } ] } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 1 } }, { @@ -153072,33 +141891,40 @@ "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" } } ] } }, "metadata": { - "usage.skimage": 3, - "usage.xarray": 38, - "usage.matplotlib": 4 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 13, - "usage.matplotlib": 1 + "usage.matplotlib": 1, + "usage.sklearn": 1 } }, { @@ -153107,12 +141933,16 @@ "type": { "name": "int" } + }, + "_1": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } } }, "metadata": { - "usage.skimage": 8, - "usage.xarray": 14, - "usage.matplotlib": 3 + "usage.matplotlib": 1 } }, { @@ -153132,487 +141962,368 @@ }, { "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" + "name": "float" } }, { "type": { - "name": "int" + "name": "float" } } ] } }, "metadata": { - "usage.skimage": 2, - "usage.xarray": 2 + "usage.matplotlib": 1 } }, { "pos_only_required": { 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"uint" + ] } }, "metadata": { - "usage.skimage": 4, - "usage.matplotlib": 3 + "usage.xarray": 2 } }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "datetime64[us]" + ] } }, "metadata": { - "usage.skimage": 34, - "usage.xarray": 13, - "usage.matplotlib": 13, - "usage.sample-usage": 1 + "usage.xarray": 2 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "type": "str", + "options": [ + "bool" ] } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1, + "usage.matplotlib": 2 } }, { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "type", + "name": { + "name": "object" } } }, "metadata": { - "usage.skimage": 3 + "usage.xarray": 6, + "usage.sklearn": 4 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "int64" - } + "type": "str", + "options": [ + "u1" + ] } }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "datetime64" - } + "type": "str", + "options": [ + "timedelta64[ns]" + ] } }, "metadata": { - "usage.xarray": 4 + "usage.xarray": 4, + "usage.matplotlib": 4 } }, { "pos_only_required": { "_0": { - "type": { - "module": "datetime", - "name": "timedelta" - } + "type": "str", + "options": [ + "datetime64[ms]" + ] } }, "metadata": { @@ -154873,36 +143854,36 @@ { "pos_only_required": { "_0": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" - } + "type": "str", + "options": [ + "datetime64[ns]" + ] } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 4 } }, { "pos_only_required": { "_0": { - "type": { - "module": "xarray.core.dataset", - "name": "Dataset" - } + "type": "str", + "options": [ + "f8" + ] } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "timedelta64" - } + "type": "str", + "options": [ + "f4" + ] } }, "metadata": { @@ -154910,11 +143891,11 @@ } }, { - "pos_only_required": { - "_0": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" + "kw_only_required": { + "dtype": { + "type": "type", + "name": { + "name": "int" } } }, @@ -154925,43 +143906,56 @@ { "pos_only_required": { "_0": { - "type": "object" + "type": "str", + "options": [ + "datetime64[s]" + ] } }, "metadata": { - "usage.pandas": 289, - "usage.scipy": 2001, - "usage.dask": 78, - "usage.sklearn": 353 + "usage.xarray": 1, + "usage.matplotlib": 10 } - } - ], - "__matmul__": [ + }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "str", + "options": [ + "timedelta64[s]" + ] } }, "metadata": { - "usage.skimage": 47, - "usage.sample-usage": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { + "type": "object" + } + }, + "kw_only_optional": { + "copy": { "type": { - "module": "numpy", - "name": "matrix" + "name": "bool" + } + }, + "casting": { + "type": "str", + "options": [ + "safe" + ] + }, + "subok": { + "type": { + "name": "bool" } } }, "metadata": { - "usage.skimage": 1 + "usage.pandas": 1453 } }, { @@ -154970,660 +143964,591 @@ "type": "union", "options": [ { - "type": { - "module": "numpy", - "name": "matrix" - } + "type": "type" }, { "type": { "module": "numpy", - "name": "ndarray" + "name": "dtype" } }, { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "complex" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "complex" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] - } - } - ] - } + "type": "str" } ] } }, + "kw_only_optional": { + "copy": { + "type": { + "name": "bool" + } + }, + "casting": { + "type": "str", + "options": [ + "unsafe", + "safe" + ] + }, + "order": { + "type": "str", + "options": [ + "C" + ] + }, + "subok": { + "type": { + "name": "bool" + } + } + }, "metadata": { - "usage.scipy": 423 + "usage.scipy": 1950 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "scipy.sparse.dok", - "name": "dok_matrix" - } - }, - { - "type": { - "module": "scipy.sparse.csc", - "name": "csc_matrix" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "scipy.sparse.csr", - "name": "csr_matrix" - } - }, - { - "type": { - "module": "scipy.sparse.lil", - "name": "lil_matrix" - } - } - ] + "type": "type", + "name": { + "name": "float" + } + } + }, + "kw_only_required": { + "copy": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.sklearn": 35 + "usage.matplotlib": 2, + "usage.sklearn": 5 } - } - ], - "__rmatmul__": [ + }, { "pos_only_required": { "_0": { - 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"float64" } } }, "metadata": { - "usage.skimage": 14, + "usage.skimage": 31, "usage.xarray": 1, - "usage.matplotlib": 2 + "usage.matplotlib": 11, + "usage.sklearn": 51 } }, { @@ -155672,14 +144601,15 @@ "_0": { "type": { "module": "numpy", - "name": "float32" + "name": "int64" } } }, "metadata": { - "usage.skimage": 2, - "usage.xarray": 1, - "usage.matplotlib": 1 + "usage.skimage": 9, + "usage.xarray": 3, + "usage.matplotlib": 1, + "usage.sklearn": 1 } }, { @@ -155687,7 +144617,7 @@ "_0": { "type": { "module": "numpy", - "name": "float16" + "name": "uint8" } } }, @@ -155698,84 +144628,136 @@ { "pos_only_required": { "_0": { - "type": { - "name": "int" - } + "type": "tuple", + "items": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "float" + } + } + ] } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 1, - "usage.matplotlib": 2 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.scipy": 155 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "float128" - } + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } } - } + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "int64" - } + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } }, { @@ -155785,65 +144767,46 @@ }, { "type": { - "module": "numpy", - "name": "matrix" + "name": "int" } }, { "type": { - "name": "float" + "name": "int" } } ] } }, "metadata": { - "usage.sklearn": 153 - } - } - ], - "__neg__": [ - { - "metadata": { - "usage.skimage": 41, - "usage.xarray": 24, - "usage.pandas": 25, - "usage.scipy": 785, - "usage.matplotlib": 68, - "usage.sample-usage": 1, - "usage.dask": 7, - "usage.sklearn": 141 + "usage.skimage": 1 } - } - ], - "__isub__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "float16" } } }, "metadata": { - "usage.skimage": 16, - "usage.xarray": 1, - "usage.matplotlib": 10, - "usage.sample-usage": 1, - "usage.dask": 2 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "module": "numpy", + "name": "float32" } } }, "metadata": { - "usage.skimage": 4, - "usage.matplotlib": 3 + "usage.skimage": 1, + "usage.sklearn": 3 } }, { @@ -155851,146 +144814,147 @@ "_0": { "type": { "module": "numpy", - "name": "ndarray" + "name": "uint64" } } }, "metadata": { - "usage.skimage": 17, - "usage.matplotlib": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "int64" + "type": "list", + "item": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeNoLeap" + } } } }, "metadata": { - "usage.skimage": 1, - "usage.matplotlib": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "float64" + "type": "list", + "item": { + "type": { + "module": "cftime._cftime", + "name": "Datetime360Day" + } } } }, "metadata": { - "usage.skimage": 3, - "usage.matplotlib": 14 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "uint8" + "type": "list", + "item": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeJulian" + } } } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "list", + "item": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeAllLeap" } - ] + } } }, "metadata": { - "usage.pandas": 8 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": "list", + "item": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeGregorian" + } + } } }, "metadata": { - "usage.scipy": 109, - "usage.sklearn": 142 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "bool_" + "type": "list", + "item": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeProlepticGregorian" + } } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "float32" + "module": "datetime", + "name": "timedelta" } } }, "metadata": { - "usage.matplotlib": 2 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "float128" + "module": "cftime._cftime", + "name": "DatetimeGregorian" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 2 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "cftime._cftime", + "name": "DatetimeProlepticGregorian" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 2 } }, { @@ -155998,128 +144962,102 @@ "_0": { "type": { "module": "numpy", - "name": "uint64" + "name": "datetime64" } } }, "metadata": { + "usage.xarray": 2, "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "type": { + "module": "cftime._cftime", + "name": "DatetimeNoLeap" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 1 } - } - ], - "min": [ + }, { + "pos_only_required": { + "_0": { + "type": { + "module": "cftime._cftime", + "name": "Datetime360Day" + } + } + }, "metadata": { - "usage.skimage": 89, - "usage.xarray": 18, - "usage.matplotlib": 50 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "cftime._cftime", + "name": "DatetimeJulian" } } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1 } }, { - "pos_only_optional": { + "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - } - }, - "kw_only_optional": { - "axis": { "type": { - "name": "int" + "module": "cftime._cftime", + "name": "DatetimeAllLeap" } } }, "metadata": { - "usage.pandas": 48 + "usage.xarray": 1 } }, { - "kw_only_optional": { - "axis": { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "xarray.core.variable", + "name": "Variable" } } }, "metadata": { - "usage.scipy": 84, - "usage.sklearn": 82 + "usage.xarray": 1 } }, { - "kw_only_optional": { - "axis": { - "type": { - "name": "int" - } - }, - "keepdims": { - "type": { - "name": "bool" - } - }, - "out": { + "pos_only_required": { + "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "xarray.core.variable", + "name": "IndexVariable" } } }, "metadata": { - "usage.dask": 8 + "usage.xarray": 1 } - } - ], - "__contains__": [ + }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } + "type": "object" } }, "metadata": { - "usage.skimage": 5, - "usage.xarray": 3, - "usage.matplotlib": 1, - "usage.sample-usage": 1 + "usage.pandas": 228, + "usage.scipy": 2435 } }, { @@ -156129,32 +145067,37 @@ "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } } ] } }, "metadata": { - "usage.skimage": 6 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "int64" + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "float64" + } } } }, "metadata": { - "usage.skimage": 2 + "usage.matplotlib": 1 } }, { @@ -156165,359 +145108,285 @@ { "type": { "module": "numpy", - "name": "uint64" + "name": "ndarray" } }, { "type": { - "module": "numpy", - "name": "int64" + "name": "float" } }, { "type": { "name": "int" } - }, - { - "type": "str", - "options": [ - "one", - "bar" - ] } ] } }, "metadata": { - "usage.pandas": 13 + "usage.dask": 45 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "complex128" - } + "type": "list", + "item": { + "type": { + "name": "float" } - ] + } } }, "metadata": { - "usage.scipy": 11 + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "module": "numpy", + "name": "memmap" } } }, "metadata": { - "usage.matplotlib": 2 + "usage.sklearn": 1 } - }, + } + ], + "__rsub__": [ { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.sklearn": 41 + "usage.skimage": 183, + "usage.xarray": 35, + "usage.matplotlib": 133, + "usage.sklearn": 526 } - } - ], - "any": [ + }, { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, "metadata": { - "usage.skimage": 15, - "usage.xarray": 10, - "usage.matplotlib": 21, - "usage.dask": 14 + "usage.skimage": 20, + "usage.xarray": 2, + "usage.matplotlib": 25, + "usage.sample-usage": 1, + "usage.sklearn": 79 } }, { - "pos_only_optional": { + "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - } - }, - "kw_only_optional": { - "axis": { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.pandas": 175 + "usage.skimage": 4, + "usage.matplotlib": 11, + "usage.sklearn": 12 } }, { - "kw_only_optional": { - "axis": { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "name": "float" } } }, "metadata": { - "usage.scipy": 105, - "usage.sklearn": 55 + "usage.skimage": 13, + "usage.matplotlib": 19, + "usage.sklearn": 13 } }, { - "kw_only_required": { - "axis": { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "float32" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 1, + "usage.sklearn": 1 } - } - ], - "nonzero": [ + }, { + "pos_only_required": { + "_0": { + "type": { + "module": "dask.array.core", + "name": "Array" + } + } + }, "metadata": { - "usage.skimage": 4, - "usage.xarray": 4, - "usage.pandas": 29, - "usage.scipy": 31, - "usage.matplotlib": 1, - "usage.dask": 1, - "usage.sklearn": 7 + "usage.skimage": 1 } - } - ], - "__pow__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "xarray.coding.cftimeindex", + "name": "CFTimeIndex" } } }, "metadata": { - "usage.skimage": 154, - "usage.xarray": 11, - "usage.matplotlib": 69, - "usage.sample-usage": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "cftime._cftime", + "name": "DatetimeNoLeap" } } }, "metadata": { - "usage.skimage": 2 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "module": "cftime._cftime", + "name": "Datetime360Day" } } }, "metadata": { - "usage.skimage": 11, - "usage.matplotlib": 16 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "cftime._cftime", + "name": "DatetimeJulian" + } } }, "metadata": { - "usage.pandas": 63 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "complex" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "type": { + "module": "cftime._cftime", + "name": "DatetimeAllLeap" + } } }, "metadata": { - "usage.scipy": 872 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "type": { + "module": "cftime._cftime", + "name": "DatetimeGregorian" + } } }, "metadata": { - "usage.dask": 12 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "cftime._cftime", + "name": "DatetimeProlepticGregorian" + } } }, "metadata": { - "usage.sklearn": 302 + "usage.xarray": 1 } - } - ], - "__ge__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "module": "datetime", + "name": "timedelta" } } }, "metadata": { - "usage.skimage": 1, - "usage.matplotlib": 9 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "xarray.core.dataarray", + "name": "DataArray" } } }, "metadata": { - "usage.skimage": 43, - "usage.xarray": 3, - "usage.matplotlib": 9 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "int64" + "module": "xarray.core.variable", + "name": "Variable" } } }, "metadata": { - "usage.skimage": 1, - "usage.matplotlib": 2 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "xarray.core.variable", + "name": "IndexVariable" } } }, "metadata": { - "usage.skimage": 5, - "usage.xarray": 7, - "usage.matplotlib": 2 + "usage.xarray": 1 } }, { @@ -156527,8 +145396,8 @@ } }, "metadata": { - "usage.pandas": 90, - "usage.scipy": 464 + "usage.pandas": 273, + "usage.scipy": 2130 } }, { @@ -156536,12 +145405,13 @@ "_0": { "type": { "module": "numpy", - "name": "float64" + "name": "int64" } } }, "metadata": { - "usage.matplotlib": 16 + "usage.matplotlib": 2, + "usage.sklearn": 2 } }, { @@ -156554,7 +145424,7 @@ } }, "metadata": { - "usage.matplotlib": 1 + "usage.matplotlib": 2 } }, { @@ -156562,103 +145432,50 @@ "_0": { "type": "union", "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "int" - } - }, { "type": { "module": "numpy", "name": "ndarray" } }, - { - "type": { - "name": "float" - } - } - ] - } - }, - "metadata": { - "usage.dask": 17 - } - }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ { "type": { "name": "int" } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - }, - { - "type": { - "name": "float" - } } ] } }, "metadata": { - "usage.sklearn": 85 + "usage.dask": 20 } - } - ], - "__le__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "matrix" } } }, "metadata": { - "usage.skimage": 16, - "usage.matplotlib": 14 + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "list", + "item": { + "type": { + "name": "int" + } } } }, "metadata": { - "usage.skimage": 5, - "usage.xarray": 7, - "usage.matplotlib": 2 + "usage.sklearn": 3 } }, { @@ -156666,221 +145483,318 @@ "_0": { "type": { "module": "numpy", - "name": "int64" + "name": "memmap" } } }, "metadata": { - "usage.skimage": 6, - "usage.matplotlib": 1 + "usage.sklearn": 1 + } + } + ], + "mean": [ + { + "metadata": { + "usage.skimage": 26, + "usage.matplotlib": 6, + "usage.sample-usage": 2, + "usage.sklearn": 93 } }, { - "pos_only_required": { - "_0": { + "kw_only_required": { + "axis": { "type": { - "name": "float" + "name": "int" } } }, "metadata": { - "usage.skimage": 1, - "usage.matplotlib": 7 + "usage.skimage": 16, + "usage.xarray": 2, + "usage.matplotlib": 8, + "usage.sklearn": 114 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "timedelta64" + "name": "int" } } }, "metadata": { - "usage.xarray": 15 + "usage.skimage": 2, + "usage.xarray": 1, + "usage.sklearn": 43 } }, { - "pos_only_required": { - "_0": { - "type": "object" + "kw_only_required": { + "axis": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.pandas": 134, - "usage.scipy": 561 + "usage.skimage": 3 } }, { - "pos_only_required": { - "_0": { + "kw_only_required": { + "axis": { "type": { - "module": "numpy", - "name": "float64" + "name": "int" + } + }, + "keepdims": { + "type": { + "name": "bool" } } }, "metadata": { - "usage.matplotlib": 15 + "usage.xarray": 2 } }, { - "pos_only_required": { - "_0": { + "kw_only_required": { + "keepdims": { "type": { - "module": "numpy", - "name": "uint8" + "name": "bool" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 1 } }, { - "pos_only_required": { - "_0": { + "kw_only_optional": { + "axis": { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "name": "int" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.pandas": 11 } }, { - "pos_only_required": { + "pos_only_optional": { "_0": { + "type": { + "name": "int" + } + } + }, + "kw_only_optional": { + "axis": { "type": "union", "options": [ { - "type": { - "module": "numpy", - "name": "float64" - } + "type": "None" }, { "type": { "name": "int" } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "float" - } } ] + }, + "keepdims": { + "type": { + "name": "bool" + } + }, + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.dask": 35 + "usage.scipy": 110 + } + }, + { + "pos_only_required": { + "_0": { + "type": "None" + } + }, + "kw_only_required": { + "keepdims": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { + "type": { + "name": "int" + } + } + }, + "kw_only_required": { + "keepdims": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "kw_only_optional": { + "axis": { "type": "union", "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, { "type": { "name": "int" } }, { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "None" }, { - "type": { - "module": "numpy", - "name": "int64" + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "name": "int" + } + } + ] } } ] + }, + "keepdims": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.sklearn": 192 + "usage.dask": 12 } } ], - "fill": [ + "__getitem__": [ { "pos_only_required": { "_0": { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } } }, "metadata": { - "usage.skimage": 4 + "usage.skimage": 64, + "usage.xarray": 22, + "usage.matplotlib": 53, + "usage.sklearn": 412 } }, { "pos_only_required": { "_0": { - "type": { - "name": "bool" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 25, + "usage.xarray": 8, + "usage.matplotlib": 45, + "usage.sklearn": 184 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "name": "int" + } } }, "metadata": { - "usage.pandas": 107, - "usage.scipy": 60 + "usage.skimage": 360, + "usage.xarray": 105, + "usage.matplotlib": 433, + "usage.sample-usage": 2, + "usage.sklearn": 1005 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "float" - } - }, + "type": "tuple", + "items": [ { - "type": { - "name": "bool" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { @@ -156892,11 +145806,12 @@ } }, "metadata": { - "usage.sklearn": 29 + "usage.skimage": 109, + "usage.xarray": 16, + "usage.matplotlib": 130, + "usage.sklearn": 483 } - } - ], - "__rpow__": [ + }, { "pos_only_required": { "_0": { @@ -156907,93 +145822,164 @@ } }, "metadata": { - "usage.skimage": 2 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.skimage": 1, - "usage.matplotlib": 9, - "usage.sample-usage": 2 + "usage.skimage": 244, + "usage.xarray": 22, + "usage.matplotlib": 182, + "usage.sample-usage": 1, + "usage.sklearn": 927 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + } + ] } }, "metadata": { - "usage.pandas": 50 + "usage.skimage": 92, + "usage.matplotlib": 4, + "usage.sklearn": 3 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "float64" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { - "type": { - "name": "float" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { "type": { "name": "int" } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } } ] } }, "metadata": { - "usage.scipy": 192 + "usage.skimage": 21, + "usage.xarray": 1, + "usage.matplotlib": 9, + "usage.sklearn": 6 } }, { "pos_only_required": { "_0": { - "type": { - "name": "float" - } + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + } + ] } }, "metadata": { - "usage.matplotlib": 7 + "usage.skimage": 5, + "usage.xarray": 2, + "usage.matplotlib": 3, + "usage.sklearn": 3 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "ellipsis" } }, { @@ -157005,75 +145991,44 @@ } }, "metadata": { - "usage.dask": 3 + "usage.skimage": 100, + "usage.xarray": 6, + "usage.matplotlib": 16 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "name": "float" + "name": "int" } } ] } }, "metadata": { - "usage.sklearn": 2 - } - } - ], - "sum": [ - { - "metadata": { - "usage.skimage": 68, - "usage.xarray": 8, - "usage.matplotlib": 14 - } - }, - { - "kw_only_required": { - "axis": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.skimage": 15, - "usage.xarray": 3 + "usage.skimage": 5, + "usage.matplotlib": 6, + "usage.sklearn": 23 } }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.skimage": 17, - "usage.xarray": 4, - "usage.matplotlib": 2 - } - }, - { - "pos_only_optional": { - "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "name": "int" + "name": "ellipsis" } }, { @@ -157082,170 +146037,121 @@ ] } }, - "kw_only_optional": { - "dtype": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "float64", - "float32", - "int64" - ] - }, + "metadata": { + "usage.skimage": 37, + "usage.xarray": 2, + "usage.matplotlib": 8, + "usage.sklearn": 4 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "dtype" + "name": "int" } }, - { - "type": "type" - } - ] - }, - "axis": { - "type": "union", - "options": [ { "type": { "name": "int" } }, { - "type": "None" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } } ] } }, "metadata": { - "usage.pandas": 101 + "usage.skimage": 6 } }, { - "pos_only_optional": { + "pos_only_required": { "_0": { - "type": { - "name": "int" - } - } - }, - "kw_only_optional": { - "axis": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - }, - "dtype": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "dtype" + "name": "ellipsis" } }, { - "type": "str", - "options": [ - "d" - ] + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } } ] } }, "metadata": { - "usage.scipy": 264 + "usage.skimage": 13, + "usage.xarray": 4, + "usage.matplotlib": 5, + "usage.sklearn": 2 } }, { - "kw_only_optional": { - "axis": { - "type": "union", - "options": [ + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { - "name": "int" + "name": "ellipsis" } }, { - "type": "None" - }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] + "type": { + "name": "int" } - } - ] - }, - "dtype": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "f8", - "i8" - ] }, { - "type": { - "module": "numpy", - "name": "dtype" - } + "type": "None" } ] - }, - "keepdims": { - "type": { - "name": "bool" - } - }, - "out": { - "type": { - "module": "numpy", - "name": "ndarray" - } } }, "metadata": { - "usage.dask": 144 + "usage.skimage": 2, + "usage.matplotlib": 1 } }, { - "pos_only_optional": { + "pos_only_required": { "_0": { - "type": { - "name": "int" - } - } - }, - "kw_only_optional": { - "axis": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": "None" + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } }, { "type": { @@ -157253,148 +146159,165 @@ } } ] - }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" - } - }, - "keepdims": { - "type": { - "name": "bool" - } } }, "metadata": { - "usage.sklearn": 458 - } - } - ], - "all": [ - { - "metadata": { - "usage.skimage": 28, - "usage.xarray": 45, - "usage.matplotlib": 16 + "usage.skimage": 24, + "usage.xarray": 3, + "usage.matplotlib": 1 } }, { - "pos_only_optional": { + "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": "None" }, { "type": { - "name": "int" + "name": "ellipsis" } } ] } }, - "kw_only_optional": { - "axis": { - "type": { - "name": "int" - } - } - }, "metadata": { - "usage.pandas": 248 + "usage.skimage": 11, + "usage.xarray": 2 } }, { - "kw_only_optional": { - "axis": { - "type": { - "name": "int" - } - }, - "keepdims": { + "pos_only_required": { + "_0": { "type": { - "name": "bool" + "module": "numpy", + "name": "bool_" } } }, "metadata": { - "usage.scipy": 147 + "usage.skimage": 2 } }, { - "kw_only_required": { - "axis": { - "type": { - "name": "int" - } + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": "None" + }, + { + "type": { + "name": "ellipsis" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + } + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 1 } }, { - "kw_only_optional": { - "axis": { - "type": { - "name": "int" - } + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.dask": 90, - "usage.sklearn": 72 - } - } - ], - "flatten": [ - { - "metadata": { - "usage.skimage": 16, - "usage.xarray": 5, - "usage.pandas": 1, - "usage.matplotlib": 29, - "usage.dask": 6, - "usage.sklearn": 17 + "usage.skimage": 128, + "usage.xarray": 15, + "usage.matplotlib": 82, + "usage.sklearn": 61 } }, { - "pos_only_optional": { + "pos_only_required": { "_0": { - "type": "str", - "options": [ - "F" + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } ] } }, "metadata": { - "usage.scipy": 74 - } - } - ], - "ravel": [ - { - "metadata": { - "usage.skimage": 82, - "usage.xarray": 46, - "usage.matplotlib": 77, - "usage.sklearn": 285 + "usage.skimage": 1, + "usage.matplotlib": 2, + "usage.sklearn": 5 } }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "F" + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } ] } }, "metadata": { - "usage.skimage": 6 + "usage.skimage": 27, + "usage.xarray": 25, + "usage.matplotlib": 10, + "usage.sklearn": 51 } }, { @@ -157402,204 +146325,117 @@ "_0": { "type": "str", "options": [ - "C" + "L1" ] } }, "metadata": { - "usage.skimage": 9 + "usage.skimage": 4 } }, { - "kw_only_required": { - "order": { + "pos_only_required": { + "_0": { "type": "str", "options": [ - "F" + "a1" ] } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 4 } }, { - "pos_only_optional": { + "pos_only_required": { "_0": { "type": "str", "options": [ - "F" - ] - } - }, - "kw_only_optional": { - "order": { - "type": "str", - "options": [ - "F", - "K", - "C" + "b1" ] } }, "metadata": { - "usage.pandas": 171 + "usage.skimage": 4 } }, { - "pos_only_optional": { + "pos_only_required": { "_0": { "type": "str", "options": [ - "A" - ] - } - }, - "kw_only_optional": { - "order": { - "type": "str", - "options": [ - "F" + "L2" ] } }, "metadata": { - "usage.scipy": 439 + "usage.skimage": 4 } }, { - "kw_only_optional": { - "order": { + "pos_only_required": { + "_0": { "type": "str", "options": [ - "K", - "F" + "a2" ] } }, "metadata": { - "usage.dask": 35 - } - } - ], - "cumsum": [ - { - "metadata": { - "usage.skimage": 1, - "usage.matplotlib": 2, - "usage.sklearn": 6 - } - }, - { - "kw_only_required": { - "axis": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.skimage": 6, - "usage.matplotlib": 1 - } - }, - { - "pos_only_optional": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.pandas": 6 - } - }, - { - "kw_only_optional": { - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "int64" - } - } - }, - "metadata": { - "usage.scipy": 8 + "usage.skimage": 4 } }, - { - "kw_only_optional": { - "axis": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.dask": 7 - } - } - ], - "__ifloordiv__": [ { "pos_only_required": { "_0": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "b2" + ] } }, "metadata": { - "usage.skimage": 6, - "usage.scipy": 8, - "usage.sample-usage": 1 + "usage.skimage": 4 } - } - ], - "transpose": [ + }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "dE" + ] } }, "metadata": { "usage.skimage": 1 } }, - { - "metadata": { - "usage.skimage": 4, - "usage.matplotlib": 2 - } - }, { "pos_only_required": { "_0": { - "type": "list", - "item": { + "type": "slice", + "start": { "type": { "name": "int" } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } } }, "metadata": { - "usage.xarray": 8 + "usage.skimage": 10, + "usage.xarray": 10, + "usage.matplotlib": 44, + "usage.sample-usage": 1, + "usage.sklearn": 101 } }, { @@ -157608,20 +146444,28 @@ "type": "tuple", "items": [ { - "type": { - "name": "int" - } + "type": "None" }, { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } } ] } }, "metadata": { - "usage.xarray": 3 + "usage.skimage": 4, + "usage.xarray": 9, + "usage.matplotlib": 10, + "usage.sklearn": 24 } }, { @@ -157631,24 +146475,22 @@ "items": [ { "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" + "module": "numpy", + "name": "int64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } } ] } }, "metadata": { - "usage.xarray": 3 + "usage.skimage": 10, + "usage.sklearn": 4 } }, { @@ -157657,30 +146499,40 @@ "type": "tuple", "items": [ { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } }, { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } } ] } }, "metadata": { - "usage.xarray": 2 + "usage.skimage": 5, + "usage.matplotlib": 8, + "usage.sklearn": 14 } }, { @@ -157689,359 +146541,302 @@ "type": "tuple", "items": [ { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { - "type": { - "name": "int" - } + "type": "None" } ] } }, "metadata": { - "usage.xarray": 2 + "usage.skimage": 12, + "usage.xarray": 14, + "usage.matplotlib": 42, + "usage.sklearn": 266 } }, { "pos_only_required": { "_0": { - "type": { - "name": "range" - } - } - }, - "metadata": { - "usage.xarray": 3 - } - }, - { - "pos_only_optional": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.pandas": 33 - } - }, - { - "pos_only_optional": { - "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": "list", - "item": { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { "type": { "name": "int" } + }, + "step": { + "type": "None" } }, { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } }, { - "type": "tuple", - "items": { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { "type": { "name": "int" } + }, + "step": { + "type": "None" } } ] - }, - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": { - "name": "int" - } - }, - "_3": { - "type": { - "name": "int" - } - }, - "_4": { - "type": { - "name": "int" - } - }, - "_5": { - "type": { - "name": "int" - } } }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ], - [ - "_4", - "_5" - ], - [ - "_0", - "_1" - ], - [ - "_3", - "_4" - ], - [ - "_2", - "_3" - ] - ], "metadata": { - "usage.scipy": 97 + "usage.skimage": 20, + "usage.xarray": 1, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } + "type": "slice", + "start": { + "type": { + "module": "numpy", + "name": "int64" } - ] - }, - { - "type": { - "name": "int" + }, + "stop": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "step": { + "type": "None" } }, { - "type": "list", - "item": { + "type": "slice", + "start": { "type": { - "name": "int" + "module": "numpy", + "name": "int64" + } + }, + "stop": { + "type": { + "module": "numpy", + "name": "int64" } + }, + "step": { + "type": "None" } } ] } }, - "pos_only_optional": { - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": { - "name": "int" - } - } - }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ] - ], "metadata": { - "usage.dask": 10 + "usage.skimage": 9 } }, { - "pos_only_optional": { + "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - { - "type": { - "name": "int" - } + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" } - ] - }, - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": { - "name": "int" } } }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ], - [ - "_0", - "_1" - ] - ], "metadata": { - "usage.sklearn": 12 - } - } - ], - "argmin": [ - { - "metadata": { - "usage.skimage": 1, + "usage.skimage": 2, "usage.xarray": 2, - "usage.matplotlib": 3 + "usage.matplotlib": 6, + "usage.sklearn": 17 } }, { "pos_only_required": { "_0": { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "module": "numpy", + "name": "int64" + } } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 2 } }, { - "pos_only_optional": { + "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": "None" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": { + "name": "int" + } + } }, { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": { + "name": "int" + } } } ] } }, "metadata": { - "usage.pandas": 13 + "usage.skimage": 1, + "usage.xarray": 1 } }, { - "kw_only_required": { - "axis": { - "type": { - "name": "int" - } + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] } }, "metadata": { - "usage.scipy": 1, - "usage.matplotlib": 1, - "usage.dask": 2 + "usage.skimage": 25, + "usage.xarray": 6, + "usage.matplotlib": 11, + "usage.sklearn": 70 } }, - { - "kw_only_optional": { - "axis": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.sklearn": 10 - } - } - ], - "__iand__": [ { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "ndarray" + "name": "int64" } } }, "metadata": { - "usage.skimage": 2, - "usage.sklearn": 4 + "usage.skimage": 25, + "usage.xarray": 5, + "usage.matplotlib": 13, + "usage.sklearn": 155 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "module": "pandas.core.series", - "name": "Series" + "module": "numpy", + "name": "ndarray" } }, { @@ -158054,131 +146849,158 @@ } }, "metadata": { - "usage.pandas": 4 + "usage.skimage": 11, + "usage.xarray": 9, + "usage.matplotlib": 5, + "usage.sklearn": 6 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "bool_" - } + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } } ] } }, "metadata": { - "usage.scipy": 20 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.sample-usage": 1 - } - } - ], - "__and__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.skimage": 12, - "usage.xarray": 1, - "usage.matplotlib": 46, - "usage.sklearn": 11 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.skimage": 2, - "usage.sample-usage": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "dask.array.core", - "name": "Array" - } - } - }, - "metadata": { - "usage.xarray": 1 + "usage.skimage": 6, + "usage.xarray": 4, + "usage.matplotlib": 4, + "usage.sklearn": 28 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "bool_" - } + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + } + ] } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 23, + "usage.xarray": 7, + "usage.matplotlib": 3, + "usage.sklearn": 106 } }, { "pos_only_required": { "_0": { - "type": { - "module": "sparse._coo.core", - "name": "COO" - } + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 25, + "usage.xarray": 8, + "usage.matplotlib": 4, + "usage.sklearn": 43 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "name": "bool" + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } }, { @@ -158191,37 +147013,37 @@ } }, "metadata": { - "usage.pandas": 88 - } - }, - { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.scipy": 296 + "usage.skimage": 3, + "usage.xarray": 2 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } } }, "metadata": { - "usage.matplotlib": 2 + "usage.skimage": 1, + "usage.xarray": 4, + "usage.matplotlib": 1, + "usage.sklearn": 17 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -158229,119 +147051,184 @@ } }, { - "type": { - "name": "bool" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } } ] } }, "metadata": { - "usage.dask": 5 - } - } - ], - "__rand__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.skimage": 12, - "usage.xarray": 1, - 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"options": [ - "z" + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": { + "name": "int" + } + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": { + "name": "ellipsis" + } + } ] } }, "metadata": { - "usage.xarray": 5 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "ellipsis" + } + } + ] } }, "metadata": { - "usage.pandas": 142, - "usage.scipy": 273, - "usage.sklearn": 182 + "usage.xarray": 1 } }, { @@ -165226,32 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{ + "name": "int" + } + }, + "step": { + "type": "None" } }, { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "ellipsis" } } ] } }, "metadata": { - "usage.pandas": 15 - } - }, - { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.scipy": 253 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "bool_" - } - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "float128" - } - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", 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+ "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } }, { @@ -166632,31 +159005,55 @@ }, { "type": { - "name": "float" + "name": "ellipsis" } } ] } }, "metadata": { - "usage.dask": 10 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": { + "name": "int" + } } }, { - "type": { - "module": "numpy", - "name": "float64" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": { + "name": "int" + } } }, { @@ -166666,44 +159063,68 @@ }, { "type": { - "name": 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}, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] } }, "metadata": { - "usage.pandas": 26, - "usage.scipy": 1100, - "usage.sklearn": 233 + "usage.xarray": 3 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "float128" - } + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": { + "name": "int" + } + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] } }, "metadata": { - "usage.matplotlib": 2 + "usage.xarray": 2 } }, { "pos_only_required": { "_0": { - "type": { - "module": "kiwisolver", - "name": "Variable" - } + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + } + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 3 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" - } + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": { + "name": "int" + } + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": { + "name": "int" + } + } + } + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 2 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "module": "pandas.core.series", - "name": "Series" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": { + "name": "int" + } } }, { @@ -167036,124 +159809,12 @@ }, { "type": { - "module": "numpy", - "name": "float64" + "name": "ellipsis" } } ] } }, - "metadata": { - "usage.dask": 13 - } - } - ], - "__rtruediv__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.skimage": 35, - "usage.matplotlib": 24 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "float64" - } - } - }, - "metadata": { - "usage.skimage": 49, - "usage.xarray": 1, - "usage.matplotlib": 91 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.skimage": 12, - "usage.xarray": 1, - 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"usage.skimage": 1, - "usage.xarray": 2, - "usage.matplotlib": 15 + "usage.skimage": 83, + "usage.xarray": 148, + "usage.matplotlib": 58, + "usage.sample-usage": 1, + "usage.sklearn": 59 } }, { @@ -170735,51 +164687,61 @@ } }, "metadata": { - "usage.skimage": 2, - "usage.xarray": 4, - "usage.matplotlib": 14 + "usage.skimage": 29, + "usage.matplotlib": 26, + "usage.sklearn": 38 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "name": "float" } } }, "metadata": { - "usage.skimage": 4, - "usage.matplotlib": 14 + "usage.skimage": 36, + "usage.xarray": 16, + "usage.matplotlib": 83, + "usage.sklearn": 60 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "dtype" - } + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.matplotlib": 2 + "usage.skimage": 4 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "name": "float" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "dtype" + "name": "int" } }, { @@ -170789,275 +164751,145 @@ }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } } ] } }, "metadata": { - "usage.dask": 13 - } - } - ] - }, - "classmethods": { - "__ne__": { - "pos_only_required": { - "_0": { - "type": "object" + "usage.skimage": 3 } }, - "metadata": { - "usage.pandas": 31, - "usage.skimage": 7, - "usage.xarray": 6, - "usage.scipy": 116, - "usage.matplotlib": 45, - "usage.dask": 13, - "usage.sklearn": 43 - } - } - }, - "properties": { - "ndim": [ - { - "usage.skimage": 1, - "usage.pandas": 2, - "usage.scipy": 24, - "usage.matplotlib": 1, - "usage.dask": 6 - }, - { - "type": "bottom" - } - ], - "dtype": [ - { - "usage.xarray": 4, - "usage.pandas": 9, - "usage.scipy": 26, - "usage.dask": 12, - "usage.sklearn": 10 - }, - { - "type": "bottom" - } - ], - "shape": [ - { - "usage.xarray": 1, - "usage.scipy": 10, - 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+ } + } + }, + "metadata": { + "usage.skimage": 2, + "usage.matplotlib": 3, + "usage.sklearn": 1 + } }, { - "type": "bottom" - } - ] - } - }, - "int64": { - "constructor_overloads": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "int64" + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "float16" + } } + }, + "metadata": { + "usage.skimage": 1 } }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "float64" + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "float32" + } } + }, + "metadata": { + "usage.skimage": 1, + "usage.sklearn": 2 } }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "name": "bool" + } + } } + }, + "metadata": { + "usage.skimage": 2 } }, - "metadata": { - "usage.xarray": 7, - "usage.matplotlib": 1, - "usage.sample-usage": 1 - } - } - ], - "method_overloads": { - "__rmul__": [ { "pos_only_required": { "_0": { "type": { - "name": "float" + "module": "dask.array.core", + "name": "Array" } } }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "timedelta64" } } }, "metadata": { - "usage.skimage": 3, - "usage.matplotlib": 3 + "usage.xarray": 4 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "sparse._coo.core", + "name": "COO" } } }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 3 + "usage.xarray": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "object" + } + }, + "metadata": { + "usage.xarray": 561, + "usage.pandas": 256, + "usage.scipy": 2345, + "usage.dask": 69 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "int64" + "module": "xarray.core.variable", + "name": "Variable" } } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "xarray.core.variable", + "name": "IndexVariable" + } } }, "metadata": { @@ -171067,44 +164899,37 @@ { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } + "type": "list", + "item": { + "type": { + "name": "int" } - ] + } } }, "metadata": { - "usage.xarray": 1 + "usage.matplotlib": 1, + "usage.sklearn": 5 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "name": "complex" + } } }, "metadata": { - "usage.pandas": 59, - "usage.scipy": 193 + "usage.matplotlib": 3 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "float64" + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, @@ -171115,83 +164940,47 @@ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "numpy", + "name": "int32" + } } }, "metadata": { - "usage.dask": 4 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } + "type": "list", + "item": { + "type": { + "name": "float" } - ] + } } }, "metadata": { - "usage.sklearn": 19 + "usage.sklearn": 2 } } ], - "__add__": [ + "__rmul__": [ { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.skimage": 25, - "usage.xarray": 7, - "usage.matplotlib": 25 + "usage.skimage": 215, + "usage.xarray": 17, + "usage.matplotlib": 154, + "usage.sklearn": 355 } }, { @@ -171204,99 +164993,93 @@ } }, "metadata": { - "usage.skimage": 2, + "usage.skimage": 28, "usage.xarray": 2, - "usage.matplotlib": 9 + "usage.matplotlib": 30, + "usage.sklearn": 115 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "int64" + "name": "float" } } }, "metadata": { - "usage.skimage": 6, - "usage.matplotlib": 10 + "usage.skimage": 152, + "usage.xarray": 22, + "usage.matplotlib": 177, + "usage.sklearn": 270 } }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "name": "int" } } }, "metadata": { - "usage.skimage": 2, - "usage.xarray": 3, - "usage.matplotlib": 6 + "usage.skimage": 120, + "usage.xarray": 27, + "usage.matplotlib": 82, + "usage.sample-usage": 2, + "usage.sklearn": 178 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "complex" } } }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 1 + "usage.skimage": 6, + "usage.xarray": 4, + "usage.matplotlib": 2, + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "numpy", + "name": "int64" + } } }, "metadata": { - "usage.pandas": 63, - "usage.scipy": 282 + "usage.skimage": 2, + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "type": { + "module": "dask.array.core", + "name": "Array" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 4 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } }, { @@ -171308,29 +165091,34 @@ } }, "metadata": { - "usage.dask": 20 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, + "type": "tuple", + "items": [ { "type": { - "name": "bool" + "name": "int" } - }, + } + ] + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } }, { @@ -171340,97 +165128,106 @@ }, { "type": { - "name": "float" + "name": "int" } } ] } }, "metadata": { - "usage.sklearn": 49 + "usage.skimage": 1 } - } - ], - "__gt__": [ + }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "xarray.core.variable", + "name": "Variable" } } }, "metadata": { - "usage.skimage": 3, - "usage.matplotlib": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "xarray.core.variable", + "name": "IndexVariable" } } }, "metadata": { - "usage.skimage": 12, - "usage.matplotlib": 7 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "int64" + "type": "object" + } + }, + "metadata": { + "usage.pandas": 243, + "usage.scipy": 4244, + "usage.dask": 77 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "name": "float" + } } } }, "metadata": { - "usage.skimage": 5, - "usage.xarray": 2, - "usage.matplotlib": 16 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { "type": { - "module": "dask.array.core", - "name": "Array" + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 5 } }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "module": "scipy.sparse.csr", + "name": "csr_matrix" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "float64" + "module": "scipy.sparse.csc", + "name": "csc_matrix" } } }, "metadata": { - "usage.xarray": 1, - "usage.matplotlib": 2 + "usage.sklearn": 1 } }, { @@ -171438,164 +165235,104 @@ "_0": { "type": { "module": "numpy", - "name": "uint8" + "name": "float32" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 10 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas._libs.missing", - "name": "NAType" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "name": "float" - } - } - ] + "type": { + "module": "scipy.sparse.dia", + "name": "dia_matrix" + } } }, "metadata": { - "usage.pandas": 24 + "usage.sklearn": 1 + } + } + ], + "__eq__": [ + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.skimage": 104, + "usage.xarray": 256, + "usage.matplotlib": 46, + "usage.sample-usage": 2, + "usage.sklearn": 214 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] + "type": { + "name": "float" + } } }, "metadata": { - "usage.scipy": 50 + "usage.skimage": 9, + "usage.xarray": 3, + "usage.matplotlib": 4, + "usage.sklearn": 31 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - } - ] + "type": { + "module": "numpy", + "name": "int64" + } } }, "metadata": { - "usage.dask": 20 + "usage.skimage": 7, + "usage.xarray": 10, + "usage.matplotlib": 4, + "usage.sklearn": 38 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] + "type": { + "name": "int" + } } }, "metadata": { - "usage.sklearn": 42 + "usage.skimage": 111, + "usage.xarray": 11, + "usage.matplotlib": 28, + "usage.sklearn": 269 } - } - ], - "__rsub__": [ + }, { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "ndarray" + "name": "uint8" } } }, "metadata": { - "usage.skimage": 9, - "usage.xarray": 3, + "usage.skimage": 3, + "usage.xarray": 1, "usage.matplotlib": 1 } }, @@ -171603,14 +165340,16 @@ "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 10, - "usage.xarray": 3, - "usage.matplotlib": 2 + "usage.skimage": 9, + "usage.xarray": 4, + "usage.matplotlib": 3, + "usage.sklearn": 42 } }, { @@ -171618,100 +165357,89 @@ "_0": { "type": { "module": "numpy", - "name": "int64" + "name": "float32" } } }, "metadata": { - "usage.skimage": 9, - "usage.xarray": 4, - "usage.matplotlib": 8 + "usage.skimage": 1, + "usage.xarray": 2, + "usage.sklearn": 5 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": "str", + "options": [ + "type-2-x" + ] } }, "metadata": { - "usage.pandas": 32 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } + "type-2-y" ] } }, "metadata": { - "usage.scipy": 93 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "float64" - } + "type": "str", + "options": [ + "type-3-x" + ] } }, "metadata": { - "usage.matplotlib": 3 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { - "type": { - "name": "float" - } + "type": "str", + "options": [ + "type-3-y" + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ + "type-4" + ] + } + }, + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } }, { @@ -171723,14 +165451,44 @@ } }, "metadata": { - "usage.dask": 3 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.skimage": 5 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + } + }, + "metadata": { + "usage.skimage": 4, + "usage.xarray": 2, + "usage.matplotlib": 1, + "usage.sklearn": 12 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -171738,47 +165496,33 @@ }, { "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } ] } }, "metadata": { - "usage.sklearn": 25 + "usage.skimage": 1, + "usage.sklearn": 1 } - } - ], - "__eq__": [ + }, { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "flatiter" + "name": "complex128" } } }, "metadata": { - "usage.skimage": 2 + "usage.skimage": 1 } }, { @@ -171786,28 +165530,25 @@ "_0": { "type": { "module": "numpy", - "name": "ndarray" + "name": "uint64" } } }, "metadata": { - "usage.skimage": 7, - "usage.xarray": 10, - "usage.matplotlib": 4 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "bytes_" } } }, "metadata": { - "usage.skimage": 46, - "usage.xarray": 22, - "usage.matplotlib": 26 + "usage.xarray": 1 } }, { @@ -171815,14 +165556,12 @@ "_0": { "type": { "module": "numpy", - "name": "int64" + "name": "int8" } } }, "metadata": { - "usage.skimage": 16, - "usage.xarray": 4, - "usage.matplotlib": 22 + "usage.xarray": 1 } }, { @@ -171830,25 +165569,25 @@ "_0": { "type": { "module": "numpy", - "name": "float64" + "name": "int16" } } }, "metadata": { - "usage.skimage": 8 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "uint8" + "module": "dask.array.core", + "name": "Array" } } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 35 } }, { @@ -171856,77 +165595,92 @@ "_0": { "type": { "module": "numpy", - "name": "uint64" + "name": "int32" } } }, "metadata": { - "usage.skimage": 2 + "usage.xarray": 1, + "usage.sklearn": 3 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "int32" - } + "type": "str", + "options": [ + "float32" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "int8" + "module": "cftime._cftime", + "name": "DatetimeGregorian" } } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "int16" - } + "type": "str", + "options": [ + "_not_supplied" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "longlong" - } + "type": "str", + "options": [ + "dim2" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "uint16" - } + "type": "str", + "options": [ + "dim1" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "foo" + ] + } + }, + "metadata": { + "usage.xarray": 3, + "usage.sklearn": 1 } }, { @@ -171934,12 +165688,12 @@ "_0": { "type": { "module": "numpy", - "name": "uint32" + "name": "datetime64" } } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 4 } }, { @@ -171947,20 +165701,21 @@ "_0": { "type": { "module": "numpy", - "name": "ulonglong" + "name": "str_" } } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 1, + "usage.sklearn": 23 } }, { "pos_only_required": { "_0": { "type": { - "module": "dask.array.core", - "name": "Array" + "module": "sparse._coo.core", + "name": "COO" } } }, @@ -171971,27 +165726,39 @@ { "pos_only_required": { "_0": { - "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" + "type": "list", + "item": { + "type": "str", + "options": [ + "d", + "b", + "a" + ] } } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 2 } }, { "pos_only_required": { "_0": { - "type": { - "module": "xarray.core.variable", - "name": "Variable" + "type": "list", + "item": { + "type": "str", + "options": [ + "e", + "d", + "c", + "b", + "a" + ] } } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 2 } }, { @@ -172001,143 +165768,92 @@ } }, "metadata": { - "usage.pandas": 513, - "usage.scipy": 175, - "usage.dask": 82, - "usage.sklearn": 213 + "usage.xarray": 5, + "usage.pandas": 895, + "usage.scipy": 616, + "usage.dask": 192 } }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "module": "xarray.core.variable", + "name": "Variable" } } }, "metadata": { - "usage.matplotlib": 2 + "usage.xarray": 2 } - } - ], - "__rtruediv__": [ + }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "pandas._libs.tslibs.period", + "name": "Period" } } }, "metadata": { - "usage.skimage": 6, - "usage.xarray": 1, - "usage.matplotlib": 2 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "a" + ] } }, "metadata": { - "usage.skimage": 2 + "usage.xarray": 4, + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "int64" - } + "type": "str", + "options": [ + "z" + ] } }, "metadata": { - "usage.skimage": 2 + "usage.xarray": 5 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "float64" + "name": "bytes" } } }, "metadata": { - "usage.skimage": 1, "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": { - "name": "float" - } - } - }, - "metadata": { - "usage.xarray": 1, - "usage.matplotlib": 5 - } - }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas._libs.tslibs.nattype", - "name": "NaTType" - } - }, + "type": "tuple", + "items": [ { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } }, - { - "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" - } - } - ] - } - }, - "metadata": { - "usage.pandas": 5 - } - }, - { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.scipy": 47, - "usage.dask": 10 - } - }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } }, { @@ -172150,39 +165866,25 @@ "type": { "name": "int" } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "float" - } } ] } }, "metadata": { - "usage.sklearn": 35 + "usage.matplotlib": 1 } - } - ], - "__sub__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "sklearn.ensemble._hist_gradient_boosting.splitting", + "name": "_memoryviewslice" } } }, "metadata": { - "usage.skimage": 12, - "usage.xarray": 2, - "usage.matplotlib": 12 + "usage.sklearn": 1 } }, { @@ -172190,315 +165892,211 @@ "_0": { "type": { "module": "numpy", - "name": "int64" + "name": "bool_" } } }, "metadata": { - "usage.skimage": 9, - "usage.xarray": 4, - "usage.matplotlib": 8 + "usage.sklearn": 7 } }, { "pos_only_required": { "_0": { - "type": { - "name": "float" - } + "type": "None" } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 2, - "usage.matplotlib": 5 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "float64" - } + "type": "str", + "options": [ + "NAN" + ] } }, "metadata": { - "usage.xarray": 2, - "usage.matplotlib": 5 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": "str", + "options": [ + "" + ] } }, "metadata": { - "usage.pandas": 26, - "usage.scipy": 150 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "int64" + } } } }, "metadata": { - "usage.matplotlib": 2 + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } + "type": "str", + "options": [ + "bar" + ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - } + "spam" ] } }, "metadata": { - "usage.dask": 7 + "usage.sklearn": 4 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "type": { + "module": "_pytest.python_api", + "name": "ApproxNumpy" + } } }, "metadata": { - "usage.sklearn": 46 + "usage.sklearn": 12 } - } - ], - "__floordiv__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "_pytest.python_api", + "name": "ApproxSequencelike" } } }, "metadata": { - "usage.skimage": 5, - "usage.xarray": 1, - "usage.matplotlib": 2, - "usage.sklearn": 2 + "usage.sklearn": 4 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "module": "pandas._libs.missing", - "name": "NAType" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" - } - }, - { - "type": { - "name": "int" - } - } + "c" ] } }, "metadata": { - "usage.pandas": 6 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "int" - } - } + "b" ] } }, "metadata": { - "usage.scipy": 12 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } + "def" ] } }, "metadata": { - "usage.dask": 2 + "usage.sklearn": 1 } - } - ], - "__isub__": [ + }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "int64" + "module": "_pytest.python_api", + "name": "ApproxScalar" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "one" + ] } }, "metadata": { - "usage.skimage": 1, - "usage.scipy": 1, - "usage.matplotlib": 1, - "usage.dask": 1 + "usage.sklearn": 4 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - } + "two" ] } }, "metadata": { - "usage.pandas": 6, "usage.sklearn": 4 } - } - ], - "__le__": [ + }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "str", + "options": [ + "three" + ] } }, "metadata": { - "usage.skimage": 1, - "usage.matplotlib": 2 + "usage.sklearn": 4 } - }, + } + ], + "__mod__": [ { "pos_only_required": { "_0": { @@ -172509,61 +166107,36 @@ }, "metadata": { "usage.skimage": 3, - "usage.matplotlib": 3 + "usage.xarray": 3, + "usage.matplotlib": 10, + "usage.sample-usage": 1, + "usage.sklearn": 16 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "int64" + "name": "float" } } }, "metadata": { "usage.skimage": 1, - "usage.matplotlib": 3 + "usage.matplotlib": 2 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas._libs.missing", - "name": "NAType" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 27 + "usage.skimage": 1 } }, { @@ -172573,7 +166146,7 @@ } }, "metadata": { - "usage.scipy": 99 + "usage.pandas": 52 } }, { @@ -172581,21 +166154,16 @@ "_0": { "type": "union", "options": [ - { - "type": { - "name": "float" - } - }, { "type": { "module": "numpy", - "name": "int64" + "name": "ndarray" } }, { "type": { - "module": "pandas.core.series", - "name": "Series" + "module": "numpy", + "name": "float64" } }, { @@ -172607,7 +166175,7 @@ } }, "metadata": { - "usage.dask": 35 + "usage.scipy": 23 } }, { @@ -172615,18 +166183,6 @@ "_0": { "type": "union", "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, { "type": { "name": "int" @@ -172634,179 +166190,131 @@ }, { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } } ] } }, "metadata": { - "usage.sklearn": 53 + "usage.dask": 12 } } ], - "__iadd__": [ + "__gt__": [ { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "int64" + "name": "float" } } }, "metadata": { - "usage.skimage": 1, - "usage.matplotlib": 1, - "usage.dask": 1 + "usage.skimage": 13, + "usage.xarray": 1, + "usage.matplotlib": 10, + "usage.sklearn": 58 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 90, + "usage.xarray": 9, + "usage.matplotlib": 26, + "usage.sample-usage": 1, + "usage.sklearn": 77 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.xarray": 2, - "usage.matplotlib": 2, - "usage.sklearn": 4 + "usage.skimage": 14, + "usage.matplotlib": 4, + "usage.sklearn": 20 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 3 + "usage.skimage": 18, + "usage.xarray": 1, + "usage.matplotlib": 9, + "usage.sklearn": 18 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "longlong" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] + "type": { + "module": "numpy", + "name": "float32" + } } }, "metadata": { - "usage.scipy": 29 + "usage.skimage": 1, + "usage.sklearn": 1 } - } - ], - "__rfloordiv__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "uint8" } } }, "metadata": { - "usage.skimage": 1, - "usage.scipy": 1, - "usage.matplotlib": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "type": { + "module": "numpy", + "name": "int64" + } } }, "metadata": { - "usage.pandas": 2 + "usage.skimage": 9, + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] + "type": "object" } }, "metadata": { - "usage.dask": 2 + "usage.pandas": 82, + "usage.scipy": 477 } }, { @@ -172816,41 +166324,38 @@ "options": [ { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } }, { "type": { "name": "int" } + }, + { + "type": { + "name": "float" + } } ] } }, "metadata": { - "usage.sklearn": 3 + "usage.dask": 27 } } ], - "__lt__": [ + "__setitem__": [ { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "int64" + "name": "ndarray" } - } - }, - "metadata": { - "usage.skimage": 5, - "usage.xarray": 2, - "usage.matplotlib": 16 - } - }, - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { "module": "numpy", "name": "ndarray" @@ -172858,22 +166363,31 @@ } }, "metadata": { - "usage.skimage": 9, - "usage.matplotlib": 1 + "usage.skimage": 52, + "usage.xarray": 4, + "usage.matplotlib": 45, + "usage.sklearn": 114 } }, { "pos_only_required": { "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "_1": { "type": { "name": "int" } } }, "metadata": { - "usage.skimage": 10, - "usage.xarray": 3, - "usage.matplotlib": 13 + "usage.skimage": 68, + "usage.xarray": 1, + "usage.matplotlib": 20, + "usage.sklearn": 130 } }, { @@ -172881,49 +166395,61 @@ "_0": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" + } + }, + "_1": { + "type": { + "name": "float" } } }, "metadata": { - "usage.xarray": 1, - "usage.matplotlib": 4 + "usage.skimage": 14, + "usage.xarray": 6, + "usage.matplotlib": 9, + "usage.sklearn": 65 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { "type": { "module": "numpy", - "name": "uint8" + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 3, + "usage.matplotlib": 3, + "usage.sklearn": 12 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas._libs.missing", - "name": "NAType" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, + "type": "tuple", + "items": [ { "type": { - "module": "pandas.core.arrays.categorical", - "name": "Categorical" + "name": "ellipsis" } }, { @@ -172932,129 +166458,173 @@ } } ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 10 + "usage.skimage": 49, + "usage.matplotlib": 11, + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "int64" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } } ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.scipy": 7 + "usage.skimage": 7, + "usage.matplotlib": 8, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "module": "dask.dataframe.core", - "name": "Series" + "name": "ellipsis" } }, { - "type": { - "name": "float" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } - }, + } + ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.skimage": 4, + "usage.matplotlib": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "int64" + "name": "ellipsis" } }, { "type": { "name": "int" } - }, - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } } ] + }, + "_1": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.dask": 13 + "usage.skimage": 4, + "usage.xarray": 1, + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "name": "int" + "name": "ellipsis" } }, { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } } ] - } - }, - "metadata": { - "usage.sklearn": 7 - } - } - ], - "__ge__": [ - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { - "name": "int" + "name": "float" } } }, "metadata": { - "usage.skimage": 3, - "usage.xarray": 2, - "usage.matplotlib": 6 + "usage.skimage": 2, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { + "type": { + "module": "numpy", + "name": "bool_" + } + }, + "_1": { "type": { "module": "numpy", "name": "ndarray" @@ -173062,76 +166632,127 @@ } }, "metadata": { - "usage.skimage": 6, - "usage.matplotlib": 1 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "ellipsis" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { "type": { - "name": "float" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 7, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } } }, "metadata": { - "usage.skimage": 1, - "usage.matplotlib": 3 + "usage.skimage": 69, + "usage.matplotlib": 1, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "module": "numpy", "name": "ndarray" } }, - { - "type": { - "name": "int" - } - }, { "type": { "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "pandas._libs.missing", - "name": "NAType" + "name": "ndarray" } } ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 19 + "usage.skimage": 14, + "usage.xarray": 3, + "usage.matplotlib": 1, + "usage.sklearn": 20 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -173141,13 +166762,17 @@ { "type": { "module": "numpy", - "name": "int32" + "name": "ndarray" } - }, + } + ] + }, + "_1": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } }, { @@ -173157,49 +166782,59 @@ }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } } ] } }, "metadata": { - "usage.scipy": 51 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.dask": 39 + "usage.skimage": 34, + "usage.xarray": 6, + "usage.matplotlib": 31, + "usage.sklearn": 146 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -173207,154 +166842,240 @@ }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.sklearn": 32 + "usage.skimage": 10, + "usage.matplotlib": 9, + "usage.sklearn": 13 } - } - ], - "__radd__": [ + }, { "pos_only_required": { "_0": { + "type": { + "name": "int" + } + }, + "_1": { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } } }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 2 + "usage.skimage": 14, + "usage.matplotlib": 26, + "usage.sklearn": 73 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "_1": { "type": { "name": "int" } } }, "metadata": { - "usage.skimage": 6, + "usage.skimage": 125, "usage.xarray": 1, - "usage.matplotlib": 8 + "usage.sklearn": 7 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "_1": { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } } }, "metadata": { - "usage.skimage": 3, - "usage.matplotlib": 1 + "usage.skimage": 5 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "int64" - } - } - }, - "metadata": { - "usage.skimage": 6, - "usage.matplotlib": 10 - } - }, - { - "pos_only_required": { - "_0": { - "type": "object" + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.pandas": 63, - "usage.scipy": 145, - "usage.dask": 26, - "usage.sklearn": 23 + "usage.skimage": 32, + "usage.xarray": 1, + "usage.matplotlib": 2 } - } - ], - "__pow__": [ + }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { "type": { "name": "int" } } }, "metadata": { - "usage.skimage": 2, - "usage.scipy": 13 + "usage.skimage": 12, + "usage.xarray": 1, + "usage.sklearn": 17 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas._libs.missing", - "name": "NAType" - } - }, - { - "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" - } - }, - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "float64" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } } ] + }, + "_1": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.pandas": 10 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "module": "numpy", - "name": "int64" + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } }, { @@ -173363,20 +167084,10 @@ } } ] - } - }, - "metadata": { - "usage.dask": 3 - } - } - ], - "__mul__": [ - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } }, @@ -173387,76 +167098,161 @@ { "pos_only_required": { "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + "_1": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 8, + "usage.xarray": 2, + "usage.matplotlib": 2, + "usage.sklearn": 9 } }, { "pos_only_required": { "_0": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + "_1": { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 2, + "usage.sklearn": 10 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "_1": { "type": { - "name": "int" + "name": "bool" } } }, "metadata": { - "usage.xarray": 1, - "usage.matplotlib": 6 + "usage.skimage": 5, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "stop": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "stop": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.pandas": 59, - "usage.scipy": 103 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "name": "int" + } + }, + "_1": { + "type": { + "name": "int" } } }, "metadata": { - "usage.matplotlib": 3 + "usage.skimage": 50, + "usage.xarray": 2, + "usage.matplotlib": 8, + "usage.sample-usage": 1, + "usage.sklearn": 59 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -173464,181 +167260,209 @@ }, { "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" + "name": "int" } } ] + }, + "_1": { + "type": { + "name": "float" + } } }, "metadata": { - "usage.dask": 9 + "usage.skimage": 30, + "usage.xarray": 7, + "usage.matplotlib": 15, + "usage.sklearn": 34 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "float" - } - }, + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "float64" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } } ] - } - }, - "metadata": { - "usage.sklearn": 19 - } - } - ], - "astype": [ - { - "pos_only_required": { - "_0": { - "type": "type", - "name": { - "module": "numpy", - "name": "int64" + }, + "_1": { + "type": { + "name": "bool" } } }, "metadata": { - "usage.skimage": 3 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { "type": { - "module": "numpy", - "name": "dtype" + "name": "bool" } } }, "metadata": { - "usage.pandas": 3 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": "type", - "name": { - "name": "float" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { - "type": "str", - "options": [ - "d" - ] + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } } ] - } - }, - "metadata": { - "usage.scipy": 3 - } - }, - { - "pos_only_required": { - "_0": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" + }, + "_1": { + "type": { + "name": "bool" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "type", - "name": { - "module": "numpy", - "name": "float32" + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { + "type": { + "name": "bool" } } }, "metadata": { - 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"union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -173750,27 +167646,34 @@ }, { "type": { - "name": "float" + "name": "int" } } ] + }, + "_1": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.dask": 6 + "usage.skimage": 151, + "usage.xarray": 1, + "usage.matplotlib": 9, + "usage.sklearn": 34 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, + "type": { + "name": "int" + } + }, + "_1": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -173778,54 +167681,87 @@ }, { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } }, { "type": { - "name": "float" + "name": "int" } } ] } }, "metadata": { - "usage.sklearn": 25 + "usage.skimage": 2 } - } - ], - "__itruediv__": [ + }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } } }, "metadata": { - "usage.skimage": 2 + "usage.skimage": 22, + "usage.sklearn": 3 } }, { "pos_only_required": { "_0": { - "type": { - "name": "float" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } - } - }, - "metadata": { - "usage.pandas": 1 - } - } - ], - "__mod__": [ - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { "name": "int" } @@ -173833,26 +167769,18 @@ }, "metadata": { "usage.skimage": 1, - "usage.scipy": 11, - "usage.matplotlib": 3, - "usage.sklearn": 2 + "usage.xarray": 2, + "usage.sklearn": 9 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas._libs.missing", - "name": "NAType" - } - }, + "type": "tuple", + "items": [ { "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" + "name": "int" } }, { @@ -173862,22 +167790,31 @@ }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } ] + }, + "_1": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.pandas": 10 + "usage.skimage": 30 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ + { + "type": { + "name": "ellipsis" + } + }, { "type": { "name": "int" @@ -173885,82 +167822,65 @@ }, { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } } ] - } - }, - "metadata": { - "usage.dask": 2 - } - } - ], - "__neg__": [ - { - "metadata": { - "usage.skimage": 6, - "usage.xarray": 1, - "usage.pandas": 3, - "usage.scipy": 20, - "usage.matplotlib": 1, - "usage.dask": 5, - "usage.sklearn": 8 - } - 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"module": "numpy", "name": "ndarray" @@ -178654,59 +172586,114 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 4, + "usage.sklearn": 8 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.arrays.boolean", - "name": "BooleanArray" - } - }, + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "bool_" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } } ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 9 + "usage.skimage": 1, + "usage.sklearn": 4 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { "module": "numpy", - "name": "int64" + "name": "ndarray" } }, { "type": { "module": "numpy", - "name": "bool_" + "name": "ndarray" } }, { @@ -178717,34 +172704,48 @@ }, { "type": { - "name": "bool" + "module": "numpy", + "name": "ndarray" } } ] - } - }, - "metadata": { - "usage.scipy": 135 - } - } - ], - "__rand__": [ - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { - "module": "numpy", - "name": "bool_" + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "_1": { "type": { "module": "numpy", "name": "ndarray" @@ -178752,104 +172753,166 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 4, + "usage.xarray": 2, + "usage.sklearn": 22 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { "type": { - "module": "dask.array.core", - "name": "Array" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 3 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "module": "pandas.core.arrays.boolean", - "name": "BooleanArray" + "name": "int" } }, { - "type": { - "module": "numpy", - "name": "bool_" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } } ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 9 + "usage.skimage": 3 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "bool" - } - }, + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } }, { - "type": { - "module": "numpy", - "name": "bool_" + "type": "slice", 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"name": "int" } } }, "metadata": { - "usage.pandas": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] + }, + "_1": { "type": { "module": "numpy", "name": "float64" @@ -179620,32 +174245,66 @@ } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { "type": { - "name": "float" + "name": "int" } } }, "metadata": { - "usage.sklearn": 1 + "usage.skimage": 3 } - } - ], - "__rmul__": [ + }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "module": "numpy", - "name": "bool_" + "name": "int64" } }, { @@ -179657,721 +174316,439 @@ { "type": { "module": "numpy", - "name": "float64" + "name": "int64" + } + } + ] + }, + "_1": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "int64" } }, { "type": { - "name": "float" + "module": "numpy", + "name": "int64" } }, { "type": { "module": "numpy", - "name": "ndarray" + "name": "int64" } } ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.scipy": 11 + "usage.skimage": 1 } - } - ], - "__rsub__": [ + }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } + } + }, + { + "type": "slice", + "start": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } + } + } + ] + }, + "_1": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.scipy": 13 + "usage.skimage": 2 } - } - ], - "__iand__": [ + }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } + } + }, + { + "type": "slice", + "start": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } + } + }, + { + "type": "slice", + "start": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } + } + } + ] + }, + "_1": { "type": { "module": "numpy", - "name": "bool_" + "name": "ndarray" } } }, "metadata": { - "usage.scipy": 1 + "usage.skimage": 2 } - } - ], - "sum": [ + }, { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { + "type": { + "name": "bool" + } + } + }, "metadata": { - "usage.scipy": 2 + "usage.skimage": 1 } - } - ], - "__ior__": [ + }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + 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} - }, - "__radd__": { - "pos_only_required": { - "_0": { - "type": "object" + "usage.skimage": 2, + "usage.matplotlib": 7, + "usage.sklearn": 18 } }, - "metadata": { - "usage.pandas": 6, - "usage.scipy": 41 - } - }, - "squeeze": { - "metadata": { - "usage.pandas": 1 - } - }, - "__lt__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } } - }, - { + ] + }, + "_1": { + "type": "list", + "item": { "type": { - "module": "pandas._libs.missing", - "name": "NAType" + "name": "int" } } - ] - } - }, - "metadata": { - "usage.pandas": 1, - "usage.matplotlib": 1 - } - }, - "__le__": { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas._libs.missing", - "name": "NAType" - } - } - }, - "metadata": { - "usage.pandas": 2 - } - }, - "__ge__": { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas._libs.missing", - "name": "NAType" } + }, + "metadata": { + "usage.skimage": 3 } }, - "metadata": { - "usage.pandas": 2 - } - }, - "__gt__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "pandas._libs.missing", - "name": "NAType" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] - } - }, - "metadata": { - "usage.pandas": 1, - "usage.matplotlib": 1, - "usage.sklearn": 1 - } - }, - "__rmul__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "bool_" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "float" + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } } - }, - { + ] + }, + "_1": { + "type": "list", + "item": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } - ] - } - }, - "metadata": { - "usage.scipy": 11 - } - }, - "__rsub__": { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.scipy": 13 - } - }, - "__iand__": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "bool_" - } - } - }, - "metadata": { - "usage.scipy": 1 - } - }, - "sum": { - "metadata": { - "usage.scipy": 2 - } - }, - "__ior__": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "bool_" } + }, + "metadata": { + "usage.skimage": 3 } }, - "metadata": { - "usage.scipy": 2 - } - }, - "__getitem__": { - "pos_only_required": { - "_0": { - "type": "tuple", - "items": { - "type": "union", - "options": [ + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { - "type": "None" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } }, { - "type": { - "name": "ellipsis" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } } ] - } - } - }, - "metadata": { - "usage.dask": 2 - } - } - }, - "classmethod_overloads": { - "__ne__": [ - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { "module": "numpy", - "name": "dtype" + "name": "ndarray" } } }, @@ -180382,78 +174759,72 @@ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas._libs.missing", - "name": "NAType" - } - }, + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "_1": { + "type": "tuple", + "items": [ { "type": { - "name": "bool" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "dtype" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "bool_" + "name": "int" } } ] } }, "metadata": { - "usage.pandas": 16 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "dtype" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } } }, { - "type": { - "module": "numpy", - "name": "bool_" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } } } ] - } - }, - "metadata": { - "usage.scipy": 29 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "bool_" - } - } - }, - 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@@ -183043,79 +176868,56 @@ { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.pandas": 84 - } - }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { - "type": { - "name": "int" - } - } - ] - } - }, - "metadata": { - "usage.scipy": 6 - } - }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "uint64" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } } ] - } - }, - "metadata": { - "usage.sklearn": 7 - } - } - ], - "__rtruediv__": [ - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { - "name": "float" + "module": "numpy", + "name": "float64" } } }, @@ -183126,36 +176928,53 @@ { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { - "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { - "type": { - "module": "pandas._libs.tslibs.nattype", - "name": "NaTType" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } } ] - } - }, - "metadata": { - "usage.pandas": 5 - } - }, - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { "module": "numpy", "name": "float64" @@ -183163,57 +176982,56 @@ } }, "metadata": { - "usage.scipy": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "uint64" - } - } - }, - "metadata": { - "usage.dask": 1 - } - } - ], - "__ge__": [ - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.skimage": 1, - "usage.pandas": 1 - } - } - ], - "__lt__": [ - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.skimage": 3 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "module": "numpy", + "name": "int64" + } + } + }, + { + "type": "slice", + "start": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "module": "numpy", + "name": "int64" + } + } + } + ] + }, + "_1": { "type": { "module": "numpy", - "name": "uint64" + "name": "ndarray" } } }, @@ -183224,58 +177042,83 @@ { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { "type": { "module": "numpy", - "name": "uint64" + "name": "int64" } } ] - } - }, - "metadata": { - "usage.pandas": 2 - } - }, - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.skimage": 1 } - } - ], - "__truediv__": [ + }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { "type": { "name": "int" } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 2, + "usage.matplotlib": 1, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -183283,35 +177126,37 @@ } }, { - "type": { - "module": "pandas.core.arrays.timedeltas", - "name": "TimedeltaArray" - } - }, - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } } ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 4 + "usage.skimage": 2, + "usage.xarray": 3, + "usage.sklearn": 7 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -183324,30 +177169,8 @@ } } ] - } - }, - "metadata": { - "usage.scipy": 3 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "uint64" - } - } - }, - "metadata": { - "usage.dask": 1 - } - } - ], - "__rsub__": [ - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { "module": "numpy", "name": "ndarray" @@ -183355,192 +177178,140 @@ } }, "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas.core.arrays.timedeltas", - "name": "TimedeltaArray" - } - } - }, - "metadata": { - "usage.pandas": 1 + "usage.skimage": 1, + "usage.sklearn": 4 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "uint64" - } - } - }, - "metadata": { - "usage.dask": 1 - } - } - ], - "__rmul__": [ - { - "pos_only_required": { - "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { "type": { "name": "float" } } }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, "metadata": { "usage.skimage": 1, - "usage.dask": 2 + "usage.matplotlib": 5, + "usage.sklearn": 13 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } }, { "type": { - "module": "numpy", - "name": "uint64" + "name": "int" } } ] - } - }, - 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"type": "tuple", + "items": [ + { + "type": { + "name": "float" } }, { "type": { - "module": "numpy", - "name": "uint64" + "name": "float" } } ] } }, "metadata": { - "usage.pandas": 272 - } - }, - { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.scipy": 18 - } - } - ], - "__iadd__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "uint64" - } - } - }, - "metadata": { - "usage.pandas": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "ulonglong" + "name": "int" } }, { @@ -183550,185 +177321,226 @@ }, { "type": { - "module": "numpy", - "name": "uint64" + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] + }, + "_1": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.scipy": 3 + "usage.skimage": 2, + "usage.xarray": 1 } - } - ], - 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"union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "float64" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { "type": { - "module": "numpy", - "name": "float32" + "name": "int" + } + } + ] + }, + "_1": { + "type": "tuple", + "items": [ + { + "type": { + "name": "float" } }, { "type": { - "name": "int" + "name": "float" } } ] } }, "metadata": { - "usage.pandas": 9 + "usage.matplotlib": 2 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "silverman", - "scott" - ] - }, + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -187190,29 +181298,15 @@ }, { "type": { - "module": "numpy", - "name": "float32" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } ] - } - }, - "metadata": { - "usage.scipy": 18 - } - }, - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { - "module": "numpy", - "name": "float64" + "module": "matplotlib.axes._subplots", + "name": "AxesSubplot" } } }, @@ -187223,8 +181317,20 @@ { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, { "type": { "name": "int" @@ -187232,147 +181338,296 @@ }, { "type": { - "module": "numpy", - "name": "float32" + "name": "int" } } ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.dask": 3 - } + "usage.matplotlib": 17, + "usage.sklearn": 1 + } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, { "type": { - "module": "numpy", - "name": "float32" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" + } + } + ] + }, + "_1": { + "type": { + "name": "float" + } + } + }, + "metadata": { + "usage.matplotlib": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { "type": { "name": "int" } + } + ] + }, + "_1": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "float64" + } }, { "type": { "module": "numpy", - "name": "flatiter" + "name": "float64" } } ] } }, "metadata": { - "usage.sklearn": 14 + "usage.matplotlib": 2 } - } - ], - "__rmul__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "name": "int" } + }, + "_1": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + } + ] } }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 1 + "usage.matplotlib": 2 } }, { "pos_only_required": { "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + "_1": { "type": { - "name": "int" + "module": "numpy", + "name": "uint8" } } }, "metadata": { - "usage.skimage": 1, - "usage.dask": 2 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { + "type": { + "name": "int" + } + }, + "_1": { "type": { "module": "numpy", - "name": "ndarray" + "name": "uint8" } } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 14 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { + "type": "tuple", + "items": [ { "type": { - "module": "pandas.core.series", - "name": "Series" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] } }, "metadata": { - "usage.pandas": 3 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.scipy": 119 + "usage.matplotlib": 4, + "usage.sklearn": 30 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, + "type": { + "name": "int" + } + }, + "_1": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "float32" + "name": "float" } }, { "type": { - "name": "int" + "name": "float" } }, { @@ -187382,122 +181637,178 @@ }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "float" } } ] } }, "metadata": { - "usage.sklearn": 20 - } - } - ], - "__lt__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.skimage": 1, - "usage.scipy": 3 + "usage.matplotlib": 3 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { "type": { - "name": "float" + "name": "int" } } }, "metadata": { - "usage.skimage": 5 + "usage.matplotlib": 5 } }, { "pos_only_required": { "_0": { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + "_1": { "type": { "module": "numpy", - "name": "float64" + "name": "uint8" } } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 1, - "usage.matplotlib": 1 + "usage.matplotlib": 4 } }, { "pos_only_required": { "_0": { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } + } + }, + "_1": { "type": { - "name": "int" + "name": "float" } } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 1, - "usage.matplotlib": 1 + "usage.matplotlib": 3 } }, { "pos_only_required": { "_0": { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + "_1": { "type": { "module": "numpy", - "name": "float32" + "name": "ndarray" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.matplotlib": 11, + "usage.sklearn": 10 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float32" - } - }, + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "float64" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } } }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } ] - } - }, - "metadata": { - "usage.sklearn": 3 - } - } - ], - "__sub__": [ - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { "module": "numpy", "name": "ndarray" @@ -187505,80 +181816,108 @@ } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 12 } }, { "pos_only_required": { "_0": { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } + } + }, + "_1": { "type": { "module": "numpy", - "name": "float64" + "name": "uint8" } } }, "metadata": { - "usage.skimage": 2 + "usage.matplotlib": 8 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "float32" + "name": "int" + } + }, + "_1": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "float64" + } } } }, "metadata": { - "usage.skimage": 2, "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "module": "pandas.core.arrays.timedeltas", - "name": "TimedeltaArray" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": { + "name": "int" + } } }, { "type": { "name": "int" } - }, - { - "type": { - "module": "numpy", - "name": "float32" - } - }, - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } } ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 7 + "usage.matplotlib": 6 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "float32" + "name": "int" } }, { @@ -187587,17 +181926,21 @@ } } ] + }, + "_1": { + "type": "None" } }, "metadata": { - "usage.scipy": 2 + "usage.matplotlib": 4, + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -187605,102 +181948,132 @@ }, { "type": { - "module": "numpy", - "name": "float32" + "name": "int" } } ] + }, + "_1": { + "type": { + "module": "matplotlib.axes._subplots", + "name": "PolarAxesSubplot" + } } }, "metadata": { - "usage.dask": 2 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "float32" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } + "type": "slice", + "start": { + "type": { + "name": "int" } - ] - } - }, - "metadata": { - "usage.sklearn": 7 - } - } - ], - "__gt__": [ - { - "pos_only_required": { - "_0": { + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": { + "name": "int" + } + } + }, + "_1": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.skimage": 2, - "usage.pandas": 1, - "usage.scipy": 1, - "usage.matplotlib": 2 + "usage.matplotlib": 4, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "name": "int" } + }, + "_1": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + } + ] } }, "metadata": { - "usage.skimage": 3 + "usage.matplotlib": 4 } }, { "pos_only_required": { "_0": { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } + } + }, + "_1": { "type": { - "module": "numpy", - "name": "float64" + "name": "bool" } } }, "metadata": { - "usage.skimage": 1, - "usage.xarray": 1, "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { "type": { - "module": "numpy", - "name": "float32" + "module": "matplotlib.axes._subplots", + "name": "AitoffAxesSubplot" } } }, @@ -187711,12 +182084,33 @@ { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, { "type": { - "module": "numpy", - "name": "float32" + "name": "int" + } + } + ] + }, + "_1": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" } }, { @@ -187728,188 +182122,166 @@ } }, "metadata": { - "usage.sklearn": 5 + "usage.matplotlib": 2 } - } - ], - "__add__": [ + }, { "pos_only_required": { "_0": { + "type": "str", + "options": [ + "flags" + ] + }, + "_1": { "type": { "name": "int" } } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 2 } }, { "pos_only_required": { "_0": { + "type": "str", + "options": [ + "points" + ] + }, + "_1": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } } }, "metadata": { - "usage.skimage": 1, - "usage.matplotlib": 1 + "usage.matplotlib": 2 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "module": "pandas.core.arrays.timedeltas", - "name": "TimedeltaArray" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - } + "colors" ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 3 - } - }, - { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.scipy": 22 + "usage.matplotlib": 2 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "float32" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } } ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.dask": 3 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "float32" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } } }, { "type": { "name": "int" } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } } ] - } - }, - "metadata": { - "usage.sklearn": 9 - } - } - ], - "__truediv__": [ - { - "pos_only_required": { - "_0": { + }, + "_1": { "type": { "name": "float" } } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 4 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } - } - }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, + }, + "_1": { + "type": "tuple", + "items": [ { "type": { - "module": "pandas.core.arrays.timedeltas", - "name": "TimedeltaArray" + "module": "numpy", + "name": "float64" } }, { "type": { "module": "numpy", - "name": "float32" + "name": "float64" } }, { "type": { - "module": "pandas.core.series", - "name": "Series" + "module": "numpy", + "name": "float64" } }, { @@ -187921,193 +182293,282 @@ } }, "metadata": { - "usage.pandas": 14 + "usage.matplotlib": 3 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { + "type": "tuple", + "items": [ { "type": { "module": "numpy", - "name": "float32" + "name": "float64" } }, { "type": { - "module": "scipy.signal.ltisys", - "name": "StateSpaceContinuous" + "name": "int" } } ] } }, "metadata": { - "usage.scipy": 5 + "usage.matplotlib": 2 } }, { "pos_only_required": { "_0": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + "_1": { "type": { - "module": "numpy", - "name": "float32" + "name": "bool" } } }, "metadata": { - "usage.dask": 1 + 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"stop": { + "type": "None" + }, + "step": { + "type": "None" } }, { "type": { - "module": "numpy", - "name": "uint32" + "name": "int" } - }, + } + ] + }, + "_1": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -189638,20 +184064,54 @@ }, { "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" - } + "module": "numpy", + "name": "float64" + } } ] } }, "metadata": { - "usage.pandas": 66 + "usage.matplotlib": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { + "type": { + "module": "matplotlib.axes._subplots", + "name": "Axes3DSubplot" + } + } + }, + "metadata": { + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { + "type": { + "module": "numpy", + "name": "int64" + } + }, + "_1": { "type": { "module": "numpy", "name": "ndarray" @@ -189659,56 +184119,139 @@ } }, "metadata": { - "usage.scipy": 2 + "usage.sklearn": 16 } - } - ], - "__sub__": [ + }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "module": "pandas.core.arrays.timedeltas", - "name": "TimedeltaArray" - } + "type": { + "module": "numpy", + "name": "int64" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "float64" + } + } + }, + "metadata": { + "usage.sklearn": 8 + } + }, + { + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + "_1": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" } - ] + } } }, "metadata": { - "usage.pandas": 3 + "usage.sklearn": 21 } }, { "pos_only_required": { "_0": { + "type": "slice", + "start": { + "type": { + "module": "numpy", + "name": "int32" + } + }, + "stop": { + "type": { + "module": "numpy", + "name": "int32" + } + }, + "step": { + "type": "None" + } + }, + "_1": { "type": { "module": "numpy", - "name": "uint32" + "name": "ndarray" } } }, "metadata": { - "usage.dask": 1 + "usage.sklearn": 3 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "module": "sklearn.utils._fast_dict", + "name": "IntFloatDict" + } + } + }, + "metadata": { + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { + "type": "tuple", + "items": [ + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { "type": { "name": "int" } @@ -189717,40 +184260,54 @@ "metadata": { "usage.sklearn": 2 } - } - ], - "__rsub__": [ + }, { "pos_only_required": { "_0": { "type": { - "module": "pandas.core.arrays.timedeltas", - "name": "TimedeltaArray" + "name": "int" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "float32" } } }, "metadata": { - "usage.pandas": 1 + "usage.sklearn": 9 } }, { "pos_only_required": { "_0": { + "type": { + "name": "int" + } + }, + "_1": { "type": { "module": "numpy", - "name": "uint32" + "name": "matrix" } } }, "metadata": { - "usage.dask": 1 + "usage.sklearn": 2 } - } - ], - "__rmul__": [ + }, { "pos_only_required": { "_0": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + "_1": { "type": { "module": "numpy", "name": "ndarray" @@ -189758,158 +184315,334 @@ } }, "metadata": { - "usage.pandas": 1 + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "int64" } } }, "metadata": { - "usage.scipy": 1 + "usage.sklearn": 5 } }, { "pos_only_required": { "_0": { "type": { - "name": 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+ "_1": { "type": { - "module": "numpy", - "name": "dtype" + "name": "int" } } }, "metadata": { - "usage.pandas": 1 + "usage.sklearn": 1 } - } - ], - "__eq__": [ + }, { "pos_only_required": { "_0": { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + "_1": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } } } }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 4 + "usage.sklearn": 1 } - }, + } + ], + "reshape": [ { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "int64" - } + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 34, + "usage.xarray": 51, + "usage.matplotlib": 50, + "usage.sample-usage": 1, + "usage.sklearn": 85 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" + } + }, + "_1": { + "type": { + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 52, + "usage.xarray": 86, + "usage.matplotlib": 35, + "usage.sample-usage": 1, + "usage.sklearn": 230 } }, { @@ -192049,19 +186344,19 @@ "_0": { "type": { "module": "numpy", - "name": "int32" + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 2 + "usage.skimage": 2 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "module": "numpy", @@ -192070,14 +186365,24 @@ }, { "type": { - "module": "pandas.core.series", - "name": "Series" + "name": "int" } - }, + } + ] + } + }, + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "int32" + "name": "int" } }, { @@ -192087,33 +186392,32 @@ }, { "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" + "name": "int" } } ] } }, "metadata": { - "usage.pandas": 77 + "usage.skimage": 12, + "usage.xarray": 27, + "usage.matplotlib": 20, + "usage.sklearn": 12 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": "str", - "options": [ - "silverman", - "scott" - ] + "type": { + "name": "int" + } }, { "type": { - "module": "numpy", - "name": "int32" + "name": "int" } }, { @@ -192123,67 +186427,46 @@ }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } ] } }, "metadata": { - "usage.scipy": 34 + "usage.skimage": 7, + "usage.xarray": 13, + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "int32" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } }, { "type": { "name": "int" } - } - ] - } - }, - "metadata": { - "usage.dask": 5 - } - }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "mle" - ] }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "int32" + "name": "int" } }, { @@ -192195,105 +186478,104 @@ } }, "metadata": { - "usage.sklearn": 16 + "usage.skimage": 1 } - } - ], - "__getitem__": [ + }, { "pos_only_required": { "_0": { - "type": { - "name": "ellipsis" + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.skimage": 3, + "usage.xarray": 38, + "usage.matplotlib": 4, + "usage.sklearn": 7 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "name": "int" + } } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1, + "usage.xarray": 13, + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "ellipsis" - } - } - ] + "type": { + "name": "int" } } }, "metadata": { - "usage.dask": 4 + "usage.skimage": 8, + "usage.xarray": 14, + "usage.matplotlib": 3, + "usage.sklearn": 22 } - } - ], - "__add__": [ + }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "int32" + "name": "int" } }, { "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" + "name": "int" } }, { "type": { - "module": "pandas.core.series", - "name": "Series" + "name": "int" } }, { "type": { - "module": "pandas.core.arrays.timedeltas", - "name": "TimedeltaArray" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } ] } }, "metadata": { - "usage.pandas": 8 - } - }, - { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.scipy": 25 + "usage.skimage": 2, + "usage.xarray": 2 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -192309,14 +186591,14 @@ } }, "metadata": { - "usage.dask": 3 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -192325,35 +186607,163 @@ { "type": { "module": "numpy", - "name": "int32" + "name": "int64" } }, { "type": { - "name": "bool" + "module": "numpy", + "name": "int64" + } + } + ] + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "name": "int" + } + }, + "_2": { + "type": { + "name": "int" + } + }, + "_3": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 2, + "usage.xarray": 2, + "usage.sklearn": 5 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "name": "int" + } + }, + "_2": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 7, + "usage.xarray": 36, + "usage.matplotlib": 1, + "usage.sklearn": 10 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": { + "type": "None" + } + } + }, + "metadata": { + "usage.xarray": 15, + "usage.matplotlib": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": "None" + } + ] } }, { - "type": { - "module": "numpy", - "name": "int64" + "type": "list", + "item": { + "type": { + "name": "int" + } } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } } ] } }, + "pos_only_optional": { + "_1": { + "type": { + "name": "int" + } + }, + "_2": { + "type": { + "name": "int" + } + } + }, + "pos_only_optional_ordering": [ + [ + "_1", + "_2" + ] + ], + "kw_only_optional": { + "order": { + "type": "str", + "options": [ + "F" + ] + } + }, "metadata": { - "usage.sklearn": 6 + "usage.pandas": 373 } - } - ], - "__sub__": [ + }, { "pos_only_required": { "_0": { @@ -192366,33 +186776,112 @@ }, { "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "module": "pandas.core.series", - "name": "Series" + "module": "numpy", + "name": "int64" } }, { - "type": { - "module": "pandas.core.arrays.timedeltas", - "name": "TimedeltaArray" + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": "None" + } + ] } }, { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "int32" + } + }, + { + "type": { + "name": "int" + } + } + ] } } ] } }, + "pos_only_optional": { + "_1": { + "type": { + "name": "int" + } + }, + "_2": { + "type": { + "name": "int" + } + }, + "_3": { + "type": { + "name": "int" + } + }, + "_4": { + "type": { + "name": "int" + } + } + }, + "pos_only_optional_ordering": [ + [ + "_2", + "_3" + ], + [ + "_1", + "_2" + ], + [ + "_3", + "_4" + ] + ], + "kw_only_optional": { + "order": { + "type": "str", + "options": [ + "F" + ] + } + }, "metadata": { - "usage.pandas": 5 + "usage.scipy": 896 } }, { @@ -192402,73 +186891,121 @@ "options": [ { "type": { - "module": "numpy", - "name": "int32" + "name": "int" } }, { - "type": { - "name": "int" + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "name": "int" + } + } + ] } } ] } }, + "pos_only_optional": { + "_1": { + "type": { + "name": "int" + } + }, + "_2": { + "type": { + "name": "int" + } + }, + "_3": { + "type": { + "name": "int" + } + } + }, + "pos_only_optional_ordering": [ + [ + "_2", + "_3" + ], + [ + "_1", + "_2" + ] + ], "metadata": { - "usage.scipy": 18, - "usage.sklearn": 5 + "usage.dask": 187 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "int32" + "name": "int" + } + }, + "_1": { + "type": { + "name": "int" + } + }, + "_2": { + "type": { + "name": "int" + } + }, + "_3": { + "type": { + "name": "int" + } + }, + "_4": { + "type": { + "name": "int" } } }, "metadata": { - "usage.dask": 1 + "usage.sklearn": 1 } } ], - "__rsub__": [ + "__imul__": [ { "pos_only_required": { "_0": { "type": { - "module": "pandas.core.arrays.timedeltas", - "name": "TimedeltaArray" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.pandas": 1 + "usage.skimage": 16, + "usage.matplotlib": 2, + "usage.sklearn": 77 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int32" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "name": "float" + } } }, "metadata": { - "usage.scipy": 3, - "usage.sklearn": 5 + "usage.skimage": 10, + "usage.xarray": 1, + "usage.matplotlib": 12, + "usage.sklearn": 46 } }, { @@ -192476,66 +187013,88 @@ "_0": { "type": { "module": "numpy", - "name": "int32" + "name": "float64" } } }, "metadata": { - "usage.dask": 1 + "usage.skimage": 4, + "usage.xarray": 1, + "usage.matplotlib": 4, + "usage.sklearn": 19 } - } - ], - "__rmul__": [ + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 4, + "usage.xarray": 1, + "usage.matplotlib": 9, + "usage.sample-usage": 1, + "usage.dask": 1, + "usage.sklearn": 35 + } + }, { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "ndarray" + "name": "uint8" } } }, "metadata": { - "usage.pandas": 1, + "usage.skimage": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "float32" + } + } + }, + "metadata": { + "usage.skimage": 1, + "usage.xarray": 1, "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "numpy", + "name": "int16" + } } }, "metadata": { - "usage.scipy": 2 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "uint16" } } }, "metadata": { - "usage.dask": 2 + "usage.skimage": 1 } - } - ], - "__mul__": [ + }, { "pos_only_required": { "_0": { @@ -192543,26 +187102,13 @@ "options": [ { "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "pandas.core.arrays.timedeltas", - "name": "TimedeltaArray" - } - }, - { - "type": { - "module": "pandas.core.series", - "name": "Series" + "name": "int" } }, { "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" + "module": "numpy", + "name": "uint64" } } ] @@ -192575,132 +187121,90 @@ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": "object" } }, "metadata": { - "usage.scipy": 7 + "usage.scipy": 244 } }, { "pos_only_required": { "_0": { - "type": { - "name": "int" + "type": "list", + "item": { + "type": { + "name": "int" + } } } }, "metadata": { - "usage.dask": 1 + "usage.matplotlib": 2 } } ], - "__rtruediv__": [ + "__iadd__": [ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" - } - }, - { - "type": { - "module": "pandas._libs.tslibs.nattype", - "name": "NaTType" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 5 + "usage.skimage": 78, + "usage.xarray": 3, + "usage.matplotlib": 38, + "usage.sklearn": 161 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } }, "metadata": { - "usage.scipy": 2 + "usage.skimage": 27, + "usage.xarray": 2, + "usage.matplotlib": 8, + "usage.sample-usage": 1, + "usage.sklearn": 33 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "int32" + "name": "float" } } }, "metadata": { - "usage.dask": 1 + "usage.skimage": 9, + "usage.matplotlib": 14, + "usage.sklearn": 28 } - } - ], - "__truediv__": [ + }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "pandas.core.arrays.timedeltas", - "name": "TimedeltaArray" - } - }, - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" - } - } - ] + "type": { + "module": "numpy", + "name": "int64" + } } }, "metadata": { - "usage.pandas": 4 + "usage.skimage": 3, + "usage.xarray": 1, + "usage.matplotlib": 2, + "usage.sklearn": 6 } }, { @@ -192708,137 +187212,116 @@ "_0": { "type": { "module": "numpy", - "name": "int32" + "name": "float64" } } }, "metadata": { - "usage.dask": 1 + "usage.skimage": 3, + "usage.matplotlib": 4, + "usage.sklearn": 29 } - } - ], - "__le__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "float16" } } }, "metadata": { - "usage.pandas": 3 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int32" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - } - ] + "type": { + "module": "numpy", + "name": "float32" + } } }, "metadata": { - "usage.scipy": 10 + "usage.skimage": 1, + "usage.sklearn": 3 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "float64" } - ] + } } }, "metadata": { - "usage.sklearn": 8 + "usage.skimage": 2 } - } - ], - "__gt__": [ + }, { "pos_only_required": { "_0": { - "type": { - "name": "int" + "type": "list", + "item": { + "type": { + "name": "int" + } } } }, "metadata": { - "usage.pandas": 2, - "usage.sklearn": 1 + "usage.skimage": 1, + "usage.matplotlib": 2, + "usage.sklearn": 5 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int32" - } - }, - { - "type": { - "name": "int" + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "name": "int" + } } - } - ] + ] + } } }, "metadata": { - "usage.scipy": 10 + "usage.skimage": 3 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" + "module": "numpy", + "name": "int32" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 1, + "usage.sklearn": 1 } - } - ], - "__floordiv__": [ + }, { "pos_only_required": { "_0": { @@ -192847,29 +187330,15 @@ { "type": { "module": "numpy", - "name": "ndarray" + "name": "int64" } }, { "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" + "module": "pandas.core.arrays.sparse.array", + "name": "SparseArray" } - } - ] - } - }, - "metadata": { - "usage.pandas": 2 - } - } - ], - "__pow__": [ - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ + }, { "type": { "module": "numpy", @@ -192878,31 +187347,57 @@ }, { "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" + "module": "numpy", + "name": "uint64" + } + }, + { + "type": { + "name": "int" } } ] } }, "metadata": { - "usage.pandas": 2 + "usage.pandas": 13 } }, { "pos_only_required": { "_0": { - "type": { - "name": "int" + "type": "object" + } + }, + "metadata": { + "usage.scipy": 368 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + } + ] } } }, "metadata": { - "usage.scipy": 2 + "usage.matplotlib": 2 } - } - ], - "__mod__": [ + }, { "pos_only_required": { "_0": { @@ -192911,134 +187406,143 @@ { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } }, { "type": { - "module": "pandas.core.arrays.integer", - "name": "IntegerArray" + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } } ] } }, "metadata": { - "usage.pandas": 2 + "usage.dask": 12 } } ], - "__radd__": [ + "__truediv__": [ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas._libs.missing", - "name": "NAType" - } - }, - { - "type": { - "module": "numpy", - "name": "int32" - } - } - ] + "type": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.pandas": 6 + "usage.skimage": 35, + "usage.matplotlib": 24, + "usage.sklearn": 86 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.scipy": 20 + "usage.skimage": 68, + "usage.xarray": 1, + "usage.matplotlib": 37, + "usage.sklearn": 204 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "int32" + "name": "float" } } }, "metadata": { - "usage.sklearn": 2 + "usage.skimage": 62, + "usage.matplotlib": 48, + "usage.sklearn": 72 } - } - ], - "__rfloordiv__": [ + }, { "pos_only_required": { "_0": { "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" + "name": "int" } } }, "metadata": { - "usage.pandas": 1 + "usage.skimage": 44, + "usage.xarray": 4, + "usage.matplotlib": 53, + "usage.sklearn": 88 } - } - ], - "__and__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } } }, "metadata": { - "usage.scipy": 8 + "usage.skimage": 6, + "usage.xarray": 1, + "usage.matplotlib": 2, + "usage.sklearn": 15 } - } - ], - "__neg__": [ + }, { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + }, "metadata": { - "usage.scipy": 3 + "usage.xarray": 4 } - } - ], - "__rmod__": [ + }, { "pos_only_required": { "_0": { - "type": "str" + "type": "object" } }, "metadata": { - "usage.scipy": 2 + "usage.pandas": 420, + "usage.scipy": 1657 } }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "%d" - ] + "type": { + "module": "numpy", + "name": "float32" + } } }, "metadata": { - "usage.sklearn": 1 + "usage.matplotlib": 1, + "usage.sklearn": 4 } - } - ], - "__iadd__": [ + }, { "pos_only_required": { "_0": { @@ -193052,575 +187556,146 @@ { "type": { "module": "numpy", - "name": "int32" + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "name": "float" } } ] } }, "metadata": { - "usage.scipy": 2 + "usage.dask": 23 } - } - ], - "__bool__": [ + }, { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "matrix" + } + } + }, "metadata": { - "usage.scipy": 1 - } - } - ] - }, - "methods": { - "__ge__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int32" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] - } - }, - "metadata": { - "usage.skimage": 1, - "usage.pandas": 3, - "usage.scipy": 12, - "usage.matplotlib": 1, - "usage.sklearn": 6 - } - }, - "astype": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "dtype" - } - }, - { - 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"pos_only_required": { - "_0": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "float64" + } } + }, + "metadata": { + "usage.skimage": 3, + "usage.matplotlib": 5, + "usage.sklearn": 22 } }, - "metadata": { - "usage.scipy": 8 - } - }, - "__neg__": { - "metadata": { - "usage.scipy": 3 - } - }, - "__rmod__": { - "pos_only_required": { - "_0": { - "type": "str" - } - }, - "metadata": { - "usage.scipy": 2, - "usage.sklearn": 1 - } - }, - "__iadd__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int32" - } + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" } - ] + } + }, + "metadata": { + "usage.skimage": 18, + "usage.xarray": 1, + "usage.matplotlib": 9, + "usage.sklearn": 18 } }, - "metadata": { - "usage.scipy": 2 - } - }, - "__bool__": { - "metadata": { - "usage.scipy": 1 - } - } - }, - "classmethod_overloads": { - "__ne__": [ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int32" - } - }, - { - "type": "type", - "name": { - "module": "numpy", - "name": "int64" - } - } - ] + "type": "object" } }, "metadata": { - "usage.pandas": 9 + "usage.pandas": 48 } }, { @@ -193631,7 +187706,7 @@ { "type": { "module": "numpy", - "name": "int64" + "name": "ndarray" } }, { @@ -193641,35 +187716,20 @@ }, { "type": { - "module": "numpy", - "name": "int32" + "name": "float" } }, { - "type": "type", - "name": { + "type": { "module": "numpy", - "name": "int32" + "name": "float64" } } ] } }, "metadata": { - "usage.scipy": 33 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "dtype" - } - } - }, - "metadata": { - "usage.dask": 1 + "usage.scipy": 75 } }, { @@ -193680,211 +187740,134 @@ { "type": { "module": "numpy", - "name": "dtype" - } - }, - { - "type": { - "module": "numpy", - "name": "int32" + "name": "ndarray" } }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } ] } }, "metadata": { - "usage.sklearn": 8 + "usage.dask": 3 } } ], - "__lt__": [ + "copy": [ { - "pos_only_required": { + "metadata": { + "usage.skimage": 93, + "usage.xarray": 17, + "usage.pandas": 284, + "usage.matplotlib": 21, + "usage.dask": 7, + "usage.sklearn": 213 + } + }, + { + "pos_only_optional": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "dtype" - } - }, - { - "type": { - "module": "numpy", - "name": "int32" - } - } + "F", + "C" + ] + } + }, + "kw_only_optional": { + "order": { + "type": "str", + "options": [ + "F", + "C" ] } }, "metadata": { - "usage.scipy": 3 + "usage.scipy": 850 } }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "C" + ] } }, "metadata": { - "usage.skimage": 2, - "usage.matplotlib": 2 + "usage.sklearn": 3 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" - } + "type": "str", + "options": [ + "F" + ] } }, "metadata": { - "usage.matplotlib": 1 - } - } - ] - }, - "classmethods": { - "__ne__": { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.pandas": 9, - "usage.scipy": 33, - "usage.dask": 1, - "usage.sklearn": 8 - } - }, - "__lt__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" - } - }, - { - "type": { - "module": "numpy", - "name": "dtype" - } - }, - { - "type": { - "module": "numpy", - "name": "int32" - } - } - ] + "usage.sklearn": 7 } }, - "metadata": { - "usage.scipy": 3, - "usage.skimage": 2, - "usage.matplotlib": 3 - } - } - }, - "properties": { - "ndim": [ - { - "usage.xarray": 1, - "usage.dask": 3 - }, - { - "type": "bottom" - } - ], - "dtype": [ - { - 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"usage.skimage": 123, + "usage.xarray": 16, + "usage.matplotlib": 57, + "usage.sklearn": 71 } }, - "metadata": { - "usage.pandas": 1 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": "list", - "item": { + { + "pos_only_optional": { + "_0": { "type": "union", "options": [ { @@ -193893,82 +187876,105 @@ } }, { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "float" - } - } - ] - } + "type": "None" } ] } + }, + "kw_only_optional": { + "axis": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.pandas": 48 } }, - "metadata": { - "usage.scipy": 3 - } - } - ], - "method_overloads": { - "__rmatmul__": [ { - "pos_only_required": { + "pos_only_optional": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" + } + } + }, + "kw_only_optional": { + "axis": { + "type": { + "name": "int" } } }, "metadata": { - "usage.skimage": 1 + "usage.scipy": 146 } }, { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "matrix" - } - } - ] + "kw_only_optional": { + "axis": { + "type": { + "name": "int" + } + }, + "keepdims": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.scipy": 237 + "usage.dask": 10 + } + }, + { + "kw_only_required": { + "axis": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.sklearn": 17 } } ], - "__neg__": [ + "ptp": [ { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 1, + "usage.matplotlib": 1 + } + }, + { + "metadata": { + "usage.skimage": 3, + "usage.matplotlib": 3 + } + }, + { + "kw_only_optional": { + "axis": { + "type": { + "name": "int" + } + } + }, "metadata": { - "usage.skimage": 2, "usage.scipy": 6 } } ], - "__matmul__": [ + "__rtruediv__": [ { "pos_only_required": { 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"type": "object" } }, - "pos_only_optional": { - "_1": { - "type": { - "name": "int" + "metadata": { + "usage.pandas": 420, + "usage.scipy": 1157 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "float64" + } } } }, - "kw_only_optional": { - "order": { - "type": "str", - "options": [ - "F", - "C" - ] - } - }, "metadata": { - "usage.scipy": 21 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, { "type": { "name": "int" @@ -194165,19 +188093,15 @@ } }, "metadata": { - "usage.dask": 1 + "usage.dask": 13 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "int" + "module": "numpy", + "name": "memmap" } } }, @@ -194186,209 +188110,200 @@ } } ], - "__gt__": [ + "__radd__": [ { "pos_only_required": { "_0": { - "type": "object" + "type": { + "name": "float" + } } }, "metadata": { - "usage.scipy": 319 + "usage.skimage": 18, + "usage.xarray": 2, + "usage.matplotlib": 26, + "usage.sklearn": 33 } - } - ], - "__setitem__": [ + }, { "pos_only_required": { "_0": { - "type": "object" - }, - "_1": { - "type": "object" + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.scipy": 303 + "usage.skimage": 177, + "usage.xarray": 26, + "usage.matplotlib": 213, + "usage.sample-usage": 1, + "usage.sklearn": 274 } }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "module": "numpy", + "name": "float64" } - }, - "_1": { + } + }, + "metadata": { + "usage.skimage": 4, + "usage.matplotlib": 3, + "usage.sklearn": 10 + } + }, + { + "pos_only_required": { + "_0": { "type": { "name": "int" } } }, "metadata": { - "usage.dask": 1 + "usage.skimage": 34, + "usage.xarray": 13, + "usage.matplotlib": 13, + "usage.sample-usage": 1, + "usage.sklearn": 22 } - } - ], - "copy": [ + }, { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, "metadata": { - "usage.scipy": 168, - "usage.dask": 1 + "usage.skimage": 1 } - } - ], - "__rmul__": [ + }, { "pos_only_required": { "_0": { - "type": "object" + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } } }, "metadata": { - "usage.scipy": 213 + "usage.skimage": 3 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } } }, "metadata": { - "usage.sklearn": 1 + "usage.skimage": 2, + "usage.matplotlib": 1 } - } - ], - "sum": [ + }, { - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": { - "type": "None" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - 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"object" + "type": { + "module": "xarray.core.dataset", + "name": "Dataset" + } } }, "metadata": { - "usage.scipy": 869 + "usage.xarray": 2 } }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "timedelta64" } } }, "metadata": { - "usage.sklearn": 1 + "usage.xarray": 1 } - } - ], - "__radd__": [ + }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "xarray.core.variable", + "name": "Variable" + } } }, "metadata": { - "usage.scipy": 888 + "usage.xarray": 1 } - } - ], - "__sub__": [ + }, { "pos_only_required": { "_0": { @@ -194396,7 +188311,37 @@ } }, "metadata": { - "usage.scipy": 610 + "usage.pandas": 289, + "usage.scipy": 2001, + "usage.dask": 78 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + } + }, + "metadata": { + "usage.sklearn": 12 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "scipy.sparse.csr", + "name": "csr_matrix" + } + } + }, + "metadata": { + "usage.sklearn": 1 } }, { @@ -194404,36 +188349,42 @@ "_0": { "type": { "module": "numpy", - "name": "ndarray" + "name": "float32" } } }, "metadata": { - "usage.sklearn": 2 + "usage.sklearn": 1 } } ], - "__mul__": [ + "__matmul__": [ { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.scipy": 69 + "usage.skimage": 47, + "usage.sample-usage": 1, + "usage.sklearn": 23 } - } - ], - "__eq__": [ + }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "numpy", + "name": "matrix" + } } }, "metadata": { - "usage.scipy": 325 + "usage.skimage": 1 } }, { @@ -194443,116 +188394,162 @@ "options": [ { "type": { - "name": "int" + "module": "numpy", + "name": "matrix" } }, { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "complex" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "complex" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] + } + } + ] } } ] } }, "metadata": { - "usage.sklearn": 3 + "usage.scipy": 423 } - } - ], - "__rsub__": [ + }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "scipy.sparse.csc", + "name": "csc_matrix" + } } }, "metadata": { - "usage.scipy": 598 + "usage.sklearn": 5 } - } - ], - "max": [ + }, { - "pos_or_kw_required": { - "axis": { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "scipy.sparse.csr", + "name": "csr_matrix" } } }, "metadata": { - "usage.scipy": 7 + "usage.sklearn": 5 } }, { + "pos_only_required": { + "_0": { + "type": { + "module": "scipy.sparse.lil", + "name": "lil_matrix" + } + } + }, "metadata": { - "usage.sklearn": 3 + "usage.sklearn": 1 } - } - ], - "min": [ + }, { - "pos_or_kw_required": { - "axis": { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "scipy.sparse.dok", + "name": "dok_matrix" } } }, "metadata": { - "usage.scipy": 7 + "usage.sklearn": 1 } } ], - "__ne__": [ + "__rmatmul__": [ { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.scipy": 345 + "usage.skimage": 47, + "usage.sample-usage": 1, + "usage.sklearn": 23 } - } - ], - "astype": [ + }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": "type" - }, - { - "type": "str", - "options": [ - "int32", - "int16" - ] + "type": "list", + "item": { + "type": { + "name": "int" } - ] + } } }, "metadata": { - "usage.scipy": 151 + "usage.skimage": 2 } - } - ], - "__le__": [ + }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "numpy", + "name": "matrix" + } } }, "metadata": { - "usage.scipy": 690 + "usage.skimage": 1 } - } - ], - "__ge__": [ + }, { "pos_only_required": { "_0": { @@ -194560,227 +188557,364 @@ "options": [ { "type": { - "module": "scipy.sparse.bsr", - "name": "bsr_matrix" + "module": "numpy", + "name": "matrix" } }, { "type": { - "module": "scipy.sparse.csc", - "name": "csc_matrix" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "module": "scipy.sparse.csr", - "name": "csr_matrix" + "module": "scipy.sparse.dia", + "name": "dia_matrix" } }, { "type": { - "module": "numpy", - "name": "matrix" + "module": "scipy.sparse.csc", + "name": "csc_matrix" } }, { - "type": { - "name": "int" + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + { + "type": { + "name": "complex" + } + }, + { + "type": { + "name": "int" + } + } + ] } } ] } }, "metadata": { - "usage.scipy": 676 + "usage.scipy": 432 } - } - ], - "__lt__": [ + }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "scipy.sparse.bsr", - "name": "bsr_matrix" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "matrix" - } - }, - { - "type": { - "module": "scipy.sparse.csr", - "name": "csr_matrix" - } - }, - { - "type": { - "module": "scipy.sparse.csc", - "name": "csc_matrix" - } - } - ] + "type": { + "module": "scipy.sparse.csr", + "name": "csr_matrix" + } } }, "metadata": { - "usage.scipy": 390 + "usage.sklearn": 6 } - } - ], - "mean": [ + }, { - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - }, - "dtype": { - "type": "type" - }, - "out": { + "pos_only_required": { + "_0": { "type": { - "module": "numpy", - "name": "matrix" + "module": "scipy.sparse.csc", + "name": "csc_matrix" } } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "out" - ] - ], "metadata": { - "usage.scipy": 187 + "usage.sklearn": 6 } - } - ], - "__pow__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "scipy.sparse.dia", + "name": "dia_matrix" } } }, "metadata": { - "usage.scipy": 1 + "usage.sklearn": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "scipy.sparse.coo", + "name": "coo_matrix" + } + } + }, + "metadata": { + "usage.sklearn": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "scipy.sparse.lil", + "name": "lil_matrix" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "scipy.sparse.dok", + "name": "dok_matrix" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "scipy.sparse.bsr", + "name": "bsr_matrix" + } + } + }, + "metadata": { + "usage.sklearn": 1 } } ], - "view": [ + "__add__": [ { "pos_only_required": { "_0": { - "type": "type", - "name": { + "type": { + "name": "float" + } + } + }, + "metadata": { + "usage.skimage": 28, + "usage.xarray": 21, + "usage.matplotlib": 54, + "usage.sklearn": 61 + } + }, + { + "pos_only_required": { + "_0": { + "type": { "module": "numpy", "name": "ndarray" } } }, "metadata": { - "usage.scipy": 1 + "usage.skimage": 177, + "usage.xarray": 26, + "usage.matplotlib": 213, + "usage.sample-usage": 1, + "usage.sklearn": 274 } }, { - "kw_only_required": { - "type": { - "type": "type", - "name": { + "pos_only_required": { + "_0": { + "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } } }, "metadata": { - "usage.dask": 2 + "usage.skimage": 18, + "usage.xarray": 1, + "usage.matplotlib": 10, + "usage.sklearn": 51 } - } - ], - "__imul__": [ + }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "name": "int" + } } }, "metadata": { - "usage.scipy": 5 + "usage.skimage": 41, + "usage.xarray": 22, + "usage.matplotlib": 94, + "usage.sample-usage": 1, + "usage.sklearn": 94 } - } - ], - "__itruediv__": [ + }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": { + "module": "numpy", + "name": "int64" + } + } + }, + "metadata": { + "usage.skimage": 2, + "usage.matplotlib": 2, + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "dask.array.core", + "name": "Array" + } + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "datetime", + "name": "timedelta" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "xarray.coding.cftime_offsets", + "name": "Day" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "xarray.coding.cftime_offsets", + "name": "Hour" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "xarray.core.dataarray", + "name": "DataArray" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "xarray.core.dataset", + "name": "Dataset" + } + } + }, + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": "object" + } + }, + "metadata": { + "usage.pandas": 272, + "usage.scipy": 2298, + "usage.dask": 194 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { - "name": "float" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } } ] } }, "metadata": { - "usage.scipy": 4 - } - } - ], - "nonzero": [ - { - "metadata": { - "usage.scipy": 1, - "usage.sklearn": 2 + "usage.matplotlib": 4 } - } - ], - "__truediv__": [ + }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -194788,46 +188922,41 @@ }, { "type": { - "name": "complex" + "name": "int" } } ] } }, "metadata": { - "usage.scipy": 2 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": { - "name": "float" + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "float64" + } } } }, "metadata": { - "usage.sklearn": 1 - } - } - ], - "all": [ - { - "metadata": { - "usage.scipy": 2, - "usage.dask": 2 + "usage.matplotlib": 5 } - } - ], - "__rtruediv__": [ + }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } }, { @@ -194839,968 +188968,254 @@ } }, "metadata": { - "usage.scipy": 2 + "usage.matplotlib": 2 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { + "type": "list", + "item": { + "type": "list", + "item": { "type": { "name": "float" } } - ] + } } }, "metadata": { - "usage.sklearn": 3 + "usage.matplotlib": 1 } - } - ], - "any": [ + }, { + "pos_only_required": { + "_0": { + "type": { + "name": "range" + } + } + }, "metadata": { - "usage.sklearn": 1 - } - } - ] - }, - "methods": { - "__rmatmul__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "matrix" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "usage.matplotlib": 2 } }, - "metadata": { - "usage.skimage": 1, - "usage.scipy": 237 - } - }, - "__neg__": { - "metadata": { - "usage.skimage": 2, - "usage.scipy": 6 - } - }, - "__matmul__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "matrix" - } - }, - { + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { "type": 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"metadata": { - "usage.skimage": 1 + "usage.matplotlib": 2, + "usage.sklearn": 2 } - } - ], - "__pow__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "str_" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 } - } - ], - "__rmul__": [ + }, { "pos_only_required": { "_0": { - "type": { - "name": "float" - } + "type": "str", + "options": [ + "spam" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 } }, { @@ -196832,84 +189813,74 @@ } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } - } - ], - "__eq__": [ + }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "c" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } - } - ], - "__gt__": [ + }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "float64" - } + "type": "str", + "options": [ + "b" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } - } - ], - "__ge__": [ + }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "str", + "options": [ + "def" + ] } }, "metadata": { - "usage.scipy": 3 + "usage.sklearn": 1 } - } - ], - "__lt__": [ + }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "str", + "options": [ + "ghi" + ] } }, "metadata": { - "usage.scipy": 3 + "usage.sklearn": 1 } - } - ], - "__mul__": [ + }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "a" + ] } }, "metadata": { - "usage.scipy": 1 + "usage.sklearn": 1 } - } - ], - "__ne__": [ + }, { "pos_only_required": { "_0": { @@ -196920,235 +189891,159 @@ } }, "metadata": { - "usage.scipy": 1 + "usage.sklearn": 2 } } - ] - }, - "methods": { - "__sub__": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "float16" - } - } - }, - "metadata": { - "usage.skimage": 2 - } - }, - "__rsub__": { - 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"__eq__": { - "pos_only_required": { - "_0": { - "type": { - "name": "int" } + }, + "metadata": { + "usage.pandas": 175 } }, - "metadata": { - "usage.skimage": 1 - } - }, - "__gt__": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "float64" + { + "kw_only_optional": { + "axis": { + "type": { + "name": "int" + } } + }, + "metadata": { + "usage.scipy": 105 } }, - "metadata": { - "usage.skimage": 1 - } - }, - "__ge__": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" + { + "kw_only_required": { + "axis": { + "type": { + "name": "int" + } } + }, + "metadata": { + "usage.matplotlib": 1, + "usage.sklearn": 6 } - }, - "metadata": { - "usage.scipy": 3 } - }, - "__lt__": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + ], + "nonzero": [ + { + "metadata": { + "usage.skimage": 4, + "usage.xarray": 4, + "usage.pandas": 29, + "usage.scipy": 31, + "usage.matplotlib": 1, + "usage.dask": 1, + "usage.sklearn": 7 } - }, - "metadata": { - "usage.scipy": 3 } - }, - "__mul__": { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + ], + "__pow__": [ + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } } + }, + "metadata": { + "usage.skimage": 154, + "usage.xarray": 11, + "usage.matplotlib": 69, + "usage.sample-usage": 1, + "usage.sklearn": 274 } }, - "metadata": { - "usage.scipy": 1 - } - }, - "__ne__": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "float64" + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } } + }, + "metadata": { + "usage.skimage": 2 } }, - "metadata": { - "usage.scipy": 1 - } - } - }, - "properties": { - "real": [ - { - "usage.scipy": 1 - }, - { - "type": "bottom" - } - ], - "dtype": [ - { - "usage.scipy": 1 - }, - { - "type": "bottom" - } - ], - "ndim": [ - { - "usage.dask": 1 - }, { - "type": "bottom" - } - ] - } - }, - "nditer": { - 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} - }, - "pos_only_optional": { - "_1": { - "type": "list", - "item": { - "type": "bottom" - } }, - "_2": { - "type": "list", - "item": { - "type": "list", - "item": { - "type": "str", - "options": [ - "readonly", - "allocate", - "writeonly" - ] - } - } + "metadata": { + "usage.scipy": 872 } }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ] - ], - "kw_only_optional": { - "flags": { - "type": "list", - "item": { - "type": "str", - "options": [ - "multi_index" - ] - } - }, - "op_flags": { - "type": "list", - "item": { - "type": "list", - "item": { - "type": "str", - "options": [ - "readonly" - ] - } - } - }, - "op_dtypes": { - "type": "list", - "item": { + { + "pos_only_required": { + "_0": { "type": "union", "options": [ { - "type": "None" + "type": { + "name": "int" + } }, { - "type": "type" + "type": { + "module": "numpy", + "name": "ndarray" + } } ] } + }, + "metadata": { + "usage.dask": 12 } }, - "metadata": { - "usage.scipy": 19 - } - } - ], - "constructor": { - 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"options": [ - "readonly" - ] - } - } - }, - "op_dtypes": { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": "type" - } - ] - } - } - }, - "metadata": { - "usage.scipy": 19 - } - }, - "method_overloads": { - "__iter__": [ + "__ge__": [ { + "pos_only_required": { + "_0": { + "type": { + "name": "float" + } + } + }, "metadata": { "usage.skimage": 1, - "usage.scipy": 11 + "usage.matplotlib": 9, + "usage.sklearn": 17 } - } - ], - "__getitem__": [ + }, { "pos_only_required": { "_0": { @@ -197356,120 +190125,117 @@ } }, "metadata": { - "usage.scipy": 3 + "usage.skimage": 43, + "usage.xarray": 3, + "usage.matplotlib": 9, + "usage.sklearn": 47 } - } - ], - "iternext": [ + }, { - "metadata": { - "usage.scipy": 3 - } - } - ] - }, - "methods": { - "__iter__": { - "metadata": { - "usage.skimage": 1, - "usage.scipy": 11 - } - }, - "__getitem__": { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "int64" + } } + }, + "metadata": { + "usage.skimage": 1, + "usage.matplotlib": 2 } }, - "metadata": { - "usage.scipy": 3 - } - }, - "iternext": { - "metadata": { - "usage.scipy": 3 - } - } - }, - "properties": { - "multi_index": [ { - "usage.skimage": 1, - "usage.scipy": 3 + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.skimage": 5, + "usage.xarray": 7, + "usage.matplotlib": 2, + "usage.sklearn": 7 + } }, { - "type": "bottom" - } - ], - "operands": [ - { - "usage.scipy": 10 + "pos_only_required": { + "_0": { + "type": "object" + } + }, + "metadata": { + "usage.pandas": 90, + "usage.scipy": 464 + } }, { - "type": "bottom" - } - ], - "finished": [ - { - "usage.scipy": 3 + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "float64" + } + } + }, + "metadata": { + "usage.matplotlib": 16, + "usage.sklearn": 5 + } }, { - "type": "bottom" - } - ] - } - }, - "longlong": { - "constructor_overloads": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "int64" + "pos_only_required": { + "_0": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } } + }, + "metadata": { + "usage.matplotlib": 1, + "usage.sklearn": 2 } }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "complex" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "list", - "item": { + { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { "type": { "name": "int" } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "float" + } } - } - ] + ] + } + }, + "metadata": { + "usage.dask": 17 } - }, - "metadata": { - "usage.scipy": 3 } - } - ], - "method_overloads": { - "__lt__": [ + ], + "__le__": [ { "pos_only_required": { "_0": { @@ -197479,21 +190245,25 @@ } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 16, + "usage.matplotlib": 14, + "usage.sklearn": 22 } - } - ], - "__eq__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 5, + "usage.xarray": 7, + "usage.matplotlib": 2, + "usage.sklearn": 7 } }, { @@ -197506,45 +190276,35 @@ } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 6, + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "name": "float" + } } }, "metadata": { - "usage.pandas": 8 + "usage.skimage": 1, + "usage.matplotlib": 7, + "usage.sklearn": 24 } - } - ], - "__add__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "numpy", + "name": "timedelta64" } } }, "metadata": { - "usage.pandas": 1, - "usage.sklearn": 1 + "usage.xarray": 15 } }, { @@ -197554,31 +190314,61 @@ } }, "metadata": { - "usage.scipy": 23 + "usage.pandas": 134, + "usage.scipy": 561 } - } - ], - "__rmul__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.pandas": 1, - "usage.scipy": 1 + "usage.matplotlib": 15, + "usage.sklearn": 11 } - } - ], - "__le__": [ + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "uint8" + } + } + }, + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + }, + "metadata": { + "usage.matplotlib": 1 + } + }, { "pos_only_required": { "_0": { "type": "union", "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, { "type": { "name": "int" @@ -197587,31 +190377,36 @@ { "type": { "module": "numpy", - "name": "longlong" + "name": "ndarray" + } + }, + { + "type": { + "name": "float" } } ] } }, "metadata": { - "usage.scipy": 2 + "usage.dask": 35 } - } - ], - "__ge__": [ + }, { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "longlong" + "name": "float32" } } }, "metadata": { - "usage.scipy": 1 + "usage.sklearn": 1 } - }, + } + ], + "fill": [ { "pos_only_required": { "_0": { @@ -197621,78 +190416,99 @@ } }, "metadata": { - "usage.sklearn": 1 + "usage.skimage": 4, + "usage.sklearn": 15 } - } - ], - "__gt__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "int" + "name": "bool" } } }, "metadata": { - "usage.scipy": 1, - "usage.sklearn": 1 + "usage.skimage": 1, + "usage.sklearn": 2 } - } - ], - "__sub__": [ + }, + { + "pos_only_required": { + "_0": { + "type": "object" + } + }, + "metadata": { + "usage.pandas": 107, + "usage.scipy": 60 + } + }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "longlong" + "name": "float" } } }, "metadata": { - "usage.scipy": 1 + "usage.sklearn": 10 } - } - ], - "__rsub__": [ + }, { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "longlong" + "name": "float64" } } }, "metadata": { - "usage.scipy": 1 + "usage.sklearn": 2 } } ], - "__bool__": [ + "__rpow__": [ { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, "metadata": { - "usage.scipy": 1 + "usage.skimage": 2 } - } - ], - "__ne__": [ + }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "longlong" + "name": "int" } } }, "metadata": { - "usage.scipy": 2 + "usage.skimage": 1, + "usage.matplotlib": 9, + "usage.sample-usage": 2, + "usage.sklearn": 1 } - } - ], - "__mul__": [ + }, + { + "pos_only_required": { + "_0": { + "type": "object" + } + }, + "metadata": { + "usage.pandas": 50 + } + }, { "pos_only_required": { "_0": { @@ -197701,639 +190517,667 @@ { "type": { "module": "numpy", - "name": "int64" + "name": "float64" + } + }, + { + "type": { + "name": "float" } }, { "type": { "name": "int" } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } ] } }, "metadata": { - "usage.scipy": 4 + "usage.scipy": 192 } - } - ], - "__radd__": [ + }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "name": "float" + } } }, "metadata": { - "usage.scipy": 18 - } - } - ] - }, - "methods": { - "__lt__": { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } + "usage.matplotlib": 7, + "usage.sklearn": 1 } }, - "metadata": { - "usage.skimage": 1 - } - }, - "__eq__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" + { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "int" + } } - } - ] + ] + } + }, + "metadata": { + "usage.dask": 3 } - }, - "metadata": { - "usage.skimage": 2, - "usage.pandas": 8 } - }, - "__add__": { - "pos_only_required": { - "_0": { - "type": "object" + ], + "sum": [ + { + "metadata": { + "usage.skimage": 68, + "usage.xarray": 8, + "usage.matplotlib": 14, + "usage.sklearn": 242 } }, - "metadata": { - "usage.pandas": 1, - "usage.scipy": 23, - "usage.sklearn": 1 - } - }, - "__rmul__": { - "pos_only_required": { - "_0": { - "type": { - "name": "float" + { + "kw_only_required": { + "axis": { + "type": { + "name": "int" + } } + }, + "metadata": { + "usage.skimage": 15, + "usage.xarray": 3, + "usage.sklearn": 164 } }, - "metadata": { - "usage.pandas": 1, - "usage.scipy": 1 - } - }, - "__le__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "longlong" - } + { + "pos_only_required": { + "_0": { + "type": { 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}, - "metadata": { - "usage.scipy": 2 - } - }, - "__mul__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "uint64" - } - }, - { - "type": { - "name": "int" - } - } - ] + { + "kw_only_optional": { + "order": { + "type": "str", + "options": [ + "K", + "F" + ] + } + }, + "metadata": { + "usage.dask": 35 } - }, - "metadata": { - "usage.scipy": 4 } - }, - "__radd__": { - "pos_only_required": { - "_0": { - "type": "object" + ], + "cumsum": [ + { + "metadata": { + "usage.skimage": 1, + "usage.matplotlib": 2, + "usage.sklearn": 6 } }, - "metadata": { - "usage.scipy": 18 - } - }, - "__rmul__": { - "pos_only_required": { - "_0": { - "type": { - "name": "float" + { + "kw_only_required": { + "axis": { + "type": { + "name": "int" + } } + }, + "metadata": { + "usage.skimage": 6, + "usage.matplotlib": 1 } }, - "metadata": { - "usage.scipy": 1 - } - }, - "__add__": { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.scipy": 17 - } - } - }, - "properties": { - "itemsize": [ { - "usage.pandas": 1 + "pos_only_optional": { + "_0": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.pandas": 6 + } }, { - "type": "bottom" - } - ], - "dtype": [ - { - "usage.scipy": 4 + "kw_only_optional": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "int64" + } + } + }, + "metadata": { + "usage.scipy": 8 + } }, { - "type": "bottom" + "kw_only_optional": { + "axis": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.dask": 7 + } } ], - "size": [ - { - "usage.scipy": 1 - }, + "__ifloordiv__": [ { - "type": "bottom" + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 6, + "usage.scipy": 8, + "usage.sample-usage": 1 + } } ], - "ndim": [ + "transpose": [ { - "usage.dask": 1 + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "name": "int" + } + }, + "_2": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 1, + "usage.sklearn": 2 + } }, { - "type": "bottom" - } - ] - }, - "classproperties": { - "__name__": [ - { - "usage.pandas": 2 + "metadata": { + "usage.skimage": 4, + "usage.matplotlib": 2, + "usage.sklearn": 8 + } }, - { - "type": "bottom" - } - ] - } - }, - "void": { - "method_overloads": { - "__getitem__": [ { "pos_only_required": { "_0": { - "type": { - "name": "int" + "type": "list", + "item": { + "type": { + "name": "int" + } } } }, "metadata": { - "usage.skimage": 33 + "usage.xarray": 8 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": "str" + "type": { + "name": "int" + } }, { "type": { @@ -198344,66 +191188,55 @@ } }, "metadata": { - "usage.scipy": 30 + "usage.xarray": 3 } }, { "pos_only_required": { "_0": { "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "ellipsis" - } + "items": [ + { + "type": { + "name": "int" } - ] - } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.dask": 2 + "usage.xarray": 3, + "usage.sklearn": 2 } }, { "pos_only_required": { "_0": { - "type": "str" - } - }, - "metadata": { - "usage.sklearn": 21 - } - } - ], - "__setitem__": [ - { - "pos_only_required": { - "_0": { - "type": "str", - "options": [ - "f2", - "f1" - ] - }, - "_1": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { - "type": "str", - "options": [ - "not perl", - "python" - ] + "type": { + "name": "int" + } }, { "type": { - "name": "float" + "name": "int" + } + }, + { + "type": { + "name": "int" } }, { @@ -198415,17 +191248,14 @@ } }, "metadata": { - "usage.scipy": 4 + "usage.xarray": 2 } }, { "pos_only_required": { "_0": { - "type": "str" - }, - "_1": { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "name": "int" @@ 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{ - "_0": { - "type": "str", - "options": [ - "asdf" - ] - } - }, - "metadata": { - "usage.xarray": 1 - } - } - ], - "method_overloads": { - "find": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "str_" - } - } - }, - "metadata": { - "usage.xarray": 1 + "pos_only_required": { + "_0": { + "type": "type", + "name": { + "module": "numpy", + "name": "int16" + } + } + }, + "metadata": { + "usage.skimage": 1 } }, { @@ -199544,174 +192002,87 @@ "_0": { "type": { "module": "numpy", - "name": "str_" - } - }, - "_1": { - "type": { - "name": "int" + "name": "dtype" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 2, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "str_" - } - }, - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - } - ], - "rfind": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "str_" - } + "type": "str", + "options": [ + "|S24" + ] } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1 } }, { - "pos_only_required": { - "_0": { - "type": { + "kw_only_required": { + "dtype": { + "type": "type", + "name": { "module": "numpy", - "name": "str_" - } - }, - "_1": { - "type": { - "name": "int" + "name": "uint8" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 4 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "str_" - } - }, - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - } - ], - "__getitem__": [ - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": { - "name": "int" - } - } + "type": "str", + "options": [ + "uint8" + ] } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": { - "name": "int" - } - } + "type": "str", + "options": [ + "|S3" + ] } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ - { - "type": { - "name": "ellipsis" - } - }, - { - "type": "None" - } + "type": "str", + "options": [ + "S1" ] } }, "metadata": { - "usage.dask": 1 + "usage.xarray": 6 } - } - ], - "__iadd__": [ + }, { "pos_only_required": { "_0": { "type": "str", "options": [ - "sh" + "S6" ] } }, @@ -199724,7 +192095,7 @@ "_0": { "type": "str", "options": [ - "a " + "S4" ] } }, @@ -199737,7 +192108,7 @@ "_0": { "type": "str", "options": [ - "ev" + "S3" ] } }, @@ -199750,7 +192121,7 @@ "_0": { "type": "str", "options": [ - "" + "S2" ] } }, @@ -199763,7 +192134,7 @@ "_0": { "type": "str", "options": [ - "shor" + "S0" ] } }, @@ -199772,81 +192143,189 @@ } }, { - "pos_only_required": { + "pos_only_optional": { "_0": { - "type": "str", + "type": "union", "options": [ - "a bit longe" + { + "type": "str" + }, + { + "type": { + "module": "numpy", + "name": "dtype" + } + }, + { + "type": "tuple", + "items": [ + { + "type": "type", + "name": { + "module": "numpy", + "name": "str_" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + { + "type": "type" + } + ] + } + }, + "kw_only_optional": { + "dtype": { + "type": "union", + "options": [ + { + "type": "type" + }, + { + "type": "str" + }, + { + "type": { + "module": "numpy", + "name": "dtype" + } + } ] } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 247 } }, { - "pos_only_required": { + "pos_only_optional": { "_0": { - "type": "str", + "type": "union", "options": [ - "evenlongerthantha" + { + "type": "type" + }, + { + "type": "str", + "options": [ + "D", + "b" + ] + } + ] + } + }, + "kw_only_optional": { + "dtype": { + "type": "union", + "options": [ + { + "type": "dict", + "key": { + "type": "str", + "options": [ + "formats", + "names" + ] + }, + "value": { + "type": "list", + "item": { + "type": "str" + } + } + }, + { + "type": "str", + "options": [ + ">c", + ">d", + ">f", + ">i", + ">b" + ] + } ] + }, + "type": { + "type": "type", + "name": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.xarray": 1 + "usage.scipy": 42 } }, { "pos_only_required": { "_0": { - "type": { + "type": "type", + "name": { "module": "numpy", - "name": "str_" + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 1 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "short" - ] + "type": "type", + "name": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } } }, "metadata": { - "usage.xarray": 1 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "a bit " - ] + "type": "type" } }, "metadata": { - "usage.xarray": 1 + "usage.matplotlib": 1 } }, { - "pos_only_required": { + "pos_only_optional": { "_0": { - "type": "str", + "type": "union", "options": [ - "evenlo" + { + "type": "str", + "options": [ + "i1", + "i4", + "i2" + ] + }, + { + "type": { + "module": "numpy", + "name": "dtype" + } + } ] } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 35 } }, { @@ -199854,88 +192333,105 @@ "_0": { "type": "str", "options": [ - "evenlong" + ">u1" ] } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "1" - ] + "type": "list", + "item": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "" + ] + }, + { + "type": { + "module": "numpy", + "name": "dtype" + } + } + ] + } } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { + "kw_only_required": { + "dtype": { "type": "str", "options": [ - "\n" + "|S16" ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 } - } - ], - "__radd__": [ + }, { - "pos_only_required": { - "_0": { + "kw_only_required": { + "dtype": { "type": "str", "options": [ - "sh" + "|S80" ] } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_only_required": { - "_0": { + "kw_only_required": { + "dtype": { "type": "str", "options": [ - "a " + "|S512" ] } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } - }, + } + ], + "__or__": [ { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "ev" - ] + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 4, + "usage.xarray": 3, + "usage.matplotlib": 6, + "usage.sklearn": 7 } }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "" - ] + "type": { + "module": "numpy", + "name": "bool_" + } } }, "metadata": { @@ -199945,10 +192441,10 @@ { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "shor" - ] + "type": { + "module": "dask.array.core", + "name": "Array" + } } }, "metadata": { @@ -199958,10 +192454,10 @@ { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "a bit longe" - ] + "type": { + "module": "sparse._coo.core", + "name": "COO" + } } }, "metadata": { @@ -199971,105 +192467,141 @@ { "pos_only_required": { "_0": { - "type": "str", + "type": "union", "options": [ - "evenlongerthantha" + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "bool" + } + }, + { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + } ] } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 61 } }, { "pos_only_required": { "_0": { - "type": "str", + "type": "union", "options": [ - "short" + { + "type": { + "module": "numpy", + "name": "bool_" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } ] } }, "metadata": { - "usage.xarray": 1 + "usage.scipy": 35 } }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "a bit " - ] + "type": { + "name": "int" + } } }, "metadata": { - "usage.xarray": 1 + "usage.sample-usage": 1 } }, { "pos_only_required": { "_0": { - "type": "str", + "type": "union", "options": [ - "evenlo" + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "bool" + } + } ] } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 3 } - }, + } + ], + "__ror__": [ { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "evenlong" - ] + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 4, + "usage.xarray": 3, + "usage.matplotlib": 6, + "usage.sklearn": 7 } }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - " " - ] + "type": { + "name": "bool" + } } }, "metadata": { - "usage.pandas": 1 + "usage.xarray": 3 } - } - ], - "__contains__": [ + }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - " " - ] + "type": { + "module": "dask.array.core", + "name": "Array" + } } }, "metadata": { "usage.xarray": 1 } - } - ], - "__ne__": [ + }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "z" - ] + "type": { + "module": "numpy", + "name": "bool_" + } } }, "metadata": { @@ -200079,14 +192611,31 @@ { "pos_only_required": { "_0": { - "type": "str", + "type": "union", "options": [ - "space" + { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + }, + { + "type": { + "module": "pandas.core.arrays.sparse.array", + "name": "SparseArray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } ] } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 61 } }, { @@ -200094,71 +192643,93 @@ "_0": { "type": "union", "options": [ + { + "type": { + "name": "bool" + } + }, { "type": { "module": "numpy", - "name": "str_" + "name": "ndarray" } }, { - "type": "str" + "type": { + "module": "numpy", + "name": "bool_" + } } ] } }, "metadata": { - "usage.pandas": 10 + "usage.scipy": 40 } }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "" - ] + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } } }, "metadata": { - "usage.matplotlib": 2 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "0" - ] + "type": { + "name": "int" + } } }, "metadata": { - "usage.matplotlib": 1 + "usage.sample-usage": 1 } }, { "pos_only_required": { "_0": { - "type": "str", + "type": "union", "options": [ - "a" + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "bool" + } + } ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.dask": 3 } - }, + } + ], + "__ior__": [ { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "b" - ] + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { + "usage.skimage": 3, + "usage.xarray": 1, + "usage.scipy": 4, "usage.matplotlib": 1 } }, @@ -200174,98 +192745,81 @@ } }, { - "type": "str", - "options": [ - "foo", - "baz", - "bar" - ] + "type": { + "name": "bool" + } } ] } }, "metadata": { - "usage.sklearn": 9 + "usage.pandas": 9 } - } - ], - "__eq__": [ + }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "a" - ] - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "str", - "options": [ - "b" - ] + "type": { + "name": "int" + } } }, "metadata": { - "usage.xarray": 1 + "usage.sample-usage": 1 } - }, + } + ], + "__floordiv__": [ { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "c" - ] + "type": { + "name": "int" + } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 8, + "usage.xarray": 1, + "usage.sample-usage": 1, + "usage.sklearn": 7 } }, { "pos_only_required": { "_0": { "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 2, + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { "type": { - "module": "xarray.core.variable", - "name": "Variable" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "object" } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 58 } }, { @@ -200274,19 +192828,21 @@ "type": "union", "options": [ { - "type": "str" + "type": { + "name": "int" + } }, { "type": { "module": "numpy", - "name": "str_" + "name": "ndarray" } } ] } }, "metadata": { - "usage.pandas": 33 + "usage.scipy": 18 } }, { @@ -200296,8 +192852,7 @@ "options": [ { "type": { - "module": "numpy", - "name": "int64" + "name": "float" } }, { @@ -200308,859 +192863,1146 @@ }, { "type": { - "module": "numpy", - "name": "str_" + "name": "int" } } ] } }, "metadata": { - "usage.sklearn": 26 + "usage.dask": 5 } - } - ], - "__add__": [ + }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "%" - ] + "type": "list", + "item": { + "type": { + "name": "int" + } + } } }, "metadata": { - "usage.pandas": 1 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": { - "name": "float" + "type": "list", + "item": { + "type": { + "name": "float" + } } } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 3 } } ], - "startswith": [ + "swapaxes": [ { "pos_only_required": { "_0": { - "type": "str" + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.pandas": 9 + "usage.skimage": 2, + "usage.pandas": 7, + "usage.scipy": 10, + "usage.matplotlib": 1, + "usage.dask": 5, + "usage.sklearn": 1 } } ], - "__le__": [ + "argmax": [ + { + "kw_only_required": { + "axis": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 1, + "usage.sklearn": 28 + } + }, + { + "metadata": { + "usage.skimage": 5, + "usage.xarray": 2, + "usage.matplotlib": 2, + "usage.sklearn": 11 + } + }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "str_" + "name": "int" } } }, "metadata": { - "usage.scipy": 2 + "usage.skimage": 1, + "usage.sklearn": 1 } - } - ], - "__ge__": [ + }, { - "pos_only_required": { + "pos_only_optional": { "_0": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.pandas": 16 + } + }, + { + "kw_only_optional": { + "axis": { "type": { - "module": "numpy", - "name": "str_" + "name": "int" } } }, "metadata": { - "usage.scipy": 2 + "usage.scipy": 3 } } ], - "__iter__": [ + "argsort": [ { "metadata": { - "usage.sklearn": 2 + "usage.skimage": 5, + "usage.xarray": 4, + "usage.scipy": 12, + "usage.matplotlib": 4, + "usage.sklearn": 16 } - } - ], - "__rmod__": [ + }, { "pos_only_required": { "_0": { - "type": "str", - "options": [ - "not %s", - "%s" - ] + "type": { + "name": "int" + } } }, "metadata": { - "usage.sklearn": 2 - } - } - ] - }, - "methods": { - "find": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "str_" - } + "usage.skimage": 1 } }, - "pos_only_optional": { - "_1": { - "type": { - "name": "int" + { + "kw_only_optional": { + "kind": { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "heapsort", + "mergesort", + "quicksort" + ] + }, + { + "type": "None" + } + ] + }, + "axis": { + "type": { + "name": "int" + } + }, + "order": { + "type": "None" } }, - "_2": { - "type": { - "name": "int" - } + "metadata": { + "usage.pandas": 79 } - }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ] - ], - "metadata": { - "usage.xarray": 3 } - }, - "rfind": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "str_" - } + ], + "tolist": [ + { + "metadata": { + "usage.skimage": 7, + "usage.xarray": 21, + "usage.pandas": 44, + "usage.scipy": 43, + "usage.matplotlib": 21, + "usage.dask": 39, + "usage.sklearn": 54 + } + } + ], + "std": [ + { + "metadata": { + "usage.skimage": 64, + "usage.xarray": 2, + "usage.sklearn": 13 } }, - 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"metadata": { - "usage.dask": 1 - } - } - ], - "constructor": { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.dask": 1 - } - }, - "method_overloads": { - "upper": [ { + "kw_only_optional": { + "kind": { + "type": "str", + "options": [ + "mergesort" + ] + } + }, "metadata": { - "usage.xarray": 1 + "usage.dask": 4 } } ], - "find": [ + "item": [ + { + "metadata": { + "usage.xarray": 22, + "usage.pandas": 31, + "usage.matplotlib": 1, + "usage.dask": 21, + "usage.sklearn": 3 + } + }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "bytes_" + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 2 } }, { - "pos_only_required": { + "pos_only_optional": { "_0": { "type": { - "module": "numpy", - "name": "bytes_" + "name": "int" } }, "_1": { @@ -201169,44 +194011,56 @@ } } }, + "pos_only_optional_ordering": [ + [ + "_0", + "_1" + ] + ], "metadata": { - "usage.xarray": 1 + "usage.scipy": 46 } - }, + } + ], + "searchsorted": [ { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "bytes_" - } - }, - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": { - "name": "int" + "module": "cftime._cftime", + "name": "DatetimeNoLeap" } } }, + "kw_only_required": { + "side": { + "type": "str", + "options": [ + "left" + ] + } + }, "metadata": { "usage.xarray": 1 } - } - ], - "rfind": [ + }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "bytes_" + "module": "cftime._cftime", + "name": "DatetimeNoLeap" } } }, + "kw_only_required": { + "side": { + "type": "str", + "options": [ + "right" + ] + } + }, "metadata": { "usage.xarray": 1 } @@ -201215,16 +194069,19 @@ "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "bytes_" - } - }, - "_1": { - "type": { - "name": "int" + "module": "cftime._cftime", + "name": "Datetime360Day" } } }, + "kw_only_required": { + "side": { + "type": "str", + "options": [ + "left" + ] + } + }, "metadata": { "usage.xarray": 1 } @@ -201233,48 +194090,40 @@ "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "bytes_" - } - }, - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": { - "name": "int" + "module": "cftime._cftime", + "name": "Datetime360Day" } } }, + "kw_only_required": { + "side": { + "type": "str", + "options": [ + "right" + ] + } + }, "metadata": { "usage.xarray": 1 } - } - ], - "__getitem__": [ + }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": { - "name": "int" - } + "type": { + "module": "cftime._cftime", + "name": "DatetimeJulian" } } }, + "kw_only_required": { + "side": { + "type": "str", + "options": [ + "left" + ] + } + }, "metadata": { "usage.xarray": 1 } @@ -201282,34 +194131,41 @@ { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": { - "name": "int" - } + "type": { + "module": "cftime._cftime", + "name": "DatetimeJulian" } } }, + "kw_only_required": { + "side": { + "type": "str", + "options": [ + "right" + ] + } + }, "metadata": { "usage.xarray": 1 } - } - ], - "__iadd__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "bytes" + "module": "cftime._cftime", + "name": "DatetimeAllLeap" } } }, + "kw_only_required": { + "side": { + "type": "str", + "options": [ + "left" + ] + } + }, "metadata": { "usage.xarray": 1 } @@ -201318,40 +194174,61 @@ "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "bytes_" + "module": "cftime._cftime", + "name": "DatetimeAllLeap" } } }, + "kw_only_required": { + "side": { + "type": "str", + "options": [ + "right" + ] + } + }, "metadata": { "usage.xarray": 1 } - } - ], - "__radd__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "bytes" + "module": "cftime._cftime", + "name": "DatetimeGregorian" } } }, + "kw_only_required": { + "side": { + "type": "str", + "options": [ + "left" + ] + } + }, "metadata": { "usage.xarray": 1 } - } - ], - "__eq__": [ + }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "cftime._cftime", + "name": "DatetimeGregorian" } } }, + "kw_only_required": { + "side": { + "type": "str", + "options": [ + "right" + ] + } + }, "metadata": { "usage.xarray": 1 } @@ -201360,634 +194237,543 @@ "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "bytes_" + "module": "cftime._cftime", + "name": "DatetimeProlepticGregorian" } } }, + "kw_only_required": { + "side": { + "type": "str", + "options": [ + "left" + ] + } + }, "metadata": { - "usage.pandas": 2 + "usage.xarray": 1 } - } - ], - "decode": [ + }, { - "pos_or_kw_required": { - "encoding": { + "pos_only_required": { + "_0": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeProlepticGregorian" + } + } + }, + "kw_only_required": { + "side": { "type": "str", "options": [ - "utf-8" + "right" ] } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 1 } - } - ], - "__add__": [ + }, { "pos_only_required": { "_0": { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.matplotlib": 1 - } - } - ] - }, - "methods": { - "upper": { - "metadata": { - "usage.xarray": 1 - } - }, - "find": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "bytes_" - } + "usage.xarray": 4 } }, - "pos_only_optional": { - "_1": { - "type": { - "name": "int" + { + "kw_only_required": { + "v": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "_2": { - "type": { - "name": "int" - } + "metadata": { + "usage.xarray": 4 } }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ] - ], - "metadata": { - "usage.xarray": 3 - } - }, - "rfind": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "bytes_" + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } } + }, + "metadata": { + "usage.xarray": 1 } }, - "pos_only_optional": { - "_1": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": "object" } }, - "_2": { - "type": { - "name": "int" - } - } - }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ] - ], - "metadata": { - "usage.xarray": 3 - } - }, - "__getitem__": { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { + "pos_only_optional": { + "_1": { + "type": "str", + "options": [ + "left", + "right" + ] + }, + "_2": { "type": "union", "options": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { "type": "None" } ] + } + }, + "pos_only_optional_ordering": [ + [ + "_1", + "_2" + ] + ], + "kw_only_optional": { + "side": { + "type": "str", + "options": [ + "left", + "right" + ] }, - "stop": { + "sorter": { "type": "union", "options": [ + { + "type": "None" + }, { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { - "type": "None" + "type": { + "name": "range" + } } ] - }, - "step": { - "type": { - "name": "int" - } } + }, + "metadata": { + "usage.pandas": 162 } }, - "metadata": { - "usage.xarray": 2 - } - }, - "__iadd__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "bytes_" - } - }, - { - "type": { - "name": "bytes" - } + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" } - ] - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - "__radd__": { - "pos_only_required": { - "_0": { - "type": { - "name": "bytes" } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - "__eq__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "bytes_" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] - } - }, - "metadata": { - "usage.xarray": 1, - "usage.pandas": 2 - } - }, - "decode": { - "pos_or_kw_required": { - "encoding": { - "type": "str", - "options": [ - "utf-8" - ] - } - }, - "metadata": { - "usage.matplotlib": 1 - } - }, - "__add__": { - "pos_only_required": { - "_0": { - "type": { - "name": "float" + }, + "pos_only_optional": { + "_1": { + "type": "str", + "options": [ + "left", + "right" + ] } - } - }, - "metadata": { - "usage.matplotlib": 1 - } - } - }, - "properties": { - "ndim": [ - { - "usage.dask": 1 - }, - { - "type": "bottom" - } - ] - }, - "classproperties": { - "__name__": [ - { - "usage.pandas": 2 - }, - { - "type": "bottom" - } - ], - "__mro__": [ - { - "usage.matplotlib": 1 - }, - { - "type": "bottom" - } - ] - } - }, - "timedelta64": { - "constructor_overloads": [ - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + }, + "kw_only_optional": { + "side": { + "type": "str", + "options": [ + "right" + ] } }, - "_1": { - "type": "str", - "options": [ - "ns" - ] + "metadata": { + "usage.scipy": 16 } }, - "metadata": { - "usage.xarray": 9 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": { + "name": "float" + } } }, - "_1": { - "type": "str", - "options": [ - "D" - ] + "metadata": { + "usage.matplotlib": 2 } }, - "metadata": { - "usage.xarray": 7, - "usage.dask": 7 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "float64" + } } }, - "_1": { - "type": "str", - "options": [ - "s" - ] + "metadata": { + "usage.matplotlib": 2, + "usage.sklearn": 2 } }, - "metadata": { - "usage.xarray": 19 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "_1": { + "type": "str", + "options": [ + "left" + ] } }, - "_1": { - "type": "str", - "options": [ - "h" - ] + "metadata": { + "usage.matplotlib": 1 } }, - "metadata": { - "usage.xarray": 7 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "_1": { + "type": "str", + "options": [ + "right" + ] } }, - "_1": { - "type": "str", - "options": [ - "ns" - ] + "metadata": { + "usage.matplotlib": 1 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "int64" + } } }, - "_1": { - "type": "str", - "options": [ - "ms" - ] + "metadata": { + "usage.sklearn": 1 } }, - "metadata": { - "usage.xarray": 4 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "str_" + } } }, - "_1": { - "type": "str", - "options": [ - "us" - ] + "metadata": { + "usage.sklearn": 2 } }, - "metadata": { - "usage.xarray": 3 - } - }, - { - "pos_only_required": { - "_0": { - "type": "str", - "options": [ - "NaT" - ] + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "one" + ] + } }, - "_1": { - "type": "str", - "options": [ - "ns" - ] - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_only_required": { - "_0": { - "type": "str", - "options": [ - "NaT" - ] + "metadata": { + "usage.sklearn": 2 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "two" + ] } }, - "_1": { - "type": "str", - "options": [ - "ps" - ] + "metadata": { + "usage.sklearn": 2 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "datetime", - "name": "timedelta" + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "three" + ] } + }, + "metadata": { + "usage.sklearn": 1 } - }, - "metadata": { - "usage.xarray": 3 } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "datetime", - "name": "timedelta" + ], + "squeeze": [ + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } } }, - "_1": { - "type": "str", - "options": [ - "ns" - ] + "metadata": { + "usage.xarray": 6 } }, - "metadata": { - "usage.xarray": 1 - } - } - ], - "constructor": { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + { + "metadata": { + "usage.xarray": 3, + "usage.pandas": 12, + "usage.scipy": 40, + "usage.matplotlib": 6, + "usage.sklearn": 17 } }, - "_1": { - "type": "str", - "options": [ - "D" - ] - } - }, - "metadata": { - "usage.dask": 7 - } - }, - "method_overloads": { - "__rtruediv__": [ { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "kw_only_required": { + "axis": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + { + "type": "None" + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.xarray": 4 + "usage.dask": 3 } }, { - "pos_only_required": { - "_0": { + "kw_only_required": { + "axis": { "type": { - "module": "pandas.core.indexes.timedeltas", - "name": "TimedeltaIndex" + "name": "int" } } }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 4 + } + } + ], + "round": [ + { + "metadata": { + "usage.xarray": 2, + "usage.scipy": 2, + "usage.sklearn": 6 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "timedelta64" + "name": "int" } } }, "metadata": { - "usage.xarray": 3 + "usage.xarray": 2, + "usage.dask": 2, + "usage.sklearn": 4 } }, { - "pos_only_required": { - "_0": { - "type": "object" + "kw_only_required": { + "decimals": { + "type": { + "name": "int" + } + }, + "out": { + "type": "None" } }, "metadata": { - "usage.pandas": 36 + "usage.xarray": 2 } }, { - "pos_only_required": { + "pos_only_optional": { "_0": { "type": "union", "options": [ { "type": { "module": "numpy", - "name": "ndarray" + "name": "int64" } }, { "type": { - "module": "dask.array.core", - "name": "Array" + "name": "int" } } ] } }, "metadata": { - "usage.dask": 2 + "usage.pandas": 13 } - } - ], - "astype": [ + }, { - "pos_only_required": { - "_0": { - "type": "str", - "options": [ - "timedelta64[ns]" - ] + "kw_only_required": { + "decimals": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.xarray": 2, - "usage.matplotlib": 1 + "usage.sklearn": 2 } - }, + } + ], + "take": [ { "pos_only_required": { "_0": { - "type": "type", - "name": { - "name": "float" + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "kw_only_required": { + "axis": { + "type": { + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 6, + "usage.sklearn": 69 } }, { "pos_only_required": { "_0": { - "type": "type", - "name": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + } + ] + } + }, + "kw_only_optional": { + "axis": { + "type": { + "name": "int" + } + }, + "mode": { + "type": "str", + "options": [ + "wrap" + ] + }, + "out": { + "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 2, - "usage.matplotlib": 2 + "usage.pandas": 287 } }, { "pos_only_required": { "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "kw_only_optional": { + "axis": { + "type": { + "name": "int" + } + }, + "mode": { "type": "str", "options": [ - "timedelta64[us]", - "m8[ns]", - "int64", - "timedelta64[ns]" + "clip" ] } }, "metadata": { - "usage.pandas": 15 + "usage.scipy": 26 } }, { @@ -201995,16 +194781,27 @@ "_0": { "type": { "module": "numpy", - "name": "dtype" + "name": "ndarray" + } + } + }, + "kw_only_required": { + "axis": { + "type": { + "name": "int" } + }, + "mode": { + "type": "str", + "options": [ + "clip" + ] } }, "metadata": { - "usage.dask": 1 + "usage.matplotlib": 4 } - } - ], - "__ge__": [ + }, { "pos_only_required": { "_0": { @@ -202015,46 +194812,48 @@ } }, "metadata": { - "usage.xarray": 15 + "usage.dask": 1, + "usage.sklearn": 29 } - } - ], - "__truediv__": [ + }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "timedelta64" + "name": "int" } } }, + "kw_only_required": { + "axis": { + "type": "None" + } + }, "metadata": { - "usage.xarray": 3 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "int64" + } + } } }, - "metadata": { - "usage.pandas": 29 - } - } - ], - "__rmul__": [ - { - "pos_only_required": { - "_0": { + "kw_only_required": { + "axis": { "type": { - "name": "float" + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 6 } }, { @@ -202066,47 +194865,84 @@ } } }, + "kw_only_required": { + "mode": { + "type": "str", + "options": [ + "clip" + ] + } + }, "metadata": { - "usage.xarray": 4 + "usage.sklearn": 13 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "name": "int" + } + } + }, + "kw_only_required": { + "axis": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.pandas": 20 + "usage.sklearn": 1 } - } - ], - "__add__": [ + }, { "pos_only_required": { "_0": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + } + }, + "kw_only_required": { + "axis": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 4 } - }, + } + ], + "conjugate": [ { - "pos_only_required": { - "_0": { - "type": "object" + "metadata": { + "usage.xarray": 2, + "usage.scipy": 26 + } + } + ], + "setflags": [ + { + "kw_only_required": { + "write": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.pandas": 59 + "usage.pandas": 25, + "usage.sklearn": 5 } } ], - "__ne__": [ + "put": [ { "pos_only_required": { "_0": { @@ -202114,12 +194950,30 @@ "module": "numpy", "name": "ndarray" } + }, + "_1": { + "type": "union", + "options": [ + { + "type": { + "name": "bool" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 3 } - }, + } + ], + "__rshift__": [ { "pos_only_required": { "_0": { @@ -202127,132 +194981,180 @@ "options": [ { "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" + "name": "int" } }, { "type": { - "module": "pandas._libs.tslibs.nattype", - "name": "NaTType" + "module": "numpy", + "name": "ndarray" } } ] } }, "metadata": { - "usage.pandas": 3 + "usage.pandas": 5 } - } - ], - "__rsub__": [ + }, { "pos_only_required": { "_0": { "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" + "name": "int" } } }, "metadata": { - "usage.xarray": 1 + "usage.scipy": 1, + "usage.sample-usage": 1, + "usage.dask": 1 } - }, + } + ], + "__ixor__": [ { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 75 + "usage.pandas": 4 } } ], - "__eq__": [ + "__ilshift__": [ { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "timedelta64" + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 4 + "usage.pandas": 1 } }, { "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.sample-usage": 1 + } + } + ], + "prod": [ + { + "pos_only_optional": { "_0": { "type": "union", "options": [ { - "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" - } + "type": "None" }, { "type": { - "module": "numpy", - "name": "timedelta64" + "name": "int" } } ] } }, + "kw_only_optional": { + "axis": { + "type": { + "name": "int" + } + } + }, "metadata": { - "usage.pandas": 65 + 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{ - "metadata": { - "usage.scipy": 2 - } - } - ], - "__rmul__": [ - { - "pos_only_required": { - "_0": { + ] + }, + "desired": { + "type": "list", + "item": { + "type": "list", + "item": { "type": "union", "options": [ - { - "type": { - "module": "numpy", - "name": "complex128" - } - }, { "type": { "name": "float" } }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "complex64" - } - }, { "type": { "name": "int" @@ -206005,258 +249265,264 @@ } ] } - }, - "metadata": { - "usage.scipy": 14 } } - ], - "__iadd__": [ - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "complex64" - } - }, - { - "type": { - "name": "complex" - } - }, - { - "type": { - "module": "numpy", - "name": "complex128" - } - } - ] + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_or_kw_required": { + "actual": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } } - }, - "metadata": { - "usage.scipy": 4 - } - } - ], - "__rsub__": [ - { - "pos_only_required": { - "_0": { + ] + }, + "desired": { + "type": "tuple", + "items": [ + { "type": { - "name": "float" + "name": "int" } } - }, - "metadata": { - "usage.scipy": 1 + ] + } + }, + "metadata": { + "usage.skimage": 4 + } + }, + { + "pos_or_kw_required": { + "actual": { + "type": { + "module": "numpy", + "name": "ndarray" } - } - ], - "__pow__": [ - { - "pos_only_required": { - "_0": { + }, + "desired": { + "type": "list", + "item": { + "type": "list", + "item": { "type": { "name": "int" } } - }, - "metadata": { - "usage.scipy": 1 } } - ], - "__bool__": [ - { - "metadata": { - "usage.scipy": 1 + }, + "metadata": { + "usage.skimage": 8 + } + }, + { + "pos_or_kw_required": { + "actual": { + "type": { + "module": "numpy", + "name": "ndarray" } - } - ], - "__ne__": [ - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "complex128" - } - }, - { - "type": { - "module": "numpy", - "name": "complex64" - } - } - ] + }, + "desired": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } } - }, - "metadata": { - "usage.scipy": 3 - } + ] } - ], - "__truediv__": [ - { - "pos_only_required": { - "_0": { + }, + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_or_kw_required": { + "actual": { + "type": "tuple", + "items": [ + { "type": { "module": "numpy", - "name": "ndarray" + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" } } - }, - "metadata": { - "usage.scipy": 1 - } - } - ], - "__add__": [ - { - "pos_only_required": { - "_0": { - "type": "object" + ] + }, + "desired": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } } - }, - "metadata": { - "usage.scipy": 17 - } + ] } - ], - 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"metadata": { - "usage.pandas": 2, - "usage.scipy": 1 + "desired": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, - "__radd__": { - "pos_only_required": { - "_0": { - "type": "object" - } + "metadata": { + "usage.skimage": 40 + } + }, + { + "pos_or_kw_required": { + "actual": { + "type": "None" }, - "metadata": { - "usage.scipy": 21 + "desired": { + "type": "None" } }, - "__mul__": { - "pos_only_required": { - "_0": { + "metadata": { + "usage.skimage": 6 + } + }, + { + "pos_or_kw_required": { + "actual": { + "type": "list", + "item": { "type": "union", "options": [ { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "complex64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "str", + "options": [ + "a", + "z" + ] }, { "type": { - "name": "float" + "name": "int" } } ] } }, - "metadata": { - "usage.scipy": 29 - } - }, - "conj": { - "metadata": { - 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"pos_or_kw_required": { - "shape": { - "type": "tuple", - "items": [ - { + }, + { + "type": "slice", + "start": { "type": { - "module": "numpy", - "name": "int32" - } - } - ] - }, - "dtype": { - "type": "list", - "item": { - "type": "tuple", - "items": [ - { - "type": "tuple", - "items": [ - { - "type": "str", - "options": [ - "h", - "g" - ] - }, - { - "type": "str", - "options": [ - "H", - "G" - ] - } - ] - }, - { - "type": "type", - "name": { - "module": "numpy", - "name": "object_" - } + "name": "int" } - ] - } - } - }, - "metadata": { - "usage.scipy": 1 - } - } - ], - "method_overloads": { - "__iter__": [ - { - "metadata": { - "usage.pandas": 1 - } - } - ], - "__getitem__": [ - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "level_0", - "index", - "EXPIRY", - "price", - "date" - ] - }, - { - "type": { - "name": "int" - } + }, + "stop": { + "type": { + "name": "int" } - ] - } - }, - "metadata": { - "usage.pandas": 13 - } - }, - { - "pos_only_required": { - "_0": { - "type": "str" - } - }, - "metadata": { - "usage.scipy": 19 - } - }, - { - "pos_only_required": { - "_0": { - "type": "str", - "options": [ - "a" - ] + }, + "step": { + "type": "None" + } } - }, - "metadata": { - "usage.matplotlib": 1 - } + ] }, - { - "pos_only_required": { - "_0": { + "desired": { + "type": "tuple", + "items": [ + { "type": "slice", "start": { - "type": "None" + "type": { + "name": "int" + } }, "stop": { "type": { @@ -206581,1624 +249726,1419 @@ "step": { "type": "None" } - } - }, - "metadata": { - "usage.dask": 1 - } - } - ], - "reshape": [ - { - "pos_only_required": { - "_0": { - "type": "list", - "item": { + }, + { + "type": "slice", + "start": { "type": { - "module": "numpy", - "name": "int32" - } - } - } - }, - "metadata": { - "usage.scipy": 3 - } - } - ], - "__eq__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.dask": 3 - } - } - ], - 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+262087,29 @@ "actual": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } }, "desired": { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } }, - "decimal": { + "atol": { "type": { - "name": "int" + "name": "float" } + }, + "err_msg": { + "type": "str", + "options": [ + "RidgeClassifier" + ] } }, "metadata": { - "usage.skimage": 35, - "usage.matplotlib": 3 + "usage.sklearn": 1 } }, { @@ -212806,23 +262117,29 @@ "actual": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } }, "desired": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } }, - "decimal": { + "atol": { "type": { - "name": "int" + "name": "float" } + }, + "err_msg": { + "type": "str", + "options": [ + "RidgeClassifierCV" + ] } }, "metadata": { - "usage.skimage": 6 + "usage.sklearn": 1 } }, { @@ -212830,22 +262147,29 @@ "actual": { "type": { "module": "numpy", - "name": "float16" + "name": "ndarray" } }, "desired": { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } }, - "decimal": { + "atol": { "type": { - "name": "int" + "name": "float" } + }, + "err_msg": { + "type": "str", + "options": [ + "SGDClassifier" + ] } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 1 } }, { @@ -212861,11 +262185,21 @@ "module": "numpy", "name": "ndarray" } + }, + "atol": { + "type": { + "name": "float" + } + }, + "err_msg": { + "type": "str", + "options": [ + "SGDRegressor" + ] } }, "metadata": { - "usage.skimage": 116, - "usage.matplotlib": 23 + "usage.sklearn": 2 } }, { @@ -212878,17 +262212,24 @@ }, "desired": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, - "decimal": { + "atol": { "type": { - "name": "int" + "name": "float" } + }, + "err_msg": { + "type": "str", + "options": [ + "SVC" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { @@ -212900,21 +262241,25 @@ } }, "desired": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "type": { + "module": "numpy", + "name": "ndarray" } }, - "decimal": { + "atol": { "type": { - "name": "int" + "name": "float" } + }, + "err_msg": { + "type": "str", + "options": [ + "SVR" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 } }, { @@ -212928,17 +262273,23 @@ "desired": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } }, - "decimal": { + "atol": { "type": { - "name": "int" + "name": "float" } + }, + "err_msg": { + "type": "str", + "options": [ + "TheilSenRegressor" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 } }, { @@ -212950,23 +262301,25 @@ } }, "desired": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "atol": { + "type": { + "name": "float" + } + }, + "err_msg": { + "type": "str", + "options": [ + "TweedieRegressor" ] } }, "metadata": { - "usage.skimage": 12 + "usage.sklearn": 2 } }, { @@ -212974,22 +262327,29 @@ "actual": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } }, "desired": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, - "decimal": { + "rtol": { "type": { - "name": "int" + "name": "float" } + }, + "err_msg": { + "type": "str", + "options": [ + "solver svd" + ] } }, "metadata": { - "usage.skimage": 5 + "usage.sklearn": 1 } }, { @@ -213001,16 +262361,25 @@ } }, "desired": { - "type": "list", - "item": { - "type": { - "name": "float" - } + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "rtol": { + "type": { + "name": "float" } + }, + "err_msg": { + "type": "str", + "options": [ + "solver lsqr" + ] } }, "metadata": { - "usage.skimage": 2 + "usage.sklearn": 1 } }, { @@ -213022,16 +262391,25 @@ } }, "desired": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "type": { + "module": "numpy", + "name": "ndarray" } + }, + "rtol": { + "type": { + "name": "float" + } + }, + "err_msg": { + "type": "str", + "options": [ + "solver eigen" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { @@ -213039,19 +262417,23 @@ "actual": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } }, "desired": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" + } + }, + "atol": { + "type": { + "name": "int" } } }, "metadata": { - "usage.skimage": 21, - "usage.matplotlib": 2 + "usage.sklearn": 1 } }, { @@ -213061,17 +262443,22 @@ "items": [ { "type": { - "name": "int" + "name": "float" } }, { "type": { - "name": "int" + "name": "float" } }, { "type": { - "name": "int" + "name": "float" + } + }, + { + "type": { + "name": "float" } } ] @@ -213091,6 +262478,12 @@ "name": "float64" } }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, { "type": { "module": "numpy", @@ -213099,14 +262492,14 @@ } ] }, - "decimal": { + "rtol": { "type": { - "name": "int" + "name": "float" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 1 } }, { @@ -213119,12 +262512,24 @@ }, "desired": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" + } + }, + "atol": { + "type": { + "name": "float" } + }, + "err_msg": { + "type": "str", + "options": [ + "estimator_name" + ] } }, "metadata": { - "usage.skimage": 5 + "usage.sklearn": 2 } }, { @@ -213136,19 +262541,249 @@ } }, "desired": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "atol": { + "type": { + "name": "float" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "actual": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + } + }, + "desired": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + } + }, + "rtol": { "type": { - "module": "numpy", - "name": "float64" + "name": "float" + } + }, + "atol": { + "type": { + "name": "float" } + }, + "err_msg": { + "type": "str", + "options": [ + "" + ] } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "actual": { + "type": { + "module": "pandas.core.frame", + "name": "DataFrame" + } + }, + "desired": { + "type": { + "module": "pandas.core.frame", + "name": "DataFrame" + } + }, + "rtol": { + "type": { + "name": "float" + } + }, + "atol": { + "type": { + "name": "float" + } + }, + "err_msg": { + "type": "str", + "options": [ + "" + ] + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "actual": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + "desired": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + "rtol": { + "type": { + "name": "float" + } + }, + "atol": { + "type": { + "name": "float" + } + }, + "err_msg": { + "type": "str", + "options": [ + "" + ] + } + }, + "metadata": { + "usage.sklearn": 2 } }, { "pos_or_kw_required": { "actual": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + }, + "desired": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + }, + "rtol": { + "type": { + "name": "float" + } + }, + "atol": { + "type": { + "name": "float" + } + }, + "err_msg": { + "type": "str", + "options": [ + "" + ] + } + }, + "metadata": { + "usage.sklearn": 1 + } + } + ], + "assert_array_almost_equal": [ + { + "pos_or_kw_required": { + "x": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "y": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + } + } + }, + "metadata": { + "usage.skimage": 4 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "y": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.skimage": 39, + "usage.matplotlib": 29, + "usage.sklearn": 886 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "y": { "type": "tuple", "items": [ { @@ -213156,34 +262791,135 @@ "name": "int" } }, + { + "type": { + "name": "int" + } + }, { "type": { "name": "int" } } ] + } + }, + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, - "desired": { + "y": { "type": "list", "item": { "type": { - "name": "float" + "name": "int" } } + } + }, + "metadata": { + "usage.skimage": 6, + "usage.matplotlib": 2, + "usage.sklearn": 61 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, - "decimal": { + "y": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + } + ] + } + } + }, + "metadata": { + "usage.skimage": 3, + "usage.matplotlib": 1, + "usage.sklearn": 4 + } + }, + { + "pos_or_kw_required": { + "x": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" + } + }, + "y": { + "type": "list", + "item": { + "type": { + "name": "float" + } } } }, "metadata": { - "usage.skimage": 1 + "usage.skimage": 1, + "usage.sklearn": 67 } }, { "pos_or_kw_required": { - "actual": { + "x": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "y": { + "type": "list", + "item": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + } + }, + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_or_kw_required": { + "x": { "type": "tuple", "items": [ { @@ -213196,6 +262932,11 @@ "name": "int" } }, + { + "type": { + "name": "int" + } + }, { "type": { "name": "int" @@ -213203,47 +262944,30 @@ } ] }, - "desired": { + "y": { "type": "tuple", "items": [ { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" + } + }, + { + "type": { + "name": "int" } } - ] - } - }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_or_kw_required": { - "actual": { - "type": { - "module": "numpy", - "name": "int64" - } - }, - "desired": { - "type": { - "module": "numpy", - "name": "int64" - } + ] } }, "metadata": { @@ -213252,7 +262976,7 @@ }, { "pos_or_kw_required": { - "actual": { + "x": { "type": "tuple", "items": [ { @@ -213270,6 +262994,16 @@ "name": "int" } }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, { "type": { "name": "int" @@ -213277,7 +263011,7 @@ } ] }, - "desired": { + "y": { "type": "tuple", "items": [ { @@ -213295,6 +263029,16 @@ "name": "int" } }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, { "type": { "name": "int" @@ -213309,24 +263053,25 @@ }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { "name": "int" } }, - "desired": { + "y": { "type": { "name": "int" } } }, "metadata": { - "usage.skimage": 3 + "usage.skimage": 1, + "usage.sklearn": 7 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": "tuple", "items": [ { @@ -213343,143 +263088,67 @@ } ] }, - "desired": { + "y": { "type": "tuple", "items": [ { "type": { - "module": "numpy", - "name": "float64" + "name": "float" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "float" } } ] } }, - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_or_kw_required": { - "actual": { - "type": { - "name": "float" - } - }, - "desired": { - "type": { - "name": "float" - } - } - }, - "metadata": { - "usage.skimage": 16, - "usage.matplotlib": 1 - } - }, - { - "pos_or_kw_required": { - "actual": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" - } - } - }, - "desired": { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "float64" - } - } - } - }, "metadata": { "usage.skimage": 2 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": "tuple", "items": [ { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "module": "numpy", + "name": "float64" } }, { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" } } ] }, - "desired": { + "y": { "type": "tuple", "items": [ { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "name": "float" } }, { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "name": "float" + } + }, + { + "type": { + "name": "float" } } ] @@ -213491,84 +263160,146 @@ }, { "pos_or_kw_required": { - "actual": { + "x": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "y": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.skimage": 5, + "usage.sklearn": 14 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": "object" + }, + "y": { + "type": "object" + } + }, + "pos_or_kw_optional": { + "decimal": { + "type": { + "name": "int" + } + }, + "err_msg": { + "type": "str" + } + }, + "pos_or_kw_optional_ordering": [ + [ + "decimal", + "err_msg" + ] + ], + "metadata": { + "usage.scipy": 3904 + } + }, + { + "pos_or_kw_required": { + "x": { "type": "tuple", "items": [ { "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" + "module": "numpy", + "name": "float32" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float32" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float32" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float32" } } ] }, - "desired": { + "y": { "type": "tuple", "items": [ { "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" + "name": "float" } }, { "type": { - "name": "int" + "name": "float" } }, { "type": { - "name": "int" + "name": "float" } }, { "type": { - "name": "int" + "name": "float" } } ] + }, + "decimal": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { - "actual": { + "x": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "y": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "decimal": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.matplotlib": 4, + "usage.sklearn": 226 + } + }, + { + "pos_or_kw_required": { + "x": { "type": "tuple", "items": [ { @@ -213583,6 +263314,12 @@ "name": "float64" } }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, { "type": { "module": "numpy", @@ -213591,7 +263328,7 @@ } ] }, - "desired": { + "y": { "type": "tuple", "items": [ { @@ -213606,6 +263343,12 @@ "name": "float64" } }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, { "type": { "module": "numpy", @@ -213613,249 +263356,193 @@ } } ] + }, + "decimal": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { "module": "numpy", - "name": "uint8" + "name": "float64" } }, - "desired": { + "y": { + "type": { + "name": "float" + } + } + }, + "metadata": { + "usage.matplotlib": 4, + "usage.sklearn": 7 + } + }, + { + "pos_or_kw_required": { + "x": { "type": { "module": "numpy", - "name": "uint8" + "name": "ndarray" + } + }, + "y": { + "type": "list", + "item": { + "type": "bottom" } } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": "tuple", "items": [ { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "module": "numpy", + "name": "float64" } }, { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "module": "numpy", + "name": "float64" } }, { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" } } ] }, - "desired": { + "y": { "type": "tuple", "items": [ { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "name": "int" } }, { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "name": "int" } }, { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] + }, + "decimal": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 2 } }, { "pos_or_kw_required": { - "actual": { - "type": { - "module": "numpy", - "name": "int64" + "x": { + "type": "list", + "item": { + "type": { + "name": "float" + } } }, - "desired": { + "y": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.skimage": 2 + "usage.matplotlib": 1, + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "actual": { - "type": { - "name": "float" - } - }, - "desired": { + "x": { "type": { "module": "numpy", "name": "float64" } }, - "decimal": { - "type": { - "name": "int" + "y": { + "type": "list", + "item": { + "type": { + "name": "int" + } } } }, "metadata": { - "usage.skimage": 3 + "usage.matplotlib": 1, + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { - "name": "float" + "module": "numpy.ma.core", + "name": "MaskedArray" } }, - "desired": { + "y": { "type": { - "name": "int" + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, "metadata": { - "usage.skimage": 8 - } - }, - { - "pos_or_kw_required": { - "actual": { - "type": "tuple", - "items": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - } - ] - }, - "desired": { - "type": "tuple", - "items": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - } - ] - } - }, - "metadata": { - "usage.skimage": 3, - "usage.matplotlib": 1 + "usage.matplotlib": 4 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" } }, - "desired": { + "y": { "type": "list", "item": { "type": "union", @@ -213875,74 +263562,71 @@ } }, "metadata": { - "usage.skimage": 2 + "usage.matplotlib": 3 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { "module": "numpy", "name": "ndarray" } }, - "desired": { - "type": "list", - "item": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } + "y": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, "metadata": { - "usage.skimage": 4 + "usage.matplotlib": 2 } }, { "pos_or_kw_required": { - "actual": { - "type": "object" + "x": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } }, - "desired": { - "type": "object" + "y": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } } }, - "pos_or_kw_optional": { - "decimal": { + "metadata": { + "usage.matplotlib": 2 + } + }, + { + "pos_or_kw_required": { + "x": { "type": { - "name": "int" + "module": "numpy.ma.core", + "name": "MaskedArray" } }, - "err_msg": { - "type": "str" - }, - "verbose": { + "y": { "type": { - "name": "bool" + "module": "numpy", + "name": "ndarray" } } }, - "pos_or_kw_optional_ordering": [ - [ - "decimal", - "err_msg" - ], - [ - "decimal", - "verbose" - ] - ], "metadata": { - "usage.scipy": 1344 + "usage.matplotlib": 7 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": "list", "item": { "type": { @@ -213951,109 +263635,141 @@ } } }, - "desired": { + "y": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, "metadata": { - "usage.matplotlib": 3 + "usage.matplotlib": 2 } }, { "pos_or_kw_required": { - "actual": { - "type": "list", - "item": { - "type": "bottom" + "x": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" } }, - "desired": { - "type": { - "module": "numpy", - "name": "ndarray" + "y": { + "type": "list", + "item": { + "type": { + "name": "float" + } } } }, "metadata": { - "usage.matplotlib": 1 + "usage.matplotlib": 2 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { "module": "numpy", "name": "ndarray" } }, - "desired": { + "y": { "type": { - "name": "float" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.matplotlib": 4 + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": "tuple", "items": [ { "type": { - "name": "float" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "float" + "module": "numpy", + "name": "float64" } } ] }, - "desired": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } + "y": { + "type": "list", + "item": { + "type": { + "name": "float" } - ] + } + }, + "decimal": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.matplotlib": 1 + "usage.matplotlib": 2 } }, { "pos_or_kw_required": { - "actual": { + "x": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "y": { "type": "list", "item": { "type": { - "module": "numpy", - "name": "float64" + "name": "float" } } }, - "desired": { + "decimal": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.matplotlib": 4, + "usage.sklearn": 61 + } + }, + { + "pos_or_kw_required": { + "x": { "type": "list", "item": { "type": { "name": "float" } } + }, + "y": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "decimal": { + "type": { + "name": "int" + } } }, "metadata": { @@ -214062,15 +263778,18 @@ }, { "pos_or_kw_required": { - "actual": { + "x": { "type": "list", "item": { - "type": { - "name": "float" + "type": "list", + "item": { + "type": { + "name": "float" + } } } }, - "desired": { + "y": { "type": { "module": "numpy", "name": "ndarray" @@ -214078,24 +263797,39 @@ } }, "metadata": { - "usage.matplotlib": 2 + "usage.matplotlib": 5, + "usage.sklearn": 6 } }, { "pos_or_kw_required": { - "actual": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, + "x": { + "type": { + "module": "numpy", + "name": "float64" + } + }, + "y": { + "type": { + "module": "numpy", + "name": "float64" + } + } + }, + "metadata": { + "usage.matplotlib": 3, + "usage.sklearn": 25 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": "tuple", + "items": [ { "type": { "module": "numpy", - "name": "int64" + "name": "float64" } }, { @@ -214103,474 +263837,401 @@ "module": "numpy", "name": "float64" } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } } ] }, - "desired": { - "type": "object" + "y": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "pos_or_kw_optional": { - "decimal": { + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "x": { "type": { - "name": "int" + "module": "dask.array.core", + "name": "Array" } }, - "err_msg": { - "type": "str" + "y": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "pos_or_kw_optional_ordering": [ - [ - "decimal", - "err_msg" - ] - ], "metadata": { - "usage.sklearn": 965 + "usage.dask": 1 } - } - ], - "assert_equal": [ + }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } }, - "desired": { + "y": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "x": { "type": { "name": "float" } + }, + "y": { + "type": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.skimage": 10, - "usage.xarray": 1 + "usage.sklearn": 2 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { - "name": "int" + "name": "float" } }, - "desired": { + "y": { + "type": { 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"actual": { + "x": { "type": { - "module": "xarray.core.dataarray", - "name": "DataArray" + "name": "float" } }, - "desired": { + "y": { "type": { "module": "numpy", "name": "ndarray" @@ -216100,247 +265513,351 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 3 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { - "module": "numpy", - "name": "int32" + "name": "int" } }, - "desired": { + "y": { "type": { "module": "numpy", - "name": "int32" + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { "module": "numpy", "name": "ndarray" } }, - "desired": { + "y": { "type": { - "module": "numpy", - "name": "int32" + "name": "float" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 3 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { - "module": "numpy", - "name": "float32" + "name": "int" } }, - "desired": { + "y": { "type": { "module": "numpy", - "name": "float32" + "name": "float64" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } }, - "desired": { + "y": { "type": { - "module": "numpy", - "name": "float32" + "name": "int" } + }, + "err_msg": { + "type": "str" } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 3 } - }, + } + ], + "assert_array_almost_equal_nulp": [ { "pos_or_kw_required": { - "actual": { - "type": "object" + "x": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, - "desired": { - "type": "object" + "y": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1, + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" } }, - "desired": { - "type": "object" + "y": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1 } }, { "pos_or_kw_required": { - "actual": { + "x": { "type": { - "module": "numpy", - "name": "str_" + "module": "numpy.ma.core", + "name": "MaskedArray" } }, - "desired": { - "type": "str", - "options": [ - "foo" - ] + "y": { + "type": { + "module": "numpy", + "name": "float64" + } } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 2 } }, { "pos_or_kw_required": { - "actual": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "x": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "matrix" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] }, - "desired": { - "type": "str", + "y": { + "type": "union", "options": [ - "foo" + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + } + ] + } + }, + "pos_or_kw_optional": { + "nulp": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + } ] } }, "metadata": { - "usage.xarray": 1 + "usage.scipy": 67 + } + } + ], + "assert_no_warnings": [ + { + "metadata": { + "usage.skimage": 1 } }, { - "pos_or_kw_required": { - "actual": { - "type": { - "module": "numpy", - "name": "datetime64" - } - }, - "desired": { - "type": { - "module": "numpy", - "name": "datetime64" - } + "var_pos": [ + "args", + { + "type": "str", + "options": [ + "v", + "t" + ] } - }, + ], "metadata": { - "usage.xarray": 2 + "usage.sklearn": 1 } - }, + } + ], + "assert_string_equal": [ { "pos_or_kw_required": { "actual": { - "type": { - "module": "numpy", - "name": "timedelta64" - } + "type": "str" }, "desired": { - "type": { - "module": "numpy", - "name": "timedelta64" - } + "type": "str" } }, "metadata": { - "usage.xarray": 2 + "usage.scipy": 2 } - }, + } + ], + "assert_approx_equal": [ { "pos_or_kw_required": { "actual": { - "type": "object" + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "name": "float" + } + } + ] }, "desired": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "err_msg": { "type": "union", "options": [ { - "type": "str" + "type": { + "name": "float" + } }, { "type": { "name": "int" } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } } ] - }, - "verbose": { + } + }, + "pos_or_kw_optional": { + "significant": { "type": { - "name": "bool" + "name": "int" } + }, + "err_msg": { + "type": "str" } }, + "pos_or_kw_optional_ordering": [ + [ + "significant", + "err_msg" + ] + ], "metadata": { - "usage.scipy": 4569 + "usage.scipy": 220 } }, { "pos_or_kw_required": { "actual": { - "type": "list", - "item": { - "type": "bottom" + "type": { + "module": "numpy", + "name": "float64" } }, "desired": { - "type": "list", - "item": { - "type": "bottom" + "type": { + "name": "float" } } }, "metadata": { - "usage.matplotlib": 2 + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { "actual": { "type": { - "name": "float" + "module": "numpy", + "name": "float64" } }, "desired": { @@ -216351,7 +265868,7 @@ } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 } }, { @@ -216359,108 +265876,728 @@ "actual": { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } }, "desired": { - "type": "list", - "item": { - "type": { - "name": "float" - } + "type": { + "module": "numpy", + "name": "float64" + } + }, + "significant": { + "type": { + "name": "int" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.sklearn": 1 + } + } + ] + }, + "functions": { + "assert_almost_equal": { + "pos_or_kw_required": { + "actual": { + "type": "object" + }, + "desired": { + "type": "object" } }, - { - "pos_or_kw_required": { - "actual": { - "type": { - "module": "numpy", - "name": "ndarray" + "pos_or_kw_optional": { + "decimal": { + "type": { + "name": "int" + } + }, + "err_msg": { + "type": "str" + }, + "verbose": { + "type": { + "name": "bool" + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "decimal", + "err_msg" + ], + [ + "decimal", + "verbose" + ] + ], + "metadata": { + "usage.skimage": 333, + "usage.scipy": 1344, + "usage.matplotlib": 50, + "usage.sklearn": 965 + } + }, + "assert_equal": { + "pos_or_kw_required": { + "actual": { + "type": "object" + }, + "desired": { + "type": "object" + } + }, + "pos_or_kw_optional": { + "err_msg": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": "str" } - }, - "desired": { - "type": "list", - "item": { + ] + }, + "verbose": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.skimage": 630, + "usage.xarray": 40, + "usage.scipy": 4569, + "usage.matplotlib": 12, + "usage.dask": 26, + "usage.sklearn": 27 + } + }, + "assert_array_equal": { + "pos_or_kw_required": { + "x": { + "type": "object" + }, + "y": { + "type": "object" + } + }, + "pos_or_kw_optional": { + "err_msg": { + "type": "str" + } + }, + "metadata": { + "usage.skimage": 430, + "usage.xarray": 400, + "usage.scipy": 1623, + "usage.matplotlib": 186, + "usage.dask": 36, + "usage.sklearn": 1475 + } + }, + "assert_allclose": { + "pos_or_kw_required": { + "actual": { + "type": "object" + }, + "desired": { + "type": "object" + } + }, + "pos_or_kw_optional": { + "rtol": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { "type": { "name": "bool" } } + ] + }, + "atol": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "float32" + } + }, + { + "type": { + "module": "numpy", + "name": "float128" + } + } + ] + }, + "err_msg": { + "type": "union", + "options": [ + { + "type": "str" + }, + { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": { + "name": "complex" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + } + ] + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "verbose": { + "type": { + "name": "bool" } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "rtol", + "atol" + ], + [ + "rtol", + "err_msg" + ], + [ + "err_msg", + "verbose" + ], + [ + "atol", + "err_msg" + ] + ], + "metadata": { + "usage.skimage": 158, + "usage.xarray": 38, + "usage.scipy": 4704, + "usage.matplotlib": 184, + "usage.sklearn": 760 + } + }, + "assert_array_almost_equal": { + "pos_or_kw_required": { + "x": { + "type": "object" }, - "metadata": { - "usage.matplotlib": 2 + "y": { + "type": "object" } }, - { - "pos_or_kw_required": { - "actual": { - "type": "list", - "item": { + "pos_or_kw_optional": { + "decimal": { + "type": "union", + "options": [ + { + "type": { + "name": "bool" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "err_msg": { + "type": "str" + } + }, + "pos_or_kw_optional_ordering": [ + [ + "decimal", + "err_msg" + ] + ], + "metadata": { + "usage.skimage": 69, + "usage.scipy": 3904, + "usage.matplotlib": 86, + "usage.dask": 1, + "usage.sklearn": 1569 + } + }, + "assert_warns": { + "pos_or_kw_required": { + "warning_class": { + "type": "type" + } + }, + "var_pos": [ + "args", + { + "type": "str", + "options": [ + "v", + "t" + ] + } + ], + "metadata": { + "usage.skimage": 5, + "usage.scipy": 39, + "usage.sklearn": 1 + } + }, + "assert_": { + "pos_or_kw_required": { + "val": { + "type": "object" + } + }, + "pos_or_kw_optional": { + "msg": { + "type": "object" + } + }, + "metadata": { + "usage.skimage": 78, + "usage.scipy": 1706 + } + }, + "assert_array_less": { + "pos_or_kw_required": { + "x": { + "type": "object" + }, + "y": { + "type": "object" + } + }, + "pos_or_kw_optional": { + "err_msg": { + "type": "str" + } + }, + "metadata": { + "usage.skimage": 20, + "usage.scipy": 92, + "usage.matplotlib": 3, + "usage.sklearn": 18 + } + }, + "assert_array_almost_equal_nulp": { + "pos_or_kw_required": { + "x": { + "type": "object" + }, + "y": { + "type": "union", + "options": [ + { "type": { "module": "numpy", "name": "ndarray" } + }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } } - }, - "desired": { - "type": { - "module": "numpy", - "name": "ndarray" + ] + } + }, + "pos_or_kw_optional": { + "nulp": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } } - } + ] + } + }, + "metadata": { + "usage.skimage": 4, + "usage.scipy": 67, + "usage.matplotlib": 1 + } + }, + "assert_no_warnings": { + "var_pos": [ + "args", + { + "type": "str", + "options": [ + "v", + "t" + ] + } + ], + "metadata": { + "usage.skimage": 1, + "usage.sklearn": 1 + } + }, + "assert_string_equal": { + "pos_or_kw_required": { + "actual": { + "type": "str" }, - "metadata": { - "usage.matplotlib": 1 + "desired": { + "type": "str" } }, - { - "pos_or_kw_required": { - "actual": { - "type": "list", - "item": { + "metadata": { + "usage.scipy": 2 + } + }, + "assert_approx_equal": { + "pos_or_kw_required": { + "actual": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { "type": { "name": "float" } } - }, - "desired": { - "type": { - "module": "numpy", - "name": "ndarray" + ] + }, + "desired": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } } + ] + } + }, + "pos_or_kw_optional": { + "significant": { + "type": { + "name": "int" } }, - "metadata": { - "usage.matplotlib": 1 + "err_msg": { + "type": "str" } }, - { - "pos_or_kw_required": { - "actual": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "bool_" - } + "pos_or_kw_optional_ordering": [ + [ + "significant", + "err_msg" + ] + ], + "metadata": { + "usage.scipy": 220, + "usage.matplotlib": 1, + "usage.sklearn": 2 + } + } + }, + "classes": { + "suppress_warnings": { + "method_overloads": { + "filter": [ + { + "pos_or_kw_optional": { + "category": { + "type": "type" }, - { - "type": "dict", - "key": { - "type": "tuple", - "items": { + "message": { + "type": "str" + } + }, + "pos_or_kw_optional_ordering": [ + [ + "category", + "message" + ] + ], + "metadata": { + "usage.scipy": 298 + } + } + ], + "record": [ + { + "pos_or_kw_required": { + "message": { + "type": "str" + } + }, + "pos_or_kw_optional": { + "category": { + "type": "type" + } + }, + "metadata": { + "usage.scipy": 11 + } + } + ] + }, + "methods": { + "filter": { + "pos_or_kw_optional": { + "category": { + "type": "type" + }, + "message": { + "type": "str" + } + }, + "pos_or_kw_optional_ordering": [ + [ + "category", + "message" + ] + ], + "metadata": { + "usage.scipy": 298 + } + }, + "record": { + "pos_or_kw_required": { + "message": { + "type": "str" + } + }, + "pos_or_kw_optional": { + "category": { + "type": "type" + } + }, + "metadata": { + "usage.scipy": 11 + } + } + } + } + } + }, + "numpy.testing": { + "properties": { + "assert_allclose": [ + { + "usage.skimage": 10, + "usage.scipy": 30, + "usage.matplotlib": 6, + "usage.sklearn": 6 + }, + { + "type": "bottom" + } + ], + "assert_almost_equal": [ + { + "usage.skimage": 3, + "usage.scipy": 15, + "usage.sklearn": 3 + }, + { + "type": "bottom" + } + ], + "assert_equal": [ + { + "usage.scipy": 1 + }, + { + "type": "bottom" + } + ], + "assert_approx_equal": [ + { + "usage.sklearn": 1 + }, + { + "type": "bottom" + } + ] + } + }, + "numpy.lib.index_tricks": { + "classes": { + "RClass": { + "method_overloads": { + "__getitem__": [ + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "float" + } + } + ] + } + }, + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": "list", + "item": { "type": "union", "options": [ { @@ -216469,280 +266606,223 @@ } }, { - "type": "str", - "options": [ - "y" - ] + "type": { + "name": "float" + } } ] } - }, - "value": { - "type": "tuple", - "items": [ - { - "type": "function", - "name": { - "module": "_operator", - "name": "getitem" - } - }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "x" - ] - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] - } - }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] - } - } - ] } - }, - { + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { "type": "list", "item": { - "type": "tuple", - "items": [ - { - "type": 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"options": [ + { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + { + "type": { + "name": "int" + } + } + ] } }, - { - "type": { - "name": "bool" + "metadata": { + "usage.pandas": 4 + } + }, + { + "pos_only_optional": { + "_0": { + "type": { + "module": "numpy", + "name": "int64" + } + } + }, + "kw_only_optional": { + "size": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } + }, + "metadata": { + "usage.scipy": 2 } - ] - }, - "atol": { - "type": "union", - "options": [ - { - "type": { - "name": "int" + }, + { + "pos_only_optional": { + "_0": { + "type": { + "name": "int" + } } }, - { - "type": { - "name": "float" + "kw_only_optional": { + "size": { + "type": "tuple", + "items": { + "type": { + "name": "int" + } + } } }, - { - "type": { - "module": "numpy", - "name": "float64" + "metadata": { + "usage.dask": 5 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } } }, - { - "type": { - "module": "numpy", - "name": "float32" + "metadata": { + "usage.sklearn": 21 + } + }, + { + "kw_only_required": { + "size": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] } }, - { - "type": { - "module": "numpy", - "name": "float128" + "metadata": { + "usage.sklearn": 4 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } + }, + "metadata": { + "usage.sklearn": 2 } - ] - }, - "err_msg": { - "type": "union", - "options": [ - { - "type": "str" + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] + } }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ { "type": { - "name": "complex" + "name": "int" } }, { @@ -223683,464 +275419,538 @@ }, { "type": { - "name": "float" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" + } + }, + { + "type": { + "name": "int" } } ] } }, - { - "type": { - "name": "float" + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } + "metadata": { + "usage.sklearn": 1 } - ] - }, - "verbose": { - "type": { - "name": "bool" } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "rtol", - "atol" - ], - [ - "rtol", - "err_msg" - ], - [ - "err_msg", - "verbose" ], - [ - "atol", - "err_msg" - ] - ], - "metadata": { - "usage.skimage": 158, - "usage.xarray": 38, - "usage.scipy": 4704, - "usage.matplotlib": 184, - "usage.sklearn": 760 - } - }, - "assert_array_almost_equal": { - "pos_or_kw_required": { - "x": { - "type": "object" - }, - "y": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "decimal": { - "type": "union", - "options": [ - { - "type": { - "name": "bool" + "permutation": [ + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - { - "type": { - "name": "int" - } + "metadata": { + "usage.skimage": 1, + "usage.sklearn": 3 } - ] - }, - "err_msg": { - "type": "str" - } - }, - "pos_or_kw_optional_ordering": [ - [ - "decimal", - "err_msg" - ] - ], - "metadata": { - "usage.skimage": 69, - "usage.scipy": 3904, - "usage.matplotlib": 86, - "usage.dask": 1, - "usage.sklearn": 1569 - } - }, - "assert_warns": { - "pos_or_kw_required": { - "warning_class": { - "type": "type" - } - }, - "var_pos": [ - "args", - { - "type": "str", - "options": [ - "v", - "t" - ] - } - ], - "metadata": { - "usage.skimage": 5, - "usage.scipy": 39, - "usage.sklearn": 1 - } - }, - "assert_": { - "pos_or_kw_required": { - "val": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "msg": { - "type": "object" - } - }, - "metadata": { - "usage.skimage": 78, - "usage.scipy": 1706 - } - }, - "assert_array_less": { - "pos_or_kw_required": { - "x": { - "type": "object" - }, - "y": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "err_msg": { - "type": "str" - } - }, - "metadata": { - "usage.skimage": 20, - "usage.scipy": 92, - "usage.matplotlib": 3, - "usage.sklearn": 18 - } - }, - "assert_array_almost_equal_nulp": { - "pos_or_kw_required": { - "x": { - "type": "object" - }, - "y": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" + }, + { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + { + "type": { + "name": "int" + } + } + ] } }, - { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "metadata": { + "usage.pandas": 17 + } + }, + { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "range" + } + }, + { + "type": "list", + "item": { + "type": { + "name": "float" + } + } + } + ] } }, - { - "type": { - "name": "float" + "metadata": { + "usage.scipy": 11 + } + }, + { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, - { - "type": { - "name": "int" + "metadata": { + "usage.dask": 4 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } } }, - { - "type": { - "module": "numpy", - "name": "float64" - } + "metadata": { + "usage.sklearn": 19 } - ] - } - }, - "pos_or_kw_optional": { - "nulp": { - "type": "union", - "options": [ - { - "type": { - "name": "int" + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "int64" + } } }, - { - "type": { - "name": "float" + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "int64" + } + } } + }, + "metadata": { + "usage.sklearn": 16 } - ] - } - }, - "metadata": { - "usage.skimage": 4, - "usage.scipy": 67, - "usage.matplotlib": 1 - } - }, - "assert_no_warnings": { - "var_pos": [ - "args", - { - "type": "str", - "options": [ - "v", - "t" - ] - } - ], - "metadata": { - "usage.skimage": 1, - "usage.sklearn": 1 - } - }, - "assert_string_equal": { - "pos_or_kw_required": { - "actual": { - "type": "str" - }, - "desired": { - "type": "str" - } - }, - "metadata": { - "usage.scipy": 2 - } - }, - "assert_approx_equal": { - "pos_or_kw_required": { - "actual": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" + } + ], + "standard_normal": [ + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, - { - "type": { - "name": "float" + "metadata": { + "usage.skimage": 8, + "usage.sklearn": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] } + }, + "metadata": { + "usage.skimage": 2 } - ] - }, - "desired": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" + }, + { + "pos_only_optional": { + "_0": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] } }, - { - "type": { - "name": "int" + "kw_only_optional": { + "size": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] + } + }, + { + "type": { + "name": "int" + } + } + ] } }, - { - "type": { - "name": "float" + "metadata": { + "usage.scipy": 33 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } } + }, + "metadata": { + "usage.matplotlib": 12 } - ] - } - }, - "pos_or_kw_optional": { - "significant": { - "type": { - "name": "int" - } - }, - "err_msg": { - "type": "str" - } - }, - "pos_or_kw_optional_ordering": [ - [ - "significant", - "err_msg" - ] - ], - "metadata": { - "usage.scipy": 220, - "usage.matplotlib": 1, - "usage.sklearn": 2 - } - } - }, - "classes": { - "suppress_warnings": { - "method_overloads": { - "filter": [ + }, { - "pos_or_kw_optional": { - "category": { - "type": "type" - }, - "message": { - "type": "str" + "pos_only_required": { + "_0": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } } }, - "pos_or_kw_optional_ordering": [ - [ - "category", - "message" - ] - ], "metadata": { - "usage.scipy": 298 + "usage.matplotlib": 8 } - } - ], - "record": [ + }, { - "pos_or_kw_required": { - "message": { - "type": "str" + "kw_only_required": { + "size": { + "type": "tuple", + "items": { + "type": { + "name": "int" + } + } } }, - "pos_or_kw_optional": { - "category": { - "type": "type" + "kw_only_optional": { + "dtype": { + "type": "str", + "options": [ + "float64" + ] } }, "metadata": { - "usage.scipy": 11 + "usage.dask": 3 } } - ] - }, - "methods": { - "filter": { - "pos_or_kw_optional": { - "category": { - "type": "type" + ], + "gamma": [ + { + "pos_only_required": { + "_0": { + "type": { + "name": "float" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "float64" + } + } }, - "message": { - "type": "str" + "metadata": { + "usage.skimage": 2 } }, - "pos_or_kw_optional_ordering": [ - [ - "category", - "message" - ] - ], - "metadata": { - "usage.scipy": 298 - } - }, - "record": { - "pos_or_kw_required": { - "message": { - "type": "str" + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "kw_only_required": { + "size": { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + } + }, + "metadata": { + "usage.scipy": 4 } }, - "pos_or_kw_optional": { - "category": { - "type": "type" + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "name": "int" + } + } + }, + "kw_only_required": { + "size": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.dask": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, + "kw_only_required": { + "size": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.sklearn": 1 } }, - "metadata": { - "usage.scipy": 11 - } - } - } - } - } - }, - "numpy.testing": { - "properties": { - "assert_allclose": [ - { - "usage.skimage": 10, - "usage.scipy": 30, - "usage.matplotlib": 6, - "usage.sklearn": 6 - }, - { - "type": "bottom" - } - ], - "assert_almost_equal": [ - { - "usage.skimage": 3, - "usage.scipy": 15, - "usage.sklearn": 3 - }, - { - "type": "bottom" - } - ], - "assert_equal": [ - { - "usage.scipy": 1 - }, - { - "type": "bottom" - } - ], - "assert_approx_equal": [ - { - "usage.sklearn": 1 - }, - { - "type": "bottom" - } - ] - } - }, - "numpy.lib.index_tricks": { - "classes": { - "RClass": { - "method_overloads": { - "__getitem__": [ { "pos_only_required": { "_0": { + "type": { + "name": "float" + } + }, + "_1": { + "type": { + "name": "float" + } + }, + "_2": { "type": "tuple", "items": [ { "type": { - "name": "float" + "name": "int" } }, { "type": { - "name": "float" + "name": "int" } } ] } }, + "metadata": { + "usage.sklearn": 2 + } + } + ], + "poisson": [ + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, "metadata": { "usage.skimage": 2 } @@ -224148,8 +275958,43 @@ { "pos_only_required": { "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "_1": { "type": "tuple", - "items": [ + "items": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": "None" + } + ] + } + } + }, + "metadata": { + "usage.scipy": 8 + } + }, + { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ { "type": { "name": "int" @@ -224157,139 +276002,292 @@ }, { "type": { - "name": "int" + "name": "float" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } - }, + } + ] + } + }, + "kw_only_required": { + "size": { + "type": "union", + "options": [ { "type": { "name": "int" } }, { - "type": { - "name": "int" + "type": "tuple", + "items": { + "type": { + "name": "int" + } } } ] } }, "metadata": { - "usage.skimage": 1 + "usage.dask": 3 } }, { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - } - ] - } + "type": { + "name": "int" } } }, "metadata": { - "usage.skimage": 1 + "usage.sklearn": 2 + } + }, + { + "kw_only_required": { + "lam": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.sklearn": 1 } }, + { + "kw_only_required": { + "size": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.sklearn": 3 + } + } + ], + "shuffle": [ { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } + "type": { + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.skimage": 1 + "usage.xarray": 2, + "usage.pandas": 3, + "usage.matplotlib": 1, + "usage.sklearn": 26 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ + "type": "union", + "options": [ { - "type": { - "name": "int" + "type": "list", + "item": { + "type": { + "name": "int" + } } }, { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } - }, + } + ] + } + }, + "metadata": { + "usage.scipy": 31 + } + }, + { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { + "type": { + "name": "int" + } } } ] } }, "metadata": { - "usage.skimage": 3 + "usage.dask": 10 + } + } + ], + "beta": [ + { + "pos_only_required": { + "_0": { + "type": { + "name": "float" + } + }, + "_1": { + "type": { + "name": "float" + } + } + }, + "kw_only_required": { + "size": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.pandas": 4 } }, { "pos_only_required": { "_0": { - "type": "tuple", - "items": [ + "type": "union", + "options": [ { "type": { - "name": "float" + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "_1": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" } }, { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } } ] + }, + "_2": { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": "None" + } + ] + } } }, "metadata": { - "usage.skimage": 2 + "usage.scipy": 8 } }, { "pos_only_required": { "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "name": "int" + } + } + }, + "kw_only_required": { + "size": { "type": "tuple", "items": [ { "type": { - "name": "float" + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.dask": 1 + } + } + ], + "get_state": [ + { + "metadata": { + "usage.pandas": 1, + "usage.scipy": 8 + } + } + ], + "set_state": [ + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "MT19937" + ] + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } }, { @@ -224301,252 +276299,212 @@ "type": { "name": "int" } + }, + { + "type": { + "name": "float" + } } ] } }, "metadata": { - "usage.skimage": 2 + "usage.pandas": 1 } - }, + } + ], + "lognormal": [ { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "float" + } + }, + "_1": { + "type": { + "name": "float" + } + }, + "_2": { + "type": { + "name": "int" } } }, "metadata": { - "usage.skimage": 3 + "usage.pandas": 1 } }, { - "pos_only_required": { - "_0": { + "kw_only_required": { + "size": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.scipy": 1, + "usage.sklearn": 3 + } + }, + { + "kw_only_required": { + "mean": { + "type": { + "name": "float" + } + }, + "sigma": { + "type": { + "name": "float" + } + }, + "size": { "type": "tuple", "items": [ { - "type": "tuple", - "items": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "float" - } - } - ] + "type": { + "name": "int" + } }, { "type": { - "name": "float" + "name": "int" } } ] } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 1 } }, { "pos_only_required": { "_0": { + "type": { + "name": "float" + } + }, + "_1": { + "type": { + "name": "float" + } + } + }, + "kw_only_required": { + "size": { "type": "tuple", "items": [ { "type": { "name": "int" } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } } ] } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 1 } - }, + } + ], + "multivariate_normal": [ { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "pos_only_optional": { + "_2": { + "type": { + "name": "int" + } + } + }, + "kw_only_optional": { + "size": { + "type": { + "name": "int" } } }, "metadata": { - "usage.xarray": 2 + "usage.scipy": 5 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": [ - { - "type": "union", - "options": [ - { - "type": { - "name": "bool" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] - }, - { - "type": "union", - "options": [ - { - "type": { - "name": "bool" - } - }, - { - "type": { - "module": 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- "metadata": { - "usage.matplotlib": 6 + ] } }, - { - "pos_only_optional": { - "_0": { + "metadata": { + "usage.skimage": 2, + "usage.scipy": 4, + "usage.dask": 1, + "usage.sklearn": 3 + } + }, + "poisson": { + "pos_only_optional": { + "_1": { + "type": "tuple", + "items": { "type": "union", "options": [ - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - } - }, { "type": { "name": "int" } - } - ] - } - }, - "kw_only_optional": { - "size": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } + }, + { + "type": "None" } ] } }, - "metadata": { - "usage.dask": 157 + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + 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}, - "_3": { - "type": { - "name": "int" - } - }, - "_4": { - "type": { - "name": "int" + "metadata": { + "usage.xarray": 2, + "usage.pandas": 3, + "usage.scipy": 31, + "usage.matplotlib": 1, + "usage.dask": 10, + "usage.sklearn": 26 + } + }, + "beta": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } - } + ] }, - "metadata": { - "usage.skimage": 5, - "usage.xarray": 1 - } - }, - { - "metadata": { - "usage.skimage": 1 - } - }, - { - "pos_only_optional": { - "_1": { - "type": { - "name": "int" - } - }, - "_0": { - "type": { - "name": "int" - } - }, - "_2": { - "type": { - "name": "int" + "_1": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": 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"name": "float" + } } - } - }, - "metadata": { - "usage.skimage": 2 + ] } }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "int" - } - } - }, - "kw_only_required": { - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "uint8" - } - }, - "size": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "pos_only_optional": { + "_1": { + "type": { + "name": "int" } - }, - "metadata": { - "usage.skimage": 1 } }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" + "kw_only_optional": { + "size": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } } + ] + }, + { + "type": { + "name": "int" } - ] - } - }, - "metadata": { - "usage.xarray": 1 + } + ] } }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + "metadata": { + "usage.scipy": 7, + "usage.dask": 1 + } + }, + "standard_gamma": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } - }, - "_1": { - "type": { - "name": "int" + ] + } + }, + "pos_only_optional": { + "_1": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } } - } - }, - "kw_only_required": { - "size": { - "type": "list", - "item": { + ] + } + }, + "kw_only_optional": { + "size": { + "type": "tuple", + "items": [ + { "type": { "name": "int" } } - } - }, - "metadata": { - "usage.xarray": 6 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.xarray": 4 + ] } }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "pos_only_optional": { - "_2": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } + "metadata": { + "usage.scipy": 8, + "usage.dask": 1 + } + }, + "chisquare": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" } - ] - }, - "_1": { - "type": { - "name": "int" - } - } - }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ] - ], - "kw_only_optional": { - "size": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" } - ] - }, - "dtype": { - "type": "str", - "options": [ - "int64" - ] - }, - "high": { - "type": { - "name": "int" } - } - }, - "metadata": { - "usage.pandas": 160 + ] } }, - { - "pos_only_optional": { - "_1": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } + "pos_only_optional": { + "_1": { + "type": "union", + "options": [ + { + "type": { + "name": "int" } - ] - }, - "_2": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": [ + }, + { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "None" + }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } }, { @@ -228586,48 +279979,47 @@ } } ] - }, - { - "type": { - "name": "int" - } } - ] - }, - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" + } + ] + } + }, + "kw_only_optional": { + "size": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } } + ] + }, + { + "type": { + "module": "numpy", + "name": "int64" } - ] - } - }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ], - [ - "_0", - "_1" + } ] - ], - "kw_only_optional": { - "high": { + } + }, + "metadata": { + "usage.scipy": 15, + "usage.dask": 1, + "usage.sklearn": 2 + } + }, + "standard_exponential": { + "pos_only_optional": { + "_0": { + "type": "tuple", + "items": { "type": "union", "options": [ - { - "type": "None" - }, { "type": { "name": "int" @@ -228636,120 +280028,82 @@ { "type": { "module": "numpy", - "name": "ndarray" - } - } - ] - }, - "dtype": { - "type": "union", - "options": [ - { - "type": "type", - "name": { - "module": "numpy", - "name": "uint32" - } - }, - { - "type": { - "module": "numpy", - "name": "dtype" + "name": "int64" } }, { - "type": "str", - "options": [ - "int64" - ] + "type": "None" } ] - }, - "size": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - }, - { - "type": "list", - "item": { + } + } + }, + "kw_only_optional": { + "size": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": [ + { "type": { "name": "int" } } - }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - } - ] - } + ] + }, + { + "type": { + "name": "int" } - ] - }, - "low": { - "type": { - "name": "int" } - } - }, - "metadata": { - "usage.scipy": 66 + ] } }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" + "metadata": { + "usage.scipy": 13, + "usage.dask": 1 + } + }, + "f": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } - } + ] }, - "kw_only_required": { - "size": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } + "_1": { + "type": "union", + "options": [ + { + "type": { + "name": "int" } - ] - } - }, - "metadata": { - "usage.matplotlib": 1 + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } }, - { - "pos_only_required": { - "_0": { + "pos_only_optional": { + "_2": { + "type": "tuple", + "items": { "type": "union", "options": [ { @@ -228762,125 +280116,76 @@ "module": "numpy", "name": "int64" } - } - ] - } - }, - "pos_only_optional": { - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } }, { - "type": { - "name": "int" - } + "type": "None" } ] } - }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" + } + }, + "kw_only_optional": { + "size": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } ] - ], - "kw_only_optional": { - "size": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - } + } + }, + "metadata": { + "usage.scipy": 3, + "usage.dask": 1 + } + }, + "wald": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "float" } - ] - }, - "dtype": { - "type": "str", - "options": [ - "l", - "uint8" - ] - }, - "high": { - "type": { - "name": "int" } - } + ] }, - "metadata": { - "usage.dask": 133 + "_1": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + } + ] } }, - { - "pos_only_optional": { - "_1": { - "type": { - "name": "int" - } - }, - "_2": { + "kw_only_required": { + "size": { + "type": "tuple", + "items": { "type": "union", "options": [ - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, { "type": { "name": "int" @@ -228891,101 +280196,66 @@ "module": "numpy", "name": "int64" } - } - ] - }, - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } }, { - "type": { - "module": "numpy", - "name": "float64" - } + "type": "None" } ] } + } + }, + "metadata": { + "usage.scipy": 9, + "usage.dask": 1 + } + }, + "laplace": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ], - [ - "_0", - "_1" + "_1": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } ] - ], - "kw_only_optional": { - "size": { + } + }, + "kw_only_required": { + "size": { + "type": "tuple", + "items": { "type": "union", "options": [ { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - }, - { - "type": { - "name": "int" - } + "type": "None" }, { "type": { "module": "numpy", "name": "int64" } - } - ] - }, - "dtype": { - "type": "union", - "options": [ - { - "type": "type" - }, - { - "type": "str", - "options": [ - "u8" - ] - } - ] - }, - "high": { - "type": { - "name": "int" - } - }, - "low": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.sklearn": 213 - } - } - ], - "uniform": [ - { - "kw_only_required": { - "size": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } }, { "type": { @@ -228994,63 +280264,45 @@ } ] } - }, - "metadata": { - "usage.skimage": 21 } }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "float" - } - }, - "_1": { - "type": { - "name": "int" - } + "metadata": { + "usage.scipy": 3, + "usage.dask": 1 + } + }, + "logistic": { + "pos_only_optional": { + "_0": { + "type": { + "name": "float" } }, - "metadata": { - "usage.skimage": 2 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "float" - } - }, - "_1": { - "type": { - "name": "float" - } + "_1": { + "type": { + "name": "float" } - }, - "metadata": { - "usage.skimage": 1 } }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": "tuple", - "items": [ + "pos_only_optional_ordering": [ + [ + "_0", + "_1" + ] + ], + "kw_only_required": { + "size": { + "type": "tuple", + "items": { + "type": "union", + "options": [ + { + "type": "None" + }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } }, { @@ -229060,26 +280312,70 @@ } ] } - }, - "metadata": { - "usage.skimage": 2 } }, - { - "kw_only_required": { - "high": { - "type": { - "name": "float" + "metadata": { + "usage.scipy": 3, + "usage.dask": 1 + } + }, + "noncentral_f": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } - }, - "low": { - "type": { - "name": "float" + ] + }, + "_1": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } - }, - "size": { - "type": "tuple", - "items": [ + ] + }, + "_2": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + } + }, + "pos_only_optional": { + "_3": { + "type": "tuple", + "items": { + "type": "union", + "options": [ { "type": { "name": "int" @@ -229087,164 +280383,142 @@ }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } + }, + { + "type": "None" } ] } - }, - "metadata": { - "usage.skimage": 1 } }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "int" - } - }, - "_2": { - "type": { - "name": "int" + "kw_only_optional": { + "size": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } } - } - }, - "metadata": { - "usage.skimage": 2 + ] } }, - { - "kw_only_required": { - "size": { - "type": "list", - "item": { + "metadata": { + "usage.scipy": 3, + "usage.dask": 1 + } + }, + "noncentral_chisquare": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { "type": { "name": "int" } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } - } + ] }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "kw_only_optional": { - "size": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - { - "type": { - "name": "int" - } + "_1": { + "type": "union", + "options": [ + { + "type": { + "name": "int" } - ] - } - }, - "metadata": { - "usage.pandas": 6 + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } }, - { - "pos_only_optional": { - "_2": { + "pos_only_optional": { + "_2": { + "type": "tuple", + "items": { "type": "union", "options": [ { - "type": { - "name": "int" - } + "type": "None" }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - } - } - ] - }, - "_0": { - "type": "union", - "options": [ { "type": { "module": "numpy", - "name": "float64" + "name": "int64" } }, { "type": { "name": "int" } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "float" - } } - ] - }, - "_1": { + ] + } + } + }, + "kw_only_optional": { + "size": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.scipy": 4, + "usage.dask": 1 + } + }, + "standard_t": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + } + }, + "kw_only_required": { + "size": { + "type": "tuple", + "items": { "type": "union", "options": [ { - "type": { - "module": "numpy", - "name": "float64" - } + "type": "None" }, { "type": { "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "float" + "name": "int64" } }, { @@ -229254,96 +280528,187 @@ } ] } + } + }, + "metadata": { + "usage.scipy": 3, + "usage.dask": 1 + } + }, + "triangular": { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ], - [ - "_0", - "_1" + "_1": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } ] - ], - "kw_only_optional": { - "size": { + }, + "_2": { + "type": { + "name": "int" + } + } + }, + "pos_only_optional": { + "_3": { + "type": "tuple", + "items": { "type": "union", "options": [ { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } }, { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - } + "type": "None" } ] - }, - "high": { - "type": { - "name": "float" + } + } + }, + "kw_only_optional": { + "size": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } } - }, - "low": { + ] + } + }, + "metadata": { + "usage.scipy": 3, + "usage.dask": 1 + } + }, + "vonmises": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + } + ] + }, + "_1": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + } + }, + "kw_only_required": { + "size": { + "type": "tuple", + "items": { "type": "union", "options": [ { - "type": { - "name": "int" - } + "type": "None" }, { "type": { - "name": "float" + "name": "int" } } ] } - }, - "metadata": { - "usage.scipy": 100 } }, - { - "kw_only_required": { - "high": { - "type": { - "name": "int" + "metadata": { + "usage.scipy": 2, + "usage.dask": 1 + } + }, + "binomial": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } - }, - "low": { - "type": { - "name": "int" + ] + }, + "_1": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } - }, - "size": { - "type": "tuple", - "items": [ + ] + } + }, + "pos_only_optional": { + "_2": { + "type": "tuple", + "items": { + "type": "union", + "options": [ { "type": { "name": "int" @@ -229351,36 +280716,72 @@ }, { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } + }, + { + "type": "None" } ] } - }, - "metadata": { - "usage.matplotlib": 1 } }, - { - "kw_only_required": { - "size": { - "type": { - "name": "int" + "kw_only_optional": { + "size": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": "tuple", + "items": { + "type": { + "name": "int" + } + } } - } - }, - "metadata": { - "usage.matplotlib": 2 + ] } }, - { - "pos_only_optional": { - "_2": { - "type": { - "name": "int" + "metadata": { + "usage.scipy": 10, + "usage.dask": 1, + "usage.sklearn": 11 + } + }, + "geometric": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } } - }, - "_0": { + ] + } + }, + "kw_only_required": { + "size": { + "type": "tuple", + "items": { "type": "union", "options": [ { @@ -229390,12 +280791,83 @@ }, { "type": { - "name": "float" + "module": "numpy", + "name": "int64" } + }, + { + "type": "None" } ] - }, - "_1": { + } + } + }, + "metadata": { + "usage.scipy": 15, + "usage.dask": 1 + } + }, + "hypergeometric": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "_1": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] + }, + "_2": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + } + }, + "kw_only_required": { + "size": { + "type": "tuple", + "items": { "type": "union", "options": [ { @@ -229405,24 +280877,45 @@ }, { "type": { - "name": "float" + "module": "numpy", + "name": "int64" } + }, + { + "type": "None" } ] } - }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ], - [ - "_0", - "_1" + } + }, + "metadata": { + "usage.scipy": 4, + "usage.dask": 1 + } + }, + "logseries": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } ] - ], - "kw_only_optional": { - "size": { + } + }, + "kw_only_required": { + "size": { + "type": "tuple", + "items": { "type": "union", "options": [ { @@ -229431,49 +280924,72 @@ } }, { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": "None" } ] } + } + }, + "metadata": { + "usage.scipy": 4, + "usage.dask": 1 + } + }, + "negative_binomial": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] }, - "metadata": { - "usage.dask": 9 + "_1": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } }, - { - "pos_only_optional": { - "_2": { + "pos_only_optional": { + "_2": { + "type": "tuple", + "items": { "type": "union", "options": [ { - "type": "list", - "item": { - "type": { - "name": "int" - } - } + "type": "None" }, { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "numpy", + "name": "int64" + } }, { "type": { @@ -229481,8 +280997,49 @@ } } ] - }, - "_0": { + } + } + }, + "kw_only_optional": { + "size": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.scipy": 7, + "usage.dask": 1 + } + }, + "zipf": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + } + }, + "kw_only_required": { + "size": { + "type": "tuple", + "items": { "type": "union", "options": [ { @@ -229493,129 +281050,507 @@ { "type": { "module": "numpy", - "name": "ndarray" + "name": "int64" } }, { - "type": { - "module": "numpy", - "name": "float64" + "type": "None" + } + ] + } + } + }, + "metadata": { + "usage.scipy": 4, + "usage.dask": 1 + } + }, + "gumbel": { + "pos_only_optional": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "name": "float" + } + } + }, + "pos_only_optional_ordering": [ + [ + "_0", + "_1" + ] + ], + "kw_only_required": { + "size": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } } - }, - { + ] + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.scipy": 2, + "usage.dask": 1 + } + }, + "dirichlet": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "kw_only_required": { + "size": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.scipy": 4 + } + }, + "multinomial": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "_1": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { "type": { "name": "float" } } - ] - }, - "_1": { - "type": "union", + } + ] + } + }, + "pos_only_optional": { + "_2": { + "type": { + "name": "int" + } + } + }, + "kw_only_optional": { + "size": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": "tuple", + "items": { + "type": { + "name": "int" + } + } + } + ] + } + }, + "metadata": { + "usage.scipy": 2, + "usage.dask": 4, + "usage.sklearn": 6 + } + }, + "rayleigh": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "pos_only_optional": { + "_1": { + "type": { + "name": "int" + } + } + }, + "kw_only_optional": { + "size": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.scipy": 1, + "usage.dask": 1 + } + }, + "bytes": { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.dask": 1 + } + }, + "power": { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, + "kw_only_required": { + "size": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.dask": 1 + } + }, + "weibull": { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, + "kw_only_required": { + "size": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.dask": 1 + } + } + }, + "classproperties": { + "__module__": [ + { + "usage.dask": 1 + }, + { + "type": "bottom" + } + ] + } + } + }, + "properties": { + "_rand": [ + { + "usage.skimage": 1, + "usage.scipy": 1, + "usage.sklearn": 3 + }, + { + "type": "bottom" + } + ] + } + }, + "numpy.random": { + "properties": { + "rand": [ + { + "usage.skimage": 18, + "usage.scipy": 19 + }, + { + "type": "bottom" + } + ], + "randint": [ + { + "usage.skimage": 14, + "usage.xarray": 2, + "usage.pandas": 84, + "usage.scipy": 18, + "usage.matplotlib": 3, + "usage.dask": 70, + "usage.sklearn": 8 + }, + { + "type": "bottom" + } + ], + "uniform": [ + { + "usage.skimage": 22, + "usage.xarray": 1, + "usage.pandas": 2, + "usage.scipy": 28, + "usage.matplotlib": 3, + "usage.dask": 1, + "usage.sklearn": 4 + }, + { + "type": "bottom" + } + ], + "seed": [ + { + "usage.skimage": 4, + "usage.matplotlib": 1, + "usage.dask": 1 + }, + { + "type": "bottom" + } + ], + "normal": [ + { + "usage.skimage": 5, + "usage.pandas": 6, + "usage.scipy": 32, + "usage.matplotlib": 22, + "usage.dask": 11, + "usage.sklearn": 1 + }, + { + "type": "bottom" + } + ], + "RandomState": [ + { + "usage.skimage": 1, + "usage.pandas": 4, + "usage.scipy": 9, + "usage.dask": 5, + "usage.sklearn": 22 + }, + { + "type": "bottom" + } + ], + "randn": [ + { + "usage.skimage": 10, + "usage.xarray": 6, + "usage.pandas": 4, + "usage.scipy": 20 + }, + { + "type": "bottom" + } + ], + "choice": [ + { + "usage.skimage": 2, + "usage.xarray": 2, + "usage.pandas": 15, + "usage.scipy": 4, + "usage.dask": 20 + }, + { + "type": "bottom" + } + ], + "random": [ + { + "usage.xarray": 4, + "usage.pandas": 11, + "usage.scipy": 9, + "usage.dask": 15 + }, + { + "type": "bottom" + } + ], + "Generator": [ + { + "usage.scipy": 2 + }, + { + "type": "bottom" + } + ], + "multivariate_normal": [ + { + "usage.scipy": 3 + }, + { + "type": "bottom" + } + ], + "standard_cauchy": [ + { + "usage.scipy": 1 + }, + { + "type": "bottom" + } + ], + "exponential": [ + { + "usage.scipy": 2 + }, + { + "type": "bottom" + } + ], + "pareto": [ + { + "usage.scipy": 5 + }, + { + "type": "bottom" + } + ], + "lognormal": [ + { + "usage.scipy": 1, + "usage.matplotlib": 1 + }, + { + "type": "bottom" + } + ], + "dirichlet": [ + { + "usage.scipy": 3 + }, + { + "type": "bottom" + } + ], + "random_sample": [ + { + "usage.scipy": 1 + }, + { + "type": "bottom" + } + ], + "multinomial": [ + { + "usage.dask": 1 + }, + { + "type": "bottom" + } + ] + } + }, + "numpy.lib.npyio": { + "classes": { + "NpzFile": { + "method_overloads": { + "__getitem__": [ + { + "pos_only_required": { + "_0": { + "type": "str", "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } + "arr_0" ] } }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ], - [ - "_0", - "_1" - ] - ], - "kw_only_optional": { - "size": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - } - ] - }, - "high": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - "low": { - "type": "union", + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - } + "autolevel" ] } }, "metadata": { - "usage.sklearn": 84 + "usage.skimage": 1 } - } - ], - "choice": [ + }, { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": "function" - } + "type": "str", + "options": [ + "autolevel_percentile" + ] } }, "metadata": { @@ -229625,22 +281560,10 @@ { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "kw_only_required": { - "replace": { - "type": { - "name": "bool" - } - }, - "size": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "equalize" + ] } }, "metadata": { @@ -229650,47 +281573,36 @@ { "pos_only_required": { "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "int" - } - } - }, - "kw_only_required": { - "replace": { - "type": { - "name": "bool" - } + "type": "str", + "options": [ + "gradient" + ] } }, "metadata": { - "usage.skimage": 2, - "usage.xarray": 2 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "_1": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "gradient_percentile" + ] } }, - "kw_only_required": { - "replace": { - "type": { - "name": "bool" - } + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "maximum" + ] } }, "metadata": { @@ -229700,76 +281612,48 @@ { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": { - "name": "bool" - } - } + "type": "str", + "options": [ + "mean" + ] } }, - "kw_only_required": { - "p": { - "type": "list", - "item": { - "type": { - "name": "float" - } - } - }, - "size": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "geometric_mean" ] } }, "metadata": { - "usage.skimage": 2 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": { - "name": "bool" - } - } + "type": "str", + "options": [ + "mean_percentile" + ] } }, - "kw_only_required": { - "p": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - "size": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "mean_bilateral" ] } }, @@ -229780,1747 +281664,1836 @@ { "pos_only_required": { "_0": { - "type": { - "name": "range" - } - }, - "_1": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "subtract_mean" + ] } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": "str", - "options": [ - "d", - "c", - "b", - "a" - ] - } + "type": "str" } }, - "kw_only_required": { - "size": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } + "metadata": { + "usage.skimage": 1, + "usage.scipy": 256 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "median" + ] } }, "metadata": { - "usage.xarray": 2 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "minimum" + ] } }, "metadata": { - "usage.xarray": 1 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "name": "range" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" - } - }, - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": "str" - }, - { - "type": { - "name": "int" - } - } - ] - } - } + "modal" ] } }, - "pos_only_optional": { - "_1": { - "type": "union", + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } - } - ] - } + "enhance_contrast" ] } }, - "kw_only_optional": { - "p": { - "type": "union", + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "None" - } + "pop" ] - }, - "replace": { - "type": { - "name": "bool" - } - }, - "size": { - "type": "union", + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } + "pop_percentile" ] } }, "metadata": { - "usage.pandas": 36 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "range" - } - }, - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": "str" - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] - } - } + "pop_bilateral" ] } }, - "pos_only_optional": { - "_1": { - "type": { - "name": "int" - } + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "sum" + ] } }, - "kw_only_optional": { - "replace": { - "type": { - "name": "bool" - } - }, - "size": { - "type": { - "name": "int" - } - }, - "p": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "sum_bilateral" + ] } }, "metadata": { - "usage.scipy": 12 + "usage.skimage": 1 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" - } - }, - { - "type": { - "module": "pandas.core.indexes.base", - "name": "Index" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "bool" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": "str" - } - ] - } - } + "sum_percentile" ] } }, - "pos_only_optional": { - "_1": { - "type": { - "name": "int" - } + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + 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{ - "_0": { - "type": "union", - "options": [ + }, + { + "type": "tuple", + "items": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { @@ -231564,16 +283521,31 @@ } ] } - }, - "metadata": { - "usage.dask": 4 + ] + }, + "ndim": { + "type": { + "name": "int" } }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ + "as_index": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": "tuple", + "items": [ + { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -231581,37 +283553,12 @@ }, { "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "list", - "item": { - "type": { - "module": "numpy", - "name": "int64" - } + "name": "int" } } ] - } - }, - "metadata": { - "usage.sklearn": 39 - } - } - ], - "standard_normal": [ - { - "pos_only_required": { - "_0": { + }, + { "type": "tuple", "items": [ { @@ -231625,16 +283572,8 @@ } } ] - } - }, - "metadata": { - "usage.skimage": 8, - "usage.sklearn": 2 - } - }, - { - "pos_only_required": { - "_0": { + }, + { "type": "tuple", "items": [ { @@ -231642,11 +283581,6 @@ "name": "int" } }, - { - "type": { - "name": "int" - } - }, { "type": { "name": "int" @@ -231654,1930 +283588,2153 @@ } ] } - }, - "metadata": { - "usage.skimage": 2 + ] + }, + "ndim": { + "type": { + "name": "int" } }, - { - "pos_only_optional": { - "_0": { + "as_index": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": { + "name": "int" + } + }, + "ndim": { + "type": { + "name": "int" + } + }, + "as_index": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": "list", + "item": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "int64" + } + 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"pos_or_kw_optional": { + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" } - }, - "metadata": { - "usage.dask": 1 } }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] - } - }, - "pos_only_optional": { - "_2": { + "s": { + "type": "union", + "options": [ + { "type": "tuple", "items": [ { @@ -233591,472 +285748,337 @@ } } ] + }, + { + "type": "None" } - }, - "kw_only_optional": { - "size": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - }, - { - "type": { - "name": "int" - } - } - ] - } - }, - "metadata": { - "usage.sklearn": 11 - } + ] } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] ], - "geometric": [ - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": 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"name": "int" } } + }, + "pos_or_kw_optional_ordering": [ + [ + "a", + "n" + ], + [ + "a", + "axis" + ], + [ + "n", + "axis" + ] ], - "hypergeometric": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "_1": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] - }, - "_2": { + "kw_only_optional": { + "axes": { + "type": "tuple", + "items": [ + { "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "kw_only_required": { - "size": { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - } - ] + "name": "int" } } - }, - "metadata": { - "usage.scipy": 4 + ] + } + }, + "metadata": { + "usage.dask": 35 + } + } + ], + "ihfft": [ + { + "pos_only_optional": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "pos_or_kw_optional": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" } }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { + "n": { + "type": "union", + "options": [ + { "type": { "name": "int" } }, - "_2": { - "type": { - "name": "int" - } - } - }, - "kw_only_required": { - "size": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - } - ] + { + "type": "None" } - }, - "metadata": { - "usage.dask": 1 + ] + }, + "axis": { + "type": { + "name": "int" } } + }, + "pos_or_kw_optional_ordering": [ + [ + "a", + "n" + ], + [ + "a", + "axis" + ], + [ + "n", + "axis" + ] ], - "logseries": [ - { - "pos_only_required": { - "_0": { + "kw_only_optional": { + "axes": { + "type": "tuple", + "items": [ + { "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "kw_only_required": { - "size": { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - } - ] + "name": "int" } } - }, - "metadata": { - "usage.scipy": 4 + ] + } + }, + "metadata": { + "usage.dask": 35 + } + } + ], + "ifft2": [ + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "float" - } - } - }, - "kw_only_required": { - "size": { + } + }, + "pos_or_kw_optional": { + "s": { + "type": "union", + "options": [ + { "type": "tuple", "items": [ { "type": { "name": "int" } - } - ] - } - }, - "metadata": { - "usage.dask": 1 - } - } - ], - "negative_binomial": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "_1": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "_2": { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, 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"metadata": { - "usage.scipy": 4 + "metadata": { + "usage.dask": 33 + } + } + ], + "ifftn": [ + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "kw_only_required": { - "size": { + } + }, + "pos_or_kw_optional": { + "s": { + "type": "union", + "options": [ + { "type": "tuple", "items": [ + { + "type": { + "name": "int" + } + }, { "type": { "name": "int" } } ] + }, + { + "type": "None" + } + ] + }, + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" } - }, - "metadata": { - "usage.dask": 1 } } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] ], - "gumbel": [ - { - "kw_only_required": { - "size": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.scipy": 2 + "metadata": { + "usage.dask": 33 + } + } + ], + "rfft2": [ + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "float" - } - } - }, - "kw_only_required": { - "size": { + } + }, + "pos_or_kw_optional": { + "s": { + "type": "union", + "options": [ + { "type": "tuple", "items": [ + { + "type": { + "name": "int" + } + }, { "type": { "name": "int" } } ] + }, + { + "type": "None" + } + ] + }, + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" } - }, - "metadata": { - "usage.dask": 1 } } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] ], - "dirichlet": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "kw_only_required": { - "size": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.scipy": 4 + "metadata": { + "usage.dask": 33 + } + } + ], + "irfft2": [ + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" } } - ], - "multinomial": [ - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ + }, + "pos_or_kw_optional": { + "s": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -234064,76 +286086,56 @@ }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } ] }, - "_1": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": { - "name": "float" - } - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] - } - }, - "pos_only_optional": { - "_2": { - "type": { - "name": "int" - } - } - }, - "kw_only_optional": { - "size": { - "type": { - "name": "int" - } + { + "type": "None" } - }, - "metadata": { - "usage.scipy": 2 - } + ] }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": "list", - "item": { - "type": { - "name": "float" - } - } + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" } - }, - "kw_only_required": { - "size": { - "type": "union", - "options": [ + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] + ], + "metadata": { + "usage.dask": 33 + } + } + ], + "rfftn": [ + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "pos_or_kw_optional": { + "s": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": [ { - "type": "tuple", - "items": { - "type": { - "name": "int" - } + "type": { + "name": "int" } }, { @@ -234142,1038 +286144,642 @@ } } ] - } - }, - "metadata": { - "usage.dask": 4 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } }, - "_1": { - "type": { - "module": "numpy", - "name": "ndarray" - } + { + "type": "None" } - }, - "kw_only_optional": { - "size": { - "type": { - "name": "int" - } + ] + }, + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" } - }, - "metadata": { - "usage.sklearn": 6 } } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] ], - "rayleigh": [ - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.scipy": 1 + "metadata": { + "usage.dask": 33 + } + } + ], + "irfftn": [ + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "float" - } - } - }, - "kw_only_required": { - "size": { + } + }, + "pos_or_kw_optional": { + "s": { + "type": "union", + "options": [ + { "type": "tuple", "items": [ + { + "type": { + "name": "int" + } + }, { "type": { "name": "int" } } ] + }, + { + "type": "None" + } + ] + }, + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" } - }, - "metadata": { - "usage.dask": 1 } } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] ], - "bytes": [ - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } + "metadata": { + "usage.dask": 33 + } + } + ] + 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- } - }, - { - "type": { - "name": "int" - } - } - ] - } - }, - "kw_only_optional": { - "size": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - } - }, - { - "type": { - "name": "int" - } - } - ] + } + }, + "metadata": { + "usage.scipy": 1, + "usage.dask": 1 + } + }, + "fft2": { + "pos_or_kw_required": { + "a": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } }, - "dtype": { - "type": "str", - "options": [ - "float64" - ] + { + "type": { + "module": "dask.array.core", + "name": "Array" + } + } + ] + } + }, + "pos_or_kw_optional": { + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" } - }, - "metadata": { - "usage.skimage": 10, - "usage.scipy": 33, - "usage.matplotlib": 20, - "usage.dask": 3, - "usage.sklearn": 2 } }, - "gamma": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] - } - }, - "pos_only_optional": { - "_1": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - "_2": { + "s": { + "type": "union", + "options": [ + { "type": "tuple", "items": [ { @@ -235187,182 +286793,169 @@ } } ] + }, + { + "type": "None" } - }, - "pos_only_optional_ordering": [ - [ - "_1", - "_2" - ] - ], - "kw_only_optional": { - "size": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - } - ] - } - } - ] - } - }, - "metadata": { - "usage.skimage": 2, - "usage.scipy": 4, - "usage.dask": 1, - "usage.sklearn": 3 + ] + } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] + ], + "metadata": { + "usage.dask": 35 + } + }, + "hfft": { + "pos_only_optional": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "pos_or_kw_optional": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" } }, - "poisson": { - "pos_only_optional": { - "_1": { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": "None" - } - ] + "n": { + "type": "union", + "options": [ + { + "type": { + "name": "int" } }, - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] + { + "type": "None" } - }, - "pos_only_optional_ordering": [ - [ - "_0", - "_1" - ] - ], - "kw_only_optional": { - "size": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - } - ] - }, - "lam": { + ] + }, + "axis": { + "type": { + "name": "int" + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "a", + "n" + ], + [ + "a", + "axis" + ], + [ + "n", + "axis" + ] + ], + "kw_only_optional": { + "axes": { + "type": "tuple", + "items": [ + { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } - }, - "metadata": { - "usage.skimage": 2, - "usage.scipy": 8, - "usage.dask": 3, - "usage.sklearn": 6 + ] + } + }, + "metadata": { + "usage.dask": 35 + } + }, + "ihfft": { + "pos_only_optional": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "pos_or_kw_optional": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" } }, - "shuffle": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - } - ] + "n": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": "None" } - }, - "metadata": { - "usage.xarray": 2, - "usage.pandas": 3, - "usage.scipy": 31, - "usage.matplotlib": 1, - "usage.dask": 10, - "usage.sklearn": 26 - } + ] }, - "beta": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ + "axis": { + "type": { + "name": "int" + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "a", + "n" + ], + [ + "a", + "axis" + ], + [ + "n", + "axis" + ] + ], + "kw_only_optional": { + "axes": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.dask": 35 + } + }, + "ifft2": { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "pos_or_kw_optional": { + "s": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -235370,131 +286963,155 @@ }, { "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } ] }, - "_1": { - "type": "union", - "options": [ + { + "type": "None" + } + ] + }, + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" + } + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] + ], + "metadata": { + "usage.dask": 33 + } + }, + "ifftn": { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "pos_or_kw_optional": { + "s": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": [ { "type": { "name": "int" } }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] - } - }, - "pos_only_optional": { - "_2": { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": "None" - } - ] - } - } - }, - "kw_only_optional": { - "size": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - } - ] - }, { "type": { "name": "int" } } ] + }, + { + "type": "None" } - }, - "metadata": { - "usage.pandas": 4, - "usage.scipy": 8, - "usage.dask": 1 - } + ] }, - "get_state": { - "metadata": { - "usage.pandas": 1, - "usage.scipy": 8 + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" + } + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] + ], + "metadata": { + "usage.dask": 33 + } + }, + "rfft2": { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" } - }, - "set_state": { - "pos_only_required": { - "_0": { + } + }, + "pos_or_kw_optional": { + "s": { + "type": "union", + "options": [ + { "type": "tuple", "items": [ - { - "type": "str", - "options": [ - "MT19937" - ] - }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { "type": { "name": "int" } - }, + } + ] + }, + { + "type": "None" + } + ] + }, + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" + } + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] + ], + "metadata": { + "usage.dask": 33 + } + }, + "irfft2": { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "pos_or_kw_optional": { + "s": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -235502,22 +287119,51 @@ }, { "type": { - "name": "float" + "name": "int" } } ] + }, + { + "type": "None" } - }, - "metadata": { - "usage.pandas": 1 - } + ] }, - "lognormal": { - "var_pos": [ - "_args", + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" + } + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] + ], + "metadata": { + "usage.dask": 33 + } + }, + "rfftn": { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "pos_or_kw_optional": { + "s": { + "type": "union", + "options": [ { - "type": "union", - "options": [ + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -235525,260 +287171,628 @@ }, { "type": { - "name": "float" + "name": "int" } } ] + }, + { + "type": "None" } - ], - "kw_only_optional": { - "size": { - "type": "union", - "options": [ + ] + }, + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" + } + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] + ], + "metadata": { + "usage.dask": 33 + } + }, + "irfftn": { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "pos_or_kw_optional": { + "s": { + "type": "union", + "options": [ + { + "type": "tuple", + "items": [ { "type": { "name": "int" } }, { - "type": "tuple", - "items": { - "type": { - "name": "int" - } + "type": { + "name": "int" } } ] }, - "mean": { - "type": { - "name": "float" - } - }, - "sigma": { - "type": { - "name": "float" - } + { + "type": "None" + } + ] + }, + "axes": { + "type": "tuple", + "items": { + "type": { + "name": "int" + } + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "s", + "axes" + ] + ], + "metadata": { + "usage.dask": 33 + } + } + }, + "properties": { + "__name__": [ + { + "usage.dask": 1 + }, + { + "type": "bottom" + } + ] + } + }, + "numpy.ma.core": { + "function_overloads": { + "isMaskedArray": [ + { + "pos_or_kw_required": { + "x": { + "type": { + "module": "numpy", + "name": "ndarray" } - }, - "metadata": { - "usage.pandas": 1, - "usage.scipy": 1, - "usage.matplotlib": 1, - "usage.dask": 1, - "usage.sklearn": 3 } }, - "multivariate_normal": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "_1": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "metadata": { + "usage.skimage": 7 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + }, + "metadata": { + "usage.skimage": 11 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": "object" + } + }, + "metadata": { + "usage.scipy": 69 + } + } + ], + "array": [ + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" } }, - "pos_only_optional": { - "_2": { - "type": { - "name": "int" + "mask": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "name": "int" + } } } + } + }, + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, - "kw_only_optional": { - "size": { - "type": { - "name": "int" - } + "mask": { + "type": { + "name": "bool" } }, - "metadata": { - "usage.scipy": 5, - "usage.sklearn": 13 + "fill_value": { + "type": { + "name": "float" + } } }, - "standard_cauchy": { - "kw_only_required": { - "size": { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" } }, - "metadata": { - "usage.scipy": 2, - "usage.dask": 1 + "mask": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "fill_value": { + "type": { + "name": "float" + } } }, - "exponential": { - "pos_only_optional": { - "_0": { - "type": { - "name": "int" - } + "metadata": { + "usage.skimage": 2 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" } }, - "kw_only_required": { - "size": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - } - ] + "mask": { + "type": { + "module": "numpy", + "name": "ndarray" } }, - "metadata": { - "usage.scipy": 3, - "usage.dask": 2, - "usage.sklearn": 1 + "fill_value": { + "type": { + "module": "numpy", + "name": "float64" + } } }, - "pareto": { - "pos_only_required": { - "_0": { + "metadata": { + "usage.skimage": 1 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "mask": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.skimage": 1, + "usage.matplotlib": 7, + "usage.dask": 2, + "usage.sklearn": 4 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { "type": "union", "options": [ { "type": { - "name": "int" + "name": "float" } }, { "type": { - "name": "float" + "name": "int" } } ] } }, - "pos_only_optional": { - "_1": { + "mask": { + "type": "list", + "item": { "type": { - "name": "int" + "name": "bool" } } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } }, - "kw_only_optional": { - "size": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": [ + "mask": { + "type": "list", + "item": { + "type": { + "name": "bool" + } + } + } + }, + "metadata": { + "usage.pandas": 1 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, { "type": { "name": "int" } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "name": "float" + } } ] - }, - { - "type": { - "name": "int" - } } - ] - } - }, - "metadata": { - "usage.scipy": 7, - "usage.dask": 1 + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + ] } }, - "standard_gamma": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } + "pos_or_kw_optional": { + "copy": { + "type": "union", + "options": [ + { + "type": { + "name": "bool" } - ] - } + }, + { + "type": { + "module": "numpy", + "name": "bool_" + } + } + ] }, - "pos_only_optional": { - "_1": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { + "mask": { + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": "list", + "item": { "type": { "name": "int" } } - ] - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } + }, + { + "type": { + "name": "bool" + } + }, + { + "type": { + "name": "int" + } + } + ] } - ] + }, + { + "type": { + "module": "numpy", + "name": "bool_" + } + }, + { + "type": { + "name": "bool" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "subok": { + "type": { + "name": "bool" } }, - "kw_only_optional": { - "size": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - } - ] + "ndmin": { + "type": { + "name": "int" } }, - "metadata": { - "usage.scipy": 8, - "usage.dask": 1 + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } } }, - "chisquare": { - "pos_only_required": { - "_0": { + "pos_or_kw_optional_ordering": [ + [ + "copy", + "mask" + ], + [ + "subok", + "ndmin" + ], + [ + "dtype", + "copy" + ], + [ + "copy", + "subok" + ] + ], + "metadata": { + "usage.scipy": 92 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } + }, + "copy": { + "type": { + "name": "bool" + } + }, + "mask": { + "type": { + "module": "numpy", + "name": "bool_" + } + } + }, + "metadata": { + "usage.matplotlib": 9 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "copy": { + "type": { + "name": "bool" + } + }, + "mask": { + "type": { + "module": "numpy", + "name": "bool_" + } + } + }, + "metadata": { + "usage.matplotlib": 4 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "dtype": { + "type": { + "module": "numpy", + "name": "dtype" + } + }, + "copy": { + "type": { + "name": "bool" + } + }, + "mask": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.matplotlib": 2 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "copy": { + "type": { + "name": "bool" + } + }, + "mask": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.matplotlib": 5 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { + "type": "None" + } + }, + "dtype": { + "type": "type", + "name": { + "name": "float" + } + }, + "mask": { + "type": "list", + "item": { + "type": { + "name": "bool" + } + } + } + }, + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + "mask": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.matplotlib": 2 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { "type": "union", "options": [ { @@ -235788,120 +287802,122 @@ }, { "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" + "name": "float" } } ] } }, - "pos_only_optional": { - "_1": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "name": "int" - } - } - ] - } - } - ] + "mask": { + "type": "list", + "item": { + "type": { + "name": "bool" + } + } + } + }, + "metadata": { + "usage.matplotlib": 2 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" } }, - "kw_only_optional": { - "size": { - "type": "union", + "mask": { + "type": { + "module": "numpy", + "name": "bool_" + } + } + }, + "metadata": { + "usage.matplotlib": 2 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.matplotlib": 3 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { + "type": "str", "options": [ - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - } - ] - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } + "b", + "k" ] } }, - "metadata": { - "usage.scipy": 15, - "usage.dask": 1, - "usage.sklearn": 2 + "mask": { + "type": "list", + "item": { + "type": { + "name": "bool" + } + } } }, - "standard_exponential": { - "pos_only_optional": { - "_0": { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": "None" - } - ] + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": { + "name": "int" + } } } - }, - "kw_only_optional": { - "size": { + } + }, + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + }, + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { "type": "union", "options": [ { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - } - ] + "type": "None" }, { "type": { @@ -235911,30 +287927,30 @@ ] } }, - "metadata": { - "usage.scipy": 13, - "usage.dask": 1 + "dtype": { + "type": "type", + "name": { + "name": "float" + } + }, + "mask": { + "type": "list", + "item": { + "type": { + "name": "bool" + } + } } }, - "f": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] - }, - "_1": { + "metadata": { + "usage.matplotlib": 3 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { "type": "union", "options": [ { @@ -235943,236 +287959,413 @@ } }, { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "None" } ] } }, - "pos_only_optional": { - "_2": { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": "None" - } - ] - } + "dtype": { + "type": "type", + "name": { + "name": "float" } }, - "kw_only_optional": { - "size": { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - } - ] + "mask": { + "type": "list", + "item": { + "type": { + "name": "bool" + } + } + } + }, + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { + "type": { + "name": "int" + } } }, - "metadata": { - "usage.scipy": 3, - "usage.dask": 1 + "mask": { + "type": "list", + "item": { + "type": { + "name": "int" + } + } } }, - "wald": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "float" - } + "metadata": { + "usage.matplotlib": 1 + } + } + ], + "getdata": [ + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.skimage": 2, + "usage.matplotlib": 16 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + }, + "metadata": { + "usage.skimage": 1, + "usage.matplotlib": 4, + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "module": "numpy.ma.mrecords", + "name": "MaskedRecords" + } + } + }, + "metadata": { + "usage.pandas": 1 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" } - ] - }, - "_1": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } - ] + } + ] + } + }, + "metadata": { + "usage.dask": 9 + } + } + ], + "getmaskarray": [ + { + "pos_or_kw_required": { + "arr": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" } - }, - "kw_only_required": { - "size": { - "type": "tuple", - "items": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": "None" - } - ] + } + }, + "metadata": { + "usage.skimage": 1, + "usage.xarray": 2, + "usage.pandas": 7, + "usage.matplotlib": 8, + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "arr": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } } + ] + } + }, + "metadata": { + "usage.scipy": 5, + "usage.dask": 9 + } + } + ], + "masked_where": [ + { + "pos_or_kw_required": { + "condition": { + "type": { + "module": "numpy", + "name": "ndarray" } }, - "metadata": { - "usage.scipy": 9, - "usage.dask": 1 + "a": { + 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}, + { + "type": { + "module": "numpy", + "name": "bool_" + } + } + ] + }, + "x": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "y": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedConstant" + } + } + ] } }, "metadata": { - "usage.skimage": 6 + "usage.scipy": 10 } - }, + } + ], + "mask_rowcols": [ { "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" } }, - "full_matrices": { + "axis": { "type": { - "name": "bool" + "name": "int" } } }, "metadata": { - "usage.skimage": 1 + "usage.scipy": 2 } - }, + } + ], + "fix_invalid": [ { "pos_or_kw_required": { "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "union", + "options": [ + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + } + ] + } + } + ] } }, "pos_or_kw_optional": { - "full_matrices": { - "type": { - "name": "bool" - } - }, - "compute_uv": { + "copy": { "type": { "name": "bool" } } }, "metadata": { - "usage.scipy": 10 + "usage.scipy": 16 } }, { "pos_or_kw_required": { "a": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "fill_value": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } }, - "pos_or_kw_optional": { - "full_matrices": { + "metadata": { + "usage.dask": 4 + } + } + ], + "filled": [ + { + "pos_or_kw_required": { + "a": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + }, + "fill_value": { "type": { "name": "int" } } }, "metadata": { - "usage.dask": 4 + "usage.scipy": 3 } }, { @@ -238108,39 +290002,48 @@ "module": "numpy", "name": "ndarray" } + }, + "fill_value": { + "type": { + "name": "float" + } } }, - "pos_or_kw_optional": { - "full_matrices": { + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "a": { "type": { - "name": "bool" + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, "metadata": { - "usage.sklearn": 6 + "usage.matplotlib": 18 } - } - ], - "inv": [ + }, { "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + "fill_value": { + "type": { + "name": "float" } } }, "metadata": { - "usage.skimage": 7, - "usage.scipy": 12, - "usage.matplotlib": 2, - "usage.sklearn": 6 + "usage.matplotlib": 2 } - } - ], - "eigvalsh": [ + }, { "pos_or_kw_required": { "a": { @@ -238148,28 +290051,33 @@ "module": "numpy", "name": "ndarray" } + }, + "fill_value": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.skimage": 2 + "usage.matplotlib": 1 } - } - ], - "det": [ + }, { "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + "fill_value": { + "type": { + "name": "bool" } } }, "metadata": { - "usage.skimage": 5, - "usage.scipy": 18, - "usage.matplotlib": 1, - "usage.sklearn": 1 + "usage.matplotlib": 1 } }, { @@ -238177,46 +290085,139 @@ "a": { "type": "union", "options": [ + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, { "type": { "module": "numpy", "name": "ndarray" } + } + ] + } + }, + "pos_or_kw_optional": { + "fill_value": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } }, { "type": { - "module": "dask.array.core", - "name": "Array" + "name": "int" } + }, + { + "type": "None" } ] } }, "metadata": { - "usage.dask": 2 + "usage.dask": 31 } } ], - "solve": [ + "allclose": [ { "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" } }, "b": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + }, + "metadata": { + "usage.scipy": 1 + } + } + ], + "masked_values": [ + { + "pos_or_kw_required": { + "x": { "type": { "module": "numpy", "name": "ndarray" } + }, + "value": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.skimage": 2 + "usage.scipy": 1 } }, + { + "pos_or_kw_required": { + "x": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "value": { + "type": { + "name": "int" + } + } + }, + "pos_or_kw_optional": { + "rtol": { + "type": { + "name": "float" + } + }, + "atol": { + "type": { + "name": "float" + } + }, + "shrink": { + "type": { + "name": "bool" + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "atol", + "shrink" + ], + [ + "rtol", + "atol" + ] + ], + "metadata": { + "usage.dask": 4 + } + } + ], + "power": [ { "pos_or_kw_required": { "a": { @@ -238224,18 +290225,35 @@ "options": [ { "type": { - "module": "numpy", - "name": "matrix" + "module": "numpy.ma.core", + "name": "MaskedArray" } }, { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } } ] }, + "b": { + "type": { + "name": "float" + } + } + }, + "metadata": { + "usage.scipy": 4 + } + }, + { + "pos_or_kw_required": { + "a": { + "type": { + "name": "float" + } + }, "b": { "type": { "module": "numpy", @@ -238244,33 +290262,75 @@ } }, "metadata": { - "usage.scipy": 25 + "usage.matplotlib": 1 } - } - ], - "norm": [ + }, { "pos_or_kw_required": { - "x": { + "a": { + "type": { + "name": "float" + } + }, + "b": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + }, + "metadata": { + "usage.matplotlib": 1 + } + }, + { + "pos_or_kw_required": { + "a": { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" + } + }, + "b": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, "metadata": { - "usage.skimage": 8, - "usage.matplotlib": 3 + "usage.matplotlib": 1 } }, { "pos_or_kw_required": { - "x": { - "type": "object" + "a": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + "b": { + "type": { + "name": "float" + } } }, - "pos_or_kw_optional": { - "axis": { + "metadata": { + "usage.matplotlib": 1 + } + } + ], + "masked_less": [ + { + "pos_or_kw_required": { + "x": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + "value": { "type": "union", "options": [ { @@ -238279,19 +290339,37 @@ } }, { - "type": "tuple", - "items": { - "type": { - "name": "int" - } + "type": { + "name": "float" + } + } + ] + } + }, + "metadata": { + "usage.scipy": 3 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" } }, { - "type": "None" + "type": { + "module": "dask.array.core", + "name": "Array" + } } ] }, - "ord": { + "value": { "type": "union", "options": [ { @@ -238301,102 +290379,64 @@ }, { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } - }, - { - "type": "str", - "options": [ - "f", - "fro" - ] - }, - { - "type": "None" } ] - }, - "keepdims": { - "type": { - "name": "bool" - } } }, - "pos_or_kw_optional_ordering": [ - [ - "ord", - "axis" - ], - [ - "axis", - "keepdims" - ] - ], "metadata": { - "usage.scipy": 394 + "usage.dask": 3 } - }, + } + ], + "make_mask": [ { "pos_or_kw_required": { - "x": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "axis": { + "m": { "type": { - "name": "int" + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.scipy": 2 } - }, + } + ], + "sort": [ { "pos_or_kw_required": { - "x": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "ord": { + "a": { "type": "union", "options": [ { - "type": { - "name": "int" + "type": "list", + "item": { + "type": { + "name": "int" + } } }, { "type": { - "name": "float" + "module": "numpy.ma.core", + "name": "MaskedArray" } }, { - "type": "None" - }, - { - "type": "str", - "options": [ - "nuc", - "fro" - ] + "type": { + "module": "numpy", + "name": "ndarray" + } } ] }, "axis": { "type": "union", "options": [ - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - }, { "type": "None" }, @@ -238406,25 +290446,30 @@ } } ] + } + }, + "metadata": { + "usage.scipy": 4 + } + } + ], + "masked_less_equal": [ + { + "pos_or_kw_required": { + "x": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } }, - "keepdims": { + "value": { "type": { - "name": "bool" + "name": "int" } } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "keepdims" - ], - [ - "ord", - "axis" - ] - ], "metadata": { - "usage.dask": 25 + "usage.scipy": 2 } }, { @@ -238434,75 +290479,150 @@ "module": "numpy", "name": "ndarray" } + }, + "value": { + "type": { + "name": "int" + } + }, + "copy": { + "type": { + "name": "bool" + } } }, - "pos_or_kw_optional": { - "ord": { + "metadata": { + "usage.matplotlib": 2 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + "value": { + "type": { + "name": "int" + } + }, + "copy": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.matplotlib": 5 + } + }, + { + "pos_or_kw_required": { + "x": { "type": "union", "options": [ { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } }, { "type": { - "name": "int" + "module": "dask.array.core", + "name": "Array" } - }, - { - "type": "str", - "options": [ - "fro" - ] } ] }, - "axis": { + "value": { "type": "union", "options": [ { - "type": "None" + "type": { + "name": "int" + } }, { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } ] } }, - "pos_or_kw_optional_ordering": [ - [ - "ord", - "axis" - ] - ], "metadata": { - "usage.sklearn": 45 + "usage.dask": 3 } } ], - "pinv": [ + "masked_greater_equal": [ { "pos_or_kw_required": { - "a": { + "x": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + "value": { + "type": { + "name": "int" } } }, "metadata": { - "usage.skimage": 1, - "usage.scipy": 3 + "usage.scipy": 2 + } + }, + { + "pos_or_kw_required": { + "x": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "dask.array.core", + "name": "Array" + } + } + ] + }, + "value": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + } + }, + "metadata": { + "usage.dask": 3 } } ], - "eig": [ + "min": [ { "pos_or_kw_required": { - "a": { + "obj": { "type": { "module": "numpy", "name": "ndarray" @@ -238510,16 +290630,14 @@ } }, "metadata": { - "usage.skimage": 3, - "usage.scipy": 15, - "usage.sklearn": 2 + "usage.matplotlib": 2 } } ], - "matrix_rank": [ + "max": [ { "pos_or_kw_required": { - "M": { + "obj": { "type": { "module": "numpy", "name": "ndarray" @@ -238527,56 +290645,66 @@ } }, "metadata": { - "usage.skimage": 1 + "usage.matplotlib": 2 } - }, + } + ], + "reshape": [ { "pos_or_kw_required": { - "M": { + "a": { "type": { "module": "numpy", "name": "ndarray" } - } - }, - "pos_or_kw_optional": { - "tol": { - "type": "union", - "options": [ + }, + "new_shape": { + "type": "tuple", + "items": [ { "type": { - "name": "float" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } } ] } }, "metadata": { - "usage.scipy": 24 + "usage.matplotlib": 1 } } ], - "cholesky": [ + "masked_not_equal": [ { "pos_or_kw_required": { - "a": { + "x": { "type": "union", "options": [ { - "type": "list", - "item": { - "type": "list", - "item": { - "type": { - "name": "float" - } - } + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "dask.array.core", + "name": "Array" + } + } + ] + }, + "value": { + "type": "union", + "options": [ + { + "type": { + "name": "int" } }, { @@ -238589,131 +290717,121 @@ } }, "metadata": { - "usage.scipy": 5 + "usage.dask": 3 } } ], - "lstsq": [ + "masked_inside": [ { "pos_or_kw_required": { - "a": { + "x": { "type": { "module": "numpy", "name": "ndarray" } }, - "b": { + "v1": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, - "rcond": { + "v2": { "type": { "name": "int" } } }, "metadata": { - "usage.scipy": 9 + "usage.dask": 3 } - }, + } + ], + "masked_outside": [ { "pos_or_kw_required": { - "a": { + "x": { "type": { "module": "numpy", "name": "ndarray" } }, - "b": { + "v1": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, - "rcond": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "int" - } - } - ] + "v2": { + "type": { + "name": "int" + } } }, "metadata": { "usage.dask": 3 } - }, + } + ], + "minimum_fill_value": [ { "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "b": { + "obj": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, - "pos_or_kw_optional": { - "rcond": { - "type": "None" - } - }, "metadata": { - "usage.sklearn": 2 + "usage.dask": 1 } } ], - "cond": [ + "maximum_fill_value": [ { "pos_or_kw_required": { - "x": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "pos_or_kw_optional": { - "p": { + "obj": { "type": { - "name": "int" + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, "metadata": { - "usage.scipy": 8 + "usage.dask": 1 } } ], - "eigvals": [ + "_check_fill_value": [ { "pos_or_kw_required": { - "a": { + "fill_value": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "ndtype": { "type": { "module": "numpy", - "name": "ndarray" + "name": "dtype" } } }, "metadata": { - "usage.scipy": 2 + "usage.dask": 2 } - }, + } + ], + "set_fill_value": [ { "pos_or_kw_required": { "a": { @@ -238727,155 +290845,846 @@ }, { "type": { - "module": "dask.array.core", - "name": "Array" + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + ] + }, + "fill_value": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } } ] } }, "metadata": { - "usage.dask": 2 + "usage.dask": 3 } } - ], - "matrix_power": [ - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" + ] + }, + "functions": { + "isMaskedArray": { + "pos_or_kw_required": { + "x": { + "type": "object" + } + }, + "metadata": { + "usage.skimage": 18, + "usage.scipy": 69 + } + }, + "array": { + "pos_or_kw_required": { + "data": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + { + "type": "list", + "item": { + "type": "object" + } } - }, - "n": { - "type": { - "name": "int" + ] + } + }, + "pos_or_kw_optional": { + "fill_value": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "name": "float" + } + } + ] + }, + "mask": { + "type": 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"name": "float" } }, { "type": { - "name": "int" + "module": "numpy.ma.core", + "name": "MaskedConstant" } } ] - }, - "compute_uv": { - "type": { - "name": "bool" - } } }, "metadata": { - "usage.skimage": 7, - "usage.scipy": 10, - "usage.dask": 4, - "usage.sklearn": 6 + "usage.scipy": 10 } }, - "inv": { + "mask_rowcols": { "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" } - } - }, - "metadata": { - "usage.skimage": 7, - "usage.scipy": 12, - "usage.matplotlib": 2, - "usage.sklearn": 6 - } - }, - "eigvalsh": { - "pos_or_kw_required": { - "a": { + }, + "axis": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } }, "metadata": { - "usage.skimage": 2 + "usage.scipy": 2 } }, - "det": { + "fix_invalid": { "pos_or_kw_required": { "a": { "type": "union", @@ -238888,22 +291697,67 @@ }, { "type": { - "module": "dask.array.core", - "name": "Array" + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } + } + ] + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] } } ] } }, + "pos_or_kw_optional": { + "copy": { + "type": { + "name": "bool" + } + }, + "fill_value": { + "type": { + "name": "int" + } + } + }, "metadata": { - "usage.skimage": 5, - "usage.scipy": 18, - "usage.matplotlib": 1, - "usage.dask": 2, - "usage.sklearn": 1 + "usage.scipy": 16, + "usage.dask": 4 } }, - "solve": { + "filled": { "pos_or_kw_required": { "a": { "type": "union", @@ -238917,62 +291771,25 @@ { "type": { "module": "numpy", - "name": "matrix" + "name": "float64" + } + }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" } } ] - }, - "b": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.skimage": 2, - "usage.scipy": 25 - } - }, - "norm": { - "pos_or_kw_required": { - "x": { - "type": "object" } }, "pos_or_kw_optional": { - "axis": { + "fill_value": { "type": "union", "options": [ - { - "type": { - "name": "int" - } - }, { "type": "None" }, - { - "type": "tuple", - "items": { - "type": { - "name": "int" - } - } - } - ] - }, - "ord": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "fro", - "nuc", - "f" - ] - }, { "type": { "name": "int" @@ -238984,151 +291801,233 @@ } }, { - "type": "None" + "type": { + "name": "bool" + } } ] - }, - "keepdims": { - "type": { - "name": "bool" - } } }, - "pos_or_kw_optional_ordering": [ - [ - "ord", - "axis" - ], - [ - "axis", - "keepdims" - ] - ], "metadata": { - "usage.skimage": 8, - "usage.scipy": 394, - "usage.matplotlib": 4, - "usage.dask": 25, - "usage.sklearn": 45 + "usage.scipy": 3, + "usage.matplotlib": 23, + "usage.dask": 31 } }, - "pinv": { + "allclose": { "pos_or_kw_required": { "a": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + "b": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, "metadata": { - "usage.skimage": 1, - "usage.scipy": 3 + "usage.scipy": 1 } }, - "eig": { + "masked_values": { "pos_or_kw_required": { - "a": { + "x": { "type": { "module": "numpy", "name": "ndarray" } + }, + "value": { + "type": { + "name": "int" + } } }, - "metadata": { - "usage.skimage": 3, - "usage.scipy": 15, - "usage.sklearn": 2 - } - }, - "matrix_rank": { - "pos_or_kw_required": { - "M": { + "pos_or_kw_optional": { + "rtol": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "float" + } + }, + "atol": { + "type": { + "name": "float" + } + }, + "shrink": { + "type": { + "name": "bool" } } }, - "pos_or_kw_optional": { - "tol": { + "pos_or_kw_optional_ordering": [ + [ + "atol", + "shrink" + ], + [ + "rtol", + "atol" + ] + ], + "metadata": { + "usage.scipy": 1, + "usage.dask": 4 + } + }, + "power": { + "pos_or_kw_required": { + "a": { "type": "union", "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, { "type": { "name": "float" } }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + ] + }, + "b": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" + } + }, + { + "type": { + "name": "float" } } ] } }, "metadata": { - "usage.skimage": 1, - "usage.scipy": 24 + "usage.scipy": 4, + "usage.matplotlib": 4 } }, - "cholesky": { + "masked_less": { "pos_or_kw_required": { - "a": { + "x": { "type": "union", "options": [ { - "type": "list", - "item": { - "type": "list", - "item": { - "type": { - "name": "float" - } - } + "type": { + "module": "dask.array.core", + "name": "Array" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" } }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + ] + }, + "value": { + "type": "union", + "options": [ { "type": { "module": "numpy", "name": "ndarray" } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "float" + } } ] } }, "metadata": { - "usage.scipy": 5 + "usage.scipy": 3, + "usage.dask": 3 } }, - "lstsq": { + "make_mask": { "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "b": { + "m": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "numpy.ma.core", + "name": "MaskedArray" } } }, - "pos_or_kw_optional": { - "rcond": { + "metadata": { + "usage.scipy": 2 + } + }, + "sort": { + "pos_or_kw_required": { + "a": { "type": "union", "options": [ { - "type": "None" + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } }, { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } + } + ] + }, + "axis": { + "type": "union", + "options": [ + { + "type": "None" }, { "type": { @@ -239139,34 +292038,67 @@ } }, "metadata": { - "usage.scipy": 9, - "usage.dask": 3, - "usage.sklearn": 2 + "usage.scipy": 4 } }, - "cond": { + "masked_less_equal": { "pos_or_kw_required": { "x": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "union", + "options": [ + { + "type": { + "module": "dask.array.core", + "name": "Array" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + ] + }, + "value": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "pos_or_kw_optional": { - "p": { + "copy": { "type": { - "name": "int" + "name": "bool" } } }, "metadata": { - "usage.scipy": 8 + "usage.scipy": 2, + "usage.matplotlib": 7, + "usage.dask": 3 } }, - "eigvals": { + "masked_greater_equal": { "pos_or_kw_required": { - "a": { + "x": { "type": "union", "options": [ { @@ -239180,36 +292112,40 @@ "module": "numpy", "name": "ndarray" } + }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } } ] - } - }, - "metadata": { - "usage.scipy": 2, - "usage.dask": 2 - } - }, - "matrix_power": { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } }, - "n": { - "type": { - "name": "int" - } + "value": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.scipy": 16 + "usage.scipy": 2, + "usage.dask": 3 } }, - "qr": { + "min": { "pos_or_kw_required": { - "a": { + "obj": { "type": { "module": "numpy", "name": "ndarray" @@ -239217,13 +292153,12 @@ } }, "metadata": { - "usage.scipy": 1, - "usage.dask": 5 + "usage.matplotlib": 2 } }, - "eigh": { + "max": { "pos_or_kw_required": { - "a": { + "obj": { "type": { 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"type": "None" + } + ] + }, + "stop": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": "None" + } + ] + }, + "step": { + "type": "None" + } + }, + { + "type": "None" + }, + { + "type": { + "name": "ellipsis" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + } + }, + "metadata": { + "usage.dask": 25 } } - }, - "metadata": { - "usage.scipy": 1, - "usage.dask": 1 - } - } - ], - "fft2": [ - { - "pos_or_kw_required": { - "a": { - "type": "union", - "options": [ - { + ], + "__isub__": [ + { + "pos_only_required": { + "_0": { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } - }, - { + } + }, + "metadata": { + "usage.skimage": 2, + "usage.matplotlib": 1 + } + }, + { + "pos_only_required": { + "_0": { "type": { - "module": "dask.array.core", - "name": "Array" + "module": "numpy.ma.core", + "name": "MaskedArray" } } - ] + }, + "metadata": { + "usage.scipy": 2 + } } - }, - "pos_or_kw_optional": { - "axes": { - "type": "tuple", - "items": { - "type": { - "name": "int" + ], + "__eq__": [ + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } } + }, + "metadata": { + "usage.skimage": 1, + "usage.matplotlib": 6 } }, - "s": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": [ + { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ { "type": { - "name": "int" + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" } }, { @@ -241394,173 +293607,114 @@ } } ] - }, - { - "type": "None" } - ] - } - }, - "pos_or_kw_optional_ordering": [ - [ - "s", - "axes" - ] - ], - "metadata": { - "usage.dask": 35 - } - } - ], - "hfft": [ - { - "pos_only_optional": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "pos_or_kw_optional": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" + }, + "metadata": { + "usage.scipy": 8 } }, - "n": { - "type": "union", - "options": [ - { + { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "numpy.ma.core", + "name": "MaskedArray" } - }, - { - "type": "None" } - ] - }, - "axis": { - "type": { - "name": "int" + }, + "metadata": { + "usage.matplotlib": 2 } } - }, - "pos_or_kw_optional_ordering": [ - [ - "a", - "n" - ], - [ - "a", - "axis" - ], - [ - "n", - "axis" - ] ], - "kw_only_optional": { - "axes": { - "type": "tuple", - "items": [ - { + "__setitem__": [ + { + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + }, + "_1": { "type": { - "name": "int" + "module": "numpy.ma.core", + "name": "MaskedConstant" } } - ] - } - }, - "metadata": { - "usage.dask": 35 - } - } - ], - "ihfft": [ - { - "pos_only_optional": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "pos_or_kw_optional": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" + }, + "metadata": { + "usage.skimage": 4 } }, - "n": { - "type": "union", - "options": [ - { + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { "type": { "name": "int" } - }, - { - "type": "None" } - ] - }, - "axis": { - "type": { - "name": "int" + }, + "metadata": { + "usage.skimage": 1 } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "a", - "n" - ], - [ - "a", - "axis" - ], - [ - "n", - "axis" - ] - ], - "kw_only_optional": { - "axes": { - "type": "tuple", - "items": [ - { + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { "type": { "name": "int" } } - ] - } - }, - "metadata": { - "usage.dask": 35 - } - } - ], - "ifft2": [ - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" + }, + "metadata": { + "usage.skimage": 1 } - } - }, - "pos_or_kw_optional": { - "s": { - "type": "union", - "options": [ - { + }, + { + "pos_only_required": { + "_0": { "type": "tuple", "items": [ { @@ -241575,105 +293729,256 @@ } ] }, - { - "type": "None" + "_1": { + "type": { + "name": "float" + } } - ] + }, + "metadata": { + "usage.xarray": 1 + } }, - "axes": { - "type": "tuple", - "items": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "module": "numpy", + "name": "bool_" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { + "type": { + "name": "int" + } } + }, + "metadata": { + "usage.xarray": 3 } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "s", - "axes" - ] - ], - "metadata": { - "usage.dask": 33 - } - } - ], - "ifftn": [ - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "pos_or_kw_optional": { - "s": { - "type": "union", - "options": [ - { + }, + { + "pos_only_required": { + "_0": { "type": "tuple", "items": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "name": "int" + } + } + ] + }, + "_1": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ { "type": { "name": "int" } }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + ] + }, + "_1": { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "2001-01-02T00:00:00", + "2001-01-03T00:00:00", + "2001-01-01T00:00:00" + ] + }, + { + "type": { + "name": "bool" + } + }, { "type": { "name": "int" } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "datetime64" + } } ] + } + }, + "metadata": { + "usage.pandas": 18 + } + }, + { + "pos_only_required": { + "_0": { + "type": "object" }, - { - "type": "None" + "_1": { + "type": "object" } - ] + }, + "metadata": { + "usage.scipy": 41 + } }, - "axes": { - "type": "tuple", - "items": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedConstant" + } } + }, + "metadata": { + "usage.matplotlib": 6 } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "s", - "axes" - ] - ], - "metadata": { - "usage.dask": 33 - } - } - ], - "rfft2": [ - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" + }, + { + "pos_only_required": { + "_0": { + "type": "tuple", + "items": [ + { + "type": { + "name": "int" + } + }, + { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } + } + ] + }, + "_1": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + }, + "metadata": { + "usage.matplotlib": 2 } - } - }, - "pos_or_kw_optional": { - "s": { - "type": "union", - "options": [ - { + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "float64" + } + } + }, + "metadata": { + "usage.matplotlib": 4, + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + "_1": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + } + }, + "metadata": { + "usage.matplotlib": 4 + } + }, + { + "pos_only_required": { + "_0": { "type": "tuple", "items": [ { "type": { - "name": "int" + "name": "ellipsis" } }, { @@ -241683,51 +293988,25 @@ } ] }, - { - "type": "None" - } - ] - }, - "axes": { - "type": "tuple", - "items": { - "type": { - "name": "int" + "_1": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } } + }, + "metadata": { + "usage.matplotlib": 2 } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "s", - "axes" - ] - ], - "metadata": { - "usage.dask": 33 - } - } - ], - "irfft2": [ - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "pos_or_kw_optional": { - "s": { - "type": "union", - "options": [ - { + }, + { + "pos_only_required": { + "_0": { "type": "tuple", "items": [ { "type": { - "name": "int" + "name": "ellipsis" } }, { @@ -241737,100 +294016,204 @@ } ] }, - { - "type": "None" + "_1": { + "type": { + "name": "int" + } } - ] + }, + "metadata": { + "usage.matplotlib": 1 + } }, - "axes": { - "type": "tuple", - "items": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + "_1": { + "type": { + "name": "float" + } } + }, + "metadata": { + "usage.dask": 1 } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "s", - "axes" - ] - ], - "metadata": { - "usage.dask": 33 - } - } - ], - "rfftn": [ - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "module": "numpy", + "name": "int64" + } + } + }, + "metadata": { + "usage.sklearn": 1 } - } - }, - "pos_or_kw_optional": { - "s": { - "type": "union", - "options": [ - { - "type": "tuple", - "items": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "int" - } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "name": "float" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": "str", + "options": [ + "soft" + ] + } + }, + "metadata": { + 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"irfftn": [ - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "ndarray" + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": "str", + "options": [ + "hinge" + ] + } + }, + "metadata": { + "usage.sklearn": 1 } - } - }, - "pos_or_kw_optional": { - "s": { - "type": "union", - "options": [ - { + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { "type": "tuple", "items": [ { @@ -241844,2176 +294227,1828 @@ } } ] - }, - { - "type": "None" } - ] + }, + "metadata": { + "usage.sklearn": 1 + } }, - "axes": { - "type": "tuple", - "items": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + }, + "_1": { + "type": "str", + "options": [ + "squared_hinge" + ] } + }, + "metadata": { + "usage.sklearn": 1 } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "s", - "axes" - ] - ], - "metadata": { - "usage.dask": 33 - } - } - ] - }, - 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"keepdims": { + "type": { + "name": "bool" } } }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" - } + "pos_or_kw_optional_ordering": [ + [ + "axis", + "keepdims" + ] + ], + "metadata": { + "usage.scipy": 32 } }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "numpy", - "name": "int64" + "argsort": { + "pos_or_kw_optional": { + "axis": { + "type": "None" } + }, + "metadata": { + "usage.scipy": 8 } }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": "list", - "item": { + "ravel": { + "metadata": { + "usage.scipy": 14 + } + }, + "__rmul__": { + "pos_only_required": { + "_0": { "type": "union", "options": [ + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, { "type": { "name": "float" } }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, { "type": { "name": "int" @@ -244609,193 +296456,244 @@ } ] } + }, + "metadata": { + "usage.scipy": 23, + "usage.matplotlib": 10, + "usage.dask": 2 } }, - "metadata": { - "usage.matplotlib": 1 - } - }, - { - "pos_or_kw_required": { - "a": { - "type": { - "module": "matplotlib.tests.test_units", - "name": "Quantity" + "__gt__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy.ma.core", + "name": "MaskedConstant" + } + } + ] } }, - "dtype": { - "type": "type", - "name": { - "name": "float" - } + "metadata": { + "usage.scipy": 9, + "usage.matplotlib": 2 } }, - "metadata": { - "usage.matplotlib": 1 - } - } - ], - "masked_equal": [ - { - "pos_or_kw_required": { - "x": { - "type": { - "module": "numpy", - "name": "ndarray" + "sum": { + "pos_or_kw_optional": { + "axis": { + "type": 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"usage.matplotlib": 1 + "usage.dask": 1 } } ], - "copy": [ + "union": [ { + "pos_or_kw_required": { + "other": { + "type": "union", + "options": [ + { + "type": { + "module": "pandas.core.indexes.datetimes", + "name": "DatetimeIndex" + } + }, + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + } + ] + } + }, "metadata": { - "usage.dask": 1 + "usage.dask": 6 } } ], - "cumsum": [ + "to_frame": [ { - "pos_or_kw_required": { - "axis": { + "pos_or_kw_optional": { + "index": { "type": { - "name": "int" + "name": "bool" } + }, + "name": { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "bar", + "foo" + ] + }, + { + "type": "None" + } + ] } }, + "pos_or_kw_optional_ordering": [ + [ + "index", + "name" + ] + ], "metadata": { - "usage.dask": 1 + "usage.dask": 6 } } ], - "cumprod": [ + "__ne__": [ { - "pos_or_kw_required": { - "axis": { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "pandas.core.indexes.base", + "name": "Index" } } }, "metadata": { - "usage.dask": 1 + "usage.sklearn": 2 } } ] }, "methods": { - "__sub__": { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.skimage": 4, - "usage.scipy": 27, - "usage.matplotlib": 7, - "usage.dask": 5, - "usage.sklearn": 1 - } - }, - "__rsub__": { + "__getitem__": { "pos_only_required": { "_0": { "type": "union", "options": [ { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "numpy", + "name": "ndarray" } }, { @@ -251176,83 +301796,82 @@ } }, { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "bool" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, { - "type": { - "module": "numpy", - "name": "float64" + "type": "slice", + "start": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "name": "int" + } + } + ] + }, + "stop": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "name": "int" + } + } + ] + }, + "step": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "name": "int" + } + } + ] } } ] } }, "metadata": { - "usage.skimage": 2, - "usage.scipy": 13, - "usage.matplotlib": 3, - "usage.dask": 4, - "usage.sklearn": 1 - } - }, - "__truediv__": { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.skimage": 1, - "usage.scipy": 36, - "usage.matplotlib": 8, - "usage.dask": 1 - } - }, - "__rtruediv__": { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.skimage": 1, - "usage.scipy": 33, - "usage.matplotlib": 1, - "usage.dask": 1 - } - }, - "__add__": { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.skimage": 1, - "usage.scipy": 16, - "usage.matplotlib": 7, - "usage.dask": 1 + "usage.xarray": 15, + "usage.dask": 27, + "usage.sklearn": 6 } }, - "__getitem__": { - "pos_only_required": { - "_0": { - "type": "object" - } - }, + "__iter__": { "metadata": { - "usage.skimage": 6, - "usage.xarray": 1, - "usage.scipy": 66, - "usage.matplotlib": 101, - "usage.dask": 25, - "usage.sklearn": 21 + "usage.xarray": 4, + "usage.dask": 22, + "usage.sklearn": 1 } }, - "__isub__": { + "__rsub__": { "pos_only_required": { "_0": { "type": "union", @@ -251260,418 +301879,174 @@ { "type": { "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - } - ] - } - }, - "metadata": { - "usage.skimage": 2, - "usage.scipy": 2, - "usage.matplotlib": 1 - } - }, - "__eq__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" + "name": "ndarray" } }, { "type": { - "module": "numpy", - "name": "float64" + "module": "xarray.coding.cftimeindex", + "name": "CFTimeIndex" } } ] } }, "metadata": { - "usage.skimage": 1, - "usage.scipy": 8, - "usage.matplotlib": 8 - } - }, - "__setitem__": { - "pos_only_required": { - "_0": { - "type": "object" - }, - "_1": { - "type": "object" - } - }, - "metadata": { - "usage.skimage": 6, - "usage.xarray": 5, - "usage.pandas": 18, - "usage.scipy": 41, - "usage.matplotlib": 19, - "usage.dask": 1, - "usage.sklearn": 25 + "usage.xarray": 1, + "usage.pandas": 4 } }, - "__imul__": { - "pos_only_required": { - "_0": { + "equals": { + "pos_or_kw_required": { + "other": { "type": "union", "options": [ { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - }, - { - "type": { - "name": "int" + "module": "pandas.core.indexes.base", + "name": "Index" } }, { "type": { - "module": "numpy", - "name": "ndarray" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } } ] } }, "metadata": { - "usage.xarray": 1, - "usage.scipy": 2, - "usage.matplotlib": 2 - } - }, - "__mul__": { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.xarray": 1, - "usage.scipy": 18, - "usage.matplotlib": 16, + "usage.xarray": 7, "usage.dask": 3 } }, - "__radd__": { - "pos_only_required": { - "_0": { - "type": "object" + "copy": { + "pos_or_kw_required": { + "deep": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.xarray": 1, - "usage.scipy": 8, - "usage.matplotlib": 7, - "usage.dask": 3 - } - }, - "soften_mask": { - "metadata": { - "usage.pandas": 4 + "usage.xarray": 1 } }, - "astype": { - "pos_only_required": { - "_0": { + "get_indexer": { + "pos_or_kw_required": { + "target": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "method": { "type": "union", "options": [ { "type": "str", "options": [ - "bool", - "d" + "pad" ] }, { - "type": "type" - }, - { - "type": { - "module": "numpy", - "name": "dtype" - } - } - ] - } - }, - "metadata": { - "usage.pandas": 2, - "usage.scipy": 8, - "usage.matplotlib": 7, - 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"pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } } ] + }, + "method": { + "type": "None" + }, + "tolerance": { + "type": "None" } }, "metadata": { - "usage.scipy": 55 + "usage.xarray": 22 } }, - "min": { - "pos_or_kw_optional": { - "axis": { + "drop": { + "pos_or_kw_required": { + "labels": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.scipy": 4, - "usage.matplotlib": 1, - "usage.sklearn": 2 - } - }, - "__ge__": { - "pos_only_required": { - "_0": { - "type": "union", + "pos_or_kw_optional": { + "errors": { + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - } + "ignore", + "raise" ] } }, "metadata": { - "usage.scipy": 5, - "usage.matplotlib": 2 - } - }, - "__le__": { - "pos_only_required": { - 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} } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "keepdims" - ] - ], - "metadata": { - "usage.scipy": 32, - "usage.sklearn": 1 - } - }, - "argsort": { - "pos_or_kw_optional": { - "axis": { - "type": "None" - } - }, - "metadata": { - "usage.scipy": 8 - } - }, - "ravel": { "metadata": { - "usage.scipy": 14 + "usage.pandas": 1 } }, - "__rmul__": { + "__eq__": { "pos_only_required": { "_0": { "type": "union", "options": [ { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "type": "list", + "item": { + "type": "str" } }, { @@ -251682,679 +302057,440 @@ }, { "type": { - "name": "float" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "int" + "module": "pandas.core.indexes.base", + "name": "Index" } } ] } }, "metadata": { - "usage.scipy": 23, - "usage.matplotlib": 10, - "usage.dask": 2 + "usage.pandas": 4, + "usage.dask": 14, + "usage.sklearn": 6 } }, - "__gt__": { + "__contains__": { "pos_only_required": { "_0": { 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[ { - "usage.xarray": 3, - "usage.scipy": 12, - "usage.matplotlib": 18, - "usage.dask": 1, - "usage.sklearn": 2 + "usage.xarray": 1 }, { "type": "bottom" } ], - "size": [ + "nlevels": [ { - "usage.scipy": 14, - "usage.matplotlib": 50 + "usage.xarray": 1 }, { "type": "bottom" } ], - "T": [ + "array": [ { - "usage.scipy": 11, - "usage.matplotlib": 2 + "usage.dask": 1 }, { "type": "bottom" } ], - "_mask": [ + "names": [ { - "usage.scipy": 16 + "usage.dask": 3 }, { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "bool_" - } - } - ] + "type": "list", + "item": { + "type": "str" + } } ], - "_data": [ + "str": [ { - "usage.scipy": 2 + "usage.dask": 2, + "usage.sklearn": 1 }, { "type": "bottom" } - ], - "__class__": [ + ] + }, + "classproperties": { + "get_loc": [ { - "usage.dask": 1 + "usage.xarray": 2 }, { "type": "bottom" } ], - "__array_priority__": [ - { - "usage.dask": 1 - }, - { - "type": "bottom" - } - ] - }, - "classproperties": { - "__module__": [ + "__name__": [ { "usage.dask": 2 }, @@ -252363,55 +302499,94 @@ } ] } - }, - "_MaskedBinaryOperation": { + } + } + }, + "pandas.core.indexes.range": { + "classes": { + "RangeIndex": { "method_overloads": { - "reduce": [ + "equals": [ { "pos_or_kw_required": { - "target": { + "other": { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } } }, "metadata": { - "usage.scipy": 1 + "usage.xarray": 3 + } + }, + { + "pos_or_kw_required": { + "other": { + "type": { + "module": "pandas.core.indexes.range", + "name": "RangeIndex" + } + } + }, + "metadata": { + "usage.dask": 2 } } - ] - }, - "methods": { - "reduce": { - "pos_or_kw_required": { - "target": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + ], + "__getitem__": [ + { + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } } + }, + "metadata": { + "usage.xarray": 1 } }, - "metadata": { - "usage.scipy": 1 - } - } - } - }, - "MaskedConstant": { - "method_overloads": { - "__lt__": [ { "pos_only_required": { "_0": { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "name": "int" } } }, "metadata": { - "usage.scipy": 2 + "usage.xarray": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } + } + } + }, + "metadata": { + "usage.xarray": 3 } }, { @@ -252419,74 +302594,134 @@ "_0": { "type": { "module": "numpy", - "name": "int32" + "name": "ndarray" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.xarray": 1 } - } - ], - "__ge__": [ + }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } } }, "metadata": { - "usage.scipy": 1 + "usage.xarray": 1 } - } - ], - "__rsub__": [ + }, { "pos_only_required": { "_0": { + "type": "union", + "options": [ + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + }, + "metadata": { + "usage.dask": 5 + } + } + ], + "__iter__": [ + { + "metadata": { + "usage.xarray": 1 + } + } + ], + "get_indexer": [ + { + "pos_or_kw_required": { + "target": { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "numpy", + "name": "ndarray" } + }, + "method": { + "type": "None" + }, + "tolerance": { + "type": "None" } }, "metadata": { - "usage.scipy": 1 + "usage.xarray": 1 } } ], - "__eq__": [ + "get_loc": [ { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "key": { "type": { "name": "int" } + }, + "method": { + "type": "None" + }, + "tolerance": { + "type": "None" } }, "metadata": { - "usage.scipy": 1 + "usage.xarray": 1 } } ], - "__pow__": [ + "copy": [ { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "deep": { "type": { - "name": "float" + "name": "bool" } } }, "metadata": { - "usage.scipy": 1 + "usage.xarray": 1 } } ], - "__truediv__": [ + "__mul__": [ { "pos_only_required": { "_0": { @@ -252494,31 +302729,37 @@ "options": [ { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - }, - { - "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" + "module": "numpy", + "name": "ndarray" } }, { "type": { "module": "numpy", - "name": "int64" + "name": "timedelta64" } } ] } }, "metadata": { - "usage.scipy": 6 + "usage.pandas": 5 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.dask": 1 } } ], - "__rtruediv__": [ + "__rmul__": [ { "pos_only_required": { "_0": { @@ -252526,38 +302767,37 @@ "options": [ { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - }, - { - "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" + "module": "numpy", + "name": "timedelta64" } }, { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } } ] } }, "metadata": { - "usage.scipy": 8 + "usage.pandas": 2 } - } - ], - "__neg__": [ + }, { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, "metadata": { - "usage.scipy": 1 + "usage.dask": 1 } } ], - "__mul__": [ + "__rtruediv__": [ { "pos_only_required": { "_0": { @@ -252566,37 +302806,39 @@ { "type": { "module": "numpy", - "name": "float64" + "name": "timedelta64" } }, { "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" + "module": "numpy", + "name": "ndarray" } } ] } }, "metadata": { - "usage.scipy": 4 + "usage.pandas": 2 } - }, + } + ], + "__rfloordiv__": [ { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } } }, "metadata": { - "usage.matplotlib": 3 + "usage.pandas": 1 } } ], - "__rmul__": [ + "__truediv__": [ { "pos_only_required": { "_0": { @@ -252605,158 +302847,201 @@ { "type": { "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" + "name": "ndarray" } }, { "type": { "module": "numpy", - "name": "int64" + "name": "timedelta64" } } ] } }, "metadata": { - "usage.scipy": 5 + "usage.pandas": 5 } } ], - "__radd__": [ + "__floordiv__": [ { "pos_only_required": { "_0": { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.scipy": 3 + "usage.pandas": 4 } - }, + } + ], + "__add__": [ { "pos_only_required": { "_0": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" - } + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + ] } }, "metadata": { - "usage.matplotlib": 3 + "usage.pandas": 2 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } } }, "metadata": { - "usage.matplotlib": 3 + "usage.dask": 1 } } ], - "__add__": [ + "__radd__": [ { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "float64" - } + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + ] } }, "metadata": { - "usage.scipy": 1, - "usage.matplotlib": 5 + "usage.pandas": 2 } - }, + } + ], + "__sub__": [ { "pos_only_required": { "_0": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" - } + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + ] } }, "metadata": { - "usage.matplotlib": 3 + "usage.pandas": 2 } } ], - "__sub__": [ + "__rsub__": [ { "pos_only_required": { "_0": { - "type": { - "name": "float" - } + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + ] } }, "metadata": { - "usage.scipy": 1 + "usage.pandas": 2 } - }, + } + ], + "__mod__": [ { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.pandas": 3 } } ], - "__gt__": [ + "__eq__": [ { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "int32" + "name": "ndarray" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.pandas": 4 } } ], - "astype": [ + "__contains__": [ { "pos_only_required": { "_0": { "type": "union", "options": [ + { + "type": { + "name": "int" + } + }, { "type": "str", "options": [ - "bool" + "dtype", + "divisions" ] - }, - { - "type": { - "module": "numpy", - "name": "dtype" - } } ] } @@ -252766,16 +303051,95 @@ } } ], - "__getitem__": [ + "min": [ { - "pos_only_required": { - "_0": { - "type": "tuple", - "items": { - "type": "None" + "metadata": { + "usage.dask": 5 + } + } + ], + "max": [ + { + "metadata": { + "usage.dask": 5 + } + } + ], + "tolist": [ + { + "metadata": { + "usage.dask": 1 + } + } + ], + "drop_duplicates": [ + { + "metadata": { + "usage.dask": 1 + } + } + ], + "map": [ + { + "pos_or_kw_required": { + "mapper": { + "type": "function" + }, + "na_action": { + "type": "None" + } + }, + "metadata": { + "usage.dask": 1 + } + } + ], + "__neg__": [ + { + "metadata": { + "usage.dask": 1 + } + } + ], + "to_series": [ + { + "metadata": { + "usage.dask": 1 + } + } + ], + "to_frame": [ + { + "pos_or_kw_required": { + "name": { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "bar" + ] + }, + { + "type": "None" + } + ] + } + }, + "pos_or_kw_optional": { + "index": { + "type": { + "name": "bool" } } }, + "metadata": { + "usage.dask": 3 + } + } + ], + "memory_usage": [ + { "metadata": { "usage.dask": 1 } @@ -252783,147 +303147,249 @@ ] }, "methods": { - "__lt__": { - "pos_only_required": { - "_0": { + "equals": { + "pos_or_kw_required": { + "other": { "type": "union", "options": [ { "type": { - "module": "numpy", - "name": "int32" + "module": "pandas.core.indexes.range", + "name": "RangeIndex" } }, { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } } ] } }, "metadata": { - "usage.scipy": 2, - "usage.matplotlib": 1 + "usage.xarray": 3, + "usage.dask": 2 } }, - "__ge__": { + "__getitem__": { "pos_only_required": { "_0": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "slice", + "start": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": "None" + } + ] + }, + "stop": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": { + "name": "int" + } + } + ] + }, + "step": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": "None" + } + ] + } + } + ] } }, "metadata": { - "usage.scipy": 1 + "usage.xarray": 7, + "usage.dask": 5 } }, - "__rsub__": { - "pos_only_required": { - "_0": { + "__iter__": { + "metadata": { + "usage.xarray": 1 + } + }, + "get_indexer": { + "pos_or_kw_required": { + "target": { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "numpy", + "name": "ndarray" } + }, + "method": { + "type": "None" + }, + "tolerance": { + "type": "None" } }, "metadata": { - "usage.scipy": 1 + "usage.xarray": 1 } }, - "__eq__": { - "pos_only_required": { - "_0": { + "get_loc": { + "pos_or_kw_required": { + "key": { "type": { "name": "int" } + }, + "method": { + "type": "None" + }, + "tolerance": { + "type": "None" } }, "metadata": { - "usage.scipy": 1 + "usage.xarray": 1 } }, - "__pow__": { - "pos_only_required": { - "_0": { + "copy": { + "pos_or_kw_required": { + "deep": { "type": { - "name": "float" + "name": "bool" } } }, "metadata": { - "usage.scipy": 1 + "usage.xarray": 1 } }, - "__truediv__": { + "__mul__": { "pos_only_required": { "_0": { "type": "union", "options": [ { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "name": "int" } }, { "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" + "module": "numpy", + "name": "timedelta64" } }, { "type": { "module": "numpy", - "name": "int64" + "name": "ndarray" } } ] } }, "metadata": { - "usage.scipy": 6 + "usage.pandas": 5, + "usage.dask": 1 } }, - "__rtruediv__": { + "__rmul__": { "pos_only_required": { "_0": { "type": "union", "options": [ { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "name": "int" } }, { "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" + "module": "numpy", + "name": "ndarray" } }, { "type": { "module": "numpy", - "name": "float64" + "name": "timedelta64" } } ] } }, "metadata": { - "usage.scipy": 8 + "usage.pandas": 2, + "usage.dask": 1 } }, - "__neg__": { + "__rtruediv__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + } + }, "metadata": { - "usage.scipy": 1 + "usage.pandas": 2 } }, - "__mul__": { + "__rfloordiv__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.pandas": 1 + } + }, + "__truediv__": { "pos_only_required": { "_0": { "type": "union", @@ -252931,51 +303397,63 @@ { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } }, { "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" + "module": "numpy", + "name": "timedelta64" } } ] } }, "metadata": { - "usage.scipy": 4, - "usage.matplotlib": 3 + "usage.pandas": 5 + } + }, + "__floordiv__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.pandas": 4 } }, - "__rmul__": { + "__add__": { "pos_only_required": { "_0": { "type": "union", "options": [ { "type": { - "module": "numpy", - "name": "float64" + "name": "int" } }, { "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" + "module": "numpy", + "name": "timedelta64" } }, { "type": { "module": "numpy", - "name": "int64" + "name": "datetime64" } } ] } }, "metadata": { - "usage.scipy": 5 + "usage.pandas": 2, + "usage.dask": 1 } }, "__radd__": { @@ -252983,33 +303461,26 @@ "_0": { "type": "union", "options": [ - { - "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" - } - }, { "type": { "module": "numpy", - "name": "float64" + "name": "datetime64" } }, { "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" + "module": "numpy", + "name": "timedelta64" } } ] } }, "metadata": { - "usage.scipy": 3, - "usage.matplotlib": 6 + "usage.pandas": 2 } }, - "__add__": { + "__sub__": { "pos_only_required": { "_0": { "type": "union", @@ -253017,24 +303488,23 @@ { "type": { "module": "numpy", - "name": "float64" + "name": "datetime64" } }, { "type": { - "module": "numpy.ma.core", - "name": "MaskedConstant" + "module": "numpy", + "name": "timedelta64" } } ] } }, "metadata": { - "usage.scipy": 1, - "usage.matplotlib": 8 + "usage.pandas": 2 } }, - "__sub__": { + "__rsub__": { "pos_only_required": { "_0": { "type": "union", @@ -253042,51 +303512,64 @@ { "type": { "module": "numpy", - "name": "float64" + "name": "datetime64" } }, { "type": { - "name": "float" + "module": "numpy", + "name": "timedelta64" } } ] } }, "metadata": { - "usage.scipy": 1, - "usage.matplotlib": 1 + "usage.pandas": 2 } }, - "__gt__": { + "__mod__": { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "int32" + "name": "ndarray" } } }, "metadata": { - "usage.matplotlib": 1 + "usage.pandas": 3 } }, - "astype": { + "__eq__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.pandas": 4 + } + }, + "__contains__": { "pos_only_required": { "_0": { "type": "union", "options": [ + { + "type": { + "name": "int" + } + }, { "type": "str", "options": [ - "bool" + "dtype", + "divisions" ] - }, - { - "type": { - "module": "numpy", - "name": "dtype" - } } ] } @@ -253095,1076 +303578,3003 @@ "usage.dask": 3 } }, - "__getitem__": { - "pos_only_required": { - "_0": { - "type": "tuple", - "items": { - "type": "None" + "min": { + "metadata": { + "usage.dask": 5 + } + }, + "max": { + "metadata": { + "usage.dask": 5 + } + }, + "tolist": { + "metadata": { + "usage.dask": 1 + } + }, + "drop_duplicates": { + "metadata": { + "usage.dask": 1 + } + }, + "map": { + "pos_or_kw_required": { + "mapper": { + "type": "function" + }, + "na_action": { + "type": "None" + } + }, + "metadata": { + "usage.dask": 1 + } + }, + "__neg__": { + "metadata": { + "usage.dask": 1 + } + }, + "to_series": { + "metadata": { + "usage.dask": 1 + } + }, + "to_frame": { + "pos_or_kw_required": { + "name": { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "bar" + ] + }, + { + "type": "None" + } + ] + } + }, + "pos_or_kw_optional": { + "index": { + "type": { + "name": "bool" } } }, + "metadata": { + "usage.dask": 3 + } + }, + "memory_usage": { "metadata": { "usage.dask": 1 } } }, "properties": { - "shape": [ + "dtype": [ { - "usage.scipy": 3, - "usage.dask": 3 + "usage.xarray": 4, + "usage.dask": 11 }, { "type": "bottom" } ], - "mask": [ + "values": [ { - "usage.scipy": 1 + "usage.xarray": 3 }, { "type": "bottom" } ], - "ndim": [ + "name": [ { - "usage.dask": 3 + "usage.xarray": 4, + "usage.dask": 16 + }, + { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "ix", + "renamed" + ] + }, + { + "type": "None" + } + ] + } + ], + "size": [ + { + "usage.xarray": 3 }, { "type": "bottom" } - ] - } - }, - "_extrema_operation": { - "method_overloads": { - "reduce": [ + ], + "is_unique": [ { - "pos_or_kw_required": { - "target": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - } - }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.scipy": 8 - } + "usage.xarray": 6, + "usage.sklearn": 2 + }, + { + "type": "bottom" } - ] - }, - "methods": { - "reduce": { - "pos_or_kw_required": { - "target": { - "type": { - "module": "numpy.ma.core", - "name": "MaskedArray" - } - } + ], + "shape": [ + { + "usage.xarray": 1 }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" - } - } + { + "type": "bottom" + } + ], + "is_monotonic": [ + { + "usage.xarray": 2, + "usage.dask": 1 }, - "metadata": { - "usage.scipy": 8 + { + "type": "bottom" } - } - } - }, - "MaskError": { - "constructor_overloads": [ - { - "pos_only_required": { - "_0": { - "type": "str" - } + ], + "array": [ + { + "usage.dask": 1 }, - "metadata": { + { + "type": "bottom" + } + ], + "names": [ + { "usage.dask": 1 + }, + { + "type": "bottom" } - } - ], - "constructor": { - "pos_only_required": { - "_0": { - "type": "str" + ], + "is_all_dates": [ + { + "usage.dask": 1 + }, + { + "type": "bottom" } - }, - "metadata": { - "usage.dask": 1 - } + ] + }, + "classproperties": { + "__module__": [ + { + "usage.dask": 1 + }, + { + "type": "bottom" + } + ], + "__name__": [ + { + "usage.dask": 2 + }, + { + "type": "bottom" + } + ] } } } }, - "numpy.ma": { + "pandas.core.indexes.datetimes": { "function_overloads": { - "zeros": [ + "date_range": [ { - "var_pos": [ - "args", - { + "pos_or_kw_required": { + "start": { "type": "str", "options": [ - "v", - "t" + "2000-01-01" ] + }, + "periods": { + "type": { + "name": "int" + } } - ], + }, "metadata": { - "usage.skimage": 5, - "usage.pandas": 1 + "usage.xarray": 27 } - } - ], - "ones": [ + 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"dim_1" - ] + "__contains__": { + "pos_only_required": { + "_0": { + "type": "str" } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 4 } }, - { + "set_names": { "pos_or_kw_required": { - "data": { - "type": { - "name": "range" + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "b" + ] } }, - "name": { - "type": "str", - "options": [ - "columns" - ] - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { + "inplace": { "type": { - "name": "range" + "name": "bool" } - }, - "name": { - "type": "str", - "options": [ - "rows" - ] } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 1 } }, - { + "equals": { "pos_or_kw_required": { - "data": { + "other": { "type": { - "name": "range" + "module": "pandas.core.indexes.numeric", + "name": "Float64Index" } - }, - "name": { - "type": "str", - "options": [ - "dim_2" - ] } }, "metadata": { + "usage.dask": 1 + } + } + }, + "properties": { + "dtype": [ + { + "usage.xarray": 4, + "usage.dask": 7 + }, + { + "type": "bottom" + } + ], + "values": [ + { + "usage.xarray": 3, + "usage.dask": 2 + }, + { + "type": "bottom" + } + ], + "size": [ + { + "usage.xarray": 2 + }, + { + "type": "bottom" + } + ], + "is_unique": [ + { + "usage.xarray": 3 + }, + { + "type": "bottom" + } + ], + "is_monotonic": [ + { + "usage.xarray": 2, + "usage.dask": 1 + }, + { + "type": "bottom" + } + ], + "shape": [ + { "usage.xarray": 1 + }, + { + "type": "bottom" } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "name": "range" + ], + "name": [ + { + "usage.xarray": 1, + "usage.dask": 9 + }, + { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "x" + ] + }, + { + "type": "None" } - }, - "name": { - "type": "str", - "options": [ - "band" - ] + ] + } + ], + "nlevels": [ + { + "usage.xarray": 1 + }, + { + "type": "bottom" + } + ], + "array": [ + { + "usage.dask": 1 + }, + { + "type": "bottom" + } + ], + "names": [ + { + "usage.dask": 4 + }, + { + "type": "list", + "item": { + "type": "str" } + } + ] + }, + "classproperties": { + "__module__": [ + { + "usage.dask": 1 }, - "metadata": { - "usage.xarray": 1 + { + "type": "bottom" } - }, + ], + "__name__": [ + { + "usage.dask": 2 + }, + { + "type": "bottom" + } + ] + } + }, + "Int64Index": { + "constructor_overloads": [ { "pos_or_kw_required": { "data": { "type": "list", "item": { - "type": "str" + "type": "bottom" } } }, @@ -255615,31 +308751,10 @@ "data": { "type": "list", "item": { - "type": "str", - "options": [ - "a", - "b" - ] - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" + "type": { + "name": "int" + } } - }, - "name": { - "type": "str", - "options": [ - "time" - ] } }, "metadata": { @@ -255649,18 +308764,6 @@ ], "method_overloads": { "__getitem__": [ - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.xarray": 3 - } - }, { "pos_only_required": { "_0": { @@ -255671,7 +308774,7 @@ } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 4 } }, { @@ -255682,42 +308785,29 @@ "type": "None" }, "stop": { + "type": "None" + }, + "step": { "type": { "name": "int" } - }, - "step": { - "type": "None" } } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": { - "name": "int" - } + "type": { + "name": "int" } } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 3 } }, { @@ -255725,20 +308815,20 @@ "_0": { "type": "slice", "start": { + "type": "None" + }, + "stop": { "type": { "name": "int" } }, - "stop": { - "type": "None" - }, "step": { "type": "None" } } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 3 } }, { @@ -255746,10 +308836,14 @@ "_0": { "type": "slice", "start": { - "type": "None" + "type": { + "name": "int" + } }, "stop": { - "type": "None" + "type": { + "name": "int" + } }, "step": { "type": "None" @@ -255757,7 +308851,7 @@ } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1 } }, { @@ -255770,9 +308864,7 @@ } }, "stop": { - "type": { - "name": "int" - } + "type": "None" }, "step": { "type": "None" @@ -255780,7 +308872,7 @@ } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 3 } }, { @@ -255794,9 +308886,7 @@ "type": "None" }, "step": { - "type": { - "name": "int" - } + "type": "None" } } }, @@ -255814,96 +308904,13 @@ "start": { "type": "union", "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - }, - "stop": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - }, - "step": { - "type": "union", - "options": [ - { - "type": "None" - }, { "type": { "name": "int" } - } - ] - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "bool" - } }, - { - "type": { - "name": "int" - } - } - ] - } - } - ] - } - }, - "metadata": { - "usage.dask": 27 - } - }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": "slice", - "start": { - "type": "union", - "options": [ { "type": "None" - }, - { - "type": { - "name": "int" - } } ] }, @@ -255916,125 +308923,25 @@ "type": "None" } }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] - } - }, - "metadata": { - "usage.sklearn": 6 - } - } - ], - "__iter__": [ - { - "metadata": { - "usage.xarray": 4, - "usage.dask": 22, - "usage.sklearn": 1 - } - } - ], - "__rsub__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "xarray.coding.cftimeindex", - "name": "CFTimeIndex" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.pandas": 4 - } - } - ], - "equals": [ - { - "pos_or_kw_required": { - "other": { - "type": { - "module": "pandas.core.indexes.base", - "name": "Index" - } - } - }, - "metadata": { - "usage.xarray": 7 - } - }, - { - "pos_or_kw_required": { - "other": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" - } - }, { "type": { - "module": "pandas.core.indexes.base", - "name": "Index" + "name": "int" } } ] } }, "metadata": { - "usage.dask": 3 - } - } - ], - "copy": [ - { - "pos_or_kw_required": { - "deep": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.xarray": 1 + "usage.dask": 11 } } ], - "get_indexer": [ + "get_loc": [ { "pos_or_kw_required": { - "target": { + "key": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, "method": { @@ -256045,115 +308952,53 @@ } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1 } }, { "pos_or_kw_required": { - "target": { + "key": { "type": { "module": "numpy", - "name": "ndarray" + "name": "int64" } }, "method": { "type": "str", "options": [ - "pad" - ] - }, - "tolerance": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - } - ], - "get_loc": [ - { - "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "b" + "nearest" ] - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 2 } }, { "pos_or_kw_required": { "key": { "type": { - "name": "bool" + "module": "numpy", + "name": "float64" } }, "method": { - "type": "None" - }, - "tolerance": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "a" - ] - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "key": { "type": "str", "options": [ - "c" + "nearest" ] - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 2 } }, { "pos_or_kw_required": { "key": { - "type": "str", - "options": [ - "2000-01-01" - ] + "type": { + "name": "float" + } }, "method": { "type": "None" @@ -256165,14 +309010,16 @@ "metadata": { "usage.xarray": 1 } - }, + } + ], + "get_indexer": [ { "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "2000-01-02" - ] + "target": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, "method": { "type": "None" @@ -256182,41 +309029,27 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 3 } }, { "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "2000-01-03" - ] + "target": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, "method": { - "type": "None" - }, - "tolerance": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "key": { "type": "str", "options": [ - "d" + "backfill" ] }, - "method": { - "type": "None" - }, "tolerance": { - "type": "None" + "type": { + "name": "int" + } } }, "metadata": { @@ -256225,33 +309058,22 @@ }, { "pos_or_kw_required": { - "key": { - "type": "str" + "target": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, "method": { - "type": "None" - }, - "tolerance": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "key": { "type": "str", "options": [ - "row0" + "backfill" ] }, - "method": { - "type": "None" - }, "tolerance": { - "type": "None" + "type": { + "name": "float" + } } }, "metadata": { @@ -256260,34 +309082,18 @@ }, { "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "col0" - ] + "target": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, "method": { - "type": "None" - }, - "tolerance": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "key": { "type": "str", "options": [ - "col1" + "backfill" ] }, - "method": { - "type": "None" - }, "tolerance": { "type": "None" } @@ -256298,34 +309104,18 @@ }, { "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "col2" - ] + "target": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, "method": { - "type": "None" - }, - "tolerance": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "key": { "type": "str", "options": [ - "row1" + "pad" ] }, - "method": { - "type": "None" - }, "tolerance": { "type": "None" } @@ -256336,262 +309126,249 @@ }, { "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "one" - ] + "target": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, "method": { - "type": "None" - }, - "tolerance": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "key": { "type": "str", "options": [ - "two" + "pad" ] }, - "method": { - "type": "None" - }, "tolerance": { - "type": "None" + "type": { + "name": "float" + } } }, "metadata": { "usage.xarray": 1 } - }, + } + ], + "equals": [ { "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "three" - ] - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" + "other": { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } } }, "metadata": { "usage.xarray": 1 } - }, + } + ], + "copy": [ { "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "five" - ] - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" + "deep": { + "type": { + "name": "bool" + } } }, "metadata": { "usage.xarray": 1 } - }, + } + ], + "drop": [ { "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "A" - ] - }, - "method": { - "type": "None" + "labels": { + "type": { + "module": "numpy", + "name": "ndarray" + } }, - "tolerance": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "key": { + "errors": { "type": "str", "options": [ - "B" + "raise" ] - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 2 } }, { "pos_or_kw_required": { - "key": { + "labels": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "errors": { "type": "str", "options": [ - "C" + "ignore" ] - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" } }, "metadata": { "usage.xarray": 1 } - }, + } + ], + "__iter__": [ { - "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "C++" - ] - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" - } - }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 2, + "usage.dask": 4 } - }, + } + ], + "__eq__": [ { - "pos_or_kw_required": { - "key": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { - "type": "str" + "type": { + "module": "numpy", + "name": "ndarray" + } }, { "type": { "module": "numpy", - "name": "str_" + "name": "int64" } } ] } }, "metadata": { - "usage.sklearn": 20 + "usage.pandas": 9 } } ], - "drop": [ + "__rmul__": [ { - "pos_or_kw_required": { - "labels": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "errors": { - "type": "str", + "pos_only_required": { + "_0": { + "type": "union", "options": [ - "raise" + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } ] } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 5 } - }, + } + ], + "__mul__": [ { - "pos_or_kw_required": { - "labels": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "errors": { - "type": "str", + "pos_only_required": { + "_0": { + "type": "union", "options": [ - "ignore" + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } ] } }, "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "labels": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.dask": 1 + "usage.pandas": 6 } } ], - "__radd__": [ + "__rtruediv__": [ { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } }, "metadata": { - "usage.pandas": 1 + "usage.pandas": 4 } } ], - "__sub__": [ + "__floordiv__": [ { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "uint64" + } + } + ] } }, "metadata": { - "usage.pandas": 1 + "usage.pandas": 11 } } ], - "__eq__": [ + "__rfloordiv__": [ { "pos_only_required": { "_0": { @@ -256602,251 +309379,249 @@ } }, "metadata": { - "usage.pandas": 4 + "usage.pandas": 1 } - }, + } + ], + "__truediv__": [ { "pos_only_required": { "_0": { "type": "union", "options": [ { - "type": "list", - "item": { - "type": "str", - "options": [ - "x", - "b", - "c", - "a" - ] + "type": { + "module": "numpy", + "name": "ndarray" } }, { "type": { - "module": "pandas.core.indexes.base", - "name": "Index" + "module": "numpy", + "name": "timedelta64" } } ] } }, "metadata": { - "usage.dask": 14 + "usage.pandas": 5 } - }, + } + ], + "__add__": [ { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": "str" - } + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "datetime64" + } + } + ] } }, "metadata": { - "usage.sklearn": 6 + "usage.pandas": 3 } } ], - "__contains__": [ + "__radd__": [ { "pos_only_required": { "_0": { - "type": "str" + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + ] } }, "metadata": { - "usage.dask": 131 + "usage.pandas": 4 } } ], - "difference": [ + "__sub__": [ { - "pos_or_kw_required": { - "other": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { - "type": "list", - "item": { - "type": "str", - "options": [ - "A" - ] + "type": { + "module": "numpy", + "name": "int64" } }, { "type": { - "module": "pandas.core.indexes.base", - "name": "Index" + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "datetime64" } } ] } }, "metadata": { - "usage.dask": 6 + "usage.pandas": 3 } } ], - "append": [ + "__rsub__": [ { - "pos_or_kw_required": { - "other": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { - "type": "list", - "item": { - "type": "object" + "type": { + "module": "numpy", + "name": "datetime64" } }, { "type": { - "module": "pandas.core.indexes.category", - "name": "CategoricalIndex" + "module": "numpy", + "name": "timedelta64" } } ] } }, "metadata": { - "usage.dask": 16 + "usage.pandas": 4 } } ], - "intersection": [ + "__mod__": [ { - "pos_or_kw_required": { - "other": { + "pos_only_required": { + "_0": { "type": { - "module": "pandas.core.indexes.base", - "name": "Index" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.dask": 2 + "usage.pandas": 3 } } ], - "astype": [ + "__contains__": [ { - "pos_or_kw_required": { - "dtype": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { - "type": "str", - "options": [ - "int64" - ] + "type": "None" }, { - "type": { - "module": "pandas.core.dtypes.dtypes", - "name": "CategoricalDtype" - } + "type": "str" }, { "type": { - "module": "numpy", - "name": "dtype" + "name": "int" } } ] } }, "metadata": { - "usage.dask": 12 + "usage.dask": 8 } } ], - "_get_level_values": [ + "__le__": [ { - "pos_or_kw_required": { - "level": { - "type": { - "name": "int" - } + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.dask": 7 - } - } - ], - "min": [ - { - "metadata": { - "usage.dask": 1 - } - } - ], - "max": [ - { - "metadata": { - "usage.dask": 1 - } - } - ], - "to_series": [ - { - "metadata": { - "usage.dask": 11 + "usage.dask": 3 } } ], - "get_slice_bound": [ + "__ge__": [ { - "pos_or_kw_required": { - "label": { - "type": "object" - }, - "side": { - "type": "str", - "options": [ - "left", - "right" - ] - }, - "kind": { - "type": "str", + "pos_only_required": { + "_0": { + "type": "union", "options": [ - "loc" + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } ] } }, - "metadata": { - "usage.dask": 30 - } - } - ], - "memory_usage": [ - { "metadata": { "usage.dask": 3 } } ], - "dropna": [ + "min": [ { "metadata": { "usage.dask": 1 } } ], - "rename": [ + "max": [ { - "pos_or_kw_required": { - "name": { - "type": "str", - "options": [ - "renamed" - ] - } - }, "metadata": { "usage.dask": 1 } @@ -256855,14 +309630,7 @@ "drop_duplicates": [ { "metadata": { - "usage.dask": 1 - } - } - ], - "unique": [ - { - "metadata": { - "usage.dask": 3 + "usage.dask": 2 } } ], @@ -256878,6 +309646,18 @@ "name": "Series" } }, + { + "type": { + "module": "dask.delayed", + "name": "Delayed" + } + }, + { + "type": { + "module": "dask.delayed", + "name": "DelayedLeaf" + } + }, { "type": "list", "item": { @@ -256894,52 +309674,72 @@ } } ], - "shift": [ + "dropna": [ { - "pos_or_kw_required": { - "periods": { - "type": { - "name": "int" - } - }, - "freq": { - "type": "None" - } - }, "metadata": { "usage.dask": 1 } } ], - "union": [ + "__gt__": [ { - "pos_or_kw_required": { - "other": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" + "name": "float" } }, { "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" + "name": "int" } } ] } }, "metadata": { - "usage.dask": 6 + "usage.dask": 2 + } + } + ], + "set_names": [ + { + "pos_or_kw_required": { + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "a", + "b", + "key" + ] + } + }, + "inplace": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.dask": 3 + } + } + ], + "to_series": [ + { + "metadata": { + "usage.dask": 1 } } ], "to_frame": [ { - "pos_or_kw_optional": { + "pos_or_kw_required": { "index": { "type": { "name": "bool" @@ -256951,8 +309751,7 @@ { "type": "str", "options": [ - "bar", - "foo" + "bar" ] }, { @@ -256961,29 +309760,8 @@ ] } }, - "pos_or_kw_optional_ordering": [ - [ - "index", - "name" - ] - ], - "metadata": { - "usage.dask": 6 - } - } - ], - "__ne__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas.core.indexes.base", - "name": "Index" - } - } - }, "metadata": { - "usage.sklearn": 2 + "usage.dask": 2 } } ] @@ -256994,33 +309772,15 @@ "_0": { "type": "union", "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, { "type": { "name": "int" } }, { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": { - "name": "bool" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" } }, { @@ -257028,13 +309788,13 @@ "start": { "type": "union", "options": [ - { - "type": "None" - }, { "type": { "name": "int" } + }, + { + "type": "None" } ] }, @@ -257054,13 +309814,13 @@ "step": { "type": "union", "options": [ - { - "type": "None" - }, { "type": { "name": "int" } + }, + { + "type": "None" } ] } @@ -257069,66 +309829,120 @@ } }, "metadata": { - "usage.xarray": 15, - "usage.dask": 27, - "usage.sklearn": 6 - } - }, - "__iter__": { - "metadata": { - "usage.xarray": 4, - "usage.dask": 22, - "usage.sklearn": 1 + "usage.xarray": 16, + "usage.dask": 11 } }, - "__rsub__": { - "pos_only_required": { - "_0": { + "get_loc": { + "pos_or_kw_required": { + "key": { "type": "union", "options": [ + { + "type": { + "name": "float" + } + }, { "type": { "module": "numpy", - "name": "ndarray" + "name": "int64" } }, { "type": { - "module": "xarray.coding.cftimeindex", - "name": "CFTimeIndex" + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" } } ] + }, + "method": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": "str", + "options": [ + "nearest" + ] + } + ] + } + }, + "pos_or_kw_optional": { + "tolerance": { + "type": "None" } }, "metadata": { - "usage.xarray": 1, - "usage.pandas": 4 + "usage.xarray": 6 } }, - "equals": { + "get_indexer": { "pos_or_kw_required": { - "other": { + "target": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "method": { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "pad", + "backfill" + ] + }, + { + "type": "None" + } + ] + }, + "tolerance": { "type": "union", "options": [ { "type": { - "module": "pandas.core.indexes.base", - "name": "Index" + "name": "float" } }, { "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" + "name": "int" } + }, + { + "type": "None" } ] } }, "metadata": { - "usage.xarray": 7, - "usage.dask": 3 + "usage.xarray": 8 + } + }, + "equals": { + "pos_or_kw_required": { + "other": { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + } + }, + "metadata": { + "usage.xarray": 1 } }, "copy": { @@ -257143,101 +309957,165 @@ "usage.xarray": 1 } }, - "get_indexer": { + "drop": { "pos_or_kw_required": { - "target": { + "labels": { "type": { "module": "numpy", "name": "ndarray" } }, - "method": { + "errors": { + "type": "str", + "options": [ + "raise", + "ignore" + ] + } + }, + "metadata": { + "usage.xarray": 3 + } + }, + "__iter__": { + "metadata": { + "usage.xarray": 2, + "usage.dask": 4 + } + }, + "__eq__": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { - "type": "str", - "options": [ - "pad" - ] + "type": { + "module": "numpy", + "name": "ndarray" + } }, { - "type": "None" + "type": { + "module": "numpy", + "name": "int64" + } } ] - }, - "tolerance": { - "type": "None" } }, "metadata": { - "usage.xarray": 3 + "usage.pandas": 9 } }, - "get_loc": { - "pos_or_kw_required": { - "key": { + "__rmul__": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { "type": { "module": "numpy", - "name": "str_" + "name": "timedelta64" } }, { "type": { - "name": "bool" + "module": "numpy", + "name": "ndarray" } - }, - { - "type": "str" } ] } }, - "pos_or_kw_optional": { - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" + "metadata": { + "usage.pandas": 5 + } + }, + "__mul__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + ] } }, - "pos_or_kw_optional_ordering": [ - [ - "method", - "tolerance" - ] - ], "metadata": { - "usage.xarray": 22, - "usage.sklearn": 20 + "usage.pandas": 6 } }, - "drop": { - "pos_or_kw_required": { - "labels": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "__rtruediv__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } }, - "pos_or_kw_optional": { - "errors": { - "type": "str", + "metadata": { + "usage.pandas": 4 + } + }, + "__floordiv__": { + "pos_only_required": { + "_0": { + "type": "union", "options": [ - "ignore", - "raise" + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "uint64" + } + } ] } }, "metadata": { - "usage.xarray": 2, - "usage.dask": 1 + "usage.pandas": 11 } }, - "__radd__": { + "__rfloordiv__": { "pos_only_required": { "_0": { "type": { @@ -257250,236 +310128,231 @@ "usage.pandas": 1 } }, - "__sub__": { + "__truediv__": { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + ] } }, "metadata": { - "usage.pandas": 1 + "usage.pandas": 5 } }, - "__eq__": { + "__add__": { "pos_only_required": { "_0": { "type": "union", "options": [ { - "type": "list", - "item": { - "type": "str" + "type": { + "module": "numpy", + "name": "int64" } }, { "type": { "module": "numpy", - "name": "ndarray" + "name": "timedelta64" } }, { "type": { - "module": "pandas.core.indexes.base", - "name": "Index" + "module": "numpy", + "name": "datetime64" } } ] } }, "metadata": { - "usage.pandas": 4, - "usage.dask": 14, - "usage.sklearn": 6 + "usage.pandas": 3 } }, - "__contains__": { + "__radd__": { "pos_only_required": { "_0": { - "type": "str" + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + ] } }, "metadata": { - "usage.dask": 131 + "usage.pandas": 4 } }, - "difference": { - "pos_or_kw_required": { - "other": { + "__sub__": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { - "type": "list", - "item": { - "type": "str", - "options": [ - "A" - ] + "type": { + "module": "numpy", + "name": "int64" } }, { "type": { - "module": "pandas.core.indexes.base", - "name": "Index" + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "datetime64" } } ] } }, "metadata": { - "usage.dask": 6 + "usage.pandas": 3 } }, - "append": { - "pos_or_kw_required": { - "other": { + "__rsub__": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { - "type": "list", - "item": { - "type": "object" + "type": { + "module": "numpy", + "name": "datetime64" } }, { "type": { - "module": "pandas.core.indexes.category", - "name": "CategoricalIndex" + "module": "numpy", + "name": "timedelta64" } } ] } }, "metadata": { - "usage.dask": 16 + "usage.pandas": 4 } }, - "intersection": { - "pos_or_kw_required": { - "other": { + "__mod__": { + "pos_only_required": { + "_0": { "type": { - "module": "pandas.core.indexes.base", - "name": "Index" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.dask": 2 + "usage.pandas": 3 } }, - "astype": { - "pos_or_kw_required": { - "dtype": { + "__contains__": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { - "type": "str", - "options": [ - "int64" - ] + "type": "None" }, { - "type": { - "module": "pandas.core.dtypes.dtypes", - "name": "CategoricalDtype" - } + "type": "str" }, { "type": { - "module": "numpy", - "name": "dtype" + "name": "int" } } ] } }, "metadata": { - "usage.dask": 12 + "usage.dask": 8 } }, - "_get_level_values": { - "pos_or_kw_required": { - "level": { - "type": { - "name": "int" - } + "__le__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.dask": 7 - } - }, - "min": { - "metadata": { - "usage.dask": 1 - } - }, - "max": { - "metadata": { - "usage.dask": 1 - } - }, - "to_series": { - "metadata": { - "usage.dask": 11 + "usage.dask": 3 } }, - "get_slice_bound": { - "pos_or_kw_required": { - "label": { - "type": "object" - }, - "side": { - "type": "str", - "options": [ - "left", - "right" - ] - }, - "kind": { - "type": "str", + "__ge__": { + "pos_only_required": { + "_0": { + "type": "union", "options": [ - "loc" + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } ] } }, - "metadata": { - "usage.dask": 30 - } - }, - "memory_usage": { "metadata": { "usage.dask": 3 } }, - "dropna": { + "min": { "metadata": { "usage.dask": 1 } }, - "rename": { - "pos_or_kw_required": { - "name": { - "type": "str", - "options": [ - "renamed" - ] - } - }, + "max": { "metadata": { "usage.dask": 1 } }, "drop_duplicates": { "metadata": { - "usage.dask": 1 - } - }, - "unique": { - "metadata": { - "usage.dask": 3 + "usage.dask": 2 } }, "isin": { @@ -257493,6 +310366,18 @@ "name": "Series" } }, + { + "type": { + "module": "dask.delayed", + "name": "Delayed" + } + }, + { + "type": { + "module": "dask.delayed", + "name": "DelayedLeaf" + } + }, { "type": "list", "item": { @@ -257508,47 +310393,63 @@ "usage.dask": 4 } }, - "shift": { - "pos_or_kw_required": { - "periods": { - "type": { - "name": "int" - } - }, - "freq": { - "type": "None" - } - }, + "dropna": { "metadata": { "usage.dask": 1 } }, - "union": { - "pos_or_kw_required": { - "other": { + "__gt__": { + "pos_only_required": { + "_0": { "type": "union", "options": [ { "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" + "name": "float" } }, { "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" + "name": "int" } } ] } }, "metadata": { - "usage.dask": 6 + "usage.dask": 2 + } + }, + "set_names": { + "pos_or_kw_required": { + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "a", + "b", + "key" + ] + } + }, + "inplace": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.dask": 3 + } + }, + "to_series": { + "metadata": { + "usage.dask": 1 } }, "to_frame": { - "pos_or_kw_optional": { + "pos_or_kw_required": { "index": { "type": { "name": "bool" @@ -257560,37 +310461,17 @@ { "type": "str", "options": [ - "bar", - "foo" - ] - }, - { - "type": "None" - } - ] - } - }, - "pos_or_kw_optional_ordering": [ - [ - "index", - "name" - ] - ], - "metadata": { - "usage.dask": 6 - } - }, - "__ne__": { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas.core.indexes.base", - "name": "Index" - } + "bar" + ] + }, + { + "type": "None" + } + ] } }, "metadata": { - "usage.sklearn": 2 + "usage.dask": 2 } } }, @@ -257598,8 +310479,7 @@ "dtype": [ { "usage.xarray": 6, - "usage.dask": 13, - "usage.sklearn": 2 + "usage.dask": 11 }, { "type": "bottom" @@ -257607,46 +310487,34 @@ ], "values": [ { - "usage.xarray": 6, - "usage.dask": 1 + "usage.xarray": 3, + "usage.dask": 3 }, { "type": "bottom" } ], - "name": [ + "size": [ { - "usage.xarray": 13, - "usage.dask": 16 + "usage.xarray": 10, + "usage.dask": 2 }, { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": "str", - "options": [ - "A", - "a" - ] - } - ] + "type": "bottom" } ], - "size": [ + "is_unique": [ { - "usage.xarray": 10 + "usage.xarray": 5, + "usage.dask": 1 }, { "type": "bottom" } ], - "is_unique": [ + "shape": [ { - "usage.xarray": 6, - "usage.dask": 1 + "usage.xarray": 2 }, { "type": "bottom" @@ -257661,17 +310529,34 @@ "type": "bottom" } ], - "shape": [ + "name": [ { - "usage.xarray": 1 + "usage.xarray": 13, + "usage.dask": 21 }, { - "type": "bottom" + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": "str", + "options": [ + "foo", + "C", + "b", + "idx", + "a" + ] + } + ] } ], "nlevels": [ { - "usage.xarray": 1 + "usage.xarray": 1, + "usage.dask": 1 }, { "type": "bottom" @@ -257687,19 +310572,37 @@ ], "names": [ { - "usage.dask": 3 + "usage.dask": 6 }, { - "type": "list", - "item": { - "type": "str" - } + "type": "union", + "options": [ + { + "type": { + "module": "pandas.core.indexes.frozen", + "name": "FrozenList" + } + }, + { + "type": "list", + "item": { + "type": "str" + } + } + ] } ], - "str": [ + "nbytes": [ { - "usage.dask": 2, - "usage.sklearn": 1 + "usage.dask": 2 + }, + { + "type": "bottom" + } + ], + "__class__": [ + { + "usage.dask": 1 }, { "type": "bottom" @@ -257707,258 +310610,70 @@ ] }, "classproperties": { - "get_loc": [ + "__name__": [ { - "usage.xarray": 2 + "usage.dask": 3 }, { "type": "bottom" } ], - "__name__": [ + "__module__": [ { - "usage.dask": 2 + "usage.dask": 1 }, { "type": "bottom" } ] } - } - } - }, - "pandas.core.indexes.range": { - "classes": { - "RangeIndex": { - "method_overloads": { - "equals": [ - { - "pos_or_kw_required": { - "other": { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" - } - } - }, - "metadata": { - "usage.xarray": 3 - } - }, - { - "pos_or_kw_required": { - "other": { - "type": { - "module": "pandas.core.indexes.range", - "name": "RangeIndex" - } - } - }, - "metadata": { - "usage.dask": 2 - } - } - ], - "__getitem__": [ - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { + }, + "UInt64Index": { + "constructor_overloads": [ + { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { "type": { "name": "int" } } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": { - "name": "int" - } - } - } - }, - "metadata": { - "usage.xarray": 3 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" - } - } - }, - "metadata": { - "usage.xarray": 1 + "name": { + "type": "str", + "options": [ + "foo" + ] } }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" - } - }, - { - "type": { - "name": "int" - } - } - ] - } - }, - "metadata": { - "usage.dask": 5 - } - } - ], - "__iter__": [ - { - "metadata": { - "usage.xarray": 1 - } - } - ], - "get_indexer": [ - { - "pos_or_kw_required": { - "target": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } + "metadata": { + "usage.dask": 1 } - ], - "get_loc": [ - { - "pos_or_kw_required": { - "key": { - "type": { - "name": "int" - } - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" + } + ], + "constructor": { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { + "type": { + "name": "int" } - }, - "metadata": { - "usage.xarray": 1 } }, - { - "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "random" - ] - } - }, - "metadata": { - "usage.sklearn": 1 - } - } - ], - "copy": [ - { - "pos_or_kw_required": { - "deep": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.xarray": 1 - } + "name": { + "type": "str", + "options": [ + "foo" + ] } - ], + }, + "metadata": { + "usage.dask": 1 + } + }, + "method_overloads": { "__mul__": [ { "pos_only_required": { @@ -257983,18 +310698,6 @@ "metadata": { "usage.pandas": 5 } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.dask": 1 - } } ], "__rmul__": [ @@ -258021,18 +310724,6 @@ "metadata": { "usage.pandas": 2 } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.dask": 1 - } } ], "__rtruediv__": [ @@ -258141,18 +310832,6 @@ "metadata": { "usage.pandas": 2 } - }, - { - "pos_only_required": { - "_0": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.dask": 1 - } } ], "__radd__": [ @@ -258249,21 +310928,6 @@ } ], "__eq__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.pandas": 4 - } - } - ], - "__contains__": [ { "pos_only_required": { "_0": { @@ -258271,313 +310935,48 @@ "options": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { - "type": "str", - "options": [ - "dtype", - "divisions" - ] - } - ] - } - }, - "metadata": { - "usage.dask": 3 - } - } - ], - "min": [ - { - "metadata": { - "usage.dask": 5 - } - } - ], - "max": [ - { - "metadata": { - "usage.dask": 5 - } - } - ], - "tolist": [ - { - "metadata": { - "usage.dask": 1 - } - } - ], - "drop_duplicates": [ - { - "metadata": { - "usage.dask": 1 - } - } - ], - "map": [ - { - "pos_or_kw_required": { - "mapper": { - "type": "function" - }, - "na_action": { - "type": "None" - } - }, - "metadata": { - "usage.dask": 1 - } - } - ], - "__neg__": [ - { - "metadata": { - "usage.dask": 1 - } - } - ], - "to_series": [ - { - "metadata": { - "usage.dask": 1 - } - } - ], - "to_frame": [ - { - "pos_or_kw_required": { - "name": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "bar" - ] - }, - { - "type": "None" + "type": { + "module": "numpy", + "name": "uint64" + } } ] } }, - "pos_or_kw_optional": { - "index": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.dask": 3 - } - } - ], - "memory_usage": [ - { "metadata": { - "usage.dask": 1 + "usage.pandas": 7 } } ] }, "methods": { - "equals": { - "pos_or_kw_required": { - "other": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.indexes.range", - "name": "RangeIndex" - } - }, - { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" - } - } - ] - } - }, - "metadata": { - "usage.xarray": 3, - "usage.dask": 2 - } - }, - "__getitem__": { + "__mul__": { "pos_only_required": { "_0": { "type": "union", "options": [ - { - "type": { - "name": "int" - } - }, { "type": { "module": "numpy", "name": "ndarray" } }, - { - "type": "slice", - "start": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - }, - "stop": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - }, - "step": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - } - } - ] - } - }, - "metadata": { - "usage.xarray": 7, - "usage.dask": 5 - } - }, - "__iter__": { - "metadata": { - "usage.xarray": 1 - } - }, - "get_indexer": { - "pos_or_kw_required": { - "target": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - "get_loc": { - "pos_or_kw_required": { - "key": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "random" - ] - }, - { - "type": { - "name": "int" - } - } - ] - } - }, - "pos_or_kw_optional": { - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" - } - }, - "pos_or_kw_optional_ordering": [ - [ - "method", - "tolerance" - ] - ], - "metadata": { - "usage.xarray": 1, - "usage.sklearn": 1 - } - }, - "copy": { - "pos_or_kw_required": { - "deep": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - "__mul__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, { "type": { "module": "numpy", "name": "timedelta64" } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } } ] } }, "metadata": { - "usage.pandas": 5, - "usage.dask": 1 + "usage.pandas": 5 } }, "__rmul__": { @@ -258585,29 +310984,23 @@ "_0": { "type": "union", "options": [ - { - "type": { - "name": "int" - } - }, { "type": { "module": "numpy", - "name": "ndarray" + "name": "timedelta64" } }, { "type": { "module": "numpy", - "name": "timedelta64" + "name": "ndarray" } } ] } }, "metadata": { - "usage.pandas": 2, - "usage.dask": 1 + "usage.pandas": 2 } }, "__rtruediv__": { @@ -258689,29 +311082,23 @@ "_0": { "type": "union", "options": [ - { - "type": { - "name": "int" - } - }, { "type": { "module": "numpy", - "name": "timedelta64" + "name": "datetime64" } }, { "type": { "module": "numpy", - "name": "datetime64" + "name": "timedelta64" } } ] } }, "metadata": { - "usage.pandas": 2, - "usage.dask": 1 + "usage.pandas": 2 } }, "__radd__": { @@ -258800,361 +311187,323 @@ } }, "__eq__": { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.pandas": 4 - } - }, - "__contains__": { "pos_only_required": { "_0": { "type": "union", "options": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } }, { - "type": "str", - "options": [ - "dtype", - "divisions" - ] + "type": { + "module": "numpy", + "name": "uint64" + } } ] } }, "metadata": { - "usage.dask": 3 - } - }, - "min": { - "metadata": { - "usage.dask": 5 + "usage.pandas": 7 } - }, - "max": { - "metadata": { - "usage.dask": 5 + } + }, + "properties": { + "name": [ + { + "usage.dask": 2 + }, + { + "type": "bottom" } - }, - "tolist": { - "metadata": { + ] + }, + "classproperties": { + "__module__": [ + { "usage.dask": 1 + }, + { + "type": "bottom" } - }, - "drop_duplicates": { - "metadata": { - "usage.dask": 1 + ] + } + } + } + }, + "pandas.core.tools.datetimes": { + "function_overloads": { + "to_datetime": [ + { + "pos_or_kw_required": { + "arg": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "map": { - "pos_or_kw_required": { - "mapper": { - "type": "function" - }, - "na_action": { - "type": "None" + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "arg": { + "type": "list", + "item": { + "type": "str", + "options": [ + "NaT", + "2000-01-02", + "2000-01-01" + ] } - }, - "metadata": { - "usage.dask": 1 } }, - "__neg__": { - "metadata": { - "usage.dask": 1 + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_or_kw_required": { + "arg": { + "type": "list", + "item": { + "type": "str", + "options": [ + "NaT" + ] + } } }, - "to_series": { - "metadata": { - "usage.dask": 1 + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "arg": { + "type": "list", + "item": { + "type": "str", + "options": [ + "2000-01-01T00:00:00Z", + "NaT" + ] + } } }, - "to_frame": { - "pos_or_kw_required": { - "name": { - "type": "union", + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "arg": { + "type": "list", + "item": { + "type": "str", "options": [ - { - "type": "str", - "options": [ - "bar" - ] - }, - { - "type": "None" - } + "2000-01-02T00:00:00Z", + "2000-01-01T00:00:00Z", + "NaT" ] } - }, - "pos_or_kw_optional": { - "index": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.dask": 3 } }, - "memory_usage": { - "metadata": { - "usage.dask": 1 - } + "metadata": { + "usage.xarray": 1 } }, - "properties": { - "dtype": [ - { - "usage.xarray": 4, - "usage.dask": 11 - }, - { - "type": "bottom" - } - ], - "values": [ - { - "usage.xarray": 3 - }, - { - "type": "bottom" - } - ], - "name": [ - { - "usage.xarray": 4, - "usage.dask": 16 - }, - { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "ix", - "renamed" - ] - }, - { - "type": "None" - } - ] - } - ], - "size": [ - { - "usage.xarray": 3 - }, - { - "type": "bottom" - } - ], - "is_unique": [ - { - "usage.xarray": 6, - "usage.sklearn": 2 - }, - { - "type": "bottom" - } - ], - "shape": [ - { - "usage.xarray": 1 - }, - { - "type": "bottom" + { + "pos_or_kw_required": { + "arg": { + "type": "list", + "item": { + "type": "str", + "options": [ + "2000-01-03T06", + "2000-01-02T18", + "2000-01-01T18" + ] + } } - ], - "is_monotonic": [ - { - "usage.xarray": 2, - "usage.dask": 1 - }, - { - "type": "bottom" + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "arg": { + "type": "list", + "item": { + "type": "str", + "options": [ + "2002", + "2000", + "2001" + ] + } } - ], - "array": [ - { - "usage.dask": 1 - }, - { - "type": "bottom" + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "arg": { + "type": "object" } - ], - "names": [ - { - "usage.dask": 1 + }, + "pos_or_kw_optional": { + "infer_datetime_format": { + "type": { + "name": "bool" + } }, - { - "type": "bottom" - } - ], - "is_all_dates": [ - { - "usage.dask": 1 + "unit": { + "type": "str", + "options": [ + "ns" + ] }, - { - "type": "bottom" + "utc": { + "type": { + "name": "bool" + } } - ] + }, + "metadata": { + "usage.dask": 21 + } + } + ] + }, + "functions": { + "to_datetime": { + "pos_or_kw_required": { + "arg": { + "type": "object" + } }, - "classproperties": { - "__module__": [ - { - "usage.dask": 1 - }, - { - "type": "bottom" + "pos_or_kw_optional": { + "infer_datetime_format": { + "type": { + "name": "bool" } - ], - "__name__": [ - { - "usage.dask": 2 - }, - { - "type": "bottom" + }, + "unit": { + "type": "str", + "options": [ + "ns" + ] + }, + "utc": { + "type": { + "name": "bool" } - ] + } + }, + "metadata": { + "usage.xarray": 8, + "usage.dask": 21 } } } }, - "pandas.core.indexes.datetimes": { + "pandas.core.dtypes.missing": { "function_overloads": { - "date_range": [ + "notna": [ { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 27 + "usage.xarray": 3 } }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "1999-01-05" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } } }, + "metadata": { + "usage.dask": 2 + } + } + ], + "isna": [ + { + "pos_or_kw_required": { + "obj": { + "type": "None" + } + }, "metadata": { "usage.xarray": 1 } }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-02-01" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "numpy", + "name": "float64" } } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 3 } }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2010-01-01" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } - }, - "freq": { - "type": "str", - "options": [ - "1D" - ] } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 22 } }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-02-01" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "numpy", + "name": "float32" } - }, - "freq": { - "type": "str", - "options": [ - "A" - ] } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1 } }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-02-01" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "numpy", + "name": "bytes_" } - }, - "freq": { - "type": "str", - "options": [ - "M" - ] } }, "metadata": { @@ -259163,64 +311512,36 @@ }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-02-01" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "name": "bytes" } - }, - "freq": { - "type": "str", - "options": [ - "D" - ] } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1 } }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "numpy", + "name": "int8" } } }, "metadata": { - "usage.xarray": 5 + "usage.xarray": 2 } }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-02T01:03:51" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "numpy", + "name": "uint8" } - }, - "freq": { - "type": "str", - "options": [ - "1777S" - ] } }, "metadata": { @@ -259229,21 +311550,23 @@ }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01T12:07:01" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "numpy", + "name": "int16" } - }, - "freq": { + } + }, + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_or_kw_required": { + "obj": { "type": "str", "options": [ - "8003D" + "XXX" ] } }, @@ -259253,21 +311576,10 @@ }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01T12:07:01" - ] - }, - "periods": { - "type": { - "name": "int" - } - }, - "freq": { + "obj": { "type": "str", "options": [ - "6H" + "" ] } }, @@ -259277,22 +311589,11 @@ }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01T12:07:01" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "numpy", + "name": "int32" } - }, - "freq": { - "type": "str", - "options": [ - "3D" - ] } }, "metadata": { @@ -259301,46 +311602,24 @@ }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01T12:07:01" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "numpy", + "name": "int64" } - }, - "freq": { - "type": "str", - "options": [ - "11D" - ] } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 2 } }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01T12:07:01" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "numpy", + "name": "timedelta64" } - }, - "freq": { - "type": "str", - "options": [ - "3MS" - ] } }, "metadata": { @@ -259349,22 +311628,11 @@ }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01T12:07:01" - ] - }, - "periods": { + "obj": { "type": { - "name": "int" + "module": "sparse._coo.core", + "name": "COO" } - }, - "freq": { - "type": "str", - "options": [ - "7M" - ] } }, "metadata": { @@ -259373,21 +311641,10 @@ }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01T12:07:01" - ] - }, - "periods": { - "type": { - "name": "int" - } - }, - "freq": { + "obj": { "type": "str", "options": [ - "43QS-AUG" + "Z" ] } }, @@ -259397,21 +311654,10 @@ }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01T12:07:01" - ] - }, - "periods": { - "type": { - "name": "int" - } - }, - "freq": { + "obj": { "type": "str", "options": [ - "11Q-JUN" + "A" ] } }, @@ -259421,876 +311667,1806 @@ }, { "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01T12:07:01" - ] + "obj": { + "type": "object" + } + }, + "metadata": { + "usage.dask": 116 + } + } + ] + }, + "functions": { + "notna": { + "pos_or_kw_required": { + "obj": { + "type": "union", + "options": [ + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + } + }, + "metadata": { + "usage.xarray": 3, + "usage.dask": 2 + } + }, + "isna": { + "pos_or_kw_required": { + "obj": { + "type": "object" + } + }, + "metadata": { + "usage.xarray": 44, + "usage.dask": 116 + } + } + } + }, + "pandas._libs.tslibs.timestamps": { + "classes": { + "Timestamp": { + "constructor_overloads": [ + { + "pos_or_kw_required": { + "ts_input": { + "type": { + "module": "numpy", + "name": "datetime64" + } + } }, - "periods": { - "type": { - "name": "int" + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_or_kw_required": { + "ts_input": { + "type": "str", + "options": [ + "2000-01-01 00:00:00" + ] + } + }, + "metadata": { + "usage.xarray": 3 + } + }, + { + "pos_or_kw_required": { + "ts_input": { + "type": "str", + "options": [ + "1950-01-01" + ] + } + }, + "metadata": { + "usage.xarray": 3 + } + }, + { + "pos_or_kw_required": { + "ts_input": { + "type": "str", + "options": [ + "0001-01-01" + ] + } + }, + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_or_kw_required": { + "ts_input": { + "type": "str" + } + }, + "metadata": { + "usage.xarray": 3 + } + }, + { + "pos_or_kw_required": { + "ts_input": { + "type": "str", + "options": [ + "1996-1-1" + ] + } + }, + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_or_kw_required": { + "ts_input": { + "type": "str", + "options": [ + "1996-01-01" + ] + } + }, + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_or_kw_required": { + "ts_input": { + "type": "str", + "options": [ + "1900-01-01" + ] + } + }, + "metadata": { + "usage.xarray": 3 + } + }, + { + "pos_or_kw_required": { + "ts_input": { + "type": "str", + "options": [ + "1999-01-01" + ] + } + }, + "metadata": { + "usage.xarray": 3 + } + }, + { + "pos_or_kw_required": { + "ts_input": { + "type": "str", + "options": [ + "2000-01-01" + ] + } + }, + "metadata": { + "usage.xarray": 6 + } + }, + { + "pos_or_kw_required": { + "ts_input": { + "type": "str", + "options": [ + 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[ - "2MS" - ] + "metadata": { + "usage.pandas": 1 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01" - ] - }, - "periods": { - "type": { - "name": "int" + "__floordiv__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "freq": { - "type": "str", - "options": [ - "H" - ] + "metadata": { + "usage.pandas": 1 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "1/1/2011" - ] - }, - "periods": { - "type": { - "name": "int" + "__truediv__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "freq": { - "type": "str", - "options": [ - "H" - ] + "metadata": { + "usage.pandas": 1 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01" - ] - }, - "periods": { - "type": { - "name": "int" + "__pow__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "freq": { - "type": "str", - "options": [ - "M" - ] + "metadata": { + "usage.pandas": 1 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "20100101" - ] - }, - "periods": { - "type": { - "name": "int" + "__mod__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } } + }, + "metadata": { + "usage.pandas": 1 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01" - ] - }, - "periods": { - "type": { - "name": "int" + "__eq__": { + "pos_only_required": { + "_0": { + "type": "object" } }, - "freq": { - "type": "str", - "options": [ - "3H" - ] + "metadata": { + "usage.pandas": 8, + "usage.dask": 43 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "20130101" - ] - }, - "periods": { - "type": { - "name": "int" + "__ne__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "pandas._libs.tslibs.timestamps", + "name": "Timestamp" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] } }, - "tz": { - "type": "str", - "options": [ - "US/Eastern" - ] + "metadata": { + "usage.pandas": 4, + "usage.dask": 10 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "15/12/1999" - ] - }, - "periods": { - "type": { - "name": "int" + "__ge__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, + { + "type": { + "module": "pandas._libs.tslibs.timestamps", + "name": "Timestamp" + } + }, + { + "type": { + "module": "pandas.core.indexes.datetimes", + "name": "DatetimeIndex" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } + }, + "metadata": { + "usage.pandas": 2, + "usage.dask": 21 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000" - ] - }, - "periods": { - "type": { - "name": "int" + "__lt__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "pandas._libs.tslibs.timestamps", + "name": "Timestamp" + } + }, + { + "type": { + "module": "pandas.core.indexes.datetimes", + "name": "DatetimeIndex" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] } }, - "freq": { - "type": "str", - "options": [ - "7D" - ] + "metadata": { + "usage.pandas": 1, + "usage.dask": 9 } }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2010-08-01" - ] - }, - "end": { - "type": "str", - "options": [ - "2010-08-15" - ] + "__le__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "pandas._libs.tslibs.timestamps", + "name": "Timestamp" + } + }, + { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + } + ] + } }, - "freq": { - "type": "str", - "options": [ - "15min" - ] + "metadata": { + "usage.dask": 20 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2010-08-01" - ] - }, - "end": { - "type": "str", - "options": [ - "2010-08-15" - ] + "tz_localize": { + "pos_or_kw_required": { + "tz": { + "type": "None" + } }, - "freq": { - "type": "str", - "options": [ - "24H" - ] + "metadata": { + "usage.dask": 3 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "01-01-2001" - ] - }, - "periods": { - "type": { - "name": "int" + "__gt__": { + "pos_only_required": { + "_0": { + "type": { + "module": "pandas._libs.tslibs.timestamps", + "name": "Timestamp" + } } }, - "freq": { - "type": "str", - "options": [ - "D" - ] + "metadata": { + "usage.dask": 8 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "01-01-2001" - ] - }, - "periods": { - "type": { - "name": "int" + "ceil": { + "pos_or_kw_required": { + "freq": { + "type": "str", + "options": [ + "1M", + "15s" + ] } }, - "freq": { - "type": "str", - "options": [ - "H" - ] + "metadata": { + "usage.dask": 2 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2001-01-01" - ] - }, - "periods": { - "type": { - "name": "int" + "round": { + "pos_or_kw_required": { + "freq": { + "type": "str", + "options": [ + "15s" + ] } }, - "freq": { - "type": "str", - "options": [ - "H" - ] + "metadata": { + "usage.dask": 1 } - }, - "metadata": { - "usage.xarray": 2 } }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "20000101" - ] + "properties": { + "tz": [ + { + "usage.xarray": 1, + "usage.dask": 5 }, - "periods": { - "type": { - "name": "int" - } + { + "type": "bottom" } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-01-01" - ] + ], + "value": [ + { + "usage.xarray": 1, + "usage.dask": 1 }, - "end": { - "type": "str", - "options": [ - "2000-01-10" - ] + { + "type": "bottom" } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "2000-1-1" - ] + ], + "dtype": [ + { + "usage.dask": 1 }, - "periods": { - "type": { - "name": "int" - } + { + "type": "bottom" } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "15/12/1999" - ] + ], + "freq": [ + { + "usage.dask": 1 }, - "periods": { - "type": { - "name": "int" - } + { + "type": "bottom" + } + ], + "tzinfo": [ + { + "usage.dask": 1 }, - "freq": { - "type": { - "module": "pandas._libs.tslibs.offsets", - "name": "DateOffset" - } + { + "type": "bottom" } - }, - "metadata": { - "usage.xarray": 1 - } + ] }, + "classproperties": { + "__module__": [ + { + "usage.dask": 1 + }, + { + "type": "bottom" + } + ], + "__name__": [ + { + "usage.dask": 1 + }, + { + "type": "bottom" + } + ] + } + } + } + }, + "pandas.core.indexes.timedeltas": { + "function_overloads": { + "timedelta_range": [ { "pos_or_kw_required": { "start": { - "type": "str", - "options": [ - "2011-09-01" - ] - }, - "periods": { "type": { "name": "int" } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { - "type": "str", - "options": [ - "10-09-2010" - ] }, "periods": { "type": { "name": "int" } - }, - "freq": { - "type": "str", - "options": [ - "1y" - ] } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1 } }, { "pos_or_kw_required": { "start": { - "type": "str", + "type": "union", "options": [ - "2000-01-01" + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": "str", + "options": [ + "1 day", + "1 days" + ] + } ] }, "periods": { @@ -260299,251 +313475,203 @@ } }, "freq": { - "type": "str", - "options": [ - "1h" - ] - }, - "tz": { - "type": "object" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "start": { "type": "union", "options": [ { - "type": "str" + "type": "None" + }, + { + "type": "str", + "options": [ + "H", + "D", + "T" + ] }, { "type": { - "name": "int" + "module": "pandas.tseries.offsets", + "name": "Hour" } }, { "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" + "module": "pandas.tseries.offsets", + "name": "Minute" } - } - ] - } - }, - "pos_or_kw_optional": { - "end": { - "type": "union", - "options": [ - { - "type": "str" }, { "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" + "module": "pandas.tseries.offsets", + "name": "Day" } } ] - }, - "periods": { - "type": { - "name": "int" - } - }, - "tz": { - "type": "object" - }, + } + }, + "pos_or_kw_optional": { "name": { "type": "union", "options": [ - { - "type": "None" - }, - { - "type": "str" - } - ] - }, - "freq": { - "type": "object" - }, - "closed": { - "type": "union", - "options": [ - { - "type": "None" - }, { "type": "str", "options": [ - "left" + "timedelta", + "foo" ] + }, + { + "type": "None" } ] } }, - "pos_or_kw_optional_ordering": [ - [ - "end", - "name" - ], - [ - "end", - "freq" - ], - [ - "tz", - "name" - ], - [ - "periods", - "tz" - ], - [ - "periods", - "freq" - ], - [ - "freq", - "tz" - ], - [ - "freq", - "name" - ], - [ - "name", - "closed" - ] - ], "metadata": { - "usage.dask": 97 + "usage.dask": 9 } } ] }, "functions": { - "date_range": { + "timedelta_range": { "pos_or_kw_required": { "start": { "type": "union", "options": [ + { + "type": "str", + "options": [ + "1 day", + "1 days" + ] + }, { "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" + "module": "numpy", + "name": "timedelta64" } }, { "type": { "name": "int" } - }, - { - "type": "str" } ] + }, + "periods": { + "type": { + "name": "int" + } } }, "pos_or_kw_optional": { "freq": { - "type": "object" - }, - "end": { "type": "union", "options": [ + { + "type": "None" + }, + { + "type": "str", + "options": [ + "H", + "D", + "T" + ] + }, { "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" + "module": "pandas.tseries.offsets", + "name": "Hour" } }, { - "type": "str" - } - ] - }, - "periods": { - "type": { - "name": "int" - } - }, - "tz": { - "type": "object" - }, - "name": { - "type": "union", - "options": [ - { - "type": "None" + "type": { + "module": "pandas.tseries.offsets", + "name": "Minute" + } }, { - "type": "str" + "type": { + "module": "pandas.tseries.offsets", + "name": "Day" + } } ] }, - "closed": { + "name": { "type": "union", "options": [ - { - "type": "None" - }, { "type": "str", "options": [ - "left" + "timedelta", + "foo" ] + }, + { + "type": "None" } ] } }, - "pos_or_kw_optional_ordering": [ - [ - "end", - "freq" - ], - [ - "periods", - "freq" - ], - [ - "periods", - "tz" - ], - [ - "freq", - "tz" - ], - [ - "end", - "name" - ], - [ - "tz", - "name" - ], - [ - "freq", - "name" - ], - [ - "name", - "closed" - ] - ], "metadata": { - "usage.xarray": 122, - "usage.dask": 97 + "usage.xarray": 1, + "usage.dask": 9 } } }, "classes": { - "DatetimeIndex": { + "TimedeltaIndex": { "constructor_overloads": [ + { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { + "type": { + "module": "datetime", + "name": "timedelta" + } + } + } + }, + "metadata": { + "usage.xarray": 6 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "unit": { + "type": "str", + "options": [ + "us" + ] + } + }, + "metadata": { + "usage.xarray": 1 + } + }, { "pos_or_kw_required": { "data": { @@ -260551,31 +313679,122 @@ "module": "numpy", "name": "ndarray" } + }, + "unit": { + "type": "str", + "options": [ + "d" + ] + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + }, + "name": { + "type": "str", + "options": [ + "timedelta", + "foo" + ] + } + }, + "pos_or_kw_optional": { + "freq": { + "type": "str", + "options": [ + "d" + ] + } + }, + "metadata": { + "usage.dask": 2 + } + } + ], + "constructor": { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + }, + "name": { + "type": "str", + "options": [ + "timedelta", + "foo" + ] + } + }, + "pos_or_kw_optional": { + "freq": { + "type": "str", + "options": [ + "d" + ] + } + }, + "metadata": { + "usage.dask": 2 + } + }, + "method_overloads": { + "__truediv__": [ + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + }, + "metadata": { + "usage.xarray": 2 } }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": "list", - "item": { - "type": "str", + { + "pos_only_required": { + "_0": { + "type": "union", "options": [ - "2000-01-01T00:00:00" + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } ] } + }, + "metadata": { + "usage.pandas": 21 } - }, - "metadata": { - "usage.xarray": 1 } - } - ], - "method_overloads": { - "__sub__": [ + ], + "__add__": [ { "pos_only_required": { "_0": { @@ -260586,15 +313805,15 @@ } }, "metadata": { - "usage.xarray": 3 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "timedelta64" + "module": "xarray.coding.cftimeindex", + "name": "CFTimeIndex" } } }, @@ -260629,7 +313848,7 @@ } }, "metadata": { - "usage.pandas": 75 + "usage.pandas": 22 } } ], @@ -260642,48 +313861,6 @@ } } }, - "metadata": { - "usage.xarray": 6 - } - }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - } - }, "metadata": { "usage.xarray": 2 } @@ -260720,46 +313897,6 @@ } } }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": { - "name": "int" - } - } - } - }, - "metadata": { - "usage.xarray": 7 - } - }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" - }, - "step": { - "type": "None" - } - } - }, "metadata": { "usage.xarray": 1 } @@ -260772,43 +313909,17 @@ { "type": "slice", "start": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] + "type": { + "name": "int" + } }, "stop": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] + "type": { + "name": "int" + } }, "step": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] + "type": "None" } }, { @@ -260820,96 +313931,90 @@ } }, "metadata": { - "usage.dask": 22 + "usage.dask": 5 } } ], - "equals": [ + "__radd__": [ { - "pos_or_kw_required": { - "other": { + "pos_only_required": { + "_0": { "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" + "module": "xarray.coding.cftimeindex", + "name": "CFTimeIndex" } } }, "metadata": { - "usage.xarray": 2 - } - } - ], - "floor": [ - { - "var_pos": [ - "args", - { - "type": "str", - "options": [ - "v", - "t" - ] - } - ], - "metadata": { - "usage.xarray": 3 + "usage.xarray": 1 } - } - ], - "ceil": [ + }, { - "var_pos": [ - "args", - { - "type": "str", - "options": [ - "v", - "t" - ] + "pos_only_required": { + "_0": { + "type": { + "module": "pandas._libs.tslibs.timestamps", + "name": "Timestamp" + } } - ], + }, "metadata": { - "usage.xarray": 3 + "usage.xarray": 1 } - } - ], - "round": [ + }, { - "var_pos": [ - "args", - { - "type": "str", + "pos_only_required": { + "_0": { + "type": "union", "options": [ - "v", - "t" + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } ] } - ], + }, "metadata": { - "usage.xarray": 3 + "usage.pandas": 18 } } ], - "__add__": [ + "equals": [ { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "other": { "type": { - "module": "pandas._libs.tslibs.offsets", - "name": "Hour" + "module": "pandas.core.indexes.timedeltas", + "name": "TimedeltaIndex" } } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 4 } - }, + } + ], + "__rsub__": [ { "pos_only_required": { "_0": { "type": { - "module": "datetime", - "name": "timedelta" + "module": "xarray.coding.cftimeindex", + "name": "CFTimeIndex" } } }, @@ -260931,30 +314036,20 @@ { "type": { "module": "numpy", - "name": "timedelta64" + "name": "datetime64" } }, { "type": { "module": "numpy", - "name": "datetime64" + "name": "timedelta64" } } ] } }, "metadata": { - "usage.pandas": 103 - } - }, - { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.dask": 8 + "usage.pandas": 16 } } ], @@ -260977,20 +314072,19 @@ "metadata": { "usage.xarray": 1 } - }, + } + ], + "get_loc": [ { "pos_or_kw_required": { - "target": { + "key": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "pandas._libs.tslibs.timedeltas", + "name": "Timedelta" } }, "method": { - "type": "str", - "options": [ - "pad" - ] + "type": "None" }, "tolerance": { "type": "None" @@ -260999,197 +314093,113 @@ "metadata": { "usage.xarray": 1 } - }, + } + ], + "copy": [ { "pos_or_kw_required": { - "target": { + "deep": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "bool" } - }, - "method": { - "type": "str", - "options": [ - "backfill" - ] - }, - "tolerance": { - "type": "None" } }, "metadata": { "usage.xarray": 1 } - }, + } + ], + "__ge__": [ { - "pos_or_kw_required": { - "target": { + "pos_only_required": { + "_0": { "type": { "module": "numpy", "name": "ndarray" } - }, - "method": { - "type": "str", - "options": [ - "nearest" - ] - }, - "tolerance": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 1 } - }, + } + ], + "__gt__": [ { - "pos_or_kw_required": { - "target": { + "pos_only_required": { + "_0": { "type": { "module": "numpy", - "name": "ndarray" + "name": "timedelta64" } - }, - "method": { - "type": "str", - "options": [ - "pad" - ] - }, - "tolerance": { - "type": "str", - "options": [ - "12H" - ] } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 4 } - }, + } + ], + "__le__": [ { - "pos_or_kw_required": { - "target": { + "pos_only_required": { + "_0": { "type": { "module": "numpy", "name": "ndarray" } - }, - "method": { - "type": "str", - "options": [ - "backfill" - ] - }, - "tolerance": { - "type": "str", - "options": [ - "12H" - ] } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 2 } - }, + } + ], + "__lt__": [ { - "pos_or_kw_required": { - "target": { + "pos_only_required": { + "_0": { "type": { "module": "numpy", "name": "ndarray" } - }, - "method": { - "type": "str", - "options": [ - "nearest" - ] - }, - "tolerance": { - "type": "str", - "options": [ - "6H" - ] - } - }, - "metadata": { - "usage.xarray": 1 - } - } - ], - "to_numpy": [ - { - "pos_or_kw_required": { - "dtype": { - "type": "str", - "options": [ - "datetime64[ns]" - ] } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 1 } } ], - "copy": [ + "__eq__": [ { - "pos_or_kw_required": { - "deep": { + "pos_only_required": { + "_0": { "type": { - "name": "bool" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 5 } } ], - "get_loc": [ + "__ne__": [ { - "pos_or_kw_required": { - "key": { + "pos_only_required": { + "_0": { "type": { "module": "numpy", - "name": "datetime64" + "name": "ndarray" } - }, - "method": { - "type": "str", - "options": [ - "nearest" - ] - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "key": { - "type": "str", - "options": [ - "2000-01-01" - ] - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 1 } } ], - "__eq__": [ + "__iadd__": [ { "pos_only_required": { "_0": { @@ -261204,18 +314214,18 @@ { "type": { "module": "numpy", - "name": "datetime64" + "name": "timedelta64" } } ] } }, "metadata": { - "usage.pandas": 32 + "usage.pandas": 2 } } ], - "__gt__": [ + "__isub__": [ { "pos_only_required": { "_0": { @@ -261230,7 +314240,7 @@ { "type": { "module": "numpy", - "name": "datetime64" + "name": "timedelta64" } } ] @@ -261239,22 +314249,9 @@ "metadata": { "usage.pandas": 2 } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" - } - } - }, - "metadata": { - "usage.dask": 1 - } } ], - "__ge__": [ + "__sub__": [ { "pos_only_required": { "_0": { @@ -261271,16 +314268,22 @@ "module": "numpy", "name": "datetime64" } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } } ] } }, "metadata": { - "usage.pandas": 3 + "usage.pandas": 18 } } ], - "__le__": [ + "__mul__": [ { "pos_only_required": { "_0": { @@ -261295,61 +314298,24 @@ { "type": { "module": "numpy", - "name": "datetime64" + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" } } ] } }, "metadata": { - "usage.pandas": 5 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" - } - } - }, - "metadata": { - "usage.dask": 1 - } - } - ], - "__lt__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.pandas": 1 - } - } - ], - "__ne__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.pandas": 2 + "usage.pandas": 18 } } ], - "__iadd__": [ + "__rmul__": [ { "pos_only_required": { "_0": { @@ -261366,16 +314332,22 @@ "module": "numpy", "name": "timedelta64" } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } } ] } }, "metadata": { - "usage.pandas": 4 + "usage.pandas": 14 } } ], - "__isub__": [ + "__rtruediv__": [ { "pos_only_required": { "_0": { @@ -261397,107 +314369,97 @@ } }, "metadata": { - "usage.pandas": 3 + "usage.pandas": 19 } } ], - "__radd__": [ + "__floordiv__": [ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "datetime64" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "type": { + "module": "numpy", + "name": "timedelta64" + } } }, "metadata": { - "usage.pandas": 59 + "usage.pandas": 21 } } ], - "__rsub__": [ + "__rfloordiv__": [ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - }, - { - "type": { - "module": "numpy", - "name": "datetime64" - } - } - ] + "type": { + "module": "numpy", + "name": "timedelta64" + } } }, "metadata": { - "usage.pandas": 32 + "usage.pandas": 7 } } ], - "min": [ + "__mod__": [ { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + }, "metadata": { - "usage.dask": 1 + "usage.pandas": 6 } } ], - "max": [ + "__rmod__": [ { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + }, "metadata": { - "usage.dask": 1 + "usage.pandas": 5 } } ], - "to_series": [ + "__rpow__": [ { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + }, "metadata": { - "usage.dask": 4 + "usage.pandas": 4 } } ], - "__contains__": [ + "__pow__": [ { "pos_only_required": { "_0": { "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" + "module": "numpy", + "name": "timedelta64" } } }, "metadata": { - "usage.dask": 2 + "usage.pandas": 4 } } ], @@ -261518,7 +314480,6 @@ { "type": "str", "options": [ - "W", "S" ] }, @@ -261532,89 +314493,400 @@ } }, "metadata": { - "usage.dask": 11 + "usage.dask": 8 + } + } + ] + }, + "methods": { + "__truediv__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + ] + } + }, + "metadata": { + "usage.xarray": 2, + "usage.pandas": 21 + } + }, + "__add__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "pandas._libs.tslibs.timestamps", + "name": "Timestamp" + } + }, + { + "type": { + "module": "xarray.coding.cftimeindex", + "name": "CFTimeIndex" + } + } + ] + } + }, + "metadata": { + "usage.xarray": 2, + "usage.pandas": 22 + } + }, + "__getitem__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } + } + ] + } + }, + "metadata": { + "usage.xarray": 4, + "usage.dask": 5 + } + }, + "__radd__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "xarray.coding.cftimeindex", + "name": "CFTimeIndex" + } + }, + { + "type": { + "module": "pandas._libs.tslibs.timestamps", + "name": "Timestamp" + } + } + ] + } + }, + "metadata": { + "usage.xarray": 2, + "usage.pandas": 18 + } + }, + "equals": { + "pos_or_kw_required": { + "other": { + "type": { + "module": "pandas.core.indexes.timedeltas", + "name": "TimedeltaIndex" + } + } + }, + "metadata": { + "usage.xarray": 4 + } + }, + "__rsub__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "xarray.coding.cftimeindex", + "name": "CFTimeIndex" + } + } + ] + } + }, + "metadata": { + "usage.xarray": 1, + "usage.pandas": 16 + } + }, + "get_indexer": { + "pos_or_kw_required": { + "target": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "method": { + "type": "None" + }, + "tolerance": { + "type": "None" + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + "get_loc": { + "pos_or_kw_required": { + "key": { + "type": { + "module": "pandas._libs.tslibs.timedeltas", + "name": "Timedelta" + } + }, + "method": { + "type": "None" + }, + "tolerance": { + "type": "None" + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + "copy": { + "pos_or_kw_required": { + "deep": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + "__ge__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.pandas": 1 + } + }, + "__gt__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + }, + "metadata": { + "usage.pandas": 4 + } + }, + "__le__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.pandas": 2 + } + }, + "__lt__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.pandas": 1 + } + }, + "__eq__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } } + }, + "metadata": { + "usage.pandas": 5 } - ], - "_maybe_cast_slice_bound": [ - { - "pos_or_kw_required": { - "label": { - "type": "str" - }, - "side": { - "type": "str", - "options": [ - "right", - "left" - ] - }, - "kind": { - "type": "str", - "options": [ - "loc" - ] + }, + "__ne__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" } - }, - "metadata": { - "usage.dask": 23 } + }, + "metadata": { + "usage.pandas": 1 } - ], - "to_frame": [ - { - "pos_or_kw_required": { - "name": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "foo" - ] - }, - { - "type": "None" + }, + "__iadd__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" } - ] - } - }, - "pos_or_kw_optional": { - "index": { - "type": { - "name": "bool" } - } - }, - "metadata": { - "usage.dask": 3 + ] } + }, + "metadata": { + "usage.pandas": 2 } - ], - "tz_localize": [ - { - "var_pos": [ - "args", - { - "type": "str", - "options": [ - "v", - "t" - ] - } - ], - "metadata": { - "usage.dask": 2 + }, + "__isub__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + ] } + }, + "metadata": { + "usage.pandas": 2 } - ] - }, - "methods": { + }, "__sub__": { "pos_only_required": { "_0": { "type": "union", "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, { "type": { "module": "numpy", @@ -261626,7 +314898,19 @@ "module": "numpy", "name": "timedelta64" } - }, + } + ] + } + }, + "metadata": { + "usage.pandas": 18 + } + }, + "__mul__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ { "type": { "module": "numpy", @@ -261635,28 +314919,58 @@ }, { "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" } } ] } }, "metadata": { - "usage.xarray": 4, - "usage.pandas": 75 + "usage.pandas": 18 } }, - "__getitem__": { + "__rmul__": { "pos_only_required": { "_0": { "type": "union", "options": [ { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" } }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] + } + }, + "metadata": { + "usage.pandas": 14 + } + }, + "__rtruediv__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ { "type": { "module": "numpy", @@ -261664,928 +314978,1032 @@ } }, { - "type": "slice", - "start": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - }, - "stop": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - }, - "step": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] + "type": { + "module": "numpy", + "name": "timedelta64" } } ] } }, "metadata": { - "usage.xarray": 20, - "usage.dask": 22 + "usage.pandas": 19 } }, - "equals": { - "pos_or_kw_required": { - "other": { + "__floordiv__": { + "pos_only_required": { + "_0": { "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" + "module": "numpy", + "name": "timedelta64" } } }, "metadata": { - "usage.xarray": 2 + "usage.pandas": 21 } }, - "floor": { - "var_pos": [ - "args", - { - "type": "str", - "options": [ - "v", - "t" - ] + "__rfloordiv__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "timedelta64" + } } - ], + }, "metadata": { - "usage.xarray": 3 + "usage.pandas": 7 } }, - "ceil": { - "var_pos": [ - "args", - { - "type": "str", - "options": [ - "v", - "t" - ] + "__mod__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "timedelta64" + } } - ], + }, "metadata": { - "usage.xarray": 3 + "usage.pandas": 6 } }, - "round": { - "var_pos": [ - "args", - { - "type": "str", - "options": [ - "v", - "t" - ] + "__rmod__": { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "timedelta64" + } } - ], + }, "metadata": { - "usage.xarray": 3 + "usage.pandas": 5 } }, - "__add__": { + "__rpow__": { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "numpy", + "name": "timedelta64" + } } }, "metadata": { - "usage.xarray": 2, - "usage.pandas": 103, - "usage.dask": 8 + "usage.pandas": 4 } }, - "get_indexer": { - "pos_or_kw_required": { - "target": { + "__pow__": { + "pos_only_required": { + "_0": { "type": { "module": "numpy", - "name": 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{ "type": "union", "options": [ { - "type": "slice", - "start": { - "type": { - "name": "int" - } - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" + "type": { + "module": "numpy", + "name": "ndarray" } }, { "type": { - "name": "int" + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" } } ] } }, "metadata": { - "usage.dask": 5 + "usage.pandas": 17 } } ], - "get_loc": [ - { - "pos_or_kw_required": { - "key": { - "type": { - "name": "float" - } - }, - "method": { - "type": "str", - "options": [ - "nearest" - ] - }, - "tolerance": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, + "to_numpy": [ { - "pos_or_kw_required": { - "key": { - "type": { - "name": "float" - } - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" - } - }, "metadata": { "usage.xarray": 1 } - }, + } + ], + "to_timedelta64": [ { - "pos_or_kw_required": { - "key": { - "type": { - "name": 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"usage.xarray": 5 - } - }, + "__eq__": [ { - "pos_or_kw_required": { - "target": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "method": { - "type": "str", + "pos_only_required": { + "_0": { + "type": "union", "options": [ - "pad" + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } ] - }, - "tolerance": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 58 } }, { - "pos_or_kw_required": { - "target": { + "pos_only_required": { + "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "pandas._libs.tslibs.timedeltas", + "name": "Timedelta" } - }, - "method": { - "type": "str", - "options": [ - "backfill" - ] - }, - "tolerance": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 6 } - }, + } + ], + "__mul__": [ { - "pos_or_kw_required": { - "target": { + "pos_only_required": { + "_0": { "type": { "module": "numpy", "name": "ndarray" } - }, - "method": { - "type": "str", - "options": [ - "nearest" - ] - }, - "tolerance": { - "type": "None" } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 6 } } ], - "identical": [ + "__truediv__": [ { - "pos_or_kw_required": { - "other": { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Float64Index" - } + "pos_only_required": { + "_0": { + "type": "object" } }, "metadata": { - "usage.xarray": 1 + "usage.pandas": 33 } } ], - "__eq__": [ + "__sub__": [ { "pos_only_required": { "_0": { @@ -262977,16 +316158,28 @@ "module": "numpy", "name": "float64" } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } } ] } }, "metadata": { - "usage.pandas": 9 + "usage.pandas": 18 } } ], - "__mul__": [ + "__rtruediv__": [ { "pos_only_required": { "_0": { @@ -262995,20 +316188,20 @@ { "type": { "module": "numpy", - "name": "ndarray" + "name": "timedelta64" } }, { "type": { "module": "numpy", - "name": "timedelta64" + "name": "ndarray" } } ] } }, "metadata": { - "usage.pandas": 5 + "usage.pandas": 3 } } ], @@ -263021,7 +316214,7 @@ { "type": { "module": "numpy", - "name": "timedelta64" + "name": "float64" } }, { @@ -263029,21 +316222,45 @@ "module": "numpy", "name": "ndarray" } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } } ] } }, "metadata": { - "usage.pandas": 2 + "usage.pandas": 7 } } ], - "__rtruediv__": [ + "__floordiv__": [ { "pos_only_required": { "_0": { "type": "union", "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": { + "module": "numpy", + "name": "uint8" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, { "type": { "module": "numpy", @@ -263053,33 +316270,18 @@ { "type": { "module": "numpy", - "name": "ndarray" + "name": "int32" } } ] } }, "metadata": { - "usage.pandas": 2 - } - } - ], - "__rfloordiv__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.pandas": 1 + "usage.pandas": 14 } } ], - "__truediv__": [ + "__radd__": [ { "pos_only_required": { "_0": { @@ -263091,41 +316293,63 @@ "name": "ndarray" } }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "numpy", + "name": "datetime64" + } + }, { "type": { "module": "numpy", "name": "timedelta64" } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } } ] } }, "metadata": { - "usage.pandas": 5 + "usage.pandas": 8 } - } - ], - "__floordiv__": [ + }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "pandas._libs.tslibs.timestamps", + "name": "Timestamp" } } }, "metadata": { - "usage.pandas": 4 + "usage.dask": 1 } } ], - "__add__": [ + "__rsub__": [ { "pos_only_required": { "_0": { "type": "union", "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, { "type": { "module": "numpy", @@ -263138,6 +316362,12 @@ "name": "timedelta64" } }, + { + "type": { + "module": "numpy", + "name": "int64" + } + }, { "type": { "module": "numpy", @@ -263148,11 +316378,39 @@ } }, "metadata": { - "usage.pandas": 3 + "usage.pandas": 10 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "pandas._libs.tslibs.timestamps", + "name": "Timestamp" + } + } + }, + "metadata": { + "usage.dask": 3 } } ], - "__radd__": [ + "__mod__": [ + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "timedelta64" + } + } + }, + "metadata": { + "usage.pandas": 2 + } + } + ], + "__rmod__": [ { "pos_only_required": { "_0": { @@ -263161,7 +316419,7 @@ { "type": { "module": "numpy", - "name": "datetime64" + "name": "ndarray" } }, { @@ -263178,24 +316436,27 @@ } } ], - "__sub__": [ + "__rfloordiv__": [ + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.pandas": 1 + } + } + ], + "__ne__": [ { "pos_only_required": { "_0": { "type": "union", "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "datetime64" - } - }, { "type": { "module": "numpy", @@ -263205,834 +316466,1700 @@ { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } } ] } }, "metadata": { - "usage.pandas": 5 + "usage.pandas": 2 } } ], - "__rsub__": [ + "__gt__": [ + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.pandas": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + } + }, + "metadata": { + "usage.dask": 2 + } + } + ], + "__ge__": [ + { + "pos_only_required": { + "_0": { + "type": { + "module": "pandas._libs.tslibs.timedeltas", + "name": 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+ "metadata": { + "usage.xarray": 1 } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": "list", - "item": { - "type": { - "name": "int" + { + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "str", + "options": [ + "0001-12-30" + ] + }, + "step": { + "type": "None" } } + }, + "metadata": { + "usage.xarray": 1 } }, - "metadata": { - "usage.xarray": 1 - } - } - ], - "method_overloads": { - "__getitem__": [ { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" + "type": "slice", + "start": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeNoLeap" + } + }, + "stop": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeNoLeap" + } + }, + "step": { + "type": "None" } } }, "metadata": { - "usage.xarray": 4 + "usage.xarray": 1 } }, { @@ -264043,12 +318170,13 @@ "type": "None" }, "stop": { - "type": "None" - }, - "step": { "type": { - "name": "int" + "module": "cftime._cftime", + "name": "DatetimeNoLeap" } + }, + "step": { + "type": "None" } } }, @@ -264060,12 +318188,13 @@ "pos_only_required": { "_0": { "type": { - "name": "int" + "module": "cftime._cftime", + "name": "Datetime360Day" } } }, "metadata": { - "usage.xarray": 3 + "usage.xarray": 1 } }, { @@ -264073,11 +318202,15 @@ "_0": { "type": "slice", "start": { - "type": "None" + "type": { + "module": "cftime._cftime", + "name": "Datetime360Day" + } }, "stop": { "type": { - "name": "int" + "module": "cftime._cftime", + "name": "Datetime360Day" } }, "step": { @@ -264086,7 +318219,7 @@ } }, "metadata": { - "usage.xarray": 3 + "usage.xarray": 1 } }, { @@ -264094,13 +318227,12 @@ "_0": { "type": "slice", "start": { - "type": { - "name": "int" - } + "type": "None" }, "stop": { "type": { - "name": "int" + "module": "cftime._cftime", + "name": "Datetime360Day" } }, "step": { @@ -264112,17 +318244,34 @@ "usage.xarray": 1 } }, + { + "pos_only_required": { + "_0": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeJulian" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, { "pos_only_required": { "_0": { "type": "slice", "start": { "type": { - "name": "int" + "module": "cftime._cftime", + "name": "DatetimeJulian" } }, "stop": { - "type": "None" + "type": { + "module": "cftime._cftime", + "name": "DatetimeJulian" + } }, "step": { "type": "None" @@ -264130,7 +318279,7 @@ } }, "metadata": { - "usage.xarray": 3 + "usage.xarray": 1 } }, { @@ -264141,7 +318290,10 @@ "type": "None" }, "stop": { - "type": "None" + "type": { + "module": "cftime._cftime", + "name": "DatetimeJulian" + } }, "step": { "type": "None" @@ -264155,58 +318307,35 @@ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": "slice", - "start": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] - }, - "stop": { - "type": { - "name": "int" - } - }, - "step": { - "type": "None" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "module": "cftime._cftime", + "name": "DatetimeAllLeap" + } } }, "metadata": { - "usage.dask": 11 + "usage.xarray": 1 } - } - ], - "get_loc": [ + }, { - "pos_or_kw_required": { - "key": { - "type": { - "name": "int" + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeAllLeap" + } + }, + "stop": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeAllLeap" + } + }, + "step": { + "type": "None" } - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" } }, "metadata": { @@ -264214,99 +318343,93 @@ } }, { - "pos_or_kw_required": { - "key": { - "type": { - "module": "numpy", - "name": "int64" + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeAllLeap" + } + }, + "step": { + "type": "None" } - }, - "method": { - "type": "str", - "options": [ - "nearest" - ] } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1 } }, { - "pos_or_kw_required": { - "key": { + "pos_only_required": { + "_0": { "type": { - "module": "numpy", - "name": "float64" + "module": "cftime._cftime", + "name": "DatetimeGregorian" } - }, - "method": { - "type": "str", - "options": [ - "nearest" - ] } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1 } }, { - "pos_or_kw_required": { - "key": { - "type": { - "name": "float" + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeGregorian" + } + }, + "stop": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeGregorian" + } + }, + "step": { + "type": "None" } - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" } }, "metadata": { "usage.xarray": 1 } - } - ], - "get_indexer": [ + }, { - "pos_or_kw_required": { - "target": { - "type": { - "module": "numpy", - "name": "ndarray" + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeGregorian" + } + }, + "step": { + "type": "None" } - }, - "method": { - "type": "None" - }, - "tolerance": { - "type": "None" } }, "metadata": { - "usage.xarray": 3 + "usage.xarray": 1 } }, { - "pos_or_kw_required": { - "target": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "method": { - "type": "str", - "options": [ - "backfill" - ] - }, - "tolerance": { + "pos_only_required": { + "_0": { "type": { - "name": "int" + "module": "cftime._cftime", + "name": "DatetimeProlepticGregorian" } } }, @@ -264315,22 +318438,23 @@ } }, { - "pos_or_kw_required": { - "target": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "method": { - "type": "str", - "options": [ - "backfill" - ] - }, - "tolerance": { - "type": { - "name": "float" + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeProlepticGregorian" + } + }, + "stop": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeProlepticGregorian" + } + }, + "step": { + "type": "None" } } }, @@ -264339,21 +318463,21 @@ } }, { - "pos_or_kw_required": { - "target": { - "type": { - "module": "numpy", - "name": "ndarray" + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeProlepticGregorian" + } + }, + "step": { + "type": "None" } - }, - "method": { - "type": "str", - "options": [ - "backfill" - ] - }, - "tolerance": { - "type": "None" } }, "metadata": { @@ -264361,21 +318485,11 @@ } }, { - "pos_or_kw_required": { - "target": { + "pos_only_required": { + "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } - }, - "method": { - "type": "str", - "options": [ - "pad" - ] - }, - "tolerance": { - "type": "None" } }, "metadata": { @@ -264383,28 +318497,35 @@ } }, { - "pos_or_kw_required": { - "target": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "method": { - "type": "str", - "options": [ - "pad" - ] - }, - "tolerance": { - "type": { - "name": "float" + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" + } } } }, "metadata": { "usage.xarray": 1 } + }, + { + "pos_only_required": { + "_0": { + "type": "object" + } + }, + "metadata": { + "usage.dask": 84 + } } ], "equals": [ @@ -264412,445 +318533,389 @@ "pos_or_kw_required": { "other": { "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" + "module": "pandas.core.series", + "name": "Series" } } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 8, + "usage.dask": 3 } } ], - "copy": [ + "dropna": [ { - "pos_or_kw_required": { - "deep": { - "type": { - "name": "bool" - } - } - }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 2, + "usage.dask": 7 } } ], - "drop": [ + "groupby": [ { "pos_or_kw_required": { - "labels": { + "by": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "pandas.core.resample", + "name": "TimeGrouper" } - }, - "errors": { - "type": "str", - "options": [ - "raise" - ] } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1 } }, { - "pos_or_kw_required": { - "labels": { + "pos_or_kw_optional": { + "by": { + "type": "object" + }, + "group_keys": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "bool" } }, - "errors": { - "type": "str", + "level": { + "type": "union", "options": [ - "ignore" + { + "type": "list", + "item": { + "type": { + "name": "int" + } + } + }, + { + "type": { + "name": "int" + } + } ] + }, + "sort": { + "type": { + "name": "bool" + } } }, + "pos_or_kw_optional_ordering": [ + [ + "by", + "group_keys" + ], + [ + "level", + "sort" + ] + ], "metadata": { - "usage.xarray": 1 + "usage.dask": 64 } } ], - "__iter__": [ + "isnull": [ { "metadata": { - "usage.xarray": 2, - "usage.dask": 4 + "usage.xarray": 1, + "usage.dask": 5 } } ], - "__eq__": [ + "any": [ { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] + "metadata": { + "usage.xarray": 1, + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_optional": { + "axis": { + "type": { + "name": "int" + } + }, + "skipna": { + "type": { + "name": "bool" + } } }, + "pos_or_kw_optional_ordering": [ + [ + "axis", + "skipna" + ] + ], "metadata": { - "usage.pandas": 9 + "usage.dask": 8 } } ], - "__rmul__": [ + "duplicated": [ { - "pos_only_required": { - "_0": { - "type": "union", + "pos_or_kw_required": { + "keep": { + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } + "last" ] } }, "metadata": { - "usage.pandas": 5 + "usage.xarray": 1 } } ], - "__mul__": [ + "__invert__": [ { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - } - ] - } - }, "metadata": { - "usage.pandas": 6 + "usage.xarray": 1, + "usage.dask": 1 } } ], - "__rtruediv__": [ + "where": [ { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "pos_or_kw_required": { + "cond": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } } }, "metadata": { - "usage.pandas": 4 + "usage.xarray": 1 } - } - ], - "__floordiv__": [ + }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "cond": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + } + }, + "pos_or_kw_optional": { + "axis": { + "type": { + "name": "int" + } + }, + "other": { "type": "union", "options": [ { "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" + "module": "pandas.core.series", + "name": "Series" } }, { "type": { "module": "numpy", - "name": "ndarray" + "name": "float64" } }, { "type": { - "module": "numpy", - "name": "uint64" + "name": "float" } } ] } }, "metadata": { - "usage.pandas": 11 + "usage.dask": 8 } } ], - "__rfloordiv__": [ + "median": [ { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, "metadata": { - "usage.pandas": 1 + "usage.xarray": 1 } } ], - "__truediv__": [ + "unstack": [ { - "pos_only_required": { - "_0": { - "type": "union", + "metadata": { + "usage.xarray": 1 + } + } + ], + "resample": [ + { + "pos_or_kw_required": { + "rule": { + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - } + "24H" ] } }, "metadata": { - "usage.pandas": 5 + "usage.xarray": 2 } - } - ], - "__add__": [ + }, { - "pos_only_required": { - "_0": { - "type": "union", + "pos_or_kw_required": { + "rule": { + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - }, - { - "type": { - "module": "numpy", - "name": "datetime64" - } - } + "24H" + ] + }, + "loffset": { + "type": "str", + "options": [ + "-12H" ] } }, "metadata": { - "usage.pandas": 3 + "usage.xarray": 2 } - } - ], - "__radd__": [ + }, { - "pos_only_required": { - "_0": { - "type": "union", + "pos_or_kw_required": { + "rule": { + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "datetime64" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - } + "3H" ] } }, "metadata": { - "usage.pandas": 4 + "usage.xarray": 8 } - } - ], - "__sub__": [ + }, { - "pos_only_required": { - "_0": { - "type": "union", + "pos_or_kw_required": { + "rule": { + "type": "str", "options": [ - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - }, - { - "type": { - "module": "numpy", - "name": "datetime64" - } - } + "1H" ] } }, "metadata": { - "usage.pandas": 3 + "usage.xarray": 2 } - } - ], - "__rsub__": [ + }, { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "rule": { + "type": "object" + } + }, + "pos_or_kw_optional": { + "closed": { "type": "union", "options": [ { - "type": { - "module": "numpy", - "name": "datetime64" - } + "type": "None" }, { - "type": { - "module": "numpy", - "name": "timedelta64" - } + "type": "str", + "options": [ + "left", + "right" + ] + } + ] + }, + "label": { + "type": "union", + "options": [ + { + "type": "None" + }, + { + "type": "str", + "options": [ + "left", + "right" + ] } ] } }, + "pos_or_kw_optional_ordering": [ + [ + "closed", + "label" + ] + ], "metadata": { - "usage.pandas": 4 + "usage.dask": 88 } } ], - "__mod__": [ + "to_frame": [ { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, "metadata": { - "usage.pandas": 3 + "usage.xarray": 2, + "usage.sklearn": 2 } - } - ], - "__contains__": [ + }, { - "pos_only_required": { - "_0": { + "pos_or_kw_optional": { + "name": { "type": "union", "options": [ { - "type": "None" - }, - { - "type": "str" + "type": "str", + "options": [ + "a", + "A", + "__series__", + "bar", + "s" + ] }, { - "type": { - "name": "int" - } + "type": "None" } ] } }, "metadata": { - "usage.dask": 8 + "usage.dask": 29 } } ], - "__le__": [ + "__setitem__": [ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } - ] + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "name": "float" + } } }, "metadata": { - "usage.dask": 3 + "usage.xarray": 2 } - } - ], - "__ge__": [ + }, { "pos_only_required": { "_0": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + }, + "_1": { "type": "union", "options": [ { @@ -264871,84 +318936,203 @@ } } ], - "min": [ + "shift": [ { + "pos_or_kw_required": { + "periods": { + "type": { + "name": "int" + } + } + }, "metadata": { - "usage.dask": 1 + "usage.xarray": 1 } - } - ], - "max": [ + }, { + "pos_or_kw_optional": { + "periods": { + "type": { + "name": "int" + } + }, + "freq": { + "type": "object" + } + }, + "pos_or_kw_optional_ordering": [ + [ + "periods", + "freq" + ] + ], "metadata": { - "usage.dask": 1 + "usage.dask": 32 } } ], - "drop_duplicates": [ + "rolling": [ { + "pos_or_kw_required": { + "window": { + "type": { + "name": "int" + } + }, + "min_periods": { + "type": "None" + }, + "center": { + "type": { + "name": "bool" + } + } + }, "metadata": { - "usage.dask": 2 + "usage.xarray": 1 } - } - ], - "isin": [ + }, { "pos_or_kw_required": { - "values": { + "window": { + "type": { + "name": "int" + } + }, + "min_periods": { + "type": { + "name": "int" + } + }, + "center": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_or_kw_required": { + "window": { "type": "union", "options": [ { "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "module": "dask.delayed", - "name": "Delayed" + "module": "pandas.tseries.offsets", + "name": "Second" } }, { "type": { - "module": "dask.delayed", - "name": "DelayedLeaf" + "name": "int" } }, { - "type": "list", - "item": { - "type": { - "name": "int" - } - } + "type": "str", + "options": [ + "3S", + "2S", + "1S" + ] } ] } }, + "pos_or_kw_optional": { + "min_periods": { + "type": "None" + }, + "win_type": { + "type": "None" + }, + "axis": { + "type": { + "name": "int" + } + }, + "center": { + "type": { + "name": "bool" + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "center", + "win_type" + ], + [ + "min_periods", + "center" + ], + [ + "win_type", + "axis" + ], + [ + "min_periods", + "win_type" + ] + ], "metadata": { - "usage.dask": 4 + "usage.dask": 24 } } ], - "dropna": [ + "iteritems": [ { "metadata": { - "usage.dask": 1 + "usage.xarray": 1, + "usage.dask": 4 } } ], - "__gt__": [ + "sum": [ { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "skipna": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "skipna": { + "type": { + "name": "bool" + } + }, + "min_count": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_optional": { + "axis": { "type": "union", "options": [ { - "type": { - "name": "float" - } + "type": "str", + "options": [ + "columns" + ] + }, + { + "type": "None" }, { "type": { @@ -264956,983 +319140,990 @@ } } ] - } - }, - "metadata": { - "usage.dask": 2 - } - } - ], - "set_names": [ - { - 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"module": "pandas.core.series", + "name": "Series" + } + } + ] + } + }, + "metadata": { + "usage.dask": 4 + } } - }, - "metadata": { - "usage.dask": 1 - } - }, - "method_overloads": { - "__mul__": [ + ], + "__le__": [ { "pos_only_required": { "_0": { @@ -265947,18 +320138,58 @@ { "type": { "module": "numpy", - "name": "timedelta64" + "name": "float64" } } ] } }, "metadata": { - "usage.pandas": 5 + "usage.pandas": 7 + } + }, + { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": { + "name": "float" + } + }, + { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" + } + } + ] + } + }, + "metadata": { + "usage.dask": 16 } } ], - "__rmul__": [ + "__rfloordiv__": [ { "pos_only_required": { "_0": { @@ -265967,7 +320198,13 @@ { "type": { "module": "numpy", - "name": "timedelta64" + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" } }, { @@ -265975,16 +320212,22 @@ "module": "numpy", "name": "ndarray" } + }, + { + "type": { + "module": "numpy", + "name": "timedelta64" + } } ] } }, "metadata": { - "usage.pandas": 2 + "usage.pandas": 6 } } ], - "__rtruediv__": [ + "__floordiv__": [ { "pos_only_required": { "_0": { @@ -266006,26 +320249,33 @@ } }, "metadata": { - "usage.pandas": 2 + "usage.pandas": 6 } - } - ], - "__rfloordiv__": [ + }, { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] } }, "metadata": { - "usage.pandas": 1 + "usage.dask": 3 } } ], - "__truediv__": [ + "__mod__": [ { "pos_only_required": { "_0": { @@ -266034,39 +320284,36 @@ { "type": { "module": "numpy", - "name": "ndarray" + "name": "timedelta64" } }, { "type": { "module": "numpy", - "name": "timedelta64" + "name": "ndarray" } } ] } }, "metadata": { - "usage.pandas": 5 + "usage.pandas": 57 } - } - ], - "__floordiv__": [ + }, { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } }, "metadata": { - "usage.pandas": 4 + "usage.dask": 5 } } ], - "__add__": [ + "__rpow__": [ { "pos_only_required": { "_0": { @@ -266075,7 +320322,13 @@ { "type": { "module": "numpy", - "name": "datetime64" + "name": "float64" + } + }, + { + "type": { + "module": "numpy", + "name": "int64" } }, { @@ -266088,11 +320341,11 @@ } }, "metadata": { - "usage.pandas": 2 + "usage.pandas": 10 } } ], - "__radd__": [ + "__gt__": [ { "pos_only_required": { "_0": { @@ -266101,50 +320354,84 @@ { "type": { "module": "numpy", - "name": "datetime64" + "name": "timedelta64" } }, { "type": { "module": "numpy", - "name": "timedelta64" + "name": "ndarray" + } + } + ] + } + }, + "metadata": { + "usage.pandas": 5 + } + }, + { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "int64" + } + }, + { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + }, + { + "type": { + "name": "int" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" } } ] } }, "metadata": { - "usage.pandas": 2 + "usage.dask": 59 + } + } + ], + "__ge__": [ + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } + }, + "metadata": { + "usage.pandas": 7 } - } - ], - "__sub__": [ + }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "datetime64" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - } - ] + "type": "object" } }, "metadata": { - "usage.pandas": 2 + "usage.dask": 17 } } ], - "__rsub__": [ + "__rmod__": [ { "pos_only_required": { "_0": { @@ -266153,7 +320440,7 @@ { "type": { "module": "numpy", - "name": "datetime64" + "name": "float64" } }, { @@ -266170,22 +320457,20 @@ } } ], - "__mod__": [ + "__pow__": [ { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "ndarray" + "name": "timedelta64" } } }, "metadata": { - "usage.pandas": 3 + "usage.pandas": 1 } - } - ], - "__eq__": [ + }, { "pos_only_required": { "_0": { @@ -266193,1773 +320478,1970 @@ "options": [ { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, { "type": { - "module": "numpy", - "name": "uint64" + "name": "float" } } ] } }, "metadata": { - "usage.pandas": 7 + "usage.dask": 5 } } - ] - }, - "methods": { - "__mul__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } + ], + "__ror__": [ + { + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" } - ] + } + }, + "metadata": { + "usage.pandas": 1 } }, - "metadata": { - "usage.pandas": 5 - } - }, - 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"type": { - "module": "numpy", - "name": "int64" + "name": "int" } + }, + { + "type": "None" } ] } }, + "pos_or_kw_optional_ordering": [ + [ + "axis", + "limit" + ], + [ + "value", + "method" + ], + [ + "method", + "axis" + ], + [ + "method", + "limit" + ] + ], "metadata": { - "usage.pandas": 23 + "usage.dask": 23 } - }, + } + ], + "nlargest": [ { - "pos_only_required": { - "_0": { - "type": "object" + "pos_or_kw_required": { + "n": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.dask": 8 + "usage.dask": 4 } } ], - "__sub__": [ + "to_timestamp": [ { - "pos_only_required": { - "_0": { + "pos_or_kw_optional": { + "freq": { + "type": "union", + "options": [ + { + "type": "str", + "options": [ + "M" + ] + }, + { + "type": "None" + } + ] + }, + "how": { + "type": "str", + "options": [ + "s", + "start" + ] + }, + "copy": { + "type": { + "name": "int" + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "freq", + "how" + ], + [ + "how", + "copy" + ] + ], + "metadata": { + "usage.dask": 4 + } + } + ], + "abs": [ + { + "metadata": { + "usage.dask": 3 + } + } + ], + "cov": [ + { + "pos_or_kw_required": { + "other": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + } + }, + "pos_or_kw_optional": { + "min_periods": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.dask": 2 + } + } + ], + "replace": [ + { + "pos_or_kw_required": { + "to_replace": { "type": "union", "options": [ + { + "type": "dict", + "key": { + "type": { + "name": "int" + } + }, + "value": { + "type": { + "name": "int" + } + } + }, { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } }, + { + "type": "str", + "options": [ + "c" + ] + } + ] + } + }, + "pos_or_kw_optional": { + "regex": { + "type": { + "name": "bool" + } + }, + "value": { + "type": "union", + "options": [ + { + "type": "None" + }, { "type": { - "module": "numpy", - "name": "timedelta64" + "name": "float" } }, { "type": { - "module": "numpy", - "name": "int64" + "name": "int" } } ] } }, + "pos_or_kw_optional_ordering": [ + [ + "value", + "regex" + ] + ], + "metadata": { + "usage.dask": 5 + } + } + ], + "autocorr": [ + { + "pos_or_kw_required": { + "lag": { + "type": { + "name": "int" + } + } + }, "metadata": { - "usage.pandas": 14 + "usage.dask": 4 } - }, + } + ], + "nsmallest": [ { - "pos_only_required": { - "_0": { - "type": "object" + "pos_or_kw_required": { + "n": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.dask": 2 + } + } + ], + "idxmax": [ + { + "pos_or_kw_optional": { + "axis": { + "type": { + "name": "int" + } + }, + "skipna": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.dask": 8 + } + } + ], + "idxmin": [ + { + "pos_or_kw_required": { + "skipna": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.dask": 8 + "usage.dask": 4 } } ], - "__mul__": [ + "diff": [ { - "pos_only_required": { - "_0": { + "pos_or_kw_optional": { + "periods": { "type": { - "module": "numpy", - "name": "ndarray" + "name": "int" } } }, "metadata": { - "usage.pandas": 1 + "usage.dask": 7 } } ], - "__floordiv__": [ + "first": [ { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "pos_or_kw_required": { + "offset": { + "type": "str" } }, "metadata": { - "usage.pandas": 1 + "usage.dask": 24 } } ], - "__truediv__": [ + "last": [ { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "pos_or_kw_required": { + "offset": { + "type": "str" } }, "metadata": { - "usage.pandas": 1 + "usage.dask": 8 } } ], - "__pow__": [ + "take": [ { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "indices": { "type": { "module": "numpy", "name": "ndarray" @@ -267967,142 +322449,103 @@ } }, "metadata": { - "usage.pandas": 1 + "usage.dask": 1 } } ], - "__mod__": [ + "mode": [ { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, "metadata": { - "usage.pandas": 1 + "usage.dask": 1 } } ], - "__eq__": [ + "explode": [ { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "datetime64" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] - } - }, "metadata": { - "usage.pandas": 8 + "usage.dask": 5 } - }, + } + ], + "__itruediv__": [ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" - } - }, - { - "type": "str", - "options": [ - "2000" - ] - } - ] + "type": { + "module": "pandas.core.series", + "name": "Series" + } } }, "metadata": { - "usage.dask": 43 + "usage.dask": 1 } } ], - "__ne__": [ + "reindex": [ { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "index": { "type": "union", "options": [ { "type": { - "module": "numpy", - "name": "int64" + "module": "pandas.core.indexes.datetimes", + "name": "DatetimeIndex" } }, { "type": { - "module": "numpy", - "name": "ndarray" + "module": "pandas.core.indexes.multi", + "name": "MultiIndex" } }, { "type": { - "module": "numpy", - "name": "datetime64" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } }, { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } } ] } }, "metadata": { - "usage.pandas": 4 + "usage.dask": 8 } - }, + } + ], + "items": [ { - "pos_only_required": { - "_0": { + "metadata": { + "usage.dask": 1 + } + } + ], + "append": [ + { + "pos_or_kw_required": { + "to_append": { "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" + "module": "pandas.core.series", + "name": "Series" } } }, "metadata": { - "usage.dask": 10 + "usage.dask": 2 } } ], - "__ge__": [ + "__array_wrap__": [ { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "result": { "type": { "module": "numpy", "name": "ndarray" @@ -268110,182 +322553,810 @@ } }, "metadata": { - "usage.pandas": 2 + "usage.dask": 3 + } + } + ] + }, + "methods": { + "rank": { + "pos_or_kw_required": { + "method": { + "type": "str", + "options": [ + "dense" + ] + }, + "ascending": { + "type": { + "name": "bool" + } } }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" - } - }, - { - "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" - } - }, - { + "metadata": { + "usage.xarray": 1 + } + }, + "astype": { + "pos_or_kw_required": { + "dtype": { + "type": "union", + "options": [ + { + "type": { + "module": "pandas.core.dtypes.dtypes", + "name": "CategoricalDtype" + } + }, + { + "type": { + "module": "numpy", + "name": "dtype" + } + }, + { + "type": "str" + }, + { + "type": "type" + } + ] + } + }, + "pos_or_kw_optional": { + "copy": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.xarray": 1, + "usage.dask": 53, + "usage.sklearn": 10 + } + }, + "__getitem__": { + "pos_only_required": { + "_0": { + "type": "object" + } + }, + "metadata": { + "usage.xarray": 23, + "usage.dask": 84 + } + }, + "equals": { + "pos_or_kw_required": { + "other": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + } + }, + "metadata": { + "usage.xarray": 8, + "usage.dask": 3 + } + }, + "dropna": { + "metadata": { + "usage.xarray": 2, + "usage.dask": 7 + } + }, + "groupby": { + "pos_or_kw_optional": { + "by": { + "type": "object" + }, + "group_keys": { + "type": { + "name": "bool" + } + }, + "level": { + "type": "union", + "options": [ + { + "type": "list", + "item": { "type": { - "module": "numpy", - "name": "datetime64" + "name": "int" } } - ] + }, + { + "type": { + "name": "int" + } + } + ] + }, + "sort": { + "type": { + "name": "bool" + } + } + }, + "pos_or_kw_optional_ordering": [ + [ + "by", + "group_keys" + ], + [ + "level", + "sort" + ] + ], + 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}, - "pos_or_kw_optional": { - "unit": { - "type": "str" - }, - "errors": { - "type": "str", - "options": [ - "coerce", - "raise" - ] - } - }, - "pos_or_kw_optional_ordering": [ - [ - "unit", - "errors" - ] - ], - "metadata": { - "usage.xarray": 54, - "usage.dask": 16 - } - } - } - }, - "pandas._libs.tslibs.timedeltas": { - "classes": { - "Timedelta": { - "constructor_overloads": [ - { + "autocorr": { "pos_or_kw_required": { - "value": { + "lag": { "type": { - "module": "numpy", - "name": "timedelta64" + "name": "int" } } }, "metadata": { - "usage.xarray": 2 + "usage.dask": 4 } }, - { + "nsmallest": { "pos_or_kw_required": { - "value": { - "type": "str", - "options": [ - "0 days" - ] + "n": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 2 } }, - { - "pos_or_kw_required": { - "value": { - "type": "str", - "options": [ - "0h" - ] + "idxmax": { + "pos_or_kw_optional": { + "axis": { + "type": { + "name": "int" + } + }, + "skipna": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 8 } }, - { + "idxmin": { "pos_or_kw_required": { - "value": { - "type": "str", - "options": [ - "10 days 1 hour" - ] + "skipna": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 4 } }, - { - "pos_or_kw_required": { - "value": { - "type": "str", - "options": [ - "-3 days" - ] + "diff": { + "pos_or_kw_optional": { + "periods": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 7 } }, - { + "first": { "pos_or_kw_required": { - "value": { - "type": "str", - "options": [ - "3 hours" - ] + "offset": { + "type": "str" } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 24 } }, - { + "last": { "pos_or_kw_required": { - "value": { - "type": "str", - "options": [ - "NaT" - ] + "offset": { + "type": "str" } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 8 } }, - { + "take": { "pos_or_kw_required": { - "value": { + "indices": { "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 1 } }, - { + "mode": { + "metadata": { + "usage.dask": 1 + } + }, + "explode": { + "metadata": { + "usage.dask": 5 + } + }, + "__itruediv__": { + "pos_only_required": { + "_0": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + } + }, + "metadata": { + "usage.dask": 1 + } + }, + "reindex": { "pos_or_kw_required": { - "value": { - "type": "str", + "index": { + "type": "union", "options": [ - "1 day" + { + "type": { + "module": "pandas.core.indexes.datetimes", + "name": "DatetimeIndex" + } + }, + { + "type": { + "module": "pandas.core.indexes.multi", + "name": "MultiIndex" + } + }, + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + }, + { + "type": { + "module": "numpy", + "name": "ndarray" + } + } ] } }, "metadata": { - "usage.xarray": 1 + "usage.dask": 8 } }, - { + "items": { + "metadata": { + "usage.dask": 1 + } + }, + "append": { "pos_or_kw_required": { - "value": { - "type": "str", - "options": [ - "1", - "1 hours" - ] - }, - "unit": { - "type": "str", - "options": [ - "ms" - ] + "to_append": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } } }, "metadata": { "usage.dask": 2 } - } - ], - "constructor": { - "pos_or_kw_required": { - "value": { - "type": "str", - "options": [ - "1", - "1 hours" - ] + }, + "__array_wrap__": { + "pos_or_kw_required": { + "result": { + "type": { + "module": "numpy", + "name": "ndarray" + } + } }, - "unit": { - "type": "str", - "options": [ - "ms" - ] + "metadata": { + "usage.dask": 3 } - }, - "metadata": { - "usage.dask": 2 } }, - "method_overloads": { - "__add__": [ + "classmethod_overloads": { + "__ne__": [ { "pos_only_required": { "_0": { - "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" + "type": "type", + "name": { + "module": "pandas.core.series", + "name": "Series" } } }, "metadata": { - "usage.xarray": 2 + "usage.dask": 4 } }, { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 17 + "usage.pandas": 6 } } ], - "to_numpy": [ + "__rmod__": [ { + "pos_only_required": { + "_0": { + "type": "str" + } + }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 + } + } + ] + }, + "classmethods": { + "__ne__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + { + "type": "type", + "name": { + "module": "pandas.core.series", + "name": "Series" + } + } + ] + } + }, + "metadata": { + "usage.dask": 4, + "usage.pandas": 6 + } + }, + "__rmod__": { + "pos_only_required": { + "_0": { + "type": "union", + "options": [ + { + "type": { + "module": "numpy", + "name": "timedelta64" + } + }, + { + "type": { + "module": "numpy", + "name": "float64" + } + }, + { + "type": "str" + } + ] } + }, + "metadata": { + "usage.sklearn": 1, + "usage.pandas": 2 + } + } + }, + "properties": { + "values": [ + { + "usage.xarray": 16, + "usage.dask": 40 + }, + { + "type": "bottom" } ], - "to_timedelta64": [ + "loc": [ { - "metadata": { - "usage.xarray": 1 - } + "usage.xarray": 3, + "usage.dask": 25, + "usage.sklearn": 1 + }, + { + "type": "bottom" } ], - "total_seconds": [ + "iloc": [ { - "metadata": { - "usage.xarray": 1 - } + "usage.xarray": 3, + "usage.dask": 31, + "usage.sklearn": 1 + }, + { + "type": "bottom" } ], - "__eq__": [ + "index": [ { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - } - ] + "usage.xarray": 23, + "usage.dask": 83, + "usage.sklearn": 3 + }, + { + "type": "object" + } + ], + "dt": [ + { + "usage.xarray": 2, + "usage.dask": 2 + }, + { + "type": "bottom" + } + ], + "name": [ + { + "usage.xarray": 5, + "usage.dask": 48, + "usage.sklearn": 6 + }, + { + "type": "union", + "options": [ + { + "type": "str" + }, + { + "type": "None" } - }, - "metadata": { - "usage.pandas": 58 - } + ] + } + ], + "dtype": [ + { + "usage.dask": 65, + "usage.sklearn": 9 + }, + { + "type": "bottom" + } + ], + "_data": [ + { + "usage.dask": 1 + }, + { + "type": "bottom" + } + ], + "cat": [ + { + "usage.dask": 28 + }, + { + "type": "bottom" + } + ], + "str": [ + { + "usage.dask": 26 + }, + { + "type": "bottom" + } + ], + "div": [ + { + "usage.dask": 1 + }, + { + "type": "bottom" + } + ], + "divide": [ + { + "usage.dask": 1 + }, + { + "type": "bottom" + } + ], + "rdiv": [ + { + "usage.dask": 1 + }, + { + "type": "bottom" + } + ], + "__class__": [ + { + "usage.dask": 6, + "usage.sklearn": 1 + }, + { + "type": "bottom" + } + ], + "shape": [ + { + "usage.dask": 7, + "usage.sklearn": 12 + }, + { + "type": "bottom" + } + ], + "ndim": [ + { + "usage.dask": 6, + "usage.sklearn": 4 + }, + { + "type": "bottom" + } + ], + "_values": [ + { + "usage.dask": 3 + }, + { + "type": "bottom" + } + ], + "_constructor": [ + { + "usage.dask": 2 + }, + { + "type": "bottom" + } + ], + "size": [ + { + "usage.dask": 2 + }, + { + "type": "bottom" + } + ], + "empty": [ + { + "usage.dask": 1 }, { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" - } - } - }, - "metadata": { - "usage.dask": 6 - } + "type": "bottom" } ], - "__mul__": [ + "nbytes": [ { - "pos_only_required": { - "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.pandas": 6 - } + "usage.dask": 2 + }, + { + "type": "bottom" } ], - "__truediv__": [ + "dtypes": [ { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.pandas": 33 - } + "usage.sklearn": 2 + }, + { + "type": "bottom" + } + ] + }, + "classproperties": { + "__name__": [ + { + "usage.dask": 7, + "usage.sklearn": 1 + }, + { + "type": "bottom" } ], - "__sub__": [ + "__module__": [ + { + "usage.dask": 4 + }, + { + "type": "bottom" + } + ] + } + } + } + }, + "pandas._libs.tslibs.nattype": { + "classes": { + "NaTType": { + "method_overloads": { + "__add__": [ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - } - ] + "type": { + "module": "pandas._libs.tslibs.timestamps", + "name": "Timestamp" + } } }, "metadata": { - "usage.pandas": 18 + "usage.xarray": 2 } - } - ], - "__rtruediv__": [ + }, { "pos_only_required": { "_0": { @@ -271459,11 +325918,11 @@ } }, "metadata": { - "usage.pandas": 3 + "usage.pandas": 9 } } ], - "__rmul__": [ + "__sub__": [ { "pos_only_required": { "_0": { @@ -271472,7 +325931,7 @@ { "type": { "module": "numpy", - "name": "float64" + "name": "timedelta64" } }, { @@ -271480,140 +325939,48 @@ "module": "numpy", "name": "ndarray" } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } } ] } }, "metadata": { - "usage.pandas": 7 + "usage.pandas": 9 } } ], - "__floordiv__": [ + "__truediv__": [ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "uint8" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - }, - { - "type": { - "module": "numpy", - "name": "int32" - } - } - ] + "type": "object" } }, "metadata": { - "usage.pandas": 14 + "usage.pandas": 21 } } ], - "__radd__": [ - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "datetime64" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] - } - }, - "metadata": { - "usage.pandas": 8 - } - }, + "__rtruediv__": [ { "pos_only_required": { "_0": { "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.dask": 1 + "usage.pandas": 1 } } ], - "__rsub__": [ + "__radd__": [ { "pos_only_required": { "_0": { "type": "union", "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, { "type": { "module": "numpy", @@ -271623,43 +325990,24 @@ { "type": { "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "datetime64" + "name": "ndarray" } } ] } }, "metadata": { - "usage.pandas": 10 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas._libs.tslibs.timestamps", - "name": "Timestamp" - } - } - }, - "metadata": { - "usage.dask": 3 + "usage.pandas": 8 } } ], - "__mod__": [ + "__rsub__": [ { "pos_only_required": { "_0": { "type": { "module": "numpy", - "name": "timedelta64" + "name": "ndarray" } } }, @@ -271668,7 +326016,7 @@ } } ], - "__rmod__": [ + "__ne__": [ { "pos_only_required": { "_0": { @@ -271677,7 +326025,7 @@ { "type": { "module": "numpy", - "name": "ndarray" + "name": "datetime64" } }, { @@ -271690,26 +326038,26 @@ } }, "metadata": { - "usage.pandas": 2 + "usage.pandas": 3 } } ], - "__rfloordiv__": [ + "__ge__": [ { "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "pandas._libs.tslibs.nattype", + "name": "NaTType" } } }, "metadata": { - "usage.pandas": 1 + "usage.dask": 2 } } ], - "__ne__": [ + "__le__": [ { "pos_only_required": { "_0": { @@ -271717,21 +326065,21 @@ "options": [ { "type": { - "module": "numpy", - "name": "timedelta64" + "module": "pandas.core.series", + "name": "Series" } }, { "type": { - "module": "numpy", - "name": "ndarray" + "module": "pandas._libs.tslibs.nattype", + "name": "NaTType" } } ] } }, "metadata": { - "usage.pandas": 2 + "usage.dask": 3 } } ], @@ -271740,56 +326088,28 @@ "pos_only_required": { "_0": { "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.pandas": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - } - }, - "metadata": { - "usage.dask": 2 - } - } - ], - "__ge__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" + "module": "pandas._libs.tslibs.nattype", + "name": "NaTType" } } }, "metadata": { - "usage.dask": 3 + "usage.dask": 1 } } ], - "__le__": [ + "__lt__": [ { "pos_only_required": { "_0": { "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" + "module": "pandas._libs.tslibs.nattype", + "name": "NaTType" } } }, "metadata": { - "usage.dask": 3 + "usage.dask": 1 } } ] @@ -271803,19 +326123,13 @@ { "type": { "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" + "name": "ndarray" } }, { "type": { "module": "numpy", - "name": "ndarray" + "name": "timedelta64" } }, { @@ -271829,53 +326143,20 @@ }, "metadata": { "usage.xarray": 2, - "usage.pandas": 17 - } - }, - "to_numpy": { - "metadata": { - "usage.xarray": 1 - } - }, - "to_timedelta64": { - "metadata": { - "usage.xarray": 1 - } - }, - "total_seconds": { - "metadata": { - "usage.xarray": 1 + "usage.pandas": 9 } }, - "__eq__": { + "__sub__": { "pos_only_required": { "_0": { "type": "union", "options": [ - { - "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" - } - }, { "type": { "module": "numpy", "name": "timedelta64" } }, - { - "type": { - "module": "numpy", - "name": "int64" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, { "type": { "module": "numpy", @@ -271886,50 +326167,37 @@ } }, "metadata": { - "usage.pandas": 58, - "usage.dask": 6 + "usage.pandas": 9 } }, - "__mul__": { + "__truediv__": { "pos_only_required": { "_0": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "type": "object" } }, "metadata": { - "usage.pandas": 6 + "usage.pandas": 21 } }, - "__truediv__": { + "__rtruediv__": { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 33 + "usage.pandas": 1 } }, - "__sub__": { + "__radd__": { "pos_only_required": { "_0": { "type": "union", "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, { "type": { "module": "numpy", @@ -271939,41 +326207,30 @@ { "type": { "module": "numpy", - "name": "int64" + "name": "ndarray" } } ] } }, "metadata": { - "usage.pandas": 18 + "usage.pandas": 8 } }, - "__rtruediv__": { + "__rsub__": { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.pandas": 3 + "usage.pandas": 2 } }, - "__rmul__": { + "__ne__": { "pos_only_required": { "_0": { "type": "union", @@ -271981,13 +326238,7 @@ { "type": { "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" + "name": "datetime64" } }, { @@ -272000,211 +326251,660 @@ } }, "metadata": { - "usage.pandas": 7 + "usage.pandas": 3 } }, - "__floordiv__": { + "__ge__": { + "pos_only_required": { + "_0": { + "type": { + "module": "pandas._libs.tslibs.nattype", + "name": "NaTType" + } + } + }, + "metadata": { + "usage.dask": 2 + } + }, + "__le__": { "pos_only_required": { "_0": { "type": "union", "options": [ { "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "uint8" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" + "module": "pandas.core.series", + "name": "Series" } }, { "type": { - "module": "numpy", - "name": "int32" + "module": "pandas._libs.tslibs.nattype", + "name": "NaTType" } } ] } }, "metadata": { - "usage.pandas": 14 + "usage.dask": 3 } }, - "__radd__": { + "__gt__": { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "pandas._libs.tslibs.nattype", + "name": "NaTType" + } } }, "metadata": { - "usage.pandas": 8, "usage.dask": 1 } }, - "__rsub__": { + "__lt__": { "pos_only_required": { "_0": { - "type": "object" + "type": { + "module": "pandas._libs.tslibs.nattype", + "name": "NaTType" + } } }, "metadata": { - "usage.pandas": 10, - "usage.dask": 3 + "usage.dask": 1 } - }, - "__mod__": { + } + } + } + } + }, + "pandas._libs.tslibs.np_datetime": { + "classes": { + "OutOfBoundsDatetime": { + "constructor_overloads": [ + { "pos_only_required": { "_0": { + "type": "str" + } + }, + "metadata": { + "usage.xarray": 1 + } + } + ], + "constructor": { + "pos_only_required": { + "_0": { + "type": "str" + } + }, + "metadata": { + "usage.xarray": 1 + } + } + } + } + }, + "pandas.core.arrays.categorical": { + "classes": { + "Categorical": { + "constructor_overloads": [ + { + "pos_or_kw_required": { + "values": { "type": { "module": "numpy", - "name": "timedelta64" + "name": "ndarray" + } + }, + "ordered": { + 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{ + "usage.dask": 1 + } + }, + "as_unordered": { "metadata": { - "usage.pandas": 2 + "usage.dask": 1 } }, - "__gt__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - ] - } - }, + "remove_categories": { "metadata": { - "usage.pandas": 1, - "usage.dask": 2 + "usage.dask": 1 } }, - "__ge__": { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" - } - } - }, + "rename_categories": { "metadata": { - "usage.dask": 3 + "usage.dask": 1 } }, - "__le__": { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas._libs.tslibs.timedeltas", - "name": "Timedelta" - } - } - }, + "reorder_categories": { "metadata": { - "usage.dask": 3 + "usage.dask": 1 } } }, - "classproperties": { - "__name__": [ + "properties": { + "categories": [ { - "usage.xarray": 1 + "usage.dask": 18 }, { "type": "bottom" } ], - "__module__": [ + "ordered": [ { - "usage.dask": 1 + "usage.dask": 6 + }, + { + "type": "bottom" + } + ], + "codes": [ + { + "usage.dask": 2 }, { "type": "bottom" @@ -272214,58 +326914,49 @@ } } }, - "pandas.core.series": { + "pandas.core.indexes.multi": { "classes": { - "Series": { + "MultiIndex": { "constructor_overloads": [ { "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.xarray": 5 - } - }, - { - "pos_or_kw_required": { - "data": { + "levels": { "type": "list", "item": { - "type": { - "name": "int" - } + "type": "union", + "options": [ + { + "type": { + "module": "pandas.core.indexes.base", + "name": "Index" + } + }, + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + } + ] } }, - "index": { - "type": { - "module": "xarray.coding.cftimeindex", - "name": "CFTimeIndex" - } - } - }, - "metadata": { - "usage.xarray": 3 - } - }, - { - "pos_or_kw_required": { - "data": { + "codes": { "type": "list", "item": { "type": { - "name": "float" + "module": "numpy", + "name": "ndarray" } } }, - "index": { - "type": { - "module": "xarray.coding.cftimeindex", - "name": "CFTimeIndex" + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "z", + "y" + ] } } }, @@ -272275,142 +326966,80 @@ }, { "pos_or_kw_required": { - "data": { - "type": "list", - "item": { - "type": { - "name": "int" + "levels": { + "type": "tuple", + "items": [ + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + }, + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } } - } + ] }, - "name": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeNoLeap" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { + "codes": { "type": "list", "item": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } }, - "name": { + "sortorder": { "type": { - "module": "cftime._cftime", - "name": "Datetime360Day" + "name": "int" } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { + }, + "names": { "type": "list", "item": { - "type": { - "name": "int" - } - } - }, - "name": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeJulian" + "type": "str", + "options": [ + "y", + "x" + ] } } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1 } }, { "pos_or_kw_required": { - "data": { + "levels": { "type": "list", "item": { "type": { - "name": "int" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } } }, - "name": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeAllLeap" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { + "codes": { "type": "list", "item": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } }, - "name": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeGregorian" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { + "names": { "type": "list", "item": { - "type": { - "name": "int" - } - } - }, - "name": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeProlepticGregorian" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "index": { - "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" + "type": "str", + "options": [ + "y", + "x" + ] } } }, @@ -272420,196 +327049,44 @@ }, { "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "index": { - "type": { - "module": "xarray.coding.cftimeindex", - "name": "CFTimeIndex" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { + "levels": { "type": "list", "item": { "type": "union", "options": [ { "type": { - "name": "int" + "module": "pandas.core.indexes.base", + "name": "Index" } }, { "type": { - "name": "float" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } } ] } }, - "index": { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "index": { - "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" - } - }, - "name": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "pandas.core.indexes.base", - "name": "Index" - } - }, - "index": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "index": { - "type": { - "module": "pandas.core.indexes.base", - "name": "Index" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "pandas.core.indexes.range", - "name": "RangeIndex" - } - }, - "index": { - "type": { - "module": "numpy", - "name": "ndarray" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "index": { - "type": { - "module": "pandas.core.indexes.range", - "name": "RangeIndex" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "index": { - "type": { - "module": "pandas.core.indexes.multi", - "name": "MultiIndex" - } - }, - "name": { - "type": "str", - "options": [ - "foo" - ] - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" + "codes": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "index": { - "type": { - "module": "pandas.core.indexes.range", - "name": "RangeIndex" + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "y", + "x" + ] } - }, - "name": { - "type": "str", - "options": [ - "x" - ] } }, "metadata": { @@ -272618,93 +327095,43 @@ }, { "pos_or_kw_required": { - "data": { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - "index": { + "levels": { "type": "list", "item": { - "type": "tuple", - "items": [ + "type": "union", + "options": [ { - "type": "str", - "options": [ - "a", - "b" - ] + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } }, { "type": { - "name": "int" + "module": "pandas.core.indexes.base", + "name": "Index" } } ] } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { + }, + "codes": { "type": "list", "item": { - "type": "bottom" - } - }, - "dtype": { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "index": { - "type": { - "module": "pandas.core.indexes.range", - "name": "RangeIndex" - } - }, - "name": { - "type": "None" - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "index": { - "type": { - "module": "pandas.core.indexes.category", - "name": "CategoricalIndex" + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "level_2", + "level_1" + ] } } }, @@ -272714,91 +327141,45 @@ }, { "pos_or_kw_required": { - "data": { - "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { + "levels": { "type": "list", "item": { - "type": "str", + "type": "union", "options": [ - "bar", - "foo" + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + }, + { + "type": { + "module": "pandas.core.indexes.base", + "name": "Index" + } + } ] } }, - "dtype": { - "type": "str", - "options": [ - "category" - ] - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "data": { + "codes": { "type": "list", "item": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { + }, + "names": { "type": "list", "item": { "type": "str", "options": [ - "bar", - "baz" + "level_3", + "level_2", + "level_1" ] } - }, - "dtype": { - "type": { - "module": "pandas.core.dtypes.dtypes", - "name": "CategoricalDtype" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" - } - }, - "index": { - "type": { - "module": "numpy", - "name": "ndarray" - } } }, "metadata": { @@ -272807,41 +327188,52 @@ }, { "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" - } + "levels": { + "type": "tuple", + "items": [ + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + }, + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + } + ] }, - "index": { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" + "codes": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "index": { + "sortorder": { "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" + "name": "int" } }, - "name": { - "type": "str", - "options": [ - "bar" + "names": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "x" + ] + }, + { + "type": "str", + "options": [ + "y" + ] + } ] } }, @@ -272851,44 +327243,33 @@ }, { "pos_or_kw_required": { - "data": { + "levels": { "type": "list", "item": { - "type": "str", - "options": [ - "foo", - "bar" - ] + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } } }, - "dtype": { - "type": { - "module": "pandas.core.dtypes.dtypes", - "name": "CategoricalDtype" + "codes": { + "type": "list", + "item": { + "type": "list", + "item": { + "type": "bottom" + } } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { + }, + "names": { "type": "list", "item": { "type": "str", "options": [ - "baz", - "bar" + "y", + "x" ] } - }, - "dtype": { - "type": { - "module": "pandas.core.dtypes.dtypes", - "name": "CategoricalDtype" - } } }, "metadata": { @@ -272897,69 +327278,45 @@ }, { "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - "index": { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" - } - }, - "name": { - "type": "str", - "options": [ - "da" + "levels": { + "type": "tuple", + "items": [ + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + }, + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + } ] - } - }, - "metadata": { - "usage.xarray": 3 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "numpy", - "name": "ndarray" + }, + "codes": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, - "index": { + "sortorder": { "type": { - "module": "pandas.core.indexes.multi", - "name": "MultiIndex" + "name": "int" } }, - "name": { - "type": "str", - "options": [ - "da" - ] - } - }, - "metadata": { - "usage.xarray": 3 - } - }, - { - "pos_or_kw_required": { - "data": { - "type": "dict", - "key": { - "type": { - "module": "numpy", - "name": "int64" - } - }, - "value": { - "type": { - "module": "numpy", - "name": "float64" - } + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "b", + "a" + ] } } }, @@ -272969,42 +327326,45 @@ }, { "pos_or_kw_required": { - "data": { - "type": "dict", - "key": { - "type": { - "module": "numpy", - "name": "float64" - } - }, - "value": { - "type": { - "module": "numpy", - "name": "float64" + "levels": { + "type": "tuple", + "items": [ + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + }, + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } } - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_required": { - "data": { + ] + }, + "codes": { "type": "list", "item": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } }, - "index": { + "sortorder": { + "type": { + "name": "int" + } + }, + "names": { "type": "list", "item": { - "type": { - "name": "int" - } + "type": "str", + "options": [ + "d", + "c" + ] } } }, @@ -273014,861 +327374,761 @@ }, { "pos_or_kw_required": { - "data": { + "levels": { + "type": "tuple", + "items": [ + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + }, + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + } + ] + }, + "codes": { "type": "list", "item": { - "type": "str" + "type": { + "module": "numpy", + "name": "ndarray" + } } - } - }, - "pos_or_kw_optional": { - "dtype": { - "type": "str", - "options": [ - "category" - ] - } - }, - "metadata": { - "usage.dask": 5 - } - }, - { - "pos_or_kw_required": { - "data": { + }, + "sortorder": { + "type": { + "name": "int" + } + }, + "names": { "type": "list", "item": { "type": "str", "options": [ - "c", - "b", - "a" + "x", + "y" ] } } }, "metadata": { - "usage.sklearn": 1 - } - } - ], - "constructor": { - "pos_or_kw_required": { - "data": { - "type": "list", - "item": { - "type": "str" - } - } - }, - "pos_or_kw_optional": { - "dtype": { - "type": "str", - "options": [ - "category" - ] + "usage.xarray": 1 } }, - "metadata": { - "usage.dask": 5, - "usage.sklearn": 1 - } - }, - "method_overloads": { - "rank": [ - { - "pos_or_kw_required": { - "method": { - "type": "str", - "options": [ - "dense" - ] - }, - "ascending": { + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + }, + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + } + ] + }, + "codes": { + "type": "list", + "item": { "type": { - "name": "bool" + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.xarray": 1 - } - } - ], - "astype": [ - { - "pos_or_kw_required": { - "dtype": { - "type": "type", - "name": { - "name": "int" - } + "sortorder": { + "type": { + "name": "int" } }, - "metadata": { - "usage.xarray": 1 + "names": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "ny" + ] + }, + { + "type": "str", + "options": [ + "nx" + ] + } + ] } }, - { - "pos_or_kw_required": { - "dtype": { + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "levels": { + "type": "list", + "item": { "type": "union", "options": [ - { - "type": "type" - }, - { - "type": "str" - }, { "type": { - "module": "pandas.core.dtypes.dtypes", - "name": "CategoricalDtype" - } - }, - { - "type": { - "module": "numpy", - "name": "dtype" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } - } - ] - } - }, - "metadata": { - "usage.dask": 53 - } - }, - { - "pos_or_kw_required": { - "dtype": { - "type": "union", - "options": [ - { - "type": "type" - }, - { - "type": "str", - "options": [ - "float", - "int", - "category" - ] }, { "type": { - "module": "pandas.core.dtypes.dtypes", - "name": "CategoricalDtype" + "module": "pandas.core.indexes.base", + "name": "Index" } } ] } }, - "pos_or_kw_optional": { - "copy": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.sklearn": 10 - } - } - ], - "__getitem__": [ - { - "pos_only_required": { - "_0": { + "codes": { + "type": "list", + "item": { "type": { - "module": "cftime._cftime", - "name": "DatetimeNoLeap" + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { + "names": { + "type": "list", + "item": { "type": "str", "options": [ - "0001" + "c", + "cat" ] } - }, - "metadata": { - "usage.xarray": 1 } }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "str", - "options": [ - "0001-01-01" - ] - }, - "stop": { - "type": "str", - "options": [ - "0001-12-30" - ] + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } }, - "step": { - "type": "None" + { + "type": { + "module": "pandas.core.indexes.base", + "name": "Index" + } } - } + ] }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "str", - "options": [ - "0001-12-30" - ] - }, - "step": { - "type": "None" + "codes": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.xarray": 1 + "sortorder": { + "type": { + "name": "int" + } + }, + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "y", + "x" + ] + } } }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { "type": { - "module": "cftime._cftime", - "name": "DatetimeNoLeap" + "module": "pandas.core.indexes.base", + "name": "Index" } }, - "stop": { + { "type": { - "module": "cftime._cftime", - "name": "DatetimeNoLeap" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } - }, - "step": { - "type": "None" } - } + ] }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeNoLeap" - } - }, - "step": { - "type": "None" + "codes": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "cftime._cftime", - "name": "Datetime360Day" - } + "sortorder": { + "type": { + "name": "int" } }, - "metadata": { - "usage.xarray": 1 + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "x", + "y" + ] + } } }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { "type": { - "module": "cftime._cftime", - "name": "Datetime360Day" + "module": "pandas.core.indexes.base", + "name": "Index" } }, - "stop": { + { "type": { - "module": "cftime._cftime", - "name": "Datetime360Day" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } - }, - "step": { - "type": "None" } - } + ] }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "module": "cftime._cftime", - "name": "Datetime360Day" - } - }, - "step": { - "type": "None" + "codes": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeJulian" - } + "sortorder": { + "type": { + "name": "int" } }, - "metadata": { - "usage.xarray": 1 + "names": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "variable" + ] + }, + { + "type": "str", + "options": [ + "y" + ] + } + ] } }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { "type": { - "module": "cftime._cftime", - "name": "DatetimeJulian" + "module": "pandas.core.indexes.base", + "name": "Index" } }, - "stop": { + { "type": { - "module": "cftime._cftime", - "name": "DatetimeJulian" + "module": "pandas.core.indexes.base", + "name": "Index" } - }, - "step": { - "type": "None" } - } + ] }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeJulian" - } - }, - "step": { - "type": "None" + "codes": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeAllLeap" - } + "sortorder": { + "type": { + "name": "int" } }, - "metadata": { - "usage.xarray": 1 + "names": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "variable" + ] + }, + { + "type": "str", + "options": [ + "y" + ] + } + ] } }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { "type": { - "module": "cftime._cftime", - "name": "DatetimeAllLeap" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } }, - "stop": { + { "type": { - "module": "cftime._cftime", - "name": "DatetimeAllLeap" + "module": "pandas.core.indexes.numeric", + "name": "Float64Index" } - }, - "step": { - "type": "None" } - } + ] }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeAllLeap" - } - }, - "step": { - "type": "None" + "codes": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeGregorian" - } + "sortorder": { + "type": { + "name": "int" } }, - "metadata": { - "usage.xarray": 1 + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "dim2", + "dim1" + ] + } } }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { "type": { - "module": "cftime._cftime", - "name": "DatetimeGregorian" + "module": "pandas.core.indexes.base", + "name": "Index" } }, - "stop": { + { "type": { - "module": "cftime._cftime", - "name": "DatetimeGregorian" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } - }, - "step": { - "type": "None" } - } + ] }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeGregorian" - } - }, - "step": { - "type": "None" + "codes": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": { - "module": "cftime._cftime", - "name": "DatetimeProlepticGregorian" - } + "sortorder": { + "type": { + "name": "int" } }, - "metadata": { - "usage.xarray": 1 + "names": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "x" + ] + }, + { + "type": "str", + "options": [ + "y" + ] + } + ] } }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { "type": { - "module": "cftime._cftime", - "name": "DatetimeProlepticGregorian" + "module": "pandas.core.indexes.base", + "name": "Index" } }, - "stop": { + { "type": { - "module": "cftime._cftime", - "name": "DatetimeProlepticGregorian" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } - }, - "step": { - "type": "None" + } + ] + }, + "codes": { + "type": "list", + "item": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.xarray": 1 + "sortorder": { + "type": { + "name": "int" + } + }, + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "y", + "x" + ] + } } }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { "type": { - "module": "cftime._cftime", - "name": "DatetimeProlepticGregorian" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } }, - "step": { - "type": "None" + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } } - } + ] }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { + "codes": { + "type": "list", + "item": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.xarray": 1 + "sortorder": { + "type": { + "name": "int" + } + }, + "names": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "lat" + ] + }, + { + "type": "str", + "options": [ + "lon" + ] + } + ] } }, - { - "pos_only_required": { - "_0": { - "type": "slice", - "start": { - "type": "None" - }, - "stop": { - "type": "None" + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } }, - "step": { + { "type": { - "name": "int" + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" } } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_only_required": { - "_0": { - "type": "object" - } + ] }, - "metadata": { - "usage.dask": 84 - } - } - ], - "equals": [ - { - "pos_or_kw_required": { - "other": { + "codes": { + "type": "list", + "item": { "type": { - "module": "pandas.core.series", - "name": "Series" + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.xarray": 8, - "usage.dask": 3 - } - } - ], - "dropna": [ - { - "metadata": { - "usage.xarray": 2, - "usage.dask": 7 - } - } - ], - "groupby": [ - { - "pos_or_kw_required": { - "by": { - "type": { - "module": "pandas.core.resample", - "name": "TimeGrouper" - } + "sortorder": { + "type": { + "name": "int" } }, - "metadata": { - "usage.xarray": 1 - } - }, - { - "pos_or_kw_optional": { - "by": { - "type": "object" - }, - "group_keys": { - "type": { - "name": "bool" - } - }, - "level": { - "type": "union", + "names": { + "type": "list", + "item": { + "type": "str", "options": [ - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - { - "type": { - "name": "int" - } - } + "lon", + "lat" ] - }, - "sort": { - "type": { - "name": "bool" - } } - }, - "pos_or_kw_optional_ordering": [ - [ - "by", - "group_keys" - ], - [ - "level", - "sort" - ] - ], - "metadata": { - "usage.dask": 64 - } - } - ], - "isnull": [ - { - "metadata": { - "usage.xarray": 1, - "usage.dask": 5 - } - } - ], - "any": [ - { - "metadata": { - "usage.xarray": 1, - "usage.sklearn": 2 } }, - { - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + }, + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } } - }, - "skipna": { + ] + }, + "codes": { + "type": "list", + "item": { "type": { - "name": "bool" + "module": "numpy", + "name": "ndarray" } } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "skipna" - ] - ], - "metadata": { - "usage.dask": 8 - } - } - ], - "duplicated": [ - { - "pos_or_kw_required": { - "keep": { - "type": "str", - "options": [ - "last" - ] + "sortorder": { + "type": { + "name": "int" } }, - "metadata": { - "usage.xarray": 1 - } - } - ], - "__invert__": [ - { - "metadata": { - "usage.xarray": 1, - "usage.dask": 1 + "names": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "replicate" + ] + }, + { + "type": "str", + "options": [ + "rsample" + ] + } + ] } + }, + "metadata": { + "usage.xarray": 1 } - ], - "where": [ - { - "pos_or_kw_required": { - "cond": { + }, + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + }, + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } + } + ] + }, + "codes": { + "type": "list", + "item": { "type": { - "module": "pandas.core.series", - "name": "Series" + "module": "numpy", + "name": "ndarray" } } }, - "metadata": { - "usage.xarray": 1 + "sortorder": { + "type": { + "name": "int" + } + }, + "names": { + "type": { + "name": "set" + } } }, - { - "pos_or_kw_required": { - "cond": { - "type": { - "module": "pandas.core.series", - "name": "Series" + "metadata": { + "usage.xarray": 1 + } + }, + { + "pos_or_kw_required": { + "levels": { + "type": "tuple", + "items": [ + { + "type": { + "module": "pandas.core.indexes.base", + "name": "Index" + } + }, + { + "type": { + "module": "pandas.core.indexes.numeric", + "name": "Int64Index" + } } - } + ] }, - "pos_or_kw_optional": { - "axis": { + "codes": { + "type": "list", + "item": { "type": { - "name": "int" + "module": "numpy", + "name": "ndarray" } - }, - "other": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "float" - } - } - ] } }, - "metadata": { - "usage.dask": 8 - } - } - ], - "median": [ - { - "metadata": { - "usage.xarray": 1 - } - } - ], - "unstack": [ - { - "metadata": { - "usage.xarray": 1 + "sortorder": { + "type": { + "name": "int" + } + }, + "names": { + "type": "None" } + }, + "metadata": { + "usage.xarray": 1 } - ], - "resample": [ + } + ], + "method_overloads": { + "__getitem__": [ { - "pos_or_kw_required": { - "rule": { - "type": "str", - "options": [ - "24H" - ] + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } } }, "metadata": { @@ -273876,169 +328136,147 @@ } }, { - "pos_or_kw_required": { - "rule": { - "type": "str", - "options": [ - "24H" - ] - }, - "loffset": { - "type": "str", - "options": [ - "-12H" - ] + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" + } } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 6 } }, { - "pos_or_kw_required": { - "rule": { - "type": "str", - "options": [ - "3H" - ] + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" + } } }, "metadata": { - "usage.xarray": 8 + "usage.xarray": 3 } }, { - "pos_or_kw_required": { - "rule": { - "type": "str", - "options": [ - "1H" - ] + "pos_only_required": { + "_0": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 3 } }, { - "pos_or_kw_required": { - "rule": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "closed": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": "str", - "options": [ - "left", - "right" - ] + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": { + "name": "int" } - ] - }, - "label": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": "str", - "options": [ - "left", - "right" - ] + }, + "stop": { + "type": { + "name": "int" } - ] + }, + "step": { + "type": "None" + } } }, - "pos_or_kw_optional_ordering": [ - [ - "closed", - "label" - ] - ], "metadata": { - "usage.dask": 88 - } - } - ], - "to_frame": [ - { - "metadata": { - "usage.xarray": 2, - "usage.sklearn": 2 + "usage.xarray": 1 } }, { - "pos_or_kw_optional": { - "name": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "a", - "A", - "__series__", - "bar", - "s" - ] - }, - { - "type": "None" + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": { + "name": "int" } - ] + } } }, "metadata": { - "usage.dask": 29 + "usage.xarray": 1 } - } - ], - "__setitem__": [ + }, { "pos_only_required": { "_0": { - "type": { - "name": "int" - } - }, - "_1": { - "type": { - "name": "float" + "type": "slice", + "start": { + "type": "None" + }, + "stop": { + "type": "None" + }, + "step": { + "type": "None" } } }, "metadata": { - "usage.xarray": 2 + "usage.xarray": 1 } }, { "pos_only_required": { "_0": { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - "_1": { "type": "union", "options": [ { "type": { - "name": "float" + "name": "int" } }, { - "type": { - "name": "int" + "type": "slice", + "start": { + "type": { + "name": "int" + } + }, + "stop": { + "type": { + "name": "int" + } + }, + "step": { + "type": "None" } } ] @@ -274049,55 +328287,46 @@ } } ], - "shift": [ + "get_loc": [ { "pos_or_kw_required": { - "periods": { - "type": { - "name": "int" - } + "key": { + "type": "str", + "options": [ + "2001-01" + ] } }, "metadata": { "usage.xarray": 1 } - }, - { - "pos_or_kw_optional": { - "periods": { - "type": { - "name": "int" - } - }, - "freq": { - "type": "object" - } - }, - "pos_or_kw_optional_ordering": [ - [ - "periods", - "freq" - ] - ], - "metadata": { - "usage.dask": 32 - } } ], - "rolling": [ + "get_loc_level": [ { "pos_or_kw_required": { - "window": { - "type": { - "name": "int" - } - }, - "min_periods": { - "type": "None" + "key": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "a" + ] + }, + { + "type": { + "name": "int" + } + } + ] }, - "center": { - "type": { - "name": "bool" + "level": { + "type": "list", + "item": { + "type": { + "name": "int" + } } } }, @@ -274107,108 +328336,23 @@ }, { "pos_or_kw_required": { - "window": { - "type": { - "name": "int" - } - }, - "min_periods": { - "type": { - "name": "int" - } - }, - "center": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.xarray": 2 - } - }, - { - "pos_or_kw_required": { - "window": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.tseries.offsets", - "name": "Second" - } - }, - { - "type": { - "name": "int" - } - }, + "key": { + "type": "tuple", + "items": [ { "type": "str", "options": [ - "3S", - "2S", - "1S" + "a" ] } ] - } - }, - "pos_or_kw_optional": { - "min_periods": { - "type": "None" - }, - "win_type": { - "type": "None" - }, - "axis": { - "type": { - "name": "int" - } }, - "center": { - "type": { - "name": "bool" - } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "center", - "win_type" - ], - [ - "min_periods", - "center" - ], - [ - "win_type", - "axis" - ], - [ - "min_periods", - "win_type" - ] - ], - "metadata": { - "usage.dask": 24 - } - } - ], - "iteritems": [ - { - "metadata": { - "usage.xarray": 1, - "usage.dask": 4 - } - } - ], - "sum": [ - { - "pos_or_kw_required": { - "skipna": { - "type": { - "name": "bool" + "level": { + "type": "list", + "item": { + "type": { + "name": "int" + } } } }, @@ -274218,12 +328362,13 @@ }, { "pos_or_kw_required": { - "skipna": { - "type": { - "name": "bool" - } + "key": { + "type": "str", + "options": [ + "a" + ] }, - "min_count": { + "level": { "type": { "name": "int" } @@ -274234,19 +328379,16 @@ } }, { - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ + "pos_or_kw_required": { + "key": { + "type": "tuple", + "items": [ { "type": "str", "options": [ - "columns" + "a" ] }, - { - "type": "None" - }, { "type": { "name": "int" @@ -274254,40 +328396,51 @@ } ] }, - "skipna": { - "type": { - "name": "bool" - } - }, "level": { - "type": { - "name": "int" - } + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "one" + ] + }, + { + "type": "str", + "options": [ + "two" + ] + } + ] } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "skipna" - ] - ], "metadata": { - "usage.dask": 41 + "usage.xarray": 1 } }, - { - "metadata": { - "usage.sklearn": 1 - } - } - ], - "min": [ { "pos_or_kw_required": { - "skipna": { - "type": { - "name": "bool" - } + "key": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "a" + ] + } + ] + }, + "level": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "one" + ] + } + ] } }, "metadata": { @@ -274295,19 +328448,10 @@ } }, { - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "columns" - ] - }, - { - "type": "None" - }, + "pos_or_kw_required": { + "key": { + "type": "tuple", + "items": [ { "type": { "name": "int" @@ -274315,30 +328459,16 @@ } ] }, - "skipna": { - "type": { - "name": "bool" - } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "skipna" - ] - ], - "metadata": { - "usage.dask": 17 - } - } - ], - "max": [ - { - "pos_or_kw_required": { - "skipna": { - "type": { - "name": "bool" - } + "level": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "x" + ] + } + ] } }, "metadata": { @@ -274346,50 +328476,28 @@ } }, { - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ + "pos_or_kw_required": { + "key": { + "type": "tuple", + "items": [ { "type": "str", "options": [ - "columns" + "a" ] - }, - { - "type": "None" - }, - { - "type": { - "name": "int" - } } ] }, - "skipna": { - "type": { - "name": "bool" - } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "skipna" - ] - ], - "metadata": { - "usage.dask": 22 - } - } - ], - "mean": [ - { - "pos_or_kw_required": { - "skipna": { - "type": { - "name": "bool" - } + "level": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "y" + ] + } + ] } }, "metadata": { @@ -274397,55 +328505,57 @@ } }, { - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ + "pos_or_kw_required": { + "key": { + "type": "tuple", + "items": [ { "type": "str", "options": [ - "columns" + "a" ] - }, - { - "type": "None" - }, - { - "type": { - "name": "int" - } } ] }, - "skipna": { - "type": { - "name": "bool" - } + "level": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "variable" + ] + } + ] } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "skipna" - ] - ], "metadata": { - "usage.dask": 25 + "usage.xarray": 1 } - } - ], - "var": [ + }, { "pos_or_kw_required": { - "skipna": { - "type": { - "name": "bool" - } + "key": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "b" + ] + } + ] }, - "ddof": { - "type": { - "name": "int" - } + "level": { + "type": "tuple", + "items": [ + { + "type": "str", + "options": [ + "variable" + ] + } + ] } }, "metadata": { @@ -274453,69 +328563,40 @@ } }, { - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "columns" - ] - }, - { - "type": "None" - }, + "pos_or_kw_required": { + "key": { + "type": "tuple", + "items": [ { "type": { "name": "int" } - } - ] - }, - "skipna": { - "type": "union", - "options": [ + }, { "type": { - "name": "bool" + "name": "int" } - }, - { - "type": "None" } ] }, - "ddof": { + "level": { "type": { "name": "int" } } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "skipna" - ], - [ - "skipna", - "ddof" - ] - ], "metadata": { - "usage.dask": 14 + "usage.xarray": 1 } - } - ], - "prod": [ + }, { "pos_or_kw_required": { - "skipna": { + "key": { "type": { - "name": "bool" + "name": "int" } }, - "min_count": { + "level": { "type": { "name": "int" } @@ -274526,19 +328607,16 @@ } }, { - "pos_or_kw_optional": { - "axis": { - "type": "union", - "options": [ + "pos_or_kw_required": { + "key": { + "type": "tuple", + "items": [ { "type": "str", "options": [ - "columns" + "a" ] }, - { - "type": "None" - }, { "type": { "name": "int" @@ -274546,1431 +328624,1858 @@ } ] }, - "skipna": { - "type": { - "name": "bool" - } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "skipna" - ] - ], - "metadata": { - "usage.dask": 10 - } - } - ], - "__eq__": [ - { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.pandas": 52, - "usage.dask": 43 - } - }, - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ + "level": { + "type": "tuple", + "items": [ { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": "type" - }, - { - "type": { - "module": "pandas.core.dtypes.dtypes", - "name": "CategoricalDtype" - } - } - ] - } + "type": "str", + "options": [ + "one" + ] }, { - "type": "type", - "name": { - "module": "numpy", - "name": "float64" - } + "type": "str", + "options": [ + "three" + ] } ] } }, "metadata": { - "usage.sklearn": 10 + "usage.xarray": 1 } } ], - "__or__": [ + "get_indexer": [ { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "target": { "type": { "module": "numpy", "name": "ndarray" } + }, + "method": { + "type": "None" + }, + "tolerance": { + "type": "None" } }, "metadata": { - "usage.pandas": 3 + "usage.xarray": 2 } - }, + } + ], + "equals": [ { - "pos_only_required": { - "_0": { + "pos_or_kw_required": { + "other": { "type": { - "module": "pandas.core.series", - "name": "Series" + "module": "pandas.core.indexes.multi", + "name": "MultiIndex" } } }, "metadata": { - "usage.dask": 3 + "usage.xarray": 2 } } ], - "__contains__": [ + "copy": [ { - "pos_only_required": { - "_0": { - "type": "str", - "options": [ - "bool" - ] + "pos_or_kw_required": { + "deep": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.pandas": 1 + "usage.xarray": 1 } } ], - "__add__": [ + "set_names": [ { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - }, - { - "type": { - "module": "numpy", - "name": "datetime64" - } - } - ] + "pos_or_kw_required": { + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "level1", + "level0" + ] + } } }, "metadata": { - "usage.pandas": 39 + "usage.xarray": 1 } }, { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.dask": 55 - } - } - ], - "__iadd__": [ - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - } - ] - } - }, - "metadata": { - "usage.pandas": 2 - } - } - ], - "__sub__": [ - { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "datetime64" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - } - ] + "pos_or_kw_required": { + "names": { + "type": "list", + "item": { + "type": "str", + "options": [ + "b", + "a", + "c" + ] + } + }, + "inplace": { + "type": { + "name": "bool" + } } }, "metadata": { - "usage.pandas": 35 + "usage.dask": 2 } - }, + } + ], + "__eq__": [ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - } - ] + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.dask": 18 + "usage.pandas": 4 } } ], - "__isub__": [ + "get_level_values": [ { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "numpy", - "name": "timedelta64" - } - } - ] + "pos_or_kw_required": { + "level": { + "type": { + "name": "int" + } } }, "metadata": { - "usage.pandas": 2 + "usage.dask": 5 } } ], - "__radd__": [ + "_get_level_values": [ { - "pos_only_required": { - "_0": { - "type": "object" + "pos_or_kw_required": { + 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- } - } - ], - "round": [ - { - "pos_or_kw_optional": { - "decimals": { - "type": { - "name": "int" - } + "tolerance": { + "type": "None" } }, "metadata": { - "usage.dask": 2 + "usage.xarray": 1 } - } - ], - "between": [ + }, { "pos_or_kw_required": { - "left": { + "target": { "type": { - "name": "int" + "module": "pandas.core.indexes.base", + "name": "Index" } }, - "right": { - "type": { - "name": "int" - } - } - }, - "pos_or_kw_optional": { - "inclusive": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.dask": 2 - } - } - ], - "notnull": [ - { - "metadata": { - "usage.dask": 3 - } - } - ], - "fillna": [ - { - "pos_or_kw_optional": { "method": { - "type": "union", + "type": "str", "options": [ - { - "type": "None" - }, - { - "type": "str", - "options": [ - "bfill", - "ffill", - "pad" - ] - } + "pad" ] }, - "axis": { - "type": { - "name": "int" - } - }, "limit": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - }, - "value": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "module": "numpy", - "name": "float64" - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "None" - } - ] + "type": "None" } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "limit" - ], - [ - "value", - "method" - ], - [ - "method", - "axis" - ], - [ - "method", - "limit" - ] - ], "metadata": { - "usage.dask": 23 + "usage.xarray": 1 } - } - ], - "nlargest": [ + }, { "pos_or_kw_required": { - "n": { + "target": { "type": { - "name": "int" + "module": "pandas.core.indexes.base", + "name": "Index" } - } - }, - "metadata": { - "usage.dask": 4 - } - } - ], - "to_timestamp": [ - { - "pos_or_kw_optional": { - "freq": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "M" - ] - }, - { - "type": "None" - } - ] }, - "how": { + "method": { "type": "str", "options": [ - "s", - "start" + "backfill" ] }, - "copy": { - "type": { - "name": "int" - } - } - }, - "pos_or_kw_optional_ordering": [ - [ - "freq", - "how" - ], - [ - "how", - "copy" - ] - ], - "metadata": { - "usage.dask": 4 - } - } - ], - "abs": [ - { - "metadata": { - "usage.dask": 3 - } - } - ], - "cov": [ - { - "pos_or_kw_required": { - "other": { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - } - }, - "pos_or_kw_optional": { - "min_periods": { - "type": { - "name": "int" - } + "limit": { + "type": "None" } }, "metadata": { - "usage.dask": 2 + "usage.xarray": 1 } - } - ], - "replace": [ + }, { "pos_or_kw_required": { - "to_replace": { - "type": "union", - "options": [ - { - "type": "dict", - "key": { - "type": { - "name": "int" - } - }, - "value": { - "type": { - "name": "int" - } - } - }, - { - "type": { - "name": "int" - } - }, - { - "type": "str", - "options": [ - "c" - ] - } - ] - } - }, - "pos_or_kw_optional": { - "regex": { + "target": { "type": { - "name": "bool" + "module": "numpy", + "name": "ndarray" } }, - "value": { - "type": "union", + "method": { + "type": "str", "options": [ - { - "type": "None" - }, - { - "type": { - "name": "float" - } - }, - { - "type": { - "name": "int" - } - } + "nearest" ] - } - }, - "pos_or_kw_optional_ordering": [ - [ - "value", - "regex" - ] - ], - "metadata": { - "usage.dask": 5 - } - } - ], - "autocorr": [ - { - "pos_or_kw_required": { - "lag": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.dask": 4 - } - } - ], - "nsmallest": [ - { - "pos_or_kw_required": { - "n": { - "type": { - "name": "int" - } - } - }, - "metadata": { - "usage.dask": 2 - } - } - ], - "idxmax": [ - { - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" - } }, - "skipna": { - "type": { - "name": "bool" - } + "tolerance": { + "type": "None" } }, "metadata": { - "usage.dask": 8 + "usage.xarray": 1 } - } - ], - "idxmin": [ + }, { "pos_or_kw_required": { - "skipna": { + "target": { "type": { - "name": "bool" + "module": "numpy", + "name": "ndarray" } - } - }, - "metadata": { - "usage.dask": 4 - } - } - ], - "diff": [ - { - "pos_or_kw_optional": { - "periods": { + }, + "method": { + "type": "str", + "options": [ + "nearest" + ] + }, + "tolerance": { "type": { - "name": "int" + "module": "datetime", + "name": "timedelta" } } }, "metadata": { - "usage.dask": 7 - } - } - ], - "first": [ - { - "pos_or_kw_required": { - "offset": { - "type": "str" - } - }, - "metadata": { - "usage.dask": 24 - } - } - ], - "last": [ - { - "pos_or_kw_required": { - "offset": { - "type": "str" - } - }, - "metadata": { - "usage.dask": 8 + "usage.xarray": 1 } - } - ], - "take": [ + }, { "pos_or_kw_required": { - "indices": { + "target": { "type": { "module": "numpy", "name": "ndarray" } - } - }, - "metadata": { - "usage.dask": 1 - } - } - ], - "mode": [ - { - "metadata": { - "usage.dask": 1 - } - } - ], - "explode": [ - { - "metadata": { - "usage.dask": 5 - } - } - ], - "__itruediv__": [ - { - "pos_only_required": { - "_0": { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - } - }, - "metadata": { - "usage.dask": 1 - } - } - ], - "reindex": [ - { - "pos_or_kw_required": { - "index": { - "type": "union", + }, + "method": { + "type": "str", "options": [ - { - "type": { - "module": "pandas.core.indexes.datetimes", - "name": "DatetimeIndex" - } - }, - { - "type": { - "module": "pandas.core.indexes.multi", - "name": "MultiIndex" - } - }, - { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - } + "pad" ] + }, + "tolerance": { + "type": "None" } }, "metadata": { - "usage.dask": 8 - } - } - ], - "items": [ - { - "metadata": { - "usage.dask": 1 - } - } - ], - "append": [ - { - "pos_or_kw_required": { - "to_append": { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - } - }, - "metadata": { - "usage.dask": 2 + "usage.xarray": 1 } - } - ], - "__array_wrap__": [ + }, { "pos_or_kw_required": { - "result": { + "target": { "type": { "module": "numpy", "name": "ndarray" } + }, + "method": { + "type": "str", + "options": [ + "pad" + ] + }, + "tolerance": { + "type": { + "module": "datetime", + "name": "timedelta" + } } - }, - "metadata": { - "usage.dask": 3 - } - } - ] - }, - "methods": { - "rank": { - "pos_or_kw_required": { - "method": { - "type": "str", - "options": [ - "dense" - ] - }, - "ascending": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.xarray": 1 - } - }, - "astype": { - "pos_or_kw_required": { - "dtype": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.dtypes.dtypes", - "name": "CategoricalDtype" - } - }, - { - "type": { - "module": "numpy", - "name": "dtype" - } - }, - { - "type": "str" - }, - { - "type": "type" - } - ] - } - }, - "pos_or_kw_optional": { - "copy": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.xarray": 1, - "usage.dask": 53, - "usage.sklearn": 10 - } - }, - "__getitem__": { - "pos_only_required": { - "_0": { - "type": "object" - } - }, - "metadata": { - "usage.xarray": 23, - "usage.dask": 84 - } - }, - "equals": { - "pos_or_kw_required": { - "other": { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - } - }, - "metadata": { - "usage.xarray": 8, - "usage.dask": 3 - } - }, - "dropna": { - "metadata": { - "usage.xarray": 2, - "usage.dask": 7 - } - }, - "groupby": { - "pos_or_kw_optional": { - "by": { - "type": "object" - }, - "group_keys": { - "type": { - "name": "bool" - } - }, - "level": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - }, - { - "type": { - "name": "int" - } - } - ] - }, - "sort": { - "type": { - "name": "bool" - } + }, + "metadata": { + "usage.xarray": 1 } }, - "pos_or_kw_optional_ordering": [ - [ - "by", - "group_keys" - ], - [ - "level", - "sort" - ] - ], - "metadata": { - "usage.xarray": 1, - "usage.dask": 64 - } - }, - "isnull": { - "metadata": { - "usage.xarray": 1, - "usage.dask": 5 - } - }, - "any": { - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" + { + "pos_or_kw_required": { + "target": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "method": { + "type": "str", + "options": [ + "backfill" + ] + }, + "tolerance": { + "type": "None" } }, - "skipna": { - "type": { - "name": "bool" - } + "metadata": { + "usage.xarray": 1 } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "skipna" - ] - ], - "metadata": { - "usage.xarray": 1, - "usage.dask": 8, - "usage.sklearn": 2 - } - }, - "duplicated": { - "pos_or_kw_required": { - "keep": { - "type": "str", - "options": [ - "last" - ] + { + "pos_or_kw_required": { + "target": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "method": { + "type": "str", + "options": [ + "backfill" + ] + }, + "tolerance": { + "type": { + "module": "datetime", + "name": "timedelta" + } + } + }, + "metadata": { + "usage.xarray": 1 } - }, - "metadata": { - "usage.xarray": 1 - } - }, - "__invert__": { - "metadata": { - "usage.xarray": 1, - "usage.dask": 1 } - }, - "where": { - "pos_or_kw_required": { - "cond": { - "type": { - "module": "pandas.core.series", - "name": "Series" + ], + "equals": [ + { + "pos_or_kw_required": { + "other": { + "type": { + "module": "xarray.coding.cftimeindex", + "name": "CFTimeIndex" + } } + }, + "metadata": { + "usage.xarray": 11 } - }, - "pos_or_kw_optional": { - "axis": { - "type": { - "name": "int" + } + ], + "copy": [ + { + "pos_or_kw_required": { + "deep": { + "type": { + "name": "bool" + } } }, - "other": { + "metadata": { + "usage.xarray": 1 + } + } + ] + }, + "methods": { + "get_indexer": { + "pos_or_kw_required": { + "target": { "type": "union", "options": [ - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, { "type": { "module": "numpy", - "name": "float64" + "name": "ndarray" } }, { "type": { - "name": "float" + "module": "pandas.core.indexes.base", + "name": "Index" } } ] - } - }, - "metadata": { - "usage.xarray": 1, - "usage.dask": 8 - } - }, - "median": { - "metadata": { - "usage.xarray": 1 - } - }, - "unstack": { - "metadata": { - "usage.xarray": 1 - } - }, - "resample": { - "pos_or_kw_required": { - "rule": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "loffset": { - "type": "str", - "options": [ - "-12H" - ] - }, - "closed": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": "str", - "options": [ - "left", - "right" - ] - } - ] }, - "label": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": "str", - "options": [ - "left", - "right" - ] - } - ] - } - }, - "pos_or_kw_optional_ordering": [ - [ - "closed", - "label" - ] - ], - "metadata": { - "usage.xarray": 14, - "usage.dask": 88 - } - }, - "to_frame": { - "pos_or_kw_optional": { - "name": { + "method": { "type": "union", "options": [ { "type": "str", "options": [ - "a", - "A", - "__series__", - "bar", - "s" + "backfill", + "pad", + "nearest" ] }, { @@ -277911,2022 +332147,2348 @@ ] } }, - "metadata": { - "usage.xarray": 2, - "usage.dask": 29, - "usage.sklearn": 2 - } - }, - "__setitem__": { - "pos_only_required": { - "_0": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "name": "int" - } - } - ] + "pos_or_kw_optional": { + "limit": { + "type": "None" }, - "_1": { + "tolerance": { "type": "union", "options": [ { "type": { - "name": "int" + "module": "datetime", + "name": "timedelta" } }, { - "type": { - "name": "float" - } + "type": "None" } ] } }, "metadata": { - "usage.xarray": 2, - "usage.dask": 3 + "usage.xarray": 9 } }, - "shift": { - "pos_or_kw_optional": { - "periods": { + "equals": { + "pos_or_kw_required": { + "other": { "type": { - "name": "int" + "module": "xarray.coding.cftimeindex", + "name": "CFTimeIndex" } - }, - "freq": { - "type": "object" } }, - "pos_or_kw_optional_ordering": [ - [ - "periods", - "freq" - ] - ], "metadata": { - "usage.xarray": 1, - "usage.dask": 32 + "usage.xarray": 11 } }, - "rolling": { + "copy": { "pos_or_kw_required": { - "window": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "3S", - "2S", - "1S" - ] - }, - { - "type": { - "name": "int" - } - }, - { - "type": { - "module": "pandas.tseries.offsets", - "name": "Second" - } - } - ] - } - }, - "pos_or_kw_optional": { - "min_periods": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - }, - "center": { + "deep": { "type": { "name": "bool" } - }, - "win_type": { - "type": "None" - }, - "axis": { - "type": { - "name": "int" - } } }, - "pos_or_kw_optional_ordering": [ - [ - "center", - "win_type" - ], - [ - "min_periods", - "center" - ], - [ - "win_type", - "axis" - ], - [ - "min_periods", - "win_type" - ] - ], - "metadata": { - "usage.xarray": 3, - "usage.dask": 24 - } - }, - "iteritems": { "metadata": { - "usage.xarray": 1, - "usage.dask": 4 + "usage.xarray": 1 } - }, - "sum": { - "pos_or_kw_optional": { - "min_count": { - "type": { - "name": "int" - } - }, - "skipna": { - "type": { - "name": "bool" - } - }, - "axis": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "columns" - ] - }, - { - "type": "None" - }, - { - "type": { - "name": "int" - } + } + } + } + } + }, + "pandas.core.indexing": { + "classes": { + "_LocIndexer": { + "method_overloads": { + "__getitem__": [ + { + "pos_only_required": { + "_0": { + "type": { + "module": "cftime._cftime", + "name": "DatetimeNoLeap" } - ] - }, - "level": { - "type": { - "name": "int" } + }, + "metadata": { + "usage.xarray": 2 } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "skipna" - ] - ], - "metadata": { - "usage.xarray": 2, - "usage.dask": 41, - "usage.sklearn": 1 - } - }, - "min": { - "pos_or_kw_optional": { - "skipna": { - "type": { - "name": "bool" + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "0001" + ] } }, - "axis": { - "type": "union", - "options": [ - { + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": "slice", + "start": { "type": "str", "options": [ - "columns" + "0001-01-01" ] }, - { - "type": "None" - }, - { - "type": { - "name": "int" - } - } - ] - } - }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "skipna" - ] - ], - "metadata": { - "usage.xarray": 1, - "usage.dask": 17 - } - }, - "max": { - "pos_or_kw_optional": { - "skipna": { - "type": { - "name": "bool" - } - }, - "axis": { - "type": "union", - "options": [ - { + "stop": { "type": "str", "options": [ - "columns" + "0001-12-30" ] }, - { + "step": { "type": "None" - }, - { - "type": { - "name": "int" - } } - ] - } - }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "skipna" - ] - ], - "metadata": { - "usage.xarray": 1, - "usage.dask": 22 - } - }, - "mean": { - "pos_or_kw_optional": { - "skipna": { - "type": { - "name": "bool" } }, - "axis": { - "type": "union", - "options": [ - { + "metadata": { + "usage.xarray": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": "slice", + "start": { + "type": "None" + }, + "stop": { "type": "str", "options": [ - "columns" + "0001-12-30" ] }, - { + "step": { "type": "None" - }, - { - "type": { - "name": "int" - } } - ] + } + }, + "metadata": { + "usage.xarray": 2 } }, - "pos_or_kw_optional_ordering": [ - [ - "axis", - "skipna" - ] - ], - "metadata": { - "usage.xarray": 1, - "usage.dask": 25 - } - }, - "var": { - "pos_or_kw_optional": { - "skipna": { - "type": "union", - "options": [ - { - "type": "None" - }, - { + { + "pos_only_required": { + "_0": { + "type": "slice", + "start": { "type": { - "name": "bool" + "module": "cftime._cftime", + "name": "DatetimeNoLeap" } - } - ] - }, - "ddof": { - "type": { - "name": "int" - } - }, - "axis": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "columns" - ] - }, - { - "type": "None" }, - { + "stop": { "type": { - "name": "int" + "module": 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+ "type": { + "module": "pandas.core.series", + "name": "Series" + } } }, - { - "type": { - "module": "pandas.core.indexes.base", - "name": "Index" + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "cbond" + ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } } }, - { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "marvi" + ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } } }, - { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Float64Index" - } + "metadata": { + "usage.sklearn": 1 } - ] - }, - "sort": { - "type": { - "name": "bool" - } - } - }, - "metadata": { - "usage.xarray": 4 - } - } - } - }, - "xarray.coding.cftimeindex": { - "classes": { - "CFTimeIndex": { - 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"str", + "options": [ + "MMIN" + ] }, - "method": { + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { "type": "str", "options": [ - "backfill" + "MMAX" ] }, - "tolerance": { - "type": "None" + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "target": { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "CACH" + ] + }, + "_1": { "type": { - "module": "numpy", - "name": "ndarray" + "module": "pandas.core.series", + "name": "Series" } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "CHMIN" + ] }, - "method": { + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + 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{ - "pos_or_kw_required": { - "target": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "pandas.core.indexes.base", - "name": "Index" - } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "fnlwgt:" + ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" } - ] + } }, - "method": { - "type": "union", - "options": [ - { - "type": "str", - "options": [ - "backfill", - "pad", - "nearest" - ] - }, - { - "type": "None" + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "education:" + ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" } - ] + } + }, + "metadata": { + "usage.sklearn": 1 } }, - "pos_or_kw_optional": { - "limit": { - "type": "None" + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "education-num:" + ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + } }, - "tolerance": { - "type": "union", - "options": [ - { - "type": { - "module": "datetime", - "name": "timedelta" - } - }, - { - "type": "None" + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "marital-status:" + ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" } - ] + } + }, + "metadata": { + "usage.sklearn": 1 } }, - "metadata": { - "usage.xarray": 9 - } - }, - "equals": { - "pos_or_kw_required": { - "other": { - "type": { - "module": "xarray.coding.cftimeindex", - "name": "CFTimeIndex" + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "occupation:" + ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } } + }, + "metadata": { + "usage.sklearn": 1 } }, - "metadata": { - "usage.xarray": 11 - } - }, - "copy": { - "pos_or_kw_required": { - "deep": { - "type": { - "name": "bool" + { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "relationship:" + ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } } + }, + "metadata": { + "usage.sklearn": 1 } }, - "metadata": { - "usage.xarray": 1 - } - } - } - } - } - }, - "pandas.core.indexing": { - "classes": { - "_LocIndexer": { - "method_overloads": { - "__getitem__": [ { "pos_only_required": { "_0": { + "type": "str", + "options": [ + "race:" + ] + }, + "_1": { "type": { - "module": "cftime._cftime", - "name": "DatetimeNoLeap" + "module": "pandas.core.series", + "name": "Series" } } }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 1 } }, { @@ -287257,2637 +340867,2070 @@ "_0": { "type": "str", "options": [ - "0001" + "sex:" ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } } }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "slice", - "start": { - 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}, { - "pos_or_kw_required": { - "keys": { + "pos_only_required": { + "_0": { "type": "str", "options": [ - "i1" + "ticket" ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "keys": { + "pos_only_required": { + "_0": { "type": "str", "options": [ - "x" + "fare" ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } } }, "metadata": { - "usage.xarray": 2 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "keys": { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "cabin" + ] + }, + "_1": { "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" + "module": "pandas.core.series", + "name": "Series" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "keys": { - "type": "object" - } - }, - "pos_or_kw_optional": { - "drop": { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "embarked" + ] + }, + "_1": { "type": { - "name": "bool" + "module": "pandas.core.series", + "name": "Series" } } }, "metadata": { - "usage.dask": 119 + "usage.sklearn": 1 } - } - ], - "__getitem__": [ + }, { "pos_only_required": { "_0": { "type": "str", "options": [ - "foo" + "boat" ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } } }, "metadata": { - "usage.xarray": 3 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "list", - "item": { - "type": "str", - "options": [ - "foo", - "C" - ] + "type": "str", + "options": [ + "body" + ] + }, + "_1": { + "type": { + "module": "pandas.core.series", + "name": "Series" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { + "type": "str", + "options": [ + "home.dest" + ] + }, + "_1": { "type": { - "name": "int" + "module": "pandas.core.series", + "name": "Series" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { @@ -289895,199 +342938,131 @@ "_0": { "type": "str", "options": [ - "x" + "correlated_feature" ] + }, + "_1": { + "type": { + "module": "numpy", + "name": "ndarray" + } } }, "metadata": { - "usage.xarray": 3 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "object" + "type": { + "name": "int" + } + }, + "_1": { + "type": { + "module": "pandas.core.arrays.categorical", + "name": "Categorical" + } } }, "metadata": { - "usage.dask": 487 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": "str" - }, - { - "type": "list", - "item": { - "type": "str" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "name": "int" - } - } + "1" ] + }, + "_1": { + "type": { + "module": "pandas.core.arrays.sparse.array", + "name": "SparseArray" + } } }, "metadata": { - "usage.sklearn": 213 - } - } - ], - "stack": [ - { - "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { - "pos_or_kw_required": { - "dropna": { + "pos_only_required": { + "_0": { + "type": "str", + "options": [ + "2" + ] + }, + "_1": { "type": { - "name": "bool" + "module": "pandas.core.arrays.sparse.array", + "name": "SparseArray" } } }, "metadata": { - "usage.dask": 1 + "usage.sklearn": 1 } - } - ], - "__setitem__": [ + }, { "pos_only_required": { "_0": { "type": "str", "options": [ - "C" + "3" ] }, "_1": { - "type": "list", - "item": { - "type": { - "name": "int" - } + "type": { + "module": "pandas.core.arrays.sparse.array", + "name": "SparseArray" } } }, "metadata": { - "usage.xarray": 1 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": "str" - }, - { - "type": "list", - "item": { - "type": "str", - "options": [ - "b", - "a" - ] - } - }, - { - "type": { - "module": "pandas.core.indexes.base", - "name": "Index" - } - }, - { - "type": { - "module": "pandas.core.frame", - "name": "DataFrame" - } - }, - { - "type": { - "module": "pandas.core.indexes.numeric", - "name": "Int64Index" - } - } + "0" ] }, "_1": { - "type": "object" + "type": { + "module": "pandas.core.arrays.sparse.array", + "name": "SparseArray" + } } }, "metadata": { - "usage.dask": 129 + "usage.sklearn": 1 } }, { "pos_only_required": { "_0": { - "type": "union", + "type": "str", "options": [ - { - "type": "str" - }, - { - "type": { - "name": "int" - } - } + "c" ] }, "_1": { - "type": "union", - "options": [ - { - "type": { - "module": "pandas.core.series", - "name": "Series" - } - }, - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": { - "module": "pandas.core.arrays.categorical", - "name": "Categorical" - } - }, - { - "type": { - "module": "pandas.core.arrays.sparse.array", - "name": "SparseArray" - } - }, - { - "type": "list", - "item": { - "type": { - "name": "int" - } - } - } - ] + "type": { + "module": "pandas.core.series", + "name": "Series" + } } }, "metadata": { - "usage.sklearn": 178 + "usage.sklearn": 2 } } ], @@ -290957,23 +343932,25 @@ { "pos_only_required": { "_0": { - "type": "union", - "options": [ - { - "type": { - "name": "int" - } - }, - { - "type": { - "name": "float" - } - } - ] + "type": { + "name": "float" + } } }, "metadata": { - "usage.sklearn": 4 + "usage.sklearn": 2 + } + }, + { + "pos_only_required": { + "_0": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.sklearn": 2 } } ], @@ -291425,19 +344402,53 @@ { "pos_or_kw_required": { "dtype": { - "type": "union", - "options": [ - { - "type": "type" - }, - { - "type": "None" - } - ] + "type": "None" } }, "metadata": { - "usage.sklearn": 4 + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float64" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "float32" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "dtype": { + "type": "type", + "name": { + "module": "numpy", + "name": "int16" + } + } + }, + "metadata": { + "usage.sklearn": 1 } } ], @@ -291629,59 +344640,189 @@ { "pos_or_kw_required": { "include": { - "type": "union", - "options": [ - { - "type": "list", - "item": { - "type": "union", - "options": [ - { - "type": "type" - }, - { - "type": "str", - "options": [ - "category" - ] - } - ] - } - }, - { - "type": "None" - }, - { - "type": "type" + "type": "type", + "name": { + "module": "numpy", + "name": "number" + } + }, + "exclude": { + "type": "None" + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "include": { + "type": "None" + }, + "exclude": { + "type": "type", + "name": { + "name": "object" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "include": { + "type": "list", + "item": { + "type": "type" + } + }, + "exclude": { + "type": "None" + } + }, + "metadata": { + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "include": { + "type": "list", + "item": { + "type": "type", + "name": { + "name": "object" } - ] + } }, "exclude": { - "type": "union", - "options": [ - { - "type": "None" - }, - { - "type": "list", - "item": { + "type": "None" + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "include": { + "type": "type", + "name": { + "name": "object" + } + }, + "exclude": { + "type": "None" + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "include": { + "type": "type", + "name": { + "name": "float" + } + }, + "exclude": { + "type": "None" + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "include": { + "type": "list", + "item": { + "type": "type", + "name": { + "module": "numpy", + "name": "number" + } + } + }, + "exclude": { + "type": "None" + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "include": { + "type": "list", + "item": { + "type": "type", + "name": { + "name": "int" + } + } + }, + "exclude": { + "type": "None" + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "include": { + "type": "None" + }, + "exclude": { + "type": "list", + "item": { + "type": "type", + "name": { + "name": "int" + } + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "include": { + "type": "list", + "item": { + "type": "union", + "options": [ + { "type": "type", "name": { - "name": "int" + "name": "object" } + }, + { + "type": "str", + "options": [ + "category" + ] } - }, - { - "type": "type", - "name": { - "name": "object" - } - } - ] + ] + } + }, + "exclude": { + "type": "None" } }, "metadata": { - "usage.sklearn": 11 + "usage.sklearn": 1 } } ], @@ -302992,7 +356133,8 @@ } }, "metadata": { - "usage.xarray": 1 + "usage.xarray": 1, + "usage.sklearn": 12 } }, { @@ -303012,6 +356154,426 @@ "metadata": { "usage.dask": 1 } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "Iris-virginica", + "Iris-versicolor", + "Iris-setosa" + ] + } + } + }, + "metadata": { + "usage.sklearn": 3 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "G", + "H", + "C" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "T" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "X", + "A", + "S" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "5", + "4", + "3", + "2", + "1" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "N" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "M", + "P" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "G", + "F", + "E", + "D" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "Y" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "M", + "B" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "C" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "P" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "C", + "V", + "R", + "B" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "SHEET", + "COIL" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "N", + "Y" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "760", + "600", + "500", + "0" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "3", + "2", + "1" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "Black", + "Other", + "Amer-Indian-Eskimo", + "Asian-Pac-Islander", + "White" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "Male", + "Female" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + ">50K", + "<=50K" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "TRUE", + "FALSE" + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "male", + "female" + ] + } + } + }, + "metadata": { + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "S", + "Q", + "C" + ] + } + } + }, + "metadata": { + "usage.sklearn": 2 + } + }, + { + "pos_or_kw_required": { + "categories": { + "type": "list", + "item": { + "type": "str", + "options": [ + "1", + "0" + ] + } + } + }, + "metadata": { + "usage.sklearn": 2 + } } ], "method_overloads": { @@ -314164,6 +367726,69 @@ "pandas.core.arrays.sparse.array": { "classes": { "SparseArray": { + "constructor_overloads": [ + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "numpy", + "name": "ndarray" + } + }, + "fill_value": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "pandas.core.series", + "name": "Series" + } + }, + "fill_value": { + "type": { + "name": "int" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "data": { + "type": "list", + "item": { + "type": "union", + "options": [ + { + "type": { + "name": "float" + } + }, + { + "type": { + "name": "int" + } + } + ] + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + } + ], "method_overloads": { "__radd__": [ { @@ -316419,7 +370044,8 @@ } }, "metadata": { - "usage.dask": 1 + "usage.dask": 1, + "usage.sklearn": 1 } }, { @@ -316430,30 +370056,36 @@ "name": "MaskedArray" } }, + "axis": { + "type": { + "name": "int" + } + }, "weights": { - "type": "union", - "options": [ - { - "type": { - "module": "numpy", - "name": "ndarray" - } - }, - { - "type": "None" - } - ] + "type": "None" } }, - "pos_or_kw_optional": { - "axis": { + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "a": { "type": { - "name": "int" + "module": "numpy.ma.core", + "name": "MaskedArray" + } + }, + "weights": { + "type": { + "module": "numpy", + "name": "ndarray" } } }, "metadata": { - "usage.sklearn": 8 + "usage.sklearn": 6 } } ] @@ -319480,8 +373112,7 @@ "A1": [ { "metadata": { - "usage.scipy": 3, - "usage.sklearn": 1 + "usage.scipy": 3 } } ], @@ -319495,8 +373126,7 @@ "mean": [ { "metadata": { - "usage.scipy": 30, - "usage.sklearn": 2 + "usage.scipy": 30 } } ], @@ -319531,8 +373161,7 @@ }, "A1": { "metadata": { - "usage.scipy": 3, - "usage.sklearn": 1 + "usage.scipy": 3 } }, "sum": { @@ -319542,8 +373171,7 @@ }, "mean": { "metadata": { - "usage.scipy": 30, - "usage.sklearn": 2 + "usage.scipy": 30 } }, "max": { @@ -321388,22 +375016,36 @@ "name": "DataFrame" } } + }, + "axis": { + "type": { + "name": "int" + } } }, - "pos_or_kw_optional": { - "ignore_index": { - "type": { - "name": "bool" + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "objs": { + "type": "list", + "item": { + "type": { + "module": "pandas.core.frame", + "name": "DataFrame" + } } }, - "axis": { + "ignore_index": { "type": { - "name": "int" + "name": "bool" } } }, "metadata": { - "usage.sklearn": 3 + "usage.sklearn": 2 } } ] @@ -321513,8 +375155,7 @@ "tolist": [ { "metadata": { - "usage.dask": 14, - "usage.sklearn": 1 + "usage.dask": 14 } } ], @@ -321558,8 +375199,7 @@ "methods": { "tolist": { "metadata": { - "usage.dask": 14, - "usage.sklearn": 1 + "usage.dask": 14 } }, "min": { @@ -322017,10 +375657,6 @@ "pat": { "type": "str", "options": [ - "str$", - "^col_s", - "float|str", - "^col_int", "at$" ] }, @@ -322031,7 +375667,79 @@ } }, "metadata": { - "usage.sklearn": 5 + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "pat": { + "type": "str", + "options": [ + "^col_int" + ] + }, + "regex": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "pat": { + "type": "str", + "options": [ + "float|str" + ] + }, + "regex": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "pat": { + "type": "str", + "options": [ + "^col_s" + ] + }, + "regex": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + }, + { + "pos_or_kw_required": { + "pat": { + "type": "str", + "options": [ + "str$" + ] + }, + "regex": { + "type": { + "name": "bool" + } + } + }, + "metadata": { + "usage.sklearn": 1 } } ], @@ -324518,60 +378226,6 @@ ] } }, - "pandas.core.arrays.sparse.accessor": { - "classes": { - "SparseFrameAccessor": { - "method_overloads": { - "to_coo": [ - { - "metadata": { - "usage.sklearn": 1 - } - } - ] - }, - "methods": { - "to_coo": { - "metadata": { - "usage.sklearn": 1 - } - } - }, - "classmethod_overloads": { - "from_spmatrix": [ - { - "pos_or_kw_required": { - "data": { - "type": { - "module": "scipy.sparse.csr", - "name": "csr_matrix" - } - } - }, - "metadata": { - "usage.sklearn": 1 - } - } - ] - }, - "classmethods": { - "from_spmatrix": { - "pos_or_kw_required": { - "data": { - "type": { - "module": "scipy.sparse.csr", - "name": "csr_matrix" - } - } - }, - "metadata": { - "usage.sklearn": 1 - } - } - } - } - } - }, "numpy.distutils.core": { "function_overloads": { "setup": [ @@ -324593,24 +378247,30 @@ "numpy.distutils.misc_util": { "classes": { "Configuration": { - "method_overloads": { - "set_options": [ - { - "metadata": { - "usage.sklearn": 1 + "constructor_overloads": [ + { + "pos_or_kw_required": { + "package_name": { + "type": "None" + }, + "parent_name": { + "type": "str", + "options": [ + "" + ] + }, + "top_path": { + "type": "None" } + }, + "metadata": { + "usage.sklearn": 1 } - ], - "add_subpackage": [ + } + ], + "method_overloads": { + "set_options": [ { - "pos_or_kw_required": { - "subpackage_name": { - "type": "str", - "options": [ - "sklearn" - ] - } - }, "metadata": { "usage.sklearn": 1 } @@ -324622,19 +378282,6 @@ "metadata": { "usage.sklearn": 1 } - }, - "add_subpackage": { - "pos_or_kw_required": { - "subpackage_name": { - "type": "str", - "options": [ - "sklearn" - ] - } - }, - "metadata": { - "usage.sklearn": 1 - } } }, "properties": { @@ -324715,6 +378362,44 @@ } } } + }, + "pandas.core.arrays.sparse.accessor": { + "classes": { + "SparseFrameAccessor": { + "classmethod_overloads": { + "from_spmatrix": [ + { + "pos_or_kw_required": { + "data": { + "type": { + "module": "scipy.sparse.csr", + "name": "csr_matrix" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + } + ] + }, + "classmethods": { + "from_spmatrix": { + "pos_or_kw_required": { + "data": { + "type": { + "module": "scipy.sparse.csr", + "name": "csr_matrix" + } + } + }, + "metadata": { + "usage.sklearn": 1 + } + } + } + } + } } } } \ No newline at end of file diff --git a/data/typing/numpy.distutils.misc_util.py b/data/typing/numpy.distutils.misc_util.py index 45f1822..5188687 100644 --- a/data/typing/numpy.distutils.misc_util.py +++ b/data/typing/numpy.distutils.misc_util.py @@ -6,12 +6,6 @@ class Configuration: # usage.sklearn: 2 ext_modules: List[numpy.distutils.extension.Extension] - def add_subpackage(self, /, subpackage_name: Literal["sklearn"]): - """ - usage.sklearn: 1 - """ - ... - def set_options(self, /): """ usage.sklearn: 1 diff --git a/data/typing/numpy.lib.index_tricks.py b/data/typing/numpy.lib.index_tricks.py index 6326d8c..634c6bd 100644 --- a/data/typing/numpy.lib.index_tricks.py +++ b/data/typing/numpy.lib.index_tricks.py @@ -6,6 +6,7 @@ class CClass: def __getitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], /): """ usage.skimage: 1 + usage.sklearn: 1 """ ... @@ -14,6 +15,7 @@ def __getitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray], /): """ usage.matplotlib: 1 usage.pandas: 3 + usage.sklearn: 38 """ ... @@ -25,9 +27,9 @@ def __getitem__(self, _0: Tuple[Union[numpy.ndarray, List[complex]], ...], /): ... @overload - def __getitem__(self, _0: Union[Tuple[numpy.ndarray, ...], numpy.ndarray], /): + def __getitem__(self, _0: numpy.ndarray, /): """ - usage.sklearn: 40 + usage.sklearn: 1 """ ... @@ -58,7 +60,6 @@ def __getitem__(self, _0: Tuple[slice[int, int, int], slice[int, int, int]], /): def __getitem__(self, _0: slice[None, None, None], /): """ usage.pandas: 2 - usage.sklearn: 2 """ ... @@ -85,6 +86,20 @@ def __getitem__(self, _0: slice[int, int, int], /): """ ... + @overload + def __getitem__(self, _0: slice[None, None, None], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: slice[None, None, None], /): + """ + usage.sklearn: 1 + """ + ... + def __getitem__( self, _0: Union[ @@ -551,6 +566,7 @@ def __getitem__(self, _0: Tuple[Tuple[float, float], float], /): @overload def __getitem__(self, _0: Tuple[int, numpy.ndarray], /): """ + usage.sklearn: 3 usage.xarray: 1 """ ... @@ -608,13 +624,116 @@ def __getitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], /) def __getitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray], /): """ usage.matplotlib: 4 + usage.sklearn: 16 """ ... @overload - def __getitem__(self, _0: Union[tuple, List[int]], /): + def __getitem__( + self, + _0: Tuple[ + List[int], List[int], List[int], List[int], List[int], int, List[int] + ], + /, + ): """ - usage.sklearn: 37 + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, + _0: Tuple[ + List[int], List[int], List[int], List[int], List[int], int, int, List[int] + ], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[List[int], List[int], List[int]], /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __getitem__(self, _0: List[int], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, + _0: Tuple[ + numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray + ], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[numpy.ndarray, int], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[bool, numpy.ndarray, bool], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[numpy.float64, numpy.ndarray], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[List[int], List[int]], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[numpy.ndarray, numpy.float64], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[numpy.int64, numpy.ndarray], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[numpy.float64, numpy.ndarray, numpy.float64], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[numpy.int64, numpy.ndarray, numpy.int64], /): + """ + usage.sklearn: 1 """ ... diff --git a/data/typing/numpy.lib.stride_tricks.py b/data/typing/numpy.lib.stride_tricks.py index a89137e..3fc902d 100644 --- a/data/typing/numpy.lib.stride_tricks.py +++ b/data/typing/numpy.lib.stride_tricks.py @@ -175,11 +175,33 @@ def as_strided( @overload def as_strided( x: numpy.ndarray, - shape: Tuple[Union[int, numpy.int64], ...], - strides: Tuple[int, ...], + shape: Tuple[numpy.int64, numpy.int64, numpy.int64, int, int, int], + strides: Tuple[int, int, int, int, int, int], ): """ - usage.sklearn: 6 + usage.sklearn: 3 + """ + ... + + +@overload +def as_strided( + x: numpy.ndarray, shape: Tuple[numpy.int64, int], strides: Tuple[int, int] +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def as_strided( + x: numpy.ndarray, + shape: Tuple[numpy.int64, numpy.int64, int, int], + strides: Tuple[int, int, int, int], +): + """ + usage.sklearn: 2 """ ... diff --git a/data/typing/numpy.linalg.py b/data/typing/numpy.linalg.py index 1418dab..9e4befa 100644 --- a/data/typing/numpy.linalg.py +++ b/data/typing/numpy.linalg.py @@ -119,9 +119,17 @@ def lstsq(a: numpy.ndarray, b: numpy.ndarray, rcond: Union[numpy.float64, int]): @overload -def lstsq(a: numpy.ndarray, b: numpy.ndarray, rcond: None = ...): +def lstsq(a: numpy.ndarray, b: numpy.ndarray, rcond: None): """ - usage.sklearn: 2 + usage.sklearn: 1 + """ + ... + + +@overload +def lstsq(a: numpy.ndarray, b: numpy.ndarray): + """ + usage.sklearn: 1 """ ... @@ -173,6 +181,7 @@ def norm(x: numpy.ndarray): """ usage.matplotlib: 3 usage.skimage: 8 + usage.sklearn: 30 """ ... @@ -194,6 +203,7 @@ def norm( def norm(x: numpy.ndarray, axis: int): """ usage.matplotlib: 1 + usage.sklearn: 3 """ ... @@ -212,13 +222,41 @@ def norm( @overload -def norm( - x: numpy.ndarray, - ord: Union[float, int, Literal["fro"]] = ..., - axis: Union[None, int] = ..., -): +def norm(x: numpy.ndarray, ord: Literal["fro"]): """ - usage.sklearn: 45 + usage.sklearn: 4 + """ + ... + + +@overload +def norm(x: numpy.ndarray, ord: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def norm(x: numpy.ndarray, ord: int, axis: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def norm(x: numpy.ndarray, ord: float, axis: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def norm(x: numpy.ndarray, ord: float, axis: None): + """ + usage.sklearn: 2 """ ... @@ -291,6 +329,7 @@ def solve(a: Union[numpy.ndarray, numpy.matrix], b: numpy.ndarray): def svd(a: numpy.ndarray): """ usage.skimage: 6 + usage.sklearn: 3 """ ... @@ -299,6 +338,7 @@ def svd(a: numpy.ndarray): def svd(a: numpy.ndarray, full_matrices: bool): """ usage.skimage: 1 + usage.sklearn: 3 """ ... @@ -319,14 +359,6 @@ def svd(a: numpy.ndarray, full_matrices: int = ...): ... -@overload -def svd(a: numpy.ndarray, full_matrices: bool = ...): - """ - usage.sklearn: 6 - """ - ... - - def svd( a: numpy.ndarray, full_matrices: Union[bool, int] = ..., compute_uv: bool = ... ): diff --git a/data/typing/numpy.ma.core.py b/data/typing/numpy.ma.core.py index a4214f5..0052b46 100644 --- a/data/typing/numpy.ma.core.py +++ b/data/typing/numpy.ma.core.py @@ -2557,6 +2557,7 @@ def __setitem__( def __setitem__(self, _0: int, _1: numpy.float64, /): """ usage.matplotlib: 4 + usage.sklearn: 1 """ ... @@ -2591,9 +2592,170 @@ def __setitem__(self, _0: numpy.ma.core.MaskedArray, _1: float, /): ... @overload - def __setitem__(self, _0: int, _1: object, /): + def __setitem__(self, _0: int, _1: numpy.int64, /): """ - usage.sklearn: 25 + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: int, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: float, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["soft"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: List[float], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["hard"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["SAMME"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["SAMME.R"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["hinge"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Tuple[int, int], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["squared_hinge"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["l1"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["l2"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["mean"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["median"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["most_frequent"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: sklearn.linear_model._base.LinearRegression, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: sklearn.linear_model._ridge.Ridge, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["rbf"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["poly"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: None, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["log"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: bool, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Literal["modified_huber"], /): + """ + usage.sklearn: 1 """ ... @@ -2824,27 +2986,12 @@ def fill(self, _0: int, /): """ ... - @overload def filled(self, /, fill_value: Union[float, bool, int]): """ usage.scipy: 7 """ ... - @overload - def filled(self, /, fill_value: int): - """ - usage.sklearn: 1 - """ - ... - - def filled(self, /, fill_value: Union[int, bool, float]): - """ - usage.scipy: 7 - usage.sklearn: 1 - """ - ... - def harden_mask(self, /): """ usage.pandas: 2 @@ -2863,27 +3010,12 @@ def max(self, /, axis: int): """ ... - @overload def mean(self, /, axis: Union[None, int] = ..., keepdims: bool = ...): """ usage.scipy: 32 """ ... - @overload - def mean(self, /): - """ - usage.sklearn: 1 - """ - ... - - def mean(self, /, axis: Union[None, int] = ..., keepdims: bool = ...): - """ - usage.scipy: 32 - usage.sklearn: 1 - """ - ... - @overload def min(self, /): """ @@ -2929,7 +3061,6 @@ def reshape(self, /, *s: Literal["v", "t"]): """ usage.dask: 9 usage.scipy: 16 - usage.sklearn: 2 """ ... diff --git a/data/typing/numpy.ma.extras.py b/data/typing/numpy.ma.extras.py index 60d1732..6dc0f08 100644 --- a/data/typing/numpy.ma.extras.py +++ b/data/typing/numpy.ma.extras.py @@ -17,16 +17,23 @@ def apply_along_axis( def average(a: numpy.ma.core.MaskedArray, axis: int, weights: numpy.ndarray): """ usage.dask: 1 + usage.sklearn: 1 """ ... @overload -def average( - a: numpy.ma.core.MaskedArray, weights: Union[numpy.ndarray, None], axis: int = ... -): +def average(a: numpy.ma.core.MaskedArray, axis: int, weights: None): """ - usage.sklearn: 8 + usage.sklearn: 1 + """ + ... + + +@overload +def average(a: numpy.ma.core.MaskedArray, weights: numpy.ndarray): + """ + usage.sklearn: 6 """ ... diff --git a/data/typing/numpy.matrixlib.defmatrix.py b/data/typing/numpy.matrixlib.defmatrix.py index cf92f49..9f5bbbd 100644 --- a/data/typing/numpy.matrixlib.defmatrix.py +++ b/data/typing/numpy.matrixlib.defmatrix.py @@ -5,7 +5,6 @@ class matrix: def A1(self, /): """ usage.scipy: 3 - usage.sklearn: 1 """ ... @@ -18,7 +17,6 @@ def max(self, /): def mean(self, /): """ usage.scipy: 30 - usage.sklearn: 2 """ ... diff --git a/data/typing/numpy.py b/data/typing/numpy.py index b6222a9..27ad79a 100644 --- a/data/typing/numpy.py +++ b/data/typing/numpy.py @@ -1660,6 +1660,7 @@ def all(a: numpy.ndarray): """ usage.matplotlib: 27 usage.skimage: 93 + usage.sklearn: 139 usage.xarray: 21 """ ... @@ -1669,6 +1670,7 @@ def all(a: numpy.ndarray): def all(a: numpy.ndarray, axis: int): """ usage.skimage: 1 + usage.sklearn: 5 usage.xarray: 3 """ ... @@ -1687,6 +1689,7 @@ def all(a: List[bool]): """ usage.matplotlib: 1 usage.skimage: 1 + usage.sklearn: 1 """ ... @@ -1814,12 +1817,25 @@ def all( @overload -def all( - a: Union[List[bool], pandas.core.series.Series, bool, numpy.bool_, numpy.ndarray], - axis: int = ..., -): +def all(a: numpy.bool_): """ - usage.sklearn: 163 + usage.sklearn: 3 + """ + ... + + +@overload +def all(a: bool): + """ + usage.sklearn: 5 + """ + ... + + +@overload +def all(a: pandas.core.series.Series): + """ + usage.sklearn: 10 """ ... @@ -1866,6 +1882,7 @@ def allclose(a: List[int], b: dask.array.core.Array): def allclose(a: numpy.float64, b: int): """ usage.skimage: 3 + usage.sklearn: 2 """ ... @@ -1883,6 +1900,7 @@ def allclose(a: numpy.ndarray, b: numpy.ndarray): """ usage.matplotlib: 3 usage.skimage: 18 + usage.sklearn: 21 usage.xarray: 6 """ ... @@ -1892,6 +1910,7 @@ def allclose(a: numpy.ndarray, b: numpy.ndarray): def allclose(a: numpy.float64, b: numpy.float64): """ usage.skimage: 1 + usage.sklearn: 9 """ ... @@ -1900,6 +1919,7 @@ def allclose(a: numpy.float64, b: numpy.float64): def allclose(a: numpy.ndarray, b: int): """ usage.skimage: 1 + usage.sklearn: 5 usage.xarray: 4 """ ... @@ -1909,6 +1929,7 @@ def allclose(a: numpy.ndarray, b: int): def allclose(a: numpy.ndarray, b: numpy.ndarray, rtol: int, atol: float): """ usage.skimage: 1 + usage.sklearn: 3 """ ... @@ -1917,6 +1938,7 @@ def allclose(a: numpy.ndarray, b: numpy.ndarray, rtol: int, atol: float): def allclose(a: numpy.ndarray, b: List[int]): """ usage.skimage: 1 + usage.sklearn: 1 """ ... @@ -1925,6 +1947,7 @@ def allclose(a: numpy.ndarray, b: List[int]): def allclose(a: numpy.ndarray, b: numpy.ndarray, rtol: float, atol: float): """ usage.skimage: 1 + usage.sklearn: 4 """ ... @@ -1933,6 +1956,7 @@ def allclose(a: numpy.ndarray, b: numpy.ndarray, rtol: float, atol: float): def allclose(a: numpy.ndarray, b: numpy.ndarray, atol: float): """ usage.skimage: 1 + usage.sklearn: 3 """ ... @@ -1973,6 +1997,7 @@ def allclose(a: xarray.core.dataarray.DataArray, b: float): def allclose(a: numpy.ndarray, b: numpy.ndarray, rtol: float): """ usage.matplotlib: 1 + usage.sklearn: 2 usage.xarray: 5 """ ... @@ -2106,6 +2131,7 @@ def allclose(a: numpy.float64, b: numpy.ndarray): def allclose(a: numpy.float64, b: float): """ usage.matplotlib: 3 + usage.sklearn: 1 """ ... @@ -2114,6 +2140,7 @@ def allclose(a: numpy.float64, b: float): def allclose(a: numpy.ndarray, b: numpy.float64): """ usage.matplotlib: 4 + usage.sklearn: 1 """ ... @@ -2127,14 +2154,65 @@ def allclose(a: object, b: object, equal_nan: bool = ...): @overload -def allclose( - a: Union[numpy.ndarray, int, float, numpy.float64], - b: object, - rtol: Union[float, int] = ..., - atol: float = ..., -): +def allclose(a: float, b: float): """ - usage.sklearn: 63 + usage.sklearn: 2 + """ + ... + + +@overload +def allclose(a: numpy.ndarray, b: int, atol: float): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def allclose(a: numpy.ndarray, b: numpy.float32): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def allclose(a: numpy.ndarray, b: float): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def allclose(a: numpy.ndarray, b: List[numpy.float64], rtol: float): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def allclose(a: numpy.float64, b: numpy.float64, rtol: float): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def allclose(a: int, b: numpy.ndarray): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def allclose(a: numpy.ndarray, b: float, atol: float): + """ + usage.sklearn: 1 """ ... @@ -2171,6 +2249,7 @@ def amax(a: numpy.ndarray): """ usage.matplotlib: 35 usage.skimage: 70 + usage.sklearn: 52 usage.xarray: 9 """ ... @@ -2181,6 +2260,7 @@ def amax(a: List[int]): """ usage.matplotlib: 2 usage.skimage: 2 + usage.sklearn: 2 """ ... @@ -2198,6 +2278,7 @@ def amax(a: numpy.ndarray, axis: int): """ usage.matplotlib: 6 usage.skimage: 6 + usage.sklearn: 14 usage.xarray: 6 """ ... @@ -2280,6 +2361,7 @@ def amax(a: Tuple[int, int, int, int]): @overload def amax(a: numpy.ndarray, axis: None): """ + usage.sklearn: 2 usage.xarray: 7 """ ... @@ -2453,14 +2535,6 @@ def amax( ... -@overload -def amax(a: Union[numpy.ndarray, List[int]], axis: Union[None, int] = ...): - """ - usage.sklearn: 70 - """ - ... - - def amax( _0: Union[numpy.ndarray, numpy.ma.core.MaskedArray] = ..., /, @@ -2488,6 +2562,7 @@ def amin(a: numpy.ndarray): """ usage.matplotlib: 31 usage.skimage: 49 + usage.sklearn: 46 usage.xarray: 10 """ ... @@ -2498,6 +2573,7 @@ def amin(a: List[int]): """ usage.matplotlib: 2 usage.skimage: 1 + usage.sklearn: 2 """ ... @@ -2522,6 +2598,7 @@ def amin(a: numpy.ndarray, axis: Tuple[int, int]): def amin(a: Tuple[int, int]): """ usage.skimage: 1 + usage.sklearn: 1 """ ... @@ -2530,6 +2607,7 @@ def amin(a: Tuple[int, int]): def amin(a: numpy.ndarray, axis: int): """ usage.matplotlib: 6 + usage.sklearn: 6 usage.xarray: 6 """ ... @@ -2538,6 +2616,7 @@ def amin(a: numpy.ndarray, axis: int): @overload def amin(a: numpy.ndarray, axis: None): """ + usage.sklearn: 2 usage.xarray: 6 """ ... @@ -2636,6 +2715,7 @@ def amin(a: object, axis: int = ..., keepdims: bool = ...): def amin(a: List[numpy.float64]): """ usage.matplotlib: 20 + usage.sklearn: 10 """ ... @@ -2644,6 +2724,7 @@ def amin(a: List[numpy.float64]): def amin(a: List[float]): """ usage.matplotlib: 1 + usage.sklearn: 1 """ ... @@ -2711,18 +2792,17 @@ def amin( @overload -def amin( - a: Union[ - numpy.ndarray, - float, - int, - List[Union[int, float, numpy.float64]], - Tuple[int, int], - ], - axis: Union[None, int] = ..., -): +def amin(a: float): """ - usage.sklearn: 73 + usage.sklearn: 3 + """ + ... + + +@overload +def amin(a: int): + """ + usage.sklearn: 2 """ ... @@ -2809,6 +2889,7 @@ def any(a: numpy.bool_): """ usage.matplotlib: 1 usage.skimage: 2 + usage.sklearn: 4 """ ... @@ -2818,6 +2899,7 @@ def any(a: numpy.ndarray): """ usage.matplotlib: 11 usage.skimage: 33 + usage.sklearn: 61 usage.xarray: 4 """ ... @@ -2827,6 +2909,7 @@ def any(a: numpy.ndarray): def any(a: List[bool]): """ usage.skimage: 1 + usage.sklearn: 4 """ ... @@ -2834,6 +2917,7 @@ def any(a: List[bool]): @overload def any(a: numpy.ndarray, axis: int): """ + usage.sklearn: 5 usage.xarray: 4 """ ... @@ -2944,6 +3028,7 @@ def any(a: object, axis: int = ...): def any(a: bool): """ usage.matplotlib: 3 + usage.sklearn: 1 """ ... @@ -2975,12 +3060,25 @@ def any( @overload -def any( - a: Union[List[Union[bool, numpy.bool_]], numpy.ndarray, numpy.bool_, bool], - axis: int = ..., -): +def any(a: List[numpy.bool_]): """ - usage.sklearn: 83 + usage.sklearn: 6 + """ + ... + + +@overload +def any(a: List[Union[bool, numpy.bool_]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def any(a: List[Union[numpy.bool_, bool]]): + """ + usage.sklearn: 1 """ ... @@ -3032,6 +3130,7 @@ def append( def append(arr: numpy.ndarray, values: numpy.ndarray): """ usage.matplotlib: 1 + usage.sklearn: 7 """ ... @@ -3057,14 +3156,47 @@ def append(arr: numpy.ndarray, values: float): """ usage.dask: 1 usage.matplotlib: 2 + usage.sklearn: 3 """ ... @overload -def append(arr: numpy.ndarray, values: object, axis: int = ...): +def append(arr: numpy.ndarray, values: numpy.ndarray, axis: int): """ - usage.sklearn: 29 + usage.sklearn: 9 + """ + ... + + +@overload +def append(arr: numpy.ndarray, values: List[int]): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def append(arr: numpy.ndarray, values: numpy.float64): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def append(arr: numpy.ndarray, values: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def append(arr: numpy.ndarray, values: Literal["a"]): + """ + usage.sklearn: 1 """ ... @@ -3100,6 +3232,7 @@ def apply_along_axis(func1d: Callable, axis: int, arr: numpy.ndarray): def apply_along_axis(func1d: Callable, axis: int, arr: numpy.ndarray): """ usage.skimage: 6 + usage.sklearn: 3 """ ... @@ -3137,14 +3270,22 @@ def apply_along_axis( ... +@overload +def apply_along_axis(func1d: Callable, axis: int, arr: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + + @overload def apply_along_axis( - func1d: Union[sklearn.gaussian_process.kernels.PairwiseKernel, Callable], + func1d: sklearn.gaussian_process.kernels.PairwiseKernel, axis: int, arr: numpy.ndarray, ): """ - usage.sklearn: 5 + usage.sklearn: 1 """ ... @@ -3211,6 +3352,7 @@ def arange(_0: int, /): usage.matplotlib: 188 usage.sample-usage: 4 usage.skimage: 95 + usage.sklearn: 287 usage.xarray: 518 """ ... @@ -3229,6 +3371,7 @@ def arange(_0: int, /, *, dtype: Type[float]): def arange(_0: numpy.int64, _1: numpy.int64, /): """ usage.skimage: 1 + usage.sklearn: 1 """ ... @@ -3246,6 +3389,7 @@ def arange(_0: int, _1: int, /): """ usage.matplotlib: 21 usage.skimage: 20 + usage.sklearn: 35 usage.xarray: 66 """ ... @@ -3256,6 +3400,7 @@ def arange(_0: int, _1: int, _2: int, /): """ usage.matplotlib: 26 usage.skimage: 12 + usage.sklearn: 8 usage.xarray: 20 """ ... @@ -3273,6 +3418,7 @@ def arange(_0: int, _1: int, _2: int, _3: Type[numpy.uint8], /): def arange(_0: int, /, *, dtype: Type[numpy.float32]): """ usage.skimage: 4 + usage.sklearn: 2 usage.xarray: 4 """ ... @@ -3282,6 +3428,7 @@ def arange(_0: int, /, *, dtype: Type[numpy.float32]): def arange(_0: int, /, *, dtype: Type[numpy.float64]): """ usage.skimage: 1 + usage.sklearn: 7 usage.xarray: 1 """ ... @@ -3292,6 +3439,7 @@ def arange(_0: numpy.int64, /): """ usage.matplotlib: 2 usage.skimage: 5 + usage.sklearn: 6 """ ... @@ -3351,6 +3499,7 @@ def arange(_0: int, _1: int, _2: float, /): """ usage.matplotlib: 16 usage.skimage: 1 + usage.sklearn: 1 """ ... @@ -3359,6 +3508,7 @@ def arange(_0: int, _1: int, _2: float, /): def arange(_0: int, /, *, dtype: Type[numpy.int64]): """ usage.skimage: 1 + usage.sklearn: 7 usage.xarray: 5 """ ... @@ -3386,6 +3536,7 @@ def arange(_0: int, /, *, dtype: numpy.dtype): def arange(_0: int, /, *, dtype: Type[int]): """ usage.skimage: 1 + usage.sklearn: 3 usage.xarray: 2 """ ... @@ -3582,6 +3733,7 @@ def arange(_0: float, _1: float, /, *, dtype: Literal["float64"]): def arange(_0: int, /, *, dtype: Type[numpy.int32]): """ usage.matplotlib: 15 + usage.sklearn: 1 usage.xarray: 1 """ ... @@ -3665,6 +3817,7 @@ def arange(_0: numpy.int64, _1: numpy.int64, _2: numpy.int64, /): def arange(_0: float, _1: float, _2: float, /): """ usage.matplotlib: 17 + usage.sklearn: 4 """ ... @@ -3795,16 +3948,41 @@ def arange( @overload -def arange( - _0: Union[int, numpy.int32, float, numpy.int64], - _1: Union[int, float, numpy.int64] = ..., - _2: Union[numpy.float64, int, float] = ..., - /, - *, - dtype: type = ..., -): +def arange(_0: int, /, *, dtype: Type[numpy.uint32]): """ - usage.sklearn: 373 + usage.sklearn: 7 + """ + ... + + +@overload +def arange(_0: numpy.int64, _1: int, /): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def arange(_0: numpy.int64, /, *, dtype: Type[int]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def arange(_0: numpy.int32, _1: numpy.int64, /): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def arange(_0: int, _1: int, _2: numpy.float64, /): + """ + usage.sklearn: 1 """ ... @@ -3834,6 +4012,7 @@ def arange( def argmax(a: numpy.ndarray): """ usage.skimage: 12 + usage.sklearn: 17 usage.xarray: 4 """ ... @@ -3851,6 +4030,7 @@ def argmax(a: dask.array.core.Array): def argmax(a: numpy.ndarray, axis: int): """ usage.skimage: 3 + usage.sklearn: 57 usage.xarray: 7 """ ... @@ -3939,9 +4119,9 @@ def argmax( @overload -def argmax(a: Union[numpy.ndarray, numpy.matrix], axis: int = ...): +def argmax(a: numpy.matrix): """ - usage.sklearn: 77 + usage.sklearn: 3 """ ... @@ -3971,6 +4151,7 @@ def argmin(a: numpy.ndarray): """ usage.matplotlib: 2 usage.skimage: 5 + usage.sklearn: 9 """ ... @@ -3980,6 +4161,7 @@ def argmin(a: numpy.ndarray, axis: int): """ usage.matplotlib: 4 usage.skimage: 3 + usage.sklearn: 2 usage.xarray: 5 """ ... @@ -3989,6 +4171,7 @@ def argmin(a: numpy.ndarray, axis: int): def argmin(a: List[numpy.float64]): """ usage.skimage: 2 + usage.sklearn: 10 """ ... @@ -4075,16 +4258,17 @@ def argmin( @overload -def argmin( - a: Union[ - Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], - numpy.ndarray, - List[Union[float, numpy.float64]], - ], - axis: int = ..., -): +def argmin(a: List[float]): """ - usage.sklearn: 23 + usage.sklearn: 1 + """ + ... + + +@overload +def argmin(a: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64]): + """ + usage.sklearn: 1 """ ... @@ -4113,14 +4297,15 @@ def argmin( def argpartition(a: numpy.ndarray, kth: int, axis: int): """ usage.dask: 1 + usage.sklearn: 3 """ ... @overload -def argpartition(a: numpy.ndarray, kth: Union[int, numpy.int64], axis: int): +def argpartition(a: numpy.ndarray, kth: numpy.int64, axis: int): """ - usage.sklearn: 4 + usage.sklearn: 1 """ ... @@ -4138,6 +4323,7 @@ def argsort(a: numpy.ndarray): """ usage.matplotlib: 1 usage.skimage: 17 + usage.sklearn: 31 """ ... @@ -4191,14 +4377,57 @@ def argsort(a: numpy.ndarray, axis: int = ...): ... +@overload +def argsort(a: numpy.ndarray, kind: Literal["mergesort"]): + """ + usage.sklearn: 16 + """ + ... + + @overload def argsort( - a: Union[numpy.ndarray, numpy.matrix, Tuple[Union[numpy.float64, float], ...]], - kind: Literal["mergesort"] = ..., - axis: int = ..., + a: Tuple[ + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + ] ): """ - usage.sklearn: 57 + usage.sklearn: 1 + """ + ... + + +@overload +def argsort(a: numpy.ndarray, axis: int): + """ + usage.sklearn: 7 + """ + ... + + +@overload +def argsort( + a: Tuple[float, float, float, float, float, float, float, float, float, float] +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def argsort(a: numpy.matrix): + """ + usage.sklearn: 1 """ ... @@ -4239,6 +4468,7 @@ def around(a: Tuple[numpy.float64, numpy.float64]): @overload def around(a: numpy.ndarray): """ + usage.sklearn: 1 usage.xarray: 5 """ ... @@ -4249,6 +4479,7 @@ def around(a: numpy.ndarray, decimals: int): """ usage.dask: 1 usage.matplotlib: 2 + usage.sklearn: 8 usage.xarray: 3 """ ... @@ -4275,12 +4506,17 @@ def around( @overload -def around( - a: Union[scipy.sparse.csc.csc_matrix, numpy.ndarray, scipy.sparse.csr.csr_matrix], - decimals: int = ..., -): +def around(a: scipy.sparse.csr.csr_matrix): """ - usage.sklearn: 11 + usage.sklearn: 1 + """ + ... + + +@overload +def around(a: scipy.sparse.csc.csc_matrix): + """ + usage.sklearn: 1 """ ... @@ -4303,6 +4539,7 @@ def array(_0: List[float], /): """ usage.matplotlib: 79 usage.skimage: 39 + usage.sklearn: 158 usage.xarray: 39 """ ... @@ -4321,6 +4558,7 @@ def array(_0: List[numpy.ndarray], /): """ usage.matplotlib: 13 usage.skimage: 47 + usage.sklearn: 107 usage.xarray: 13 """ ... @@ -4331,6 +4569,7 @@ def array(_0: List[Union[int, float]], /): """ usage.matplotlib: 8 usage.skimage: 5 + usage.sklearn: 14 usage.xarray: 7 """ ... @@ -4341,6 +4580,7 @@ def array(_0: List[List[Union[float, int]]], /): """ usage.matplotlib: 7 usage.skimage: 5 + usage.sklearn: 64 usage.xarray: 4 """ ... @@ -4350,6 +4590,7 @@ def array(_0: List[List[Union[float, int]]], /): def array(_0: List[List[int]], /, *, dtype: Type[numpy.uint8]): """ usage.skimage: 62 + usage.sklearn: 1 """ ... @@ -4359,6 +4600,7 @@ def array(_0: List[List[Union[int, float]]], /): """ usage.matplotlib: 5 usage.skimage: 6 + usage.sklearn: 54 usage.xarray: 3 """ ... @@ -4377,6 +4619,7 @@ def array(_0: numpy.ndarray, /): """ usage.matplotlib: 23 usage.skimage: 14 + usage.sklearn: 72 usage.xarray: 30 """ ... @@ -4388,6 +4631,7 @@ def array(_0: List[int], /): usage.matplotlib: 41 usage.sample-usage: 1 usage.skimage: 101 + usage.sklearn: 427 usage.xarray: 79 """ ... @@ -4398,6 +4642,7 @@ def array(_0: Tuple[int, int, int], /): """ usage.matplotlib: 1 usage.skimage: 12 + usage.sklearn: 2 usage.xarray: 1 """ ... @@ -4434,6 +4679,7 @@ def array(_0: Tuple[float, float, float], /): """ usage.matplotlib: 2 usage.skimage: 1 + usage.sklearn: 2 """ ... @@ -4460,6 +4706,7 @@ def array(_0: List[List[float]], /): """ usage.matplotlib: 33 usage.skimage: 31 + usage.sklearn: 183 usage.xarray: 9 """ ... @@ -4503,6 +4750,7 @@ def array(_0: List[List[int]], /): """ usage.matplotlib: 27 usage.skimage: 153 + usage.sklearn: 422 usage.xarray: 14 """ ... @@ -4513,6 +4761,7 @@ def array(_0: List[numpy.float64], /): """ usage.matplotlib: 32 usage.skimage: 12 + usage.sklearn: 61 usage.xarray: 8 """ ... @@ -4523,6 +4772,7 @@ def array(_0: Tuple[int, int], /): """ usage.matplotlib: 2 usage.skimage: 31 + usage.sklearn: 7 usage.xarray: 1 """ ... @@ -4533,6 +4783,7 @@ def array(_0: List[numpy.int64], /): """ usage.matplotlib: 3 usage.skimage: 10 + usage.sklearn: 17 usage.xarray: 4 """ ... @@ -4542,6 +4793,7 @@ def array(_0: List[numpy.int64], /): def array(_0: List[List[List[int]]], /): """ usage.skimage: 7 + usage.sklearn: 3 usage.xarray: 1 """ ... @@ -4552,6 +4804,7 @@ def array(_0: List[List[List[float]]], /): """ usage.matplotlib: 2 usage.skimage: 1 + usage.sklearn: 3 """ ... @@ -4579,6 +4832,7 @@ def array(_0: List[int], /, *, dtype: Type[numpy.uint8]): """ usage.matplotlib: 3 usage.skimage: 8 + usage.sklearn: 2 """ ... @@ -4595,6 +4849,7 @@ def array(_0: List[int], /, *, dtype: Type[numpy.float16]): def array(_0: numpy.ndarray, /, *, dtype: Type[bool]): """ usage.skimage: 6 + usage.sklearn: 1 """ ... @@ -4604,6 +4859,7 @@ def array(_0: List[float], /, *, dtype: Type[numpy.float64]): """ usage.matplotlib: 1 usage.skimage: 2 + usage.sklearn: 3 """ ... @@ -4629,6 +4885,7 @@ def array(_0: list, /): """ usage.matplotlib: 8 usage.skimage: 11 + usage.sklearn: 13 usage.xarray: 1 """ ... @@ -4638,6 +4895,7 @@ def array(_0: list, /): def array(_0: List[Tuple[int, int]], /): """ usage.skimage: 6 + usage.sklearn: 8 """ ... @@ -4647,6 +4905,7 @@ def array(_0: List[Union[float, int]], /): """ usage.matplotlib: 14 usage.skimage: 9 + usage.sklearn: 21 usage.xarray: 3 """ ... @@ -4674,6 +4933,7 @@ def array( def array(_0: List[List[int]], /, *, dtype: Type[bool]): """ usage.skimage: 11 + usage.sklearn: 1 """ ... @@ -4683,6 +4943,7 @@ def array(_0: List[List[Union[int, numpy.float64]]], /): """ usage.matplotlib: 1 usage.skimage: 1 + usage.sklearn: 4 """ ... @@ -4700,6 +4961,7 @@ def array(_0: Tuple[numpy.ndarray, numpy.ndarray], /): """ usage.matplotlib: 2 usage.skimage: 3 + usage.sklearn: 1 """ ... @@ -4741,6 +5003,7 @@ def array(_0: List[List[bool]], /): """ usage.matplotlib: 2 usage.skimage: 11 + usage.sklearn: 5 usage.xarray: 4 """ ... @@ -4791,6 +5054,7 @@ def array(_0: List[List[int]], /, *, dtype: Type[numpy.uint32]): def array(_0: range, /): """ usage.skimage: 3 + usage.sklearn: 1 """ ... @@ -4816,6 +5080,7 @@ def array(_0: List[List[int]], /, *, dtype: Type[numpy.int32]): """ usage.matplotlib: 5 usage.skimage: 2 + usage.sklearn: 2 """ ... @@ -4824,6 +5089,7 @@ def array(_0: List[List[int]], /, *, dtype: Type[numpy.int32]): def array(_0: List[List[int]], /, *, dtype: Type[numpy.int64]): """ usage.skimage: 3 + usage.sklearn: 1 usage.xarray: 1 """ ... @@ -4833,6 +5099,7 @@ def array(_0: List[List[int]], /, *, dtype: Type[numpy.int64]): def array(_0: List[List[int]], /, *, dtype: Type[numpy.float32]): """ usage.skimage: 3 + usage.sklearn: 10 """ ... @@ -4841,6 +5108,7 @@ def array(_0: List[List[int]], /, *, dtype: Type[numpy.float32]): def array(_0: List[List[int]], /, *, dtype: Type[numpy.float64]): """ usage.skimage: 4 + usage.sklearn: 12 """ ... @@ -4849,6 +5117,7 @@ def array(_0: List[List[int]], /, *, dtype: Type[numpy.float64]): def array(_0: List[List[int]], /, *, dtype: Type[float]): """ usage.skimage: 3 + usage.sklearn: 1 usage.xarray: 1 """ ... @@ -4859,6 +5128,7 @@ def array(_0: List[List[numpy.float64]], /): """ usage.matplotlib: 9 usage.skimage: 2 + usage.sklearn: 6 """ ... @@ -4867,6 +5137,7 @@ def array(_0: List[List[numpy.float64]], /): def array(_0: List[List[Union[float, int]]], /, *, dtype: Type[numpy.float32]): """ usage.skimage: 1 + usage.sklearn: 2 """ ... @@ -4892,6 +5163,7 @@ def array(_0: int, /): """ usage.matplotlib: 3 usage.skimage: 1 + usage.sklearn: 8 usage.xarray: 12 """ ... @@ -4926,6 +5198,7 @@ def array(_0: numpy.ndarray, /, *, copy: bool): """ usage.matplotlib: 4 usage.skimage: 4 + usage.sklearn: 1 """ ... @@ -4943,6 +5216,7 @@ def array(_0: Tuple[int, int, int, int], /): def array(_0: Tuple[int], /): """ usage.skimage: 12 + usage.sklearn: 3 """ ... @@ -4952,6 +5226,7 @@ def array(_0: List[int], /, *, dtype: Type[numpy.float64]): """ usage.matplotlib: 1 usage.skimage: 2 + usage.sklearn: 8 usage.xarray: 10 """ ... @@ -4962,6 +5237,7 @@ def array(_0: List[int], /, *, dtype: numpy.dtype): """ usage.matplotlib: 3 usage.skimage: 3 + usage.sklearn: 14 """ ... @@ -5003,6 +5279,7 @@ def array(_0: List[List[float]], /, *, dtype: Type[numpy.float64]): """ usage.matplotlib: 3 usage.skimage: 1 + usage.sklearn: 5 """ ... @@ -5054,6 +5331,7 @@ def array(_0: List[List[Union[numpy.float64, float]]], /): def array(_0: numpy.ndarray, /, *, dtype: Type[numpy.float64], order: Literal["C"]): """ usage.skimage: 2 + usage.sklearn: 1 """ ... @@ -5062,6 +5340,7 @@ def array(_0: numpy.ndarray, /, *, dtype: Type[numpy.float64], order: Literal["C def array(_0: List[Tuple[numpy.float64, ...]], /): """ usage.skimage: 2 + usage.sklearn: 2 """ ... @@ -5078,6 +5357,7 @@ def array(_0: List[Tuple[numpy.float32, ...]], /): def array(_0: List[List[Union[numpy.float64, int]]], /): """ usage.skimage: 4 + usage.sklearn: 3 """ ... @@ -5102,6 +5382,7 @@ def array(_0: List[List[int]], _1: Type[numpy.uint8], /): def array(_0: List[List[int]], /, *, dtype: Type[int]): """ usage.skimage: 4 + usage.sklearn: 1 """ ... @@ -5142,6 +5423,7 @@ def array(_0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], /): def array(_0: List[List[float]], /, *, dtype: Type[numpy.float32]): """ usage.skimage: 3 + usage.sklearn: 12 """ ... @@ -5179,6 +5461,7 @@ def array( def array(_0: List[List[int]], /, *, order: Literal["F"]): """ usage.skimage: 1 + usage.sklearn: 1 """ ... @@ -5220,6 +5503,7 @@ def array(_0: List[bool], /): """ usage.matplotlib: 2 usage.skimage: 5 + usage.sklearn: 19 usage.xarray: 5 """ ... @@ -5229,6 +5513,7 @@ def array(_0: List[bool], /): def array(_0: List[numpy.bool_], /): """ usage.skimage: 1 + usage.sklearn: 1 """ ... @@ -5304,6 +5589,7 @@ def array(_0: List[int], /, *, dtype: Type[numpy.int32]): """ usage.matplotlib: 2 usage.skimage: 3 + usage.sklearn: 5 """ ... @@ -5312,6 +5598,7 @@ def array(_0: List[int], /, *, dtype: Type[numpy.int32]): def array(_0: List[int], /, *, dtype: Type[numpy.uint32]): """ usage.skimage: 3 + usage.sklearn: 18 """ ... @@ -5320,6 +5607,7 @@ def array(_0: List[int], /, *, dtype: Type[numpy.uint32]): def array(_0: List[int], /, *, dtype: Type[numpy.int64]): """ usage.skimage: 8 + usage.sklearn: 17 """ ... @@ -5361,6 +5649,7 @@ def array(_0: List[int], /, *, dtype: Type[float]): """ usage.matplotlib: 4 usage.skimage: 1 + usage.sklearn: 1 usage.xarray: 2 """ ... @@ -5436,6 +5725,7 @@ def array(_0: List[Union[int, numpy.int64]], /): def array(_0: List[numpy.ndarray], /, *, dtype: Type[int]): """ usage.skimage: 3 + usage.sklearn: 2 """ ... @@ -5468,6 +5758,7 @@ def array(_0: List[Union[numpy.uint64, numpy.uint8]], /): def array(_0: List[Tuple[numpy.int64, numpy.int64]], /): """ usage.skimage: 2 + usage.sklearn: 12 """ ... @@ -5500,6 +5791,7 @@ def array(_0: List[List[Union[int, numpy.int64]]], /): def array(_0: List[int], /, *, dtype: Type[numpy.float32]): """ usage.skimage: 3 + usage.sklearn: 11 """ ... @@ -5726,6 +6018,7 @@ def array(_0: List[Union[float, Literal["cdef", "ab"]]], /, *, dtype: Type[objec def array(_0: float, /): """ usage.matplotlib: 1 + usage.sklearn: 9 usage.xarray: 11 """ ... @@ -5940,6 +6233,7 @@ def array(_0: List[bool], /, *, dtype: Type[bool]): @overload def array(_0: numpy.ndarray, /, *, copy: bool, dtype: numpy.dtype): """ + usage.sklearn: 1 usage.xarray: 6 """ ... @@ -5949,6 +6243,7 @@ def array(_0: numpy.ndarray, /, *, copy: bool, dtype: numpy.dtype): def array(_0: List[str], /, *, dtype: Type[object]): """ usage.matplotlib: 2 + usage.sklearn: 3 usage.xarray: 1 """ ... @@ -5974,6 +6269,7 @@ def array(_0: List[Literal["", "cdef", "ab"]], /, *, dtype: Type[object]): def array(_0: List[Union[numpy.float64, int]], /): """ usage.matplotlib: 1 + usage.sklearn: 1 usage.xarray: 1 """ ... @@ -6454,6 +6750,7 @@ def array(_0: List[int], /, *, dtype: Literal["uint32"]): def array(_0: List[str], /): """ usage.matplotlib: 6 + usage.sklearn: 10 usage.xarray: 6 """ ... @@ -6502,6 +6799,7 @@ def array(_0: List[Literal["c", "a"]], /): @overload def array(_0: List[Literal["c", "b", "a"]], /): """ + usage.sklearn: 2 usage.xarray: 3 """ ... @@ -6705,6 +7003,7 @@ def array(_0: datetime.timedelta, /): def array(_0: numpy.float64, /): """ usage.matplotlib: 2 + usage.sklearn: 4 usage.xarray: 1 """ ... @@ -6713,6 +7012,7 @@ def array(_0: numpy.float64, /): @overload def array(_0: numpy.float32, /): """ + usage.sklearn: 1 usage.xarray: 1 """ ... @@ -6737,6 +7037,7 @@ def array(_0: bool, /): @overload def array(_0: List[List[int]], /, *, dtype: Literal["int64"]): """ + usage.sklearn: 2 usage.xarray: 3 """ ... @@ -6818,6 +7119,7 @@ def array(_0: int, /, *, dtype: Type[numpy.int64]): def array(_0: List[Union[int, numpy.float64]], /): """ usage.matplotlib: 2 + usage.sklearn: 3 usage.xarray: 3 """ ... @@ -6909,6 +7211,7 @@ def array(_0: List[Dict[Literal["b", "a"], numpy.int64]], /): def array(_0: Tuple[numpy.float64, numpy.float64], /): """ usage.matplotlib: 2 + usage.sklearn: 1 usage.xarray: 1 """ ... @@ -6918,6 +7221,7 @@ def array(_0: Tuple[numpy.float64, numpy.float64], /): def array(_0: Tuple[float, float], /): """ usage.matplotlib: 5 + usage.sklearn: 2 usage.xarray: 3 """ ... @@ -7007,6 +7311,7 @@ def array(_0: object, /): def array(_0: List[Literal["c", "b", "a"]], /, *, dtype: Type[object]): """ usage.matplotlib: 2 + usage.sklearn: 3 usage.xarray: 2 """ ... @@ -7342,6 +7647,7 @@ def array(_0: numpy.ndarray, /, *, copy: bool, subok: bool): def array(_0: List[Tuple[numpy.float64, numpy.float64, numpy.float64]], /): """ usage.matplotlib: 4 + usage.sklearn: 4 """ ... @@ -7382,6 +7688,7 @@ def array(_0: numpy.ndarray, /, *, order: Literal["C"]): def array(_0: Tuple[int, float], /): """ usage.matplotlib: 1 + usage.sklearn: 1 """ ... @@ -7609,6 +7916,7 @@ def array(_0: List[List[int]], /, *, copy: bool, subok: bool): def array(_0: List[List[Union[float, numpy.float64, int]]], /): """ usage.matplotlib: 1 + usage.sklearn: 1 """ ... @@ -7638,6 +7946,7 @@ def array(_0: List[List[Union[float, numpy.float64]]], /): def array(_0: numpy.ndarray, _1: Type[float], /): """ usage.matplotlib: 4 + usage.sklearn: 1 """ ... @@ -7666,6 +7975,7 @@ def array( ): """ usage.matplotlib: 5 + usage.sklearn: 1 """ ... @@ -7674,6 +7984,7 @@ def array( def array(_0: List[Union[numpy.float64, float]], /): """ usage.matplotlib: 15 + usage.sklearn: 1 """ ... @@ -7978,6 +8289,7 @@ def array(_0: List[Literal["g", "b"]], /): def array(_0: List[Union[numpy.int64, numpy.float64]], /): """ usage.matplotlib: 2 + usage.sklearn: 1 """ ... @@ -7994,6 +8306,7 @@ def array(_0: Tuple[int, int, int, float], /): def array(_0: List[None], /): """ usage.matplotlib: 2 + usage.sklearn: 1 """ ... @@ -8058,6 +8371,7 @@ def array(_0: List[Union[float, int]], _1: Type[float], /): def array(_0: List[List[numpy.int64]], /): """ usage.matplotlib: 1 + usage.sklearn: 2 """ ... @@ -8407,6 +8721,7 @@ def array(_0: List[Literal["fun", "is", "Python"]], /, *, dtype: Type[object]): def array(_0: List[Literal["b", "a"]], /, *, dtype: Type[object]): """ usage.matplotlib: 2 + usage.sklearn: 1 """ ... @@ -8840,6 +9155,7 @@ def array(_0: object, /, *, copy: bool, subok: bool): def array(_0: List[Tuple[float, float]], /): """ usage.matplotlib: 2 + usage.sklearn: 2 """ ... @@ -9346,6 +9662,7 @@ def array(_0: List[Tuple[Union[int, float], Union[numpy.float64, int]]], /): def array(_0: List[Union[float, numpy.float64]], /): """ usage.matplotlib: 4 + usage.sklearn: 1 """ ... @@ -9354,6 +9671,7 @@ def array(_0: List[Union[float, numpy.float64]], /): def array(_0: List[List[Union[float, int]]], /, *, dtype: Type[numpy.float64]): """ usage.matplotlib: 1 + usage.sklearn: 2 """ ... @@ -9477,6 +9795,7 @@ def array( def array(_0: List[list], /): """ usage.matplotlib: 1 + usage.sklearn: 3 """ ... @@ -9580,185 +9899,115 @@ def array( @overload -def array( - _0: object, - _1: type = ..., - /, - *, - dtype: Union[type, numpy.dtype, None, str] = ..., - order: Union[Literal["C", "F"], None] = ..., - copy: bool = ..., - ndmin: int = ..., -): +def array(_0: numpy.ndarray, /, *, dtype: None, order: Literal["C"]): """ - usage.sklearn: 2254 + usage.sklearn: 3 """ ... -def array( - _0: object, - _1: Union[ - type, - numpy.dtype, - str, - List[Tuple[str, Union[type, Tuple[Type[numpy.bytes_], int], Literal["i"]]]], - ] = ..., - /, - *, - dtype: object = ..., - copy: bool = ..., - ndmin: int = ..., - order: Union[None, Literal["C", "F", "c", "K"]] = ..., - subok: bool = ..., -): +@overload +def array(_0: numpy.ndarray, /, *, dtype: None, order: None): """ - usage.dask: 495 - usage.matplotlib: 812 - usage.pandas: 6865 - usage.sample-usage: 3 - usage.scipy: 7120 - usage.skimage: 927 - usage.sklearn: 2254 - usage.xarray: 665 + usage.sklearn: 9 """ ... -def array2string(a: numpy.ndarray, separator: Literal[", "]): +@overload +def array(_0: List[int], /, *, dtype: Type[numpy.int64], order: Literal["C"]): """ - usage.skimage: 1 + usage.sklearn: 14 """ ... @overload -def array_equal(a1: List[int], a2: numpy.ndarray): +def array(_0: list, /, *, dtype: Type[numpy.int64], order: Literal["C"]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def array_equal(a1: numpy.ndarray, a2: numpy.ndarray): +def array(_0: List[Tuple[numpy.int64, Union[int, numpy.int64]]], /): """ - usage.dask: 4 - usage.matplotlib: 1 - usage.skimage: 2 - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def array_equal(a1: Tuple[float, float, float], a2: Tuple[int, int, int]): +def array(_0: List[Tuple[numpy.int64, Union[numpy.int64, int]]], /): """ - usage.skimage: 1 + usage.sklearn: 9 """ ... @overload -def array_equal(a1: numpy.ndarray, a2: Tuple[int, int, int]): +def array(_0: numpy.ndarray, /, *, dtype: Type[numpy.float32], order: Literal["C"]): """ - usage.skimage: 1 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def array_equal(a1: numpy.ndarray, a2: Tuple[int, int]): +def array(_0: numpy.ndarray, /, *, dtype: numpy.dtype): """ - usage.xarray: 1 + usage.sklearn: 7 """ ... @overload -def array_equal( - a1: Union[ - numpy.ndarray, - int, - pandas.core.series.Series, - List[Union[float, bool, numpy.int32, int]], - ], - a2: Union[numpy.ndarray, pandas.core.series.Series], -): +def array(_0: List[Literal["virginica", "versicolor", "setosa"]], /): """ - usage.pandas: 142 + usage.sklearn: 1 """ ... @overload -def array_equal( - a1: Union[ - List[ - Union[ - Tuple[Union[float, int, None, Literal["2020-02-29", ""]], ...], - List[Union[int, List[int]]], - Literal["hi"], - ] - ], - numpy.ndarray, - Tuple[ - Union[None, Tuple[Union[float, int, None], Union[float, None]], float, int], - ..., - ], - Literal["hi", "hello"], - ], - a2: Union[List[list], numpy.ndarray], -): +def array(_0: List[Tuple[numpy.float64, numpy.float64]], /): """ - usage.scipy: 135 + usage.sklearn: 8 """ ... @overload -def array_equal(a1: None, a2: None): +def array( + _0: List[Tuple[numpy.float64, numpy.float64]], /, *, dtype: Type[numpy.float32] +): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload -def array_equal( - a1: Union[numpy.ndarray, List[float]], - a2: Union[List[Union[numpy.float32, numpy.float64, int, float]], numpy.ndarray], -): +def array(_0: List[Literal["b", "a"]], /, *, dtype: numpy.dtype): """ - usage.sklearn: 33 + usage.sklearn: 1 """ ... -def array_equal( - a1: object, - a2: Union[ - numpy.ndarray, - pandas.core.series.Series, - List[Union[list, float, int, numpy.float64, numpy.float32]], - None, - Tuple[int, ...], - ], +@overload +def array( + _0: List[List[Union[Literal["a", "b"], int, bool]]], /, *, dtype: Literal["O"] ): """ - usage.dask: 4 - usage.matplotlib: 2 - usage.pandas: 142 - usage.scipy: 135 - usage.skimage: 5 - usage.sklearn: 33 - usage.xarray: 3 + usage.sklearn: 1 """ ... -def array_equiv(a1: numpy.ndarray, a2: numpy.ndarray): +@overload +def array(_0: List[List[Literal["b", "a", "B", "A"]]], /, *, dtype: Type[object]): """ usage.sklearn: 1 """ @@ -9766,1760 +10015,1750 @@ def array_equiv(a1: numpy.ndarray, a2: numpy.ndarray): @overload -def array_split( - ary: Union[pandas.core.series.Series, pandas.core.frame.DataFrame], - indices_or_sections: int, -): +def array(_0: List[Literal["B", "A"]], /, *, dtype: numpy.dtype): """ - usage.pandas: 4 + usage.sklearn: 1 """ ... @overload -def array_split( - ary: Union[List[Union[Tuple[int, ...], numpy.ndarray]], numpy.ndarray], - indices_or_sections: int, -): +def array(_0: List[List[Dict[Literal["c", "b", "a"], int]]], /, *, dtype: Type[object]): """ - usage.sklearn: 7 + usage.sklearn: 2 """ ... -def array_split( - ary: Union[ - numpy.ndarray, - pandas.core.frame.DataFrame, - pandas.core.series.Series, - List[Union[numpy.ndarray, Tuple[int, ...]]], - ], - indices_or_sections: int, -): +@overload +def array(_0: List[int], /, *, dtype: Type[int]): """ - usage.pandas: 4 - usage.sklearn: 7 + usage.sklearn: 4 """ ... @overload -def asanyarray(a: numpy.ndarray): +def array(_0: List[float], /, *, dtype: Type[float]): """ - usage.matplotlib: 18 - usage.skimage: 33 + usage.sklearn: 3 """ ... @overload -def asanyarray(a: List[int]): +def array(_0: List[Literal["two", "one"]], /, *, dtype: numpy.dtype): """ - usage.matplotlib: 6 - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Union[float, int]]): +def array(_0: List[Literal["middle", "low", "high"]], /, *, dtype: numpy.dtype): """ - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[float]): +def array(_0: List[Literal["z"]], /, *, dtype: numpy.dtype): """ - usage.matplotlib: 5 - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: Tuple[int, int, int]): +def array(_0: List[Literal["x1_z", "x0_b", "x0_a"]], /, *, dtype: Type[object]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: numpy.ndarray, dtype: Type[bool]): +def array(_0: List[List[numpy.ndarray]], /): """ - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: Tuple[int, int]): +def array(_0: Tuple[float, float, float, float, float], /): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def asanyarray(a: float): +def array( + _0: List[ + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64, numpy.float64] + ], + /, +): """ - usage.matplotlib: 1 - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def asanyarray(a: object, dtype: Union[Literal["int64"], type] = ...): +def array( + _0: List[ + Union[ + numpy.ndarray, + Tuple[ + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + ], + ] + ], + /, +): """ - usage.pandas: 42 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: object, dtype: Type[numpy.int64] = ...): +def array(_0: numpy.ndarray, /, *, dtype: Type[numpy.float64], order: None): """ - usage.scipy: 185 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: Tuple[float, float]): +def array(_0: List[Literal["class_2", "class_1", "class_0"]], /): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[List[numpy.float64]]): +def array(_0: List[Literal["benign", "malignant"]], /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: list): +def array( + _0: List[List[Union[float, Literal["Iris-setosa"]]]], /, *, dtype: Literal["O"] +): """ - usage.matplotlib: 4 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[numpy.float64]): +def array(_0: List[Literal["Iris-setosa"]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 7 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: numpy.float64): +def array(_0: List[List[Union[str, float, None]]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: numpy.ma.core.MaskedArray): +def array(_0: List[None], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 14 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: Tuple[numpy.float64, numpy.float64]): +def array(_0: List[Literal["C"]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def asanyarray( - a: Tuple[Tuple[numpy.float64, numpy.float64], Tuple[numpy.float64, numpy.float64]] -): +def array(_0: List[Literal["A", "R"]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: Tuple[Tuple[float, float], Tuple[float, float]]): +def array(_0: List[Union[None, Literal["T"]]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: Tuple[Tuple[int, int], Tuple[float, float]]): +def array(_0: List[Union[Literal["S"], None]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: Tuple[Tuple[float, int], Tuple[float, int]]): +def array(_0: List[Union[Literal["3", "2"], None]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: Tuple[Tuple[float, int], Tuple[float, float]]): +def array(_0: List[Union[Literal["N"], None]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Tuple[float, float]], dtype: Type[float]): +def array(_0: List[Literal["E", "D", "G"]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: numpy.int64): +def array(_0: List[Union[None, Literal["Y"]]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 5 """ ... @overload -def asanyarray(a: Tuple[float, int]): +def array(_0: List[Union[None, Literal["B", "M"]]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: Tuple[int, float]): +def array(_0: List[Literal["SHEET", "COIL"]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: numpy.ndarray, dtype: Type[float]): +def array(_0: List[Literal["0"]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 6 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[numpy.ndarray]): +def array(_0: List[Literal["3"]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Literal["lime", "b", "y", "r"]]): +def array( + _0: List[List[Union[Literal["adviser", "amdahl"], float]]], + /, + *, + dtype: Literal["O"], +): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: numpy.ma.core.MaskedArray, dtype: Type[float]): +def array(_0: List[Literal["amdahl", "adviser"]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Union[float, int]], dtype: Type[float]): +def array(_0: List[List[float]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[int], dtype: Type[float]): +def array(_0: List[Literal["FALSE", "TRUE"]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 2 + usage.sklearn: 3 """ ... @overload -def asanyarray(a: int, dtype: Type[float]): +def array(_0: List[Literal["TRUE", "FALSE"]], /, *, dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload -def asanyarray(a: List[List[Union[int, float]]], dtype: Type[float]): +def array(_0: numpy.ndarray, /, *, dtype: None, order: Literal["F"]): """ - usage.matplotlib: 7 + usage.sklearn: 2 """ ... @overload -def asanyarray(a: List[Literal["0.8", "0.7", "0.6", "0.5"]]): +def array(_0: numpy.ndarray, /, *, order: Literal["F"]): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload -def asanyarray( - a: List[Union[Literal["lime", "b", "y"], Tuple[int, int, int]]], dtype: Type[float] +def array( + _0: Tuple[float, float, float, float, float, float, float, float, float, float], + /, + *, + dtype: Type[numpy.float64], ): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[float], dtype: Type[float]): +def array(_0: Tuple[int, int, int], /, *, dtype: Type[int]): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[List[int]], dtype: Type[float]): +def array(_0: numpy.ndarray, /, *, dtype: Type[numpy.float64]): """ - usage.matplotlib: 5 + usage.sklearn: 5 """ ... @overload -def asanyarray( - a: List[Union[Literal["0.0", "red"], List[Union[int, float]]]], dtype: Type[float] -): +def array(_0: List[Literal["x"]], /, *, dtype: Type[object]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray( - a: List[Union[Literal["C5", "0.0", "red"], List[Union[int, float]]]], - dtype: Type[float], -): +def array(_0: List[Literal["y", "x"]], /, *, dtype: numpy.dtype): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray( - a: List[Union[List[Union[int, float]], Literal["C5", "0.0", "red"]]], - dtype: Type[float], -): +def array(_0: List[bool], /, *, dtype: Type[numpy.uint8]): """ - usage.matplotlib: 1 + usage.sklearn: 6 """ ... @overload -def asanyarray( - a: List[Union[Literal["jaune", "red"], List[Union[int, float]]]], dtype: Type[float] +def array( + _0: List[importlib._bootstrap.MonotonicConstraint], /, *, dtype: Type[numpy.int8] ): """ - usage.matplotlib: 1 + usage.sklearn: 6 """ ... @overload -def asanyarray( - a: List[Union[Literal["jaune", "0.0", "red"], List[Union[int, float]]]], - dtype: Type[float], -): +def array(_0: Tuple[int, int, int, int, int], /, *, dtype: Type[int]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray( - a: List[Union[Literal["jaune", "C5", "0.0", "red"], List[Union[int, float]]]], - dtype: Type[float], -): +def array(_0: List[List[Union[int, float, None]]], /): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def asanyarray(a: Tuple[float, float, float], dtype: Type[float]): +def array(_0: numpy.ndarray, /, *, dtype: Type[numpy.float32]): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload -def asanyarray(a: List[Tuple[float, float, float]], dtype: Type[float]): +def array(_0: sklearn.utils.estimator_checks._NotAnArray, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Tuple[int, int]]): +def array(_0: List[List[int]], /, *, dtype: numpy.dtype): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload -def asanyarray(a: list, dtype: Type[float]): +def array(_0: List[Literal["two", "three", "one"]], /, *, dtype: numpy.dtype): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Tuple[numpy.float64, numpy.float64]], dtype: Type[float]): +def array(_0: List[numpy.int32], /): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload -def asanyarray(a: List[numpy.ndarray], dtype: Type[float]): +def array(_0: List[Literal["beer", "pizza"]], /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Tuple[int, int]], dtype: Type[float]): +def array(_0: List[Literal["sklearn", "scipy", "numpy"]], /): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: range, dtype: Type[float]): +def array(_0: numpy.ndarray, /, *, dtype: Type[float], order: None): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Union[int, float]]): +def array( + _0: List[List[Union[Literal["b", "a", "e", "h", "g"], int]]], /, *, dtype: None +): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Tuple[float, numpy.float64]], dtype: Type[float]): +def array( + _0: List[List[Union[Literal["b", "a", "e", "h", "g"], int]]], + /, + *, + dtype: Type[object], +): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: Tuple[Tuple[int, float], Tuple[float, float]]): +def array( + _0: List[List[Union[Literal["b", "a", "e", "h", "g"], int]]], /, *, dtype: Type[str] +): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Tuple[float, int]], dtype: Type[float]): +def array(_0: List[List[Union[float, str]]], /, *, dtype: Type[str]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Tuple[int, float]], dtype: Type[float]): +def array(_0: List[List[Union[float, str]]], /, *, dtype: numpy.dtype): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def asanyarray(a: int): +def array(_0: List[List[Union[None, str]]], /, *, dtype: Type[object]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[List[float]]): +def array(_0: List[List[str]], /, *, dtype: Type[object]): """ - usage.matplotlib: 1 + usage.sklearn: 8 """ ... @overload -def asanyarray(a: List[List[float]], dtype: Type[float]): +def array(_0: List[List[Union[float, str]]], /, *, dtype: Type[object]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Union[numpy.float64, float]]): +def array(_0: List[List[Union[int, str]]], /, *, dtype: Type[object]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Union[float, numpy.float64]]): +def array(_0: List[List[Union[str, None]]], /, *, dtype: Type[object]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Union[numpy.float64, int]]): +def array(_0: List[List[Union[str, float]]], /, *, dtype: Type[object]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: List[Union[int, numpy.float64]]): +def array(_0: List[List[Union[str, int]]], /, *, dtype: Type[object]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def asanyarray(a: object): +def array(_0: List[List[Literal["c", "b", "a"]]], /, *, dtype: Type[object]): """ - usage.dask: 45 + usage.sklearn: 1 """ ... @overload -def asanyarray( - a: Union[ - numpy.ndarray, - numpy.ma.core.MaskedArray, - numpy.float64, - List[ - Union[ - int, - Dict[Literal["max_depth", "min_samples_split"], int], - Literal["3", "2", "1"], - ] - ], - ] -): +def array(_0: List[List[int]], /, *, order: Literal["C"]): """ - usage.sklearn: 34 + usage.sklearn: 1 """ ... -def asanyarray(a: object, dtype: Union[type, Literal["int64"]] = ...): +@overload +def array(_0: List[int], /, *, order: Literal["C"]): """ - usage.dask: 45 - usage.matplotlib: 135 - usage.pandas: 42 - usage.scipy: 185 - usage.skimage: 48 - usage.sklearn: 34 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def asarray(a: List[int]): +def array(_0: List[int], /, *, order: Literal["F"]): """ - usage.matplotlib: 42 - usage.skimage: 12 - usage.xarray: 42 + usage.sklearn: 1 """ ... @overload -def asarray(a: List[Union[float, int]]): +def array(_0: List[numpy.str_], /): """ - usage.matplotlib: 12 - usage.skimage: 2 - usage.xarray: 11 + usage.sklearn: 4 """ ... @overload -def asarray(a: numpy.ndarray): +def array(_0: List[Literal["not-setosa", "setosa"]], /): """ - usage.matplotlib: 96 - usage.skimage: 43 - usage.xarray: 229 + usage.sklearn: 1 """ ... @overload -def asarray(a: float): +def array(_0: numpy.matrix, /): """ - usage.matplotlib: 8 - usage.skimage: 2 - usage.xarray: 5 + usage.sklearn: 4 """ ... @overload -def asarray(a: List[float]): +def array(_0: List[numpy.float32], /): """ - usage.matplotlib: 30 - usage.skimage: 6 - usage.xarray: 17 + usage.sklearn: 2 """ ... @overload -def asarray(a: Tuple[float, float]): +def array( + _0: Tuple[int, int, int, int, int, int, int, int, int, int], /, *, dtype: Type[int] +): """ - usage.matplotlib: 2 - usage.skimage: 1 - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def asarray(a: Tuple[Tuple[int, int, int], Tuple[int, float, int]]): +def array( + _0: Tuple[float, float, float, float, float, float, float, float, float], + /, + *, + dtype: Type[numpy.float64], +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def asarray(a: List[Union[int, float]]): +def array(_0: List[Literal["three", "two", "one"]], /): """ - usage.matplotlib: 15 - usage.skimage: 1 - usage.xarray: 19 + usage.sklearn: 1 """ ... @overload -def asarray(a: numpy.ndarray, dtype: Type[numpy.float64]): +def array(_0: numpy.ndarray, /, *, dtype: Literal["float"]): """ - usage.matplotlib: 13 - usage.skimage: 2 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def asarray(a: int): +def array(_0: List[Literal["1-a", "0-a"]], /, *, dtype: Type[object]): """ - usage.matplotlib: 4 - usage.skimage: 3 - usage.xarray: 6 + usage.sklearn: 1 """ ... @overload -def asarray(a: Tuple[float, float, float], dtype: numpy.dtype): +def array(_0: List[Literal["1-a", "0-a"]], /, *, dtype: numpy.dtype): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def asarray(a: Tuple[float, float, float], dtype: Type[float]): +def array(_0: List[Literal["red", "green", "blue"]], /): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def asarray(a: Tuple[float, float, float]): +def array(_0: List[Literal["red¢", "green¢", "blue¢"]], /): """ - usage.skimage: 8 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def asarray(a: imageio.core.util.Array): +def array(_0: List[Literal["cat", "bird", "ant"]], /): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def asarray(a: List[numpy.int64]): +def array(_0: List[Literal["yes", "no"]], /): """ - usage.matplotlib: 9 - usage.skimage: 2 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def asarray(a: Tuple[int, int]): +def array(_0: List[Literal["spam", "ham"]], /): """ - usage.matplotlib: 6 - usage.skimage: 16 + usage.sklearn: 1 """ ... @overload -def asarray(a: Tuple[int, int, int]): +def array(_0: List[Literal["ham", "spam"]], /): """ - usage.skimage: 14 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def asarray(a: Tuple[float, int]): +def array(_0: numpy.ndarray, _1: Type[int], /): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def asarray(a: numpy.ndarray, dtype: Type[numpy.uint8]): +def array(_0: List[Literal["spam", "eggs"]], /): """ - usage.matplotlib: 4 - usage.skimage: 1 + usage.sklearn: 4 """ ... @overload -def asarray(a: numpy.ndarray, dtype: Type[numpy.uint16]): +def array(_0: List[Literal["spam", "eggs"]], /, *, dtype: numpy.dtype): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def asarray(a: List[Tuple[numpy.ndarray, numpy.ndarray]]): +def array(_0: List[List[int]], /, *, dtype: Literal["object"]): """ - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def asarray(a: numpy.ndarray, dtype: Type[float]): +def array(_0: List[List[int]], /, *, dtype: Literal["int"]): """ - usage.matplotlib: 13 - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def asarray(a: List[Union[int, float]], dtype: Type[float]): +def array(_0: numpy.ndarray, /, *, dtype: Type[int]): """ - usage.skimage: 2 + usage.sklearn: 7 """ ... @overload -def asarray(a: List[int], dtype: Type[float]): +def array(_0: List[Literal["b", "a"]], /, *, dtype: Literal["u4", "points", "colors"], Tuple[int]], ...] - ], -): +def asarray(a: List[Literal["2.3", "6.1", "3.0", "7.7"]], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def empty(_0: Tuple[int], /, *, dtype: Type[float]): +def asarray(a: List[Literal["2.4", "5.6", "3.4", "6.3"]], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def empty(_0: Tuple[int, int], /, *, dtype: Type[float]): +def asarray(a: List[Literal["1.8", "5.5", "3.1", "6.4"]], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def empty(*, dtype: numpy.dtype, shape: Tuple[int, int, int]): +def asarray(a: List[Literal["1.8", "4.8", "3.0", "6.0"]], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def empty(_0: List[int], /, *, dtype: Type[numpy.float32]): +def asarray(a: List[Literal["2.1", "5.4", "3.1", "6.9"]], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def empty(_0: List[Union[numpy.int64, int]], /, *, dtype: numpy.dtype): +def asarray(a: List[Literal["2.4", "5.6", "3.1", "6.7"]], dtype: Type[numpy.float64]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def empty(_0: List[int], /, *, dtype: numpy.dtype): +def asarray(a: List[Literal["2.3", "5.1", "3.1", "6.9"]], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def empty(_0: List[int], /, *, dtype: Type[numpy.float64]): +def asarray(a: List[Literal["2.3", "5.9", "3.2", "6.8"]], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def empty(_0: int, /, *, dtype: Type[numpy.float64]): +def asarray(a: List[Literal["2.5", "5.7", "3.3", "6.7"]], dtype: Type[numpy.float64]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def empty(_0: List[int], /, *, dtype: Type[numpy.int32]): +def asarray(a: List[Literal["2.3", "5.2", "3.0", "6.7"]], dtype: Type[numpy.float64]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def empty( - _0: Union[Tuple[Union[int, None], ...], List[int], numpy.ndarray, int] = ..., - _1: int = ..., - /, - *, - shape: Tuple[int, ...] = ..., - dtype: Union[ - Literal["f8", "i1", "O"], - type, - List[Tuple[str, Union[numpy.dtype, Type[numpy.int64]]]], - numpy.dtype, - ] = ..., - order: Literal["F", "C"] = ..., -): +def asarray(a: List[Literal["1.9", "5.0", "2.5", "6.3"]], dtype: Type[numpy.float64]): """ - usage.dask: 149 + usage.sklearn: 1 """ ... @overload -def empty( - _0: Union[Tuple[Union[int, numpy.int64], ...], List[int], int] = ..., - _1: Union[Type[int], numpy.dtype] = ..., - /, - *, - dtype: Union[numpy.dtype, type, Literal["i", "float"]] = ..., - order: Literal["C", "F"] = ..., - shape: Union[int, Tuple[int, int]] = ..., -): +def asarray(a: List[Literal["2.0", "5.2", "3.0", "6.5"]], dtype: Type[numpy.float64]): """ - usage.sklearn: 189 + usage.sklearn: 1 """ ... -def empty( - _0: object = ..., - _1: Union[ - numpy.dtype, - int, - type, - str, - List[ - Tuple[Literal["mopt", "mrows", "ncols", "imagf", "namlen"], Literal["i4"]] - ], - ] = ..., - /, - *, - dtype: Union[ - str, - type, - numpy.dtype, - None, - List[Tuple[Union[Tuple[int], str, numpy.dtype, type], ...]], - ] = ..., - order: Literal["C", "F", "c"] = ..., - shape: Union[Tuple[int, ...], int] = ..., -): +@overload +def asarray(a: List[Literal["2.3", "5.4", "3.4", "6.2"]], dtype: Type[numpy.float64]): """ - usage.dask: 149 - usage.matplotlib: 30 - usage.pandas: 423 - usage.scipy: 677 - usage.skimage: 117 - usage.sklearn: 189 - usage.xarray: 23 + usage.sklearn: 1 """ ... @overload -def empty_like(_0: numpy.ndarray, /): +def asarray(a: List[Literal["1.8", "5.1", "3.0", "5.9"]], dtype: Type[numpy.float64]): """ - usage.matplotlib: 10 - usage.skimage: 33 + usage.sklearn: 1 """ ... @overload -def empty_like(_0: numpy.ndarray, /, *, dtype: Type[numpy.float64]): +def asarray(a: List[float], dtype: numpy.dtype, order: Literal["C"]): """ - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def empty_like(_0: numpy.ndarray, /, *, dtype: Type[numpy.uint16]): +def asarray(a: List[numpy.ndarray], dtype: None, order: None): """ - usage.skimage: 1 + usage.sklearn: 7 """ ... @overload -def empty_like(_0: numpy.ndarray, /, *, dtype: Type[float]): +def asarray(a: List[List[int]], dtype: Type[float], order: None): """ - usage.skimage: 2 + usage.sklearn: 2 """ ... @overload -def empty_like(_0: numpy.ndarray, /, *, dtype: Type[numpy.uint8]): +def asarray( + a: List[List[Union[int, float, Literal["a", "b"]]]], + dtype: Type[object], + order: None, +): """ - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def empty_like( - _0: numpy.ndarray, - /, - *, - dtype: Type[numpy.float64], - order: Literal["C"], - subok: bool, -): +def asarray(a: pandas.core.frame.DataFrame, dtype: None, order: None): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def empty_like( - _0: numpy.ma.core.MaskedArray, - /, - *, - dtype: Type[numpy.float64], - order: Literal["C"], - subok: bool, -): +def asarray(a: List[List[int]], dtype: Type[object], order: None): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def empty_like(_0: numpy.ndarray, _1: Type[numpy.float64], /): +def asarray(a: pandas.core.series.Series, dtype: None, order: None): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def empty_like(_0: xarray.core.variable.Variable, /): +def asarray(a: List[float], dtype: None, order: None): """ - usage.xarray: 1 + usage.sklearn: 6 """ ... @overload -def empty_like(_0: xarray.core.variable.IndexVariable, /): +def asarray( + a: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + ] +): """ - usage.xarray: 1 + usage.sklearn: 5 """ ... @overload -def empty_like( - _0: Union[numpy.ndarray, pandas.core.arrays.string_.StringArray, List[None]], - /, - *, - dtype: Union[type, Literal["float", "f8", "i8", "object"], numpy.dtype] = ..., -): +def asarray(a: List[str], dtype: Type[numpy.float64]): """ - usage.pandas: 18 + usage.sklearn: 1253 """ ... @overload -def empty_like(_0: numpy.ndarray, /, *, dtype: type = ...): +def asarray(a: Literal["24"], dtype: Type[numpy.float64]): """ - usage.scipy: 103 + usage.sklearn: 1 """ ... @overload -def empty_like( - _0: numpy.ndarray, - /, - *, - dtype: numpy.dtype = ..., - shape: Union[Tuple[int, ...], None, int] = ..., - order: Literal["F", "C"] = ..., -): +def asarray(a: Literal["21.6"], dtype: Type[numpy.float64]): """ - usage.dask: 12 + usage.sklearn: 1 """ ... @overload -def empty_like( - _0: numpy.ndarray, /, *, dtype: Union[numpy.dtype, Type[numpy.float32]] = ... -): +def asarray(a: Literal["34.7"], dtype: Type[numpy.float64]): """ - usage.sklearn: 26 + usage.sklearn: 1 """ ... -def empty_like( - _0: object, - _1: Type[numpy.float64] = ..., - /, - *, - dtype: Union[type, numpy.dtype, Literal["float", "f8", "i8", "object"]] = ..., - order: Literal["F", "C"] = ..., - subok: bool = ..., - shape: Union[Tuple[int, ...], None, int] = ..., -): +@overload +def asarray(a: Literal["33.4"], dtype: Type[numpy.float64]): """ - usage.dask: 12 - usage.matplotlib: 10 - usage.pandas: 18 - usage.scipy: 103 - usage.skimage: 48 - usage.sklearn: 26 - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def expand_dims(a: numpy.ndarray, axis: int): +def asarray(a: Literal["36.2"], dtype: Type[numpy.float64]): """ - usage.dask: 2 - usage.matplotlib: 12 - usage.xarray: 5 + usage.sklearn: 1 """ ... @overload -def expand_dims(a: Union[numpy.ndarray, numpy.float64], axis: int): +def asarray(a: Literal["28.7"], dtype: Type[numpy.float64]): """ - usage.pandas: 19 + usage.sklearn: 1 """ ... @overload -def expand_dims( - a: Union[ - numpy.int64, - numpy.ma.core.MaskedArray, - numpy.ndarray, - numpy.float64, - numpy.ma.core.MaskedConstant, - ], - axis: int, -): +def asarray(a: Literal["22.9"], dtype: Type[numpy.float64]): """ - usage.scipy: 21 + usage.sklearn: 1 """ ... @overload -def expand_dims(a: Union[numpy.float64, numpy.ndarray], axis: int): +def asarray(a: Literal["27.1"], dtype: Type[numpy.float64]): """ - usage.sklearn: 6 + usage.sklearn: 1 """ ... -def expand_dims( - a: Union[ - numpy.ndarray, - numpy.float64, - numpy.int64, - numpy.ma.core.MaskedArray, - numpy.ma.core.MaskedConstant, - ], - axis: int, -): +@overload +def asarray(a: Literal["16.5"], dtype: Type[numpy.float64]): """ - usage.dask: 2 - usage.matplotlib: 12 - usage.pandas: 19 - usage.scipy: 21 - usage.sklearn: 6 - usage.xarray: 5 + usage.sklearn: 1 """ ... @overload -def extract( - condition: object, - arr: Union[ - numpy.ma.core.MaskedArray, numpy.int64, float, numpy.float64, numpy.ndarray - ], -): +def asarray(a: Literal["18.9"], dtype: Type[numpy.float64]): """ - usage.scipy: 77 + usage.sklearn: 1 """ ... @overload -def extract(condition: numpy.ndarray, arr: numpy.ndarray): +def asarray(a: Literal["15"], dtype: Type[numpy.float64]): """ - usage.dask: 1 + usage.sklearn: 1 """ ... -def extract( - condition: object, - arr: Union[ - numpy.ndarray, numpy.float64, float, numpy.int64, numpy.ma.core.MaskedArray - ], -): +@overload +def asarray(a: Literal["21.7"], dtype: Type[numpy.float64]): """ - usage.dask: 1 - usage.scipy: 77 + usage.sklearn: 1 """ ... @overload -def eye(N: int): +def asarray(a: Literal["20.4"], dtype: Type[numpy.float64]): """ - usage.sample-usage: 1 - usage.skimage: 30 + usage.sklearn: 1 """ ... @overload -def eye(N: int, dtype: Type[int]): +def asarray(a: Literal["18.2"], dtype: Type[numpy.float64]): """ - usage.skimage: 5 + usage.sklearn: 1 """ ... @overload -def eye(N: int, M: int, dtype: Type[bool]): +def asarray(a: Literal["19.9"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def eye(N: int, dtype: Type[numpy.float64]): +def asarray(a: Literal["23.1"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def eye(N: int, M: int): +def asarray(a: Literal["17.5"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 2 - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def eye(N: int, dtype: Union[numpy.dtype, Literal["int64", "float64"]] = ...): +def asarray(a: Literal["20.2"], dtype: Type[numpy.float64]): """ - usage.pandas: 9 + usage.sklearn: 1 """ ... @overload -def eye( - N: Union[numpy.int64, int], - M: int = ..., - dtype: Union[numpy.dtype, type] = ..., - k: int = ..., -): +def asarray(a: Literal["13.6"], dtype: Type[numpy.float64]): """ - usage.scipy: 463 + usage.sklearn: 1 """ ... @overload -def eye(N: int, M: int = ..., k: int = ...): +def asarray(a: Literal["19.6"], dtype: Type[numpy.float64]): """ - usage.dask: 33 + usage.sklearn: 1 """ ... @overload -def eye(N: int, M: int = ...): +def asarray(a: Literal["15.2"], dtype: Type[numpy.float64]): """ - usage.sklearn: 52 + usage.sklearn: 1 """ ... -def eye( - N: Union[int, numpy.int64], - M: int = ..., - k: int = ..., - dtype: Union[type, Literal["int64", "float64"], numpy.dtype] = ..., -): +@overload +def asarray(a: Literal["14.5"], dtype: Type[numpy.float64]): """ - usage.dask: 33 - usage.matplotlib: 2 - usage.pandas: 9 - usage.sample-usage: 1 - usage.scipy: 463 - usage.skimage: 38 - usage.sklearn: 52 + usage.sklearn: 1 """ ... @overload -def fill_diagonal(a: numpy.ndarray, val: float): +def asarray(a: Literal["15.6"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def fill_diagonal(a: numpy.ndarray, val: Union[numpy.float64, numpy.ndarray, int]): +def asarray(a: Literal["13.9"], dtype: Type[numpy.float64]): """ - usage.scipy: 7 + usage.sklearn: 1 """ ... @overload -def fill_diagonal(a: numpy.ndarray, val: Union[numpy.ndarray, int, float]): +def asarray(a: Literal["16.6"], dtype: Type[numpy.float64]): """ - usage.sklearn: 16 + usage.sklearn: 1 """ ... -def fill_diagonal( - a: numpy.ndarray, val: Union[float, int, numpy.ndarray, numpy.float64] -): +@overload +def asarray(a: Literal["14.8"], dtype: Type[numpy.float64]): """ - usage.scipy: 7 - usage.skimage: 1 - usage.sklearn: 16 + usage.sklearn: 1 """ ... @overload -def find_common_type( - array_types: Union[ - List[numpy.dtype], - Tuple[Union[numpy.dtype, type], Union[numpy.dtype, type]], - collections.defaultdict, - ], - scalar_types: list, -): +def asarray(a: Literal["18.4"], dtype: Type[numpy.float64]): """ - usage.pandas: 58 + usage.sklearn: 1 """ ... @overload -def find_common_type( - array_types: Union[ - List[Union[numpy.dtype, type]], Tuple[Union[type, str, numpy.dtype], ...] - ], - scalar_types: Union[List[numpy.dtype], Tuple[None, ...]], -): +def asarray(a: Literal["21"], dtype: Type[numpy.float64]): """ - usage.scipy: 309 + usage.sklearn: 1 """ ... @overload -def find_common_type(array_types: List[numpy.dtype], scalar_types: list): +def asarray(a: Literal["12.7"], dtype: Type[numpy.float64]): """ - usage.sklearn: 8 + usage.sklearn: 1 """ ... -def find_common_type( - array_types: Union[ - List[Union[type, numpy.dtype]], - collections.defaultdict, - Tuple[Union[str, numpy.dtype, type], ...], - ], - scalar_types: Union[List[numpy.dtype], Tuple[None, ...]], -): +@overload +def asarray(a: Literal["13.2"], dtype: Type[numpy.float64]): """ - usage.pandas: 58 - usage.scipy: 309 - usage.sklearn: 8 + usage.sklearn: 1 """ ... @overload -def fix(x: float): +def asarray(a: Literal["13.1"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def fix(x: numpy.float64): +def asarray(a: Literal["13.5"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def fix(x: xarray.core.dataarray.DataArray): +def asarray(a: Literal["20"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def fix(x: Union[pandas.core.series.Series, numpy.ndarray]): +def asarray(a: Literal["24.7"], dtype: Type[numpy.float64]): """ - usage.pandas: 2 + usage.sklearn: 1 """ ... @overload -def fix(x: numpy.ndarray): +def asarray(a: Literal["30.8"], dtype: Type[numpy.float64]): """ - usage.dask: 3 + usage.sklearn: 1 """ ... -def fix( - x: Union[ - numpy.ndarray, - xarray.core.dataarray.DataArray, - float, - numpy.float64, - pandas.core.series.Series, - ] -): +@overload +def asarray(a: Literal["34.9"], dtype: Type[numpy.float64]): """ - usage.dask: 3 - usage.pandas: 2 - usage.skimage: 2 - usage.xarray: 1 + usage.sklearn: 1 """ ... -def flatnonzero(a: numpy.ndarray): +@overload +def asarray(a: Literal["26.6"], dtype: Type[numpy.float64]): """ - usage.dask: 2 - usage.pandas: 2 - usage.scipy: 5 - usage.skimage: 6 - usage.sklearn: 35 - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def flip(m: numpy.ndarray, axis: int): +def asarray(a: Literal["25.3"], dtype: Type[numpy.float64]): """ - usage.scipy: 1 - usage.skimage: 3 - usage.xarray: 4 + usage.sklearn: 1 """ ... @overload -def flip(m: sparse._coo.core.COO, axis: int): +def asarray(a: Literal["21.2"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def flip(m: object, axis: int): +def asarray(a: Literal["19.3"], dtype: Type[numpy.float64]): """ - usage.dask: 5 - usage.xarray: 1 + usage.sklearn: 1 """ ... -def flip(m: object, axis: int): +@overload +def asarray(a: Literal["14.4"], dtype: Type[numpy.float64]): """ - usage.dask: 5 - usage.scipy: 1 - usage.skimage: 3 - usage.xarray: 6 + usage.sklearn: 1 """ ... -def fliplr(m: numpy.ndarray): +@overload +def asarray(a: Literal["19.4"], dtype: Type[numpy.float64]): """ - usage.dask: 1 - usage.matplotlib: 1 - usage.skimage: 5 + usage.sklearn: 1 """ ... -def flipud(m: numpy.ndarray): +@overload +def asarray(a: Literal["19.7"], dtype: Type[numpy.float64]): """ - usage.dask: 1 - usage.matplotlib: 1 - usage.scipy: 4 - usage.skimage: 2 - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def frombuffer(_0: bytes, /, *, dtype: Literal["int8"]): +def asarray(a: Literal["20.5"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def frombuffer( - _0: Union[bytes, pyarrow.lib.Buffer], - /, - *, - dtype: Union[numpy.dtype, Literal["q", ">i", ">b"] = ..., - /, - *, - count: int = ..., - dtype: Union[ - str, numpy.dtype, Dict[Literal["formats", "names"], List[str]], type - ] = ..., - offset: int = ..., -): +def asarray(a: Literal["23.4"], dtype: Type[numpy.float64]): """ - usage.scipy: 31 + usage.sklearn: 1 """ ... @overload -def frombuffer(_0: numpy.ndarray, _1: Type[numpy.uint8], /): +def asarray(a: Literal["35.4"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def frombuffer(_0: numpy.ndarray, /, *, dtype: Type[numpy.uint8]): +def asarray(a: Literal["31.6"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def frombuffer(_0: bytes, /, *, dtype: Type[numpy.uint8]): +def asarray(a: Literal["23.3"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def frombuffer(_0: bytes, /, *, dtype: Literal["q", ">i", ">b"]] = ..., - /, - *, - count: int = ..., - dtype: Union[ - type, str, numpy.dtype, Dict[Literal["formats", "names"], List[str]] - ] = ..., - offset: int = ..., -): +@overload +def asarray(a: Literal["33"], dtype: Type[numpy.float64]): """ - usage.dask: 1 - usage.matplotlib: 4 - usage.pandas: 22 - usage.scipy: 31 - usage.skimage: 1 - usage.sklearn: 8 + usage.sklearn: 1 """ ... @overload -def fromfile(_0: _io.TextIOWrapper, /, *, sep: Literal[" "]): +def asarray(a: Literal["23.5"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def fromfile(_0: _io.BufferedReader, /, *, count: int, dtype: numpy.dtype): +def asarray(a: Literal["22"], dtype: Type[numpy.float64]): """ - usage.scipy: 10 + usage.sklearn: 1 """ ... -def fromfile( - _0: Union[_io.BufferedReader, _io.TextIOWrapper], - /, - *, - sep: Literal[" "] = ..., - count: int = ..., - dtype: numpy.dtype = ..., -): +@overload +def asarray(a: Literal["17.4"], dtype: Type[numpy.float64]): """ - usage.scipy: 10 - usage.skimage: 1 + usage.sklearn: 1 """ ... -def fromfunction( - function: Callable, - shape: Tuple[int, int], - *, - dtype: Union[Literal["i8", "f8"], Type[float], None], -): +@overload +def asarray(a: Literal["20.9"], dtype: Type[numpy.float64]): """ - usage.dask: 6 + usage.sklearn: 1 """ ... @overload -def fromiter(_0: generator, /, *, dtype: Union[type, Literal["i8"]]): +def asarray(a: Literal["24.2"], dtype: Type[numpy.float64]): """ - usage.pandas: 3 + usage.sklearn: 1 """ ... @overload -def fromiter( - _0: Union[List[numpy.float64], dict_values, generator], - /, - *, - dtype: Union[type, numpy.dtype], - count: int = ..., -): +def asarray(a: Literal["22.8"], dtype: Type[numpy.float64]): """ - usage.scipy: 20 + usage.sklearn: 1 """ ... @overload -def fromiter( - _0: Union[generator, itertools.chain], - /, - *, - dtype: Union[Type[numpy.float64], Literal["float64"]], - count: int = ..., -): +def asarray(a: Literal["24.1"], dtype: Type[numpy.float64]): """ - usage.sklearn: 3 + usage.sklearn: 1 """ ... -def fromiter( - _0: Union[itertools.chain, generator, dict_values, List[numpy.float64]], - /, - *, - dtype: Union[Literal["float64", "i8"], type, numpy.dtype], - count: int = ..., -): +@overload +def asarray(a: Literal["21.4"], dtype: Type[numpy.float64]): """ - usage.pandas: 3 - usage.scipy: 20 - usage.sklearn: 3 + usage.sklearn: 1 """ ... -def frompyfunc(_0: Callable, _1: int, _2: int, /): +@overload +def asarray(a: Literal["20.8"], dtype: Type[numpy.float64]): """ - usage.dask: 4 + usage.sklearn: 1 """ ... @overload -def fromstring(_0: str, /, *, dtype: Literal["float"], sep: Literal[","]): +def asarray(a: Literal["20.3"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def fromstring(_0: str, /, *, dtype: type, sep: Literal[" "]): +def asarray(a: Literal["28"], dtype: Type[numpy.float64]): """ - usage.scipy: 5 + usage.sklearn: 1 """ ... -def fromstring( - _0: str, /, *, dtype: Union[type, Literal["float"]], sep: Literal[" ", ","] -): +@overload +def asarray(a: Literal["23.9"], dtype: Type[numpy.float64]): """ - usage.scipy: 5 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int, int], fill_value: int, dtype: Literal["float64"]): +def asarray(a: Literal["24.8"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int, int, int], fill_value: int, dtype: Type[numpy.uint8]): +def asarray(a: Literal["22.5"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: int, fill_value: int, dtype: Type[float]): +def asarray(a: Literal["23.6"], dtype: Type[numpy.float64]): """ - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def full(shape: int, fill_value: float, dtype: Type[float]): +def asarray(a: Literal["22.6"], dtype: Type[numpy.float64]): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def full(shape: int, fill_value: float, dtype: numpy.dtype): +def asarray(a: Literal["20.6"], dtype: Type[numpy.float64]): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def full(shape: int, fill_value: int, dtype: numpy.dtype): +def asarray(a: Literal["28.4"], dtype: Type[numpy.float64]): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int, int], fill_value: int, dtype: Type[numpy.uint8]): +def asarray(a: Literal["38.7"], dtype: Type[numpy.float64]): """ - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int, int], fill_value: float): +def asarray(a: Literal["43.8"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 - usage.skimage: 6 - usage.xarray: 3 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int, int], fill_value: int, dtype: Type[numpy.uint16]): +def asarray(a: Literal["33.2"], dtype: Type[numpy.float64]): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def full(shape: int, fill_value: float, dtype: Literal["float64"]): +def asarray(a: Literal["27.5"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int], fill_value: int, dtype: numpy.dtype): +def asarray(a: Literal["26.5"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: numpy.ndarray, fill_value: numpy.float64, dtype: Literal["float64"]): +def asarray(a: Literal["18.6"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: int, fill_value: int, dtype: Type[numpy.int32]): +def asarray(a: Literal["20.1"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 3 - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def full(shape: numpy.ndarray, fill_value: numpy.int64, dtype: Literal["float64"]): +def asarray(a: Literal["19.5"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int, int], fill_value: bool): +def asarray(a: Literal["19.8"], dtype: Type[numpy.float64]): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int, int], fill_value: int): +def asarray(a: Literal["18.8"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[None, ...], fill_value: float): +def asarray(a: Literal["18.5"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[None, ...], fill_value: int): +def asarray(a: Literal["18.3"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int, int, int], fill_value: int): +def asarray(a: Literal["19.2"], dtype: Type[numpy.float64]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int], fill_value: int): +def asarray(a: Literal["17.3"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: int, fill_value: float, dtype: Type[numpy.float64]): +def asarray(a: Literal["15.7"], dtype: Type[numpy.float64]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int], fill_value: int, dtype: Type[float]): +def asarray(a: Literal["16.2"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int], fill_value: numpy.ndarray, dtype: Type[float]): +def asarray(a: Literal["18"], dtype: Type[numpy.float64]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int], fill_value: int, dtype: Type[int]): +def asarray(a: Literal["14.3"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[int], fill_value: numpy.ndarray, dtype: Type[int]): +def asarray(a: Literal["23"], dtype: Type[numpy.float64]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[None, ...], fill_value: numpy.int64): +def asarray(a: Literal["18.1"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: Tuple[None, ...], fill_value: numpy.float64): +def asarray(a: Literal["17.1"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full( - shape: Union[Tuple[int, ...], int], - fill_value: object, - dtype: Union[Type[object], numpy.dtype] = ..., -): +def asarray(a: Literal["13.3"], dtype: Type[numpy.float64]): """ - usage.pandas: 30 + usage.sklearn: 1 """ ... @overload -def full( - shape: Union[Tuple[Union[int, None], ...], List[int], numpy.int64, int], - fill_value: Union[float, int, complex, bool], - dtype: Union[type, numpy.dtype, Literal["float64", "f", "D", "F", "d"]] = ..., -): +def asarray(a: Literal["17.8"], dtype: Type[numpy.float64]): """ - usage.scipy: 179 + usage.sklearn: 1 """ ... @overload -def full(shape: int, fill_value: numpy.uint8, dtype: Type[numpy.uint8]): +def asarray(a: Literal["14"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload -def full(shape: int, fill_value: numpy.int64, dtype: Type[numpy.float64]): +def asarray(a: Literal["13.4"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def full(shape: int, fill_value: float): +def asarray(a: Literal["11.8"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def full( - _0: Tuple[int, ...] = ..., - /, - shape: Union[int, numpy.ndarray, List[int], Tuple[int, ...]] = ..., - fill_value: Union[int, float] = ..., - dtype: Union[Literal["i8"], numpy.dtype] = ..., - order: Literal["F", "C"] = ..., -): +def asarray(a: Literal["13.8"], dtype: Type[numpy.float64]): """ - usage.dask: 14 + usage.sklearn: 1 """ ... @overload -def full( - shape: Union[int, numpy.int64, Tuple[int, ...]], - fill_value: object, - dtype: Union[None, Literal["int"], numpy.dtype, type] = ..., -): +def asarray(a: Literal["14.6"], dtype: Type[numpy.float64]): """ - usage.sklearn: 97 + usage.sklearn: 1 """ ... -def full( - _0: Tuple[int, ...] = ..., - /, - shape: Union[ - Tuple[Union[None, int], ...], numpy.int64, int, numpy.ndarray, List[int] - ] = ..., - fill_value: object = ..., - dtype: Union[type, numpy.dtype, str, None] = ..., - order: Literal["F", "C"] = ..., -): +@overload +def asarray(a: Literal["15.4"], dtype: Type[numpy.float64]): """ - usage.dask: 14 - usage.matplotlib: 9 - usage.pandas: 30 - usage.scipy: 179 - usage.skimage: 32 - usage.sklearn: 97 - usage.xarray: 20 + usage.sklearn: 1 """ ... @overload -def full_like(a: numpy.ndarray, fill_value: int): +def asarray(a: Literal["21.5"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like(a: xarray.core.variable.Variable, fill_value: int, dtype: None): +def asarray(a: Literal["15.3"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like(a: xarray.core.variable.Variable, fill_value: bool, dtype: Type[bool]): +def asarray(a: Literal["17"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like(a: xarray.core.dataarray.DataArray, fill_value: int, dtype: None): +def asarray(a: Literal["41.3"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like(a: xarray.core.dataarray.DataArray, fill_value: bool, dtype: Type[bool]): +def asarray(a: Literal["24.3"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like(a: xarray.core.variable.IndexVariable, fill_value: int, dtype: None): +def asarray(a: Literal["27"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like(a: object, fill_value: float): +def asarray(a: Literal["50"], dtype: Type[numpy.float64]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def full_like(a: xarray.core.variable.Variable, fill_value: object, dtype: None): +def asarray(a: Literal["22.7"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like(a: xarray.core.variable.Variable, fill_value: numpy.ndarray, dtype: None): +def asarray(a: Literal["23.8"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like(a: xarray.core.dataarray.DataArray, fill_value: object, dtype: None): +def asarray(a: Literal["22.3"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like( - a: xarray.core.dataarray.DataArray, fill_value: numpy.ndarray, dtype: None -): +def asarray(a: Literal["19.1"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like(a: object, fill_value: bool, dtype: Type[bool]): +def asarray(a: Literal["29.4"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like(a: numpy.ndarray, fill_value: bool, dtype: Type[bool]): +def asarray(a: Literal["23.2"], dtype: Type[numpy.float64]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def full_like(a: xarray.core.variable.Variable, fill_value: int, dtype: Type[int]): +def asarray(a: Literal["24.6"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like(a: xarray.core.variable.Variable, fill_value: float, dtype: None): +def asarray(a: Literal["29.9"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def full_like( - a: Union[numpy.float64, float, numpy.ndarray], - fill_value: Union[float, int], - dtype: Type[numpy.float64] = ..., -): +def asarray(a: Literal["37.2"], dtype: Type[numpy.float64]): """ - usage.scipy: 29 + usage.sklearn: 1 """ ... @overload -def full_like(a: List[float], fill_value: float): +def asarray(a: Literal["39.8"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload -def full_like(a: numpy.ndarray, fill_value: float): +def asarray(a: Literal["37.9"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 4 + usage.sklearn: 1 """ ... @overload -def full_like(a: List[numpy.float64], fill_value: float): +def asarray(a: Literal["32.5"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def full_like( - a: object, - fill_value: Union[numpy.int64, numpy.float32, int, numpy.int32, numpy.float64], - dtype: Union[ - Type[numpy.float64], Literal["i8", "f8", "i4", "f4"], numpy.dtype - ] = ..., - shape: Union[Tuple[int, ...], None, int] = ..., -): +def asarray(a: Literal["26.4"], dtype: Type[numpy.float64]): """ - usage.dask: 47 + usage.sklearn: 1 """ ... @overload -def full_like( - a: Union[numpy.ndarray, List[Literal["b", "a", "c"]]], - fill_value: Union[float, int, Literal["a"]], - dtype: Type[numpy.float32] = ..., -): +def asarray(a: Literal["29.6"], dtype: Type[numpy.float64]): """ - usage.sklearn: 10 + usage.sklearn: 1 """ ... -def full_like( - a: object, - fill_value: object, - dtype: Union[type, None, Literal["i8", "f8", "i4", "f4"], numpy.dtype] = ..., - shape: Union[Tuple[int, ...], None, int] = ..., -): +@overload +def asarray(a: Literal["32"], dtype: Type[numpy.float64]): """ - usage.dask: 47 - usage.matplotlib: 8 - usage.scipy: 29 - usage.skimage: 1 - usage.sklearn: 10 - usage.xarray: 17 + usage.sklearn: 1 """ ... -def genfromtxt(fname: str): +@overload +def asarray(a: Literal["29.8"], dtype: Type[numpy.float64]): """ - usage.scipy: 1 + usage.sklearn: 1 """ ... -def get_printoptions(): +@overload +def asarray(a: Literal["37"], dtype: Type[numpy.float64]): """ usage.sklearn: 1 - usage.xarray: 3 """ ... -def geterr(): +@overload +def asarray(a: Literal["30.5"], dtype: Type[numpy.float64]): """ - usage.pandas: 1 + usage.sklearn: 1 """ ... @overload -def gradient(f: numpy.ndarray): +def asarray(a: Literal["36.4"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 - usage.skimage: 7 + usage.sklearn: 1 """ ... @overload -def gradient(f: numpy.ndarray, *, axis: int): +def asarray(a: Literal["31.1"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def gradient( - f: xarray.core.dataarray.DataArray, - *varargs: Literal["v", "t"], - axis: int, - edge_order: int, -): +def asarray(a: Literal["29.1"], dtype: Type[numpy.float64]): """ - usage.xarray: 4 + usage.sklearn: 1 """ ... @overload -def gradient(f: numpy.ndarray, *varargs: Literal["v", "t"], axis: int, edge_order: int): +def asarray(a: Literal["33.3"], dtype: Type[numpy.float64]): """ - usage.xarray: 3 + usage.sklearn: 1 """ ... @overload -def gradient( - f: sparse._coo.core.COO, *varargs: Literal["v", "t"], axis: int, edge_order: int -): +def asarray(a: Literal["30.3"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def gradient(f: object, *varargs: Literal["v", "t"], axis: int, edge_order: int): +def asarray(a: Literal["34.6"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def gradient(f: numpy.ndarray, *varargs: Literal["v", "t"]): +def asarray(a: Literal["32.9"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload -def gradient(f: numpy.ma.core.MaskedArray, *varargs: Literal["v", "t"]): +def asarray(a: Literal["42.3"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def gradient( - f: Union[numpy.ndarray, int, float], - *varargs: Literal["v", "t"], - axis: Union[int, None, Tuple[int, int]] = ..., - edge_order: int = ..., -): +def asarray(a: Literal["48.5"], dtype: Type[numpy.float64]): """ - usage.dask: 17 + usage.sklearn: 1 """ ... -def gradient( - f: object, - *varargs: Literal["v", "t"], - axis: Union[Tuple[int, int], None, int] = ..., - edge_order: int = ..., -): +@overload +def asarray(a: Literal["24.4"], dtype: Type[numpy.float64]): """ - usage.dask: 17 - usage.matplotlib: 5 - usage.skimage: 8 - usage.xarray: 9 + usage.sklearn: 1 """ ... -def hamming(M: int): +@overload +def asarray(a: Literal["22.4"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... -def hanning(M: int): +@overload +def asarray(a: Literal["28.1"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 4 - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def histogram(a: numpy.ndarray, bins: int, range: None): +def asarray(a: Literal["23.7"], dtype: Type[numpy.float64]): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def histogram(a: numpy.ndarray, bins: int, range: Tuple[int, int]): +def asarray(a: Literal["26.7"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def histogram( - a: numpy.ndarray, - bins: Tuple[ - numpy.float64, - numpy.float64, - numpy.float64, - numpy.float64, - numpy.float64, - numpy.float64, - numpy.float64, - numpy.float64, - numpy.float64, - numpy.float64, - ], - density: bool, -): +def asarray(a: Literal["30.1"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def histogram(a: numpy.ndarray, bins: numpy.int64, range: None): +def asarray(a: Literal["44.8"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def histogram(a: numpy.ndarray, bins: List[Union[int, float]]): +def asarray(a: Literal["37.6"], dtype: Type[numpy.float64]): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def histogram(a: numpy.ndarray, bins: int, range: None, weights: None): +def asarray(a: Literal["46.7"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 - usage.pandas: 2 + usage.sklearn: 1 """ ... @overload -def histogram( - a: numpy.ndarray, - bins: Union[int, numpy.ndarray], - range: Tuple[Union[numpy.float64, float], Union[numpy.float64, int]] = ..., - weights: Union[None, numpy.ndarray] = ..., -): +def asarray(a: Literal["31.5"], dtype: Type[numpy.float64]): """ - usage.scipy: 7 + usage.sklearn: 1 """ ... @overload -def histogram( - a: numpy.ndarray, bins: int, range: Tuple[numpy.int64, numpy.int64], weights: None -): +def asarray(a: Literal["31.7"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def histogram( - a: numpy.ndarray, - bins: int, - range: Tuple[numpy.float64, numpy.float64], - weights: None, -): +def asarray(a: Literal["41.7"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def histogram(a: list, bins: int, range: None, weights: None): +def asarray(a: Literal["48.3"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def histogram(a: numpy.ndarray, bins: numpy.ndarray, weights: None): +def asarray(a: Literal["29"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def histogram( - a: numpy.ndarray, - bins: int, - range: Tuple[numpy.float64, numpy.float64], - weights: None, - density: bool, -): +def asarray(a: Literal["25.1"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def histogram( - a: numpy.ndarray, - bins: List[Union[int, float]], - range: Tuple[numpy.float64, numpy.float64], - weights: None, - density: bool, -): +def asarray(a: Literal["17.6"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def histogram(a: numpy.ndarray, bins: List[Union[int, float]], density: bool): +def asarray(a: Literal["24.5"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def histogram(a: numpy.ndarray, bins: numpy.ndarray, range: None, weights: None): +def asarray(a: Literal["26.2"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def histogram( - a: numpy.ndarray, - bins: Literal["auto"], - range: Tuple[int, int], - weights: None, - density: bool, -): +def asarray(a: Literal["42.8"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def histogram(a: numpy.ndarray, bins: int, range: Tuple[int, int], weights: None): +def asarray(a: Literal["21.9"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def histogram( - a: numpy.ndarray, - bins: numpy.ndarray, - range: Tuple[numpy.float64, numpy.float64], - weights: None, -): +def asarray(a: Literal["44"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def histogram( - a: Union[numpy.ndarray, dask.array.core.Array, list], - bins: Union[numpy.ndarray, int] = ..., - range: Union[List[numpy.float64], None, Tuple[int, int]] = ..., - weights: Union[None, dask.array.core.Array, numpy.ndarray] = ..., - density: bool = ..., -): +def asarray(a: Literal["36"], dtype: Type[numpy.float64]): """ - usage.dask: 17 + usage.sklearn: 1 """ ... @overload -def histogram(a: numpy.ndarray): +def asarray(a: Literal["33.8"], dtype: Type[numpy.float64]): """ usage.sklearn: 1 """ ... -def histogram( - a: Union[numpy.ndarray, dask.array.core.Array, list], - bins: object = ..., - range: Union[ - Tuple[ - Union[numpy.int64, int, float, numpy.float64], - Union[numpy.int64, int, numpy.float64], - ], - None, - List[numpy.float64], - ] = ..., - weights: Union[numpy.ndarray, dask.array.core.Array, None] = ..., - density: bool = ..., -): +@overload +def asarray(a: Literal["43.1"], dtype: Type[numpy.float64]): """ - usage.dask: 17 - usage.matplotlib: 13 - usage.pandas: 2 - usage.scipy: 7 - usage.skimage: 7 usage.sklearn: 1 """ ... @overload -def histogram2d( - x: numpy.ndarray, y: numpy.ndarray, bins: int, weights: numpy.ndarray = ... -): +def asarray(a: Literal["48.8"], dtype: Type[numpy.float64]): """ - usage.scipy: 2 + usage.sklearn: 1 """ ... @overload -def histogram2d( - x: numpy.ndarray, - y: numpy.ndarray, - bins: int, - range: None, - normed: bool, - weights: None, -): +def asarray(a: Literal["31"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def histogram2d( - x: numpy.ndarray, y: numpy.ndarray, bins: Tuple[numpy.ndarray, numpy.ndarray] -): +def asarray(a: Literal["36.5"], dtype: Type[numpy.float64]): """ usage.sklearn: 1 """ ... -def histogram2d( - x: numpy.ndarray, - y: numpy.ndarray, - bins: Union[Tuple[numpy.ndarray, numpy.ndarray], int], - range: None = ..., - normed: bool = ..., - weights: Union[None, numpy.ndarray] = ..., -): +@overload +def asarray(a: Literal["30.7"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 2 - usage.scipy: 2 usage.sklearn: 1 """ ... @overload -def histogram_bin_edges( - a: numpy.ndarray, - bins: int, - range: Tuple[numpy.float64, numpy.float64], - weights: None, -): +def asarray(a: Literal["43.5"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def histogram_bin_edges( - a: numpy.ndarray, - bins: numpy.ndarray, - range: Tuple[numpy.float64, numpy.float64], - weights: None, -): +def asarray(a: Literal["20.7"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def histogram_bin_edges( - a: numpy.ndarray, bins: int, range: Tuple[numpy.int64, numpy.int64], weights: None -): +def asarray(a: Literal["21.1"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... -def histogram_bin_edges( - a: numpy.ndarray, - bins: Union[int, numpy.ndarray], - range: Tuple[Union[numpy.float64, numpy.int64], Union[numpy.float64, numpy.int64]], - weights: None, -): +@overload +def asarray(a: Literal["25.2"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... -def histogramdd(sample: numpy.ndarray, bins: int, weights: numpy.ndarray = ...): +@overload +def asarray(a: Literal["35.2"], dtype: Type[numpy.float64]): """ - usage.scipy: 2 + usage.sklearn: 1 """ ... @overload -def hstack(tup: List[numpy.ndarray]): +def asarray(a: Literal["32.4"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 18 - usage.skimage: 24 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def hstack(tup: Tuple[numpy.ndarray]): +def asarray(a: Literal["33.1"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def hstack(tup: List[numpy.int64]): +def asarray(a: Literal["35.1"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def hstack(tup: List[numpy.float32]): +def asarray(a: Literal["45.4"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def hstack(tup: List[numpy.float64]): +def asarray(a: Literal["46"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def hstack(tup: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): +def asarray(a: Literal["32.2"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def hstack(tup: Tuple[numpy.ndarray, numpy.ndarray]): +def asarray(a: Literal["28.5"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def hstack( - tup: Union[ - Tuple[ - Union[List[Union[bool, int, List[Literal["x", "y", "z"]]]], numpy.ndarray], - ..., - ], - List[Union[numpy.ndarray, float]], - ] -): +def asarray(a: Literal["37.3"], dtype: Type[numpy.float64]): """ - usage.pandas: 9 + usage.sklearn: 1 """ ... @overload -def hstack(tup: Union[List[Union[float, numpy.ndarray]], tuple]): +def asarray(a: Literal["27.9"], dtype: Type[numpy.float64]): """ - usage.scipy: 237 + usage.sklearn: 1 """ ... @overload -def hstack(tup: Tuple[numpy.ndarray, numpy.ndarray, numpy.int64]): +def asarray(a: Literal["28.6"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def hstack(tup: Tuple[numpy.ndarray, numpy.ndarray, numpy.float64]): +def asarray(a: Literal["36.1"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def hstack(tup: List[Union[List[numpy.uint8], numpy.ndarray]]): +def asarray(a: Literal["28.2"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def hstack(tup: List[Union[numpy.uint8, numpy.ndarray]]): +def asarray(a: Literal["16.1"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def hstack(tup: List[Union[int, numpy.ndarray]]): +def asarray(a: Literal["22.1"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 11 + usage.sklearn: 1 """ ... @overload -def hstack(tup: List[Union[numpy.float64, numpy.ndarray]]): +def asarray(a: Literal["19"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def hstack(tup: List[Union[numpy.ndarray, List[numpy.float64]]]): +def asarray(a: Literal["32.7"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def hstack(tup: object): +def asarray(a: Literal["31.2"], dtype: Type[numpy.float64]): """ - usage.dask: 7 + usage.sklearn: 1 """ ... @overload -def hstack( - tup: Union[ - list, - numpy.ndarray, - Tuple[Union[List[List[int]], numpy.ndarray, float, int, numpy.float64], ...], - ] -): +def asarray(a: Literal["17.2"], dtype: Type[numpy.float64]): """ - usage.sklearn: 179 + usage.sklearn: 1 """ ... -def hstack(tup: object): +@overload +def asarray(a: Literal["16.8"], dtype: Type[numpy.float64]): """ - usage.dask: 7 - usage.matplotlib: 37 - usage.pandas: 9 - usage.scipy: 237 - usage.skimage: 30 - usage.sklearn: 179 - usage.xarray: 1 + usage.sklearn: 1 """ ... -def i0( - x: Union[ - dask.dataframe.core.DataFrame, - dask.dataframe.core.Series, - numpy.ndarray, - pandas.core.series.Series, - pandas.core.frame.DataFrame, - ] -): +@overload +def asarray(a: Literal["10.2"], dtype: Type[numpy.float64]): """ - usage.dask: 17 + usage.sklearn: 1 """ ... @overload -def identity(n: int, dtype: numpy.dtype = ...): +def asarray(a: Literal["10.4"], dtype: Type[numpy.float64]): """ - usage.scipy: 67 + usage.sklearn: 1 """ ... @overload -def identity(n: int): +def asarray(a: Literal["10.9"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 usage.sklearn: 1 """ ... @overload -def identity(n: int, dtype: Type[float]): +def asarray(a: Literal["11.3"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def identity(n: int, dtype: Type[bool]): +def asarray(a: Literal["12.3"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... -def identity(n: int, dtype: Union[type, numpy.dtype] = ...): +@overload +def asarray(a: Literal["8.8"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 3 - usage.scipy: 67 usage.sklearn: 1 """ ... @overload -def imag(val: numpy.ndarray): +def asarray(a: Literal["7.2"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 usage.sklearn: 1 """ ... @overload -def imag(val: xarray.core.dataarray.DataArray): +def asarray(a: Literal["10.5"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def imag(val: Union[numpy.ndarray, numpy.complex128]): +def asarray(a: Literal["7.4"], dtype: Type[numpy.float64]): """ - usage.pandas: 2 + usage.sklearn: 1 """ ... @overload -def imag(val: object): +def asarray(a: Literal["11.5"], dtype: Type[numpy.float64]): """ - usage.dask: 31 - usage.scipy: 51 + usage.sklearn: 1 """ ... -def imag(val: object): +@overload +def asarray(a: Literal["15.1"], dtype: Type[numpy.float64]): """ - usage.dask: 31 - usage.pandas: 2 - usage.scipy: 51 - usage.skimage: 1 usage.sklearn: 1 - usage.xarray: 1 """ ... @overload -def in1d(ar1: numpy.flatiter, ar2: Tuple[int, int]): +def asarray(a: Literal["9.7"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def in1d(ar1: numpy.ndarray, ar2: numpy.ndarray): +def asarray(a: Literal["12.5"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 - usage.scipy: 3 - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def in1d( - ar1: Union[pandas.core.indexes.numeric.Int64Index, numpy.ndarray], - ar2: Union[Tuple[int, int, int, int], numpy.ndarray], - assume_unique: bool = ..., -): +def asarray(a: Literal["8.5"], dtype: Type[numpy.float64]): """ - usage.pandas: 6 + usage.sklearn: 1 """ ... @overload -def in1d(ar1: numpy.ndarray, ar2: numpy.ndarray, assume_unique: bool): +def asarray(a: Literal["5"], dtype: Type[numpy.float64]): """ - usage.dask: 1 + usage.sklearn: 1 """ ... @overload -def in1d( - ar1: Union[ - numpy.ndarray, - Tuple[ - Literal["mean_test_score"], - Literal["rank_test_score"], - Literal["split0_test_score"], - Literal["split1_test_score"], - Literal["split2_test_score"], - ], - ], - ar2: Union[List[Union[str, numpy.float64, numpy.int64]], numpy.ndarray], -): +def asarray(a: Literal["6.3"], dtype: Type[numpy.float64]): """ - usage.sklearn: 35 + usage.sklearn: 1 """ ... -def in1d( - ar1: Union[ - Tuple[ - Literal["mean_test_score"], - Literal["rank_test_score"], - Literal["split0_test_score"], - Literal["split1_test_score"], - Literal["split2_test_score"], - ], - numpy.ndarray, - pandas.core.indexes.numeric.Int64Index, - numpy.flatiter, - ], - ar2: Union[ - numpy.ndarray, List[Union[numpy.int64, numpy.float64, str]], Tuple[int, ...] - ], - assume_unique: bool = ..., -): +@overload +def asarray(a: Literal["5.6"], dtype: Type[numpy.float64]): """ - usage.dask: 1 - usage.matplotlib: 1 - usage.pandas: 6 - usage.scipy: 3 - usage.skimage: 2 - usage.sklearn: 35 + usage.sklearn: 1 """ ... @overload -def indices(dimensions: Tuple[int]): +def asarray(a: Literal["12.1"], dtype: Type[numpy.float64]): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def indices(dimensions: Tuple[int, int]): +def asarray(a: Literal["8.3"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 2 - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def indices(dimensions: Tuple[numpy.int64, numpy.int64], dtype: Type[numpy.float64]): +def asarray(a: Literal["11.9"], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def indices( - dimensions: Union[List[int], Tuple[int, ...]], dtype: Union[numpy.dtype, type] = ... -): +def asarray(a: Literal["17.9"], dtype: Type[numpy.float64]): """ - usage.scipy: 26 + usage.sklearn: 1 """ ... @overload -def indices(dimensions: Tuple[Union[int, None], ...], dtype: Type[float] = ...): +def asarray(a: Literal["16.3"], dtype: Type[numpy.float64]): """ - usage.dask: 8 + usage.sklearn: 1 """ ... @overload -def indices(dimensions: Union[generator, Tuple[int, int]]): +def asarray(a: Literal["7"], dtype: Type[numpy.float64]): """ - usage.sklearn: 3 + usage.sklearn: 1 """ ... -def indices( - dimensions: Union[Tuple[Union[None, int, numpy.int64], ...], generator, List[int]], - dtype: Union[type, numpy.dtype] = ..., -): +@overload +def asarray(a: Literal["7.5"], dtype: Type[numpy.float64]): """ - usage.dask: 8 - usage.matplotlib: 2 - usage.scipy: 26 - usage.skimage: 5 - usage.sklearn: 3 + usage.sklearn: 1 """ ... -def inner(_0: numpy.ndarray, _1: numpy.ndarray, /): +@overload +def asarray(a: Literal["8.4"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 - usage.scipy: 5 - usage.sklearn: 4 + usage.sklearn: 1 """ ... @overload -def insert(arr: numpy.ndarray, obj: int, values: numpy.ndarray, axis: int): +def asarray(a: Literal["16.7"], dtype: Type[numpy.float64]): """ - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def insert( - arr: numpy.ndarray, - obj: Union[numpy.ndarray, int], - values: Union[None, float, numpy.int64, int], -): +def asarray(a: Literal["14.2"], dtype: Type[numpy.float64]): """ - usage.pandas: 15 + usage.sklearn: 1 """ ... @overload -def insert( - arr: numpy.ndarray, - obj: Union[int, numpy.ndarray], - values: Union[int, numpy.ndarray], - axis: int = ..., -): +def asarray(a: Literal["11.7"], dtype: Type[numpy.float64]): """ - usage.scipy: 145 + usage.sklearn: 1 """ ... @overload -def insert(arr: numpy.ndarray, obj: int, values: float, axis: int): +def asarray(a: Literal["11"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def insert( - arr: numpy.ndarray, - obj: Union[List[int], int, slice[int, int, int]], - values: Union[int, numpy.ndarray], - axis: int, -): +def asarray(a: Literal["9.5"], dtype: Type[numpy.float64]): """ - usage.dask: 11 + usage.sklearn: 1 """ ... @overload -def insert(arr: numpy.ndarray, obj: int, values: Union[float, int, numpy.int64]): +def asarray(a: Literal["14.1"], dtype: Type[numpy.float64]): """ - usage.sklearn: 4 + usage.sklearn: 1 """ ... -def insert( - arr: numpy.ndarray, - obj: Union[int, numpy.ndarray, List[int], slice[int, int, int]], - values: Union[numpy.int64, int, float, numpy.ndarray, None], - axis: int = ..., -): +@overload +def asarray(a: Literal["9.6"], dtype: Type[numpy.float64]): """ - usage.dask: 11 - usage.matplotlib: 2 - usage.pandas: 15 - usage.scipy: 145 - usage.skimage: 3 - usage.sklearn: 4 + usage.sklearn: 1 """ ... @overload -def interp(x: numpy.flatiter, xp: numpy.ndarray, fp: numpy.ndarray): +def asarray(a: Literal["8.7"], dtype: Type[numpy.float64]): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def interp(x: numpy.ndarray, xp: numpy.ndarray, fp: numpy.ndarray): +def asarray(a: Literal["12.8"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 5 - usage.pandas: 3 - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def interp( - x: numpy.ndarray, - xp: numpy.ndarray, - fp: numpy.ndarray, - left: float, - right: float, - period: None, -): +def asarray(a: Literal["10.8"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def interp( - x: numpy.ndarray, - xp: numpy.ndarray, - fp: numpy.ndarray, - left: int, - right: int, - period: None, -): +def asarray(a: Literal["14.9"], dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def interp( - x: numpy.ndarray, - xp: Union[numpy.ndarray, List[float]], - fp: Union[numpy.ndarray, Tuple[float, float, float, float], List[float]], -): +def asarray(a: Literal["12.6"], dtype: Type[numpy.float64]): """ - usage.scipy: 13 + usage.sklearn: 1 """ ... @overload -def interp(x: int, xp: numpy.ndarray, fp: numpy.ndarray): +def asarray(a: Literal["13"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def interp(x: numpy.float64, xp: numpy.ndarray, fp: numpy.ndarray): +def asarray(a: Literal["16.4"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 4 + usage.sklearn: 1 """ ... @overload -def interp(x: numpy.ndarray, xp: numpy.ndarray, fp: numpy.ma.core.MaskedArray): +def asarray(a: Literal["17.7"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def interp(x: numpy.ma.core.MaskedArray, xp: List[float], fp: List[Union[float, int]]): +def asarray(a: Literal["12"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def interp( - x: numpy.ma.core.MaskedArray, - xp: List[Union[numpy.int64, int]], - fp: List[Union[float, int]], -): +def asarray(a: Literal["21.8"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def interp( - x: numpy.ma.core.MaskedArray, - xp: List[Union[int, numpy.int64]], - fp: List[Union[float, int]], -): +def asarray(a: Literal["8.1"], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def interp( - x: numpy.ma.core.MaskedArray, - xp: List[Union[numpy.float64, int]], - fp: List[Union[float, int]], +def asarray( + a: List[Literal["Iris-virginica", "Iris-versicolor", "Iris-setosa"]], + dtype: Literal["O"], ): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def interp( - x: numpy.ma.core.MaskedArray, - xp: List[Union[int, numpy.float64]], - fp: List[Union[float, int]], -): +def asarray(a: List[str], dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 4 """ ... @overload -def interp(x: numpy.ma.core.MaskedArray, xp: List[int], fp: List[Union[float, int]]): +def asarray(a: List[Literal["G", "H", "C"]], dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def interp( - x: numpy.ndarray, xp: numpy.ndarray, fp: numpy.ndarray, left: int, right: int -): +def asarray(a: List[Literal["SHEET", "COIL"]], dtype: Literal["O"]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def interp( - x: numpy.ndarray, - xp: numpy.ndarray, - fp: Union[ - List[Union[None, float, pandas._libs.tslibs.nattype.NaTType]], numpy.ndarray - ], -): +def asarray(a: List[Literal["-1", "1"]], dtype: Literal["O"]): """ - usage.dask: 8 + usage.sklearn: 1 """ ... @overload -def interp( - x: Union[numpy.ndarray, numpy.float64, float], - xp: Union[numpy.ndarray, List[numpy.float64]], - fp: Union[numpy.ndarray, List[numpy.float64]], -): +def asarray(a: List[Literal[">50K", "<=50K"]], dtype: Literal["O"]): """ - usage.sklearn: 8 + usage.sklearn: 1 """ ... -def interp( - x: object, - xp: Union[List[Union[float, numpy.float64, numpy.int64, int]], numpy.ndarray], - fp: Union[ - List[ - Union[None, pandas._libs.tslibs.nattype.NaTType, float, int, numpy.float64] - ], - numpy.ndarray, - numpy.ma.core.MaskedArray, - Tuple[float, float, float, float], - ], - left: Union[int, float] = ..., - right: Union[int, float] = ..., - period: None = ..., -): +@overload +def asarray(a: List[Literal["TRUE", "FALSE"]], dtype: Literal["O"]): """ - usage.dask: 8 - usage.matplotlib: 20 - usage.pandas: 3 - usage.scipy: 13 - usage.skimage: 6 - usage.sklearn: 8 - usage.xarray: 2 + usage.sklearn: 1 """ ... -def intersect1d(ar1: numpy.ndarray, ar2: numpy.ndarray): +@overload +def asarray(a: List[Literal["1", "0"]], dtype: Literal["O"]): """ - usage.pandas: 8 - usage.sklearn: 9 + usage.sklearn: 1 """ ... -def is_busday(_0: numpy.datetime64, /, *, busdaycal: numpy.busdaycalendar): +@overload +def asarray(a: List[Literal["Ts65Dn", "Control"]], dtype: Literal["O"]): """ - usage.pandas: 1 + usage.sklearn: 1 """ ... @overload -def isclose(a: numpy.float64, b: int): +def asarray(a: List[Literal["nowin", "won"]], dtype: Literal["O"]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def isclose(a: float, b: float): +def asarray(a: List[Union[float, int]], dtype: None, order: None): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def isclose(a: numpy.ndarray, b: int, atol: float): +def asarray(a: List[List[Union[int, float]]], dtype: None, order: None): """ - usage.skimage: 1 + usage.sklearn: 9 """ ... @overload -def isclose( - a: numpy.ndarray, b: numpy.ndarray, rtol: float, atol: float, equal_nan: bool -): +def asarray(a: numpy.ndarray, dtype: None, order: Literal["F"]): """ - usage.xarray: 11 + usage.sklearn: 3 """ ... @overload -def isclose(a: numpy.float64, b: numpy.float64): +def asarray(a: numpy.ndarray, dtype: Literal["float64"], order: Literal["C"]): """ - usage.matplotlib: 4 - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def isclose(a: numpy.float64, b: numpy.float64, rtol: float): +def asarray(a: numpy.memmap, dtype: None, order: None): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def isclose( - a: Union[numpy.bool_, numpy.float64, numpy.ndarray, float], - b: Union[bool, numpy.ndarray, int], - equal_nan: bool = ..., -): +def asarray(a: numpy.ndarray, dtype: Type[numpy.float64], order: Literal["F"]): """ - usage.pandas: 4 + usage.sklearn: 8 """ ... @overload -def isclose( - a: object, - b: object, - rtol: Union[float, int, numpy.float64] = ..., - atol: Union[float, int, numpy.float64] = ..., -): +def asarray(a: List[List[int]], dtype: Type[numpy.float64], order: None): """ - usage.scipy: 66 + usage.sklearn: 12 """ ... @overload -def isclose(a: numpy.float64, b: numpy.ndarray, rtol: int, atol: numpy.float64): +def asarray(a: List[List[float]], dtype: Type[numpy.float64], order: None): """ - usage.matplotlib: 2 + usage.sklearn: 10 """ ... @overload -def isclose(a: numpy.float64, b: List[float]): +def asarray(a: numpy.ndarray, dtype: numpy.dtype, order: None): """ - usage.matplotlib: 1 + usage.sklearn: 8 """ ... @overload -def isclose(a: numpy.int64, b: numpy.ndarray, rtol: int, atol: numpy.float64): +def asarray(a: numpy.ndarray, dtype: Type[numpy.uint32]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def isclose(a: numpy.float64, b: float): +def asarray(a: List[bool], dtype: Type[numpy.uint8]): """ - usage.matplotlib: 1 + usage.sklearn: 4 """ ... @overload -def isclose( - a: Union[numpy.ndarray, numpy.float64], - b: Union[numpy.ndarray, int], - rtol: float = ..., - atol: int = ..., -): +def asarray(a: List[importlib._bootstrap.MonotonicConstraint], dtype: Type[numpy.int8]): """ - usage.dask: 3 + usage.sklearn: 1 """ ... @overload -def isclose( - a: Union[numpy.float64, numpy.int64, float, numpy.ndarray], - b: Union[numpy.float64, numpy.float32, numpy.ndarray, float], - rtol: Union[int, float] = ..., - atol: Union[int, float] = ..., - equal_nan: bool = ..., -): +def asarray(a: List[int], dtype: Type[numpy.int8]): """ - usage.sklearn: 11 + usage.sklearn: 2 """ ... -def isclose( - a: object, - b: object, - rtol: Union[float, int, numpy.float64] = ..., - atol: Union[float, int, numpy.float64] = ..., - equal_nan: bool = ..., +@overload +def asarray( + a: sklearn.ensemble._hist_gradient_boosting.histogram._memoryviewslice, + dtype: numpy.dtype, ): """ - usage.dask: 3 - usage.matplotlib: 10 - usage.pandas: 4 - usage.scipy: 66 - usage.skimage: 3 - usage.sklearn: 11 - usage.xarray: 13 + usage.sklearn: 3 """ ... @overload -def iscomplex(x: xarray.core.dataarray.DataArray): +def asarray(a: Tuple[numpy.ndarray], dtype: Type[numpy.int32]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def iscomplex(x: Union[numpy.float64, int, numpy.ndarray, float]): +def asarray(a: Tuple[numpy.ndarray]): """ - usage.scipy: 12 + usage.sklearn: 2 """ ... @overload -def iscomplex(x: object): +def asarray(a: List[Literal["A", "C", "B"]], dtype: None, order: None): """ - usage.dask: 28 + usage.sklearn: 1 """ ... -def iscomplex(x: object): +@overload +def asarray(a: List[List[int]], dtype: Type[numpy.float32], order: None): """ - usage.dask: 28 - usage.scipy: 12 - usage.xarray: 1 + usage.sklearn: 14 """ ... @overload -def iscomplexobj(x: numpy.ndarray): +def asarray( + a: List[List[Literal["blue", "red", "green", "purple", "yellow"]]], + dtype: None, + order: None, +): """ - usage.matplotlib: 2 - usage.pandas: 2 - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def iscomplexobj(x: object): +def asarray(a: numpy.ndarray, dtype: Type[numpy.float32], order: Literal["F"]): """ - usage.scipy: 324 + usage.sklearn: 5 """ ... @overload -def iscomplexobj(x: dask.array.core.Array): +def asarray(a: List[str], dtype: None, order: None): """ - usage.dask: 2 + usage.sklearn: 3 """ ... -def iscomplexobj(x: object): +@overload +def asarray(a: List[List[int]], dtype: Type[numpy.float32], order: Literal["C"]): """ - usage.dask: 2 - usage.matplotlib: 2 - usage.pandas: 2 - usage.scipy: 324 - usage.skimage: 4 + usage.sklearn: 2 """ ... -def isfortran(a: numpy.ndarray): +@overload +def asarray(a: List[numpy.ndarray], dtype: Type[numpy.float32], order: Literal["C"]): """ - usage.dask: 1 - usage.matplotlib: 2 - usage.scipy: 16 - usage.sklearn: 3 + usage.sklearn: 1 """ ... @overload -def isin(element: numpy.ndarray, test_elements: List[int]): +def asarray(a: List[Literal["1", "-1"]], dtype: None, order: None): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def isin(element: numpy.ndarray, test_elements: numpy.ndarray): +def asarray(a: List[int], dtype: Type[numpy.int32]): """ - usage.scipy: 3 - usage.xarray: 3 + usage.sklearn: 20 """ ... @overload -def isin(element: dask.array.core.Array, test_elements: List[int]): +def asarray( + a: pandas.core.frame.DataFrame, dtype: Type[numpy.float64], order: Literal["C"] +): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isin(element: dask.array.core.Array, test_elements: numpy.ndarray): +def asarray(a: pandas.core.series.Series, dtype: numpy.dtype, order: Literal["C"]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isin(element: sparse._coo.core.COO, test_elements: List[int]): +def asarray(a: pandas.core.frame.DataFrame, dtype: Type[numpy.float32], order: None): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isin(element: object, test_elements: numpy.ndarray): +def asarray( + a: pandas.core.series.Series, dtype: Type[numpy.float64], order: Literal["C"] +): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def isin(element: object, test_elements: object): +def asarray(a: sklearn.utils.estimator_checks._NotAnArray): """ - usage.xarray: 1 + usage.sklearn: 12 """ ... @overload -def isin( - element: Union[dask.array.core.Array, numpy.ndarray], - test_elements: Union[numpy.ndarray, dask.array.core.Array], - assume_unique: bool = ..., - invert: bool = ..., +def asarray( + a: sklearn.utils.estimator_checks._NotAnArray, + dtype: Type[numpy.float64], + order: Literal["C"], ): """ - usage.dask: 4 + usage.sklearn: 1 """ ... -def isin( - element: object, - test_elements: object, - assume_unique: bool = ..., - invert: bool = ..., +@overload +def asarray( + a: sklearn.utils.estimator_checks._NotAnArray, + dtype: numpy.dtype, + order: Literal["C"], ): """ - usage.dask: 4 - usage.scipy: 3 - usage.xarray: 11 + usage.sklearn: 1 """ ... @overload -def isneginf(x: Union[List[Union[int, numpy.float64]], numpy.ndarray, numpy.float64]): +def asarray( + a: sklearn.utils.estimator_checks._NotAnArray, + dtype: Type[numpy.float32], + order: None, +): """ - usage.scipy: 13 + usage.sklearn: 1 """ ... @overload -def isneginf(x: numpy.ndarray): +def asarray(a: numpy.memmap): """ - usage.dask: 1 + usage.sklearn: 9 """ ... -def isneginf(x: Union[numpy.ndarray, numpy.float64, List[Union[numpy.float64, int]]]): +@overload +def asarray(a: numpy.memmap, dtype: Type[numpy.float64], order: Literal["C"]): """ - usage.dask: 1 - usage.scipy: 13 + usage.sklearn: 2 """ ... @overload -def isposinf( - x: Union[numpy.ndarray, float, numpy.float64, List[Union[numpy.float64, int]]] -): +def asarray(a: numpy.memmap, dtype: Type[numpy.float32], order: None): """ - usage.scipy: 9 + usage.sklearn: 2 """ ... @overload -def isposinf(x: numpy.ndarray): +def asarray(a: sklearn.utils.estimator_checks._NotAnArray, dtype: None, order: None): """ - usage.dask: 1 + usage.sklearn: 1 """ ... @overload -def isposinf(x: Union[int, float]): +def asarray(a: List[List[float]], dtype: Type[numpy.float32], order: None): """ - usage.sklearn: 2 + usage.sklearn: 4 """ ... -def isposinf( - x: Union[float, int, numpy.float64, numpy.ndarray, List[Union[numpy.float64, int]]] -): +@overload +def asarray(a: List[Tuple[int, int]], dtype: None, order: None): """ - usage.dask: 1 - usage.scipy: 9 - usage.sklearn: 2 + usage.sklearn: 1 """ ... @overload -def isreal(x: xarray.core.dataarray.DataArray): +def asarray(a: List[List[int]], dtype: Type[numpy.float64], order: Literal["C"]): """ - usage.xarray: 1 + usage.sklearn: 10 """ ... @overload -def isreal( - x: Union[ - numpy.ndarray, numpy.float64, numpy.complex128, numpy.complex256, numpy.float128 - ] -): +def asarray(a: List[Union[int, Literal["foo"]]]): """ - usage.scipy: 58 + usage.sklearn: 2 """ ... @overload -def isreal(x: float): +def asarray(a: List[Union[int, Literal["bar", "foo"]]]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def isreal(x: int): +def asarray(a: List[List[str]], dtype: None, order: None): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def isreal(x: numpy.float64): +def asarray(a: List[List[Literal["I", "G", "E", "C", "A"]]], dtype: None, order: None): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def isreal(x: object): +def asarray(a: numpy.ndarray, order: Literal["F"]): """ - usage.dask: 28 + usage.sklearn: 20 """ ... @overload -def isreal(x: numpy.ndarray): +def asarray(a: List[bool], dtype: None, order: None): """ - usage.sklearn: 4 + usage.sklearn: 2 """ ... -def isreal(x: object): +@overload +def asarray(a: List[numpy.int64], dtype: None, order: None): """ - usage.dask: 28 - usage.matplotlib: 3 - usage.scipy: 58 - usage.sklearn: 4 - usage.xarray: 1 + usage.sklearn: 1 """ ... -def isrealobj(x: numpy.ndarray): +@overload +def asarray(a: List[Literal["B", "AB", "A"]], dtype: None, order: None): """ - usage.scipy: 44 + usage.sklearn: 1 """ ... @overload -def isscalar(element: int): +def asarray(a: List[Literal["C", "B", "A"]], dtype: None, order: None): """ - usage.matplotlib: 2 - usage.skimage: 15 + usage.sklearn: 1 """ ... @overload -def isscalar(element: numpy.ndarray): +def asarray(a: List[List[int]], dtype: Type[numpy.float64], order: Literal["F"]): """ - usage.matplotlib: 3 - usage.skimage: 5 - usage.xarray: 21 + usage.sklearn: 4 """ ... @overload -def isscalar(element: List[int]): +def asarray( + a: List[List[Union[int, float]]], dtype: Type[numpy.float64], order: Literal["F"] +): """ - usage.matplotlib: 6 - usage.skimage: 8 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: List[Union[int, float]]): +def asarray(a: List[List[Union[int, float]]], dtype: Type[numpy.float64], order: None): """ - usage.skimage: 2 + usage.sklearn: 2 """ ... @overload -def isscalar(element: float): +def asarray( + a: pandas.core.frame.DataFrame, dtype: Type[numpy.float64], order: Literal["F"] +): """ - usage.matplotlib: 3 - usage.skimage: 6 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: List[Union[float, int]]): +def asarray(a: pandas.core.frame.DataFrame, dtype: Type[numpy.float64], order: None): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def isscalar(element: numpy.float64): +def asarray( + a: List[List[Union[Literal["b", "a", "e", "h", "g"], int]]], + dtype: Type[numpy.float64], + order: None, +): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def isscalar(element: Tuple[int, int]): +def asarray(a: List[Union[int, float]], dtype: None, order: None): """ - usage.matplotlib: 1 - usage.skimage: 4 + usage.sklearn: 3 """ ... @overload -def isscalar(element: Tuple[int, int, int]): +def asarray(a: List[int], dtype: Type[numpy.int32], order: Literal["C"]): """ - usage.skimage: 3 + usage.sklearn: 2 """ ... @overload -def isscalar(element: Tuple[int, int, int, int]): +def asarray(a: List[numpy.ndarray], dtype: Type[object], order: None): """ - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def isscalar(element: Tuple[float, float]): +def asarray( + a: pandas.core.frame.DataFrame, dtype: Type[numpy.float32], order: Literal["C"] +): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def isscalar(element: numpy.int64): +def asarray(a: numpy.memmap, dtype: Type[numpy.float64], order: Literal["F"]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: List[Dict[Literal["dd", "da", "ad"], numpy.float64]]): +def asarray(a: numpy.memmap, dtype: None, order: Literal["F"]): """ - usage.skimage: 6 + usage.sklearn: 3 """ ... @overload -def isscalar(element: List[Dict[Literal["d"], numpy.float64]]): +def asarray(a: numpy.ndarray, dtype: Literal["float64"], order: Literal["F"]): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def isscalar(element: List[Dict[str, numpy.float64]]): +def asarray(a: numpy.ndarray, dtype: Literal["float32"], order: Literal["F"]): """ - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def isscalar(element: Tuple[int]): +def asarray(a: List[List[Union[float, int]]], dtype: None, order: None): """ - usage.skimage: 2 + usage.sklearn: 7 """ ... @overload -def isscalar(element: slice[int, None, int]): +def asarray( + a: List[List[Union[float, int]]], dtype: Type[numpy.float64], order: Literal["F"] +): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def isscalar(element: xarray.core.indexing.CopyOnWriteArray): +def asarray( + a: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], dtype: Type[numpy.int32] +): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: xarray.core.indexing.NumpyIndexingAdapter): +def asarray(a: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: xarray.core.indexing.LazilyOuterIndexedArray): +def asarray(a: List[Literal["foo", "baz", "bar"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: xarray.core.indexing.LazilyVectorizedIndexedArray): +def asarray(a: Tuple[float, float], dtype: numpy.dtype): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def isscalar(element: Literal["a"]): +def asarray(a: List[Literal["three", "two", "one"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: cftime._cftime.DatetimeNoLeap): +def asarray( + a: List[Union[int, float]], dtype: Type[numpy.float64], order: Literal["C"] +): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: cftime._cftime.Datetime360Day): +def asarray(a: List[Literal["one"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: cftime._cftime.DatetimeJulian): +def asarray(a: List[Literal["three", "two"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: cftime._cftime.DatetimeAllLeap): +def asarray(a: List[Literal["spam", "ham"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: cftime._cftime.DatetimeGregorian): +def asarray(a: List[List[bool]], dtype: None, order: None): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: cftime._cftime.DatetimeProlepticGregorian): +def asarray(a: List[Literal["1-a", "0-a"]], dtype: None, order: None): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: numpy.timedelta64): +def asarray(a: List[Literal["1-a", "0-a"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: Tuple[Literal["a"], numpy.int64, numpy.int64]): +def asarray(a: list, dtype: None, order: None): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: Tuple[Literal["b"], numpy.int64, numpy.int64]): +def asarray(a: List[Literal["a"]], dtype: None, order: None): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: Literal["2000-01-01"]): +def asarray(a: List[Literal["0", "1"]]): """ - usage.xarray: 1 + usage.sklearn: 6 """ ... @overload -def isscalar(element: Literal["2000-01-02"]): +def asarray(a: List[Literal["1", "2", "0"]]): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def isscalar(element: Literal["2000-01-03"]): +def asarray(a: List[Union[numpy.int64, int]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: Literal["bar"]): +def asarray(a: List[Literal["cat", "bird", "ant"]]): """ - usage.xarray: 1 + usage.sklearn: 5 """ ... @overload -def isscalar(element: Tuple[Literal["a"], numpy.int64]): +def asarray(a: List[Literal["bird", "cat", "ant"]]): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def isscalar(element: Tuple[Literal["b"], numpy.int64]): +def asarray(a: List[Tuple[int, ...]]): """ - usage.xarray: 1 + usage.sklearn: 4 """ ... @overload -def isscalar(element: Tuple[Literal["c"], numpy.int64]): +def asarray(a: List[Literal["red", "white", "green", "blue"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: Tuple[numpy.int64, numpy.int64]): +def asarray(a: List[Literal["yes", "no"]]): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def isscalar(element: numpy.datetime64): +def asarray(a: List[Literal["ham", "spam"]]): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def isscalar(element: object): +def asarray(a: sklearn.utils._mocking.MockDataFrame, dtype: None, order: None): """ - usage.dask: 547 - usage.scipy: 289 - usage.sklearn: 26 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: pandas._libs.tslibs.period.Period): +def asarray(a: sklearn.utils._mocking.MockDataFrame): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def isscalar(element: xarray.core.indexing.MemoryCachedArray): +def asarray(a: List[numpy.int64], dtype: Type[numpy.float64], order: None): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: dask.array.core.Array): +def asarray(a: List[Literal["spam", "eggs"]]): """ - usage.xarray: 1 + usage.sklearn: 4 """ ... @overload -def isscalar(element: list): +def asarray(a: List[int], dtype: Type[numpy.float64], order: None): """ - usage.matplotlib: 1 + usage.sklearn: 6 """ ... @overload -def isscalar( - element: List[List[Literal["2017-01-01T00:00:00", "2017-01-02T00:00:00"]]] -): +def asarray(a: List[float], dtype: Type[numpy.float64], order: None): """ - usage.matplotlib: 1 + usage.sklearn: 4 """ ... @overload -def isscalar(element: List[List[str]]): +def asarray(a: List[Union[Tuple[Union[None, int], ...], int]]): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def isscalar(element: List[list]): +def asarray(a: List[Tuple[None, ...]]): """ - usage.matplotlib: 2 + usage.sklearn: 2 """ ... @overload -def isscalar(element: List[float]): +def asarray( + a: Tuple[ + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + ] +): """ - usage.matplotlib: 2 + usage.sklearn: 2 """ ... @overload -def isscalar(element: Tuple[numpy.ndarray, numpy.ndarray]): +def asarray( + a: Tuple[ + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + ] +): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def isscalar(element: List[List[int]]): +def asarray(a: numpy.ndarray, dtype: Type[bool], order: None): """ - usage.matplotlib: 2 + usage.sklearn: 3 """ ... @overload -def isscalar(element: List[Union[range, list]]): +def asarray(a: List[Literal["c", "a", "b"]]): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def isscalar(element: List[range]): +def asarray(a: List[Literal["c", "a", "b"]], dtype: None, order: None): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... -def isscalar(element: object): +@overload +def asarray(a: List[Literal["c", "b", "a"]], dtype: None, order: None): """ - usage.dask: 547 - usage.matplotlib: 30 - usage.scipy: 289 - usage.skimage: 68 - usage.sklearn: 26 - usage.xarray: 50 + usage.sklearn: 3 """ ... @overload -def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.floating]): +def asarray(a: List[Literal["0", "1"]], dtype: None, order: None): """ - usage.skimage: 2 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.floating]): +def asarray(a: range, dtype: None, order: None): """ - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.float64]): +def asarray(a: List[Literal["3", "2", "1"]], dtype: None, order: None): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.uint16]): +def asarray(a: List[Literal["3", "2", "1"]]): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[numpy.uint16], arg2: Type[numpy.float64]): +def asarray(a: Literal["wrong_type"]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.uint8]): +def asarray(a: numpy.ndarray, dtype: Type[object]): """ - usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.floating]): +def asarray(a: sklearn.neighbors._kd_tree._memoryviewslice): """ - usage.skimage: 18 - usage.xarray: 64 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.bool_]): +def asarray(a: sklearn.neighbors._ball_tree._memoryviewslice): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.integer]): +def asarray(a: List[list], dtype: None, order: None): """ - usage.matplotlib: 24 - usage.skimage: 31 - usage.xarray: 52 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.uint16], arg2: Type[numpy.floating]): +def asarray(a: List[numpy.ndarray], dtype: Type[numpy.float64], order: Literal["C"]): """ - usage.skimage: 2 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.uint16]): +def asarray(a: List[Literal["d"]], dtype: None, order: None): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.uint16], arg2: Type[numpy.uint16]): +def asarray(a: List[List[Union[float, int]]], dtype: Type[numpy.float64], order: None): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.bool_], arg2: Type[numpy.floating]): +def asarray(a: int, dtype: Type[numpy.float64], order: None): """ - usage.skimage: 1 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.float32], arg2: Type[numpy.floating]): +def asarray(a: List[List[numpy.float64]], dtype: Type[numpy.float64], order: None): """ - usage.skimage: 2 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int64], arg2: Type[numpy.floating]): +def asarray(a: numpy.ndarray, order: None): """ - usage.skimage: 2 + usage.sklearn: 6 """ ... @overload -def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.bool_]): +def asarray(a: List[int], order: None): """ - usage.skimage: 5 - usage.xarray: 3 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.float32]): +def asarray(a: List[Union[int, float]], order: None): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.float64]): +def asarray(a: List[List[int]], dtype: Type[numpy.int32]): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.uint8]): +def asarray(a: List[List[int]], dtype: Type[numpy.float32]): """ - usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.int64]): +def asarray(a: List[List[int]], dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.signedinteger]): +def asarray( + a: List[List[Union[Literal["girl", "Female", "boy", "Male"], int]]], + dtype: None, + order: None, +): """ - usage.skimage: 14 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.bool_], arg2: Type[numpy.uint8]): +def asarray( + a: List[List[Union[Literal["girl", "Female", "boy", "Male"], int]]], + dtype: Type[object], + order: None, +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.uint8]): +def asarray(a: List[List[Union[Literal["def", "abc"], int]]], dtype: None, order: None): """ - usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.unsignedinteger]): +def asarray( + a: List[List[Union[Literal["def", "abc"], int]]], dtype: Type[object], order: None +): """ - usage.skimage: 11 + usage.sklearn: 3 """ ... @overload -def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.floating]): +def asarray(a: List[List[Union[Literal["abc", "def"], int]]], dtype: None, order: None): """ - usage.skimage: 1 + usage.sklearn: 4 """ ... @overload -def issubdtype(arg1: Type[numpy.int16], arg2: Type[numpy.floating]): +def asarray( + a: List[List[Union[Literal["abc", "def"], int]]], dtype: Type[object], order: None +): """ - usage.skimage: 2 + usage.sklearn: 4 """ ... @overload -def issubdtype(arg1: Type[numpy.uint32], arg2: Type[numpy.floating]): +def asarray( + a: List[List[Union[int, Literal["a", "c", "b"]]]], dtype: None, order: None +): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[numpy.int32], arg2: Type[numpy.floating]): +def asarray( + a: List[List[Union[int, Literal["a", "c", "b"]]]], dtype: Type[object], order: None +): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[numpy.uint64], arg2: Type[numpy.floating]): +def asarray(a: List[Union[Literal["b", "c"], int]], dtype: Type[object]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.uint16]): +def asarray(a: List[List[Union[int, Literal["no", "yes"]]]], dtype: None, order: None): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.int16]): +def asarray( + a: List[List[Union[int, Literal["no", "yes"]]]], dtype: Type[object], order: None +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int16], arg2: Type[numpy.uint8]): +def asarray(a: List[List[Literal["a", "true", "false"]]], dtype: None, order: None): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.uint16], arg2: Type[numpy.uint8]): +def asarray( + a: List[List[Literal["a", "true", "false"]]], dtype: Type[object], order: None +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int16], arg2: Type[numpy.uint16]): +def asarray(a: List[List[Union[int, Literal["a", "b"]]]], dtype: None, order: None): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int64], arg2: Type[numpy.integer]): +def asarray( + a: List[List[Union[int, Literal["a", "b"]]]], dtype: Type[object], order: None +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.bool_], arg2: Type[numpy.bool_]): +def asarray( + a: List[List[Union[Literal["Male", "Female"], int]]], dtype: None, order: None +): """ - usage.skimage: 1 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.float32]): +def asarray( + a: List[List[Union[Literal["Male", "Female"], int]]], + dtype: Type[object], + order: None, +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.float16], arg2: Type[numpy.floating]): +def asarray(a: List[Union[int, Literal["def"]]], dtype: Type[object]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.uint16], arg2: Type[numpy.int16]): +def asarray(a: List[List[Literal["Male", "Female"]]], dtype: None, order: None): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.uint16], arg2: Type[numpy.float32]): +def asarray(a: List[List[Literal["Male", "Female"]]], dtype: Type[object], order: None): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.int16]): +def asarray(a: List[Union[int, Literal["ghi"]]], dtype: Type[object]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.float64]): +def asarray(a: List[Union[int, Literal["abc"]]], dtype: Type[object]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.float32]): +def asarray(a: List[Union[Literal["a", "abc"], int]], dtype: Type[object]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.uint16]): +def asarray(a: List[Union[Literal["b", "a"], int]], dtype: Type[object]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int16], arg2: Type[numpy.int16]): +def asarray(a: List[List[Union[Literal["a"], None]]], dtype: None, order: None): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int16], arg2: Type[numpy.float64]): +def asarray(a: List[Literal["pos"]]): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[numpy.int16], arg2: Type[numpy.float32]): +def asarray(a: List[Literal["neg", "pos"]]): """ - usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def issubdtype(arg1: Type[numpy.float32], arg2: Type[numpy.int16]): +def asarray(a: List[Literal["0", "ham", "eggs", "spam"]]): """ - usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def issubdtype(arg1: Type[numpy.float32], arg2: Type[numpy.float64]): +def asarray(a: List[Literal["e", "d", "b"]]): """ - usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def issubdtype(arg1: Type[numpy.float32], arg2: Type[numpy.float32]): +def asarray(a: Literal["apple"]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.float32], arg2: Type[numpy.uint16]): +def asarray( + a: List[List[Union[int, numpy.float64]]], + dtype: Type[numpy.float64], + order: Literal["C"], +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.float32], arg2: Type[numpy.uint8]): +def asarray(a: numpy.memmap, dtype: Type[numpy.float64], order: None): """ - usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.int16]): +def asarray( + a: sklearn.utils.estimator_checks._NotAnArray, + dtype: Type[numpy.float64], + order: None, +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.uint32]): +def asarray(a: numpy.memmap, dtype: Type[float], order: None): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.uint32]): +def asarray(a: pandas.core.frame.DataFrame, order: None): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.int32]): +def asarray(a: sklearn.utils.estimator_checks._NotAnArray, order: None): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.int32], arg2: Type[numpy.int8]): +def asarray(a: numpy.memmap, order: None): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[numpy.int32], arg2: Type[numpy.float32]): +def asarray(a: List[List[float]], order: None): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.uint64], arg2: Type[numpy.int16]): +def asarray( + a: sklearn.utils.estimator_checks._NotAnArray, + dtype: Type[numpy.float64], + order: Literal["F"], +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.bool_], arg2: Type[numpy.int16]): +def asarray(a: List[List[float]], dtype: Type[numpy.float64], order: Literal["F"]): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[numpy.bool_], arg2: Type[numpy.uint16]): +def asarray(a: pandas.core.frame.DataFrame, dtype: None, order: Literal["F"]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.datetime64]): +def asarray( + a: sklearn.utils.estimator_checks._NotAnArray, + dtype: Type[numpy.float32], + order: Literal["C"], +): """ - usage.matplotlib: 11 - usage.xarray: 77 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.timedelta64]): +def asarray(a: numpy.memmap, dtype: Type[numpy.float32], order: Literal["C"]): """ - usage.xarray: 67 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.complexfloating]): +def asarray(a: numpy.memmap, dtype: None, order: Literal["C"]): """ - usage.xarray: 18 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.number]): +def asarray( + a: sklearn.utils.estimator_checks._NotAnArray, dtype: Type[float], order: None +): """ - usage.xarray: 13 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[float], arg2: Type[numpy.floating]): +def asarray(a: List[List[float]], dtype: Type[float], order: None): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def issubdtype(arg1: Type[int], arg2: Type[numpy.floating]): +def asarray(a: List[List[float]], dtype: None, order: Literal["F"]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[int], arg2: Type[numpy.integer]): +def asarray( + a: sklearn.utils.estimator_checks._NotAnArray, dtype: None, order: Literal["F"] +): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.bool_], arg2: Type[numpy.integer]): +def asarray(a: pandas.core.series.Series, dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[str], arg2: Type[numpy.floating]): +def asarray(a: sklearn.utils.estimator_checks._NotAnArray, dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[str], arg2: Type[numpy.integer]): +def asarray(a: List[Literal["berlin", "amsterdam", "tokyo", "paris"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[str], arg2: Type[numpy.bool_]): +def asarray(a: int, dtype: None, order: None): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.generic], arg2: Type[numpy.floating]): +def asarray(a: List[Literal["two", "one"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.generic], arg2: Type[numpy.integer]): +def asarray(a: List[int], dtype: numpy.dtype, order: None): """ - usage.xarray: 1 + usage.sklearn: 5 """ ... @overload -def issubdtype(arg1: Type[numpy.generic], arg2: Type[numpy.timedelta64]): +def asarray(a: List[Union[float, int]], dtype: numpy.dtype, order: None): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Type[numpy.generic], arg2: Type[numpy.datetime64]): +def asarray(a: List[numpy.int64], dtype: numpy.dtype, order: None): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Union[numpy.dtype, type], arg2: Union[type, numpy.dtype]): +def asarray(a: List[Literal["ham", "eggs", "spam"]]): """ - usage.pandas: 83 + usage.sklearn: 3 """ ... @overload -def issubdtype(arg1: Union[type, None, numpy.dtype, numpy.int64], arg2: type): +def asarray(a: List[List[numpy.float64]], dtype: None, order: None): """ - usage.scipy: 466 + usage.sklearn: 1 """ ... @overload -def issubdtype(arg1: Union[Type[int], numpy.dtype], arg2: type): +def asarray(a: List[List[int]], order: None): """ - usage.dask: 48 + usage.sklearn: 7 """ ... @overload -def issubdtype(arg1: numpy.dtype, arg2: type): - """ - usage.sklearn: 44 - """ - ... - - -def issubdtype( - arg1: Union[numpy.dtype, numpy.int64, None, type], arg2: Union[type, numpy.dtype] +def asarray( + a: List[Union[float, int]], dtype: Type[numpy.float64], order: Literal["C"] ): """ - usage.dask: 48 - usage.matplotlib: 35 - usage.pandas: 83 - usage.scipy: 466 - usage.skimage: 143 - usage.sklearn: 44 - usage.xarray: 308 + usage.sklearn: 1 """ ... -def issubsctype(arg1: numpy.ndarray, arg2: Type[numpy.float32]): +@overload +def asarray(a: List[numpy.ndarray], dtype: Type[numpy.float32], order: None): """ - usage.scipy: 2 + usage.sklearn: 1 """ ... @overload -def iterable(y: object): +def asarray(a: List[Literal["def", "abc"]]): """ - usage.matplotlib: 2 - usage.pandas: 32 + usage.sklearn: 2 """ ... @overload -def iterable(y: matplotlib.gridspec.SubplotSpec): +def asarray(a: List[set]): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def iterable(y: List[int]): +def asarray(a: List[frozenset]): """ - usage.matplotlib: 70 + usage.sklearn: 2 """ ... @overload -def iterable(y: int): +def asarray(a: List[Dict[int, Literal["b", "a"]]]): """ - usage.matplotlib: 21 + usage.sklearn: 2 """ ... @overload -def iterable(y: Tuple[int]): +def asarray(a: List[List[Literal["b", "a", "d", "c"]]]): """ - usage.matplotlib: 2 + usage.sklearn: 2 """ ... @overload -def iterable(y: Tuple[float, float]): +def asarray(a: List[List[Literal["d", "a"]]]): """ - usage.matplotlib: 4 + usage.sklearn: 2 """ ... @overload -def iterable(y: Tuple[int, float]): +def asarray(a: List[Union[int, Literal["1"]]]): """ - usage.matplotlib: 4 + usage.sklearn: 2 """ ... @overload -def iterable(y: Tuple[int, int]): +def asarray(a: List[List[Union[Literal["1"], int]]]): """ - usage.matplotlib: 5 + usage.sklearn: 2 """ ... @overload -def iterable(y: float): +def asarray(a: List[List[Union[Literal["2", "1"], int]]]): """ - usage.matplotlib: 13 + usage.sklearn: 2 """ ... @overload -def iterable(y: numpy.float64): +def asarray(a: List[Literal["spam", "egg"]]): """ - usage.matplotlib: 7 + usage.sklearn: 1 """ ... @overload -def iterable(y: numpy.ndarray): +def asarray(a: List[Literal["col_2", "col_1"]]): """ - usage.matplotlib: 88 + usage.sklearn: 1 """ ... @overload -def iterable(y: numpy.ma.core.MaskedArray): +def asarray(a: Tuple[bool, bool, bool]): """ - usage.matplotlib: 26 + usage.sklearn: 1 """ ... @overload -def iterable(y: numpy.int64): +def asarray(a: numpy.matrix, dtype: Type[numpy.float64], order: None): """ - usage.matplotlib: 4 + usage.sklearn: 1 """ ... @overload -def iterable(y: range_iterator): +def asarray(a: numpy.ndarray, dtype: Type[float], order: Literal["C"]): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def iterable(y: bool): +def asarray(a: numpy.ndarray, dtype: Type[float], order: Literal["F"]): """ - usage.matplotlib: 3 + usage.sklearn: 2 """ ... @overload -def iterable(y: List[bool]): +def asarray(a: numpy.ndarray, dtype: Type[bool], order: Literal["C"]): """ - usage.matplotlib: 6 + usage.sklearn: 2 """ ... @overload -def iterable(y: List[Union[int, float]]): +def asarray(a: numpy.ndarray, dtype: Type[bool], order: Literal["F"]): """ - usage.matplotlib: 23 + usage.sklearn: 2 """ ... @overload -def iterable(y: list): +def asarray(a: numpy.ndarray, dtype: Type[object], order: Literal["C"]): """ - usage.matplotlib: 18 + usage.sklearn: 2 """ ... @overload -def iterable(y: matplotlib.lines.Line2D): +def asarray(a: numpy.ndarray, dtype: Type[object], order: Literal["F"]): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def iterable(y: List[None]): +def asarray(a: numpy.ndarray, dtype: Type[object], order: None): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def iterable(y: None): +def asarray(a: List[List[List[int]]], dtype: None, order: None): """ - usage.matplotlib: 6 + usage.sklearn: 1 """ ... @overload -def iterable(y: matplotlib.spines.Spine): +def asarray(a: List[List[Literal["12", "11", "xx", "13"]]], dtype: None, order: None): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: type): +def asarray(a: List[List[bytes]], dtype: None, order: None): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: Type[matplotlib.lines.Line2D]): +def asarray( + a: sklearn.utils._mocking.MockDataFrame, dtype: Type[numpy.float32], order: None +): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: matplotlib.testing.jpl_units.UnitDbl.UnitDbl): +def asarray(a: List[List[int]], dtype: Type[numpy.int64]): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload -def iterable(y: List[decimal.Decimal]): +def asarray(a: List[List[complex]], dtype: None, order: None): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def iterable(y: List[float]): +def asarray( + a: Tuple[Tuple[complex, complex, complex], Tuple[complex, complex, complex]], + dtype: None, + order: None, +): """ - usage.matplotlib: 25 + usage.sklearn: 1 """ ... @overload -def iterable(y: decimal.Decimal): +def asarray(a: Tuple[numpy.ndarray, numpy.ndarray], dtype: None, order: None): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... -@overload -def iterable(y: List[numpy.float64]): +def asarray( + a: object, + dtype: Union[numpy.dtype, numpy.ndarray, str, type, None] = ..., + order: Union[Literal["F", "C", "c"], None] = ..., +): """ - usage.matplotlib: 88 + usage.dask: 125 + usage.matplotlib: 675 + usage.pandas: 3941 + usage.scipy: 3858 + usage.skimage: 222 + usage.sklearn: 3078 + usage.xarray: 1078 """ ... -@overload -def iterable(y: range): +def asarray_chkfinite(a: object, dtype: type = ...): """ - usage.matplotlib: 7 + usage.scipy: 200 """ ... @overload -def iterable(y: numpy.bool_): +def ascontiguousarray(a: numpy.ndarray): """ - usage.matplotlib: 1 + usage.dask: 4 + usage.skimage: 42 + usage.sklearn: 9 """ ... @overload -def iterable(y: Tuple[int, int, int, int]): +def ascontiguousarray(a: numpy.ndarray, dtype: Type[numpy.uint8]): """ - usage.matplotlib: 4 + usage.skimage: 6 """ ... @overload -def iterable(y: matplotlib.transforms.Bbox): +def ascontiguousarray(a: List[int], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.skimage: 6 """ ... @overload -def iterable(y: Literal["2009-04-27T00:00:00"]): +def ascontiguousarray(a: List[Union[float, int]], dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.skimage: 4 """ ... @overload -def iterable(y: matplotlib.testing.jpl_units.Epoch.Epoch): +def ascontiguousarray(a: List[float], dtype: Type[numpy.float64]): """ - usage.matplotlib: 3 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["2000-01-01"]): +def ascontiguousarray(a: numpy.ndarray, dtype: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 14 """ ... @overload -def iterable(y: Literal["2010-01-01"]): +def ascontiguousarray(a: numpy.ndarray, dtype: Type[numpy.float32]): """ - usage.matplotlib: 1 + usage.skimage: 3 + usage.sklearn: 2 """ ... @overload -def iterable(y: List[str]): +def ascontiguousarray(a: numpy.ndarray, dtype: numpy.dtype): """ - usage.matplotlib: 49 + usage.skimage: 4 + usage.sklearn: 3 """ ... @overload -def iterable(y: Literal["2009-01-20T00:00:00"]): +def ascontiguousarray(a: numpy.ndarray, dtype: Type[numpy.int64]): """ - usage.matplotlib: 2 + usage.skimage: 2 """ ... @overload -def iterable(y: Literal["2009-01-21T00:00:00"]): +def ascontiguousarray(a: numpy.ndarray, dtype: Type[numpy.int32]): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def iterable(y: List[matplotlib.testing.jpl_units.Epoch.Epoch]): +def ascontiguousarray(a: List[int], dtype: numpy.dtype): """ - usage.matplotlib: 4 + usage.skimage: 1 """ ... @overload -def iterable(y: List[Union[float, int]]): +def ascontiguousarray(a: numpy.ndarray, dtype: Type[bool]): """ - usage.matplotlib: 11 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["2018-01-01T00:00:00"]): +def ascontiguousarray( + a: Union[numpy.ndarray, List[Union[List[int], float, int, complex]]], + dtype: Union[Literal["bool", "double", "complex", "float", "intc"], type] = ..., +): """ - usage.matplotlib: 2 + usage.scipy: 104 """ ... @overload -def iterable(y: datetime.timedelta): +def ascontiguousarray(a: List[List[int]]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... -@overload -def iterable(y: Literal["2018-01-01T03:00:00"]): +def ascontiguousarray( + a: Union[List[Union[int, float, complex, List[int]]], numpy.ndarray], + dtype: Union[ + numpy.dtype, type, Literal["bool", "double", "complex", "float", "intc"] + ] = ..., +): """ - usage.matplotlib: 2 + usage.dask: 4 + usage.scipy: 104 + usage.skimage: 72 + usage.sklearn: 29 """ ... -@overload -def iterable(y: Literal["2018-01-01T02:00:00"]): +def asfarray(a: Union[List[Union[numpy.float64, float, int]], numpy.ndarray]): """ - usage.matplotlib: 2 + usage.scipy: 37 """ ... @overload -def iterable(y: List[numpy.int64]): +def asfortranarray(a: numpy.ndarray): """ - usage.matplotlib: 43 + usage.dask: 2 + usage.skimage: 1 + usage.sklearn: 16 """ ... @overload -def iterable(y: List[Literal["2018-01-01T00:00:00"]]): +def asfortranarray(a: numpy.ndarray, dtype: Type[float] = ...): """ - usage.matplotlib: 2 + usage.scipy: 26 """ ... @overload -def iterable(y: List[datetime.timedelta]): +def asfortranarray(a: List[List[int]]): """ - usage.matplotlib: 1 + usage.sklearn: 4 """ ... @overload -def iterable(y: Literal["2018-01-01T01:00:00"]): +def asfortranarray(a: numpy.ndarray, dtype: numpy.dtype): """ - usage.matplotlib: 2 + usage.sklearn: 2 """ ... @overload -def iterable( - y: List[ - Literal["2019-03-01T00:00:00", "2019-02-01T00:00:00", "2019-01-01T00:00:00"] - ] -): +def asfortranarray(a: numpy.ndarray, dtype: Type[numpy.uint8]): """ - usage.matplotlib: 3 + usage.sklearn: 2 """ ... @overload -def iterable(y: List[Literal["y"]]): +def asfortranarray(a: List[List[int]], dtype: Type[numpy.uint8]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: List[Literal["c"]]): +def asfortranarray(a: numpy.ndarray, dtype: Type[numpy.float32]): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... -@overload -def iterable(y: matplotlib.ticker.FixedLocator): +def asfortranarray( + a: Union[List[List[int]], numpy.ndarray], dtype: Union[numpy.dtype, type] = ... +): """ - usage.matplotlib: 1 + usage.dask: 2 + usage.scipy: 26 + usage.skimage: 1 + usage.sklearn: 28 """ ... @overload -def iterable( - y: List[Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64]] +def asmatrix( + data: Union[ + List[Union[List[Union[int, complex, float]], int]], numpy.matrix, numpy.ndarray + ] ): """ - usage.matplotlib: 3 + usage.scipy: 42 """ ... @overload -def iterable(y: List[Literal["lime", "b", "y", "r"]]): +def asmatrix(data: numpy.ndarray): """ - usage.matplotlib: 1 + usage.dask: 1 + usage.sklearn: 2 """ ... -@overload -def iterable(y: List[List[Union[int, float]]]): +def asmatrix( + data: Union[ + numpy.ndarray, numpy.matrix, List[Union[List[Union[int, complex, float]], int]] + ] +): """ - usage.matplotlib: 3 + usage.dask: 1 + usage.scipy: 42 + usage.sklearn: 2 """ ... -@overload -def iterable(y: List[Literal["0.8", "0.7", "0.6", "0.5"]]): +def atleast_1d(*arys: Literal["v", "t"]): """ - usage.matplotlib: 1 + usage.dask: 4 + usage.matplotlib: 116 + usage.pandas: 4 + usage.scipy: 686 + usage.skimage: 11 + usage.sklearn: 70 + usage.xarray: 41 """ ... -@overload -def iterable(y: List[numpy.ndarray]): +def atleast_2d(*arys: Literal["v", "t"]): """ - usage.matplotlib: 20 + usage.dask: 3 + usage.matplotlib: 12 + usage.pandas: 18 + usage.scipy: 239 + usage.skimage: 7 + usage.sklearn: 45 + usage.xarray: 2 """ ... -@overload -def iterable(y: List[Union[numpy.float64, float]]): +def atleast_3d(*arys: Literal["v", "t"]): """ - usage.matplotlib: 2 + usage.dask: 3 + usage.matplotlib: 4 + usage.scipy: 1 + usage.skimage: 10 + usage.sklearn: 6 """ ... @overload -def iterable(y: List[Literal["2013-09-28T12:00:00", "2013-09-28T11:00:00"]]): +def average(a: numpy.ndarray, weights: Union[numpy.ndarray, None]): """ - usage.matplotlib: 2 + usage.scipy: 15 """ ... @overload -def iterable(y: List[List[float]]): +def average(a: numpy.ndarray, axis: int = ..., weights: numpy.ndarray = ...): """ - usage.matplotlib: 5 + usage.dask: 2 """ ... @overload -def iterable(y: List[List[int]]): +def average(a: numpy.ndarray, axis: int, weights: None): """ - usage.matplotlib: 10 + usage.sklearn: 23 """ ... @overload -def iterable(y: Literal["0.5"]): +def average(a: numpy.ndarray, weights: None): """ - usage.matplotlib: 1 + usage.sklearn: 27 """ ... @overload -def iterable( - y: Tuple[ - Literal["tab:orange"], - Literal["tab:pink"], - Literal["tab:cyan"], - Literal["bLacK"], - ] -): +def average(a: numpy.ndarray, weights: numpy.ndarray): """ - usage.matplotlib: 1 + usage.sklearn: 66 """ ... @overload -def iterable(y: List[Literal["b", "r"]]): +def average(a: numpy.ndarray, axis: int, weights: numpy.ndarray): """ - usage.matplotlib: 1 + usage.sklearn: 43 """ ... @overload -def iterable(y: List[Literal["dashed", "solid"]]): +def average(a: numpy.ndarray, axis: int, weights: List[int]): """ - usage.matplotlib: 1 + usage.sklearn: 14 """ ... @overload -def iterable(y: List[list]): +def average(a: numpy.ndarray, axis: int, weights: List[float]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def iterable(y: List[Tuple[float, float, float, float]]): +def average(a: numpy.ndarray, axis: int, weights: List[Union[float, int]]): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def iterable(y: Callable): +def average(a: numpy.ndarray, axis: int, weights: List[numpy.int64]): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload -def iterable(y: Tuple[numpy.float64, numpy.float64]): +def average(a: List[int], weights: List[numpy.float64]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def iterable(y: matplotlib.text.Text): +def average(a: numpy.ndarray): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def iterable(y: Tuple[numpy.int64, numpy.int64]): +def average(a: numpy.ndarray, weights: List[int]): """ - usage.matplotlib: 2 + usage.sklearn: 12 """ ... @overload -def iterable(y: Literal["2018-11-09T00:00:00"]): +def average(a: List[numpy.float64], weights: List[float]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def iterable(y: Literal["2018-11-09T01:00:00"]): +def average(a: numpy.memmap, weights: numpy.ndarray): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... -@overload -def iterable(y: numpy.uint8): +def average( + a: Union[List[Union[int, numpy.float64]], numpy.memmap, numpy.ndarray], + axis: int = ..., + weights: Union[ + List[Union[numpy.float64, numpy.int64, float, int]], None, numpy.ndarray + ] = ..., +): """ - usage.matplotlib: 1 + usage.dask: 2 + usage.scipy: 15 + usage.sklearn: 194 """ ... @overload -def iterable(y: numpy.datetime64): +def bincount(_0: numpy.ndarray, /, *, minlength: int): """ usage.matplotlib: 2 + usage.skimage: 6 + usage.sklearn: 18 """ ... @overload -def iterable(y: matplotlib.axes._subplots.Axes3DSubplot): +def bincount(_0: dask.array.core.Array, /, *, minlength: int): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def iterable(y: matplotlib.patches.Rectangle): +def bincount(_0: numpy.ndarray, /): """ - usage.matplotlib: 1 + usage.skimage: 5 + usage.sklearn: 40 """ ... @overload -def iterable(y: Literal["Здравствуйте мир"]): +def bincount(_0: numpy.ndarray, /, *, minlength: int = ...): """ - usage.matplotlib: 1 + usage.pandas: 10 """ ... @overload -def iterable(y: Literal["hello world"]): +def bincount( + _0: numpy.ndarray, + _1: Union[numpy.ndarray, None] = ..., + /, + *, + weights: numpy.ndarray = ..., + minlength: int = ..., +): """ - usage.matplotlib: 1 + usage.scipy: 40 """ ... @overload -def iterable(y: Literal["a"]): +def bincount(_0: List[int], /, *, minlength: int): """ - usage.matplotlib: 1 + usage.matplotlib: 7 """ ... @overload -def iterable(y: Literal["1"]): +def bincount(_0: list, /, *, minlength: int): """ usage.matplotlib: 1 """ @@ -23518,127 +22757,147 @@ def iterable(y: Literal["1"]): @overload -def iterable(y: Literal["A"]): +def bincount(_0: numpy.ndarray, /, *, weights: numpy.ndarray): """ - usage.matplotlib: 1 + usage.matplotlib: 5 + usage.sklearn: 4 """ ... @overload -def iterable(y: Literal["hi"]): +def bincount(_0: numpy.ndarray, /, *, minlength: int, weights: numpy.ndarray): """ usage.matplotlib: 1 + usage.sklearn: 13 """ ... @overload -def iterable(y: Literal["мир"]): +def bincount( + _0: Union[numpy.ndarray, List[int]], + /, + *, + minlength: int = ..., + weights: Union[numpy.ndarray, List[int]] = ..., +): """ - usage.matplotlib: 1 + usage.dask: 6 """ ... @overload -def iterable(y: Literal["42"]): +def bincount(_0: numpy.ndarray, _1: numpy.ndarray, /): """ - usage.matplotlib: 1 + usage.sklearn: 5 """ ... @overload -def iterable(y: List[Literal["hi", "world", "hello"]]): +def bincount(_0: numpy.ndarray, /, *, weights: None): """ - usage.matplotlib: 2 + usage.sklearn: 4 """ ... @overload -def iterable(y: Literal["hello"]): +def bincount(_0: numpy.ndarray, /, *, weights: List[int]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: List[Literal["привет", "Здравствуйте"]]): +def bincount(_0: numpy.ndarray, /, *, weights: List[float]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def iterable(y: Literal["Здравствуйте"]): +def bincount(_0: numpy.ndarray, /, *, weights: List[Union[float, int]]): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def iterable(y: List[Literal["c", "b", "a"]]): +def bincount(_0: numpy.ndarray, /, *, minlength: int, weights: None): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload -def iterable(y: List[bytes]): +def bincount(_0: numpy.ndarray, _1: numpy.ndarray, /, *, minlength: int): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def iterable(y: bytes): +def bincount(_0: Tuple[None, ...], /, *, minlength: int): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: List[Literal["3", "11", "1"]]): +def bincount(_0: Tuple[int], /, *, minlength: int): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: List[Literal["world", "happy", "hello"]]): +def bincount(_0: Tuple[int, int], /, *, minlength: int): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: List[Literal["fun", "is", "Python"]]): +def bincount(_0: Tuple[int, int, int], /, *, minlength: int): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... -@overload -def iterable(y: Literal["Python"]): +def bincount( + _0: Union[ + Tuple[Union[int, None], ...], numpy.ndarray, dask.array.core.Array, List[int] + ], + _1: Union[numpy.ndarray, None] = ..., + /, + *, + minlength: int = ..., + weights: Union[None, List[Union[int, float]], numpy.ndarray] = ..., +): """ - usage.matplotlib: 1 + usage.dask: 6 + usage.matplotlib: 16 + usage.pandas: 10 + usage.scipy: 40 + usage.skimage: 12 + usage.sklearn: 97 """ ... -@overload -def iterable(y: List[Literal["b", "a"]]): +def blackman(M: int): """ usage.matplotlib: 1 """ @@ -23646,175 +22905,208 @@ def iterable(y: List[Literal["b", "a"]]): @overload -def iterable(y: List[Literal["g", "e"]]): +def block(arrays: List[Union[List[numpy.ndarray], numpy.ndarray]]): """ - usage.matplotlib: 2 + usage.scipy: 5 """ ... @overload -def iterable(y: Literal["e"]): +def block( + arrays: Union[ + List[ + Union[ + List[Union[numpy.ndarray, dask.array.core.Array, List[numpy.ndarray]]], + numpy.ndarray, + dask.array.core.Array, + ] + ], + int, + numpy.ndarray, + ] +): """ - usage.matplotlib: 1 + usage.dask: 18 """ ... -@overload -def iterable(y: List[Literal["d", "b", "a"]]): +def block( + arrays: Union[ + numpy.ndarray, + int, + List[ + Union[ + numpy.ndarray, + dask.array.core.Array, + List[Union[List[numpy.ndarray], dask.array.core.Array, numpy.ndarray]], + ] + ], + ] +): """ - usage.matplotlib: 1 + usage.dask: 18 + usage.scipy: 5 """ ... -@overload -def iterable(y: List[Literal["b", "a", "f"]]): +def bmat(obj: List[List[numpy.ndarray]]): """ - usage.matplotlib: 2 + usage.scipy: 12 """ ... -@overload -def iterable(y: Literal["f"]): +def broadcast_arrays(*args: Literal["v", "t"]): """ - usage.matplotlib: 1 + usage.dask: 10 + usage.matplotlib: 42 + usage.scipy: 286 + usage.xarray: 3 """ ... @overload -def iterable(y: List[Literal["d", "c", "b"]]): +def broadcast_to(array: numpy.ndarray, shape: Tuple[int, int]): """ usage.matplotlib: 1 + usage.xarray: 7 """ ... @overload -def iterable(y: Literal["b"]): +def broadcast_to(array: numpy.ndarray, shape: Tuple[int, int, int]): """ - usage.matplotlib: 1 + usage.xarray: 3 """ ... @overload -def iterable(y: List[Literal["d", "e", "g"]]): +def broadcast_to(array: numpy.ndarray, shape: Tuple[int]): """ - usage.matplotlib: 2 + usage.matplotlib: 1 + usage.xarray: 7 """ ... @overload -def iterable(y: Literal["g"]): +def broadcast_to(array: numpy.ndarray, shape: Tuple[int, int, int, int]): """ - usage.matplotlib: 1 + usage.xarray: 4 """ ... @overload -def iterable(y: Literal["12"]): +def broadcast_to(array: numpy.ndarray, shape: Tuple[int, int, int, int, int]): """ - usage.matplotlib: 1 + usage.xarray: 2 """ ... @overload -def iterable(y: mpl_toolkits.mplot3d.axes3d.Axes3D): +def broadcast_to(array: float, shape: Tuple[int]): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def iterable(y: matplotlib.axes._subplots.AxesSubplot): +def broadcast_to(array: sparse._coo.core.COO, shape: Tuple[int, int, int]): """ - usage.matplotlib: 2 + usage.xarray: 1 """ ... @overload -def iterable(y: List[matplotlib.axes._subplots.AxesSubplot]): +def broadcast_to(array: object, shape: Tuple[int, int]): """ - usage.matplotlib: 4 + usage.xarray: 1 """ ... @overload -def iterable(y: List[Literal["c", "b", "g", "r"]]): +def broadcast_to(array: object, shape: Tuple[int, int, int]): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def iterable(y: numpy.float128): +def broadcast_to(array: object, shape: Tuple[int, int, int, int, int]): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def iterable(y: List[Literal["yellow", "blue", "green"]]): +def broadcast_to(array: float, shape: Tuple[None, ...]): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def iterable(y: List[Tuple[float, float, float]]): +def broadcast_to(array: object, shape: Tuple[int]): """ - usage.matplotlib: 3 + usage.xarray: 1 """ ... @overload -def iterable(y: matplotlib.ticker.MaxNLocator): +def broadcast_to(array: object, shape: Tuple[int, int, int, int]): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def iterable(y: matplotlib.axes._axes.Axes): +def broadcast_to( + array: Union[bool, numpy.datetime64, numpy.ndarray, numpy.timedelta64, int], + shape: Tuple[int, ...], +): """ - usage.matplotlib: 2 + usage.pandas: 14 """ ... @overload -def iterable(y: Tuple[Literal["r"], Literal["g"]]): +def broadcast_to( + array: Union[numpy.ndarray, int, numpy.matrix, float], + shape: Tuple[Union[numpy.int64, int, None], ...], +): """ - usage.matplotlib: 1 + usage.scipy: 59 """ ... @overload -def iterable(y: List[Literal["blue", "pink", "yellow"]]): +def broadcast_to(array: bool, shape: int): """ - usage.matplotlib: 1 + usage.matplotlib: 4 """ ... @overload -def iterable(y: List[Literal["blue", "pink", "yellow", "red"]]): +def broadcast_to(array: Literal["0"], shape: int): """ usage.matplotlib: 1 """ @@ -23822,7 +23114,7 @@ def iterable(y: List[Literal["blue", "pink", "yellow", "red"]]): @overload -def iterable(y: List[Literal["black", "blue", "pink", "yellow"]]): +def broadcast_to(array: List[Literal["b", "a"]], shape: int): """ usage.matplotlib: 1 """ @@ -23830,34 +23122,23 @@ def iterable(y: List[Literal["black", "blue", "pink", "yellow"]]): @overload -def iterable(y: Literal["2017-01-01T00:01:01"]): +def broadcast_to(array: numpy.ndarray, shape: int): """ - usage.matplotlib: 1 + usage.matplotlib: 4 """ ... @overload -def iterable(y: List[Literal["2017-01-01T01:01:01", "2017-01-01T00:01:01"]]): +def broadcast_to(array: int, shape: int): """ - usage.matplotlib: 1 + usage.matplotlib: 2 """ ... @overload -def iterable( - y: List[ - List[ - Literal[ - "2017-01-01T01:01:01", - "2017-01-01T00:01:01", - "2017-01-01T03:01:01", - "2017-01-01T02:01:01", - ] - ] - ] -): +def broadcast_to(array: Literal[""], shape: int): """ usage.matplotlib: 1 """ @@ -23865,139 +23146,151 @@ def iterable( @overload -def iterable(y: List[Literal["2009-01-21T00:00:00", "2009-01-20T00:00:00"]]): +def broadcast_to(array: List[int], shape: int): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def iterable(y: Literal["2009-01-15T00:00:00"]): +def broadcast_to(array: List[Literal["First"]], shape: int): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def iterable(y: Literal["2009-01-26T00:00:00"]): +def broadcast_to(array: float, shape: int): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def iterable(y: List[Literal["2010-01-21T00:00:00", "2000-01-20T00:00:00"]]): +def broadcast_to(array: List[float], shape: int): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def iterable(y: Literal["1998-01-30T00:00:00"]): +def broadcast_to(array: Tuple[float, float], shape: int): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def iterable(y: Literal["2012-01-11T00:00:00"]): +def broadcast_to( + array: Union[numpy.float64, int, numpy.ndarray], + shape: Union[Tuple[Union[None, int, numpy.int64], ...], List[int]], +): """ - usage.matplotlib: 2 + usage.dask: 24 """ ... -@overload -def iterable(y: List[Literal["2009-01-20T00:00:00"]]): +def broadcast_to( + array: object, + shape: Union[List[int], Tuple[Union[None, int, numpy.int64], ...], int], +): """ - usage.matplotlib: 3 + usage.dask: 24 + usage.matplotlib: 20 + usage.pandas: 14 + usage.scipy: 59 + usage.xarray: 31 """ ... -@overload -def iterable(y: Literal["2009-02-05T00:00:00"]): +def busday_offset( + _0: numpy.datetime64, + _1: int, + /, + *, + busdaycal: numpy.busdaycalendar, + roll: Literal["backward", "forward"], +): """ - usage.matplotlib: 2 + usage.pandas: 2 """ ... @overload -def iterable(y: Literal["2000-01-20T00:00:00"]): +def can_cast(_0: int, _1: numpy.dtype, /): """ - usage.matplotlib: 2 + usage.skimage: 4 """ ... @overload -def iterable(y: List[Literal["2000-01-20T00:00:00"]]): +def can_cast(_0: float, _1: numpy.dtype, /): """ - usage.matplotlib: 3 + usage.skimage: 2 """ ... @overload -def iterable(y: Literal["2000-01-15T00:00:00"]): +def can_cast(_0: numpy.ndarray, _1: numpy.dtype, /): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["2000-01-26T00:00:00"]): +def can_cast(_0: numpy.dtype, _1: Type[bool], /, *, casting: Literal["safe"]): """ - usage.matplotlib: 2 + usage.skimage: 2 """ ... @overload -def iterable(y: Tuple[Literal["2000-01-20T00:00:00"], Literal["2000-01-20T00:00:00"]]): +def can_cast(_0: object, _1: Union[Type[bool], numpy.dtype], /): """ - usage.matplotlib: 1 + usage.pandas: 21 """ ... @overload -def iterable( - y: Tuple[Literal["2001-01-01T00:00:00+00:00"], Literal["2001-01-01T00:00:01+00:00"]] +def can_cast( + _0: Union[numpy.dtype, int, float, type], + _1: Union[type, numpy.dtype, Literal["intp"]], + _2: Literal["safe"] = ..., + /, + *, + casting: Literal["same_kind"] = ..., ): """ - usage.matplotlib: 1 + usage.scipy: 185 """ ... @overload -def iterable( - y: List[ - Literal[ - "2018-09-30T10:15:00+00:00", - "2018-09-30T09:45:00+00:00", - "2018-09-30T09:15:00+00:00", - "2018-09-30T08:45:00+00:00", - "2018-09-30T08:15:00+00:00", - ] - ] -): +def can_cast(_0: numpy.dtype, _1: Type[float], _2: Literal["same_kind"], /): """ - usage.matplotlib: 1 + usage.matplotlib: 20 """ ... @overload -def iterable(y: Literal["2011-01-01T00:00:00+00:00"]): +def can_cast( + _0: Type[numpy.float128], _1: Type[numpy.float64], _2: Literal["equiv"], / +): """ usage.matplotlib: 1 """ @@ -24005,245 +23298,262 @@ def iterable(y: Literal["2011-01-01T00:00:00+00:00"]): @overload -def iterable(y: Literal["2011-01-02T00:00:00+00:00"]): +def can_cast( + _0: Union[numpy.dtype, numpy.ndarray], + _1: numpy.dtype, + /, + *, + casting: Literal["unsafe", "safe", "same_kind"], +): """ - usage.matplotlib: 1 + usage.dask: 23 """ ... -@overload -def iterable(y: Literal["2011-01-01T23:00:00+00:00"]): +def can_cast( + _0: object, + _1: Union[numpy.dtype, Literal["intp"], type], + _2: Literal["equiv", "same_kind", "safe"] = ..., + /, + *, + casting: Literal["unsafe", "safe", "same_kind"] = ..., +): """ - usage.matplotlib: 1 + usage.dask: 23 + usage.matplotlib: 21 + usage.pandas: 21 + usage.scipy: 185 + usage.skimage: 9 """ ... @overload -def iterable(y: Literal["2011-01-02T00:00:00.000001+00:00"]): +def choose(a: numpy.ndarray, choices: List[numpy.ndarray]): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["2011-01-01T20:00:00+00:00"]): +def choose( + a: numpy.ndarray, + choices: Union[ + List[Union[int, numpy.ndarray]], Tuple[Union[numpy.ndarray, int], numpy.ndarray] + ], +): """ - usage.matplotlib: 1 + usage.dask: 4 """ ... -@overload -def iterable(y: Literal["1990-01-01T00:00:00"]): +def choose( + a: numpy.ndarray, + choices: Union[ + Tuple[Union[int, numpy.ndarray], numpy.ndarray], List[Union[numpy.ndarray, int]] + ], +): """ - usage.matplotlib: 1 + usage.dask: 4 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["2189-04-27T00:00:00"]): +def clip(a: numpy.ndarray, a_min: int, a_max: int, out: numpy.ndarray): """ - usage.matplotlib: 1 + usage.matplotlib: 6 + usage.skimage: 13 + usage.sklearn: 4 """ ... @overload -def iterable(y: Literal["1990-12-31T00:00:00"]): +def clip(a: numpy.ndarray, a_min: float, a_max: float): """ - usage.matplotlib: 1 + usage.matplotlib: 2 + usage.skimage: 8 + usage.sklearn: 4 """ ... @overload -def iterable(y: Literal["1990-05-22T00:00:00"]): +def clip(a: numpy.ndarray, a_min: int, a_max: int): """ - usage.matplotlib: 1 + usage.matplotlib: 5 + usage.skimage: 18 + usage.sklearn: 1 """ ... @overload -def iterable(y: Literal["1990-02-10T00:00:00"]): +def clip(a: numpy.float64, a_min: int, a_max: None): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["1990-01-02T16:00:00"]): +def clip(a: numpy.ndarray, a_min: None, a_max: int, out: numpy.ndarray): """ - usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: Literal["1990-01-01T00:20:00"]): +def clip(a: numpy.float64, a_min: int, a_max: int): """ - usage.matplotlib: 1 + usage.skimage: 2 """ ... @overload -def iterable( - y: List[ - Literal[ - "1990-01-01T00:20:00+00:00", - "1990-01-01T00:15:00+00:00", - "1990-01-01T00:10:00+00:00", - "1990-01-01T00:05:00+00:00", - "1990-01-01T00:00:00+00:00", - ] - ] +def clip( + a: numpy.ndarray, a_min: numpy.float64, a_max: numpy.float64, out: numpy.ndarray ): """ - usage.matplotlib: 1 + usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["1990-01-01T00:00:40"]): +def clip( + a: numpy.ndarray, a_min: numpy.float32, a_max: numpy.float32, out: numpy.ndarray +): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["1990-01-01T00:00:00.001500"]): +def clip(a: numpy.ndarray, a_min: int, a_max: None): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def iterable( - y: Tuple[ - Literal["1990-01-01T00:00:00+00:00"], - Literal["1990-01-01T00:00:00.001500+00:00"], - ] -): +def clip(a: numpy.ndarray, a_min: float, a_max: float, out: numpy.ndarray): """ - usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 3 """ ... @overload -def iterable(y: Literal["1997-01-01T00:00:00"]): +def clip(a: numpy.ndarray, a_min: int, a_max: None, out: numpy.ndarray): """ - usage.matplotlib: 2 + usage.skimage: 1 + usage.sklearn: 13 """ ... @overload -def iterable(y: Literal["2196-04-27T00:00:00"]): +def clip(a: numpy.ndarray, a_min: numpy.float16, a_max: numpy.float16): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["1997-12-31T00:00:00"]): +def clip(a: numpy.ndarray, a_min: numpy.float32, a_max: numpy.float32): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["1997-05-22T00:00:00"]): +def clip(a: numpy.ndarray, a_min: numpy.float64, a_max: numpy.float64): """ - usage.matplotlib: 2 + usage.skimage: 3 + usage.sklearn: 5 """ ... @overload -def iterable(y: Literal["1997-02-10T00:00:00"]): +def clip(a: numpy.ndarray, a_min: numpy.int16, a_max: numpy.int16): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["1997-01-02T16:00:00"]): +def clip(a: int, a_min: int, a_max: numpy.ndarray): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["1997-01-01T00:20:00"]): +def clip(a: numpy.ndarray, a_min: numpy.uint8, a_max: numpy.uint8): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def iterable( - y: List[ - Literal[ - "1997-01-01T00:20:00+00:00", - "1997-01-01T00:15:00+00:00", - "1997-01-01T00:10:00+00:00", - "1997-01-01T00:05:00+00:00", - "1997-01-01T00:00:00+00:00", - ] - ] -): +def clip(a: numpy.float64, a_min: numpy.float64, a_max: numpy.ndarray): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def iterable(y: Literal["1997-01-01T00:00:40"]): +def clip(a: pandas.core.series.Series, a_min: float, a_max: float): """ - usage.matplotlib: 2 + usage.pandas: 1 """ ... @overload -def iterable(y: Literal["1997-01-01T00:00:00.001500"]): +def clip( + a: Union[float, numpy.ndarray, numpy.float64], + a_min: Union[int, float, numpy.ndarray, numpy.float64], + a_max: Union[int, numpy.float64, numpy.ndarray, float], + out: numpy.ndarray = ..., +): """ - usage.matplotlib: 1 + usage.scipy: 38 """ ... @overload -def iterable( - y: Tuple[ - Literal["1997-01-01T00:00:00+00:00"], - Literal["1997-01-01T00:00:00.001500+00:00"], - ] -): +def clip(a: int, a_min: int, a_max: int): """ - usage.matplotlib: 1 + usage.matplotlib: 3 + usage.sklearn: 2 """ ... @overload -def iterable(y: Literal["1997-01-01T00:00:02"]): +def clip(a: float, a_min: int, a_max: int): """ usage.matplotlib: 2 """ @@ -24251,9 +23561,7 @@ def iterable(y: Literal["1997-01-01T00:00:02"]): @overload -def iterable( - y: Tuple[Literal["1997-01-01T00:00:00+00:00"], Literal["1997-01-01T00:00:02+00:00"]] -): +def clip(a: numpy.float64, a_min: float, a_max: float): """ usage.matplotlib: 1 """ @@ -24261,47 +23569,54 @@ def iterable( @overload -def iterable(y: Literal["1997-01-01T00:00:00+00:00"]): +def clip(a: numpy.ndarray, a_min: numpy.float64, a_max: float): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def iterable(y: Literal["1997-01-02T16:00:00+00:00"]): +def clip(a: numpy.ma.core.MaskedArray, a_min: int, a_max: int): """ - usage.matplotlib: 2 + usage.matplotlib: 8 """ ... @overload -def iterable(y: Literal["1997-01-01T00:20:00+00:00"]): +def clip(a: numpy.ndarray, a_min: numpy.ndarray, a_max: numpy.ndarray): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def iterable(y: Literal["1997-01-01T00:00:40+00:00"]): +def clip(a: numpy.ndarray, a_min: None, a_max: numpy.float64, out: numpy.ndarray): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def iterable(y: Literal["1997-01-01T00:00:02+00:00"]): +def clip( + a: numpy.ndarray, a_min: numpy.float128, a_max: numpy.float128, out: numpy.ndarray +): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def iterable(y: Literal["1997-01-01T00:00:00-08:00"]): +def clip( + a: numpy.ndarray, + a_min: numpy.ma.core.MaskedConstant, + a_max: numpy.ma.core.MaskedConstant, + out: numpy.ndarray, +): """ usage.matplotlib: 1 """ @@ -24309,169 +23624,182 @@ def iterable(y: Literal["1997-01-01T00:00:00-08:00"]): @overload -def iterable(y: Literal["2196-04-27T00:00:00-08:00"]): +def clip( + a: Union[ + dask.dataframe.core.DataFrame, + dask.dataframe.core.Series, + numpy.ndarray, + pandas.core.series.Series, + pandas.core.frame.DataFrame, + ], + a_min: Union[float, int, None], + a_max: Union[float, int, None], +): """ - usage.matplotlib: 1 + usage.dask: 23 """ ... @overload -def iterable(y: Literal["1997-12-31T00:00:00-08:00"]): +def clip(a: numpy.float64, a_min: float, a_max: None): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: Literal["1997-05-22T00:00:00-07:00"]): +def clip(a: numpy.ndarray, a_min: None, a_max: float, out: numpy.ndarray): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: Literal["1997-02-10T00:00:00-08:00"]): +def clip(a: numpy.float64, a_min: numpy.float64, a_max: numpy.float64): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def iterable(y: Literal["1997-01-02T16:00:00-08:00"]): +def clip(a: numpy.float64, a_min: numpy.float64, a_max: None): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: Literal["1997-01-01T00:20:00-08:00"]): +def clip(a: numpy.ndarray, a_min: float, a_max: None): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable( - y: List[ - Literal[ - "1997-01-01T00:20:00-08:00", - "1997-01-01T00:15:00-08:00", - "1997-01-01T00:10:00-08:00", - "1997-01-01T00:05:00-08:00", - "1997-01-01T00:00:00-08:00", - ] - ] -): +def clip(a: numpy.ndarray, a_min: int, a_max: numpy.ndarray, out: numpy.ndarray): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def iterable(y: Literal["1997-01-01T00:00:40-08:00"]): +def clip(a: numpy.float64, a_min: numpy.float32, a_max: numpy.float64): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: list): +def clip(a: numpy.ndarray, a_min: numpy.float32, a_max: numpy.float64): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload -def iterable(y: List[numpy.datetime64]): +def clip(a: numpy.int64, a_min: int, a_max: int): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: Tuple[float, float, float, float]): +def clip(a: numpy.ndarray, a_min: numpy.float64, a_max: None, out: numpy.ndarray): """ - usage.matplotlib: 4 + usage.sklearn: 2 """ ... @overload -def iterable(y: List[Literal["sans-serif"]]): +def clip(a: numpy.ndarray, a_min: float, a_max: None, out: numpy.ndarray): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: Literal["normal"]): +def clip(a: numpy.ndarray, a_min: numpy.int64, a_max: numpy.int64): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def iterable(y: List[Literal["serif"]]): +def clip(a: numpy.ndarray, a_min: int, a_max: numpy.float64, out: numpy.ndarray): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... -@overload -def iterable(y: Literal["italic"]): +def clip(a: object, a_min: object, a_max: object, out: numpy.ndarray = ...): """ - usage.matplotlib: 1 + usage.dask: 23 + usage.matplotlib: 34 + usage.pandas: 1 + usage.scipy: 38 + usage.skimage: 58 + usage.sklearn: 51 """ ... @overload -def iterable(y: Literal["oblique"]): +def column_stack(tup: List[numpy.ndarray]): """ - usage.matplotlib: 1 + usage.matplotlib: 17 + usage.sample-usage: 1 + usage.skimage: 5 + usage.sklearn: 4 """ ... @overload -def iterable(y: Literal["small-caps"]): +def column_stack(tup: Tuple[numpy.ndarray, numpy.ndarray]): """ - usage.matplotlib: 1 + usage.matplotlib: 16 + usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def iterable(y: Literal["bold"]): +def column_stack( + tup: Union[ + Tuple[numpy.ndarray, ...], List[Union[numpy.ndarray, List[Union[float, int]]]] + ] +): """ - usage.matplotlib: 1 + usage.scipy: 43 """ ... @overload -def iterable(y: Literal["expanded"]): +def column_stack(tup: List[List[Union[int, float]]]): """ - usage.matplotlib: 1 + usage.matplotlib: 16 """ ... @overload -def iterable(y: numpy.uint16): +def column_stack(tup: Tuple[List[float], numpy.ndarray]): """ usage.matplotlib: 1 """ @@ -24479,7 +23807,7 @@ def iterable(y: numpy.uint16): @overload -def iterable(y: numpy.ma.core.MaskedConstant): +def column_stack(tup: Tuple[numpy.ndarray, List[float]]): """ usage.matplotlib: 1 """ @@ -24487,7 +23815,7 @@ def iterable(y: numpy.ma.core.MaskedConstant): @overload -def iterable(y: matplotlib.axes._subplots.AxesHostAxesSubplot): +def column_stack(tup: Tuple[List[float], List[float]]): """ usage.matplotlib: 1 """ @@ -24495,23 +23823,23 @@ def iterable(y: matplotlib.axes._subplots.AxesHostAxesSubplot): @overload -def iterable(y: matplotlib.axis.XAxis): +def column_stack(tup: List[List[Union[int, numpy.float64]]]): """ - usage.matplotlib: 1 + usage.matplotlib: 7 """ ... @overload -def iterable(y: matplotlib.axis.YAxis): +def column_stack(tup: List[numpy.ma.core.MaskedArray]): """ - usage.matplotlib: 1 + usage.matplotlib: 4 """ ... @overload -def iterable(y: List[Tuple[int, Tuple[int, int]]]): +def column_stack(tup: List[List[numpy.float64]]): """ usage.matplotlib: 1 """ @@ -24519,249 +23847,311 @@ def iterable(y: List[Tuple[int, Tuple[int, int]]]): @overload -def iterable(y: Callable): +def column_stack(tup: List[numpy.float64]): """ usage.matplotlib: 1 """ ... -@overload -def iterable(y: List[matplotlib.testing.jpl_units.UnitDbl.UnitDbl]): +def column_stack( + tup: Union[ + List[ + Union[ + List[Union[float, int, numpy.float64]], + numpy.ndarray, + numpy.ma.core.MaskedArray, + numpy.float64, + ] + ], + Tuple[Union[numpy.ndarray, List[float]], ...], + ] +): """ - usage.matplotlib: 4 + usage.matplotlib: 65 + usage.sample-usage: 1 + usage.scipy: 43 + usage.skimage: 9 + usage.sklearn: 5 """ ... -@overload -def iterable(y: Literal["2017-01-01T00:00:00"]): +def common_type(*arrays: Literal["v", "t"]): """ - usage.matplotlib: 2 + usage.scipy: 31 """ ... @overload -def iterable(y: Literal["2017-01-01T00:00:16"]): +def compress(condition: numpy.ndarray, a: List[Literal["X", "A"]]): """ - usage.matplotlib: 2 + usage.pandas: 1 """ ... @overload -def iterable(y: Literal["2000-01-01T00:00:00"]): +def compress(condition: numpy.ndarray, a: numpy.ndarray, axis: int = ...): """ - usage.matplotlib: 2 + usage.scipy: 11 """ ... @overload -def iterable(y: matplotlib.collections.LineCollection): +def compress( + condition: Union[List[bool], numpy.ndarray], + a: numpy.ndarray, + axis: Union[int, None], +): """ - usage.matplotlib: 1 + usage.dask: 4 """ ... @overload -def iterable(y: matplotlib.contour.ClabelText): +def compress(condition: numpy.ndarray, a: numpy.ndarray): """ - usage.matplotlib: 1 + usage.sklearn: 7 """ ... -@overload -def iterable(y: numpy.float32): +def compress( + condition: Union[numpy.ndarray, List[bool]], + a: Union[numpy.ndarray, List[Literal["X", "A"]]], + axis: Union[None, int] = ..., +): """ - usage.matplotlib: 1 + usage.dask: 4 + usage.pandas: 1 + usage.scipy: 11 + usage.sklearn: 7 """ ... @overload -def iterable(y: Tuple[int, int, float, float]): +def concatenate( + _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], /, *, axis: int +): """ - usage.matplotlib: 1 + usage.skimage: 3 + usage.sklearn: 2 """ ... @overload -def iterable(y: Tuple[float, int]): +def concatenate(_0: Tuple[numpy.ndarray, numpy.ndarray], /, *, axis: int): """ - usage.matplotlib: 1 + usage.sample-usage: 1 + usage.skimage: 3 + usage.sklearn: 8 + usage.xarray: 1 """ ... @overload -def iterable(y: Tuple[None, float]): +def concatenate( + _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray], + /, + *, + axis: int, +): """ - usage.matplotlib: 2 + usage.skimage: 2 """ ... @overload -def iterable(y: Literal["2020-08-17T22:40:05.392444"]): +def concatenate(_0: List[numpy.ndarray], /, *, axis: int): """ - usage.matplotlib: 2 + usage.skimage: 12 + usage.sklearn: 15 + usage.xarray: 33 """ ... @overload -def iterable(y: Literal["2020-08-17T22:40:06.879974"]): +def concatenate(_0: Tuple[numpy.ndarray], /): """ - usage.matplotlib: 2 + usage.skimage: 3 + usage.sklearn: 7 """ ... @overload -def iterable( - y: List[ - Literal[ - "2018-11-05T00:00:00+00:00", - "2018-11-04T00:00:00+00:00", - "2018-11-03T00:00:00+00:00", - ] - ] -): +def concatenate(_0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], /): """ - usage.matplotlib: 1 + usage.skimage: 4 + usage.sklearn: 3 """ ... @overload -def iterable(y: matplotlib.tests.test_units.Quantity): +def concatenate(_0: Tuple[numpy.ndarray, List[int]], /): """ - usage.matplotlib: 3 + usage.skimage: 2 """ ... @overload -def iterable(y: List[matplotlib.tests.test_units.Quantity]): +def concatenate(_0: List[Union[numpy.ndarray, List[int]]], /): """ - usage.matplotlib: 4 + usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def iterable( - y: Tuple[matplotlib.tests.test_units.Quantity, matplotlib.tests.test_units.Quantity] -): +def concatenate(_0: Tuple[numpy.ndarray, numpy.ndarray], /): """ - usage.matplotlib: 1 + usage.matplotlib: 6 + usage.skimage: 5 + usage.sklearn: 14 """ ... @overload -def iterable(y: Literal["2009-04-25T00:00:00"]): +def concatenate(_0: List[numpy.ndarray], /): """ - usage.matplotlib: 1 + usage.matplotlib: 38 + usage.skimage: 6 + usage.sklearn: 68 + usage.xarray: 3 """ ... @overload -def iterable(y: List[matplotlib.testing.jpl_units.Duration.Duration]): +def concatenate(_0: List[List[int]], /): """ - usage.matplotlib: 2 + usage.matplotlib: 4 + usage.sklearn: 2 + usage.xarray: 16 """ ... @overload -def iterable(y: matplotlib.testing.jpl_units.Duration.Duration): +def concatenate(_0: List[List[Union[int, float]]], /): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def iterable(y: Tuple[int, float, int, float]): +def concatenate(_0: List[Union[List[int], numpy.ndarray]], /): """ usage.matplotlib: 2 + usage.xarray: 3 """ ... @overload -def iterable(y: Tuple[float, int, float, int]): +def concatenate(_0: List[Union[List[float], numpy.ndarray]], /): """ - usage.matplotlib: 2 + usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def iterable(y: List[Union[numpy.float64, int]]): +def concatenate(_0: List[List[cftime._cftime.DatetimeGregorian]], /): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def iterable(y: mpl_toolkits.axes_grid1.mpl_axes.Axes): +def concatenate(_0: List[xarray.core.dataarray.DataArray], /): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def iterable(y: mpl_toolkits.axes_grid1.axes_grid.CbarAxes): +def concatenate(_0: List[sparse._coo.core.COO], /, *, axis: int): """ - usage.matplotlib: 1 + usage.xarray: 2 """ ... @overload -def iterable(y: mpl_toolkits.axes_grid1.parasite_axes.AxesHostAxes): +def concatenate(_0: list, /, *, axis: int): """ - usage.matplotlib: 1 + usage.xarray: 7 """ ... @overload -def iterable(y: matplotlib.axes._subplots.AxesZeroSubplot): +def concatenate(_0: Union[tuple, list], /, *, axis: int = ...): """ - usage.matplotlib: 1 + usage.pandas: 312 """ ... @overload -def iterable(y: mpl_toolkits.axisartist.axislines.Axes): +def concatenate( + _0: Union[ + Tuple[ + Union[ + Tuple[Union[float, int], ...], numpy.flatiter, numpy.ndarray, int, list + ], + ..., + ], + numpy.ndarray, + List[ + Union[ + numpy.ma.core.MaskedArray, + numpy.ndarray, + List[Union[numpy.ndarray, int]], + ] + ], + ], + _1: int = ..., + /, + *, + axis: Union[int, None] = ..., +): """ - usage.matplotlib: 1 + usage.scipy: 377 """ ... @overload -def iterable(y: mpl_toolkits.axisartist.axis_artist.AxisArtist): +def concatenate(_0: Tuple[numpy.ndarray, numpy.ndarray], _1: int, /): """ - usage.matplotlib: 1 + usage.matplotlib: 4 + usage.sklearn: 1 """ ... @overload -def iterable(y: mpl_toolkits.axisartist.axis_artist.GridlinesCollection): +def concatenate(_0: List[Union[List[numpy.float64], numpy.ndarray]], /): """ usage.matplotlib: 1 """ @@ -24769,597 +24159,585 @@ def iterable(y: mpl_toolkits.axisartist.axis_artist.GridlinesCollection): @overload -def iterable( - y: Union[List[Union[float, int]], float, numpy.float64, numpy.ndarray, int] -): +def concatenate(_0: List[Union[List[numpy.ndarray], numpy.ndarray]], /): """ - usage.sklearn: 9 + usage.matplotlib: 4 """ ... -def iterable(y: object): +@overload +def concatenate(_0: List[Union[List[numpy.uint8], numpy.ndarray]], /): """ - usage.matplotlib: 858 - usage.pandas: 32 - usage.sklearn: 9 + usage.matplotlib: 4 """ ... @overload -def ix_(*args: Literal["v", "t"]): +def concatenate(_0: List[Union[numpy.ndarray, List[numpy.ndarray]]], /): """ - usage.matplotlib: 6 - usage.scipy: 1 - usage.skimage: 2 - usage.sklearn: 4 - usage.xarray: 3 + usage.matplotlib: 2 """ ... @overload -def ix_(): +def concatenate(_0: List[Union[numpy.ndarray, List[numpy.uint8]]], /): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... -def ix_(*args: Literal["v", "t"]): +@overload +def concatenate( + _0: List[Union[List[Tuple[float, float]], numpy.ndarray]], /, *, axis: int +): """ - usage.matplotlib: 6 - usage.scipy: 1 - usage.skimage: 2 - usage.sklearn: 4 - usage.xarray: 4 + usage.matplotlib: 1 """ ... -def kron( - a: Union[List[Union[int, List[int]]], numpy.ndarray, numpy.matrix], - b: Union[List[List[int]], numpy.ndarray], +@overload +def concatenate( + _0: List[Union[List[Tuple[numpy.float64, numpy.float64]], numpy.ndarray]], / ): """ - usage.scipy: 53 + usage.matplotlib: 3 """ ... @overload -def lexsort(_0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], /): +def concatenate(_0: List[List[Tuple[numpy.float64, numpy.float64]]], /): """ - usage.skimage: 1 + usage.matplotlib: 2 """ ... @overload -def lexsort(_0: Tuple[xarray.core.dataarray.DataArray], /): +def concatenate(_0: List[List[numpy.uint8]], /): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def lexsort( - _0: Tuple[xarray.core.dataarray.DataArray, xarray.core.dataarray.DataArray], / +def concatenate( + _0: List[Union[numpy.ndarray, List[Tuple[numpy.float64, numpy.float64]]]], / ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def lexsort( - _0: Union[ - List[numpy.ndarray], - Tuple[ - Union[ - pandas.core.indexes.numeric.Float64Index, - numpy.ndarray, - pandas.core.indexes.numeric.Int64Index, - ], - Union[ - pandas.core.indexes.numeric.Float64Index, - numpy.ndarray, - pandas.core.indexes.numeric.Int64Index, - ], - ], - ], - /, -): +def concatenate(_0: List[list], /): """ - usage.pandas: 11 + usage.matplotlib: 2 """ ... @overload -def lexsort( - _0: Union[Tuple[numpy.ndarray, numpy.ndarray], numpy.ndarray, List[numpy.ndarray]], - /, -): +def concatenate(_0: List[List[numpy.int64]], /): """ - usage.scipy: 9 + usage.matplotlib: 14 """ ... @overload -def lexsort(_0: Tuple[numpy.ma.core.MaskedArray, numpy.ma.core.MaskedArray], /): +def concatenate(_0: Tuple[numpy.ndarray, List[numpy.float64]], /): """ - usage.matplotlib: 1 + usage.matplotlib: 4 + usage.sklearn: 1 """ ... @overload -def lexsort(_0: Tuple[numpy.ndarray, numpy.ndarray], /): +def concatenate(_0: List[List[float]], /): """ - usage.sklearn: 2 + usage.matplotlib: 2 """ ... -def lexsort( - _0: Union[ - Tuple[ - Union[ - numpy.ma.core.MaskedArray, - pandas.core.indexes.numeric.Float64Index, - pandas.core.indexes.numeric.Int64Index, - numpy.ndarray, - xarray.core.dataarray.DataArray, - ], - ..., - ], - List[numpy.ndarray], +@overload +def concatenate( + _0: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, numpy.ndarray, ], /, + *, + axis: int, ): """ - usage.matplotlib: 1 - usage.pandas: 11 - usage.scipy: 9 - usage.skimage: 1 - usage.sklearn: 2 - usage.xarray: 2 + usage.matplotlib: 2 """ ... @overload -def linspace(start: int, stop: int, num: int, endpoint: bool): +def concatenate( + _0: Tuple[ + numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray + ], + /, + *, + axis: int, +): """ usage.matplotlib: 1 - usage.skimage: 4 """ ... @overload -def linspace(start: numpy.int64, stop: numpy.int64, num: int, endpoint: bool): +def concatenate(_0: List[List[numpy.float64]], /): """ - usage.skimage: 1 + usage.matplotlib: 21 """ ... @overload -def linspace(start: numpy.float64, stop: numpy.float64, num: int, endpoint: bool): +def concatenate(_0: List[Union[List[numpy.int64], numpy.ndarray]], /): """ usage.matplotlib: 1 - usage.skimage: 2 """ ... @overload -def linspace(start: int, stop: int, num: int, dtype: Type[numpy.uint8]): +def concatenate(_0: List[List[Union[numpy.float64, numpy.int64]]], /): """ - usage.skimage: 1 + usage.matplotlib: 2 """ ... @overload -def linspace(start: int, stop: int, num: int, dtype: Type[numpy.int8]): +def concatenate(_0: list, /): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def linspace(start: int, stop: int, num: int): +def concatenate(_0: Tuple[Tuple[float], numpy.ndarray], /): """ - usage.matplotlib: 88 - usage.sample-usage: 1 - usage.skimage: 23 - usage.xarray: 193 + usage.matplotlib: 1 """ ... @overload -def linspace(start: int, stop: float, num: int): +def concatenate(_0: Tuple[numpy.ndarray, Tuple[float]], /): """ - usage.matplotlib: 17 - usage.skimage: 17 - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload -def linspace(start: numpy.float64, stop: numpy.float64, num: int): +def concatenate(_0: Tuple[Tuple[int], numpy.ndarray], /): """ - usage.matplotlib: 8 - usage.skimage: 4 + usage.matplotlib: 1 """ ... @overload -def linspace(start: int, stop: numpy.float64, num: int, endpoint: bool): +def concatenate(_0: Tuple[numpy.ndarray, Tuple[int]], /): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def linspace(start: int, stop: numpy.float32, num: int, endpoint: bool): +def concatenate( + _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray], / +): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def linspace(start: float, stop: int, num: int): +def concatenate(_0: list, _1: int = ..., /, *, axis: int = ...): """ - usage.matplotlib: 2 - usage.skimage: 2 + usage.dask: 117 """ ... @overload -def linspace(start: float, stop: float, num: int): +def concatenate(_0: Tuple[List[int], numpy.ndarray], /): """ - usage.matplotlib: 51 - usage.skimage: 4 - usage.xarray: 21 + usage.sklearn: 1 """ ... @overload -def linspace(start: float, stop: float, num: int, endpoint: bool): +def concatenate(_0: Tuple[List[numpy.float64], numpy.ndarray], /): """ - usage.skimage: 5 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def linspace(start: int, stop: float, num: int, endpoint: bool): +def concatenate( + _0: Tuple[ + numpy.ndarray, List[numpy.float64], List[numpy.float64], List[numpy.float64] + ], + /, +): """ - usage.matplotlib: 3 - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def linspace(start: int, stop: int, num: numpy.int64): +def concatenate( + _0: Tuple[ + numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray + ], + /, +): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... -@overload -def linspace(start: float, stop: numpy.float64, num: int): +def concatenate( + _0: Union[tuple, list, numpy.ndarray], + _1: int = ..., + /, + *, + axis: Union[int, None] = ..., +): """ - usage.xarray: 1 + usage.dask: 117 + usage.matplotlib: 134 + usage.pandas: 312 + usage.sample-usage: 1 + usage.scipy: 377 + usage.skimage: 42 + usage.sklearn: 126 + usage.xarray: 69 """ ... @overload -def linspace(start: float, stop: float, num: numpy.int64): +def convolve(a: numpy.ndarray, v: List[float], mode: Literal["valid"]): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def linspace(start: int, stop: int, num: int, dtype: Type[float]): +def convolve( + a: Union[numpy.ndarray, List[Union[int, numpy.complex128]]], + v: Union[numpy.ndarray, List[Union[int, numpy.complex128]]], + mode: Literal["full", "same", "valid"] = ..., +): """ - usage.xarray: 5 + usage.scipy: 654 """ ... @overload -def linspace(start: int, stop: int, num: int, dtype: Type[int]): +def convolve(a: numpy.ndarray, v: numpy.ndarray, mode: Literal["same"]): """ - usage.xarray: 5 + usage.matplotlib: 2 """ ... -@overload -def linspace( - start: Union[float, int], - stop: Union[int, float], - num: int, - endpoint: bool = ..., - dtype: Literal["int64"] = ..., +def convolve( + a: Union[numpy.ndarray, List[Union[int, numpy.complex128]]], + v: Union[numpy.ndarray, List[Union[int, numpy.complex128, float]]], + mode: Literal["same", "full", "valid"] = ..., ): """ - usage.pandas: 19 + usage.matplotlib: 2 + usage.scipy: 654 + usage.skimage: 1 """ ... @overload -def linspace( - start: Union[numpy.float64, float, int], - stop: Union[numpy.float64, int, numpy.int64, float], - num: object = ..., - endpoint: Union[bool, int] = ..., - retstep: bool = ..., -): +def copy(a: numpy.ndarray): """ - usage.scipy: 589 + usage.matplotlib: 7 + usage.pandas: 1 + usage.skimage: 12 + usage.sklearn: 40 """ ... @overload -def linspace(start: float, stop: float, num: int, endpoint: bool, retstep: bool): +def copy(a: numpy.float64): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def linspace(start: float, stop: int): +def copy(a: Union[numpy.ndarray, float, numpy.int64, List[float]]): """ - usage.matplotlib: 1 + usage.scipy: 53 """ ... @overload -def linspace(start: numpy.int64, stop: numpy.int64, num: int): +def copy(a: numpy.ndarray, order: Literal["C"]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... -@overload -def linspace(start: int, stop: int): +def copy( + a: Union[numpy.ndarray, numpy.int64, float, numpy.float64, List[float]], + order: Literal["C"] = ..., +): """ usage.matplotlib: 7 + usage.pandas: 1 + usage.scipy: 53 + usage.skimage: 13 + usage.sklearn: 41 """ ... @overload -def linspace(start: int, stop: float): +def copyto(_0: numpy.ndarray, _1: int, /, *, where: numpy.ndarray): """ - usage.matplotlib: 1 + usage.matplotlib: 2 """ ... @overload -def linspace( - start: object, - stop: Union[numpy.float64, float, int], - num: int = ..., - endpoint: bool = ..., - dtype: Union[numpy.dtype, type] = ..., - retstep: bool = ..., -): +def copyto(_0: numpy.ndarray, _1: numpy.ndarray, /, *, where: numpy.ndarray): """ - usage.dask: 26 + usage.matplotlib: 4 """ ... -@overload -def linspace( - start: Union[numpy.int64, int, float, numpy.float64], - stop: Union[numpy.int64, int, float, numpy.float64], - num: Union[numpy.int64, int], - endpoint: bool = ..., +def copyto( + _0: numpy.ndarray, _1: Union[numpy.ndarray, int], /, *, where: numpy.ndarray ): """ - usage.sklearn: 95 + usage.matplotlib: 6 """ ... -def linspace( - start: object, - stop: Union[numpy.float64, float, int, numpy.int64, numpy.float32], - num: object = ..., - endpoint: Union[bool, int] = ..., - dtype: Union[type, numpy.dtype, Literal["int64"]] = ..., - retstep: bool = ..., +@overload +def corrcoef( + x: Union[numpy.ndarray, numpy.flatiter], y: Union[numpy.ndarray, numpy.flatiter] ): """ - usage.dask: 26 - usage.matplotlib: 184 - usage.pandas: 19 - usage.sample-usage: 1 - usage.scipy: 589 - usage.skimage: 67 - usage.sklearn: 95 - usage.xarray: 231 + usage.pandas: 12 """ ... @overload -def load(file: str): +def corrcoef(x: numpy.ndarray, rowvar: int = ..., y: numpy.ndarray = ...): """ - usage.skimage: 21 + usage.dask: 5 + usage.scipy: 3 """ ... @overload -def load(file: str, allow_pickle: bool = ...): +def corrcoef(x: numpy.ndarray): """ - usage.scipy: 26 + usage.sklearn: 1 """ ... @overload -def load(file: _io.BufferedReader): +def corrcoef(x: numpy.ndarray, y: List[numpy.ndarray]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def load(file: str, mmap_mode: Literal["r"] = ...): +def corrcoef(x: numpy.ndarray, y: numpy.ndarray): """ - usage.dask: 7 + usage.sklearn: 1 """ ... -def load( - file: Union[str, _io.BufferedReader], - allow_pickle: bool = ..., - mmap_mode: Literal["r"] = ..., +def corrcoef( + x: Union[numpy.ndarray, numpy.flatiter], + y: Union[List[numpy.ndarray], numpy.ndarray, numpy.flatiter] = ..., + rowvar: int = ..., ): """ - usage.dask: 7 - usage.matplotlib: 1 - usage.scipy: 26 - usage.skimage: 21 + usage.dask: 5 + usage.pandas: 12 + usage.scipy: 3 + usage.sklearn: 3 """ ... @overload -def loadtxt(fname: str, dtype: List[Tuple[str, type]]): +def correlate( + a: numpy.ndarray, v: numpy.ndarray, mode: Literal["same", "valid", "full"] +): """ - usage.skimage: 1 + usage.scipy: 4 """ ... @overload -def loadtxt(fname: str): +def correlate(a: numpy.ndarray, v: numpy.ndarray, mode: Literal["full"]): """ - usage.pandas: 2 + usage.matplotlib: 1 + """ + ... + + +def correlate( + a: numpy.ndarray, v: numpy.ndarray, mode: Literal["full", "same", "valid"] +): + """ + usage.matplotlib: 1 + usage.scipy: 4 """ ... @overload -def loadtxt(fname: Union[str, _io.StringIO], skiprows: int = ...): +def count_nonzero(a: numpy.ndarray): """ - usage.scipy: 6 + usage.skimage: 9 + usage.sklearn: 12 """ ... @overload -def loadtxt( - fname: Union[tarfile.ExFileObject, str], - delimiter: Literal[","] = ..., - skiprows: int = ..., +def count_nonzero( + a: Union[numpy.ma.core.MaskedArray, numpy.ndarray, List[float]], axis: int = ... ): """ - usage.sklearn: 6 + usage.scipy: 22 """ ... -def loadtxt( - fname: Union[str, tarfile.ExFileObject, _io.StringIO], - dtype: List[Tuple[str, type]] = ..., - skiprows: int = ..., - delimiter: Literal[","] = ..., +@overload +def count_nonzero( + a: Union[numpy.ndarray, List[bool], str], + axis: Union[Tuple[int, ...], int, None] = ..., ): """ - usage.pandas: 2 - usage.scipy: 6 - usage.skimage: 1 - usage.sklearn: 6 + usage.dask: 21 """ ... @overload -def logspace(start: numpy.float64, stop: numpy.float64, num: int): +def count_nonzero(a: numpy.ndarray, axis: int): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... -@overload -def logspace(start: float, stop: float, num: int): +def count_nonzero( + a: Union[numpy.ndarray, numpy.ma.core.MaskedArray, List[Union[bool, float]], str], + axis: Union[int, Tuple[int, ...], None] = ..., +): """ - usage.skimage: 1 + usage.dask: 21 + usage.scipy: 22 + usage.skimage: 9 + usage.sklearn: 13 """ ... @overload -def logspace( - start: Union[float, int, numpy.float64], - stop: Union[int, numpy.float64, float], - num: object = ..., - base: float = ..., +def cov( + m: Union[numpy.ndarray, numpy.flatiter], + y: Union[numpy.flatiter, numpy.ndarray] = ..., ): """ - usage.scipy: 51 + usage.pandas: 7 """ ... @overload -def logspace(start: int, stop: int, num: int, base: float): +def cov( + m: numpy.ndarray, + rowvar: Union[int, bool] = ..., + y: numpy.ndarray = ..., + bias: Union[bool, int] = ..., + aweights: numpy.ndarray = ..., +): """ - usage.matplotlib: 1 + usage.scipy: 21 """ ... @overload -def logspace(start: int, stop: int, num: int): +def cov(m: numpy.ndarray, rowvar: int, bias: bool): """ - usage.matplotlib: 1 + usage.matplotlib: 2 """ ... @overload -def logspace(start: int, stop: int): +def cov(m: numpy.ndarray, y: numpy.ndarray, bias: int): """ usage.matplotlib: 1 """ @@ -25367,124 +24745,109 @@ def logspace(start: int, stop: int): @overload -def logspace( - start: Union[int, numpy.float64], - stop: Union[int, numpy.float64], - num: int, - base: float = ..., +def cov( + m: object, y: object = ..., rowvar: int = ..., ddof: int = ..., bias: int = ... ): """ - usage.sklearn: 17 + usage.dask: 11 """ ... -def logspace( - start: Union[numpy.float64, int, float], - stop: Union[numpy.float64, int, float], - num: object = ..., - base: float = ..., -): +@overload +def cov(m: numpy.ndarray, bias: int): """ - usage.matplotlib: 3 - usage.scipy: 51 - usage.skimage: 2 - usage.sklearn: 17 + usage.sklearn: 8 """ ... @overload -def may_share_memory(_0: numpy.ndarray, _1: numpy.ndarray, /): +def cov(m: numpy.ndarray, rowvar: float, bias: float): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def may_share_memory( - _0: Union[ - pandas.core.indexes.base.Index, - pandas.core.arrays.datetimes.DatetimeArray, - pandas.core.arrays.timedeltas.TimedeltaArray, - ], - _1: Union[pandas.core.indexes.base.Index, numpy.ndarray], - /, -): +def cov(m: numpy.ndarray, rowvar: bool): """ - usage.pandas: 3 + usage.sklearn: 1 """ ... @overload -def may_share_memory( - _0: numpy.ndarray, _1: Union[list, numpy.ndarray, numpy.ma.core.MaskedArray], / -): +def cov(m: numpy.ndarray): """ - usage.scipy: 158 + usage.sklearn: 14 """ ... -@overload -def may_share_memory(_0: object, _1: object, /): +def cov( + m: object, + rowvar: Union[float, bool, int] = ..., + y: object = ..., + bias: Union[float, int, bool] = ..., + aweights: numpy.ndarray = ..., +): """ - usage.sklearn: 96 + usage.dask: 11 + usage.matplotlib: 3 + usage.pandas: 7 + usage.scipy: 21 + usage.sklearn: 24 """ ... -def may_share_memory(_0: object, _1: object, /): +def cross(a: numpy.ndarray, b: numpy.ndarray): """ - usage.pandas: 3 - usage.scipy: 158 - usage.skimage: 2 - usage.sklearn: 96 + usage.scipy: 8 + usage.skimage: 5 """ ... @overload -def mean(a: numpy.ndarray): +def cumprod(a: Tuple[int]): """ - usage.matplotlib: 7 - usage.skimage: 35 + usage.skimage: 1 usage.xarray: 1 """ ... @overload -def mean(a: numpy.ndarray, axis: int): +def cumprod(a: Tuple[int, int, int]): """ - usage.matplotlib: 5 - usage.skimage: 13 - usage.xarray: 6 + usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def mean(a: dask.array.core.Array): +def cumprod(a: Tuple[int, int]): """ usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def mean(a: numpy.ndarray, axis: Tuple[int, int]): +def cumprod(a: Tuple[int, int, int, int]): """ usage.skimage: 1 - usage.xarray: 2 """ ... @overload -def mean(a: numpy.ndarray, axis: Tuple[int, int], dtype: Type[numpy.uint8]): +def cumprod(a: Tuple[int, int, int, int, int]): """ usage.skimage: 1 """ @@ -25492,55 +24855,55 @@ def mean(a: numpy.ndarray, axis: Tuple[int, int], dtype: Type[numpy.uint8]): @overload -def mean(a: numpy.ndarray, axis: Tuple[int, int], dtype: Type[numpy.float16]): +def cumprod(a: numpy.ndarray, axis: int): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def mean(a: numpy.ndarray, dtype: Type[numpy.float64]): +def cumprod(a: object, axis: int): """ - usage.skimage: 4 + usage.xarray: 1 """ ... @overload -def mean(a: List[numpy.float64]): +def cumprod(a: object): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload -def mean(a: numpy.ndarray, axis: None): +def cumprod(a: xarray.core.dataarray.DataArray): """ - usage.xarray: 3 + usage.xarray: 1 """ ... @overload -def mean(a: numpy.ndarray, axis: Tuple[int]): +def cumprod(a: numpy.ndarray, axis: int, dtype: None): """ - usage.xarray: 3 + usage.xarray: 1 """ ... @overload -def mean(a: xarray.core.dataarray.DataArray): +def cumprod(a: object, axis: int, dtype: None): """ - usage.xarray: 3 + usage.xarray: 1 """ ... @overload -def mean(a: xarray.core.dataarray.DataArray, keepdims: bool): +def cumprod(a: xarray.core.dataset.Dataset): """ usage.xarray: 1 """ @@ -25548,79 +24911,119 @@ def mean(a: xarray.core.dataarray.DataArray, keepdims: bool): @overload -def mean(a: numpy.ndarray, axis: None, dtype: None): +def cumprod(a: Union[numpy.ndarray, pandas.core.series.Series], axis: int = ...): """ - usage.xarray: 1 + usage.pandas: 9 """ ... @overload -def mean(a: xarray.core.dataarray.DataArray, axis: int, keepdims: bool): +def cumprod(a: List[Union[numpy.int64, int]]): """ - usage.xarray: 1 + usage.scipy: 3 """ ... @overload -def mean(a: numpy.ndarray, axis: int, dtype: None): +def cumprod( + a: Union[ + dask.dataframe.core.DataFrame, + dask.array.core.Array, + numpy.ma.core.MaskedArray, + numpy.ndarray, + ], + axis: Union[int, None] = ..., + out: Union[dask.dataframe.core.DataFrame, dask.array.core.Array] = ..., +): """ - usage.xarray: 1 + usage.dask: 14 """ ... @overload -def mean(a: numpy.ndarray, axis: None, dtype: Type[float]): +def cumprod(a: numpy.ndarray): """ - usage.xarray: 2 + usage.sklearn: 2 """ ... -@overload -def mean(a: numpy.ndarray, axis: int, dtype: Type[float]): +def cumprod( + a: object, + axis: Union[None, int] = ..., + out: Union[dask.dataframe.core.DataFrame, dask.array.core.Array] = ..., + dtype: None = ..., +): """ - usage.xarray: 2 + usage.dask: 14 + usage.pandas: 9 + usage.scipy: 3 + usage.skimage: 5 + usage.sklearn: 2 + usage.xarray: 10 + """ + ... + + +def cumproduct(*args: Literal["v", "t"]): + """ + usage.pandas: 2 """ ... @overload -def mean(a: object, axis: None): +def cumsum(a: numpy.ndarray, axis: int): """ + usage.skimage: 4 + usage.sklearn: 3 usage.xarray: 1 """ ... @overload -def mean(a: object, axis: Tuple[int]): +def cumsum(a: numpy.ndarray): """ - usage.xarray: 2 + usage.matplotlib: 4 + usage.skimage: 15 + usage.sklearn: 16 """ ... @overload -def mean(a: object): +def cumsum(a: numpy.ndarray, out: numpy.ndarray): """ - usage.xarray: 1 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def mean(a: object, axis: None, dtype: None): +def cumsum(a: dask.array.core.Array): """ - usage.xarray: 1 + usage.skimage: 9 """ ... @overload -def mean(a: object, axis: int): +def cumsum(a: List[int]): + """ + usage.matplotlib: 5 + usage.sklearn: 7 + usage.xarray: 10 + """ + ... + + +@overload +def cumsum(a: list): """ usage.xarray: 1 """ @@ -25628,7 +25031,7 @@ def mean(a: object, axis: int): @overload -def mean(a: xarray.core.dataset.Dataset): +def cumsum(a: object, axis: int): """ usage.xarray: 1 """ @@ -25636,7 +25039,7 @@ def mean(a: xarray.core.dataset.Dataset): @overload -def mean(a: object, axis: int, dtype: None): +def cumsum(a: object): """ usage.xarray: 1 """ @@ -25644,7 +25047,7 @@ def mean(a: object, axis: int, dtype: None): @overload -def mean(a: xarray.core.variable.Variable): +def cumsum(a: xarray.core.dataarray.DataArray): """ usage.xarray: 1 """ @@ -25652,23 +25055,23 @@ def mean(a: xarray.core.variable.Variable): @overload -def mean(a: numpy.ndarray, keepdims: bool): +def cumsum(a: numpy.ndarray, axis: int, dtype: None): """ - usage.xarray: 3 + usage.xarray: 1 """ ... @overload -def mean(a: numpy.ndarray, axis: int, keepdims: bool): +def cumsum(a: object, axis: int, dtype: None): """ - usage.xarray: 3 + usage.xarray: 1 """ ... @overload -def mean(a: numpy.ndarray, axis: Tuple[int, int], keepdims: bool): +def cumsum(a: xarray.core.dataset.Dataset): """ usage.xarray: 1 """ @@ -25676,16 +25079,15 @@ def mean(a: numpy.ndarray, axis: Tuple[int, int], keepdims: bool): @overload -def mean( +def cumsum( a: Union[ numpy.ndarray, pandas.core.arrays.sparse.array.SparseArray, pandas.core.series.Series, - List[float], ], - axis: Union[None, int] = ..., dtype: Type[numpy.int64] = ..., - out: numpy.float64 = ..., + out: pandas.core.arrays.sparse.array.SparseArray = ..., + axis: int = ..., ): """ usage.pandas: 26 @@ -25694,1052 +25096,1083 @@ def mean( @overload -def mean(a: object, axis: Union[None, int] = ..., keepdims: bool = ...): +def cumsum( + a: Union[List[int], numpy.ndarray], axis: int = ..., out: numpy.ndarray = ... +): """ - usage.scipy: 89 + usage.scipy: 30 """ ... @overload -def mean( - a: object, - axis: Union[None, Tuple[Union[int, None], ...], int] = ..., - dtype: Literal["float32", "i8", "f8"] = ..., - out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., - keepdims: bool = ..., -): +def cumsum(a: numpy.flatiter): """ - usage.dask: 78 + usage.matplotlib: 4 """ ... @overload -def mean( - a: Union[ - List[Union[float, numpy.ndarray, numpy.float64, int]], - numpy.float64, - numpy.ndarray, - Tuple[Union[float, numpy.float64], ...], - ], - axis: int = ..., - dtype: Type[numpy.float64] = ..., -): +def cumsum(a: List[Union[numpy.float64, float, int]]): """ - usage.sklearn: 231 + usage.matplotlib: 1 """ ... -def mean( - a: object, - axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., - out: Union[ - dask.dataframe.core.Series, dask.dataframe.core.Scalar, numpy.float64 - ] = ..., - keepdims: bool = ..., - dtype: Union[type, None, Literal["float32", "i8", "f8"]] = ..., -): +@overload +def cumsum(a: List[Union[float, int]]): """ - usage.dask: 78 - usage.matplotlib: 12 - usage.pandas: 26 - usage.scipy: 89 - usage.skimage: 58 - usage.sklearn: 231 - usage.xarray: 42 + usage.matplotlib: 9 """ ... @overload -def median(a: numpy.ndarray, axis: Tuple[int, int]): +def cumsum(a: List[Union[numpy.float64, int]]): """ - usage.skimage: 1 + usage.matplotlib: 6 """ ... @overload -def median(a: numpy.ndarray): +def cumsum(a: numpy.ndarray, axis: int, dtype: numpy.dtype): """ usage.matplotlib: 2 - usage.skimage: 4 """ ... @overload -def median(a: numpy.ndarray, axis: None): +def cumsum(a: List[Union[float, numpy.float64, int]]): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def median(a: object, axis: None): +def cumsum( + a: object, + axis: Union[int, None] = ..., + out: Union[dask.dataframe.core.DataFrame, dask.array.core.Array] = ..., +): """ - usage.xarray: 1 + usage.dask: 41 """ ... @overload -def median(a: object): +def cumsum(a: numpy.ndarray, axis: None, dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def median(a: xarray.core.dataarray.DataArray): +def cumsum(a: numpy.ndarray, axis: int, dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def median(a: xarray.core.dataset.Dataset): +def cumsum(a: List[int], axis: None, dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... -@overload -def median(a: object, axis: int): +def cumsum( + a: object, + axis: Union[None, int] = ..., + out: Union[ + numpy.ndarray, + pandas.core.arrays.sparse.array.SparseArray, + dask.dataframe.core.DataFrame, + dask.array.core.Array, + ] = ..., + dtype: Union[type, None, numpy.dtype] = ..., +): """ - usage.xarray: 1 + usage.dask: 41 + usage.matplotlib: 33 + usage.pandas: 26 + usage.scipy: 30 + usage.skimage: 29 + usage.sklearn: 33 + usage.xarray: 18 """ ... -@overload -def median(a: numpy.ndarray, axis: int): +def datetime_data(_0: numpy.dtype, /): """ + usage.pandas: 14 usage.xarray: 1 """ ... @overload -def median(a: numpy.ndarray, axis: Tuple[int]): +def delete(arr: numpy.ndarray, obj: Tuple[None, ...], axis: int): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def median( - a: Union[numpy.ndarray, pandas.core.series.Series], axis: Union[None, int] = ... -): +def delete(arr: numpy.ndarray, obj: Tuple[int, int, int], axis: int): """ - usage.pandas: 17 + usage.skimage: 1 """ ... @overload -def median( - a: Union[numpy.ndarray, List[float]], - axis: Union[None, int] = ..., - keepdims: bool = ..., +def delete( + arr: numpy.ndarray, + obj: Tuple[int, int, int, int, int, int, int, int, int, int], + axis: int, ): """ - usage.scipy: 28 + usage.skimage: 1 """ ... @overload -def median(a: numpy.ndarray, axis: int, overwrite_input: bool): +def delete( + arr: numpy.ndarray, obj: Tuple[int, int, int, int, int, int, int, int], axis: int +): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def median( - a: Union[numpy.ndarray, Tuple[int, ...]], - axis: Union[List[int], int] = ..., - keepdims: bool = ..., +def delete( + arr: numpy.ndarray, + obj: Union[ + int, numpy.ndarray, List[int], Tuple[int, int, int], slice[int, int, int] + ], + axis: int = ..., ): """ - usage.dask: 18 + usage.pandas: 28 """ ... @overload -def median(a: numpy.ndarray, axis: int = ...): - """ - usage.sklearn: 38 - """ - ... - - -def median( - a: object, - axis: Union[int, Tuple[int, ...], None, List[int]] = ..., - overwrite_input: bool = ..., - keepdims: bool = ..., +def delete( + arr: numpy.ndarray, + obj: Union[ + numpy.int64, + int, + numpy.ndarray, + slice[int, int, int], + Tuple[numpy.int64, numpy.int64], + ], + axis: int = ..., ): """ - usage.dask: 18 - usage.matplotlib: 3 - usage.pandas: 17 - usage.scipy: 28 - usage.skimage: 5 - usage.sklearn: 38 - usage.xarray: 8 + usage.scipy: 145 """ ... @overload -def meshgrid(*xi: Literal["v", "t"]): +def delete(arr: numpy.ndarray, obj: int): """ - usage.matplotlib: 74 - usage.scipy: 14 - usage.skimage: 8 - usage.xarray: 4 + usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload -def meshgrid(*xi: Literal["v", "t"], indexing: Literal["ij"]): +def delete(arr: numpy.ndarray, obj: numpy.ndarray): """ - usage.skimage: 10 - usage.xarray: 2 + usage.sklearn: 3 """ ... @overload -def meshgrid(*xi: Literal["v", "t"], indexing: Literal["ij"], sparse: bool): +def delete(arr: numpy.ndarray, obj: numpy.int64): """ - usage.skimage: 9 + usage.sklearn: 1 """ ... -@overload -def meshgrid(*xi: Literal["v", "t"], indexing: Literal["ij", "xy"], sparse: bool = ...): +def delete(arr: numpy.ndarray, obj: object, axis: int = ...): """ - usage.dask: 9 + usage.matplotlib: 1 + usage.pandas: 28 + usage.scipy: 145 + usage.skimage: 4 + usage.sklearn: 7 """ ... @overload -def meshgrid(*xi: Literal["v", "t"], indexing: Literal["ij"] = ...): +def diag(v: numpy.ndarray): """ - usage.sklearn: 6 + usage.skimage: 5 + usage.sklearn: 55 """ ... -def meshgrid( - *xi: Literal["v", "t"], indexing: Literal["ij", "xy"] = ..., sparse: bool = ... +@overload +def diag( + v: Union[numpy.ndarray, List[Union[List[int], int, float, complex, numpy.float64]]], + k: Union[numpy.int64, int] = ..., ): """ - usage.dask: 9 - usage.matplotlib: 74 - usage.scipy: 14 - usage.skimage: 27 - usage.sklearn: 6 - usage.xarray: 6 + usage.scipy: 382 """ ... @overload -def min_scalar_type(_0: int, /): +def diag(v: Union[numpy.ndarray, dask.array.core.Array]): """ - usage.skimage: 3 + usage.dask: 8 """ ... -@overload -def min_scalar_type(_0: numpy.int64, /): +def diag( + v: Union[ + numpy.ndarray, + dask.array.core.Array, + List[Union[numpy.float64, complex, float, int, List[int]]], + ], + k: Union[numpy.int64, int] = ..., +): """ - usage.skimage: 1 + usage.dask: 8 + usage.scipy: 382 + usage.skimage: 5 + usage.sklearn: 55 """ ... -@overload -def min_scalar_type(_0: numpy.float64, /): +def diag_indices(n: int): """ - usage.skimage: 1 + usage.scipy: 3 + usage.sklearn: 1 """ ... -@overload -def min_scalar_type(_0: object, /): +def diag_indices_from(arr: numpy.ndarray): """ - usage.matplotlib: 1 - usage.pandas: 10 + usage.scipy: 3 + usage.sklearn: 6 """ ... @overload -def min_scalar_type(_0: numpy.ma.core.MaskedArray, /): +def diagonal(a: numpy.ndarray): """ - usage.matplotlib: 3 + usage.scipy: 2 + usage.sklearn: 2 """ ... @overload -def min_scalar_type(_0: List[numpy.int64], /): +def diagonal( + a: Union[numpy.ndarray, dask.array.core.Array], + offset: int = ..., + axis1: int = ..., + axis2: int = ..., +): """ - usage.matplotlib: 1 + usage.dask: 23 """ ... -@overload -def min_scalar_type(_0: List[numpy.bool_], /): +def diagonal( + a: Union[numpy.ndarray, dask.array.core.Array], + offset: int = ..., + axis1: int = ..., + axis2: int = ..., +): """ - usage.matplotlib: 1 + usage.dask: 23 + usage.scipy: 2 + usage.sklearn: 2 """ ... @overload -def min_scalar_type(_0: List[numpy.float64], /): +def diff(a: numpy.ndarray, n: int, axis: int): """ - usage.matplotlib: 1 + usage.skimage: 2 + usage.xarray: 2 """ ... @overload -def min_scalar_type(_0: List[float], /): +def diff(a: numpy.ndarray): """ - usage.matplotlib: 1 + usage.matplotlib: 18 + usage.pandas: 6 + usage.skimage: 7 + usage.sklearn: 39 + usage.xarray: 4 """ ... @overload -def min_scalar_type(_0: numpy.ndarray, /): +def diff(a: numpy.ndarray, axis: int): """ usage.matplotlib: 5 + usage.skimage: 5 + usage.xarray: 7 """ ... @overload -def min_scalar_type(_0: List[Union[int, float]], /): +def diff( + a: Union[ + numpy.ndarray, + Tuple[Union[float, Tuple[float, float, float]], ...], + List[Union[float, numpy.float64, int, List[int]]], + ], + axis: int = ..., +): """ - usage.matplotlib: 2 + usage.scipy: 127 """ ... @overload -def min_scalar_type(_0: List[int], /): +def diff(a: Tuple[numpy.float64, numpy.float64]): """ - usage.matplotlib: 4 + usage.matplotlib: 2 """ ... @overload -def min_scalar_type(_0: List[numpy.float128], /): +def diff( + a: Union[ + List[Union[numpy.float64, numpy.int64]], dask.array.core.Array, numpy.ndarray + ], + n: int = ..., + axis: int = ..., +): """ - usage.matplotlib: 1 + usage.dask: 12 """ ... @overload -def min_scalar_type(_0: List[Union[float, int]], /): +def diff(a: List[numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 8 """ ... @overload -def min_scalar_type(_0: List[None], /): +def diff(a: numpy.ndarray, n: int): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... -@overload -def min_scalar_type(_0: List[numpy.ma.core.MaskedArray], /): +def diff( + a: Union[ + List[Union[numpy.int64, float, numpy.float64, int, List[int]]], + numpy.ndarray, + dask.array.core.Array, + Tuple[Union[float, numpy.float64, Tuple[float, float, float]], ...], + ], + n: int = ..., + axis: int = ..., +): """ - usage.matplotlib: 1 + usage.dask: 12 + usage.matplotlib: 25 + usage.pandas: 6 + usage.scipy: 127 + usage.skimage: 14 + usage.sklearn: 49 + usage.xarray: 13 """ ... @overload -def min_scalar_type(_0: List[numpy.uint16], /): +def digitize(x: numpy.ndarray, bins: numpy.ndarray): """ - usage.matplotlib: 1 + usage.scipy: 2 + usage.sklearn: 3 """ ... @overload -def min_scalar_type(_0: List[numpy.ma.core.MaskedConstant], /): +def digitize(x: Union[numpy.ndarray, List[int]], bins: numpy.ndarray, right: bool): """ - usage.matplotlib: 1 + usage.dask: 8 """ ... -@overload -def min_scalar_type(_0: List[numpy.float32], /): +def digitize( + x: Union[numpy.ndarray, List[int]], bins: numpy.ndarray, right: bool = ... +): """ - usage.matplotlib: 1 + usage.dask: 8 + usage.scipy: 2 + usage.sklearn: 3 """ ... @overload -def min_scalar_type(_0: Union[int, dask.array.core.Array, numpy.ndarray], /): +def dot(_0: numpy.ndarray, _1: numpy.ndarray, /): """ - usage.dask: 3 + usage.matplotlib: 21 + usage.pandas: 15 + usage.skimage: 1 + usage.sklearn: 542 """ ... -def min_scalar_type(_0: object, /): +@overload +def dot(_0: object, _1: object, /): """ - usage.dask: 3 - usage.matplotlib: 26 - usage.pandas: 10 - usage.skimage: 5 + usage.dask: 13 + usage.scipy: 1080 """ ... -def mintypecode( - typechars: Union[ - List[Literal["d", "D", "l", "e", "f"]], Tuple[numpy.ndarray, numpy.ndarray] - ] -): +@overload +def dot(_0: numpy.ndarray, _1: matplotlib.transforms.Affine2D, /): """ - usage.scipy: 37 + usage.matplotlib: 2 """ ... @overload -def moveaxis(a: numpy.ndarray, source: int, destination: int): +def dot(_0: numpy.ndarray, _1: List[List[numpy.float64]], /): """ usage.matplotlib: 1 - usage.skimage: 4 - usage.xarray: 1 """ ... @overload -def moveaxis(a: numpy.ndarray, source: numpy.ndarray, destination: numpy.ndarray): +def dot(_0: numpy.ndarray, _1: List[numpy.float64], /): """ - usage.xarray: 12 + usage.matplotlib: 1 """ ... @overload -def moveaxis(a: numpy.ndarray, source: Tuple[None, ...], destination: Tuple[None, ...]): +def dot(_0: numpy.ma.core.MaskedArray, _1: numpy.ma.core.MaskedArray, /): """ - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload -def moveaxis(a: numpy.ndarray, source: range, destination: List[int]): +def dot(_0: numpy.ndarray, _1: numpy.ndarray, /, *, out: numpy.ndarray): """ - usage.xarray: 2 + usage.sklearn: 5 """ ... @overload -def moveaxis(a: numpy.float64, source: numpy.ndarray, destination: numpy.ndarray): +def dot(_0: numpy.ndarray, _1: numpy.matrix, /): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def moveaxis(a: int, source: Tuple[None, ...], destination: Tuple[None, ...]): +def dot(_0: numpy.ndarray, _1: List[float], /): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def moveaxis(a: object, source: int, destination: int): +def dot(_0: List[List[int]], _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.sklearn: 8 """ ... @overload -def moveaxis(a: object, source: numpy.ndarray, destination: numpy.ndarray): +def dot(_0: List[List[Union[float, int]]], _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def moveaxis( - a: numpy.ndarray, source: Union[List[int], int], destination: Union[List[int], int] -): +def dot(_0: numpy.memmap, _1: numpy.memmap, /): """ - usage.scipy: 27 + usage.sklearn: 1 """ ... @overload -def moveaxis( - a: Union[dask.array.core.Array, numpy.ndarray], source: int, destination: int -): +def dot(_0: numpy.memmap, _1: numpy.ndarray, /): """ - usage.dask: 4 + usage.sklearn: 1 """ ... -def moveaxis( - a: object, - source: Union[int, numpy.ndarray, range, List[int], Tuple[None, ...]], - destination: Union[int, numpy.ndarray, List[int], Tuple[None, ...]], -): +@overload +def dot(_0: numpy.ndarray, _1: List[Union[float, int]], /): """ - usage.dask: 4 - usage.matplotlib: 1 - usage.scipy: 27 - usage.skimage: 4 - usage.xarray: 22 + usage.sklearn: 1 """ ... @overload -def nan_to_num(x: List[numpy.float64]): +def dot(_0: numpy.ndarray, _1: List[int], /): """ - usage.skimage: 1 + usage.sklearn: 5 """ ... @overload -def nan_to_num(x: numpy.ndarray): +def dot(_0: List[int], _1: List[int], /): """ - usage.scipy: 4 - usage.sklearn: 7 + usage.sklearn: 1 """ ... -@overload -def nan_to_num(x: object): +def dot(_0: object, _1: object, /, *, out: numpy.ndarray = ...): """ - usage.dask: 23 + usage.dask: 13 + usage.matplotlib: 26 + usage.pandas: 15 + usage.scipy: 1080 + usage.skimage: 1 + usage.sklearn: 572 """ ... -def nan_to_num(x: object): +@overload +def dstack(tup: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): """ - usage.dask: 23 - usage.scipy: 4 - usage.skimage: 1 - usage.sklearn: 7 + usage.skimage: 3 """ ... @overload -def nanargmax(a: numpy.ndarray): +def dstack(tup: Tuple[numpy.ndarray, numpy.ndarray]): """ - usage.xarray: 5 + usage.skimage: 3 + usage.sklearn: 4 """ ... @overload -def nanargmax(a: int): +def dstack(tup: List[skimage.feature._hessian_det_appx._memoryviewslice]): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def nanargmax(a: List[int]): +def dstack(tup: List[numpy.ndarray]): """ - usage.xarray: 2 + usage.matplotlib: 4 + usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def nanargmax(a: List[List[int]]): +def dstack(tup: Union[List[numpy.ndarray], Tuple[numpy.ndarray, ...]]): """ - usage.xarray: 1 + usage.scipy: 13 """ ... @overload -def nanargmax(a: float): +def dstack(tup: Tuple[numpy.ndarray, numpy.ndarray, numpy.ma.core.MaskedArray]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def nanargmax(a: List[float]): +def dstack(tup: object): """ - usage.xarray: 1 + usage.dask: 8 """ ... -@overload -def nanargmax(a: numpy.int32): +def dstack(tup: object): """ - usage.xarray: 1 + usage.dask: 8 + usage.matplotlib: 5 + usage.scipy: 13 + usage.skimage: 10 + usage.sklearn: 5 """ ... @overload -def nanargmax(a: numpy.ndarray, axis: int): +def ediff1d( + ary: numpy.ndarray, to_end: numpy.float64, to_begin: Union[None, numpy.float64] +): """ - usage.xarray: 1 + usage.pandas: 2 """ ... @overload -def nanargmax(a: sparse._coo.core.COO, axis: None): +def ediff1d( + ary: numpy.ndarray, + to_end: Union[float, int, None, List[int]], + to_begin: Union[float, int, None, List[int]], +): """ - usage.xarray: 1 + usage.dask: 4 """ ... @overload -def nanargmax(a: object, axis: None): +def ediff1d(ary: numpy.ndarray, to_begin: float): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... -@overload -def nanargmax(a: object, axis: int): +def ediff1d( + ary: numpy.ndarray, + to_begin: Union[float, numpy.float64, int, None, List[int]], + to_end: Union[List[int], None, int, float, numpy.float64] = ..., +): """ - usage.xarray: 1 + usage.dask: 4 + usage.pandas: 2 + usage.sklearn: 1 """ ... @overload -def nanargmax(a: numpy.ndarray, axis: None): +def einsum(*operands: Literal["v", "t"]): """ - usage.xarray: 1 + usage.scipy: 33 + usage.skimage: 1 + usage.sklearn: 15 + usage.xarray: 83 """ ... @overload -def nanargmax( - _0: numpy.ndarray = ..., - _1: Union[None, int] = ..., - /, - a: numpy.ndarray = ..., - axis: Union[int, None] = ..., - *, - keepdims: bool = ..., +def einsum( + *operands: Literal["v", "t"], + optimize: Union[ + bool, + List[Union[Tuple[int, int], Literal["einsum_path"]]], + Literal["optimal", "greedy"], + ] = ..., ): """ - usage.dask: 28 + usage.dask: 139 """ ... -def nanargmax( - _0: numpy.ndarray = ..., - _1: Union[None, int] = ..., - /, - a: object = ..., - axis: Union[None, int] = ..., - *, - keepdims: bool = ..., +def einsum( + *operands: Literal["v", "t"], + optimize: Union[ + bool, + List[Union[Tuple[int, int], Literal["einsum_path"]]], + Literal["optimal", "greedy"], + ] = ..., ): """ - usage.dask: 28 - usage.xarray: 17 + usage.dask: 139 + usage.scipy: 33 + usage.skimage: 1 + usage.sklearn: 15 + usage.xarray: 83 """ ... -@overload -def nanargmin(a: numpy.ndarray): +def einsum_path( + *operands: Literal["v", "t"], + optimize: Union[Literal["optimal", "greedy"], bool] = ..., +): """ - usage.xarray: 6 + usage.dask: 4 """ ... @overload -def nanargmin(a: int): +def empty(_0: Tuple[int, int, int], /, *, dtype: numpy.dtype): """ - usage.xarray: 1 + usage.skimage: 12 + usage.sklearn: 2 """ ... @overload -def nanargmin(a: List[int]): +def empty(_0: Tuple[int, int, int, int], /, *, dtype: numpy.dtype): """ - usage.xarray: 2 + usage.skimage: 1 """ ... @overload -def nanargmin(a: List[List[int]]): +def empty(_0: Tuple[int, int, int, int, int], /, *, dtype: numpy.dtype): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def nanargmin(a: float): +def empty(_0: Tuple[int, int], /, *, dtype: numpy.dtype): """ - usage.xarray: 1 + usage.skimage: 4 + usage.sklearn: 41 """ ... @overload -def nanargmin(a: List[float]): +def empty(_0: Tuple[int, int, int, int, int, int], /, *, dtype: numpy.dtype): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def nanargmin(a: numpy.int32): +def empty(_0: Tuple[int, int, int], _1: numpy.dtype, /): """ - usage.xarray: 1 + usage.skimage: 4 """ ... @overload -def nanargmin(a: numpy.ndarray, axis: int): +def empty(_0: Tuple[int, int], _1: Type[int], /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def nanargmin(a: sparse._coo.core.COO, axis: None): +def empty(_0: Tuple[int, int], /): """ - usage.xarray: 1 + usage.matplotlib: 3 + usage.skimage: 17 + usage.sklearn: 30 + usage.xarray: 9 """ ... @overload -def nanargmin(a: object, axis: None): +def empty(_0: Tuple[int, int, int], /, *, dtype: Type[float]): """ - usage.xarray: 1 + usage.skimage: 2 """ ... @overload -def nanargmin(a: object, axis: int): +def empty(_0: Tuple[int, int, int, int], /, *, dtype: Type[float]): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def nanargmin( - _0: numpy.ndarray = ..., - _1: Union[None, int] = ..., - /, - a: numpy.ndarray = ..., - axis: Union[int, None] = ..., - *, - keepdims: bool = ..., -): +def empty(_0: Tuple[int, int, int], /): """ - usage.dask: 28 + usage.matplotlib: 3 + usage.skimage: 5 + usage.sklearn: 15 """ ... -def nanargmin( - _0: numpy.ndarray = ..., - _1: Union[None, int] = ..., - /, - a: object = ..., - axis: Union[None, int] = ..., - *, - keepdims: bool = ..., -): +@overload +def empty(_0: Tuple[int, int], /, *, dtype: Type[numpy.float64]): """ - usage.dask: 28 - usage.xarray: 17 + usage.skimage: 2 + usage.sklearn: 5 """ ... @overload -def nancumprod(a: numpy.ndarray, axis: int, dtype: None): +def empty(_0: Tuple[int, int], _1: numpy.dtype, /): """ - usage.xarray: 1 + usage.skimage: 8 """ ... @overload -def nancumprod(a: sparse._coo.core.COO, axis: int, dtype: None): +def empty(_0: Tuple[int, int, int], /, *, dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def nancumprod(a: object, axis: int, dtype: None): +def empty(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint16]): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def nancumprod(a: numpy.ndarray, axis: Union[None, int] = ...): +def empty(_0: Tuple[int, int], _1: Type[numpy.uint8], /): """ - usage.dask: 16 + usage.skimage: 1 """ ... -def nancumprod(a: object, axis: Union[int, None] = ..., dtype: None = ...): +@overload +def empty(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint8]): """ - usage.dask: 16 - usage.xarray: 3 + usage.skimage: 1 """ ... @overload -def nancumsum(a: numpy.ndarray, axis: int, dtype: None): +def empty(_0: Tuple[int], /, *, dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.skimage: 2 """ ... @overload -def nancumsum(a: sparse._coo.core.COO, axis: int, dtype: None): +def empty(_0: int, /, *, dtype: Type[object]): """ + usage.skimage: 2 + usage.sklearn: 7 usage.xarray: 1 """ ... @overload -def nancumsum(a: object, axis: int, dtype: None): +def empty(_0: Tuple[int, int, int], _1: Type[numpy.uint8], /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def nancumsum(a: numpy.ndarray, axis: Union[None, int] = ...): +def empty(_0: Tuple[int, int], _1: Type[numpy.uint16], /): """ - usage.dask: 16 + usage.skimage: 1 """ ... -def nancumsum(a: object, axis: Union[int, None] = ..., dtype: None = ...): +@overload +def empty(_0: Tuple[int, int, int], _1: Literal["float32"], /): """ - usage.dask: 16 - usage.xarray: 3 + usage.skimage: 1 """ ... @overload -def nanmax(a: numpy.ndarray, axis: int): +def empty(_0: Tuple[int, int], /, *, dtype: Type[int]): """ - usage.xarray: 4 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def nanmax(a: numpy.ndarray, axis: Tuple[int]): +def empty(_0: int, /): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.skimage: 3 + usage.sklearn: 33 """ ... @overload -def nanmax(a: numpy.ndarray): +def empty(_0: Tuple[int, int], /, *, dtype: Type[bool]): """ - usage.matplotlib: 2 - usage.xarray: 4 + usage.skimage: 2 """ ... @overload -def nanmax(a: numpy.ndarray, axis: None): +def empty(_0: Tuple[int, int, int], /, *, dtype: Type[bool]): """ - usage.xarray: 2 + usage.skimage: 2 """ ... @overload -def nanmax(a: sparse._coo.core.COO, axis: None): +def empty(_0: numpy.ndarray, /, *, dtype: numpy.dtype, order: Literal["F"]): """ - usage.xarray: 1 + usage.skimage: 2 """ ... @overload -def nanmax(a: object, axis: None): +def empty(_0: numpy.ndarray, /, *, dtype: numpy.dtype, order: Literal["C"]): """ - usage.xarray: 1 + usage.skimage: 4 """ ... @overload -def nanmax(a: object, axis: int): +def empty(_0: int, /, *, dtype: numpy.dtype): """ - usage.xarray: 1 + usage.skimage: 2 + usage.sklearn: 4 """ ... @overload -def nanmax(a: Union[numpy.ndarray, pandas.core.series.Series]): +def empty(_0: Tuple[int], /, *, dtype: numpy.dtype): """ - usage.pandas: 7 + usage.skimage: 14 """ ... @overload -def nanmax( - _0: numpy.ndarray = ..., - /, - a: numpy.ndarray = ..., - axis: Union[Tuple[Union[None, int], ...], int, None] = ..., - keepdims: bool = ..., - *, - computing_meta: bool = ..., -): +def empty(_0: Tuple[int, int], _1: Type[numpy.uint32], /): """ - usage.dask: 62 + usage.skimage: 2 """ ... @overload -def nanmax(a: numpy.ndarray, axis: Union[None, int]): +def empty(_0: List[Union[numpy.int64, int]], /, *, dtype: Type[numpy.float64]): """ - usage.sklearn: 8 + usage.skimage: 2 """ ... -def nanmax( - _0: numpy.ndarray = ..., - /, - a: object = ..., - axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., - keepdims: bool = ..., - *, - computing_meta: bool = ..., -): +@overload +def empty(_0: Tuple[int], _1: numpy.dtype, /): """ - usage.dask: 62 - usage.matplotlib: 2 - usage.pandas: 7 - usage.sklearn: 8 - usage.xarray: 14 + usage.skimage: 10 + usage.xarray: 2 """ ... @overload -def nanmean(a: numpy.ndarray, axis: None, dtype: None): +def empty(_0: Tuple[int], _1: Type[numpy.uint16], /): """ - usage.xarray: 4 + usage.skimage: 1 """ ... @overload -def nanmean(a: numpy.ndarray, axis: int, dtype: None): +def empty(_0: Tuple[int], _1: Type[numpy.uint8], /): """ - usage.xarray: 3 + usage.skimage: 1 """ ... @overload -def nanmean(a: numpy.ndarray, axis: None, dtype: Type[numpy.float16]): +def empty(_0: Tuple[int], _1: Type[numpy.uint32], /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def nanmean(a: numpy.ndarray, axis: None, dtype: Type[numpy.float32]): +def empty(*, dtype: Literal["U"], shape: Tuple[int]): """ usage.xarray: 1 """ @@ -26747,7 +26180,7 @@ def nanmean(a: numpy.ndarray, axis: None, dtype: Type[numpy.float32]): @overload -def nanmean(a: numpy.ndarray, axis: None, dtype: Type[numpy.float64]): +def empty(*, dtype: Type[int], shape: Tuple[int]): """ usage.xarray: 1 """ @@ -26755,16 +26188,15 @@ def nanmean(a: numpy.ndarray, axis: None, dtype: Type[numpy.float64]): @overload -def nanmean(a: numpy.ndarray, axis: int): +def empty(*, dtype: Type[bool], shape: Tuple[int]): """ - usage.sklearn: 4 usage.xarray: 1 """ ... @overload -def nanmean(a: numpy.ndarray, axis: Tuple[None, ...], dtype: None): +def empty(*, dtype: Literal["S"], shape: Tuple[int]): """ usage.xarray: 1 """ @@ -26772,7 +26204,7 @@ def nanmean(a: numpy.ndarray, axis: Tuple[None, ...], dtype: None): @overload -def nanmean(a: numpy.ndarray, axis: Tuple[int], dtype: None): +def empty(_0: Tuple[None, ...], /, *, dtype: Type[object]): """ usage.xarray: 1 """ @@ -26780,31 +26212,31 @@ def nanmean(a: numpy.ndarray, axis: Tuple[int], dtype: None): @overload -def nanmean(a: numpy.ndarray): +def empty(_0: Tuple[int], /, *, dtype: Literal["M8[ns]"]): """ - usage.xarray: 3 + usage.xarray: 1 """ ... @overload -def nanmean(a: numpy.ndarray, axis: None, dtype: Type[float]): +def empty(_0: Tuple[int, int], /, *, dtype: Literal["M8[ns]"]): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload -def nanmean(a: numpy.ndarray, axis: int, dtype: Type[float]): +def empty(_0: Tuple[None, ...], /, *, dtype: Literal["M8[ns]"]): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload -def nanmean(a: numpy.ndarray, axis: Tuple[int, int], dtype: None): +def empty(_0: int, /, *, dtype: Type[numpy.int64]): """ usage.xarray: 1 """ @@ -26812,548 +26244,559 @@ def nanmean(a: numpy.ndarray, axis: Tuple[int, int], dtype: None): @overload -def nanmean(a: sparse._coo.core.COO, axis: None, dtype: None): +def empty(_0: Tuple[int], /, *, dtype: Type[object]): """ - usage.xarray: 1 + usage.xarray: 2 """ ... @overload -def nanmean(a: sparse._coo.core.COO, axis: Tuple[int], dtype: None): +def empty( + _0: Union[Tuple[Union[int, None], ...], int, numpy.int64, List[int]] = ..., + /, + *, + order: Literal["F"] = ..., + dtype: Union[ + str, + numpy.dtype, + None, + type, + List[ + Tuple[ + Literal["err", "A", "B", "C"], + Union[Literal["datetime64[h]", "str", "int32"], Type[object]], + ] + ], + ] = ..., + shape: Tuple[int, ...] = ..., +): """ - usage.xarray: 1 + usage.pandas: 423 """ ... @overload -def nanmean(a: sparse._coo.core.COO, axis: int, dtype: None): +def empty( + _0: Union[ + Tuple[Union[None, int, numpy.int64], ...], + int, + numpy.int64, + numpy.int32, + List[Union[int, numpy.int64]], + ] = ..., + _1: Union[ + str, + numpy.dtype, + List[ + Tuple[Literal["mopt", "mrows", "ncols", "imagf", "namlen"], Literal["i4"]] + ], + type, + ] = ..., + /, + *, + dtype: Union[ + numpy.dtype, + type, + List[Tuple[Literal["ii", "dd"], Literal["i8", "f8"]]], + str, + None, + ] = ..., + order: Literal["F", "c", "C"] = ..., + shape: Tuple[int, ...] = ..., +): """ - usage.xarray: 1 + usage.scipy: 677 """ ... @overload -def nanmean(a: object, axis: None, dtype: None): +def empty(_0: Tuple[int, int], /, *, dtype: Type[object]): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def nanmean(a: object, axis: int, dtype: None): +def empty(_0: int, /, *, dtype: Type[numpy.uint8]): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload -def nanmean(a: object, axis: Tuple[int], dtype: None): +def empty(_0: Tuple[int, int, int], /, *, dtype: Type[numpy.uint8]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def nanmean(a: pandas.core.series.Series): +def empty(_0: Tuple[int], /): """ - usage.pandas: 2 + usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def nanmean(a: numpy.ndarray, axis: int, keepdims: bool): +def empty(_0: int, _1: Type[numpy.uint8], /): """ - usage.scipy: 1 + usage.matplotlib: 1 """ ... @overload -def nanmean( - a: numpy.ndarray, - axis: Union[Tuple[Union[None, int], ...], int] = ..., - keepdims: bool = ..., -): +def empty(_0: Tuple[int, int], _1: Type[float], /): """ - usage.dask: 11 + usage.matplotlib: 2 """ ... -def nanmean( - a: object, - axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., - keepdims: bool = ..., - dtype: Union[None, type] = ..., -): +@overload +def empty(_0: List[int], /): """ - usage.dask: 11 - usage.pandas: 2 - usage.scipy: 1 - usage.sklearn: 4 - usage.xarray: 27 + usage.matplotlib: 1 """ ... @overload -def nanmedian(a: numpy.ndarray, axis: int): +def empty( + _0: Tuple[int], + /, + *, + dtype: List[ + Tuple[Union[Literal["u1", "flags", ">u4", "points", "colors"], Tuple[int]], ...] + ], +): """ - usage.sklearn: 2 - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def nanmedian(a: sparse._coo.core.COO, axis: None): +def empty(_0: Tuple[int], /, *, dtype: Type[float]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def nanmedian(a: object, axis: None): +def empty(_0: Tuple[int, int], /, *, dtype: Type[float]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def nanmedian(a: Union[pandas.core.series.Series, numpy.ndarray], axis: int = ...): +def empty(*, dtype: numpy.dtype, shape: Tuple[int, int, int]): """ - usage.pandas: 3 + usage.matplotlib: 1 """ ... @overload -def nanmedian(a: numpy.ndarray, axis: Union[List[int], int], keepdims: bool): +def empty(_0: List[int], /, *, dtype: Type[numpy.float32]): """ - usage.dask: 8 + usage.matplotlib: 1 """ ... -def nanmedian(a: object, axis: Union[int, None, List[int]] = ..., keepdims: bool = ...): +@overload +def empty(_0: List[Union[numpy.int64, int]], /, *, dtype: numpy.dtype): """ - usage.dask: 8 - usage.pandas: 3 - usage.sklearn: 2 - usage.xarray: 3 + usage.matplotlib: 2 """ ... @overload -def nanmin(a: numpy.ndarray, axis: Tuple[None, ...]): +def empty(_0: List[int], /, *, dtype: numpy.dtype): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def nanmin(a: numpy.ndarray, axis: int): +def empty(_0: List[int], /, *, dtype: Type[numpy.float64]): """ - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload -def nanmin(a: numpy.ndarray): +def empty(_0: int, /, *, dtype: Type[numpy.float64]): """ usage.matplotlib: 2 - usage.xarray: 4 + usage.sklearn: 6 """ ... @overload -def nanmin(a: numpy.ndarray, axis: None): +def empty(_0: List[int], /, *, dtype: Type[numpy.int32]): """ - usage.xarray: 2 + usage.matplotlib: 2 """ ... @overload -def nanmin(a: sparse._coo.core.COO, axis: None): +def empty( + _0: Union[Tuple[Union[int, None], ...], List[int], numpy.ndarray, int] = ..., + _1: int = ..., + /, + *, + shape: Tuple[int, ...] = ..., + dtype: Union[ + Literal["f8", "i1", "O"], + type, + List[Tuple[str, Union[numpy.dtype, Type[numpy.int64]]]], + numpy.dtype, + ] = ..., + order: Literal["F", "C"] = ..., +): """ - usage.xarray: 1 + usage.dask: 149 """ ... @overload -def nanmin(a: object, axis: None): +def empty(_0: int, /, *, dtype: Type[numpy.float64], order: Literal["C"]): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def nanmin(a: object, axis: int): +def empty(_0: int, /, *, dtype: Type[numpy.int8], order: Literal["C"]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def nanmin(a: Union[numpy.ndarray, pandas.core.series.Series]): +def empty(_0: Tuple[int], /, *, dtype: Type[numpy.int64], order: Literal["C"]): """ - usage.pandas: 6 + usage.sklearn: 1 """ ... @overload -def nanmin( - _0: numpy.ndarray = ..., - /, - a: numpy.ndarray = ..., - axis: Union[Tuple[Union[None, int], ...], int, None] = ..., - keepdims: bool = ..., - *, - computing_meta: bool = ..., -): +def empty(_0: Tuple[int, int], /, *, dtype: Literal["float"]): """ - usage.dask: 62 + usage.sklearn: 1 """ ... @overload -def nanmin(a: numpy.ndarray, axis: Union[None, int] = ...): +def empty(_0: Tuple[int, int], /, *, dtype: Type[numpy.float32]): """ - usage.sklearn: 9 + usage.sklearn: 1 """ ... -def nanmin( - _0: numpy.ndarray = ..., - /, - a: object = ..., - axis: Union[int, None, Tuple[Union[int, None], ...]] = ..., - keepdims: bool = ..., - *, - computing_meta: bool = ..., -): +@overload +def empty(_0: Tuple[int], /, *, dtype: Type[int]): """ - usage.dask: 62 - usage.matplotlib: 2 - usage.pandas: 6 - usage.sklearn: 9 - usage.xarray: 13 + usage.sklearn: 1 """ ... @overload -def nanpercentile(a: numpy.ndarray, q: numpy.float64, axis: None): +def empty(_0: Tuple[int, int], /, *, dtype: Type[float], order: Literal["F"]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def nanpercentile(a: numpy.ndarray, q: numpy.ndarray, axis: None): +def empty(_0: int, /, *, dtype: Type[int]): """ - usage.xarray: 2 + usage.sklearn: 5 """ ... @overload -def nanpercentile(a: numpy.ndarray, q: numpy.float64, axis: int): +def empty(_0: int, /, *, dtype: Type[numpy.int32]): """ - usage.xarray: 3 + usage.sklearn: 3 """ ... @overload -def nanpercentile(a: numpy.ndarray, q: numpy.ndarray, axis: int): +def empty(_0: int, /, *, dtype: Type[numpy.int8]): """ - usage.xarray: 3 + usage.sklearn: 1 """ ... @overload -def nanpercentile(a: numpy.ndarray, q: numpy.float64, axis: List[int]): +def empty(*, dtype: numpy.dtype, order: Literal["F"], shape: Tuple[int, int]): """ - usage.xarray: 5 + usage.sklearn: 1 """ ... @overload -def nanpercentile(a: numpy.ndarray, q: numpy.ndarray, axis: List[int]): +def empty(_0: int, /, *, dtype: Literal["i"]): """ - usage.xarray: 5 + usage.sklearn: 1 """ ... @overload -def nanpercentile( - a: numpy.ndarray, - q: List[int], - axis: Union[int, None], - interpolation: Literal["linear"], - keepdims: bool, -): +def empty(*, dtype: Type[numpy.float32], shape: Tuple[int, int]): """ - usage.scipy: 2 + usage.sklearn: 4 """ ... @overload -def nanpercentile( - a: numpy.ndarray, - q: Union[Tuple[Union[float, int], Union[float, int]], numpy.ndarray], -): +def empty(_0: Tuple[int, int, int, int], /): """ - usage.sklearn: 6 + usage.sklearn: 1 """ ... -def nanpercentile( - a: numpy.ndarray, - q: Union[ - numpy.ndarray, - numpy.float64, - Tuple[Union[int, float], Union[int, float]], - List[int], - ], - interpolation: Literal["linear"] = ..., - keepdims: bool = ..., -): +@overload +def empty(_0: int, /, *, dtype: Type[bool]): """ - usage.scipy: 2 - usage.sklearn: 6 - usage.xarray: 20 + usage.sklearn: 1 """ ... @overload -def nanprod(a: numpy.ndarray, axis: None, dtype: None, out: None): +def empty(_0: int, /, *, dtype: Type[float]): """ - usage.xarray: 3 + usage.sklearn: 1 """ ... @overload -def nanprod(a: numpy.ndarray, axis: int, dtype: None, out: None): +def empty(_0: Tuple[int, int], /, *, dtype: numpy.dtype, order: Literal["C"]): """ - usage.xarray: 3 + usage.sklearn: 2 """ ... @overload -def nanprod(a: numpy.ndarray, axis: Tuple[int, int], dtype: None, out: None): +def empty(*, dtype: numpy.dtype, order: Literal["C"], shape: Tuple[int, int]): """ - usage.xarray: 3 + usage.sklearn: 2 """ ... @overload -def nanprod(a: sparse._coo.core.COO, axis: None, dtype: None, out: None): +def empty(*, dtype: numpy.dtype, order: Literal["C"], shape: int): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def nanprod(a: numpy.ndarray, axis: Union[None, int]): +def empty(_0: Tuple[numpy.int64, numpy.int64], /, *, dtype: numpy.dtype): """ - usage.pandas: 4 + usage.sklearn: 3 """ ... @overload -def nanprod( - a: numpy.ndarray, - axis: Union[Tuple[Union[None, int], ...], int] = ..., - keepdims: bool = ..., -): - """ - usage.dask: 29 - """ - ... - - -def nanprod( - a: Union[numpy.ndarray, sparse._coo.core.COO], - axis: Union[int, Tuple[Union[int, None], ...], None] = ..., - keepdims: bool = ..., - dtype: Union[Literal["i8", "f8"], None] = ..., - out: None = ..., -): +def empty(_0: List[int], _1: numpy.dtype, /): """ - usage.dask: 29 - usage.pandas: 4 - usage.xarray: 10 + usage.sklearn: 2 """ ... @overload -def nanquantile( - a: numpy.ndarray, - q: numpy.ndarray, - axis: numpy.ndarray, - interpolation: Literal["linear"], -): +def empty(_0: Tuple[int, int], /, *, dtype: numpy.dtype, order: Literal["F"]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def nanquantile( - a: sparse._coo.core.COO, - q: numpy.ndarray, - axis: numpy.ndarray, - interpolation: Literal["linear"], -): +def empty(_0: Tuple[int], _1: Type[int], /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def nanquantile( - a: object, q: numpy.ndarray, axis: numpy.ndarray, interpolation: Literal["linear"] -): +def empty(*, shape: int): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... -def nanquantile( - a: object, q: numpy.ndarray, axis: numpy.ndarray, interpolation: Literal["linear"] +def empty( + _0: object = ..., + _1: Union[ + numpy.dtype, + int, + type, + str, + List[ + Tuple[Literal["mopt", "mrows", "ncols", "imagf", "namlen"], Literal["i4"]] + ], + ] = ..., + /, + *, + dtype: Union[ + str, + type, + numpy.dtype, + None, + List[Tuple[Union[Tuple[int], str, numpy.dtype, type], ...]], + ] = ..., + order: Literal["C", "F", "c"] = ..., + shape: Union[Tuple[int, ...], int] = ..., ): """ - usage.xarray: 4 + usage.dask: 149 + usage.matplotlib: 30 + usage.pandas: 423 + usage.scipy: 677 + usage.skimage: 117 + usage.sklearn: 189 + usage.xarray: 23 """ ... @overload -def nanstd(a: numpy.ndarray, axis: int): +def empty_like(_0: numpy.ndarray, /): """ - usage.sklearn: 1 - usage.xarray: 1 + usage.matplotlib: 10 + usage.skimage: 33 + usage.sklearn: 19 """ ... @overload -def nanstd(a: sparse._coo.core.COO, axis: None, dtype: None, ddof: int): +def empty_like(_0: numpy.ndarray, /, *, dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.skimage: 3 """ ... @overload -def nanstd(a: object, axis: None, dtype: None, ddof: int): +def empty_like(_0: numpy.ndarray, /, *, dtype: Type[numpy.uint16]): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def nanstd(a: object, axis: int, dtype: None, ddof: int): +def empty_like(_0: numpy.ndarray, /, *, dtype: Type[float]): """ - usage.xarray: 1 + usage.skimage: 2 """ ... @overload -def nanstd(a: numpy.ndarray, axis: int, ddof: int, keepdims: bool): +def empty_like(_0: numpy.ndarray, /, *, dtype: Type[numpy.uint8]): """ - usage.scipy: 1 + usage.skimage: 4 """ ... @overload -def nanstd( - a: numpy.ndarray, - axis: Union[Tuple[Union[None, int], ...], int] = ..., - keepdims: bool = ..., +def empty_like( + _0: numpy.ndarray, + /, + *, + dtype: Type[numpy.float64], + order: Literal["C"], + subok: bool, ): """ - usage.dask: 11 + usage.skimage: 2 """ ... -def nanstd( - a: object, - axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., - dtype: None = ..., - ddof: int = ..., - keepdims: bool = ..., +@overload +def empty_like( + _0: numpy.ma.core.MaskedArray, + /, + *, + dtype: Type[numpy.float64], + order: Literal["C"], + subok: bool, ): """ - usage.dask: 11 - usage.scipy: 1 - usage.sklearn: 1 - usage.xarray: 4 + usage.skimage: 1 """ ... @overload -def nansum(a: numpy.ndarray, axis: int): +def empty_like(_0: numpy.ndarray, _1: Type[numpy.float64], /): """ - usage.xarray: 3 + usage.skimage: 2 """ ... @overload -def nansum(a: numpy.ndarray): +def empty_like(_0: xarray.core.variable.Variable, /): """ - usage.scipy: 1 - usage.xarray: 4 + usage.xarray: 1 """ ... @overload -def nansum(a: numpy.ndarray, axis: None): +def empty_like(_0: xarray.core.variable.IndexVariable, /): """ usage.xarray: 1 """ @@ -27361,327 +26804,367 @@ def nansum(a: numpy.ndarray, axis: None): @overload -def nansum( - a: Union[pandas.core.series.Series, numpy.ndarray], axis: Union[None, int] = ... +def empty_like( + _0: Union[numpy.ndarray, pandas.core.arrays.string_.StringArray, List[None]], + /, + *, + dtype: Union[type, Literal["float", "f8", "i8", "object"], numpy.dtype] = ..., ): """ - usage.pandas: 6 + usage.pandas: 18 """ ... @overload -def nansum( - a: numpy.ndarray, - axis: Union[Tuple[Union[None, int], ...], int] = ..., - dtype: Union[numpy.dtype, Literal["i8", "f8"]] = ..., - keepdims: bool = ..., -): +def empty_like(_0: numpy.ndarray, /, *, dtype: type = ...): """ - usage.dask: 82 + usage.scipy: 103 """ ... @overload -def nansum( - a: Union[numpy.ndarray, numpy.ma.core.MaskedArray], - axis: int = ..., - dtype: Type[numpy.float64] = ..., -): - """ - usage.sklearn: 9 - """ - ... - - -def nansum( - a: Union[numpy.ma.core.MaskedArray, numpy.ndarray, pandas.core.series.Series], - axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., - dtype: Union[Type[numpy.float64], Literal["i8", "f8"], numpy.dtype] = ..., - keepdims: bool = ..., +def empty_like( + _0: numpy.ndarray, + /, + *, + dtype: numpy.dtype = ..., + shape: Union[Tuple[int, ...], None, int] = ..., + order: Literal["F", "C"] = ..., ): """ - usage.dask: 82 - usage.pandas: 6 - usage.scipy: 1 - usage.sklearn: 9 - usage.xarray: 8 + usage.dask: 12 """ ... @overload -def nanvar(a: numpy.ndarray, axis: int): +def empty_like(_0: numpy.ndarray, /, *, dtype: Type[numpy.float32]): """ - usage.xarray: 2 + usage.sklearn: 4 """ ... @overload -def nanvar(a: numpy.ndarray): +def empty_like(_0: numpy.ndarray, /, *, dtype: numpy.dtype): """ - usage.xarray: 4 + usage.sklearn: 3 """ ... -@overload -def nanvar(a: numpy.ndarray, axis: None, dtype: Type[float], ddof: int): +def empty_like( + _0: object, + _1: Type[numpy.float64] = ..., + /, + *, + dtype: Union[type, numpy.dtype, Literal["float", "f8", "i8", "object"]] = ..., + order: Literal["F", "C"] = ..., + subok: bool = ..., + shape: Union[Tuple[int, ...], None, int] = ..., +): """ - usage.xarray: 1 + usage.dask: 12 + usage.matplotlib: 10 + usage.pandas: 18 + usage.scipy: 103 + usage.skimage: 48 + usage.sklearn: 26 + usage.xarray: 2 """ ... @overload -def nanvar(a: numpy.ndarray, axis: None): +def expand_dims(a: numpy.ndarray, axis: int): """ - usage.xarray: 1 + usage.dask: 2 + usage.matplotlib: 12 + usage.sklearn: 5 + usage.xarray: 5 """ ... @overload -def nanvar(a: numpy.ndarray, axis: None, dtype: None, ddof: int): +def expand_dims(a: Union[numpy.ndarray, numpy.float64], axis: int): """ - usage.xarray: 1 + usage.pandas: 19 """ ... @overload -def nanvar(a: numpy.ndarray, axis: int, dtype: Type[float], ddof: int): +def expand_dims( + a: Union[ + numpy.int64, + numpy.ma.core.MaskedArray, + numpy.ndarray, + numpy.float64, + numpy.ma.core.MaskedConstant, + ], + axis: int, +): """ - usage.xarray: 1 + usage.scipy: 21 """ ... @overload -def nanvar(a: sparse._coo.core.COO, axis: None, dtype: None, ddof: int): +def expand_dims(a: numpy.float64, axis: int): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... -@overload -def nanvar(a: object, axis: None, dtype: None, ddof: int): +def expand_dims( + a: Union[ + numpy.ndarray, + numpy.float64, + numpy.int64, + numpy.ma.core.MaskedArray, + numpy.ma.core.MaskedConstant, + ], + axis: int, +): """ - usage.xarray: 1 + usage.dask: 2 + usage.matplotlib: 12 + usage.pandas: 19 + usage.scipy: 21 + usage.sklearn: 6 + usage.xarray: 5 """ ... @overload -def nanvar(a: object, axis: int, dtype: None, ddof: int): +def extract( + condition: object, + arr: Union[ + numpy.ma.core.MaskedArray, numpy.int64, float, numpy.float64, numpy.ndarray + ], +): """ - usage.xarray: 1 + usage.scipy: 77 """ ... @overload -def nanvar(a: numpy.ndarray, axis: int, ddof: int): +def extract(condition: numpy.ndarray, arr: numpy.ndarray): """ - usage.scipy: 1 + usage.dask: 1 """ ... -@overload -def nanvar( - a: numpy.ndarray, - axis: Union[Tuple[Union[None, int], ...], int] = ..., - keepdims: bool = ..., +def extract( + condition: object, + arr: Union[ + numpy.ndarray, numpy.float64, float, numpy.int64, numpy.ma.core.MaskedArray + ], ): """ - usage.dask: 11 + usage.dask: 1 + usage.scipy: 77 """ ... @overload -def nanvar(a: numpy.ndarray, axis: int = ..., dtype: Type[numpy.float64] = ...): - """ - usage.sklearn: 10 - """ - ... - - -def nanvar( - a: object, - axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., - keepdims: bool = ..., - dtype: Union[type, None] = ..., - ddof: int = ..., -): +def eye(N: int): """ - usage.dask: 11 - usage.scipy: 1 - usage.sklearn: 10 - usage.xarray: 13 + usage.sample-usage: 1 + usage.skimage: 30 + usage.sklearn: 51 """ ... @overload -def ndim(a: numpy.ndarray): +def eye(N: int, dtype: Type[int]): """ - usage.matplotlib: 7 - usage.skimage: 4 + usage.skimage: 5 """ ... @overload -def ndim(a: object): +def eye(N: int, M: int, dtype: Type[bool]): """ - usage.matplotlib: 1 - usage.pandas: 703 - usage.scipy: 92 + usage.skimage: 1 """ ... @overload -def ndim(a: numpy.int64): +def eye(N: int, dtype: Type[numpy.float64]): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def ndim(a: numpy.float64): +def eye(N: int, M: int): """ usage.matplotlib: 2 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def ndim(a: float): +def eye(N: int, dtype: Union[numpy.dtype, Literal["int64", "float64"]] = ...): """ - usage.matplotlib: 3 + usage.pandas: 9 """ ... @overload -def ndim(a: numpy.ma.core.MaskedArray): +def eye( + N: Union[numpy.int64, int], + M: int = ..., + dtype: Union[numpy.dtype, type] = ..., + k: int = ..., +): """ - usage.matplotlib: 1 + usage.scipy: 463 """ ... @overload -def ndim(a: Union[List[Union[int, List[int]]], None, int, numpy.ndarray]): +def eye(N: int, M: int = ..., k: int = ...): """ - usage.dask: 10 + usage.dask: 33 """ ... -@overload -def ndim(a: Union[numpy.ndarray, numpy.float64]): +def eye( + N: Union[int, numpy.int64], + M: int = ..., + k: int = ..., + dtype: Union[type, Literal["int64", "float64"], numpy.dtype] = ..., +): """ - usage.sklearn: 4 + usage.dask: 33 + usage.matplotlib: 2 + usage.pandas: 9 + usage.sample-usage: 1 + usage.scipy: 463 + usage.skimage: 38 + usage.sklearn: 52 """ ... -def ndim(a: object): +@overload +def fill_diagonal(a: numpy.ndarray, val: float): """ - usage.dask: 10 - usage.matplotlib: 16 - usage.pandas: 703 - usage.scipy: 92 - usage.skimage: 4 - usage.sklearn: 4 + usage.skimage: 1 + usage.sklearn: 7 """ ... @overload -def nonzero(a: numpy.ndarray): +def fill_diagonal(a: numpy.ndarray, val: Union[numpy.float64, numpy.ndarray, int]): """ - usage.dask: 2 - usage.matplotlib: 5 - usage.pandas: 4 - usage.skimage: 17 - usage.sklearn: 15 - usage.xarray: 4 + usage.scipy: 7 """ ... @overload -def nonzero(a: object): +def fill_diagonal(a: numpy.ndarray, val: int): """ - usage.scipy: 80 + usage.sklearn: 8 """ ... -def nonzero(a: object): +@overload +def fill_diagonal(a: numpy.ndarray, val: numpy.ndarray): """ - usage.dask: 2 - usage.matplotlib: 5 - usage.pandas: 4 - usage.scipy: 80 - usage.skimage: 17 - usage.sklearn: 15 - usage.xarray: 4 + usage.sklearn: 1 """ ... -@overload -def obj2sctype(rep: Type[numpy.floating]): +def fill_diagonal( + a: numpy.ndarray, val: Union[float, int, numpy.ndarray, numpy.float64] +): """ + usage.scipy: 7 usage.skimage: 1 + usage.sklearn: 16 """ ... @overload -def obj2sctype(rep: numpy.dtype): +def find_common_type( + array_types: Union[ + List[numpy.dtype], + Tuple[Union[numpy.dtype, type], Union[numpy.dtype, type]], + collections.defaultdict, + ], + scalar_types: list, +): """ - usage.skimage: 1 + usage.pandas: 58 """ ... @overload -def obj2sctype(rep: Type[numpy.uint16]): +def find_common_type( + array_types: Union[ + List[Union[numpy.dtype, type]], Tuple[Union[type, str, numpy.dtype], ...] + ], + scalar_types: Union[List[numpy.dtype], Tuple[None, ...]], +): """ - usage.skimage: 1 + usage.scipy: 309 """ ... @overload -def obj2sctype(rep: Type[numpy.uint8]): +def find_common_type(array_types: List[numpy.dtype], scalar_types: list): """ - usage.skimage: 1 + usage.sklearn: 8 """ ... -@overload -def obj2sctype(rep: Type[numpy.bool_]): +def find_common_type( + array_types: Union[ + List[Union[type, numpy.dtype]], + collections.defaultdict, + Tuple[Union[str, numpy.dtype, type], ...], + ], + scalar_types: Union[List[numpy.dtype], Tuple[None, ...]], +): """ - usage.skimage: 1 + usage.pandas: 58 + usage.scipy: 309 + usage.sklearn: 8 """ ... @overload -def obj2sctype(rep: Type[numpy.float64]): +def fix(x: float): """ usage.skimage: 1 """ @@ -27689,7 +27172,7 @@ def obj2sctype(rep: Type[numpy.float64]): @overload -def obj2sctype(rep: Literal["float32"]): +def fix(x: numpy.float64): """ usage.skimage: 1 """ @@ -27697,1013 +27180,971 @@ def obj2sctype(rep: Literal["float32"]): @overload -def obj2sctype(rep: Literal["float64"]): +def fix(x: xarray.core.dataarray.DataArray): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def obj2sctype(rep: Literal["uint8"]): +def fix(x: Union[pandas.core.series.Series, numpy.ndarray]): """ - usage.skimage: 1 + usage.pandas: 2 """ ... @overload -def obj2sctype(rep: Literal["uint16"]): +def fix(x: numpy.ndarray): """ - usage.skimage: 1 + usage.dask: 3 """ ... -@overload -def obj2sctype(rep: Literal["int64"]): +def fix( + x: Union[ + numpy.ndarray, + xarray.core.dataarray.DataArray, + float, + numpy.float64, + pandas.core.series.Series, + ] +): """ - usage.skimage: 1 + usage.dask: 3 + usage.pandas: 2 + usage.skimage: 2 + usage.xarray: 1 """ ... -@overload -def obj2sctype(rep: Type[numpy.int16]): +def flatnonzero(a: numpy.ndarray): """ - usage.skimage: 1 + usage.dask: 2 + usage.pandas: 2 + usage.scipy: 5 + usage.skimage: 6 + usage.sklearn: 35 + usage.xarray: 2 """ ... @overload -def obj2sctype(rep: Type[numpy.float32]): +def flip(m: numpy.ndarray, axis: int): """ - usage.skimage: 1 + usage.scipy: 1 + usage.skimage: 3 + usage.xarray: 4 """ ... @overload -def obj2sctype(rep: Type[numpy.uint32]): +def flip(m: sparse._coo.core.COO, axis: int): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def obj2sctype(rep: Type[numpy.int32]): +def flip(m: object, axis: int): """ - usage.skimage: 1 + usage.dask: 5 + usage.xarray: 1 """ ... -@overload -def obj2sctype(rep: Type[numpy.int8]): +def flip(m: object, axis: int): """ - usage.skimage: 1 + usage.dask: 5 + usage.scipy: 1 + usage.skimage: 3 + usage.xarray: 6 """ ... -@overload -def obj2sctype(rep: Type[float]): +def fliplr(m: numpy.ndarray): """ - usage.skimage: 1 + usage.dask: 1 + usage.matplotlib: 1 + usage.skimage: 5 """ ... -def obj2sctype( - rep: Union[ - type, Literal["int64", "uint16", "uint8", "float64", "float32"], numpy.dtype - ] -): +def flipud(m: numpy.ndarray): """ - usage.skimage: 17 + usage.dask: 1 + usage.matplotlib: 1 + usage.scipy: 4 + usage.skimage: 2 + usage.xarray: 2 """ ... @overload -def ones(shape: Tuple[int, int], dtype: Type[numpy.bool_]): +def frombuffer(_0: bytes, /, *, dtype: Literal["int8"]): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload -def ones(shape: Tuple[int, int]): +def frombuffer( + _0: Union[bytes, pyarrow.lib.Buffer], + /, + *, + dtype: Union[numpy.dtype, Literal["q", ">i", ">b"] = ..., + /, + *, + count: int = ..., + dtype: Union[ + str, numpy.dtype, Dict[Literal["formats", "names"], List[str]], type + ] = ..., + offset: int = ..., +): """ - usage.matplotlib: 1 - usage.skimage: 1 - usage.xarray: 3 + usage.scipy: 31 """ ... @overload -def ones(shape: List[int]): +def frombuffer(_0: numpy.ndarray, _1: Type[numpy.uint8], /): """ - usage.skimage: 3 - usage.xarray: 8 + usage.matplotlib: 1 """ ... @overload -def ones(shape: Tuple[int, int, int]): +def frombuffer(_0: numpy.ndarray, /, *, dtype: Type[numpy.uint8]): """ - usage.skimage: 17 - usage.xarray: 6 + usage.matplotlib: 1 """ ... @overload -def ones(shape: Tuple[int, int, int, int]): +def frombuffer(_0: bytes, /, *, dtype: Type[numpy.uint8]): """ - usage.skimage: 8 - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def ones(shape: Tuple[int, int], dtype: Type[numpy.uint8]): +def frombuffer(_0: bytes, /, *, dtype: Literal["q", ">i", ">b"]] = ..., + /, + *, + count: int = ..., + dtype: Union[ + type, str, numpy.dtype, Dict[Literal["formats", "names"], List[str]] + ] = ..., + offset: int = ..., +): """ + usage.dask: 1 + usage.matplotlib: 4 + usage.pandas: 22 + usage.scipy: 31 usage.skimage: 1 + usage.sklearn: 8 """ ... @overload -def ones(shape: Tuple[int]): +def fromfile(_0: _io.TextIOWrapper, /, *, sep: Literal[" "]): """ - usage.matplotlib: 1 - usage.skimage: 5 - usage.xarray: 4 + usage.skimage: 1 """ ... @overload -def ones(shape: int, dtype: numpy.dtype): +def fromfile(_0: _io.BufferedReader, /, *, count: int, dtype: numpy.dtype): """ - usage.matplotlib: 6 - usage.skimage: 2 + usage.scipy: 10 """ ... -@overload -def ones(shape: Tuple[int], dtype: Type[float]): +def fromfile( + _0: Union[_io.BufferedReader, _io.TextIOWrapper], + /, + *, + sep: Literal[" "] = ..., + count: int = ..., + dtype: numpy.dtype = ..., +): """ + usage.scipy: 10 usage.skimage: 1 - usage.xarray: 1 """ ... -@overload -def ones(shape: int, dtype: Type[numpy.float64]): +def fromfunction( + function: Callable, + shape: Tuple[int, int], + *, + dtype: Union[Literal["i8", "f8"], Type[float], None], +): """ - usage.matplotlib: 4 - usage.skimage: 1 + usage.dask: 6 """ ... @overload -def ones(shape: Tuple[int, int, int], dtype: Type[numpy.uint8]): +def fromiter(_0: generator, /, *, dtype: Union[type, Literal["i8"]]): """ - usage.skimage: 3 + usage.pandas: 3 """ ... @overload -def ones(shape: Tuple[int, int], dtype: Type[bool], order: Literal["F"]): +def fromiter( + _0: Union[List[numpy.float64], dict_values, generator], + /, + *, + dtype: Union[type, numpy.dtype], + count: int = ..., +): """ - usage.skimage: 1 + usage.scipy: 20 """ ... @overload -def ones(shape: Tuple[int, int, int], dtype: Type[bool], order: Literal["F"]): +def fromiter(_0: itertools.chain, /, *, count: int, dtype: Literal["float64"]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def ones(shape: Tuple[int, int, int], dtype: Type[bool]): +def fromiter(_0: generator, /, *, dtype: Type[numpy.float64]): """ - usage.skimage: 5 + usage.sklearn: 2 """ ... -@overload -def ones(shape: Tuple[int, int, int, int, int]): +def fromiter( + _0: Union[itertools.chain, generator, dict_values, List[numpy.float64]], + /, + *, + dtype: Union[Literal["float64", "i8"], type, numpy.dtype], + count: int = ..., +): """ - usage.skimage: 1 + usage.pandas: 3 + usage.scipy: 20 + usage.sklearn: 3 """ ... -@overload -def ones(shape: List[int], dtype: Type[bool]): +def frompyfunc(_0: Callable, _1: int, _2: int, /): """ - usage.skimage: 2 + usage.dask: 4 """ ... @overload -def ones(shape: Tuple[int, int], dtype: Literal["uint8"]): +def fromstring(_0: str, /, *, dtype: Literal["float"], sep: Literal[","]): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload -def ones(shape: Tuple[int, int, int], dtype: Literal["uint8"]): +def fromstring(_0: str, /, *, dtype: type, sep: Literal[" "]): """ - usage.skimage: 1 + usage.scipy: 5 """ ... -@overload -def ones(shape: Tuple[int], dtype: Type[bool]): +def fromstring( + _0: str, /, *, dtype: Union[type, Literal["float"]], sep: Literal[" ", ","] +): """ - usage.skimage: 3 + usage.scipy: 5 usage.xarray: 1 """ ... @overload -def ones(shape: Tuple[int, int, int, int], dtype: Type[bool]): +def full(shape: Tuple[int, int], fill_value: int, dtype: Literal["float64"]): """ - usage.skimage: 3 + usage.skimage: 1 """ ... @overload -def ones(shape: Tuple[int, int], dtype: Type[int]): +def full(shape: Tuple[int, int, int], fill_value: int, dtype: Type[numpy.uint8]): """ usage.skimage: 1 - usage.xarray: 4 """ ... @overload -def ones(shape: Tuple[int], dtype: Type[numpy.float64]): +def full(shape: int, fill_value: int, dtype: Type[float]): """ - usage.skimage: 1 + usage.skimage: 4 """ ... @overload -def ones(shape: numpy.ndarray): +def full(shape: int, fill_value: float, dtype: Type[float]): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload -def ones(shape: int, dtype: Type[bool]): +def full(shape: int, fill_value: float, dtype: numpy.dtype): """ - usage.skimage: 1 + usage.skimage: 2 + usage.sklearn: 6 """ ... @overload -def ones(shape: numpy.int64): +def full(shape: int, fill_value: int, dtype: numpy.dtype): """ usage.skimage: 2 + usage.sklearn: 3 """ ... @overload -def ones(shape: Tuple[int, int, int, int, int, int]): +def full(shape: Tuple[int, int], fill_value: int, dtype: Type[numpy.uint8]): """ - usage.xarray: 1 + usage.skimage: 3 """ ... @overload -def ones(shape: Tuple[int], dtype: Type[int]): +def full(shape: Tuple[int, int], fill_value: float): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.skimage: 6 + usage.sklearn: 8 + usage.xarray: 3 """ ... @overload -def ones(shape: int, dtype: Literal[">f4"]): +def full(shape: Tuple[int, int], fill_value: int, dtype: Type[numpy.uint16]): """ - usage.xarray: 1 + usage.skimage: 2 """ ... @overload -def ones( - shape: Union[int, Tuple[int, ...]], - dtype: Union[numpy.dtype, Literal["int64", "float64", "bool"], type] = ..., -): +def full(shape: int, fill_value: float, dtype: Literal["float64"]): """ - usage.pandas: 116 + usage.skimage: 1 """ ... @overload -def ones( - shape: object, dtype: Union[type, numpy.dtype, str] = ..., order: Literal["c"] = ... -): +def full(shape: Tuple[int], fill_value: int, dtype: numpy.dtype): """ - usage.scipy: 934 + usage.skimage: 1 """ ... @overload -def ones(shape: Tuple[int, int], dtype: Type[numpy.uint16]): +def full(shape: numpy.ndarray, fill_value: numpy.float64, dtype: Literal["float64"]): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def ones(shape: List[int], dtype: Type[numpy.int32]): +def full(shape: int, fill_value: int, dtype: Type[numpy.int32]): """ - usage.matplotlib: 1 + usage.matplotlib: 3 + usage.skimage: 2 + usage.sklearn: 6 """ ... @overload -def ones(shape: int, dtype: Type[numpy.int32]): +def full(shape: numpy.ndarray, fill_value: numpy.int64, dtype: Literal["float64"]): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def ones( - shape: Union[Tuple[Union[None, int], ...], int, numpy.ndarray, List[int]], - dtype: Union[ - numpy.dtype, - List[ - Tuple[ - Literal["a", "b", "c", "col1", "col2"], - Union[Literal["f8"], Tuple[Literal["f4"], Union[int, Tuple[int, int]]]], - ] - ], - type, - Literal["i4", "float32", "f8"], - ] = ..., - order: Literal["F", "C"] = ..., -): +def full(shape: Tuple[int, int], fill_value: bool): """ - usage.dask: 186 + usage.skimage: 2 """ ... @overload -def ones( - shape: Union[Tuple[int, ...], int, numpy.int64, List[int]], - dtype: Union[type, numpy.dtype, Literal["int"], None] = ..., - order: Literal["C"] = ..., -): +def full(shape: Tuple[int, int], fill_value: int): """ - usage.sklearn: 435 + usage.skimage: 1 + usage.sklearn: 1 + usage.xarray: 2 """ ... -def ones( - shape: object, - dtype: Union[ - None, - str, - numpy.dtype, - type, - List[ - Tuple[ - Literal["a", "b", "c", "col1", "col2"], - Union[Literal["f8"], Tuple[Literal["f4"], Union[int, Tuple[int, int]]]], - ] - ], - ] = ..., - order: Literal["C", "F", "c"] = ..., -): +@overload +def full(shape: Tuple[None, ...], fill_value: float): """ - usage.dask: 186 - usage.matplotlib: 62 - usage.pandas: 116 - usage.sample-usage: 1 - usage.scipy: 934 - usage.skimage: 248 - usage.sklearn: 435 - usage.xarray: 73 + usage.xarray: 1 """ ... @overload -def ones_like(a: numpy.ndarray): +def full(shape: Tuple[None, ...], fill_value: int): """ - usage.matplotlib: 8 - usage.skimage: 17 + usage.xarray: 1 """ ... @overload -def ones_like(a: numpy.ndarray, dtype: Type[bool]): +def full(shape: Tuple[int, int, int], fill_value: int): """ - usage.skimage: 3 + usage.xarray: 2 """ ... @overload -def ones_like(a: numpy.ndarray, dtype: Type[numpy.uint8]): +def full(shape: Tuple[int], fill_value: int): """ - usage.skimage: 6 + usage.xarray: 1 """ ... @overload -def ones_like(a: object): +def full(shape: int, fill_value: float, dtype: Type[numpy.float64]): """ - usage.xarray: 11 + usage.sklearn: 2 + usage.xarray: 2 """ ... @overload -def ones_like(a: xarray.core.dataarray.DataArray): +def full(shape: Tuple[int], fill_value: int, dtype: Type[float]): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload -def ones_like(a: xarray.core.variable.Variable): +def full(shape: Tuple[int], fill_value: numpy.ndarray, dtype: Type[float]): """ - usage.xarray: 4 + usage.xarray: 2 """ ... @overload -def ones_like(a: Union[pandas.core.series.Series, numpy.ndarray]): +def full(shape: Tuple[int], fill_value: int, dtype: Type[int]): """ - usage.pandas: 6 + usage.xarray: 1 """ ... @overload -def ones_like( - a: Union[List[Union[float, int]], Tuple[int, ...], float, numpy.ndarray], - dtype: type = ..., -): +def full(shape: Tuple[int], fill_value: numpy.ndarray, dtype: Type[int]): """ - usage.scipy: 62 + usage.xarray: 2 """ ... @overload -def ones_like(a: numpy.ma.core.MaskedArray, dtype: Type[numpy.float32]): +def full(shape: Tuple[None, ...], fill_value: numpy.int64): """ - usage.matplotlib: 6 + usage.xarray: 1 """ ... @overload -def ones_like(a: numpy.ma.core.MaskedArray): +def full(shape: Tuple[None, ...], fill_value: numpy.float64): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def ones_like( - a: Union[numpy.ndarray, numpy.ma.core.MaskedArray], - order: Literal["F", "C"] = ..., - shape: Union[Tuple[int, ...], int, None] = ..., +def full( + shape: Union[Tuple[int, ...], int], + fill_value: object, + dtype: Union[Type[object], numpy.dtype] = ..., ): """ - usage.dask: 11 + usage.pandas: 30 """ ... @overload -def ones_like(a: Union[List[int], numpy.ndarray, numpy.float64], dtype: type = ...): +def full( + shape: Union[Tuple[Union[int, None], ...], List[int], numpy.int64, int], + fill_value: Union[float, int, complex, bool], + dtype: Union[type, numpy.dtype, Literal["float64", "f", "D", "F", "d"]] = ..., +): """ - usage.sklearn: 37 + usage.scipy: 179 """ ... -def ones_like( - a: object, - dtype: type = ..., - order: Literal["F", "C"] = ..., - shape: Union[Tuple[int, ...], int, None] = ..., -): +@overload +def full(shape: int, fill_value: numpy.uint8, dtype: Type[numpy.uint8]): """ - usage.dask: 11 - usage.matplotlib: 15 - usage.pandas: 6 - usage.scipy: 62 - usage.skimage: 26 - usage.sklearn: 37 - usage.xarray: 17 + usage.matplotlib: 3 """ ... @overload -def outer(a: numpy.ndarray, b: Union[numpy.ndarray, List[int]]): +def full(shape: int, fill_value: numpy.int64, dtype: Type[numpy.float64]): """ - usage.scipy: 67 + usage.matplotlib: 1 """ ... @overload -def outer(a: numpy.ndarray, b: numpy.ndarray): +def full(shape: int, fill_value: float): """ usage.matplotlib: 1 - usage.sklearn: 14 + usage.sklearn: 18 """ ... @overload -def outer( - a: Union[numpy.float64, numpy.ndarray], b: Union[numpy.float64, numpy.ndarray] +def full( + _0: Tuple[int, ...] = ..., + /, + shape: Union[int, numpy.ndarray, List[int], Tuple[int, ...]] = ..., + fill_value: Union[int, float] = ..., + dtype: Union[Literal["i8"], numpy.dtype] = ..., + order: Literal["F", "C"] = ..., ): """ - usage.dask: 4 + usage.dask: 14 """ ... -def outer( - a: Union[numpy.ndarray, numpy.float64], - b: Union[numpy.ndarray, numpy.float64, List[int]], -): +@overload +def full(shape: int, fill_value: int, dtype: Type[numpy.int64]): """ - usage.dask: 4 - usage.matplotlib: 1 - usage.scipy: 67 - usage.sklearn: 14 + usage.sklearn: 2 """ ... @overload -def pad(array: numpy.ndarray, pad_width: List[List[int]], mode: Literal["reflect"]): +def full(shape: int, fill_value: int, dtype: Type[int]): """ - usage.skimage: 2 + usage.sklearn: 7 """ ... @overload -def pad(array: numpy.ndarray, pad_width: List[List[int]], mode: Literal["edge"]): +def full(shape: Tuple[int, int], fill_value: numpy.ndarray, dtype: numpy.dtype): """ - usage.skimage: 2 + usage.sklearn: 3 """ ... @overload -def pad(array: numpy.ndarray, pad_width: int, mode: Literal["constant"]): +def full(shape: Tuple[int, int], fill_value: numpy.float64, dtype: numpy.dtype): """ - usage.skimage: 13 + usage.sklearn: 4 """ ... @overload -def pad( - array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["constant"] -): +def full(shape: Tuple[int, int], fill_value: int, dtype: Type[int]): """ - usage.matplotlib: 1 - usage.skimage: 7 - usage.xarray: 16 + usage.sklearn: 1 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: Tuple[Tuple[int, int], Tuple[int, int]], - mode: Literal["constant"], +def full( + shape: int, + fill_value: importlib._bootstrap.MonotonicConstraint, + dtype: Type[numpy.int8], ): """ - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], - mode: Literal["constant"], -): +def full(shape: int, fill_value: numpy.int64, dtype: Type[int]): """ - usage.skimage: 1 - usage.xarray: 5 + usage.sklearn: 2 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: Tuple[Tuple[int, int], Tuple[int, int]], - mode: Literal["reflect"], -): +def full(shape: Tuple[int, int, int], fill_value: numpy.float64, dtype: numpy.dtype): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], - mode: Literal["reflect"], -): +def full(shape: int, fill_value: numpy.float64, dtype: numpy.dtype): """ - usage.skimage: 1 - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def pad(array: numpy.ndarray, pad_width: int, mode: Literal["edge"]): +def full(shape: Tuple[int, int], fill_value: float, dtype: numpy.dtype): """ - usage.skimage: 4 + usage.sklearn: 2 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: List[Tuple[numpy.int64, numpy.int64]], - mode: Literal["constant"], -): +def full(shape: Tuple[int, int, int], fill_value: float, dtype: numpy.dtype): """ - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def pad(array: numpy.ndarray, pad_width: Tuple[int, int], mode: Literal["constant"]): +def full(shape: Tuple[int, int], fill_value: int, dtype: numpy.dtype): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def pad( - array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["minimum"] -): +def full(shape: Tuple[int, int], fill_value: int, dtype: Type[float]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], - mode: Literal["minimum"], -): +def full(shape: Tuple[int, int], fill_value: float, dtype: Type[float]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def pad( - array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["maximum"] -): +def full(shape: int, fill_value: Literal["missing"], dtype: numpy.dtype): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], - mode: Literal["maximum"], -): +def full(shape: int, fill_value: Literal["missing_value"], dtype: numpy.dtype): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def pad(array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["mean"]): +def full(shape: int, fill_value: int): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], - mode: Literal["mean"], -): +def full(shape: int, fill_value: int, dtype: None): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def pad( - array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["median"] -): +def full(shape: numpy.int64, fill_value: numpy.float64): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], - mode: Literal["median"], -): +def full(shape: int, fill_value: int, dtype: Type[numpy.uint32]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def pad( - array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["linear_ramp"] -): +def full(shape: int, fill_value: int, dtype: Literal["int"]): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], - mode: Literal["linear_ramp"], -): +def full(shape: numpy.int64, fill_value: int, dtype: Type[int]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def pad( - array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["reflect"] -): +def full(shape: Tuple[int, int], fill_value: numpy.float64, dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def pad( - array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["symmetric"] -): +def full(shape: Tuple[int, int], fill_value: int, dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], - mode: Literal["symmetric"], -): +def full(shape: Tuple[int], fill_value: numpy.float64, dtype: Type[numpy.float64]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def pad( - array: sparse._coo.core.COO, - pad_width: List[Tuple[int, int]], - mode: Literal["constant"], -): +def full(shape: Tuple[int, int], fill_value: numpy.float64): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["constant"]): +def full(shape: int, fill_value: numpy.float64, dtype: Type[numpy.float64]): """ - usage.xarray: 4 + usage.sklearn: 2 """ ... @overload -def pad( - array: object, - pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], - mode: Literal["constant"], -): +def full(shape: int, fill_value: numpy.float64): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["mean"]): +def full(shape: Tuple[int], fill_value: float): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["median"]): +def full(shape: Tuple[int, int], fill_value: numpy.ndarray, dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["reflect"]): +def full(shape: int, fill_value: float, dtype: None): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... -@overload -def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["edge"]): +def full( + _0: Tuple[int, ...] = ..., + /, + shape: Union[ + Tuple[Union[None, int], ...], numpy.int64, int, numpy.ndarray, List[int] + ] = ..., + fill_value: object = ..., + dtype: Union[type, numpy.dtype, str, None] = ..., + order: Literal["F", "C"] = ..., +): """ - usage.xarray: 1 + usage.dask: 14 + usage.matplotlib: 9 + usage.pandas: 30 + usage.scipy: 179 + usage.skimage: 32 + usage.sklearn: 97 + usage.xarray: 20 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], - mode: Literal["edge"], -): +def full_like(a: numpy.ndarray, fill_value: int): """ + usage.skimage: 1 + usage.sklearn: 2 usage.xarray: 1 """ ... @overload -def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["linear_ramp"]): +def full_like(a: xarray.core.variable.Variable, fill_value: int, dtype: None): """ usage.xarray: 1 """ @@ -28711,7 +28152,7 @@ def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["linear_r @overload -def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["maximum"]): +def full_like(a: xarray.core.variable.Variable, fill_value: bool, dtype: Type[bool]): """ usage.xarray: 1 """ @@ -28719,7 +28160,7 @@ def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["maximum" @overload -def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["minimum"]): +def full_like(a: xarray.core.dataarray.DataArray, fill_value: int, dtype: None): """ usage.xarray: 1 """ @@ -28727,7 +28168,7 @@ def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["minimum" @overload -def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["symmetric"]): +def full_like(a: xarray.core.dataarray.DataArray, fill_value: bool, dtype: Type[bool]): """ usage.xarray: 1 """ @@ -28735,7 +28176,7 @@ def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["symmetri @overload -def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["wrap"]): +def full_like(a: xarray.core.variable.IndexVariable, fill_value: int, dtype: None): """ usage.xarray: 1 """ @@ -28743,19 +28184,15 @@ def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["wrap"]): @overload -def pad( - array: numpy.ndarray, - pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], - mode: Literal["wrap"], -): +def full_like(a: object, fill_value: float): """ - usage.xarray: 1 + usage.xarray: 2 """ ... @overload -def pad(array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["edge"]): +def full_like(a: xarray.core.variable.Variable, fill_value: object, dtype: None): """ usage.xarray: 1 """ @@ -28763,7 +28200,7 @@ def pad(array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["e @overload -def pad(array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["wrap"]): +def full_like(a: xarray.core.variable.Variable, fill_value: numpy.ndarray, dtype: None): """ usage.xarray: 1 """ @@ -28771,1057 +28208,1064 @@ def pad(array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["w @overload -def pad( - array: numpy.ndarray, - pad_width: Union[int, Tuple[int, int], List[Tuple[int, int]]], - mode: Literal["reflect", "symmetric", "edge", "wrap", "constant"], -): +def full_like(a: xarray.core.dataarray.DataArray, fill_value: object, dtype: None): """ - usage.scipy: 42 + usage.xarray: 1 """ ... @overload -def pad( - array: Union[dask.array.core.Array, numpy.ndarray], - pad_width: Union[int, Tuple[Union[Tuple[int, int], int], ...]], - mode: Union[str, Callable], +def full_like( + a: xarray.core.dataarray.DataArray, fill_value: numpy.ndarray, dtype: None ): """ - usage.dask: 142 + usage.xarray: 1 """ ... @overload -def pad( - array: numpy.ndarray, - pad_width: Union[List[Tuple[int, Union[numpy.int64, int]]], Tuple[int, int]], - mode: Literal["constant"], -): +def full_like(a: object, fill_value: bool, dtype: Type[bool]): """ - usage.sklearn: 2 + usage.xarray: 1 """ ... -def pad( - array: object, - pad_width: Union[ - Tuple[Union[Tuple[int, int], int], ...], - List[Union[List[int], Tuple[Union[int, numpy.int64], Union[int, numpy.int64]]]], - int, - ], - mode: Union[str, Callable], -): +@overload +def full_like(a: numpy.ndarray, fill_value: bool, dtype: Type[bool]): """ - usage.dask: 142 - usage.matplotlib: 1 - usage.scipy: 42 - usage.skimage: 39 - usage.sklearn: 2 - usage.xarray: 63 + usage.xarray: 2 """ ... @overload -def partition(a: numpy.ndarray, kth: Tuple[int, int], axis: int): +def full_like(a: xarray.core.variable.Variable, fill_value: int, dtype: Type[int]): """ - usage.scipy: 5 + usage.xarray: 1 """ ... @overload -def partition(a: numpy.ndarray, kth: int, axis: int): +def full_like(a: xarray.core.variable.Variable, fill_value: float, dtype: None): """ - usage.dask: 1 - usage.sklearn: 4 + usage.xarray: 1 """ ... -def partition(a: numpy.ndarray, kth: Union[int, Tuple[int, int]], axis: int): +@overload +def full_like( + a: Union[numpy.float64, float, numpy.ndarray], + fill_value: Union[float, int], + dtype: Type[numpy.float64] = ..., +): """ - usage.dask: 1 - usage.scipy: 5 - usage.sklearn: 4 + usage.scipy: 29 """ ... @overload -def percentile(a: numpy.ndarray, q: float): +def full_like(a: List[float], fill_value: float): """ - usage.skimage: 1 - usage.xarray: 2 + usage.matplotlib: 3 """ ... @overload -def percentile(a: numpy.ndarray, q: List[int]): +def full_like(a: numpy.ndarray, fill_value: float): """ - usage.matplotlib: 3 - usage.skimage: 5 + usage.matplotlib: 4 + usage.sklearn: 4 """ ... @overload -def percentile(a: numpy.ndarray, q: int): +def full_like(a: List[numpy.float64], fill_value: float): """ - usage.skimage: 3 - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload -def percentile(a: numpy.ndarray, q: numpy.float64, axis: None): +def full_like( + a: object, + fill_value: Union[numpy.int64, numpy.float32, int, numpy.int32, numpy.float64], + dtype: Union[ + Type[numpy.float64], Literal["i8", "f8", "i4", "f4"], numpy.dtype + ] = ..., + shape: Union[Tuple[int, ...], None, int] = ..., +): """ - usage.xarray: 2 + usage.dask: 47 """ ... @overload -def percentile(a: numpy.ndarray, q: numpy.ndarray, axis: None): +def full_like(a: numpy.ndarray, fill_value: int, dtype: Type[numpy.float32]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def percentile(a: numpy.ndarray, q: numpy.float64, axis: int): +def full_like(a: List[Literal["b", "a"]], fill_value: Literal["a"]): """ - usage.xarray: 2 + usage.sklearn: 2 """ ... @overload -def percentile(a: numpy.ndarray, q: numpy.ndarray, axis: int): +def full_like(a: List[Literal["b", "c", "a"]], fill_value: Literal["a"]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... -@overload -def percentile(a: numpy.ndarray, q: numpy.float64, axis: List[int]): +def full_like( + a: object, + fill_value: object, + dtype: Union[type, None, Literal["i8", "f8", "i4", "f4"], numpy.dtype] = ..., + shape: Union[Tuple[int, ...], None, int] = ..., +): """ - usage.xarray: 4 + usage.dask: 47 + usage.matplotlib: 8 + usage.scipy: 29 + usage.skimage: 1 + usage.sklearn: 10 + usage.xarray: 17 """ ... -@overload -def percentile(a: numpy.ndarray, q: numpy.ndarray, axis: List[int]): +def genfromtxt(fname: str): """ - usage.xarray: 4 + usage.scipy: 1 """ ... -@overload -def percentile( - _0: pandas.core.series.Series = ..., - /, - a: Union[ - numpy.ndarray, int, pandas.core.frame.DataFrame, pandas.core.series.Series - ] = ..., - q: Union[ - int, - numpy.ndarray, - pandas.core.frame.DataFrame, - pandas.core.series.Series, - float, - ] = ..., - axis: int = ..., - interpolation: Literal["linear", "midpoint", "nearest", "higher", "lower"] = ..., -): +def get_printoptions(): """ - usage.pandas: 54 + usage.sklearn: 1 + usage.xarray: 3 """ ... -@overload -def percentile( - a: Union[numpy.ndarray, List[float]], - q: Union[List[Union[float, int]], int], - axis: Union[Tuple[int, ...], int, None] = ..., - interpolation: str = ..., - keepdims: bool = ..., -): +def geterr(): """ - usage.scipy: 27 + usage.pandas: 1 """ ... @overload -def percentile(a: numpy.ndarray, q: numpy.ndarray): +def gradient(f: numpy.ndarray): """ usage.matplotlib: 1 + usage.skimage: 7 """ ... @overload -def percentile(a: numpy.ndarray, q: List[float]): +def gradient(f: numpy.ndarray, *, axis: int): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def percentile(a: numpy.ndarray, q: Tuple[int, int]): +def gradient( + f: xarray.core.dataarray.DataArray, + *varargs: Literal["v", "t"], + axis: int, + edge_order: int, +): """ - usage.matplotlib: 1 + usage.xarray: 4 """ ... @overload -def percentile(a: numpy.ndarray, q: list): +def gradient(f: numpy.ndarray, *varargs: Literal["v", "t"], axis: int, edge_order: int): """ - usage.matplotlib: 2 + usage.xarray: 3 """ ... @overload -def percentile( - a: numpy.ndarray, q: numpy.ndarray, interpolation: Literal["linear", "nearest"] +def gradient( + f: sparse._coo.core.COO, *varargs: Literal["v", "t"], axis: int, edge_order: int ): """ - usage.dask: 8 + usage.xarray: 1 """ ... @overload -def percentile( - a: numpy.ndarray, - q: Union[float, numpy.ndarray, int, Tuple[int, int]], - interpolation: Literal["midpoint"] = ..., - axis: int = ..., -): +def gradient(f: object, *varargs: Literal["v", "t"], axis: int, edge_order: int): """ - usage.sklearn: 25 + usage.xarray: 1 """ ... -def percentile( - _0: pandas.core.series.Series = ..., - /, - a: Union[ - numpy.ndarray, - pandas.core.series.Series, - pandas.core.frame.DataFrame, - int, - List[float], - ] = ..., - q: object = ..., - axis: Union[int, List[int], Tuple[int, ...], None] = ..., - interpolation: str = ..., - keepdims: bool = ..., -): +@overload +def gradient(f: numpy.ndarray, *varargs: Literal["v", "t"]): """ - usage.dask: 8 - usage.matplotlib: 9 - usage.pandas: 54 - usage.scipy: 27 - usage.skimage: 9 - usage.sklearn: 25 - usage.xarray: 20 + usage.matplotlib: 3 """ ... @overload -def piecewise( - x: numpy.ndarray, - condlist: List[numpy.ndarray], - funclist: List[Union[int, Callable]], -): +def gradient(f: numpy.ma.core.MaskedArray, *varargs: Literal["v", "t"]): """ - usage.scipy: 4 + usage.matplotlib: 1 """ ... @overload -def piecewise( - x: Union[int, numpy.ndarray], - condlist: Union[numpy.ndarray, List[numpy.ndarray]], - funclist: List[Union[Callable, int, numpy.ndarray]], - *args: Literal["v", "t"], +def gradient( + f: Union[numpy.ndarray, int, float], + *varargs: Literal["v", "t"], + axis: Union[int, None, Tuple[int, int]] = ..., + edge_order: int = ..., ): """ - usage.dask: 6 + usage.dask: 17 """ ... -def piecewise( - x: Union[numpy.ndarray, int], - condlist: Union[List[numpy.ndarray], numpy.ndarray], - funclist: List[Union[Callable, int, numpy.ndarray]], - *args: Literal["v", "t"], +def gradient( + f: object, + *varargs: Literal["v", "t"], + axis: Union[Tuple[int, int], None, int] = ..., + edge_order: int = ..., ): """ - usage.dask: 6 - usage.scipy: 4 + usage.dask: 17 + usage.matplotlib: 5 + usage.skimage: 8 + usage.xarray: 9 """ ... -@overload -def place(arr: numpy.ndarray, mask: numpy.ndarray, vals: object): +def hamming(M: int): """ - usage.pandas: 19 + usage.skimage: 1 """ ... -@overload -def place( - arr: Union[numpy.ma.core.MaskedArray, numpy.ndarray], mask: object, vals: object -): +def hanning(M: int): """ - usage.scipy: 269 + usage.matplotlib: 4 + usage.skimage: 1 """ ... -def place( - arr: Union[numpy.ndarray, numpy.ma.core.MaskedArray], mask: object, vals: object -): +@overload +def histogram(a: numpy.ndarray, bins: int, range: None): """ - usage.pandas: 19 - usage.scipy: 269 + usage.skimage: 2 """ ... -def poly(seq_of_zeros: Union[List[int], numpy.ndarray]): +@overload +def histogram(a: numpy.ndarray, bins: int, range: Tuple[int, int]): """ - usage.scipy: 24 + usage.skimage: 1 """ ... -def polyadd(a1: Union[numpy.ndarray, int], a2: numpy.ndarray): +@overload +def histogram( + a: numpy.ndarray, + bins: Tuple[ + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + numpy.float64, + ], + density: bool, +): """ - usage.scipy: 14 + usage.skimage: 1 """ ... -def polydiv(u: numpy.ndarray, v: numpy.ndarray): +@overload +def histogram(a: numpy.ndarray, bins: numpy.int64, range: None): """ - usage.scipy: 9 + usage.skimage: 1 """ ... -def polyfit(x: numpy.ndarray, y: numpy.ndarray, deg: int): +@overload +def histogram(a: numpy.ndarray, bins: List[Union[int, float]]): """ - usage.scipy: 3 - usage.skimage: 1 + usage.skimage: 2 """ ... -def polyint(p: Union[List[float], numpy.poly1d, numpy.ndarray]): +@overload +def histogram(a: numpy.ndarray, bins: int, range: None, weights: None): """ - usage.scipy: 3 + usage.matplotlib: 1 + usage.pandas: 2 """ ... -def polymul( - a1: Union[List[Union[float, int]], numpy.ndarray], - a2: Union[List[Union[int, float]], numpy.ndarray], +@overload +def histogram( + a: numpy.ndarray, + bins: Union[int, numpy.ndarray], + range: Tuple[Union[numpy.float64, float], Union[numpy.float64, int]] = ..., + weights: Union[None, numpy.ndarray] = ..., ): """ - usage.scipy: 26 + usage.scipy: 7 """ ... -def polysub(a1: numpy.ndarray, a2: numpy.ndarray): +@overload +def histogram( + a: numpy.ndarray, bins: int, range: Tuple[numpy.int64, numpy.int64], weights: None +): """ - usage.scipy: 2 + usage.matplotlib: 1 """ ... -def polyval( - p: Union[List[Union[float, int]], numpy.ndarray, numpy.poly1d], - x: Union[float, numpy.ndarray, numpy.complex128, numpy.float64], +@overload +def histogram( + a: numpy.ndarray, + bins: int, + range: Tuple[numpy.float64, numpy.float64], + weights: None, ): """ - usage.scipy: 36 + usage.matplotlib: 1 """ ... @overload -def prod(a: Tuple[int, int]): +def histogram(a: list, bins: int, range: None, weights: None): """ - usage.matplotlib: 2 - usage.skimage: 2 - usage.xarray: 4 + usage.matplotlib: 1 """ ... @overload -def prod(a: Tuple[int, int, int]): +def histogram(a: numpy.ndarray, bins: numpy.ndarray, weights: None): """ - usage.skimage: 3 - usage.xarray: 3 + usage.matplotlib: 2 """ ... @overload -def prod(a: List[int]): +def histogram( + a: numpy.ndarray, + bins: int, + range: Tuple[numpy.float64, numpy.float64], + weights: None, + density: bool, +): """ - usage.skimage: 1 - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def prod(a: numpy.ndarray): +def histogram( + a: numpy.ndarray, + bins: List[Union[int, float]], + range: Tuple[numpy.float64, numpy.float64], + weights: None, + density: bool, +): """ - usage.skimage: 3 + usage.matplotlib: 1 """ ... @overload -def prod(a: Tuple[int]): +def histogram(a: numpy.ndarray, bins: List[Union[int, float]], density: bool): """ - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload -def prod(a: Tuple[None, ...]): +def histogram(a: numpy.ndarray, bins: numpy.ndarray, range: None, weights: None): """ - usage.matplotlib: 2 - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def prod(a: list): +def histogram( + a: numpy.ndarray, + bins: Literal["auto"], + range: Tuple[int, int], + weights: None, + density: bool, +): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def prod(a: Tuple[int, int, int, int]): +def histogram(a: numpy.ndarray, bins: int, range: Tuple[int, int], weights: None): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def prod(a: Tuple[int, int, int, int, int]): - """ - usage.xarray: 1 - """ - ... - - -@overload -def prod(a: numpy.ndarray, axis: None): +def histogram( + a: numpy.ndarray, + bins: numpy.ndarray, + range: Tuple[numpy.float64, numpy.float64], + weights: None, +): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def prod(a: object, axis: None): +def histogram( + a: Union[numpy.ndarray, dask.array.core.Array, list], + bins: Union[numpy.ndarray, int] = ..., + range: Union[List[numpy.float64], None, Tuple[int, int]] = ..., + weights: Union[None, dask.array.core.Array, numpy.ndarray] = ..., + density: bool = ..., +): """ - usage.xarray: 1 + usage.dask: 17 """ ... @overload -def prod(a: object): +def histogram(a: numpy.ndarray): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... -@overload -def prod(a: xarray.core.dataarray.DataArray): +def histogram( + a: Union[numpy.ndarray, dask.array.core.Array, list], + bins: object = ..., + range: Union[ + Tuple[ + Union[numpy.int64, int, float, numpy.float64], + Union[numpy.int64, int, numpy.float64], + ], + None, + List[numpy.float64], + ] = ..., + weights: Union[numpy.ndarray, dask.array.core.Array, None] = ..., + density: bool = ..., +): """ - usage.xarray: 1 + usage.dask: 17 + usage.matplotlib: 13 + usage.pandas: 2 + usage.scipy: 7 + usage.skimage: 7 + usage.sklearn: 1 """ ... @overload -def prod(a: xarray.core.dataset.Dataset): +def histogram2d( + x: numpy.ndarray, y: numpy.ndarray, bins: int, weights: numpy.ndarray = ... +): """ - usage.xarray: 1 + usage.scipy: 2 """ ... @overload -def prod(a: object, axis: int): +def histogram2d( + x: numpy.ndarray, + y: numpy.ndarray, + bins: int, + range: None, + normed: bool, + weights: None, +): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def prod(a: numpy.ndarray, axis: int): +def histogram2d( + x: numpy.ndarray, y: numpy.ndarray, bins: Tuple[numpy.ndarray, numpy.ndarray] +): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... -@overload -def prod( - a: Union[numpy.ndarray, int, List[int], Tuple[Union[None, int], ...]], - dtype: Literal["i8"] = ..., - axis: Union[None, int] = ..., +def histogram2d( + x: numpy.ndarray, + y: numpy.ndarray, + bins: Union[Tuple[numpy.ndarray, numpy.ndarray], int], + range: None = ..., + normed: bool = ..., + weights: Union[None, numpy.ndarray] = ..., ): """ - usage.pandas: 52 + usage.matplotlib: 2 + usage.scipy: 2 + usage.sklearn: 1 """ ... @overload -def prod( - a: Union[ - Tuple[Union[float, int, numpy.int64, None], ...], int, numpy.ndarray, List[int] - ], - axis: int = ..., +def histogram_bin_edges( + a: numpy.ndarray, + bins: int, + range: Tuple[numpy.float64, numpy.float64], + weights: None, ): """ - usage.scipy: 63 + usage.matplotlib: 1 """ ... @overload -def prod( - a: object, - axis: Union[None, Tuple[Union[None, int], ...], int] = ..., - out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., - keepdims: bool = ..., +def histogram_bin_edges( + a: numpy.ndarray, + bins: numpy.ndarray, + range: Tuple[numpy.float64, numpy.float64], + weights: None, ): """ - usage.dask: 100 + usage.matplotlib: 1 """ ... @overload -def prod( - a: Union[numpy.ndarray, List[numpy.ndarray], Tuple[int, ...]], - axis: int = ..., - dtype: Type[numpy.float64] = ..., +def histogram_bin_edges( + a: numpy.ndarray, bins: int, range: Tuple[numpy.int64, numpy.int64], weights: None ): """ - usage.sklearn: 10 + usage.matplotlib: 1 """ ... -def prod( - a: object, - axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., - dtype: Union[type, Literal["i8", "f8", "i4", "f4"]] = ..., - out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., - keepdims: bool = ..., +def histogram_bin_edges( + a: numpy.ndarray, + bins: Union[int, numpy.ndarray], + range: Tuple[Union[numpy.float64, numpy.int64], Union[numpy.float64, numpy.int64]], + weights: None, ): """ - usage.dask: 100 - usage.matplotlib: 4 - usage.pandas: 52 - usage.scipy: 63 - usage.skimage: 9 - usage.sklearn: 10 - usage.xarray: 22 + usage.matplotlib: 3 """ ... -def product(*args: Literal["v", "t"]): +def histogramdd(sample: numpy.ndarray, bins: int, weights: numpy.ndarray = ...): """ - usage.pandas: 1 - usage.skimage: 14 - usage.sklearn: 5 + usage.scipy: 2 """ ... @overload -def promote_types(_0: numpy.dtype, _1: numpy.dtype, /): +def hstack(tup: List[numpy.ndarray]): """ - usage.pandas: 55 - usage.skimage: 1 + usage.matplotlib: 18 + usage.skimage: 24 + usage.sklearn: 79 + usage.xarray: 1 """ ... @overload -def promote_types(_0: numpy.dtype, _1: Union[Literal["float64"], numpy.dtype], /): +def hstack(tup: Tuple[numpy.ndarray]): """ - usage.scipy: 13 + usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def promote_types(_0: numpy.dtype, _1: Type[numpy.float32], /): +def hstack(tup: List[numpy.int64]): """ - usage.matplotlib: 9 + usage.skimage: 1 """ ... @overload -def promote_types(_0: numpy.dtype, _1: Union[numpy.dtype, Type[float]], /): - """ - usage.dask: 7 - """ - ... - - -def promote_types(_0: numpy.dtype, _1: Union[type, numpy.dtype, Literal["float64"]], /): +def hstack(tup: List[numpy.float32]): """ - usage.dask: 7 - usage.matplotlib: 9 - usage.pandas: 55 - usage.scipy: 13 usage.skimage: 1 """ ... @overload -def ptp(a: Union[numpy.ndarray, pandas.core.series.Series]): +def hstack(tup: List[numpy.float64]): """ - usage.pandas: 2 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def ptp(a: numpy.ndarray): +def hstack(tup: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): """ - usage.matplotlib: 8 + usage.skimage: 1 + usage.sklearn: 10 """ ... @overload -def ptp(a: numpy.ndarray, axis: int): +def hstack(tup: Tuple[numpy.ndarray, numpy.ndarray]): """ usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 39 """ ... @overload -def ptp(a: numpy.ndarray, axis: Union[int, None]): +def hstack( + tup: Union[ + Tuple[ + Union[List[Union[bool, int, List[Literal["x", "y", "z"]]]], numpy.ndarray], + ..., + ], + List[Union[numpy.ndarray, float]], + ] +): """ - usage.dask: 2 + usage.pandas: 9 """ ... @overload -def ptp(a: Union[List[int], numpy.ndarray], axis: int = ...): +def hstack(tup: Union[List[Union[float, numpy.ndarray]], tuple]): """ - usage.sklearn: 9 + usage.scipy: 237 """ ... -def ptp( - a: Union[numpy.ndarray, pandas.core.series.Series, List[int]], - axis: Union[int, None] = ..., -): +@overload +def hstack(tup: Tuple[numpy.ndarray, numpy.ndarray, numpy.int64]): """ - usage.dask: 2 - usage.matplotlib: 9 - usage.pandas: 2 - usage.sklearn: 9 + usage.matplotlib: 1 """ ... @overload -def putmask( - _0: Union[ - numpy.ndarray, - pandas.core.arrays.interval.IntervalArray, - pandas.core.arrays.categorical.Categorical, - ], - _1: Union[numpy.ndarray, pandas.core.series.Series, Literal["foo"]], - _2: object, - /, -): +def hstack(tup: Tuple[numpy.ndarray, numpy.ndarray, numpy.float64]): """ - usage.pandas: 177 + usage.matplotlib: 1 """ ... @overload -def putmask( - _0: numpy.ndarray, _1: Union[numpy.ndarray, numpy.int64, bool], _2: float, / -): +def hstack(tup: List[Union[List[numpy.uint8], numpy.ndarray]]): """ - usage.scipy: 12 + usage.matplotlib: 1 """ ... @overload -def putmask(_0: numpy.ndarray, _1: numpy.ndarray, _2: numpy.ndarray, /): +def hstack(tup: List[Union[numpy.uint8, numpy.ndarray]]): """ - usage.matplotlib: 4 + usage.matplotlib: 1 """ ... -def putmask( - _0: Union[ - numpy.ndarray, - pandas.core.arrays.interval.IntervalArray, - pandas.core.arrays.categorical.Categorical, - ], - _1: Union[ - numpy.ndarray, pandas.core.series.Series, numpy.int64, bool, Literal["foo"] - ], - _2: object, - /, -): +@overload +def hstack(tup: List[Union[int, numpy.ndarray]]): """ - usage.matplotlib: 4 - usage.pandas: 177 - usage.scipy: 12 + usage.matplotlib: 11 + usage.sklearn: 1 """ ... -def quantile( - a: numpy.ndarray, - q: numpy.ndarray, - axis: numpy.ndarray, - interpolation: Literal["linear"], -): +@overload +def hstack(tup: List[Union[numpy.float64, numpy.ndarray]]): """ - usage.xarray: 1 + usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def ravel(a: Tuple[int, int, int]): +def hstack(tup: List[Union[numpy.ndarray, List[numpy.float64]]]): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def ravel(a: numpy.int64): +def hstack(tup: object): """ - usage.skimage: 1 + usage.dask: 7 """ ... @overload -def ravel(a: Tuple[int, int, int, int]): +def hstack( + tup: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + ] +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: int): +def hstack( + tup: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + ] +): """ - usage.skimage: 1 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: numpy.ndarray): +def hstack(tup: Tuple[numpy.ndarray, float]): """ - usage.dask: 1 - usage.matplotlib: 22 - usage.skimage: 1 - usage.xarray: 27 + usage.sklearn: 1 """ ... @overload -def ravel(a: Tuple[numpy.float64, numpy.float64]): +def hstack(tup: List[pandas.core.frame.DataFrame]): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def ravel(a: float): +def hstack(tup: List[Union[pandas.core.frame.DataFrame, numpy.ndarray]]): """ - usage.skimage: 1 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: numpy.float64): +def hstack(tup: Tuple[int, numpy.ndarray]): """ - usage.skimage: 1 - usage.xarray: 1 + usage.sklearn: 4 """ ... @overload -def ravel(a: List[numpy.float64]): +def hstack(tup: numpy.ndarray): """ - usage.skimage: 1 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: numpy.matrix): +def hstack(tup: Tuple[numpy.ndarray, int]): """ - usage.skimage: 3 + usage.sklearn: 4 """ ... @overload -def ravel(a: None): +def hstack(tup: List[Union[numpy.ndarray, List[int]]]): """ - usage.xarray: 1 + usage.sklearn: 7 """ ... @overload -def ravel(a: numpy.float32): +def hstack(tup: List[float]): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def ravel(a: numpy.bytes_): +def hstack(tup: List[List[float]]): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def ravel(a: numpy.uint8): +def hstack(tup: Tuple[numpy.ndarray, numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def ravel(a: numpy.int8): +def hstack(tup: List[int]): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def ravel(a: numpy.int16): +def hstack(tup: List[Union[numpy.float64, int]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: bytes): +def hstack(tup: List[Union[int, numpy.float64]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: numpy.int32): +def hstack(tup: List[List[int]]): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def ravel(a: pandas.core.indexes.datetimes.DatetimeIndex): +def hstack(tup: List[Union[numpy.ndarray, List[Literal["1", "2", "0"]]]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: xarray.coding.cftimeindex.CFTimeIndex): +def hstack(tup: List[Union[numpy.ndarray, List[Literal["3", "1", "2", "0"]]]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: pandas.core.indexes.numeric.Int64Index): +def hstack(tup: List[Union[numpy.ndarray, List[Literal["bird", "ant"]]]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: pandas.core.indexes.numeric.Float64Index): +def hstack(tup: List[Union[numpy.ndarray, List[Literal["cat", "ant"]]]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: pandas.core.indexes.base.Index): +def hstack(tup: List[Union[numpy.ndarray, List[Literal["bird", "cat"]]]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: pandas.core.indexes.multi.MultiIndex): +def hstack(tup: List[Union[numpy.ndarray, List[Literal["ant"]]]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: pandas.core.indexes.interval.IntervalIndex): +def hstack(tup: List[Union[numpy.ndarray, List[Literal["bird"]]]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: xarray.core.variable.IndexVariable): +def hstack(tup: List[Union[numpy.ndarray, List[Literal["cat"]]]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: pandas.core.indexes.range.RangeIndex): +def hstack(tup: List[Union[numpy.ndarray, List[Literal["spam", "eggs"]]]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: xarray.core.variable.Variable): +def hstack(tup: Tuple[List[List[int]], numpy.ndarray]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... -@overload -def ravel(a: Union[pandas.core.series.Series, pandas.core.frame.DataFrame]): +def hstack(tup: object): """ - usage.pandas: 2 + usage.dask: 7 + usage.matplotlib: 37 + usage.pandas: 9 + usage.scipy: 237 + usage.skimage: 30 + usage.sklearn: 179 + usage.xarray: 1 """ ... -@overload -def ravel( - a: Union[ +def i0( + x: Union[ + dask.dataframe.core.DataFrame, + dask.dataframe.core.Series, numpy.ndarray, - numpy.matrix, - List[Union[None, float, int, List[float]]], - Tuple[int, int, int], + pandas.core.series.Series, + pandas.core.frame.DataFrame, ] ): """ - usage.scipy: 162 + usage.dask: 17 """ ... @overload -def ravel(a: List[Union[float, None]]): +def identity(n: int, dtype: numpy.dtype = ...): """ - usage.matplotlib: 1 + usage.scipy: 67 """ ... @overload -def ravel(a: List[int]): +def identity(n: int): """ usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def ravel(a: List[List[int]]): +def identity(n: int, dtype: Type[float]): """ usage.matplotlib: 1 """ @@ -29829,85 +29273,70 @@ def ravel(a: List[List[int]]): @overload -def ravel( - a: Union[ - List[ - Union[int, numpy.float64, numpy.ndarray, Literal["spam", "egg"], List[int]] - ], - numpy.ndarray, - float, - numpy.matrix, - int, - ], - order: Literal["K"] = ..., -): +def identity(n: int, dtype: Type[bool]): """ - usage.sklearn: 80 + usage.matplotlib: 1 """ ... -def ravel(a: object, order: Literal["K"] = ...): +def identity(n: int, dtype: Union[type, numpy.dtype] = ...): """ - usage.dask: 1 - usage.matplotlib: 25 - usage.pandas: 2 - usage.scipy: 162 - usage.skimage: 12 - usage.sklearn: 80 - usage.xarray: 49 + usage.matplotlib: 3 + usage.scipy: 67 + usage.sklearn: 1 """ ... @overload -def ravel_multi_index(_0: List[int], _1: Tuple[int], /, *, order: Literal["F"]): +def imag(val: numpy.ndarray): """ usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def ravel_multi_index( - _0: List[int], _1: Tuple[int, int, int], /, *, order: Literal["C"] -): +def imag(val: xarray.core.dataarray.DataArray): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def ravel_multi_index(_0: List[int], _1: Tuple[int, int], /, *, order: Literal["C"]): +def imag(val: Union[numpy.ndarray, numpy.complex128]): """ - usage.skimage: 1 + usage.pandas: 2 """ ... @overload -def ravel_multi_index( - _0: List[int], _1: Tuple[int, int, int, int], /, *, order: Literal["C"] -): +def imag(val: object): """ - usage.skimage: 1 + usage.dask: 31 + usage.scipy: 51 """ ... -@overload -def ravel_multi_index( - _0: List[int], _1: Tuple[int, int, int, int, int], /, *, order: Literal["C"] -): +def imag(val: object): """ + usage.dask: 31 + usage.pandas: 2 + usage.scipy: 51 usage.skimage: 1 + usage.sklearn: 1 + usage.xarray: 1 """ ... @overload -def ravel_multi_index(_0: List[int], _1: Tuple[int, int], /, *, order: Literal["F"]): +def in1d(ar1: numpy.flatiter, ar2: Tuple[int, int]): """ usage.skimage: 1 """ @@ -29915,99 +29344,118 @@ def ravel_multi_index(_0: List[int], _1: Tuple[int, int], /, *, order: Literal[" @overload -def ravel_multi_index(_0: Tuple[int], _1: Tuple[int], /, *, order: Literal["C"]): +def in1d(ar1: numpy.ndarray, ar2: numpy.ndarray): """ + usage.matplotlib: 1 + usage.scipy: 3 usage.skimage: 1 + usage.sklearn: 32 """ ... @overload -def ravel_multi_index( - _0: Tuple[int, int], _1: Tuple[int, int], /, *, order: Literal["C"] +def in1d( + ar1: Union[pandas.core.indexes.numeric.Int64Index, numpy.ndarray], + ar2: Union[Tuple[int, int, int, int], numpy.ndarray], + assume_unique: bool = ..., ): """ - usage.skimage: 1 + usage.pandas: 6 """ ... @overload -def ravel_multi_index( - _0: Tuple[int, int, int], _1: Tuple[int, int, int], /, *, order: Literal["C"] -): +def in1d(ar1: numpy.ndarray, ar2: numpy.ndarray, assume_unique: bool): """ - usage.skimage: 1 + usage.dask: 1 """ ... @overload -def ravel_multi_index( - _0: Tuple[int, int, int, int], - _1: Tuple[int, int, int, int], - /, - *, - order: Literal["C"], -): +def in1d(ar1: numpy.ndarray, ar2: List[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def ravel_multi_index(_0: Tuple[int], _1: Tuple[int], /, *, order: Literal["F"]): +def in1d( + ar1: Tuple[ + Literal["mean_test_score"], + Literal["rank_test_score"], + Literal["split0_test_score"], + Literal["split1_test_score"], + Literal["split2_test_score"], + ], + ar2: List[str], +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def ravel_multi_index( - _0: Tuple[int, int], _1: Tuple[int, int], /, *, order: Literal["F"] -): +def in1d(ar1: numpy.ndarray, ar2: List[numpy.int64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... -@overload -def ravel_multi_index( - _0: Tuple[int, int, int], _1: Tuple[int, int, int], /, *, order: Literal["F"] +def in1d( + ar1: Union[ + Tuple[ + Literal["mean_test_score"], + Literal["rank_test_score"], + Literal["split0_test_score"], + Literal["split1_test_score"], + Literal["split2_test_score"], + ], + numpy.ndarray, + pandas.core.indexes.numeric.Int64Index, + numpy.flatiter, + ], + ar2: Union[ + numpy.ndarray, List[Union[numpy.int64, numpy.float64, str]], Tuple[int, ...] + ], + assume_unique: bool = ..., ): """ - usage.skimage: 1 + usage.dask: 1 + usage.matplotlib: 1 + usage.pandas: 6 + usage.scipy: 3 + usage.skimage: 2 + usage.sklearn: 35 """ ... @overload -def ravel_multi_index( - _0: Tuple[int, int, int, int], - _1: Tuple[int, int, int, int], - /, - *, - order: Literal["F"], -): +def indices(dimensions: Tuple[int]): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload -def ravel_multi_index(_0: Tuple[numpy.ndarray, numpy.ndarray], _1: Tuple[int, int], /): +def indices(dimensions: Tuple[int, int]): """ - usage.skimage: 1 + usage.matplotlib: 2 + usage.skimage: 2 + usage.sklearn: 2 """ ... @overload -def ravel_multi_index(_0: numpy.ndarray, _1: Tuple[int, int], /): +def indices(dimensions: Tuple[numpy.int64, numpy.int64], dtype: Type[numpy.float64]): """ usage.skimage: 1 """ @@ -30015,431 +29463,460 @@ def ravel_multi_index(_0: numpy.ndarray, _1: Tuple[int, int], /): @overload -def ravel_multi_index( - _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], _1: Tuple[int, int, int], / +def indices( + dimensions: Union[List[int], Tuple[int, ...]], dtype: Union[numpy.dtype, type] = ... ): """ - usage.skimage: 1 + usage.scipy: 26 """ ... @overload -def ravel_multi_index(_0: numpy.ndarray, _1: Tuple[int, int, int], /): +def indices(dimensions: Tuple[Union[int, None], ...], dtype: Type[float] = ...): """ - usage.skimage: 1 + usage.dask: 8 """ ... @overload -def ravel_multi_index(_0: List[numpy.ndarray], _1: numpy.ndarray, /): +def indices(dimensions: generator): """ - usage.scipy: 3 + usage.sklearn: 1 """ ... -@overload -def ravel_multi_index(_0: List[Tuple[int, int]], _1: Tuple[int, int], /): +def indices( + dimensions: Union[Tuple[Union[None, int, numpy.int64], ...], generator, List[int]], + dtype: Union[type, numpy.dtype] = ..., +): """ - usage.matplotlib: 1 + usage.dask: 8 + usage.matplotlib: 2 + usage.scipy: 26 + usage.skimage: 5 + usage.sklearn: 3 """ ... -@overload -def ravel_multi_index(_0: Tuple[numpy.int64, ...], _1: Tuple[int, ...], /): +def inner(_0: numpy.ndarray, _1: numpy.ndarray, /): """ - usage.dask: 3 + usage.matplotlib: 1 + usage.scipy: 5 + usage.sklearn: 4 """ ... -def ravel_multi_index( - _0: Union[ - Tuple[Union[int, numpy.ndarray, numpy.int64], ...], - numpy.ndarray, - List[Union[Tuple[int, int], int, numpy.ndarray]], - ], - _1: Union[Tuple[int, ...], numpy.ndarray], - /, - *, - order: Literal["F", "C"] = ..., -): +@overload +def insert(arr: numpy.ndarray, obj: int, values: numpy.ndarray, axis: int): """ - usage.dask: 3 - usage.matplotlib: 1 - usage.scipy: 3 - usage.skimage: 18 + usage.skimage: 3 """ ... @overload -def real(val: numpy.ndarray): +def insert( + arr: numpy.ndarray, + obj: Union[numpy.ndarray, int], + values: Union[None, float, numpy.int64, int], +): """ - usage.skimage: 7 - usage.sklearn: 5 + usage.pandas: 15 """ ... @overload -def real(val: float): +def insert( + arr: numpy.ndarray, + obj: Union[int, numpy.ndarray], + values: Union[int, numpy.ndarray], + axis: int = ..., +): """ - usage.skimage: 1 + usage.scipy: 145 """ ... @overload -def real(val: xarray.core.dataarray.DataArray): +def insert(arr: numpy.ndarray, obj: int, values: float, axis: int): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def real(val: numpy.complex128): +def insert( + arr: numpy.ndarray, + obj: Union[List[int], int, slice[int, int, int]], + values: Union[int, numpy.ndarray], + axis: int, +): """ - usage.pandas: 1 + usage.dask: 11 """ ... @overload -def real(val: object): +def insert(arr: numpy.ndarray, obj: int, values: int): """ - usage.dask: 32 - usage.scipy: 106 + usage.sklearn: 2 """ ... -def real(val: object): +@overload +def insert(arr: numpy.ndarray, obj: int, values: numpy.int64): """ - usage.dask: 32 - usage.pandas: 1 - usage.scipy: 106 - usage.skimage: 8 - usage.sklearn: 5 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def repeat(a: numpy.ndarray, repeats: int, axis: int): +def insert(arr: numpy.ndarray, obj: int, values: float): """ - usage.dask: 4 - usage.matplotlib: 16 - usage.xarray: 4 + usage.sklearn: 1 """ ... -@overload -def repeat( - _0: object = ..., - _1: int = ..., - /, - a: object = ..., - repeats: object = ..., +def insert( + arr: numpy.ndarray, + obj: Union[int, numpy.ndarray, List[int], slice[int, int, int]], + values: Union[numpy.int64, int, float, numpy.ndarray, None], axis: int = ..., - *, - foo: Literal["bar"] = ..., ): """ - usage.pandas: 168 + usage.dask: 11 + usage.matplotlib: 2 + usage.pandas: 15 + usage.scipy: 145 + usage.skimage: 3 + usage.sklearn: 4 """ ... @overload -def repeat( - a: object, - repeats: Union[numpy.ndarray, int, Tuple[int], List[int]], - axis: int = ..., -): +def interp(x: numpy.flatiter, xp: numpy.ndarray, fp: numpy.ndarray): """ - usage.scipy: 48 + usage.skimage: 2 """ ... @overload -def repeat(a: numpy.ndarray, repeats: int): +def interp(x: numpy.ndarray, xp: numpy.ndarray, fp: numpy.ndarray): """ - usage.matplotlib: 8 + usage.matplotlib: 5 + usage.pandas: 3 + usage.skimage: 4 + usage.sklearn: 5 """ ... @overload -def repeat(a: List[int], repeats: int): +def interp( + x: numpy.ndarray, + xp: numpy.ndarray, + fp: numpy.ndarray, + left: float, + right: float, + period: None, +): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def repeat( - a: object, - repeats: Union[numpy.ndarray, float, numpy.int64, int, Tuple[int]], - axis: int = ..., +def interp( + x: numpy.ndarray, + xp: numpy.ndarray, + fp: numpy.ndarray, + left: int, + right: int, + period: None, ): """ - usage.sklearn: 69 + usage.xarray: 1 """ ... -def repeat( - _0: object = ..., - _1: int = ..., - /, - a: object = ..., - repeats: object = ..., - axis: int = ..., - *, - foo: Literal["bar"] = ..., +@overload +def interp( + x: numpy.ndarray, + xp: Union[numpy.ndarray, List[float]], + fp: Union[numpy.ndarray, Tuple[float, float, float, float], List[float]], ): """ - usage.dask: 4 - usage.matplotlib: 25 - usage.pandas: 168 - usage.scipy: 48 - usage.sklearn: 69 - usage.xarray: 4 + usage.scipy: 13 """ ... @overload -def require( - a: numpy.ndarray, dtype: Type[numpy.uint8], requirements: List[Literal["C"]] -): +def interp(x: int, xp: numpy.ndarray, fp: numpy.ndarray): """ - usage.skimage: 1 + usage.matplotlib: 2 """ ... @overload -def require(a: numpy.ndarray, dtype: numpy.dtype, requirements: Literal["C", "F"]): +def interp(x: numpy.float64, xp: numpy.ndarray, fp: numpy.ndarray): """ - usage.scipy: 16 + usage.matplotlib: 4 """ ... @overload -def require(a: numpy.ndarray, requirements: Literal["W"]): +def interp(x: numpy.ndarray, xp: numpy.ndarray, fp: numpy.ma.core.MaskedArray): """ - usage.sklearn: 6 + usage.matplotlib: 1 """ ... -def require( - a: numpy.ndarray, - requirements: Union[Literal["W", "C", "F"], List[Literal["C"]]], - dtype: Union[numpy.dtype, Type[numpy.uint8]] = ..., -): +@overload +def interp(x: numpy.ma.core.MaskedArray, xp: List[float], fp: List[Union[float, int]]): """ - usage.scipy: 16 - usage.skimage: 1 - usage.sklearn: 6 + usage.matplotlib: 1 """ ... @overload -def reshape(a: List[int], newshape: List[int]): +def interp( + x: numpy.ma.core.MaskedArray, + xp: List[Union[numpy.int64, int]], + fp: List[Union[float, int]], +): """ - usage.skimage: 2 + usage.matplotlib: 1 """ ... @overload -def reshape(a: numpy.ndarray, newshape: List[int]): +def interp( + x: numpy.ma.core.MaskedArray, + xp: List[Union[int, numpy.int64]], + fp: List[Union[float, int]], +): """ - usage.skimage: 2 + usage.matplotlib: 1 """ ... @overload -def reshape(a: numpy.ndarray, newshape: Tuple[int, int]): +def interp( + x: numpy.ma.core.MaskedArray, + xp: List[Union[numpy.float64, int]], + fp: List[Union[float, int]], +): """ usage.matplotlib: 2 - usage.skimage: 11 - usage.xarray: 3 """ ... @overload -def reshape(a: numpy.ndarray, newshape: Tuple[int]): +def interp( + x: numpy.ma.core.MaskedArray, + xp: List[Union[int, numpy.float64]], + fp: List[Union[float, int]], +): """ - usage.skimage: 3 + usage.matplotlib: 1 """ ... @overload -def reshape(a: numpy.ndarray, newshape: Tuple[int, int, int]): +def interp(x: numpy.ma.core.MaskedArray, xp: List[int], fp: List[Union[float, int]]): """ - usage.skimage: 5 - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def reshape(a: numpy.ndarray, newshape: Tuple[int, int, int, int]): +def interp( + x: numpy.ndarray, xp: numpy.ndarray, fp: numpy.ndarray, left: int, right: int +): """ - usage.skimage: 4 - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload -def reshape(a: numpy.ndarray, newshape: Tuple[int, int, int, int, int]): +def interp( + x: numpy.ndarray, + xp: numpy.ndarray, + fp: Union[ + List[Union[None, float, pandas._libs.tslibs.nattype.NaTType]], numpy.ndarray + ], +): """ - usage.skimage: 2 + usage.dask: 8 """ ... @overload -def reshape(a: numpy.ndarray, newshape: Tuple[numpy.int64, numpy.int64]): +def interp(x: float, xp: List[numpy.float64], fp: List[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def reshape(a: numpy.ndarray, newshape: Tuple[int, numpy.int64], order: Literal["F"]): +def interp(x: numpy.float64, xp: List[numpy.float64], fp: List[numpy.float64]): """ - usage.skimage: 3 + usage.sklearn: 2 """ ... -@overload -def reshape(a: List[float], newshape: Tuple[int, int]): +def interp( + x: object, + xp: Union[List[Union[float, numpy.float64, numpy.int64, int]], numpy.ndarray], + fp: Union[ + List[ + Union[None, pandas._libs.tslibs.nattype.NaTType, float, int, numpy.float64] + ], + numpy.ndarray, + numpy.ma.core.MaskedArray, + Tuple[float, float, float, float], + ], + left: Union[int, float] = ..., + right: Union[int, float] = ..., + period: None = ..., +): """ + usage.dask: 8 + usage.matplotlib: 20 + usage.pandas: 3 + usage.scipy: 13 + usage.skimage: 6 + usage.sklearn: 8 usage.xarray: 2 """ ... -@overload -def reshape(a: List[float], newshape: Tuple[int]): +def intersect1d(ar1: numpy.ndarray, ar2: numpy.ndarray): """ - usage.xarray: 7 + usage.pandas: 8 + usage.sklearn: 9 """ ... -@overload -def reshape(a: List[float], newshape: Tuple[None, ...]): +def is_busday(_0: numpy.datetime64, /, *, busdaycal: numpy.busdaycalendar): """ - usage.xarray: 1 + usage.pandas: 1 """ ... @overload -def reshape( - a: Union[numpy.ndarray, List[Literal["A2", "A0", "A4", "A3"]]], - newshape: Tuple[int, int], -): +def isclose(a: numpy.float64, b: int): """ - usage.pandas: 6 + usage.skimage: 1 """ ... @overload -def reshape( - a: Union[numpy.ndarray, List[int]], - newshape: Union[Tuple[Union[int, numpy.int64], ...], List[int]], -): +def isclose(a: float, b: float): """ - usage.scipy: 50 + usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def reshape(a: numpy.ndarray, newshape: int): +def isclose(a: numpy.ndarray, b: int, atol: float): """ - usage.matplotlib: 3 + usage.skimage: 1 """ ... @overload -def reshape( - a: Union[numpy.ndarray, list, Tuple[numpy.ndarray, ...]], - newshape: Tuple[Union[int, numpy.int64], ...], +def isclose( + a: numpy.ndarray, b: numpy.ndarray, rtol: float, atol: float, equal_nan: bool ): """ - usage.sklearn: 97 + usage.sklearn: 1 + usage.xarray: 11 """ ... -def reshape( - a: Union[Tuple[numpy.ndarray, ...], list, numpy.ndarray], - newshape: Union[Tuple[Union[int, numpy.int64, None], ...], List[int], int], - order: Literal["F"] = ..., -): +@overload +def isclose(a: numpy.float64, b: numpy.float64): """ - usage.matplotlib: 5 - usage.pandas: 6 - usage.scipy: 50 - usage.skimage: 33 - usage.sklearn: 97 - usage.xarray: 16 + usage.matplotlib: 4 + usage.xarray: 1 """ ... @overload -def resize(a: List[bool], new_shape: int): +def isclose(a: numpy.float64, b: numpy.float64, rtol: float): """ - usage.pandas: 3 + usage.xarray: 1 """ ... @overload -def resize(a: Union[numpy.ndarray, bool, int], new_shape: Union[Tuple[int, ...], int]): +def isclose( + a: Union[numpy.bool_, numpy.float64, numpy.ndarray, float], + b: Union[bool, numpy.ndarray, int], + equal_nan: bool = ..., +): """ - usage.scipy: 31 + usage.pandas: 4 """ ... @overload -def resize(a: numpy.ndarray, new_shape: Tuple[int]): +def isclose( + a: object, + b: object, + rtol: Union[float, int, numpy.float64] = ..., + atol: Union[float, int, numpy.float64] = ..., +): """ - usage.matplotlib: 8 + usage.scipy: 66 """ ... @overload -def resize(a: List[int], new_shape: Tuple[int]): +def isclose(a: numpy.float64, b: numpy.ndarray, rtol: int, atol: numpy.float64): """ usage.matplotlib: 2 """ @@ -30447,301 +29924,211 @@ def resize(a: List[int], new_shape: Tuple[int]): @overload -def resize(a: numpy.ndarray, new_shape: Tuple[int, int]): +def isclose(a: numpy.float64, b: List[float]): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def resize(a: numpy.ndarray, new_shape: int): +def isclose(a: numpy.int64, b: numpy.ndarray, rtol: int, atol: numpy.float64): """ - usage.matplotlib: 5 + usage.matplotlib: 1 """ ... @overload -def resize(a: numpy.ndarray, new_shape: Union[Tuple[int, int], int]): +def isclose(a: numpy.float64, b: float): """ - usage.sklearn: 15 + usage.matplotlib: 1 + usage.sklearn: 4 """ ... -def resize( - a: Union[numpy.ndarray, int, bool, List[Union[int, bool]]], - new_shape: Union[int, Tuple[int, ...]], +@overload +def isclose( + a: Union[numpy.ndarray, numpy.float64], + b: Union[numpy.ndarray, int], + rtol: float = ..., + atol: int = ..., ): """ - usage.matplotlib: 17 - usage.pandas: 3 - usage.scipy: 31 - usage.sklearn: 15 + usage.dask: 3 """ ... @overload -def result_type(_0: numpy.dtype, _1: numpy.dtype, _2: Type[numpy.float32], /): +def isclose( + a: numpy.float64, b: numpy.float64, rtol: float, atol: float, equal_nan: bool +): """ - usage.skimage: 7 + usage.sklearn: 1 """ ... @overload -def result_type(_0: numpy.ndarray, /): +def isclose(a: float, b: numpy.float32): """ - usage.xarray: 21 + usage.sklearn: 2 """ ... @overload -def result_type(_0: numpy.ndarray, _1: numpy.ndarray, /): +def isclose(a: float, b: numpy.float64): """ - usage.xarray: 25 + usage.sklearn: 1 """ ... @overload -def result_type(_0: dask.array.core.Array, /): +def isclose(a: numpy.int64, b: float): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def result_type(_0: dask.array.core.Array, _1: dask.array.core.Array, /): +def isclose(a: numpy.float64, b: numpy.float64, rtol: int, atol: int, equal_nan: bool): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... -@overload -def result_type( - _0: dask.array.core.Array, - _1: dask.array.core.Array, - _2: dask.array.core.Array, - _3: dask.array.core.Array, - _4: dask.array.core.Array, - _5: dask.array.core.Array, - _6: dask.array.core.Array, - _7: dask.array.core.Array, - _8: dask.array.core.Array, - _9: dask.array.core.Array, - _10: dask.array.core.Array, - _11: dask.array.core.Array, - _12: dask.array.core.Array, - _13: dask.array.core.Array, - _14: dask.array.core.Array, - _15: dask.array.core.Array, - _16: dask.array.core.Array, - _17: dask.array.core.Array, - _18: dask.array.core.Array, - _19: dask.array.core.Array, - /, +def isclose( + a: object, + b: object, + rtol: Union[float, int, numpy.float64] = ..., + atol: Union[float, int, numpy.float64] = ..., + equal_nan: bool = ..., ): """ - usage.xarray: 1 + usage.dask: 3 + usage.matplotlib: 10 + usage.pandas: 4 + usage.scipy: 66 + usage.skimage: 3 + usage.sklearn: 11 + usage.xarray: 13 """ ... @overload -def result_type(_0: numpy.ndarray, _1: dask.array.core.Array, /): +def iscomplex(x: xarray.core.dataarray.DataArray): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload -def result_type( - _0: numpy.ndarray, - _1: numpy.ndarray, - _2: numpy.ndarray, - _3: numpy.ndarray, - _4: numpy.ndarray, - /, -): +def iscomplex(x: Union[numpy.float64, int, numpy.ndarray, float]): """ - usage.xarray: 2 + usage.scipy: 12 """ ... @overload -def result_type(_0: numpy.ndarray, _1: numpy.ndarray, _2: numpy.ndarray, /): +def iscomplex(x: object): """ - usage.xarray: 6 + usage.dask: 28 """ ... -@overload -def result_type( - _0: numpy.ndarray, _1: numpy.ndarray, _2: numpy.ndarray, _3: numpy.ndarray, / -): +def iscomplex(x: object): """ - usage.xarray: 2 + usage.dask: 28 + usage.scipy: 12 + usage.xarray: 1 """ ... @overload -def result_type( - _0: numpy.ndarray, - _1: numpy.ndarray, - _2: numpy.ndarray, - _3: numpy.ndarray, - _4: numpy.ndarray, - _5: numpy.ndarray, - _6: numpy.ndarray, - _7: numpy.ndarray, - _8: numpy.ndarray, - _9: numpy.ndarray, - /, -): +def iscomplexobj(x: numpy.ndarray): """ - usage.xarray: 3 + usage.matplotlib: 2 + usage.pandas: 2 + usage.skimage: 4 """ ... @overload -def result_type(_0: dask.array.core.Array, _1: numpy.ndarray, /): +def iscomplexobj(x: object): """ - usage.xarray: 4 + usage.scipy: 324 """ ... @overload -def result_type( - _0: numpy.ndarray, - _1: numpy.ndarray, - _2: numpy.ndarray, - _3: numpy.ndarray, - _4: numpy.ndarray, - _5: numpy.ndarray, - _6: numpy.ndarray, - _7: numpy.ndarray, - /, -): +def iscomplexobj(x: dask.array.core.Array): """ - usage.xarray: 5 + usage.dask: 2 """ ... -@overload -def result_type( - _0: numpy.ndarray, - _1: numpy.ndarray, - _2: numpy.ndarray, - _3: numpy.ndarray, - _4: numpy.ndarray, - _5: numpy.ndarray, - _6: numpy.ndarray, - _7: numpy.ndarray, - _8: numpy.ndarray, - /, -): +def iscomplexobj(x: object): """ - usage.xarray: 2 + usage.dask: 2 + usage.matplotlib: 2 + usage.pandas: 2 + usage.scipy: 324 + usage.skimage: 4 """ ... -@overload -def result_type( - _0: numpy.ndarray, - _1: numpy.ndarray, - _2: numpy.ndarray, - _3: numpy.ndarray, - _4: numpy.ndarray, - _5: numpy.ndarray, - _6: numpy.ndarray, - _7: numpy.ndarray, - _8: numpy.ndarray, - _9: numpy.ndarray, - _10: numpy.ndarray, - _11: numpy.ndarray, - _12: numpy.ndarray, - _13: numpy.ndarray, - _14: numpy.ndarray, - _15: numpy.ndarray, - _16: numpy.ndarray, - _17: numpy.ndarray, - _18: numpy.ndarray, - _19: numpy.ndarray, - /, -): +def isfortran(a: numpy.ndarray): """ - usage.xarray: 2 + usage.dask: 1 + usage.matplotlib: 2 + usage.scipy: 16 + usage.sklearn: 3 """ ... @overload -def result_type( - _0: numpy.ndarray, - _1: numpy.ndarray, - _2: numpy.ndarray, - _3: numpy.ndarray, - _4: numpy.ndarray, - _5: numpy.ndarray, - _6: numpy.ndarray, - _7: numpy.ndarray, - _8: numpy.ndarray, - _9: numpy.ndarray, - _10: numpy.ndarray, - /, -): +def isin(element: numpy.ndarray, test_elements: List[int]): """ - usage.xarray: 1 + usage.xarray: 2 """ ... @overload -def result_type(_0: sparse._coo.core.COO, /): +def isin(element: numpy.ndarray, test_elements: numpy.ndarray): """ - usage.xarray: 1 + usage.scipy: 3 + usage.xarray: 3 """ ... @overload -def result_type(_0: sparse._coo.core.COO, _1: numpy.ndarray, /): +def isin(element: dask.array.core.Array, test_elements: List[int]): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload -def result_type( - _0: numpy.ndarray, - _1: numpy.ndarray, - _2: numpy.ndarray, - _3: numpy.ndarray, - _4: numpy.ndarray, - _5: numpy.ndarray, - _6: numpy.ndarray, - _7: numpy.ndarray, - _8: numpy.ndarray, - _9: numpy.ndarray, - _10: numpy.ndarray, - _11: numpy.ndarray, - /, -): +def isin(element: dask.array.core.Array, test_elements: numpy.ndarray): """ usage.xarray: 1 """ @@ -30749,23 +30136,23 @@ def result_type( @overload -def result_type(_0: Type[bool], /): +def isin(element: sparse._coo.core.COO, test_elements: List[int]): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload -def result_type(_0: Type[numpy.bytes_], /): +def isin(element: object, test_elements: numpy.ndarray): """ - usage.xarray: 1 + usage.xarray: 2 """ ... @overload -def result_type(_0: Type[numpy.float32], /): +def isin(element: object, test_elements: object): """ usage.xarray: 1 """ @@ -30773,109 +30160,103 @@ def result_type(_0: Type[numpy.float32], /): @overload -def result_type(_0: Type[numpy.float64], /): +def isin( + element: Union[dask.array.core.Array, numpy.ndarray], + test_elements: Union[numpy.ndarray, dask.array.core.Array], + assume_unique: bool = ..., + invert: bool = ..., +): """ - usage.xarray: 1 + usage.dask: 4 """ ... -@overload -def result_type(_0: Type[numpy.float32], _1: Type[numpy.float64], /): +def isin( + element: object, + test_elements: object, + assume_unique: bool = ..., + invert: bool = ..., +): """ - usage.xarray: 1 + usage.dask: 4 + usage.scipy: 3 + usage.xarray: 11 """ ... @overload -def result_type(_0: Type[numpy.str_], /): +def isneginf(x: Union[List[Union[int, numpy.float64]], numpy.ndarray, numpy.float64]): """ - usage.xarray: 1 + usage.scipy: 13 """ ... @overload -def result_type(_0: Type[numpy.int64], /): +def isneginf(x: numpy.ndarray): """ - usage.xarray: 1 + usage.dask: 1 """ ... -@overload -def result_type(_0: Type[numpy.str_], _1: Type[numpy.str_], /): +def isneginf(x: Union[numpy.ndarray, numpy.float64, List[Union[numpy.float64, int]]]): """ - usage.xarray: 1 + usage.dask: 1 + usage.scipy: 13 """ ... @overload -def result_type(_0: float, /): +def isposinf( + x: Union[numpy.ndarray, float, numpy.float64, List[Union[numpy.float64, int]]] +): """ - usage.xarray: 1 + usage.scipy: 9 """ ... @overload -def result_type(_0: numpy.ndarray, _1: float, /): +def isposinf(x: numpy.ndarray): """ - usage.xarray: 1 + usage.dask: 1 """ ... @overload -def result_type( - _0: numpy.ndarray, - _1: numpy.ndarray, - _2: numpy.ndarray, - _3: numpy.ndarray, - _4: numpy.ndarray, - _5: numpy.ndarray, - _6: numpy.ndarray, - _7: numpy.ndarray, - _8: numpy.ndarray, - _9: numpy.ndarray, - _10: numpy.ndarray, - _11: numpy.ndarray, - _12: numpy.ndarray, - _13: numpy.ndarray, - /, -): +def isposinf(x: float): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def result_type(_0: sparse._coo.core.COO, _1: sparse._coo.core.COO, /): +def isposinf(x: int): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... -@overload -def result_type(_0: numpy.ndarray, _1: sparse._coo.core.COO, /): +def isposinf( + x: Union[float, int, numpy.float64, numpy.ndarray, List[Union[numpy.float64, int]]] +): """ - usage.xarray: 2 + usage.dask: 1 + usage.scipy: 9 + usage.sklearn: 2 """ ... @overload -def result_type( - _0: sparse._coo.core.COO, - _1: sparse._coo.core.COO, - _2: sparse._coo.core.COO, - _3: sparse._coo.core.COO, - /, -): +def isreal(x: xarray.core.dataarray.DataArray): """ usage.xarray: 1 """ @@ -30883,713 +30264,433 @@ def result_type( @overload -def result_type( - _0: sparse._coo.core.COO, _1: sparse._coo.core.COO, _2: sparse._coo.core.COO, / +def isreal( + x: Union[ + numpy.ndarray, numpy.float64, numpy.complex128, numpy.complex256, numpy.float128 + ] ): """ - usage.xarray: 1 + usage.scipy: 58 """ ... @overload -def result_type(_0: object, /): +def isreal(x: float): """ - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload -def result_type(_0: object, _1: numpy.ndarray, /): +def isreal(x: int): """ - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload -def result_type(_0: object, _1: object, /): +def isreal(x: numpy.float64): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def result_type(_0: numpy.ndarray, _1: object, /): +def isreal(x: object): """ - usage.xarray: 1 + usage.dask: 28 """ ... @overload -def result_type( - _0: object, - _1: object, - _2: object, - _3: object, - _4: object, - _5: object, - _6: object, - _7: object, - _8: object, - _9: object, - /, -): +def isreal(x: numpy.ndarray): """ - usage.xarray: 1 + usage.sklearn: 4 """ ... -@overload -def result_type(_0: object, _1: object, _2: object, /): +def isreal(x: object): """ + usage.dask: 28 + usage.matplotlib: 3 + usage.scipy: 58 + usage.sklearn: 4 usage.xarray: 1 """ ... -@overload -def result_type(_0: object, _1: object, _2: object, _3: object, /): +def isrealobj(x: numpy.ndarray): """ - usage.xarray: 1 + usage.scipy: 44 """ ... @overload -def result_type(_0: object, _1: object, _2: object, _3: object, _4: object, /): +def isscalar(element: int): """ - usage.xarray: 1 + usage.matplotlib: 2 + usage.skimage: 15 + usage.sklearn: 3 """ ... @overload -def result_type( - _0: dask.array.core.Array, - _1: dask.array.core.Array, - _2: dask.array.core.Array, - _3: dask.array.core.Array, - _4: dask.array.core.Array, - _5: dask.array.core.Array, - _6: dask.array.core.Array, - _7: dask.array.core.Array, - _8: dask.array.core.Array, - _9: dask.array.core.Array, - /, -): +def isscalar(element: numpy.ndarray): """ - usage.xarray: 1 + usage.matplotlib: 3 + usage.skimage: 5 + usage.sklearn: 5 + usage.xarray: 21 """ ... @overload -def result_type(_0: numpy.dtype, /): +def isscalar(element: List[int]): """ + usage.matplotlib: 6 + usage.skimage: 8 + usage.sklearn: 6 usage.xarray: 1 """ ... @overload -def result_type(_0: int, /): +def isscalar(element: List[Union[int, float]]): """ - usage.xarray: 1 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def result_type(_0: numpy.dtype, _1: int, /): +def isscalar(element: float): """ + usage.matplotlib: 3 + usage.skimage: 6 + usage.sklearn: 2 usage.xarray: 1 """ ... @overload -def result_type( - _0: object, - _1: object = ..., - _2: Union[numpy.dtype, numpy.float64, float, int, type] = ..., - _3: Union[int, Type[int]] = ..., - _4: Union[int, Type[int]] = ..., - _5: Union[int, Type[int]] = ..., - _6: Union[int, Type[int]] = ..., - _7: Union[int, Type[int]] = ..., - _8: Union[int, Type[int]] = ..., - _9: Union[int, Type[int]] = ..., - _10: Union[int, Type[int]] = ..., - _11: Union[int, Type[int]] = ..., - _12: Union[int, Type[int]] = ..., - _13: Union[int, Type[int]] = ..., - _14: Union[int, Type[int]] = ..., - _15: Union[int, Type[int]] = ..., - _16: Union[int, Type[int]] = ..., - _17: Union[int, Type[int]] = ..., - _18: Union[int, Type[int]] = ..., - _19: Union[int, Type[int]] = ..., - _20: Union[int, Type[int]] = ..., - _21: Union[int, Type[int]] = ..., - _22: Union[int, Type[int]] = ..., - _23: Union[int, Type[int]] = ..., - _24: Union[int, Type[int]] = ..., - _25: Union[int, Type[int]] = ..., - _26: Union[int, Type[int]] = ..., - _27: Union[int, Type[int]] = ..., - _28: Union[int, Type[int]] = ..., - _29: Union[int, Type[int]] = ..., - _30: Union[int, Type[int]] = ..., - _31: Union[int, Type[int]] = ..., - _32: Union[int, Type[int]] = ..., - /, -): +def isscalar(element: List[Union[float, int]]): """ - usage.pandas: 128 + usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def result_type( - _0: object, - _1: Union[numpy.ndarray, numpy.dtype, type] = ..., - _2: Union[numpy.ndarray, type] = ..., - _3: object = ..., - /, -): +def isscalar(element: numpy.float64): """ - usage.scipy: 778 + usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 2 """ ... @overload -def result_type( - _0: object, - _1: object = ..., - _2: Union[Literal["f8"], Type[numpy.float64], numpy.dtype] = ..., - _3: numpy.dtype = ..., - _4: numpy.dtype = ..., - /, -): +def isscalar(element: Tuple[int, int]): """ - usage.dask: 80 + usage.matplotlib: 1 + usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def result_type( - _0: numpy.dtype, - _1: numpy.dtype = ..., - _2: numpy.dtype = ..., - _3: numpy.dtype = ..., - _4: numpy.dtype = ..., - _5: numpy.dtype = ..., - _6: numpy.dtype = ..., - _7: numpy.dtype = ..., - _8: numpy.dtype = ..., - _9: numpy.dtype = ..., - _10: numpy.dtype = ..., - _11: numpy.dtype = ..., - _12: numpy.dtype = ..., - _13: numpy.dtype = ..., - _14: numpy.dtype = ..., - _15: numpy.dtype = ..., - _16: numpy.dtype = ..., - _17: numpy.dtype = ..., - _18: numpy.dtype = ..., - _19: numpy.dtype = ..., - _20: numpy.dtype = ..., - _21: numpy.dtype = ..., - _22: numpy.dtype = ..., - _23: numpy.dtype = ..., - _24: numpy.dtype = ..., - _25: numpy.dtype = ..., - _26: numpy.dtype = ..., - _27: numpy.dtype = ..., - _28: numpy.dtype = ..., - _29: numpy.dtype = ..., - _30: numpy.dtype = ..., - _31: numpy.dtype = ..., - _32: numpy.dtype = ..., - _33: numpy.dtype = ..., - _34: numpy.dtype = ..., - _35: numpy.dtype = ..., - _36: numpy.dtype = ..., - _37: numpy.dtype = ..., - _38: numpy.dtype = ..., - _39: numpy.dtype = ..., - _40: numpy.dtype = ..., - _41: numpy.dtype = ..., - _42: numpy.dtype = ..., - _43: numpy.dtype = ..., - _44: numpy.dtype = ..., - _45: numpy.dtype = ..., - _46: numpy.dtype = ..., - _47: numpy.dtype = ..., - _48: numpy.dtype = ..., - _49: numpy.dtype = ..., - _50: numpy.dtype = ..., - _51: numpy.dtype = ..., - _52: numpy.dtype = ..., - _53: numpy.dtype = ..., - _54: numpy.dtype = ..., - _55: numpy.dtype = ..., - _56: numpy.dtype = ..., - _57: numpy.dtype = ..., - _58: numpy.dtype = ..., - _59: numpy.dtype = ..., - _60: numpy.dtype = ..., - _61: numpy.dtype = ..., - _62: numpy.dtype = ..., - _63: numpy.dtype = ..., - _64: numpy.dtype = ..., - _65: numpy.dtype = ..., - _66: numpy.dtype = ..., - _67: numpy.dtype = ..., - _68: numpy.dtype = ..., - _69: numpy.dtype = ..., - _70: numpy.dtype = ..., - _71: numpy.dtype = ..., - _72: numpy.dtype = ..., - _73: numpy.dtype = ..., - _74: numpy.dtype = ..., - _75: numpy.dtype = ..., - _76: numpy.dtype = ..., - _77: numpy.dtype = ..., - _78: numpy.dtype = ..., - _79: numpy.dtype = ..., - _80: numpy.dtype = ..., - _81: numpy.dtype = ..., - _82: numpy.dtype = ..., - _83: numpy.dtype = ..., - _84: numpy.dtype = ..., - _85: numpy.dtype = ..., - _86: numpy.dtype = ..., - _87: numpy.dtype = ..., - _88: numpy.dtype = ..., - _89: numpy.dtype = ..., - _90: numpy.dtype = ..., - _91: numpy.dtype = ..., - _92: numpy.dtype = ..., - _93: numpy.dtype = ..., - _94: numpy.dtype = ..., - _95: numpy.dtype = ..., - _96: numpy.dtype = ..., - _97: numpy.dtype = ..., - _98: numpy.dtype = ..., - _99: numpy.dtype = ..., - _100: numpy.dtype = ..., - _101: numpy.dtype = ..., - _102: numpy.dtype = ..., - _103: numpy.dtype = ..., - _104: numpy.dtype = ..., - _105: numpy.dtype = ..., - _106: numpy.dtype = ..., - _107: numpy.dtype = ..., - _108: numpy.dtype = ..., - _109: numpy.dtype = ..., - _110: numpy.dtype = ..., - _111: numpy.dtype = ..., - _112: numpy.dtype = ..., - _113: numpy.dtype = ..., - _114: numpy.dtype = ..., - _115: numpy.dtype = ..., - _116: numpy.dtype = ..., - _117: numpy.dtype = ..., - _118: numpy.dtype = ..., - _119: numpy.dtype = ..., - _120: numpy.dtype = ..., - _121: numpy.dtype = ..., - _122: numpy.dtype = ..., - _123: numpy.dtype = ..., - _124: numpy.dtype = ..., - _125: numpy.dtype = ..., - _126: numpy.dtype = ..., - _127: numpy.dtype = ..., - _128: numpy.dtype = ..., - _129: numpy.dtype = ..., - _130: numpy.dtype = ..., - _131: numpy.dtype = ..., - _132: numpy.dtype = ..., - _133: numpy.dtype = ..., - _134: numpy.dtype = ..., - _135: numpy.dtype = ..., - _136: numpy.dtype = ..., - _137: numpy.dtype = ..., - _138: numpy.dtype = ..., - _139: numpy.dtype = ..., - _140: numpy.dtype = ..., - _141: numpy.dtype = ..., - _142: numpy.dtype = ..., - _143: numpy.dtype = ..., - _144: numpy.dtype = ..., - _145: numpy.dtype = ..., - _146: numpy.dtype = ..., - _147: numpy.dtype = ..., - _148: numpy.dtype = ..., - _149: numpy.dtype = ..., - _150: numpy.dtype = ..., - _151: numpy.dtype = ..., - _152: numpy.dtype = ..., - _153: numpy.dtype = ..., - _154: numpy.dtype = ..., - _155: numpy.dtype = ..., - _156: numpy.dtype = ..., - _157: numpy.dtype = ..., - _158: numpy.dtype = ..., - _159: numpy.dtype = ..., - _160: numpy.dtype = ..., - _161: numpy.dtype = ..., - _162: numpy.dtype = ..., - _163: numpy.dtype = ..., - _164: numpy.dtype = ..., - _165: numpy.dtype = ..., - _166: numpy.dtype = ..., - _167: numpy.dtype = ..., - _168: numpy.dtype = ..., - _169: numpy.dtype = ..., - _170: numpy.dtype = ..., - _171: numpy.dtype = ..., - _172: numpy.dtype = ..., - _173: numpy.dtype = ..., - _174: numpy.dtype = ..., - _175: numpy.dtype = ..., - _176: numpy.dtype = ..., - _177: numpy.dtype = ..., - _178: numpy.dtype = ..., - _179: numpy.dtype = ..., - _180: numpy.dtype = ..., - _181: numpy.dtype = ..., - _182: numpy.dtype = ..., - _183: numpy.dtype = ..., - _184: numpy.dtype = ..., - _185: numpy.dtype = ..., - _186: numpy.dtype = ..., - _187: numpy.dtype = ..., - _188: numpy.dtype = ..., - _189: numpy.dtype = ..., - _190: numpy.dtype = ..., - _191: numpy.dtype = ..., - _192: numpy.dtype = ..., - _193: numpy.dtype = ..., - _194: numpy.dtype = ..., - _195: numpy.dtype = ..., - _196: numpy.dtype = ..., - _197: numpy.dtype = ..., - _198: numpy.dtype = ..., - _199: numpy.dtype = ..., - /, -): +def isscalar(element: Tuple[int, int, int]): """ - usage.sklearn: 20 + usage.skimage: 3 """ ... -def result_type(_0: object, /, *_args: object): +@overload +def isscalar(element: Tuple[int, int, int, int]): """ - usage.dask: 80 - usage.pandas: 128 - usage.scipy: 778 - usage.skimage: 7 - usage.sklearn: 20 - usage.xarray: 114 + usage.skimage: 4 """ ... @overload -def roll(a: numpy.ndarray, shift: int, axis: int): +def isscalar(element: Tuple[float, float]): """ - usage.matplotlib: 3 - usage.skimage: 8 - usage.xarray: 1 + usage.skimage: 2 """ ... @overload -def roll(a: List[Union[float, int]], shift: int): +def isscalar(element: numpy.int64): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload -def roll(a: numpy.ndarray, shift: Tuple[int, int], axis: Tuple[int, int]): +def isscalar(element: List[Dict[Literal["dd", "da", "ad"], numpy.float64]]): """ - usage.skimage: 2 + usage.skimage: 6 """ ... @overload -def roll(a: numpy.ndarray, shift: int): +def isscalar(element: List[Dict[Literal["d"], numpy.float64]]): """ - usage.matplotlib: 1 usage.skimage: 2 """ ... @overload -def roll(a: numpy.ndarray, shift: Union[numpy.ndarray, int], axis: int = ...): +def isscalar(element: List[Dict[str, numpy.float64]]): """ - usage.pandas: 10 + usage.skimage: 3 """ ... @overload -def roll(a: numpy.ndarray, shift: int, axis: int = ...): +def isscalar(element: Tuple[int]): """ - usage.scipy: 8 + usage.skimage: 2 """ ... @overload -def roll( - a: numpy.ndarray, - shift: Union[Tuple[int, int], int], - axis: Union[Tuple[int, int], None, int] = ..., -): +def isscalar(element: slice[int, None, int]): """ - usage.dask: 5 + usage.skimage: 1 """ ... -def roll( - a: Union[numpy.ndarray, List[Union[float, int]]], - shift: Union[int, numpy.ndarray, Tuple[int, int]], - axis: Union[int, None, Tuple[int, int]] = ..., -): +@overload +def isscalar(element: xarray.core.indexing.CopyOnWriteArray): """ - usage.dask: 5 - usage.matplotlib: 4 - usage.pandas: 10 - usage.scipy: 8 - usage.skimage: 14 usage.xarray: 1 """ ... @overload -def rollaxis(a: numpy.ndarray, axis: int): +def isscalar(element: xarray.core.indexing.NumpyIndexingAdapter): """ - usage.skimage: 12 + usage.xarray: 1 """ ... @overload -def rollaxis(a: numpy.ndarray, axis: int, start: int): +def isscalar(element: xarray.core.indexing.LazilyOuterIndexedArray): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload -def rollaxis(a: numpy.ndarray, axis: int, start: int = ...): +def isscalar(element: xarray.core.indexing.LazilyVectorizedIndexedArray): """ - usage.scipy: 73 - usage.sklearn: 5 + usage.xarray: 1 """ ... @overload -def rollaxis( - a: Union[dask.array.core.Array, numpy.ndarray], axis: int, start: int = ... -): +def isscalar(element: Literal["a"]): """ - usage.dask: 5 + usage.xarray: 1 """ ... -def rollaxis( - a: Union[numpy.ndarray, dask.array.core.Array], axis: int, start: int = ... -): +@overload +def isscalar(element: cftime._cftime.DatetimeNoLeap): """ - usage.dask: 5 - usage.scipy: 73 - usage.skimage: 14 - usage.sklearn: 5 + usage.xarray: 1 """ ... -def roots(p: Union[List[Union[float, int]], numpy.ndarray]): +@overload +def isscalar(element: cftime._cftime.Datetime360Day): """ - usage.scipy: 23 + usage.xarray: 1 """ ... @overload -def rot90(m: numpy.ndarray, k: int): +def isscalar(element: cftime._cftime.DatetimeJulian): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload -def rot90(m: numpy.ndarray): +def isscalar(element: cftime._cftime.DatetimeAllLeap): """ - usage.scipy: 5 - usage.skimage: 5 + usage.xarray: 1 """ ... -def rot90(m: numpy.ndarray, k: int = ...): +@overload +def isscalar(element: cftime._cftime.DatetimeGregorian): """ - usage.scipy: 5 - usage.skimage: 7 + usage.xarray: 1 """ ... @overload -def round_(a: float): +def isscalar(element: cftime._cftime.DatetimeProlepticGregorian): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def round_(a: List[Union[int, float]]): +def isscalar(element: numpy.timedelta64): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def round_(a: numpy.ndarray): +def isscalar(element: Tuple[Literal["a"], numpy.int64, numpy.int64]): """ - usage.matplotlib: 4 - usage.skimage: 11 + usage.xarray: 1 """ ... @overload -def round_(a: float, decimals: int): +def isscalar(element: Tuple[Literal["b"], numpy.int64, numpy.int64]): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def round_(a: Tuple[numpy.float64, numpy.float64]): +def isscalar(element: Literal["2000-01-01"]): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload -def round_(a: Tuple[int, int]): +def isscalar(element: Literal["2000-01-02"]): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def round_(a: numpy.ndarray, decimals: int): +def isscalar(element: Literal["2000-01-03"]): """ - usage.matplotlib: 2 - usage.skimage: 5 + usage.xarray: 1 """ ... @overload -def round_(a: Tuple[int, int, int]): +def isscalar(element: Literal["bar"]): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def round_(a: Tuple[int, int, int, int]): +def isscalar(element: Tuple[Literal["a"], numpy.int64]): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def round_(a: List[int]): +def isscalar(element: Tuple[Literal["b"], numpy.int64]): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def round_(a: int): +def isscalar(element: Tuple[Literal["c"], numpy.int64]): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def round_(a: numpy.float64): +def isscalar(element: Tuple[numpy.int64, numpy.int64]): """ - usage.matplotlib: 2 - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def round_(a: Tuple[numpy.int64, numpy.int64]): +def isscalar(element: numpy.datetime64): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def round_(a: Tuple[numpy.int64, numpy.int64, numpy.int64]): +def isscalar(element: object): """ - usage.skimage: 1 + usage.dask: 547 + usage.scipy: 289 + usage.xarray: 1 """ ... @overload -def round_(a: Tuple[float, float]): +def isscalar(element: pandas._libs.tslibs.period.Period): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def round_(a: object): +def isscalar(element: xarray.core.indexing.MemoryCachedArray): """ usage.xarray: 1 """ @@ -31597,217 +30698,181 @@ def round_(a: object): @overload -def round_(a: xarray.core.dataarray.DataArray): +def isscalar(element: dask.array.core.Array): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload -def round_( - a: object, - decimals: int = ..., - out: Union[pandas.core.series.Series, pandas.core.frame.DataFrame] = ..., -): +def isscalar(element: list): """ - usage.pandas: 12 + usage.matplotlib: 1 """ ... @overload -def round_(a: object, decimals: int = ...): +def isscalar( + element: List[List[Literal["2017-01-01T00:00:00", "2017-01-02T00:00:00"]]] +): """ - usage.scipy: 50 + usage.matplotlib: 1 """ ... @overload -def round_( - a: Union[numpy.ndarray, dask.array.core.Array, numpy.ma.core.MaskedArray], - decimals: int = ..., -): +def isscalar(element: List[List[str]]): """ - usage.dask: 9 + usage.matplotlib: 1 """ ... @overload -def round_(a: Union[numpy.float64, numpy.ndarray], decimals: int = ...): +def isscalar(element: List[list]): """ - usage.sklearn: 11 + usage.matplotlib: 2 """ ... -def round_( - a: object, - decimals: int = ..., - out: Union[pandas.core.series.Series, pandas.core.frame.DataFrame] = ..., -): +@overload +def isscalar(element: List[float]): """ - usage.dask: 9 - usage.matplotlib: 9 - usage.pandas: 12 - usage.scipy: 50 - usage.skimage: 30 - usage.sklearn: 11 - usage.xarray: 3 + usage.matplotlib: 2 """ ... -def save(file: str, arr: Union[numpy.ndarray, numpy.memmap]): +@overload +def isscalar(element: Tuple[numpy.ndarray, numpy.ndarray]): """ - usage.dask: 6 + usage.matplotlib: 1 """ ... -def savez(file: Literal["/tmp/tmpj7_8czx9.npz"]): +@overload +def isscalar(element: List[List[int]]): """ - usage.scipy: 1 + usage.matplotlib: 2 """ ... -def savez_compressed(file: str): +@overload +def isscalar(element: List[Union[range, list]]): """ - usage.scipy: 15 + usage.matplotlib: 1 """ ... -def sctype2char(sctype: numpy.dtype): +@overload +def isscalar(element: List[range]): """ - usage.skimage: 4 + usage.matplotlib: 2 """ ... @overload -def searchsorted(a: numpy.ndarray, v: int): +def isscalar(element: Literal["abc"]): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def searchsorted(a: numpy.ndarray, v: numpy.float64): +def isscalar(element: pandas.core.series.Series): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def searchsorted( - a: xarray.coding.cftimeindex.CFTimeIndex, - v: xarray.coding.cftimeindex.CFTimeIndex, - side: Literal["left"], -): +def isscalar(element: slice[int, int, int]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def searchsorted( - a: xarray.coding.cftimeindex.CFTimeIndex, - v: xarray.coding.cftimeindex.CFTimeIndex, - side: Literal["right"], -): +def isscalar(element: List[bool]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def searchsorted(a: numpy.ndarray, v: numpy.ndarray): +def isscalar(element: Tuple[bool, bool, bool, bool, bool, bool, bool, bool, bool]): """ - usage.matplotlib: 2 - usage.xarray: 1 + usage.sklearn: 1 """ ... -@overload -def searchsorted( - a: object, v: object, sorter: range = ..., side: Literal["right"] = ... -): +def isscalar(element: object): """ - usage.pandas: 24 + usage.dask: 547 + usage.matplotlib: 30 + usage.scipy: 289 + usage.skimage: 68 + usage.sklearn: 26 + usage.xarray: 50 """ ... @overload -def searchsorted( - a: Union[numpy.ma.core.MaskedArray, numpy.ndarray, List[Union[float, int]]], - v: object, - side: Literal["right", "left"] = ..., -): +def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.floating]): """ - usage.scipy: 52 + usage.skimage: 2 """ ... @overload -def searchsorted( - a: Union[numpy.ndarray, Tuple[int, ...], List[int]], - v: Union[numpy.ndarray, int], - side: Literal["right", "left"], -): +def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.floating]): """ - usage.dask: 17 + usage.skimage: 3 """ ... @overload -def searchsorted(a: numpy.ndarray, v: object, side: Literal["right"] = ...): +def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.float64]): """ - usage.sklearn: 66 + usage.skimage: 1 """ ... -def searchsorted( - a: object, v: object, side: Literal["right", "left"] = ..., sorter: range = ... -): +@overload +def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.uint16]): """ - usage.dask: 17 - usage.matplotlib: 3 - usage.pandas: 24 - usage.scipy: 52 - usage.skimage: 3 - usage.sklearn: 66 - usage.xarray: 3 + usage.skimage: 1 """ ... -def select( - condlist: List[Union[numpy.bool_, numpy.ndarray]], - choicelist: List[Union[float, numpy.float64, numpy.ndarray]], - default: int = ..., -): +@overload +def issubdtype(arg1: Type[numpy.uint16], arg2: Type[numpy.float64]): """ - usage.scipy: 7 + usage.skimage: 1 """ ... @overload -def set_printoptions(precision: int): +def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.uint8]): """ usage.skimage: 1 """ @@ -31815,482 +30880,402 @@ def set_printoptions(precision: int): @overload -def set_printoptions(precision: int, threshold: int, edgeitems: int, linewidth: int): +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.floating]): """ - usage.xarray: 1 + usage.skimage: 18 + usage.sklearn: 10 + usage.xarray: 64 """ ... @overload -def set_printoptions( - precision: int, - threshold: int, - edgeitems: int, - linewidth: int, - suppress: bool, - nanstr: Literal["nan"], - infstr: Literal["inf"], - formatter: None, - sign: Literal["-"], - floatmode: Literal["maxprec"], - *, - legacy: bool, -): +def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.bool_]): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def set_printoptions(threshold: int): +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.integer]): """ - usage.xarray: 1 + usage.matplotlib: 24 + usage.skimage: 31 + usage.sklearn: 4 + usage.xarray: 52 """ ... @overload -def set_printoptions( - precision: int, - threshold: int, - edgeitems: int, - linewidth: int = ..., - suppress: bool = ..., - nanstr: Literal["nan"] = ..., - infstr: Literal["inf"] = ..., - formatter: None = ..., - sign: Literal["-"] = ..., - floatmode: Literal["maxprec"] = ..., - *, - legacy: bool = ..., -): +def issubdtype(arg1: Type[numpy.uint16], arg2: Type[numpy.floating]): """ - usage.sklearn: 2 + usage.skimage: 2 """ ... -def set_printoptions( - precision: int = ..., - threshold: int = ..., - edgeitems: int = ..., - linewidth: int = ..., - suppress: bool = ..., - nanstr: Literal["nan"] = ..., - infstr: Literal["inf"] = ..., - formatter: None = ..., - sign: Literal["-"] = ..., - floatmode: Literal["maxprec"] = ..., - *, - legacy: bool = ..., -): +@overload +def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.uint16]): """ usage.skimage: 1 - usage.sklearn: 2 - usage.xarray: 3 """ ... @overload -def setdiff1d(ar1: numpy.ndarray, ar2: numpy.ndarray, assume_unique: bool): +def issubdtype(arg1: Type[numpy.uint16], arg2: Type[numpy.uint16]): """ - usage.pandas: 3 - usage.scipy: 1 + usage.skimage: 1 """ ... @overload -def setdiff1d( - ar1: Union[List[int], numpy.ndarray], - ar2: Union[numpy.ndarray, List[Union[str, int]]], - assume_unique: bool = ..., -): +def issubdtype(arg1: Type[numpy.bool_], arg2: Type[numpy.floating]): """ - usage.sklearn: 52 + usage.skimage: 1 + usage.xarray: 1 """ ... -def setdiff1d( - ar1: Union[numpy.ndarray, List[int]], - ar2: Union[List[Union[int, str]], numpy.ndarray], - assume_unique: bool = ..., -): +@overload +def issubdtype(arg1: Type[numpy.float32], arg2: Type[numpy.floating]): """ - usage.pandas: 3 - usage.scipy: 1 - usage.sklearn: 52 + usage.skimage: 2 + usage.xarray: 1 """ ... @overload -def seterr(invalid: Literal["ignore"]): +def issubdtype(arg1: Type[numpy.int64], arg2: Type[numpy.floating]): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload -def seterr( - divide: Literal["warn"], - over: Literal["warn"], - under: Literal["ignore"], - invalid: Literal["warn"], -): +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.bool_]): """ - usage.skimage: 1 + usage.skimage: 5 + usage.sklearn: 4 + usage.xarray: 3 """ ... @overload -def seterr( - divide: Literal["warn"] = ..., - over: Literal["warn"] = ..., - under: Literal["ignore"] = ..., - invalid: Literal["warn"] = ..., -): +def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.float32]): """ - usage.dask: 2 + usage.skimage: 1 """ ... @overload -def seterr( - divide: Literal["warn"] = ..., - over: Literal["warn"] = ..., - under: Literal["ignore"] = ..., - invalid: Literal["warn"] = ..., -): +def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.float64]): """ - usage.sklearn: 6 + usage.skimage: 1 """ ... -def seterr( - divide: Literal["warn"] = ..., - over: Literal["warn"] = ..., - under: Literal["ignore"] = ..., - invalid: Literal["warn", "ignore"] = ..., -): +@overload +def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.uint8]): """ - usage.dask: 2 - usage.skimage: 2 - usage.sklearn: 6 + usage.skimage: 1 """ ... @overload -def shape(a: numpy.ndarray): +def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.int64]): """ - usage.matplotlib: 9 usage.skimage: 1 - usage.sklearn: 22 - usage.xarray: 9 """ ... @overload -def shape(a: numpy.ma.core.MaskedArray): +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.signedinteger]): """ - usage.matplotlib: 1 - usage.xarray: 3 + usage.skimage: 14 + usage.sklearn: 1 """ ... @overload -def shape(a: object): +def issubdtype(arg1: Type[numpy.bool_], arg2: Type[numpy.uint8]): """ - usage.scipy: 242 + usage.skimage: 1 """ ... @overload -def shape(a: List[numpy.ndarray]): +def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.uint8]): """ - usage.matplotlib: 10 + usage.skimage: 1 """ ... @overload -def shape(a: List[numpy.float64]): +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.unsignedinteger]): """ - usage.matplotlib: 2 + usage.skimage: 11 """ ... @overload -def shape(a: List[Union[float, None]]): +def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.floating]): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def shape(a: List[List[int]]): +def issubdtype(arg1: Type[numpy.int16], arg2: Type[numpy.floating]): """ - usage.matplotlib: 4 + usage.skimage: 2 """ ... @overload -def shape(a: List[list]): +def issubdtype(arg1: Type[numpy.uint32], arg2: Type[numpy.floating]): """ - usage.matplotlib: 3 + usage.skimage: 1 """ ... @overload -def shape(a: List[float]): +def issubdtype(arg1: Type[numpy.int32], arg2: Type[numpy.floating]): """ - usage.matplotlib: 6 + usage.skimage: 1 """ ... @overload -def shape(a: List[int]): +def issubdtype(arg1: Type[numpy.uint64], arg2: Type[numpy.floating]): """ - usage.matplotlib: 6 + usage.skimage: 1 """ ... @overload -def shape(a: list): +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.uint16]): """ - usage.matplotlib: 2 + usage.skimage: 2 """ ... @overload -def shape(a: List[numpy.int64]): +def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.int16]): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def shape(a: dask.array.core.Array): +def issubdtype(arg1: Type[numpy.int16], arg2: Type[numpy.uint8]): """ - usage.dask: 1 + usage.skimage: 1 """ ... -def shape(a: object): +@overload +def issubdtype(arg1: Type[numpy.uint16], arg2: Type[numpy.uint8]): """ - usage.dask: 1 - usage.matplotlib: 46 - usage.scipy: 242 usage.skimage: 1 - usage.sklearn: 22 - usage.xarray: 12 """ ... -def shares_memory(_0: numpy.ndarray, _1: numpy.ndarray, /): +@overload +def issubdtype(arg1: Type[numpy.int16], arg2: Type[numpy.uint16]): """ - usage.pandas: 11 + usage.skimage: 1 """ ... @overload -def sinc(x: Union[float, numpy.ndarray, List[int]]): +def issubdtype(arg1: Type[numpy.int64], arg2: Type[numpy.integer]): """ - usage.scipy: 8 + usage.skimage: 1 """ ... @overload -def sinc( - x: Union[ - dask.dataframe.core.DataFrame, - dask.dataframe.core.Series, - numpy.ndarray, - pandas.core.series.Series, - pandas.core.frame.DataFrame, - ] -): +def issubdtype(arg1: Type[numpy.bool_], arg2: Type[numpy.bool_]): """ - usage.dask: 17 + usage.skimage: 1 + usage.xarray: 1 """ ... -def sinc(x: object): +@overload +def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.float32]): """ - usage.dask: 17 - usage.scipy: 8 + usage.skimage: 1 """ ... @overload -def size(a: Union[pandas.core.series.Series, numpy.ndarray]): +def issubdtype(arg1: Type[numpy.float16], arg2: Type[numpy.floating]): """ - usage.pandas: 3 + usage.skimage: 1 """ ... @overload -def size(a: object, axis: int = ...): +def issubdtype(arg1: Type[numpy.uint16], arg2: Type[numpy.int16]): """ - usage.scipy: 56 + usage.skimage: 1 """ ... @overload -def size(a: List[int]): +def issubdtype(arg1: Type[numpy.uint16], arg2: Type[numpy.float32]): """ - usage.matplotlib: 3 + usage.skimage: 1 """ ... @overload -def size(a: numpy.ndarray): +def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.int16]): """ - usage.matplotlib: 6 + usage.skimage: 1 """ ... @overload -def size(a: list): +def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def size(a: List[List[Literal["2017-01-01T00:00:00", "2017-01-02T00:00:00"]]]): +def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.float32]): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def size(a: List[List[str]]): +def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.uint16]): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def size(a: List[list]): +def issubdtype(arg1: Type[numpy.int16], arg2: Type[numpy.int16]): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def size(a: Tuple[numpy.ndarray, numpy.ndarray]): +def issubdtype(arg1: Type[numpy.int16], arg2: Type[numpy.float64]): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def size(a: List[List[int]]): +def issubdtype(arg1: Type[numpy.int16], arg2: Type[numpy.float32]): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def size(a: List[Union[range, list]]): +def issubdtype(arg1: Type[numpy.float32], arg2: Type[numpy.int16]): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def size(a: List[range]): +def issubdtype(arg1: Type[numpy.float32], arg2: Type[numpy.float64]): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload -def size(a: List[Union[float, int]]): +def issubdtype(arg1: Type[numpy.float32], arg2: Type[numpy.float32]): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload -def size(a: numpy.ndarray, axis: int): +def issubdtype(arg1: Type[numpy.float32], arg2: Type[numpy.uint16]): """ - usage.matplotlib: 4 + usage.skimage: 1 """ ... @overload -def size(a: Union[numpy.ndarray, List[int]], axis: int = ...): +def issubdtype(arg1: Type[numpy.float32], arg2: Type[numpy.uint8]): """ - usage.sklearn: 22 + usage.skimage: 1 """ ... -def size(a: object, axis: int = ...): +@overload +def issubdtype(arg1: Type[numpy.float64], arg2: Type[numpy.int16]): """ - usage.matplotlib: 25 - usage.pandas: 3 - usage.scipy: 56 - usage.sklearn: 22 + usage.skimage: 1 """ ... @overload -def sort(a: numpy.ndarray): +def issubdtype(arg1: Type[numpy.uint8], arg2: Type[numpy.uint32]): """ - usage.matplotlib: 4 - usage.pandas: 33 - usage.skimage: 13 - usage.xarray: 2 + usage.skimage: 1 """ ... @overload -def sort(a: numpy.ndarray, axis: int): +def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.uint32]): """ usage.skimage: 1 """ @@ -32298,68 +31283,47 @@ def sort(a: numpy.ndarray, axis: int): @overload -def sort( - a: Union[ - List[Union[numpy.complex128, float, complex, int, numpy.float64]], - numpy.ndarray, - numpy.ma.core.MaskedArray, - ], - axis: Union[int, None] = ..., -): +def issubdtype(arg1: Type[numpy.int8], arg2: Type[numpy.int32]): """ - usage.scipy: 217 + usage.skimage: 1 """ ... @overload -def sort(a: Union[numpy.ndarray, dask.array.core.Array], axis: int = ...): +def issubdtype(arg1: Type[numpy.int32], arg2: Type[numpy.int8]): """ - usage.dask: 14 + usage.skimage: 1 """ ... @overload -def sort( - a: Union[numpy.ndarray, List[Union[int, float, numpy.float64]]], axis: int = ... -): +def issubdtype(arg1: Type[numpy.int32], arg2: Type[numpy.float32]): """ - usage.sklearn: 48 + usage.skimage: 1 """ ... -def sort( - a: Union[ - List[Union[numpy.complex128, float, complex, int, numpy.float64]], - numpy.ndarray, - numpy.ma.core.MaskedArray, - dask.array.core.Array, - ], - axis: Union[int, None] = ..., -): +@overload +def issubdtype(arg1: Type[numpy.uint64], arg2: Type[numpy.int16]): """ - usage.dask: 14 - usage.matplotlib: 4 - usage.pandas: 33 - usage.scipy: 217 - usage.skimage: 14 - usage.sklearn: 48 - usage.xarray: 2 + usage.skimage: 1 """ ... -def sort_complex(a: numpy.ndarray): +@overload +def issubdtype(arg1: Type[numpy.bool_], arg2: Type[numpy.int16]): """ - usage.scipy: 2 + usage.skimage: 1 """ ... @overload -def split(ary: numpy.ndarray, indices_or_sections: int): +def issubdtype(arg1: Type[numpy.bool_], arg2: Type[numpy.uint16]): """ usage.skimage: 1 """ @@ -32367,56 +31331,48 @@ def split(ary: numpy.ndarray, indices_or_sections: int): @overload -def split(ary: numpy.ndarray, indices_or_sections: List[int], axis: int): +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.datetime64]): """ - usage.pandas: 4 - usage.scipy: 8 + usage.matplotlib: 11 + usage.xarray: 77 """ ... @overload -def split(ary: numpy.ndarray, indices_or_sections: numpy.ndarray): +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.timedelta64]): """ - usage.matplotlib: 2 - usage.sklearn: 7 + usage.xarray: 67 """ ... -def split( - ary: numpy.ndarray, - indices_or_sections: Union[numpy.ndarray, int, List[int]], - axis: int = ..., -): +@overload +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.complexfloating]): """ - usage.matplotlib: 2 - usage.pandas: 4 - usage.scipy: 8 - usage.skimage: 1 - usage.sklearn: 7 + usage.xarray: 18 """ ... @overload -def squeeze(a: numpy.ndarray): +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.number]): """ - usage.skimage: 11 + usage.xarray: 13 """ ... @overload -def squeeze(a: numpy.ndarray, axis: int): +def issubdtype(arg1: Type[float], arg2: Type[numpy.floating]): """ - usage.skimage: 7 + usage.xarray: 1 """ ... @overload -def squeeze(a: numpy.ndarray, axis: Tuple[int]): +def issubdtype(arg1: Type[int], arg2: Type[numpy.floating]): """ usage.xarray: 1 """ @@ -32424,7 +31380,7 @@ def squeeze(a: numpy.ndarray, axis: Tuple[int]): @overload -def squeeze(a: numpy.ndarray, axis: Tuple[int, int]): +def issubdtype(arg1: Type[int], arg2: Type[numpy.integer]): """ usage.xarray: 1 """ @@ -32432,726 +31388,689 @@ def squeeze(a: numpy.ndarray, axis: Tuple[int, int]): @overload -def squeeze( - a: Union[pandas.core.frame.DataFrame, numpy.ndarray, pandas.core.series.Series] -): +def issubdtype(arg1: Type[numpy.bool_], arg2: Type[numpy.integer]): """ - usage.pandas: 5 + usage.xarray: 1 """ ... @overload -def squeeze( - a: Union[numpy.ndarray, float, numpy.float64, int], - axis: Union[Tuple[int, int], int, None] = ..., -): +def issubdtype(arg1: Type[str], arg2: Type[numpy.floating]): """ - usage.scipy: 35 + usage.xarray: 1 """ ... @overload -def squeeze(a: numpy.ndarray, axis: Union[Tuple[int, int], None, int]): +def issubdtype(arg1: Type[str], arg2: Type[numpy.integer]): """ - usage.dask: 3 + usage.xarray: 1 """ ... @overload -def squeeze( - a: Union[numpy.ndarray, float, numpy.float64, int, List[Union[int, float]]] -): +def issubdtype(arg1: Type[str], arg2: Type[numpy.bool_]): """ - usage.sklearn: 23 + usage.xarray: 1 """ ... -def squeeze(a: object, axis: Union[int, None, Tuple[int, ...]] = ...): +@overload +def issubdtype(arg1: Type[numpy.generic], arg2: Type[numpy.floating]): """ - usage.dask: 3 - usage.pandas: 5 - usage.scipy: 35 - usage.skimage: 18 - usage.sklearn: 23 - usage.xarray: 2 + usage.xarray: 1 """ ... @overload -def stack(arrays: List[numpy.ndarray], axis: int): +def issubdtype(arg1: Type[numpy.generic], arg2: Type[numpy.integer]): """ - usage.matplotlib: 8 - usage.skimage: 12 - usage.xarray: 24 + usage.xarray: 1 """ ... @overload -def stack(arrays: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], axis: int): +def issubdtype(arg1: Type[numpy.generic], arg2: Type[numpy.timedelta64]): """ - usage.skimage: 6 + usage.xarray: 1 """ ... @overload -def stack(arrays: List[numpy.float64], axis: int): +def issubdtype(arg1: Type[numpy.generic], arg2: Type[numpy.datetime64]): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload -def stack(arrays: Tuple[numpy.ndarray, numpy.float64, numpy.float64], axis: int): +def issubdtype(arg1: Union[numpy.dtype, type], arg2: Union[type, numpy.dtype]): """ - usage.skimage: 2 + usage.pandas: 83 """ ... @overload -def stack(arrays: Tuple[numpy.float64, numpy.ndarray, numpy.float64], axis: int): +def issubdtype(arg1: Union[type, None, numpy.dtype, numpy.int64], arg2: type): """ - usage.skimage: 2 + usage.scipy: 466 """ ... @overload -def stack(arrays: Tuple[numpy.float64, numpy.float64, numpy.ndarray], axis: int): +def issubdtype(arg1: Union[Type[int], numpy.dtype], arg2: type): """ - usage.skimage: 2 + usage.dask: 48 """ ... @overload -def stack(arrays: List[numpy.ndarray]): +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.flexible]): """ - usage.skimage: 2 - usage.xarray: 2 + usage.sklearn: 13 """ ... @overload -def stack(arrays: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.str_]): """ - usage.xarray: 1 + usage.sklearn: 8 """ ... @overload -def stack(arrays: List[xarray.core.dataarray.DataArray], axis: int): +def issubdtype(arg1: numpy.dtype, arg2: Type[numpy.object_]): """ - usage.xarray: 1 + usage.sklearn: 4 """ ... -@overload -def stack(arrays: List[float], axis: int): +def issubdtype( + arg1: Union[numpy.dtype, numpy.int64, None, type], arg2: Union[type, numpy.dtype] +): """ - usage.xarray: 1 + usage.dask: 48 + usage.matplotlib: 35 + usage.pandas: 83 + usage.scipy: 466 + usage.skimage: 143 + usage.sklearn: 44 + usage.xarray: 308 """ ... -@overload -def stack(arrays: List[sparse._coo.core.COO], axis: int): +def issubsctype(arg1: numpy.ndarray, arg2: Type[numpy.float32]): """ - usage.xarray: 2 + usage.scipy: 2 """ ... @overload -def stack(arrays: list, axis: int): +def iterable(y: object): """ - usage.xarray: 3 + usage.matplotlib: 2 + usage.pandas: 32 """ ... @overload -def stack(arrays: Union[Tuple[numpy.ndarray, numpy.ndarray], List[numpy.ndarray]]): +def iterable(y: matplotlib.gridspec.SubplotSpec): """ - usage.scipy: 3 + usage.matplotlib: 1 """ ... @overload -def stack(arrays: Tuple[numpy.ndarray, numpy.ndarray], axis: int): +def iterable(y: List[int]): """ - usage.matplotlib: 2 + usage.matplotlib: 70 + usage.sklearn: 1 """ ... @overload -def stack(arrays: Union[Tuple[numpy.ndarray, ...], list], axis: int = ...): +def iterable(y: int): """ - usage.dask: 32 + usage.matplotlib: 21 + usage.sklearn: 2 """ ... -def stack( - arrays: Union[list, Tuple[Union[numpy.ndarray, numpy.float64], ...]], - axis: int = ..., -): +@overload +def iterable(y: Tuple[int]): """ - usage.dask: 32 - usage.matplotlib: 10 - usage.scipy: 3 - usage.skimage: 28 - usage.xarray: 34 + usage.matplotlib: 2 """ ... @overload -def std(a: numpy.ndarray): +def iterable(y: Tuple[float, float]): """ - usage.skimage: 1 - usage.xarray: 1 + usage.matplotlib: 4 """ ... @overload -def std(a: numpy.ndarray, axis: None): +def iterable(y: Tuple[int, float]): """ - usage.xarray: 1 + usage.matplotlib: 4 """ ... @overload -def std(a: object, axis: None): +def iterable(y: Tuple[int, int]): """ - usage.xarray: 1 + usage.matplotlib: 5 """ ... @overload -def std(a: numpy.ndarray, axis: int): +def iterable(y: float): """ - usage.xarray: 3 + usage.matplotlib: 13 + usage.sklearn: 2 """ ... @overload -def std(a: object, axis: int): +def iterable(y: numpy.float64): """ - usage.xarray: 2 + usage.matplotlib: 7 + usage.sklearn: 1 """ ... @overload -def std(a: object): +def iterable(y: numpy.ndarray): """ - usage.xarray: 1 + usage.matplotlib: 88 + usage.sklearn: 1 """ ... @overload -def std(a: xarray.core.dataarray.DataArray): +def iterable(y: numpy.ma.core.MaskedArray): """ - usage.xarray: 1 + usage.matplotlib: 26 """ ... @overload -def std(a: numpy.ndarray, axis: None, dtype: None, ddof: int): +def iterable(y: numpy.int64): """ - usage.xarray: 1 + usage.matplotlib: 4 """ ... @overload -def std(a: object, axis: None, dtype: None, ddof: int): +def iterable(y: range_iterator): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def std(a: xarray.core.dataset.Dataset): +def iterable(y: bool): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload -def std(a: object, axis: int, dtype: None, ddof: int): +def iterable(y: List[bool]): """ - usage.xarray: 1 + usage.matplotlib: 6 """ ... @overload -def std(a: numpy.ndarray, axis: int, dtype: None, ddof: int): +def iterable(y: List[Union[int, float]]): """ - usage.xarray: 1 + usage.matplotlib: 23 """ ... @overload -def std(a: numpy.ndarray, axis: Tuple[int, int]): +def iterable(y: list): """ - usage.xarray: 1 + usage.matplotlib: 18 """ ... @overload -def std( - a: Union[numpy.ndarray, pandas.core.series.Series], - axis: Union[None, int] = ..., - ddof: int = ..., -): +def iterable(y: matplotlib.lines.Line2D): """ - usage.pandas: 20 + usage.matplotlib: 1 """ ... @overload -def std( - a: Union[numpy.ma.core.MaskedArray, numpy.ndarray, list], - axis: Union[int, None, Tuple[int, int]] = ..., - ddof: int = ..., -): +def iterable(y: List[None]): """ - usage.scipy: 14 + usage.matplotlib: 1 """ ... @overload -def std( - a: object, - axis: Union[None, Tuple[Union[None, int], ...], int] = ..., - out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., - ddof: int = ..., - keepdims: bool = ..., -): +def iterable(y: None): """ - usage.dask: 55 + usage.matplotlib: 6 """ ... @overload -def std(a: numpy.ndarray, axis: int = ...): +def iterable(y: matplotlib.spines.Spine): """ - usage.sklearn: 14 + usage.matplotlib: 1 """ ... -def std( - a: object, - axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., - out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., - keepdims: bool = ..., - dtype: Union[Literal["i8", "f8"], None] = ..., - ddof: int = ..., -): +@overload +def iterable(y: type): """ - usage.dask: 55 - usage.pandas: 20 - usage.scipy: 14 - usage.skimage: 1 - usage.sklearn: 14 - usage.xarray: 16 + usage.matplotlib: 1 """ ... @overload -def sum(a: numpy.ndarray): +def iterable(y: Type[matplotlib.lines.Line2D]): """ - usage.matplotlib: 13 - usage.skimage: 91 - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload -def sum(a: numpy.ndarray, axis: int): +def iterable(y: matplotlib.testing.jpl_units.UnitDbl.UnitDbl): """ - usage.matplotlib: 14 - usage.skimage: 14 - usage.xarray: 9 + usage.matplotlib: 3 """ ... @overload -def sum(a: List[numpy.ndarray], axis: int): +def iterable(y: List[decimal.Decimal]): """ - usage.skimage: 6 + usage.matplotlib: 2 """ ... @overload -def sum(a: Tuple[int, int]): +def iterable(y: List[float]): """ - usage.skimage: 1 + usage.matplotlib: 25 + usage.sklearn: 2 """ ... @overload -def sum(a: Tuple[int, int, int]): +def iterable(y: decimal.Decimal): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def sum(a: numpy.ndarray, axis: Tuple[int, int]): +def iterable(y: List[numpy.float64]): """ - usage.skimage: 1 - usage.xarray: 4 + usage.matplotlib: 88 """ ... @overload -def sum(a: numpy.ndarray, axis: Tuple[int]): +def iterable(y: range): """ - usage.xarray: 2 + usage.matplotlib: 7 """ ... @overload -def sum(a: dask.array.core.Array, axis: Tuple[int]): +def iterable(y: numpy.bool_): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def sum(a: numpy.ndarray, axis: None): +def iterable(y: Tuple[int, int, int, int]): """ - usage.xarray: 6 + usage.matplotlib: 4 """ ... @overload -def sum(a: numpy.ndarray, axis: Tuple[int, int], dtype: None): +def iterable(y: matplotlib.transforms.Bbox): """ - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload -def sum(a: numpy.ndarray, axis: None, dtype: None): +def iterable(y: Literal["2009-04-27T00:00:00"]): """ - usage.xarray: 4 + usage.matplotlib: 1 """ ... @overload -def sum(a: xarray.core.dataarray.DataArray): +def iterable(y: matplotlib.testing.jpl_units.Epoch.Epoch): """ - usage.xarray: 3 + usage.matplotlib: 3 """ ... @overload -def sum(a: numpy.ndarray, axis: int, dtype: None): +def iterable(y: Literal["2000-01-01"]): """ - usage.xarray: 4 + usage.matplotlib: 1 """ ... @overload -def sum(a: numpy.ndarray, axis: Tuple[None, ...]): +def iterable(y: Literal["2010-01-01"]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def sum(a: numpy.ndarray, axis: int, keepdims: bool): +def iterable(y: List[str]): """ - usage.xarray: 1 + usage.matplotlib: 49 """ ... @overload -def sum(a: numpy.bool_, axis: None): +def iterable(y: Literal["2009-01-20T00:00:00"]): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def sum(a: List[Tuple[int, int]], axis: int): +def iterable(y: Literal["2009-01-21T00:00:00"]): """ - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload -def sum(a: numpy.ndarray, axis: None, dtype: Type[float]): +def iterable(y: List[matplotlib.testing.jpl_units.Epoch.Epoch]): """ - usage.xarray: 3 + usage.matplotlib: 4 """ ... @overload -def sum(a: dask.array.core.Array, axis: None): +def iterable(y: List[Union[float, int]]): """ - usage.xarray: 1 + usage.matplotlib: 11 """ ... @overload -def sum(a: numpy.ndarray, axis: None, dtype: Type[float], keepdims: bool): +def iterable(y: Literal["2018-01-01T00:00:00"]): """ - usage.xarray: 2 + usage.matplotlib: 2 """ ... @overload -def sum(a: numpy.ndarray, axis: None, dtype: None, keepdims: bool): +def iterable(y: datetime.timedelta): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def sum(a: numpy.ndarray, axis: int, dtype: Type[float]): +def iterable(y: Literal["2018-01-01T03:00:00"]): """ - usage.xarray: 3 + usage.matplotlib: 2 """ ... @overload -def sum(a: dask.array.core.Array, axis: int): +def iterable(y: Literal["2018-01-01T02:00:00"]): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def sum(a: numpy.ndarray, axis: int, dtype: Type[float], keepdims: bool): +def iterable(y: List[numpy.int64]): """ - usage.xarray: 2 + usage.matplotlib: 43 """ ... @overload -def sum(a: numpy.ndarray, axis: int, dtype: None, keepdims: bool): +def iterable(y: List[Literal["2018-01-01T00:00:00"]]): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def sum(a: sparse._coo.core.COO, axis: Tuple[int]): +def iterable(y: List[datetime.timedelta]): """ - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload -def sum(a: sparse._coo.core.COO, axis: None): +def iterable(y: Literal["2018-01-01T01:00:00"]): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def sum(a: sparse._coo.core.COO, axis: int): +def iterable( + y: List[ + Literal["2019-03-01T00:00:00", "2019-02-01T00:00:00", "2019-01-01T00:00:00"] + ] +): """ - usage.xarray: 2 + usage.matplotlib: 3 """ ... @overload -def sum(a: sparse._coo.core.COO, axis: None, dtype: None): +def iterable(y: List[Literal["y"]]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def sum(a: xarray.core.dataarray.DataArray, axis: int): +def iterable(y: List[Literal["c"]]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def sum(a: sparse._coo.core.COO, axis: int, dtype: None): +def iterable(y: matplotlib.ticker.FixedLocator): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def sum(a: object, axis: None, dtype: None): +def iterable( + y: List[Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64]] +): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload -def sum(a: object, axis: None): +def iterable(y: List[Literal["lime", "b", "y", "r"]]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def sum(a: object): +def iterable(y: List[List[Union[int, float]]]): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload -def sum(a: object, axis: int): +def iterable(y: List[Literal["0.8", "0.7", "0.6", "0.5"]]): """ - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload -def sum(a: xarray.core.dataset.Dataset): +def iterable(y: List[numpy.ndarray]): """ - usage.xarray: 1 + usage.matplotlib: 20 """ ... @overload -def sum(a: object, axis: int, dtype: None): +def iterable(y: List[Union[numpy.float64, float]]): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def sum(a: numpy.ndarray, axis: Tuple[int], dtype: None): +def iterable(y: List[Literal["2013-09-28T12:00:00", "2013-09-28T11:00:00"]]): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def sum(a: numpy.ndarray, axis: Tuple[int, int, int], dtype: None): +def iterable(y: List[List[float]]): """ - usage.xarray: 1 + usage.matplotlib: 5 """ ... @overload -def sum(a: numpy.ndarray, axis: Tuple[int, int, int]): +def iterable(y: List[List[int]]): """ - usage.xarray: 1 + usage.matplotlib: 10 """ ... @overload -def sum( - a: object, - axis: Union[int, None] = ..., - dtype: Type[numpy.int64] = ..., - out: numpy.float64 = ..., - initial: int = ..., - keepdims: bool = ..., -): +def iterable(y: Literal["0.5"]): """ - usage.pandas: 38 + usage.matplotlib: 1 """ ... @overload -def sum( - a: object, - axis: Union[int, Tuple[int, int], None] = ..., - keepdims: bool = ..., - dtype: Union[type, None] = ..., +def iterable( + y: Tuple[ + Literal["tab:orange"], + Literal["tab:pink"], + Literal["tab:cyan"], + Literal["bLacK"], + ] ): """ - usage.scipy: 442 + usage.matplotlib: 1 """ ... @overload -def sum(a: List[float]): +def iterable(y: List[Literal["b", "r"]]): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def sum(a: numpy.ma.core.MaskedArray): +def iterable(y: List[Literal["dashed", "solid"]]): """ usage.matplotlib: 1 """ @@ -33159,155 +32078,103 @@ def sum(a: numpy.ma.core.MaskedArray): @overload -def sum( - a: object, - axis: Union[None, Tuple[Union[None, int], ...], int] = ..., - dtype: Union[numpy.dtype, Literal["f8", "i8", "i4", "f4", "u4"]] = ..., - keepdims: bool = ..., - out: Union[ - dask.dataframe.core.Scalar, dask.array.core.Array, dask.dataframe.core.Series - ] = ..., -): +def iterable(y: List[list]): """ - usage.dask: 216 + usage.matplotlib: 2 """ ... @overload -def sum( - a: object, - axis: Union[int, None] = ..., - keepdims: bool = ..., - out: numpy.ndarray = ..., - dtype: type = ..., -): +def iterable(y: List[Tuple[float, float, float, float]]): """ - usage.sklearn: 397 + usage.matplotlib: 1 """ ... -def sum( - a: object, - axis: Union[None, int, Tuple[Union[None, int], ...]] = ..., - out: Union[ - numpy.ndarray, - numpy.float64, - dask.dataframe.core.Scalar, - dask.array.core.Array, - dask.dataframe.core.Series, - ] = ..., - dtype: Union[type, None, numpy.dtype, Literal["f8", "i8", "i4", "f4", "u4"]] = ..., - keepdims: bool = ..., -): +@overload +def iterable(y: Callable): """ - usage.dask: 216 - usage.matplotlib: 30 - usage.pandas: 38 - usage.scipy: 442 - usage.skimage: 114 - usage.sklearn: 397 - usage.xarray: 75 + usage.matplotlib: 3 """ ... @overload -def swapaxes(a: numpy.ndarray, axis1: int, axis2: int): +def iterable(y: Tuple[numpy.float64, numpy.float64]): """ - usage.dask: 4 - usage.scipy: 48 - usage.skimage: 2 - usage.sklearn: 2 - usage.xarray: 8 + usage.matplotlib: 2 """ ... @overload -def swapaxes(a: object, axis1: int, axis2: int): +def iterable(y: matplotlib.text.Text): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... -def swapaxes(a: object, axis1: int, axis2: int): +@overload +def iterable(y: Tuple[numpy.int64, numpy.int64]): """ - usage.dask: 4 - usage.scipy: 48 - usage.skimage: 2 - usage.sklearn: 2 - usage.xarray: 9 + usage.matplotlib: 2 """ ... @overload -def take(a: numpy.ndarray, indices: numpy.ndarray): +def iterable(y: Literal["2018-11-09T00:00:00"]): """ - usage.skimage: 1 + usage.matplotlib: 2 """ ... @overload -def take(a: numpy.ndarray, indices: int, axis: int): +def iterable(y: Literal["2018-11-09T01:00:00"]): """ - usage.xarray: 2 + usage.matplotlib: 2 """ ... @overload -def take(a: numpy.ndarray, indices: List[int], axis: int): +def iterable(y: numpy.uint8): """ - usage.xarray: 10 + usage.matplotlib: 1 """ ... @overload -def take(a: numpy.ndarray, indices: numpy.ndarray, axis: int): +def iterable(y: numpy.datetime64): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def take( - a: Union[ - pandas.core.indexes.numeric.Float64Index, - numpy.ndarray, - pandas.core.indexes.base.Index, - pandas.core.indexes.numeric.Int64Index, - List[Literal["#2ca02c", "#ff7f0e", "#1f77b4"]], - ], - indices: Union[numpy.ndarray, int, List[int]], - axis: int = ..., -): +def iterable(y: matplotlib.axes._subplots.Axes3DSubplot): """ - usage.pandas: 52 + usage.matplotlib: 1 """ ... @overload -def take( - a: Union[numpy.ndarray, Tuple[int, ...]], - indices: Union[int, numpy.ndarray, Tuple[int, ...]], - axis: int = ..., -): +def iterable(y: matplotlib.patches.Rectangle): """ - usage.scipy: 59 + usage.matplotlib: 1 """ ... @overload -def take(a: List[float], indices: List[int]): +def iterable(y: Literal["Здравствуйте мир"]): """ usage.matplotlib: 1 """ @@ -33315,15 +32182,15 @@ def take(a: List[float], indices: List[int]): @overload -def take(a: numpy.ndarray, indices: List[int]): +def iterable(y: Literal["hello world"]): """ - usage.matplotlib: 3 + usage.matplotlib: 1 """ ... @overload -def take(a: List[int], indices: List[int]): +def iterable(y: Literal["a"]): """ usage.matplotlib: 1 """ @@ -33331,233 +32198,175 @@ def take(a: List[int], indices: List[int]): @overload -def take(a: numpy.ndarray, indices: Union[numpy.ndarray, int, List[int]], axis: int): +def iterable(y: Literal["1"]): """ - usage.dask: 3 + usage.matplotlib: 1 """ ... @overload -def take( - a: Union[ - Tuple[int, int, int], - numpy.ndarray, - List[Union[int, Literal["three", "two", "one"]]], - ], - indices: numpy.ndarray, - axis: int = ..., - mode: Literal["clip"] = ..., -): +def iterable(y: Literal["A"]): """ - usage.sklearn: 10 + usage.matplotlib: 1 """ ... -def take( - a: object, - indices: Union[numpy.ndarray, int, Tuple[int, ...], List[int]], - axis: int = ..., - mode: Literal["clip"] = ..., -): +@overload +def iterable(y: Literal["hi"]): """ - usage.dask: 3 - usage.matplotlib: 5 - usage.pandas: 52 - usage.scipy: 59 - usage.skimage: 1 - usage.sklearn: 10 - usage.xarray: 13 + usage.matplotlib: 1 """ ... -def take_along_axis(arr: numpy.ndarray, indices: numpy.ndarray, axis: int): +@overload +def iterable(y: Literal["мир"]): """ - usage.dask: 3 - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def tensordot(a: numpy.ndarray, b: numpy.ndarray, axes: Tuple[int, int]): +def iterable(y: Literal["42"]): """ usage.matplotlib: 1 - usage.skimage: 1 """ ... @overload -def tensordot(a: numpy.ndarray, b: range, axes: List[int]): +def iterable(y: List[Literal["hi", "world", "hello"]]): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def tensordot(a: numpy.ndarray, b: numpy.ndarray, axes: Tuple[List[int], List[int]]): +def iterable(y: Literal["hello"]): """ - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload -def tensordot(a: numpy.ndarray, b: numpy.ndarray, axes: List[int]): +def iterable(y: List[Literal["привет", "Здравствуйте"]]): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def tensordot(a: numpy.ndarray, b: numpy.ndarray, axes: int): +def iterable(y: Literal["Здравствуйте"]): """ - usage.scipy: 2 + usage.matplotlib: 1 """ ... @overload -def tensordot( - a: object, - b: object, - axes: Union[ - Tuple[ - Union[int, Tuple[Union[int, None], ...]], - Union[int, Tuple[Union[int, None], ...]], - ], - int, - ], -): +def iterable(y: List[Literal["c", "b", "a"]]): """ - usage.dask: 23 + usage.matplotlib: 1 """ ... -def tensordot( - a: object, - b: object, - axes: Union[ - int, - Tuple[ - Union[List[int], int, Tuple[Union[None, int], ...]], - Union[List[int], int, Tuple[Union[None, int], ...]], - ], - List[int], - ], -): +@overload +def iterable(y: List[bytes]): """ - usage.dask: 23 usage.matplotlib: 1 - usage.scipy: 2 - usage.skimage: 1 - usage.xarray: 5 """ ... @overload -def tile(A: List[int], reps: Tuple[int, int]): +def iterable(y: bytes): """ - usage.skimage: 2 + usage.matplotlib: 1 """ ... @overload -def tile(A: numpy.ndarray, reps: Tuple[int, int]): +def iterable(y: List[Literal["3", "11", "1"]]): """ - usage.matplotlib: 15 - usage.skimage: 8 - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def tile(A: numpy.ndarray, reps: Tuple[int, int, int]): +def iterable(y: List[Literal["world", "happy", "hello"]]): """ - usage.skimage: 2 - usage.xarray: 5 + usage.matplotlib: 1 """ ... @overload -def tile(A: numpy.ndarray, reps: List[int]): +def iterable(y: List[Literal["fun", "is", "Python"]]): """ - usage.skimage: 1 + usage.matplotlib: 2 """ ... @overload -def tile(A: numpy.ndarray, reps: Tuple[int, int, int, int]): +def iterable(y: Literal["Python"]): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def tile(A: Tuple[int, int], reps: List[int]): +def iterable(y: List[Literal["b", "a"]]): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def tile( - A: Union[ - numpy.ndarray, - range, - pandas.core.indexes.base.Index, - List[Union[numpy.int8, int, Literal["c", "b", "a"]]], - ], - reps: Union[int, numpy.int64, Tuple[int, ...], List[int]], -): +def iterable(y: List[Literal["g", "e"]]): """ - usage.pandas: 49 + usage.matplotlib: 2 """ ... @overload -def tile( - A: Union[Tuple[Union[numpy.int64, numpy.float64], ...], numpy.ndarray], - reps: Union[Tuple[int, int], List[int], int], -): +def iterable(y: Literal["e"]): """ - usage.scipy: 51 + usage.matplotlib: 1 """ ... @overload -def tile(A: numpy.ndarray, reps: int): +def iterable(y: List[Literal["d", "b", "a"]]): """ - usage.matplotlib: 9 + usage.matplotlib: 1 """ ... @overload -def tile(A: List[Union[float, int]], reps: int): +def iterable(y: List[Literal["b", "a", "f"]]): """ - usage.matplotlib: 1 + usage.matplotlib: 2 """ ... @overload -def tile(A: Tuple[float, numpy.float64], reps: Tuple[int, int]): +def iterable(y: Literal["f"]): """ usage.matplotlib: 1 """ @@ -33565,186 +32374,186 @@ def tile(A: Tuple[float, numpy.float64], reps: Tuple[int, int]): @overload -def tile(A: Union[numpy.ndarray, List[List[int]]], reps: Union[Tuple[int, ...], int]): +def iterable(y: List[Literal["d", "c", "b"]]): """ - usage.dask: 18 + usage.matplotlib: 1 """ ... @overload -def tile(A: object, reps: Union[Tuple[int, ...], int, List[int]]): +def iterable(y: Literal["b"]): """ - usage.sklearn: 31 + usage.matplotlib: 1 """ ... -def tile(A: object, reps: Union[List[int], int, numpy.int64, Tuple[int, ...]]): +@overload +def iterable(y: List[Literal["d", "e", "g"]]): """ - usage.dask: 18 - usage.matplotlib: 26 - usage.pandas: 49 - usage.scipy: 51 - usage.skimage: 15 - usage.sklearn: 31 - usage.xarray: 6 + usage.matplotlib: 2 """ ... @overload -def trace(a: numpy.ndarray): +def iterable(y: Literal["g"]): """ - usage.scipy: 2 + usage.matplotlib: 1 """ ... @overload -def trace(a: numpy.ndarray, axis1: int = ..., axis2: int = ...): +def iterable(y: Literal["12"]): """ - usage.sklearn: 11 + usage.matplotlib: 1 """ ... -def trace(a: numpy.ndarray, axis1: int = ..., axis2: int = ...): +@overload +def iterable(y: mpl_toolkits.mplot3d.axes3d.Axes3D): """ - usage.scipy: 2 - usage.sklearn: 11 + usage.matplotlib: 1 """ ... @overload -def transpose(a: List[numpy.ndarray]): +def iterable(y: matplotlib.axes._subplots.AxesSubplot): """ usage.matplotlib: 2 - usage.skimage: 2 """ ... @overload -def transpose(a: numpy.ndarray, axes: numpy.ndarray): +def iterable(y: List[matplotlib.axes._subplots.AxesSubplot]): """ - usage.skimage: 3 + usage.matplotlib: 4 """ ... @overload -def transpose(a: numpy.ndarray): +def iterable(y: List[Literal["c", "b", "g", "r"]]): """ - usage.matplotlib: 3 - usage.skimage: 3 + usage.matplotlib: 1 """ ... @overload -def transpose(a: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): +def iterable(y: numpy.float128): """ - usage.skimage: 4 + usage.matplotlib: 1 """ ... @overload -def transpose(a: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]): +def iterable(y: List[Literal["yellow", "blue", "green"]]): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def transpose(a: Tuple[numpy.ndarray, numpy.ndarray]): +def iterable(y: List[Tuple[float, float, float]]): """ - usage.skimage: 3 + usage.matplotlib: 3 """ ... @overload -def transpose(a: numpy.ndarray, axes: Tuple[int, int, int, int]): +def iterable(y: matplotlib.ticker.MaxNLocator): """ - usage.skimage: 2 + usage.matplotlib: 1 """ ... @overload -def transpose(a: numpy.ndarray, axes: List[int]): +def iterable(y: matplotlib.axes._axes.Axes): """ - usage.matplotlib: 5 - usage.skimage: 1 + usage.matplotlib: 2 """ ... @overload -def transpose(a: numpy.ndarray, axes: Tuple[int, int, int]): +def iterable(y: Tuple[Literal["r"], Literal["g"]]): """ - usage.skimage: 1 - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload -def transpose(a: Tuple[numpy.ndarray]): +def iterable(y: List[Literal["blue", "pink", "yellow"]]): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def transpose(a: numpy.ndarray, axes: Tuple[int, int]): +def iterable(y: List[Literal["blue", "pink", "yellow", "red"]]): """ - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload -def transpose(a: sparse._coo.core.COO, axes: Tuple[int, int]): +def iterable(y: List[Literal["black", "blue", "pink", "yellow"]]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def transpose(a: object, axes: Tuple[int, int]): +def iterable(y: Literal["2017-01-01T00:01:01"]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def transpose(a: object, axes: int = ...): +def iterable(y: List[Literal["2017-01-01T01:01:01", "2017-01-01T00:01:01"]]): """ - usage.pandas: 18 + usage.matplotlib: 1 """ ... @overload -def transpose( - a: object, axes: Union[List[int], Tuple[Union[numpy.int64, int], ...]] = ... +def iterable( + y: List[ + List[ + Literal[ + "2017-01-01T01:01:01", + "2017-01-01T00:01:01", + "2017-01-01T03:01:01", + "2017-01-01T02:01:01", + ] + ] + ] ): """ - usage.scipy: 119 + usage.matplotlib: 1 """ ... @overload -def transpose(a: numpy.ma.core.MaskedArray): +def iterable(y: List[Literal["2009-01-21T00:00:00", "2009-01-20T00:00:00"]]): """ usage.matplotlib: 2 """ @@ -33752,387 +32561,356 @@ def transpose(a: numpy.ma.core.MaskedArray): @overload -def transpose(a: object, axes: Tuple[Union[None, int], ...]): +def iterable(y: Literal["2009-01-15T00:00:00"]): """ - usage.dask: 25 + usage.matplotlib: 2 """ ... @overload -def transpose( - a: Union[numpy.ndarray, List[Union[numpy.ndarray, List[Union[float, int]]]]], - axes: Tuple[int, ...] = ..., -): +def iterable(y: Literal["2009-01-26T00:00:00"]): """ - usage.sklearn: 37 + usage.matplotlib: 2 """ ... -def transpose( - a: object, - axes: Union[ - Tuple[Union[None, numpy.int64, int], ...], List[int], int, numpy.ndarray - ] = ..., -): +@overload +def iterable(y: List[Literal["2010-01-21T00:00:00", "2000-01-20T00:00:00"]]): """ - usage.dask: 25 - usage.matplotlib: 12 - usage.pandas: 18 - usage.scipy: 119 - usage.skimage: 21 - usage.sklearn: 37 - usage.xarray: 8 + usage.matplotlib: 2 """ ... @overload -def trapz( - y: xarray.core.dataarray.DataArray, x: xarray.core.dataarray.DataArray, axis: int -): +def iterable(y: Literal["1998-01-30T00:00:00"]): """ - usage.xarray: 2 + usage.matplotlib: 2 """ ... @overload -def trapz(y: dask.array.core.Array, x: numpy.ndarray, axis: int): +def iterable(y: Literal["2012-01-11T00:00:00"]): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def trapz(y: numpy.ndarray, x: numpy.ndarray, axis: int): +def iterable(y: List[Literal["2009-01-20T00:00:00"]]): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload -def trapz(y: numpy.ndarray, x: numpy.ndarray): +def iterable(y: Literal["2009-02-05T00:00:00"]): """ - usage.scipy: 1 - usage.sklearn: 2 + usage.matplotlib: 2 """ ... -def trapz( - y: Union[numpy.ndarray, xarray.core.dataarray.DataArray, dask.array.core.Array], - x: Union[numpy.ndarray, xarray.core.dataarray.DataArray], - axis: int = ..., -): +@overload +def iterable(y: Literal["2000-01-20T00:00:00"]): """ - usage.scipy: 1 - usage.sklearn: 2 - usage.xarray: 4 + usage.matplotlib: 2 """ ... @overload -def tri(N: int): +def iterable(y: List[Literal["2000-01-20T00:00:00"]]): """ - usage.skimage: 2 - usage.sklearn: 1 + usage.matplotlib: 3 """ ... @overload -def tri(N: int, M: int, k: int): +def iterable(y: Literal["2000-01-15T00:00:00"]): """ - usage.skimage: 6 + usage.matplotlib: 2 """ ... @overload -def tri(N: int, dtype: Type[numpy.int32]): +def iterable(y: Literal["2000-01-26T00:00:00"]): """ - usage.skimage: 1 + usage.matplotlib: 2 """ ... @overload -def tri(N: int, M: int, k: int, dtype: Type[numpy.bool_]): +def iterable(y: Tuple[Literal["2000-01-20T00:00:00"], Literal["2000-01-20T00:00:00"]]): """ - usage.scipy: 1 + usage.matplotlib: 1 """ ... -def tri(N: int, M: int = ..., k: int = ..., dtype: type = ...): +@overload +def iterable( + y: Tuple[Literal["2001-01-01T00:00:00+00:00"], Literal["2001-01-01T00:00:01+00:00"]] +): """ - usage.scipy: 1 - usage.skimage: 9 - usage.sklearn: 1 + usage.matplotlib: 1 """ ... @overload -def tril(m: numpy.ndarray): +def iterable( + y: List[ + Literal[ + "2018-09-30T10:15:00+00:00", + "2018-09-30T09:45:00+00:00", + "2018-09-30T09:15:00+00:00", + "2018-09-30T08:45:00+00:00", + "2018-09-30T08:15:00+00:00", + ] + ] +): """ usage.matplotlib: 1 - usage.skimage: 1 """ ... @overload -def tril(m: numpy.ndarray, k: int = ...): +def iterable(y: Literal["2011-01-01T00:00:00+00:00"]): """ - usage.dask: 7 - usage.scipy: 88 + usage.matplotlib: 1 """ ... @overload -def tril(m: numpy.ndarray, k: int): +def iterable(y: Literal["2011-01-02T00:00:00+00:00"]): """ - usage.sklearn: 1 + usage.matplotlib: 1 """ ... -def tril(m: numpy.ndarray, k: int = ...): +@overload +def iterable(y: Literal["2011-01-01T23:00:00+00:00"]): """ - usage.dask: 7 usage.matplotlib: 1 - usage.scipy: 88 - usage.skimage: 1 - usage.sklearn: 1 """ ... @overload -def tril_indices(n: int, k: int = ...): +def iterable(y: Literal["2011-01-02T00:00:00.000001+00:00"]): """ - usage.scipy: 15 + usage.matplotlib: 1 """ ... @overload -def tril_indices(n: int, k: int): +def iterable(y: Literal["2011-01-01T20:00:00+00:00"]): """ - usage.sklearn: 1 + usage.matplotlib: 1 """ ... -def tril_indices(n: int, k: int = ...): +@overload +def iterable(y: Literal["1990-01-01T00:00:00"]): """ - usage.scipy: 15 - usage.sklearn: 1 + usage.matplotlib: 1 """ ... -def tril_indices_from(arr: numpy.ndarray, k: int): +@overload +def iterable(y: Literal["2189-04-27T00:00:00"]): """ - usage.scipy: 2 + usage.matplotlib: 1 """ ... -def trim_zeros(filt: numpy.ndarray, trim: Literal["b", "f"]): +@overload +def iterable(y: Literal["1990-12-31T00:00:00"]): """ - usage.scipy: 13 + usage.matplotlib: 1 """ ... @overload -def triu(m: numpy.ndarray): +def iterable(y: Literal["1990-05-22T00:00:00"]): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def triu(m: numpy.ndarray, k: int = ...): +def iterable(y: Literal["1990-02-10T00:00:00"]): """ - usage.dask: 8 - usage.scipy: 205 + usage.matplotlib: 1 """ ... -def triu(m: numpy.ndarray, k: int = ...): +@overload +def iterable(y: Literal["1990-01-02T16:00:00"]): """ - usage.dask: 8 - usage.scipy: 205 - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def triu_indices(n: int, k: int = ...): +def iterable(y: Literal["1990-01-01T00:20:00"]): """ - usage.scipy: 2 + usage.matplotlib: 1 """ ... @overload -def triu_indices(n: int, k: int): +def iterable( + y: List[ + Literal[ + "1990-01-01T00:20:00+00:00", + "1990-01-01T00:15:00+00:00", + "1990-01-01T00:10:00+00:00", + "1990-01-01T00:05:00+00:00", + "1990-01-01T00:00:00+00:00", + ] + ] +): """ - usage.sklearn: 2 + usage.matplotlib: 1 """ ... -def triu_indices(n: int, k: int = ...): +@overload +def iterable(y: Literal["1990-01-01T00:00:40"]): """ - usage.scipy: 2 - usage.sklearn: 2 - """ - ... - - -def triu_indices_from(arr: numpy.ndarray, k: int = ...): - """ - usage.scipy: 6 + usage.matplotlib: 1 """ ... @overload -def union1d(ar1: List[Union[int, float, complex]], ar2: numpy.ndarray): +def iterable(y: Literal["1990-01-01T00:00:00.001500"]): """ - usage.scipy: 25 + usage.matplotlib: 1 """ ... @overload -def union1d( - ar1: Union[numpy.ndarray, dask.array.core.Array], - ar2: Union[numpy.ndarray, dask.array.core.Array], +def iterable( + y: Tuple[ + Literal["1990-01-01T00:00:00+00:00"], + Literal["1990-01-01T00:00:00.001500+00:00"], + ] ): """ - usage.dask: 2 + usage.matplotlib: 1 """ ... @overload -def union1d(ar1: numpy.ndarray, ar2: numpy.ndarray): - """ - usage.sklearn: 12 - """ - ... - - -def union1d( - ar1: Union[numpy.ndarray, dask.array.core.Array, List[Union[int, float, complex]]], - ar2: Union[numpy.ndarray, dask.array.core.Array], -): +def iterable(y: Literal["1997-01-01T00:00:00"]): """ - usage.dask: 2 - usage.scipy: 25 - usage.sklearn: 12 + usage.matplotlib: 2 """ ... @overload -def unique(ar: numpy.ndarray, return_inverse: bool): +def iterable(y: Literal["2196-04-27T00:00:00"]): """ - usage.skimage: 4 - usage.xarray: 3 + usage.matplotlib: 2 """ ... @overload -def unique(ar: numpy.ndarray): +def iterable(y: Literal["1997-12-31T00:00:00"]): """ - usage.matplotlib: 6 - usage.skimage: 46 - usage.xarray: 10 + usage.matplotlib: 2 """ ... @overload -def unique(ar: numpy.ndarray, return_inverse: bool, return_counts: bool): +def iterable(y: Literal["1997-05-22T00:00:00"]): """ - usage.skimage: 3 + usage.matplotlib: 2 """ ... @overload -def unique(ar: numpy.ndarray, return_counts: bool): +def iterable(y: Literal["1997-02-10T00:00:00"]): """ - usage.skimage: 7 + usage.matplotlib: 2 """ ... @overload -def unique(ar: numpy.ndarray, return_index: bool): +def iterable(y: Literal["1997-01-02T16:00:00"]): """ - usage.skimage: 4 + usage.matplotlib: 2 """ ... @overload -def unique(ar: xarray.core.dataarray.DataArray): +def iterable(y: Literal["1997-01-01T00:20:00"]): """ - usage.xarray: 2 + usage.matplotlib: 2 """ ... @overload -def unique( - ar: Union[ - pandas.core.series.Series, - numpy.ndarray, - pandas.core.arrays.categorical.Categorical, - List[str], - ], - return_inverse: bool = ..., - return_index: bool = ..., +def iterable( + y: List[ + Literal[ + "1997-01-01T00:20:00+00:00", + "1997-01-01T00:15:00+00:00", + "1997-01-01T00:10:00+00:00", + "1997-01-01T00:05:00+00:00", + "1997-01-01T00:00:00+00:00", + ] + ] ): """ - usage.pandas: 22 + usage.matplotlib: 1 """ ... @overload -def unique( - ar: Union[numpy.ndarray, List[float]], - return_inverse: bool = ..., - return_index: bool = ..., - axis: int = ..., - return_counts: bool = ..., -): +def iterable(y: Literal["1997-01-01T00:00:40"]): """ - usage.scipy: 66 + usage.matplotlib: 2 """ ... @overload -def unique(ar: numpy.ma.core.MaskedArray): +def iterable(y: Literal["1997-01-01T00:00:00.001500"]): """ usage.matplotlib: 1 """ @@ -34140,7 +32918,12 @@ def unique(ar: numpy.ma.core.MaskedArray): @overload -def unique(ar: numpy.ndarray, return_index: bool, return_inverse: bool): +def iterable( + y: Tuple[ + Literal["1997-01-01T00:00:00+00:00"], + Literal["1997-01-01T00:00:00.001500+00:00"], + ] +): """ usage.matplotlib: 1 """ @@ -34148,133 +32931,97 @@ def unique(ar: numpy.ndarray, return_index: bool, return_inverse: bool): @overload -def unique( - ar: Union[ - pandas.core.series.Series, - numpy.ndarray, - List[ - Union[ - numpy.float32, numpy.bool_, numpy.int64, numpy.float64, numpy.complex128 - ] - ], - ], - return_index: bool = ..., - return_inverse: bool = ..., - return_counts: bool = ..., -): +def iterable(y: Literal["1997-01-01T00:00:02"]): """ - usage.dask: 30 + usage.matplotlib: 2 """ ... @overload -def unique( - ar: Union[ - List[Union[str, float, int, numpy.float64]], - pandas.core.series.Series, - numpy.memmap, - numpy.ndarray, - ], - return_index: bool = ..., - return_inverse: bool = ..., - return_counts: bool = ..., +def iterable( + y: Tuple[Literal["1997-01-01T00:00:00+00:00"], Literal["1997-01-01T00:00:02+00:00"]] ): """ - usage.sklearn: 572 + usage.matplotlib: 1 """ ... -def unique( - ar: object, - return_index: bool = ..., - return_inverse: bool = ..., - return_counts: bool = ..., -): +@overload +def iterable(y: Literal["1997-01-01T00:00:00+00:00"]): """ - usage.dask: 30 - usage.matplotlib: 8 - usage.pandas: 22 - usage.scipy: 66 - usage.skimage: 64 - usage.sklearn: 572 - usage.xarray: 15 + usage.matplotlib: 2 """ ... -def unpackbits(_0: numpy.ndarray, /): +@overload +def iterable(y: Literal["1997-01-02T16:00:00+00:00"]): """ - usage.sklearn: 1 + usage.matplotlib: 2 """ ... @overload -def unravel_index(_0: numpy.int64, _1: Tuple[int, int], /): +def iterable(y: Literal["1997-01-01T00:20:00+00:00"]): """ - usage.skimage: 8 - usage.sklearn: 1 + usage.matplotlib: 2 """ ... @overload -def unravel_index(_0: numpy.ndarray, _1: Tuple[int, int], /): +def iterable(y: Literal["1997-01-01T00:00:40+00:00"]): """ - usage.skimage: 2 + usage.matplotlib: 2 """ ... @overload -def unravel_index(_0: numpy.int64, _1: Tuple[int, int, int], /): +def iterable(y: Literal["1997-01-01T00:00:02+00:00"]): """ - usage.skimage: 4 + usage.matplotlib: 2 """ ... @overload -def unravel_index(_0: numpy.int64, _1: Tuple[int, int, int, int], /): +def iterable(y: Literal["1997-01-01T00:00:00-08:00"]): """ - usage.skimage: 2 + usage.matplotlib: 1 """ ... @overload -def unravel_index(_0: int, _1: Tuple[int, int], /): +def iterable(y: Literal["2196-04-27T00:00:00-08:00"]): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def unravel_index(_0: int, _1: Tuple[int, int, int], /): +def iterable(y: Literal["1997-12-31T00:00:00-08:00"]): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def unravel_index( - _0: Union[numpy.ndarray, int], - _1: Union[numpy.ndarray, Tuple[int, int], List[int]], - _2: Literal["F"] = ..., - /, -): +def iterable(y: Literal["1997-05-22T00:00:00-07:00"]): """ - usage.scipy: 4 + usage.matplotlib: 1 """ ... @overload -def unravel_index(_0: List[numpy.int64], _1: Tuple[int, int], /): +def iterable(y: Literal["1997-02-10T00:00:00-08:00"]): """ usage.matplotlib: 1 """ @@ -34282,7 +33029,7 @@ def unravel_index(_0: List[numpy.int64], _1: Tuple[int, int], /): @overload -def unravel_index(_0: List[int], _1: Tuple[int, int], /): +def iterable(y: Literal["1997-01-02T16:00:00-08:00"]): """ usage.matplotlib: 1 """ @@ -34290,366 +33037,331 @@ def unravel_index(_0: List[int], _1: Tuple[int, int], /): @overload -def unravel_index( - _0: Union[numpy.ndarray, numpy.int64], - _1: Tuple[Union[int, None], ...] = ..., - _2: Literal["F", "C"] = ..., - /, - *, - order: Literal["F", "C"] = ..., - shape: Tuple[int, ...] = ..., -): +def iterable(y: Literal["1997-01-01T00:20:00-08:00"]): """ - usage.dask: 12 + usage.matplotlib: 1 """ ... -def unravel_index( - _0: Union[numpy.int64, int, numpy.ndarray, List[Union[int, numpy.int64]]], - _1: Union[Tuple[Union[None, int], ...], numpy.ndarray, List[int]] = ..., - _2: Literal["F", "C"] = ..., - /, - *, - order: Literal["F", "C"] = ..., - shape: Tuple[int, ...] = ..., +@overload +def iterable( + y: List[ + Literal[ + "1997-01-01T00:20:00-08:00", + "1997-01-01T00:15:00-08:00", + "1997-01-01T00:10:00-08:00", + "1997-01-01T00:05:00-08:00", + "1997-01-01T00:00:00-08:00", + ] + ] ): """ - usage.dask: 12 - usage.matplotlib: 2 - usage.scipy: 4 - usage.skimage: 18 - usage.sklearn: 1 + usage.matplotlib: 1 """ ... @overload -def unwrap(p: numpy.ndarray): +def iterable(y: Literal["1997-01-01T00:00:40-08:00"]): """ - usage.scipy: 5 + usage.matplotlib: 1 """ ... @overload -def unwrap(p: numpy.ndarray, axis: int): +def iterable(y: list): """ - usage.matplotlib: 3 + usage.matplotlib: 1 """ ... -def unwrap(p: numpy.ndarray, axis: int = ...): +@overload +def iterable(y: List[numpy.datetime64]): """ - usage.matplotlib: 3 - usage.scipy: 5 + usage.matplotlib: 1 """ ... @overload -def vander(x: numpy.ndarray, N: int = ...): +def iterable(y: Tuple[float, float, float, float]): """ - usage.scipy: 2 + usage.matplotlib: 4 """ ... @overload -def vander(x: numpy.ndarray, N: int): +def iterable(y: List[Literal["sans-serif"]]): """ - usage.sklearn: 1 + usage.matplotlib: 1 """ ... -def vander(x: numpy.ndarray, N: int = ...): +@overload +def iterable(y: Literal["normal"]): """ - usage.scipy: 2 - usage.sklearn: 1 + usage.matplotlib: 1 """ ... @overload -def var(a: numpy.ndarray, axis: int): +def iterable(y: List[Literal["serif"]]): """ - usage.xarray: 4 + usage.matplotlib: 1 """ ... @overload -def var(a: numpy.ndarray, axis: None): +def iterable(y: Literal["italic"]): """ - usage.xarray: 5 + usage.matplotlib: 1 """ ... @overload -def var(a: numpy.ndarray, axis: Tuple[None, ...]): +def iterable(y: Literal["oblique"]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def var(a: numpy.ndarray, axis: None, ddof: int): +def iterable(y: Literal["small-caps"]): """ - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload -def var(a: numpy.ndarray, axis: None, dtype: Type[float]): +def iterable(y: Literal["bold"]): """ - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload -def var(a: numpy.ndarray, axis: int, ddof: int): +def iterable(y: Literal["expanded"]): """ - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload -def var(a: numpy.ndarray, axis: int, dtype: Type[float]): +def iterable(y: numpy.uint16): """ - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload -def var(a: object, axis: None): +def iterable(y: numpy.ma.core.MaskedConstant): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def var(a: object): +def iterable(y: matplotlib.axes._subplots.AxesHostAxesSubplot): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def var(a: xarray.core.dataarray.DataArray): +def iterable(y: matplotlib.axis.XAxis): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def var(a: numpy.ndarray, axis: None, dtype: None, ddof: int): +def iterable(y: matplotlib.axis.YAxis): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def var(a: object, axis: None, dtype: None, ddof: int): +def iterable(y: List[Tuple[int, Tuple[int, int]]]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def var(a: xarray.core.dataset.Dataset): +def iterable(y: Callable): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def var(a: object, axis: int, dtype: None, ddof: int): +def iterable(y: List[matplotlib.testing.jpl_units.UnitDbl.UnitDbl]): """ - usage.xarray: 1 + usage.matplotlib: 4 """ ... @overload -def var(a: numpy.ndarray, axis: int, dtype: None, ddof: int): +def iterable(y: Literal["2017-01-01T00:00:00"]): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def var(a: object, axis: int): +def iterable(y: Literal["2017-01-01T00:00:16"]): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload -def var( - a: Union[numpy.ndarray, pandas.core.series.Series], - axis: Union[None, int] = ..., - ddof: int = ..., -): +def iterable(y: Literal["2000-01-01T00:00:00"]): """ - usage.pandas: 13 + usage.matplotlib: 2 """ ... @overload -def var(a: Union[numpy.ndarray, List[float]], axis: int = ..., ddof: int = ...): +def iterable(y: matplotlib.collections.LineCollection): """ - usage.scipy: 19 + usage.matplotlib: 1 """ ... @overload -def var( - a: object, - axis: Union[None, Tuple[Union[None, int], ...], int] = ..., - out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., - ddof: int = ..., - keepdims: bool = ..., -): +def iterable(y: matplotlib.contour.ClabelText): """ - usage.dask: 59 + usage.matplotlib: 1 """ ... @overload -def var(a: numpy.ndarray, axis: int = ..., ddof: int = ...): +def iterable(y: numpy.float32): """ - usage.sklearn: 55 + usage.matplotlib: 1 """ ... -def var( - a: object, - axis: Union[int, None, Tuple[Union[int, None], ...]] = ..., - out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., - keepdims: bool = ..., - dtype: Union[Literal["i8", "f8"], Type[float], None] = ..., - ddof: int = ..., -): +@overload +def iterable(y: Tuple[int, int, float, float]): """ - usage.dask: 59 - usage.pandas: 13 - usage.scipy: 19 - usage.sklearn: 55 - usage.xarray: 31 + usage.matplotlib: 1 """ ... -def vdot(_0: numpy.ndarray, _1: numpy.ndarray, /): +@overload +def iterable(y: Tuple[float, int]): """ - usage.dask: 2 - usage.scipy: 14 + usage.matplotlib: 1 """ ... -def vsplit(ary: numpy.ndarray, indices_or_sections: int): +@overload +def iterable(y: Tuple[None, float]): """ - usage.sklearn: 2 + usage.matplotlib: 2 """ ... @overload -def vstack(tup: Tuple[List[int], List[int]]): +def iterable(y: Literal["2020-08-17T22:40:05.392444"]): """ - usage.skimage: 1 + usage.matplotlib: 2 """ ... @overload -def vstack(tup: Tuple[List[float], List[Union[int, float]]]): +def iterable(y: Literal["2020-08-17T22:40:06.879974"]): """ - usage.skimage: 1 + usage.matplotlib: 2 """ ... @overload -def vstack(tup: Tuple[numpy.ndarray, numpy.ndarray]): +def iterable( + y: List[ + Literal[ + "2018-11-05T00:00:00+00:00", + "2018-11-04T00:00:00+00:00", + "2018-11-03T00:00:00+00:00", + ] + ] +): """ - usage.matplotlib: 9 - usage.skimage: 4 + usage.matplotlib: 1 """ ... @overload -def vstack(tup: Tuple[List[numpy.int64], List[numpy.int64]]): +def iterable(y: matplotlib.tests.test_units.Quantity): """ - usage.skimage: 1 + usage.matplotlib: 3 """ ... @overload -def vstack(tup: List[numpy.ndarray]): +def iterable(y: List[matplotlib.tests.test_units.Quantity]): """ - usage.matplotlib: 70 - usage.pandas: 49 - usage.skimage: 23 + usage.matplotlib: 4 """ ... @overload -def vstack(tup: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): +def iterable( + y: Tuple[matplotlib.tests.test_units.Quantity, matplotlib.tests.test_units.Quantity] +): """ - usage.matplotlib: 3 - usage.skimage: 2 + usage.matplotlib: 1 """ ... @overload -def vstack( - tup: Union[ - Tuple[ - Union[List[Union[List[int], float, int]], numpy.ndarray, numpy.float64], ... - ], - List[ - Union[ - Tuple[Union[numpy.float64, int], ...], - numpy.ndarray, - List[Union[int, float]], - ] - ], - ] -): +def iterable(y: Literal["2009-04-25T00:00:00"]): """ - usage.scipy: 199 + usage.matplotlib: 1 """ ... @overload -def vstack(tup: Tuple[numpy.ndarray, numpy.ndarray, List[float]]): +def iterable(y: List[matplotlib.testing.jpl_units.Duration.Duration]): """ usage.matplotlib: 2 """ @@ -34657,7 +33369,7 @@ def vstack(tup: Tuple[numpy.ndarray, numpy.ndarray, List[float]]): @overload -def vstack(tup: Tuple[numpy.ma.core.MaskedArray, numpy.ma.core.MaskedArray]): +def iterable(y: matplotlib.testing.jpl_units.Duration.Duration): """ usage.matplotlib: 1 """ @@ -34665,25 +33377,23 @@ def vstack(tup: Tuple[numpy.ma.core.MaskedArray, numpy.ma.core.MaskedArray]): @overload -def vstack( - tup: List[Union[List[Union[numpy.ndarray, Tuple[int, int]]], numpy.ndarray]] -): +def iterable(y: Tuple[int, float, int, float]): """ - usage.matplotlib: 1 + usage.matplotlib: 2 """ ... @overload -def vstack(tup: List[Union[Tuple[int, int], numpy.ndarray]]): +def iterable(y: Tuple[float, int, float, int]): """ - usage.matplotlib: 1 + usage.matplotlib: 2 """ ... @overload -def vstack(tup: Tuple[numpy.ndarray]): +def iterable(y: List[Union[numpy.float64, int]]): """ usage.matplotlib: 1 """ @@ -34691,7 +33401,7 @@ def vstack(tup: Tuple[numpy.ndarray]): @overload -def vstack(tup: List[Tuple[float, float, float, float]]): +def iterable(y: mpl_toolkits.axes_grid1.mpl_axes.Axes): """ usage.matplotlib: 1 """ @@ -34699,15 +33409,15 @@ def vstack(tup: List[Tuple[float, float, float, float]]): @overload -def vstack(tup: List[Union[List[numpy.float64], numpy.ndarray]]): +def iterable(y: mpl_toolkits.axes_grid1.axes_grid.CbarAxes): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def vstack(tup: List[Union[numpy.ndarray, List[numpy.float64]]]): +def iterable(y: mpl_toolkits.axes_grid1.parasite_axes.AxesHostAxes): """ usage.matplotlib: 1 """ @@ -34715,26 +33425,15 @@ def vstack(tup: List[Union[numpy.ndarray, List[numpy.float64]]]): @overload -def vstack( - tup: Tuple[ - List[Union[int, numpy.float64]], numpy.ndarray, List[Union[int, numpy.float64]] - ] -): +def iterable(y: matplotlib.axes._subplots.AxesZeroSubplot): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload -def vstack( - tup: Tuple[ - List[Union[float, numpy.float64]], - numpy.ndarray, - List[Union[float, numpy.float64]], - List[Union[float, numpy.float64]], - ] -): +def iterable(y: mpl_toolkits.axisartist.axislines.Axes): """ usage.matplotlib: 1 """ @@ -34742,13 +33441,7 @@ def vstack( @overload -def vstack( - tup: Tuple[ - List[Union[float, numpy.float64]], - numpy.ndarray, - List[Union[float, numpy.float64]], - ] -): +def iterable(y: mpl_toolkits.axisartist.axis_artist.AxisArtist): """ usage.matplotlib: 1 """ @@ -34756,56 +33449,65 @@ def vstack( @overload -def vstack(tup: Tuple[List[int]]): +def iterable(y: mpl_toolkits.axisartist.axis_artist.GridlinesCollection): """ usage.matplotlib: 1 """ ... -@overload -def vstack(tup: List[numpy.flatiter]): +def iterable(y: object): """ - usage.matplotlib: 1 + usage.matplotlib: 858 + usage.pandas: 32 + usage.sklearn: 9 """ ... @overload -def vstack(tup: object): +def ix_(*args: Literal["v", "t"]): """ - usage.dask: 10 + usage.matplotlib: 6 + usage.scipy: 1 + usage.skimage: 2 + usage.sklearn: 4 + usage.xarray: 3 """ ... @overload -def vstack( - tup: Union[ - Tuple[Union[numpy.ndarray, List[Union[List[Union[int, float]], int]]], ...], - List[Union[numpy.ndarray, pandas.core.series.Series, float, List[List[int]]]], - ] -): +def ix_(): """ - usage.sklearn: 166 + usage.xarray: 1 """ ... -def vstack(tup: object): - """ - usage.dask: 10 - usage.matplotlib: 98 - usage.pandas: 49 - usage.scipy: 199 - usage.skimage: 32 - usage.sklearn: 166 +def ix_(*args: Literal["v", "t"]): """ - ... + usage.matplotlib: 6 + usage.scipy: 1 + usage.skimage: 2 + usage.sklearn: 4 + usage.xarray: 4 + """ + ... + + +def kron( + a: Union[List[Union[int, List[int]]], numpy.ndarray, numpy.matrix], + b: Union[List[List[int]], numpy.ndarray], +): + """ + usage.scipy: 53 + """ + ... @overload -def where(_0: numpy.bool_, _1: float, _2: int, /): +def lexsort(_0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], /): """ usage.skimage: 1 """ @@ -34813,77 +33515,115 @@ def where(_0: numpy.bool_, _1: float, _2: int, /): @overload -def where(_0: numpy.ndarray, _1: float, _2: int, /): +def lexsort(_0: Tuple[xarray.core.dataarray.DataArray], /): """ - usage.skimage: 3 + usage.xarray: 1 """ ... @overload -def where(_0: numpy.ndarray, _1: numpy.ndarray, _2: numpy.ndarray, /): +def lexsort( + _0: Tuple[xarray.core.dataarray.DataArray, xarray.core.dataarray.DataArray], / +): """ - usage.matplotlib: 1 - usage.skimage: 1 - usage.xarray: 9 + usage.xarray: 1 """ ... @overload -def where(_0: numpy.ndarray, _1: float, _2: numpy.ndarray, /): +def lexsort( + _0: Union[ + List[numpy.ndarray], + Tuple[ + Union[ + pandas.core.indexes.numeric.Float64Index, + numpy.ndarray, + pandas.core.indexes.numeric.Int64Index, + ], + Union[ + pandas.core.indexes.numeric.Float64Index, + numpy.ndarray, + pandas.core.indexes.numeric.Int64Index, + ], + ], + ], + /, +): """ - usage.skimage: 1 - usage.xarray: 7 + usage.pandas: 11 """ ... @overload -def where(_0: numpy.bool_, _1: numpy.float64, _2: numpy.float64, /): +def lexsort( + _0: Union[Tuple[numpy.ndarray, numpy.ndarray], numpy.ndarray, List[numpy.ndarray]], + /, +): """ - usage.skimage: 1 + usage.scipy: 9 """ ... @overload -def where(_0: numpy.bool_, _1: float, _2: numpy.float64, /): +def lexsort(_0: Tuple[numpy.ma.core.MaskedArray, numpy.ma.core.MaskedArray], /): """ - usage.skimage: 1 - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload -def where(_0: numpy.ndarray, _1: numpy.ndarray, _2: int, /): +def lexsort(_0: Tuple[numpy.ndarray, numpy.ndarray], /): """ - usage.skimage: 4 - usage.xarray: 1 + usage.sklearn: 2 """ ... -@overload -def where(_0: numpy.ndarray, /): +def lexsort( + _0: Union[ + Tuple[ + Union[ + numpy.ma.core.MaskedArray, + pandas.core.indexes.numeric.Float64Index, + pandas.core.indexes.numeric.Int64Index, + numpy.ndarray, + xarray.core.dataarray.DataArray, + ], + ..., + ], + List[numpy.ndarray], + numpy.ndarray, + ], + /, +): """ usage.matplotlib: 1 - usage.skimage: 12 + usage.pandas: 11 + usage.scipy: 9 + usage.skimage: 1 + usage.sklearn: 2 + usage.xarray: 2 """ ... @overload -def where(_0: dask.array.core.Array, /): +def linspace(start: int, stop: int, num: int, endpoint: bool): """ - usage.skimage: 1 + usage.matplotlib: 1 + usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def where(_0: numpy.ndarray, _1: int, _2: int, /): +def linspace(start: numpy.int64, stop: numpy.int64, num: int, endpoint: bool): """ usage.skimage: 1 """ @@ -34891,115 +33631,132 @@ def where(_0: numpy.ndarray, _1: int, _2: int, /): @overload -def where(_0: numpy.bool_, _1: numpy.ndarray, _2: numpy.ndarray, /): +def linspace(start: numpy.float64, stop: numpy.float64, num: int, endpoint: bool): """ - usage.xarray: 3 + usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def where(_0: bool, _1: float, _2: numpy.float64, /): +def linspace(start: int, stop: int, num: int, dtype: Type[numpy.uint8]): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def where(_0: List[bool], _1: numpy.ndarray, _2: numpy.ndarray, /): +def linspace(start: int, stop: int, num: int, dtype: Type[numpy.int8]): """ - usage.xarray: 2 + usage.skimage: 1 """ ... @overload -def where(_0: sparse._coo.core.COO, _1: sparse._coo.core.COO, _2: numpy.ndarray, /): +def linspace(start: int, stop: int, num: int): """ - usage.xarray: 1 + usage.matplotlib: 88 + usage.sample-usage: 1 + usage.skimage: 23 + usage.sklearn: 44 + usage.xarray: 193 """ ... @overload -def where(_0: bool, _1: numpy.ndarray, _2: numpy.ndarray, /): +def linspace(start: int, stop: float, num: int): """ - usage.xarray: 1 + usage.matplotlib: 17 + usage.skimage: 17 + usage.sklearn: 9 + usage.xarray: 3 """ ... @overload -def where(_0: dask.array.core.Array, _1: float, _2: dask.array.core.Array, /): +def linspace(start: numpy.float64, stop: numpy.float64, num: int): """ - usage.xarray: 1 + usage.matplotlib: 8 + usage.skimage: 4 + usage.sklearn: 4 """ ... @overload -def where( - _0: numpy.ndarray, _1: float, _2: pandas.core.indexes.numeric.Float64Index, / -): +def linspace(start: int, stop: numpy.float64, num: int, endpoint: bool): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def where(_0: sparse._coo.core.COO, _1: numpy.ndarray, _2: sparse._coo.core.COO, /): +def linspace(start: int, stop: numpy.float32, num: int, endpoint: bool): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def where(_0: sparse._coo.core.COO, _1: numpy.ndarray, _2: numpy.ndarray, /): +def linspace(start: float, stop: int, num: int): """ - usage.xarray: 1 + usage.matplotlib: 2 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def where( - _0: sparse._coo.core.COO, _1: sparse._coo.core.COO, _2: sparse._coo.core.COO, / -): +def linspace(start: float, stop: float, num: int): """ - usage.xarray: 1 + usage.matplotlib: 51 + usage.skimage: 4 + usage.sklearn: 27 + usage.xarray: 21 """ ... @overload -def where(_0: numpy.ndarray, _1: numpy.ndarray, _2: sparse._coo.core.COO, /): +def linspace(start: float, stop: float, num: int, endpoint: bool): """ + usage.skimage: 5 + usage.sklearn: 1 usage.xarray: 1 """ ... @overload -def where(_0: numpy.ndarray, _1: sparse._coo.core.COO, _2: numpy.ndarray, /): +def linspace(start: int, stop: float, num: int, endpoint: bool): """ - usage.xarray: 1 + usage.matplotlib: 3 + usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def where(_0: numpy.ndarray, _1: object, _2: numpy.ndarray, /): +def linspace(start: int, stop: int, num: numpy.int64): """ - usage.xarray: 2 + usage.sklearn: 1 + usage.xarray: 1 """ ... @overload -def where(_0: numpy.ndarray, _1: object, _2: object, /): +def linspace(start: float, stop: numpy.float64, num: int): """ usage.xarray: 1 """ @@ -35007,59 +33764,67 @@ def where(_0: numpy.ndarray, _1: object, _2: object, /): @overload -def where(_0: numpy.ndarray, _1: numpy.ndarray, _2: object, /): +def linspace(start: float, stop: float, num: numpy.int64): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload -def where(_0: object, _1: object = ..., _2: object = ..., /): +def linspace(start: int, stop: int, num: int, dtype: Type[float]): """ - usage.pandas: 175 + usage.xarray: 5 """ ... @overload -def where( - _0: Union[numpy.ndarray, numpy.bool_, bool], _1: object = ..., _2: object = ..., / -): +def linspace(start: int, stop: int, num: int, dtype: Type[int]): """ - usage.scipy: 193 + usage.xarray: 5 """ ... @overload -def where(_0: numpy.ndarray, _1: numpy.ndarray, _2: numpy.ma.core.MaskedArray, /): +def linspace( + start: Union[float, int], + stop: Union[int, float], + num: int, + endpoint: bool = ..., + dtype: Literal["int64"] = ..., +): """ - usage.matplotlib: 1 + usage.pandas: 19 """ ... @overload -def where(_0: numpy.ndarray, _1: numpy.ndarray, _2: float, /): +def linspace( + start: Union[numpy.float64, float, int], + stop: Union[numpy.float64, int, numpy.int64, float], + num: object = ..., + endpoint: Union[bool, int] = ..., + retstep: bool = ..., +): """ - usage.matplotlib: 1 + usage.scipy: 589 """ ... @overload -def where( - _0: numpy.ndarray, _1: numpy.ma.core.MaskedArray, _2: numpy.ma.core.MaskedArray, / -): +def linspace(start: float, stop: float, num: int, endpoint: bool, retstep: bool): """ - usage.matplotlib: 1 + usage.matplotlib: 2 """ ... @overload -def where(_0: numpy.ndarray, _1: numpy.float32, _2: numpy.float32, /): +def linspace(start: float, stop: int): """ usage.matplotlib: 1 """ @@ -35067,125 +33832,129 @@ def where(_0: numpy.ndarray, _1: numpy.float32, _2: numpy.float32, /): @overload -def where(_0: numpy.ndarray, _1: int, _2: numpy.ndarray, /): +def linspace(start: numpy.int64, stop: numpy.int64, num: int): """ - usage.matplotlib: 1 + usage.matplotlib: 2 """ ... @overload -def where(_0: numpy.ma.core.MaskedArray, _1: numpy.ma.core.MaskedArray, _2: float, /): +def linspace(start: int, stop: int): """ - usage.matplotlib: 1 + usage.matplotlib: 7 """ ... @overload -def where( - _0: Union[numpy.ndarray, bool, int, numpy.bool_, dask.array.core.Array], - _1: Union[numpy.ndarray, numpy.float64, int, numpy.int32, float] = ..., - _2: Union[float, int, numpy.float64, dask.array.core.Array, numpy.ndarray] = ..., - /, -): +def linspace(start: int, stop: float): """ - usage.dask: 58 + usage.matplotlib: 1 """ ... @overload -def where( - _0: Union[numpy.ndarray, numpy.matrix, List[bool]], - _1: object = ..., - _2: Union[ - Literal["one", "b"], numpy.ndarray, numpy.bool_, numpy.int64, numpy.str_ - ] = ..., - /, +def linspace( + start: object, + stop: Union[numpy.float64, float, int], + num: int = ..., + endpoint: bool = ..., + dtype: Union[numpy.dtype, type] = ..., + retstep: bool = ..., ): """ - usage.sklearn: 77 + usage.dask: 26 """ ... -def where(_0: object, _1: object = ..., _2: object = ..., /): +@overload +def linspace(start: numpy.float64, stop: numpy.float64, num: numpy.int64): """ - usage.dask: 58 - usage.matplotlib: 8 - usage.pandas: 175 - usage.scipy: 193 - usage.skimage: 26 - usage.sklearn: 77 - usage.xarray: 39 + usage.sklearn: 2 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[float]): +def linspace(start: numpy.int64, stop: numpy.int64, num: numpy.int64): """ - usage.skimage: 6 + usage.sklearn: 2 """ ... -@overload -def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[numpy.float64]): +def linspace( + start: object, + stop: Union[numpy.float64, float, int, numpy.int64, numpy.float32], + num: object = ..., + endpoint: Union[bool, int] = ..., + dtype: Union[type, numpy.dtype, Literal["int64"]] = ..., + retstep: bool = ..., +): """ - usage.skimage: 11 + usage.dask: 26 + usage.matplotlib: 184 + usage.pandas: 19 + usage.sample-usage: 1 + usage.scipy: 589 + usage.skimage: 67 + usage.sklearn: 95 + usage.xarray: 231 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.int64]): +def load(file: str): """ - usage.skimage: 1 + usage.skimage: 21 """ ... @overload -def zeros(_0: Tuple[int, int], /): +def load(file: str, allow_pickle: bool = ...): """ - usage.matplotlib: 25 - usage.sample-usage: 1 - usage.skimage: 228 - usage.xarray: 26 + usage.scipy: 26 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.float64]): +def load(file: _io.BufferedReader): """ - usage.skimage: 50 + usage.matplotlib: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int], /): +def load(file: str, mmap_mode: Literal["r"] = ...): """ - usage.matplotlib: 6 - usage.skimage: 73 - usage.xarray: 13 + usage.dask: 7 """ ... -@overload -def zeros(_0: Tuple[int, int], _1: Literal["uint8"], /): +def load( + file: Union[str, _io.BufferedReader], + allow_pickle: bool = ..., + mmap_mode: Literal["r"] = ..., +): """ - usage.skimage: 39 + usage.dask: 7 + usage.matplotlib: 1 + usage.scipy: 26 + usage.skimage: 21 """ ... @overload -def zeros(_0: Tuple[int, int, int], _1: Literal["uint8"], /): +def loadtxt(fname: str, dtype: List[Tuple[str, type]]): """ usage.skimage: 1 """ @@ -35193,452 +33962,528 @@ def zeros(_0: Tuple[int, int, int], _1: Literal["uint8"], /): @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint8]): +def loadtxt(fname: str): """ - usage.skimage: 55 + usage.pandas: 2 + usage.sklearn: 2 """ ... @overload -def zeros(_0: int, /, *, dtype: Type[int]): +def loadtxt(fname: Union[str, _io.StringIO], skiprows: int = ...): """ - usage.skimage: 3 + usage.scipy: 6 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[int]): +def loadtxt(fname: str, skiprows: int): """ - usage.skimage: 35 + usage.sklearn: 2 """ ... @overload -def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[numpy.uint8]): +def loadtxt(fname: str, delimiter: Literal[","]): """ - usage.matplotlib: 2 - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[bool]): +def loadtxt(fname: tarfile.ExFileObject, delimiter: Literal[","]): """ - usage.matplotlib: 3 - usage.skimage: 28 + usage.sklearn: 1 """ ... -@overload -def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[bool]): +def loadtxt( + fname: Union[str, tarfile.ExFileObject, _io.StringIO], + dtype: List[Tuple[str, type]] = ..., + skiprows: int = ..., + delimiter: Literal[","] = ..., +): """ - usage.skimage: 6 + usage.pandas: 2 + usage.scipy: 6 + usage.skimage: 1 + usage.sklearn: 6 """ ... @overload -def zeros(_0: int, /): +def logspace(start: numpy.float64, stop: numpy.float64, num: int): """ - usage.matplotlib: 38 - usage.skimage: 15 - usage.xarray: 10 + usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.float32]): +def logspace(start: float, stop: float, num: int): """ - usage.skimage: 5 + usage.skimage: 1 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[bool], order: Literal["C"]): +def logspace( + start: Union[float, int, numpy.float64], + stop: Union[int, numpy.float64, float], + num: object = ..., + base: float = ..., +): """ - usage.skimage: 1 + usage.scipy: 51 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Literal["float32"]): +def logspace(start: int, stop: int, num: int, base: float): """ - usage.skimage: 1 + usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Literal["float64"]): +def logspace(start: int, stop: int, num: int): """ - usage.skimage: 5 + usage.matplotlib: 1 + usage.sklearn: 13 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Literal["uint8"]): +def logspace(start: int, stop: int): """ - usage.skimage: 5 + usage.matplotlib: 1 """ ... -@overload -def zeros(_0: Tuple[int, int], /, *, dtype: Literal["int"]): +def logspace( + start: Union[numpy.float64, int, float], + stop: Union[numpy.float64, int, float], + num: object = ..., + base: float = ..., +): """ + usage.matplotlib: 3 + usage.scipy: 51 usage.skimage: 2 + usage.sklearn: 17 """ ... @overload -def zeros(_0: int, /, *, dtype: Type[bool]): +def may_share_memory(_0: numpy.ndarray, _1: numpy.ndarray, /): """ - usage.matplotlib: 5 usage.skimage: 2 + usage.sklearn: 36 """ ... @overload -def zeros(_0: Tuple[int, int], _1: Type[numpy.uint8], /): +def may_share_memory( + _0: Union[ + pandas.core.indexes.base.Index, + pandas.core.arrays.datetimes.DatetimeArray, + pandas.core.arrays.timedeltas.TimedeltaArray, + ], + _1: Union[pandas.core.indexes.base.Index, numpy.ndarray], + /, +): """ - usage.skimage: 6 + usage.pandas: 3 """ ... @overload -def zeros(_0: Tuple[int, int, int, int, int], /): +def may_share_memory( + _0: numpy.ndarray, _1: Union[list, numpy.ndarray, numpy.ma.core.MaskedArray], / +): """ - usage.skimage: 3 + usage.scipy: 158 """ ... @overload -def zeros(_0: Tuple[int], /, *, dtype: Type[numpy.float64]): +def may_share_memory( + _0: scipy.sparse.csr.csr_matrix, _1: scipy.sparse.csr.csr_matrix, / +): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Literal["float"]): +def may_share_memory( + _0: scipy.sparse.csr.csr_matrix, _1: scipy.sparse.coo.coo_matrix, / +): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int, int], /): +def may_share_memory(_0: numpy.ndarray, _1: List[List[float]], /): """ - usage.skimage: 18 - usage.xarray: 1 + usage.sklearn: 10 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: numpy.dtype): +def may_share_memory(_0: numpy.ndarray, _1: pandas.core.frame.DataFrame, /): """ - usage.skimage: 7 + usage.sklearn: 2 """ ... @overload -def zeros(_0: Tuple[int], /, *, dtype: numpy.dtype): +def may_share_memory( + _0: scipy.sparse.csc.csc_matrix, _1: scipy.sparse.csc.csc_matrix, / +): """ - usage.skimage: 6 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int], _1: Type[float], /): +def may_share_memory( + _0: scipy.sparse.lil.lil_matrix, _1: scipy.sparse.lil.lil_matrix, / +): """ - usage.matplotlib: 6 - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[numpy.int64], _1: numpy.dtype, /): +def may_share_memory(_0: numpy.ndarray, _1: List[List[Union[int, float]]], /): """ - usage.skimage: 2 + usage.sklearn: 3 """ ... @overload -def zeros(_0: Tuple[int, int], _1: Type[int], /): +def may_share_memory(_0: numpy.ndarray, _1: List[List[int]], /): """ - usage.skimage: 8 + usage.sklearn: 7 """ ... @overload -def zeros( - _0: Tuple[int, int, int, int], /, *, dtype: Type[numpy.uint32], order: Literal["C"] +def may_share_memory( + _0: scipy.sparse.csr.csr_matrix, _1: scipy.sparse.csc.csc_matrix, / ): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint32]): +def may_share_memory( + _0: scipy.sparse.csc.csc_matrix, _1: scipy.sparse.csr.csr_matrix, / +): """ - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def zeros(_0: List[int], /, *, dtype: numpy.dtype): +def may_share_memory( + _0: scipy.sparse.csc.csc_matrix, _1: scipy.sparse.coo.coo_matrix, / +): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint16]): +def may_share_memory( + _0: scipy.sparse.csc.csc_matrix, _1: scipy.sparse.lil.lil_matrix, / +): """ - usage.skimage: 7 + usage.sklearn: 1 """ ... @overload -def zeros(_0: List[int], /): +def may_share_memory( + _0: scipy.sparse.csc.csc_matrix, _1: scipy.sparse.bsr.bsr_matrix, / +): """ - usage.matplotlib: 2 - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[float]): +def may_share_memory( + _0: scipy.sparse.csr.csr_matrix, _1: scipy.sparse.lil.lil_matrix, / +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[complex]): +def may_share_memory(_0: numpy.ndarray, _1: numpy.memmap, /): """ - usage.skimage: 1 + usage.sklearn: 4 """ ... @overload -def zeros(_0: Tuple[int], /): +def may_share_memory(_0: numpy.ndarray, _1: List[List[Union[float, int]]], /): """ - usage.skimage: 5 - usage.xarray: 4 + usage.sklearn: 2 """ ... @overload -def zeros(_0: List[int], /, *, dtype: Type[numpy.uint8]): +def may_share_memory(_0: numpy.ndarray, _1: List[int], /): """ - usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def zeros(_0: Tuple[int, int], _1: Literal["double"], /): +def may_share_memory(_0: numpy.ndarray, _1: List[Literal["3", "2", "1"]], /): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def zeros(_0: int, /, *, dtype: Type[numpy.uint8]): +def may_share_memory( + _0: scipy.sparse.csr.csr_matrix, _1: scipy.sparse.bsr.bsr_matrix, / +): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[numpy.float32]): +def may_share_memory( + _0: scipy.sparse.csr.csr_matrix, _1: scipy.sparse.dia.dia_matrix, / +): """ - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int, int], /, *, dtype: Type[numpy.float32]): +def may_share_memory( + _0: scipy.sparse.csr.csr_matrix, _1: scipy.sparse.dok.dok_matrix, / +): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int, int, int], /, *, dtype: Type[numpy.float32]): - """ - usage.skimage: 1 +def may_share_memory( + _0: scipy.sparse.csc.csc_matrix, _1: scipy.sparse.dia.dia_matrix, / +): + """ + usage.sklearn: 1 """ ... @overload -def zeros(_0: numpy.int64, /, *, dtype: numpy.dtype): +def may_share_memory( + _0: scipy.sparse.csc.csc_matrix, _1: scipy.sparse.dok.dok_matrix, / +): """ - usage.skimage: 5 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int], _1: Type[bool], /): +def may_share_memory(_0: numpy.ndarray, _1: List[float], /): """ - usage.skimage: 7 + usage.sklearn: 1 """ ... @overload -def zeros(_0: int, _1: Literal["bool"], /): +def may_share_memory(_0: numpy.ndarray, _1: List[List[numpy.float64]], /): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def zeros(_0: int, /, *, dtype: Type[numpy.float64]): +def may_share_memory(_0: numpy.ndarray, _1: List[numpy.ndarray], /): """ - usage.matplotlib: 7 - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint64]): +def may_share_memory(_0: numpy.ndarray, _1: List[Union[int, float]], /): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[int]): +def may_share_memory( + _0: numpy.ndarray, _1: sklearn.utils.estimator_checks._NotAnArray, / +): """ - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int, int], /, *, dtype: Type[int]): +def may_share_memory(_0: numpy.memmap, _1: numpy.ndarray, /): """ - usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.int8]): +def may_share_memory(_0: sklearn.utils.estimator_checks._NotAnArray, _1: None, /): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[numpy.int8]): +def may_share_memory(_0: numpy.ndarray, _1: numpy.matrix, /): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int], _1: Type[bool], /): +def may_share_memory( + _0: scipy.sparse.coo.coo_matrix, _1: scipy.sparse.csc.csc_matrix, / +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int], _1: Type[bool], /): +def may_share_memory( + _0: scipy.sparse.coo.coo_matrix, _1: scipy.sparse.coo.coo_matrix, / +): """ - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int, int], _1: Type[numpy.uint16], /): +def may_share_memory( + _0: scipy.sparse.dok.dok_matrix, _1: scipy.sparse.dok.dok_matrix, / +): + """ + usage.sklearn: 1 + """ + ... + + +def may_share_memory(_0: object, _1: object, /): """ + usage.pandas: 3 + usage.scipy: 158 usage.skimage: 2 + usage.sklearn: 96 """ ... @overload -def zeros(_0: Tuple[int, int, int], _1: Type[numpy.uint16], /): +def mean(a: numpy.ndarray): """ - usage.skimage: 1 + usage.matplotlib: 7 + usage.skimage: 35 + usage.sklearn: 128 + usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int], _1: Type[int], /): +def mean(a: numpy.ndarray, axis: int): """ - usage.skimage: 6 + usage.matplotlib: 5 + usage.skimage: 13 + usage.sklearn: 73 + usage.xarray: 6 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.bool_]): +def mean(a: dask.array.core.Array): """ - usage.skimage: 4 + usage.skimage: 1 """ ... @overload -def zeros(_0: Tuple[int], /, *, dtype: Type[bool]): +def mean(a: numpy.ndarray, axis: Tuple[int, int]): """ usage.skimage: 1 - usage.xarray: 1 + usage.xarray: 2 """ ... @overload -def zeros(_0: Tuple[int], /, *, dtype: Type[numpy.uint8], order: Literal["F"]): +def mean(a: numpy.ndarray, axis: Tuple[int, int], dtype: Type[numpy.uint8]): """ usage.skimage: 1 """ @@ -35646,9 +34491,7 @@ def zeros(_0: Tuple[int], /, *, dtype: Type[numpy.uint8], order: Literal["F"]): @overload -def zeros( - _0: Tuple[int, int, int], /, *, dtype: Type[numpy.uint8], order: Literal["C"] -): +def mean(a: numpy.ndarray, axis: Tuple[int, int], dtype: Type[numpy.float16]): """ usage.skimage: 1 """ @@ -35656,7688 +34499,21860 @@ def zeros( @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint8], order: Literal["C"]): +def mean(a: numpy.ndarray, dtype: Type[numpy.float64]): """ - usage.skimage: 1 + usage.skimage: 4 """ ... @overload -def zeros( - _0: Tuple[int, int, int, int], /, *, dtype: Type[numpy.uint8], order: Literal["C"] -): +def mean(a: List[numpy.float64]): """ - usage.skimage: 1 + usage.skimage: 2 + usage.sklearn: 9 """ ... @overload -def zeros( - _0: Tuple[int, int, int, int, int], - /, - *, - dtype: Type[numpy.uint8], - order: Literal["C"], -): +def mean(a: numpy.ndarray, axis: None): """ - usage.skimage: 1 + usage.xarray: 3 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint8], order: Literal["F"]): +def mean(a: numpy.ndarray, axis: Tuple[int]): """ - usage.skimage: 1 + usage.xarray: 3 """ ... @overload -def zeros(_0: Tuple[int, int, int, int], /, *, dtype: Type[numpy.uint8]): +def mean(a: xarray.core.dataarray.DataArray): """ - usage.skimage: 1 + usage.xarray: 3 """ ... @overload -def zeros(_0: Tuple[int, int, int], /, *, dtype: numpy.dtype): +def mean(a: xarray.core.dataarray.DataArray, keepdims: bool): """ - usage.matplotlib: 3 - usage.skimage: 4 usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int, int], /, *, dtype: numpy.dtype): +def mean(a: numpy.ndarray, axis: None, dtype: None): """ - usage.skimage: 4 + usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int, int, int], /, *, dtype: numpy.dtype): +def mean(a: xarray.core.dataarray.DataArray, axis: int, keepdims: bool): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int], _1: numpy.dtype, /): +def mean(a: numpy.ndarray, axis: int, dtype: None): """ - usage.matplotlib: 3 - usage.skimage: 2 + usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int], /, *, dtype: Literal["float64"]): +def mean(a: numpy.ndarray, axis: None, dtype: Type[float]): """ - usage.skimage: 2 + usage.xarray: 2 """ ... @overload -def zeros(_0: Tuple[int, int, int], /, *, dtype: Literal["float32"]): +def mean(a: numpy.ndarray, axis: int, dtype: Type[float]): """ - usage.skimage: 2 + usage.xarray: 2 """ ... @overload -def zeros(_0: numpy.ndarray, /): +def mean(a: object, axis: None): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def zeros(_0: list, /): +def mean(a: object, axis: Tuple[int]): """ - usage.skimage: 2 + usage.xarray: 2 """ ... @overload -def zeros(_0: List[int], _1: numpy.dtype, /): +def mean(a: object): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def zeros(_0: int, _1: Type[int], /): +def mean(a: object, axis: None, dtype: None): """ - usage.skimage: 4 + usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[int, numpy.int64], /): +def mean(a: object, axis: int): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int, int], /, *, dtype: Type[numpy.float64]): +def mean(a: xarray.core.dataset.Dataset): """ - usage.skimage: 3 + usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int, int, int], /, *, dtype: Type[numpy.float64]): +def mean(a: object, axis: int, dtype: None): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[int, int, int, int, int, int], /, *, dtype: Type[numpy.float64]): +def mean(a: xarray.core.variable.Variable): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload -def zeros( - _0: Tuple[int, int, int, int, int, int, int], /, *, dtype: Type[numpy.float64] -): +def mean(a: numpy.ndarray, keepdims: bool): """ - usage.skimage: 2 + usage.xarray: 3 """ ... @overload -def zeros(_0: Tuple[int, int], _1: Type[numpy.bool_], /): +def mean(a: numpy.ndarray, axis: int, keepdims: bool): """ - usage.skimage: 2 + usage.xarray: 3 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Literal["bool"]): +def mean(a: numpy.ndarray, axis: Tuple[int, int], keepdims: bool): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Literal["int8"]): +def mean( + a: Union[ + numpy.ndarray, + pandas.core.arrays.sparse.array.SparseArray, + pandas.core.series.Series, + List[float], + ], + axis: Union[None, int] = ..., + dtype: Type[numpy.int64] = ..., + out: numpy.float64 = ..., +): """ - usage.skimage: 2 + usage.pandas: 26 """ ... @overload -def zeros(_0: numpy.int64, /): +def mean(a: object, axis: Union[None, int] = ..., keepdims: bool = ...): """ - usage.matplotlib: 2 - usage.skimage: 2 + usage.scipy: 89 """ ... @overload -def zeros(_0: int, /, *, dtype: Type[numpy.int64]): +def mean( + a: object, + axis: Union[None, Tuple[Union[int, None], ...], int] = ..., + dtype: Literal["float32", "i8", "f8"] = ..., + out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., + keepdims: bool = ..., +): """ - usage.xarray: 1 + usage.dask: 78 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Literal["S1"]): +def mean( + a: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64, numpy.float64] +): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[int], /, *, dtype: Type[numpy.bytes_]): +def mean(a: Tuple[float, float, float, float, numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def zeros(_0: Tuple[None, ...], /): +def mean(a: List[numpy.ndarray], axis: int, dtype: Type[numpy.float64]): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def zeros(_0: Tuple[int], /, *, dtype: Literal["bool"]): +def mean(a: numpy.float64): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def zeros(*, dtype: Type[float], shape: Tuple[int, int]): +def mean(a: List[float]): """ - usage.xarray: 8 + usage.sklearn: 3 """ ... @overload -def zeros(*, dtype: Type[int], shape: Tuple[int, int]): +def mean(a: List[Union[float, numpy.float64]]): """ - usage.xarray: 8 + usage.sklearn: 2 """ ... @overload -def zeros( - _0: Union[int, numpy.int64, Tuple[int, ...]] = ..., - _1: Union[numpy.dtype, Literal["float64", "float32"]] = ..., - /, - *, - dtype: Union[ - Literal["uint64", "float64", "bool", "i4,f4,a10", "int64"], numpy.dtype, type - ] = ..., - shape: Tuple[int, int] = ..., -): +def mean(a: List[Union[numpy.float64, float]]): """ - usage.pandas: 125 + usage.sklearn: 1 """ ... @overload -def zeros( - _0: Union[ - List[Union[numpy.int64, int]], - Tuple[Union[numpy.int64, int, None], ...], - int, - numpy.int64, - numpy.ndarray, - ], - _1: Union[ - str, - numpy.dtype, - List[Tuple[Literal["a", "junk"], Union[Literal["S1"], numpy.dtype]]], - type, - ] = ..., - _2: Literal["F"] = ..., - /, - *, - dtype: Union[ - numpy.dtype, - type, - str, - List[Tuple[Union[str, Type[object], numpy.dtype, int], ...]], - ] = ..., - order: Union[None, Literal["f", "C", "c", "F"]] = ..., -): +def mean(a: List[numpy.ndarray], axis: int): """ - usage.scipy: 2106 + usage.sklearn: 3 """ ... @overload -def zeros(_0: Tuple[int, int], _1: numpy.dtype, /): +def mean(a: List[int]): """ - usage.matplotlib: 3 + usage.sklearn: 3 """ ... @overload -def zeros(_0: int, _1: Type[bool], /): +def mean(a: Tuple[numpy.float64, numpy.float64, numpy.float64]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... -@overload -def zeros(_0: int, _1: Literal["d"], /): +def mean( + a: object, + axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., + out: Union[ + dask.dataframe.core.Series, dask.dataframe.core.Scalar, numpy.float64 + ] = ..., + keepdims: bool = ..., + dtype: Union[type, None, Literal["float32", "i8", "f8"]] = ..., +): """ - usage.matplotlib: 3 + usage.dask: 78 + usage.matplotlib: 12 + usage.pandas: 26 + usage.scipy: 89 + usage.skimage: 58 + usage.sklearn: 231 + usage.xarray: 42 """ ... @overload -def zeros(_0: int, /, *, dtype: Type[numpy.int8]): +def median(a: numpy.ndarray, axis: Tuple[int, int]): """ - usage.matplotlib: 4 + usage.skimage: 1 """ ... @overload -def zeros(*, shape: int): +def median(a: numpy.ndarray): """ - usage.matplotlib: 1 + usage.matplotlib: 2 + usage.skimage: 4 + usage.sklearn: 23 """ ... @overload -def zeros(_0: Tuple[int], /, *, dtype: Type[numpy.int16]): +def median(a: numpy.ndarray, axis: None): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def zeros(_0: int, /, *, dtype: Type[float]): +def median(a: object, axis: None): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.int16]): +def median(a: object): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.float128]): +def median(a: xarray.core.dataarray.DataArray): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def zeros(_0: Tuple[numpy.int64, numpy.int64], /): +def median(a: xarray.core.dataset.Dataset): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def zeros(_0: List[int], /, *, dtype: Type[numpy.float64]): +def median(a: object, axis: int): """ - usage.matplotlib: 5 + usage.xarray: 1 """ ... @overload -def zeros( - _0: Union[Tuple[Union[None, int], ...], int, numpy.ndarray, List[int]] = ..., - _1: Type[numpy.float64] = ..., - /, - *, - dtype: Union[numpy.dtype, Literal["f8"], type] = ..., - shape: Tuple[int, ...] = ..., - order: Literal["F", "C"] = ..., -): +def median(a: numpy.ndarray, axis: int): """ - usage.dask: 52 + usage.sklearn: 15 + usage.xarray: 1 """ ... @overload -def zeros( - _0: Union[List[int], numpy.int64, int, Tuple[Union[int, numpy.int64], ...]] = ..., - _1: type = ..., - /, - *, - dtype: Union[numpy.dtype, Literal["int", "float"], type] = ..., - order: Literal["C", "F", "f"] = ..., - shape: Union[int, Tuple[int, int], List[int]] = ..., -): +def median(a: numpy.ndarray, axis: Tuple[int]): """ - usage.sklearn: 597 + usage.xarray: 1 """ ... -def zeros( - *_args: object, - dtype: Union[ - type, - str, - numpy.dtype, - List[Tuple[Union[str, Type[object], numpy.dtype, int], ...]], - ] = ..., - order: Union[Literal["C", "F", "f", "c"], None] = ..., - shape: Union[List[int], Tuple[int, ...], int] = ..., +@overload +def median( + a: Union[numpy.ndarray, pandas.core.series.Series], axis: Union[None, int] = ... ): """ - usage.dask: 52 - usage.matplotlib: 124 - usage.pandas: 125 - usage.sample-usage: 1 - usage.scipy: 2106 - usage.skimage: 757 - usage.sklearn: 597 - usage.xarray: 77 + usage.pandas: 17 """ ... @overload -def zeros_like(a: numpy.ndarray): +def median( + a: Union[numpy.ndarray, List[float]], + axis: Union[None, int] = ..., + keepdims: bool = ..., +): """ - usage.matplotlib: 20 - usage.skimage: 46 - usage.xarray: 3 + usage.scipy: 28 """ ... @overload -def zeros_like(a: Tuple[int, int]): +def median(a: numpy.ndarray, axis: int, overwrite_input: bool): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload -def zeros_like(a: Tuple[int, int, int]): +def median( + a: Union[numpy.ndarray, Tuple[int, ...]], + axis: Union[List[int], int] = ..., + keepdims: bool = ..., +): """ - usage.skimage: 1 + usage.dask: 18 """ ... -@overload -def zeros_like(a: numpy.ndarray, dtype: Type[bool]): +def median( + a: object, + axis: Union[int, Tuple[int, ...], None, List[int]] = ..., + overwrite_input: bool = ..., + keepdims: bool = ..., +): """ - usage.skimage: 6 - usage.xarray: 40 + usage.dask: 18 + usage.matplotlib: 3 + usage.pandas: 17 + usage.scipy: 28 + usage.skimage: 5 + usage.sklearn: 38 + usage.xarray: 8 """ ... @overload -def zeros_like(a: numpy.ndarray, dtype: Type[numpy.float64]): +def meshgrid(*xi: Literal["v", "t"]): """ + usage.matplotlib: 74 + usage.scipy: 14 usage.skimage: 8 + usage.sklearn: 4 + usage.xarray: 4 """ ... @overload -def zeros_like(a: numpy.ndarray, dtype: Type[numpy.float32]): +def meshgrid(*xi: Literal["v", "t"], indexing: Literal["ij"]): """ - usage.skimage: 1 + usage.skimage: 10 + usage.sklearn: 2 + usage.xarray: 2 """ ... @overload -def zeros_like(a: numpy.ndarray, dtype: Type[numpy.uint8]): +def meshgrid(*xi: Literal["v", "t"], indexing: Literal["ij"], sparse: bool): """ - usage.skimage: 1 + usage.skimage: 9 """ ... @overload -def zeros_like(a: numpy.ndarray, dtype: Type[numpy.int32]): +def meshgrid(*xi: Literal["v", "t"], indexing: Literal["ij", "xy"], sparse: bool = ...): """ - usage.skimage: 1 + usage.dask: 9 """ ... -@overload -def zeros_like(a: numpy.ndarray, dtype: Type[numpy.uint8], order: Literal["C"]): +def meshgrid( + *xi: Literal["v", "t"], indexing: Literal["ij", "xy"] = ..., sparse: bool = ... +): """ - usage.skimage: 2 + usage.dask: 9 + usage.matplotlib: 74 + usage.scipy: 14 + usage.skimage: 27 + usage.sklearn: 6 + usage.xarray: 6 """ ... @overload -def zeros_like(a: numpy.ndarray, dtype: Type[numpy.bool_]): +def min_scalar_type(_0: int, /): """ - usage.skimage: 1 + usage.skimage: 3 """ ... @overload -def zeros_like(a: sparse._coo.core.COO, dtype: Type[bool]): +def min_scalar_type(_0: numpy.int64, /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def zeros_like(a: object, dtype: Type[bool]): +def min_scalar_type(_0: numpy.float64, /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload -def zeros_like(a: object): +def min_scalar_type(_0: object, /): """ - usage.xarray: 2 + usage.matplotlib: 1 + usage.pandas: 10 """ ... @overload -def zeros_like(a: xarray.core.dataarray.DataArray): +def min_scalar_type(_0: numpy.ma.core.MaskedArray, /): """ - usage.xarray: 2 + usage.matplotlib: 3 """ ... @overload -def zeros_like( - a: Union[pandas.core.indexes.datetimes.DatetimeIndex, numpy.ndarray], - dtype: Literal["int64"] = ..., -): +def min_scalar_type(_0: List[numpy.int64], /): """ - usage.pandas: 20 + usage.matplotlib: 1 """ ... @overload -def zeros_like( - a: Union[numpy.ndarray, numpy.matrix, List[float]], - dtype: Union[numpy.dtype, type] = ..., -): +def min_scalar_type(_0: List[numpy.bool_], /): """ - usage.scipy: 135 + usage.matplotlib: 1 """ ... @overload -def zeros_like( - a: numpy.ndarray, - dtype: numpy.dtype = ..., - shape: Union[int, Tuple[int, ...], None] = ..., -): +def min_scalar_type(_0: List[numpy.float64], /): """ - usage.dask: 14 + usage.matplotlib: 1 """ ... @overload -def zeros_like( - a: Union[numpy.ndarray, int], dtype: type = ..., order: Literal["F"] = ... -): +def min_scalar_type(_0: List[float], /): """ - usage.sklearn: 76 + usage.matplotlib: 1 """ ... -def zeros_like( - a: object, - dtype: Union[type, Literal["int64"], numpy.dtype] = ..., - shape: Union[int, Tuple[int, ...], None] = ..., - order: Literal["F", "C"] = ..., -): +@overload +def min_scalar_type(_0: numpy.ndarray, /): """ - usage.dask: 14 - usage.matplotlib: 20 - usage.pandas: 20 - usage.scipy: 135 - usage.skimage: 68 - usage.sklearn: 76 - usage.xarray: 49 + usage.matplotlib: 5 """ ... -class AxisError: +@overload +def min_scalar_type(_0: List[Union[int, float]], /): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def min_scalar_type(_0: List[int], /): + """ + usage.matplotlib: 4 + """ + ... + + +@overload +def min_scalar_type(_0: List[numpy.float128], /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def min_scalar_type(_0: List[Union[float, int]], /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def min_scalar_type(_0: List[None], /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def min_scalar_type(_0: List[numpy.ma.core.MaskedArray], /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def min_scalar_type(_0: List[numpy.uint16], /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def min_scalar_type(_0: List[numpy.ma.core.MaskedConstant], /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def min_scalar_type(_0: List[numpy.float32], /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def min_scalar_type(_0: Union[int, dask.array.core.Array, numpy.ndarray], /): + """ + usage.dask: 3 + """ + ... + + +def min_scalar_type(_0: object, /): + """ + usage.dask: 3 + usage.matplotlib: 26 + usage.pandas: 10 + usage.skimage: 5 + """ + ... + + +def mintypecode( + typechars: Union[ + List[Literal["d", "D", "l", "e", "f"]], Tuple[numpy.ndarray, numpy.ndarray] + ] +): + """ + usage.scipy: 37 + """ + ... + + +@overload +def moveaxis(a: numpy.ndarray, source: int, destination: int): + """ + usage.matplotlib: 1 + usage.skimage: 4 + usage.xarray: 1 + """ + ... + + +@overload +def moveaxis(a: numpy.ndarray, source: numpy.ndarray, destination: numpy.ndarray): + """ + usage.xarray: 12 + """ + ... + + +@overload +def moveaxis(a: numpy.ndarray, source: Tuple[None, ...], destination: Tuple[None, ...]): + """ + usage.xarray: 3 + """ + ... + + +@overload +def moveaxis(a: numpy.ndarray, source: range, destination: List[int]): + """ + usage.xarray: 2 + """ + ... + + +@overload +def moveaxis(a: numpy.float64, source: numpy.ndarray, destination: numpy.ndarray): + """ + usage.xarray: 1 + """ + ... + + +@overload +def moveaxis(a: int, source: Tuple[None, ...], destination: Tuple[None, ...]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def moveaxis(a: object, source: int, destination: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def moveaxis(a: object, source: numpy.ndarray, destination: numpy.ndarray): + """ + usage.xarray: 1 + """ + ... + + +@overload +def moveaxis( + a: numpy.ndarray, source: Union[List[int], int], destination: Union[List[int], int] +): + """ + usage.scipy: 27 + """ + ... + + +@overload +def moveaxis( + a: Union[dask.array.core.Array, numpy.ndarray], source: int, destination: int +): + """ + usage.dask: 4 + """ + ... + + +def moveaxis( + a: object, + source: Union[int, numpy.ndarray, range, List[int], Tuple[None, ...]], + destination: Union[int, numpy.ndarray, List[int], Tuple[None, ...]], +): + """ + usage.dask: 4 + usage.matplotlib: 1 + usage.scipy: 27 + usage.skimage: 4 + usage.xarray: 22 + """ + ... + + +@overload +def nan_to_num(x: List[numpy.float64]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def nan_to_num(x: numpy.ndarray): + """ + usage.scipy: 4 + usage.sklearn: 7 + """ + ... + + +@overload +def nan_to_num(x: object): + """ + usage.dask: 23 + """ + ... + + +def nan_to_num(x: object): + """ + usage.dask: 23 + usage.scipy: 4 + usage.skimage: 1 + usage.sklearn: 7 + """ + ... + + +@overload +def nanargmax(a: numpy.ndarray): + """ + usage.xarray: 5 + """ + ... + + +@overload +def nanargmax(a: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmax(a: List[int]): + """ + usage.xarray: 2 + """ + ... + + +@overload +def nanargmax(a: List[List[int]]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmax(a: float): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmax(a: List[float]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmax(a: numpy.int32): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmax(a: numpy.ndarray, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmax(a: sparse._coo.core.COO, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmax(a: object, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmax(a: object, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmax(a: numpy.ndarray, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmax( + _0: numpy.ndarray = ..., + _1: Union[None, int] = ..., + /, + a: numpy.ndarray = ..., + axis: Union[int, None] = ..., + *, + keepdims: bool = ..., +): + """ + usage.dask: 28 + """ + ... + + +def nanargmax( + _0: numpy.ndarray = ..., + _1: Union[None, int] = ..., + /, + a: object = ..., + axis: Union[None, int] = ..., + *, + keepdims: bool = ..., +): + """ + usage.dask: 28 + usage.xarray: 17 + """ + ... + + +@overload +def nanargmin(a: numpy.ndarray): + """ + usage.xarray: 6 + """ + ... + + +@overload +def nanargmin(a: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmin(a: List[int]): + """ + usage.xarray: 2 + """ + ... + + +@overload +def nanargmin(a: List[List[int]]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmin(a: float): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmin(a: List[float]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmin(a: numpy.int32): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmin(a: numpy.ndarray, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmin(a: sparse._coo.core.COO, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmin(a: object, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmin(a: object, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanargmin( + _0: numpy.ndarray = ..., + _1: Union[None, int] = ..., + /, + a: numpy.ndarray = ..., + axis: Union[int, None] = ..., + *, + keepdims: bool = ..., +): + """ + usage.dask: 28 + """ + ... + + +def nanargmin( + _0: numpy.ndarray = ..., + _1: Union[None, int] = ..., + /, + a: object = ..., + axis: Union[None, int] = ..., + *, + keepdims: bool = ..., +): + """ + usage.dask: 28 + usage.xarray: 17 + """ + ... + + +@overload +def nancumprod(a: numpy.ndarray, axis: int, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nancumprod(a: sparse._coo.core.COO, axis: int, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nancumprod(a: object, axis: int, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nancumprod(a: numpy.ndarray, axis: Union[None, int] = ...): + """ + usage.dask: 16 + """ + ... + + +def nancumprod(a: object, axis: Union[int, None] = ..., dtype: None = ...): + """ + usage.dask: 16 + usage.xarray: 3 + """ + ... + + +@overload +def nancumsum(a: numpy.ndarray, axis: int, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nancumsum(a: sparse._coo.core.COO, axis: int, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nancumsum(a: object, axis: int, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nancumsum(a: numpy.ndarray, axis: Union[None, int] = ...): + """ + usage.dask: 16 + """ + ... + + +def nancumsum(a: object, axis: Union[int, None] = ..., dtype: None = ...): + """ + usage.dask: 16 + usage.xarray: 3 + """ + ... + + +@overload +def nanmax(a: numpy.ndarray, axis: int): + """ + usage.sklearn: 6 + usage.xarray: 4 + """ + ... + + +@overload +def nanmax(a: numpy.ndarray, axis: Tuple[int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmax(a: numpy.ndarray): + """ + usage.matplotlib: 2 + usage.xarray: 4 + """ + ... + + +@overload +def nanmax(a: numpy.ndarray, axis: None): + """ + usage.sklearn: 2 + usage.xarray: 2 + """ + ... + + +@overload +def nanmax(a: sparse._coo.core.COO, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmax(a: object, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmax(a: object, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmax(a: Union[numpy.ndarray, pandas.core.series.Series]): + """ + usage.pandas: 7 + """ + ... + + +@overload +def nanmax( + _0: numpy.ndarray = ..., + /, + a: numpy.ndarray = ..., + axis: Union[Tuple[Union[None, int], ...], int, None] = ..., + keepdims: bool = ..., + *, + computing_meta: bool = ..., +): + """ + usage.dask: 62 + """ + ... + + +def nanmax( + _0: numpy.ndarray = ..., + /, + a: object = ..., + axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., + keepdims: bool = ..., + *, + computing_meta: bool = ..., +): + """ + usage.dask: 62 + usage.matplotlib: 2 + usage.pandas: 7 + usage.sklearn: 8 + usage.xarray: 14 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray, axis: None, dtype: None): + """ + usage.xarray: 4 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray, axis: int, dtype: None): + """ + usage.xarray: 3 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray, axis: None, dtype: Type[numpy.float16]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray, axis: None, dtype: Type[numpy.float32]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray, axis: None, dtype: Type[numpy.float64]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray, axis: int): + """ + usage.sklearn: 4 + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray, axis: Tuple[None, ...], dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray, axis: Tuple[int], dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray): + """ + usage.xarray: 3 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray, axis: None, dtype: Type[float]): + """ + usage.xarray: 2 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray, axis: int, dtype: Type[float]): + """ + usage.xarray: 2 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray, axis: Tuple[int, int], dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: sparse._coo.core.COO, axis: None, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: sparse._coo.core.COO, axis: Tuple[int], dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: sparse._coo.core.COO, axis: int, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: object, axis: None, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: object, axis: int, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: object, axis: Tuple[int], dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmean(a: pandas.core.series.Series): + """ + usage.pandas: 2 + """ + ... + + +@overload +def nanmean(a: numpy.ndarray, axis: int, keepdims: bool): + """ + usage.scipy: 1 + """ + ... + + +@overload +def nanmean( + a: numpy.ndarray, + axis: Union[Tuple[Union[None, int], ...], int] = ..., + keepdims: bool = ..., +): + """ + usage.dask: 11 + """ + ... + + +def nanmean( + a: object, + axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., + keepdims: bool = ..., + dtype: Union[None, type] = ..., +): + """ + usage.dask: 11 + usage.pandas: 2 + usage.scipy: 1 + usage.sklearn: 4 + usage.xarray: 27 + """ + ... + + +@overload +def nanmedian(a: numpy.ndarray, axis: int): + """ + usage.sklearn: 2 + usage.xarray: 1 + """ + ... + + +@overload +def nanmedian(a: sparse._coo.core.COO, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmedian(a: object, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmedian(a: Union[pandas.core.series.Series, numpy.ndarray], axis: int = ...): + """ + usage.pandas: 3 + """ + ... + + +@overload +def nanmedian(a: numpy.ndarray, axis: Union[List[int], int], keepdims: bool): + """ + usage.dask: 8 + """ + ... + + +def nanmedian(a: object, axis: Union[int, None, List[int]] = ..., keepdims: bool = ...): + """ + usage.dask: 8 + usage.pandas: 3 + usage.sklearn: 2 + usage.xarray: 3 + """ + ... + + +@overload +def nanmin(a: numpy.ndarray, axis: Tuple[None, ...]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmin(a: numpy.ndarray, axis: int): + """ + usage.sklearn: 5 + usage.xarray: 3 + """ + ... + + +@overload +def nanmin(a: numpy.ndarray): + """ + usage.matplotlib: 2 + usage.sklearn: 2 + usage.xarray: 4 + """ + ... + + +@overload +def nanmin(a: numpy.ndarray, axis: None): + """ + usage.sklearn: 2 + usage.xarray: 2 + """ + ... + + +@overload +def nanmin(a: sparse._coo.core.COO, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmin(a: object, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmin(a: object, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanmin(a: Union[numpy.ndarray, pandas.core.series.Series]): + """ + usage.pandas: 6 + """ + ... + + +@overload +def nanmin( + _0: numpy.ndarray = ..., + /, + a: numpy.ndarray = ..., + axis: Union[Tuple[Union[None, int], ...], int, None] = ..., + keepdims: bool = ..., + *, + computing_meta: bool = ..., +): + """ + usage.dask: 62 + """ + ... + + +def nanmin( + _0: numpy.ndarray = ..., + /, + a: object = ..., + axis: Union[int, None, Tuple[Union[int, None], ...]] = ..., + keepdims: bool = ..., + *, + computing_meta: bool = ..., +): + """ + usage.dask: 62 + usage.matplotlib: 2 + usage.pandas: 6 + usage.sklearn: 9 + usage.xarray: 13 + """ + ... + + +@overload +def nanpercentile(a: numpy.ndarray, q: numpy.float64, axis: None): + """ + usage.xarray: 2 + """ + ... + + +@overload +def nanpercentile(a: numpy.ndarray, q: numpy.ndarray, axis: None): + """ + usage.xarray: 2 + """ + ... + + +@overload +def nanpercentile(a: numpy.ndarray, q: numpy.float64, axis: int): + """ + usage.xarray: 3 + """ + ... + + +@overload +def nanpercentile(a: numpy.ndarray, q: numpy.ndarray, axis: int): + """ + usage.xarray: 3 + """ + ... + + +@overload +def nanpercentile(a: numpy.ndarray, q: numpy.float64, axis: List[int]): + """ + usage.xarray: 5 + """ + ... + + +@overload +def nanpercentile(a: numpy.ndarray, q: numpy.ndarray, axis: List[int]): + """ + usage.xarray: 5 + """ + ... + + +@overload +def nanpercentile( + a: numpy.ndarray, + q: List[int], + axis: Union[int, None], + interpolation: Literal["linear"], + keepdims: bool, +): + """ + usage.scipy: 2 + """ + ... + + +@overload +def nanpercentile(a: numpy.ndarray, q: Tuple[float, float]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def nanpercentile(a: numpy.ndarray, q: numpy.ndarray): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def nanpercentile(a: numpy.ndarray, q: Tuple[int, int]): + """ + usage.sklearn: 1 + """ + ... + + +def nanpercentile( + a: numpy.ndarray, + q: Union[ + numpy.ndarray, + numpy.float64, + Tuple[Union[int, float], Union[int, float]], + List[int], + ], + interpolation: Literal["linear"] = ..., + keepdims: bool = ..., +): + """ + usage.scipy: 2 + usage.sklearn: 6 + usage.xarray: 20 + """ + ... + + +@overload +def nanprod(a: numpy.ndarray, axis: None, dtype: None, out: None): + """ + usage.xarray: 3 + """ + ... + + +@overload +def nanprod(a: numpy.ndarray, axis: int, dtype: None, out: None): + """ + usage.xarray: 3 + """ + ... + + +@overload +def nanprod(a: numpy.ndarray, axis: Tuple[int, int], dtype: None, out: None): + """ + usage.xarray: 3 + """ + ... + + +@overload +def nanprod(a: sparse._coo.core.COO, axis: None, dtype: None, out: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanprod(a: numpy.ndarray, axis: Union[None, int]): + """ + usage.pandas: 4 + """ + ... + + +@overload +def nanprod( + a: numpy.ndarray, + axis: Union[Tuple[Union[None, int], ...], int] = ..., + keepdims: bool = ..., +): + """ + usage.dask: 29 + """ + ... + + +def nanprod( + a: Union[numpy.ndarray, sparse._coo.core.COO], + axis: Union[int, Tuple[Union[int, None], ...], None] = ..., + keepdims: bool = ..., + dtype: Union[Literal["i8", "f8"], None] = ..., + out: None = ..., +): + """ + usage.dask: 29 + usage.pandas: 4 + usage.xarray: 10 + """ + ... + + +@overload +def nanquantile( + a: numpy.ndarray, + q: numpy.ndarray, + axis: numpy.ndarray, + interpolation: Literal["linear"], +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def nanquantile( + a: sparse._coo.core.COO, + q: numpy.ndarray, + axis: numpy.ndarray, + interpolation: Literal["linear"], +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanquantile( + a: object, q: numpy.ndarray, axis: numpy.ndarray, interpolation: Literal["linear"] +): + """ + usage.xarray: 1 + """ + ... + + +def nanquantile( + a: object, q: numpy.ndarray, axis: numpy.ndarray, interpolation: Literal["linear"] +): + """ + usage.xarray: 4 + """ + ... + + +@overload +def nanstd(a: numpy.ndarray, axis: int): + """ + usage.sklearn: 1 + usage.xarray: 1 + """ + ... + + +@overload +def nanstd(a: sparse._coo.core.COO, axis: None, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanstd(a: object, axis: None, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanstd(a: object, axis: int, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanstd(a: numpy.ndarray, axis: int, ddof: int, keepdims: bool): + """ + usage.scipy: 1 + """ + ... + + +@overload +def nanstd( + a: numpy.ndarray, + axis: Union[Tuple[Union[None, int], ...], int] = ..., + keepdims: bool = ..., +): + """ + usage.dask: 11 + """ + ... + + +def nanstd( + a: object, + axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., + dtype: None = ..., + ddof: int = ..., + keepdims: bool = ..., +): + """ + usage.dask: 11 + usage.scipy: 1 + usage.sklearn: 1 + usage.xarray: 4 + """ + ... + + +@overload +def nansum(a: numpy.ndarray, axis: int): + """ + usage.sklearn: 2 + usage.xarray: 3 + """ + ... + + +@overload +def nansum(a: numpy.ndarray): + """ + usage.scipy: 1 + usage.sklearn: 4 + usage.xarray: 4 + """ + ... + + +@overload +def nansum(a: numpy.ndarray, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nansum( + a: Union[pandas.core.series.Series, numpy.ndarray], axis: Union[None, int] = ... +): + """ + usage.pandas: 6 + """ + ... + + +@overload +def nansum( + a: numpy.ndarray, + axis: Union[Tuple[Union[None, int], ...], int] = ..., + dtype: Union[numpy.dtype, Literal["i8", "f8"]] = ..., + keepdims: bool = ..., +): + """ + usage.dask: 82 + """ + ... + + +@overload +def nansum(a: numpy.ndarray, axis: int, dtype: Type[numpy.float64]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def nansum(a: numpy.ma.core.MaskedArray): + """ + usage.sklearn: 1 + """ + ... + + +def nansum( + a: Union[numpy.ma.core.MaskedArray, numpy.ndarray, pandas.core.series.Series], + axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., + dtype: Union[Type[numpy.float64], Literal["i8", "f8"], numpy.dtype] = ..., + keepdims: bool = ..., +): + """ + usage.dask: 82 + usage.pandas: 6 + usage.scipy: 1 + usage.sklearn: 9 + usage.xarray: 8 + """ + ... + + +@overload +def nanvar(a: numpy.ndarray, axis: int): + """ + usage.sklearn: 4 + usage.xarray: 2 + """ + ... + + +@overload +def nanvar(a: numpy.ndarray): + """ + usage.sklearn: 4 + usage.xarray: 4 + """ + ... + + +@overload +def nanvar(a: numpy.ndarray, axis: None, dtype: Type[float], ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanvar(a: numpy.ndarray, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanvar(a: numpy.ndarray, axis: None, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanvar(a: numpy.ndarray, axis: int, dtype: Type[float], ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanvar(a: sparse._coo.core.COO, axis: None, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanvar(a: object, axis: None, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanvar(a: object, axis: int, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def nanvar(a: numpy.ndarray, axis: int, ddof: int): + """ + usage.scipy: 1 + """ + ... + + +@overload +def nanvar( + a: numpy.ndarray, + axis: Union[Tuple[Union[None, int], ...], int] = ..., + keepdims: bool = ..., +): + """ + usage.dask: 11 + """ + ... + + +@overload +def nanvar(a: numpy.ndarray, axis: int, dtype: Type[numpy.float64]): + """ + usage.sklearn: 2 + """ + ... + + +def nanvar( + a: object, + axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., + keepdims: bool = ..., + dtype: Union[type, None] = ..., + ddof: int = ..., +): + """ + usage.dask: 11 + usage.scipy: 1 + usage.sklearn: 10 + usage.xarray: 13 + """ + ... + + +@overload +def ndim(a: numpy.ndarray): + """ + usage.matplotlib: 7 + usage.skimage: 4 + usage.sklearn: 2 + """ + ... + + +@overload +def ndim(a: object): + """ + usage.matplotlib: 1 + usage.pandas: 703 + usage.scipy: 92 + """ + ... + + +@overload +def ndim(a: numpy.int64): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def ndim(a: numpy.float64): + """ + usage.matplotlib: 2 + usage.sklearn: 2 + """ + ... + + +@overload +def ndim(a: float): + """ + usage.matplotlib: 3 + """ + ... + + +@overload +def ndim(a: numpy.ma.core.MaskedArray): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def ndim(a: Union[List[Union[int, List[int]]], None, int, numpy.ndarray]): + """ + usage.dask: 10 + """ + ... + + +def ndim(a: object): + """ + usage.dask: 10 + usage.matplotlib: 16 + usage.pandas: 703 + usage.scipy: 92 + usage.skimage: 4 + usage.sklearn: 4 + """ + ... + + +@overload +def nonzero(a: numpy.ndarray): + """ + usage.dask: 2 + usage.matplotlib: 5 + usage.pandas: 4 + usage.skimage: 17 + usage.sklearn: 15 + usage.xarray: 4 + """ + ... + + +@overload +def nonzero(a: object): + """ + usage.scipy: 80 + """ + ... + + +def nonzero(a: object): + """ + usage.dask: 2 + usage.matplotlib: 5 + usage.pandas: 4 + usage.scipy: 80 + usage.skimage: 17 + usage.sklearn: 15 + usage.xarray: 4 + """ + ... + + +@overload +def obj2sctype(rep: Type[numpy.floating]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: numpy.dtype): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Type[numpy.uint16]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Type[numpy.uint8]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Type[numpy.bool_]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Type[numpy.float64]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Literal["float32"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Literal["float64"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Literal["uint8"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Literal["uint16"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Literal["int64"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Type[numpy.int16]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Type[numpy.float32]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Type[numpy.uint32]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Type[numpy.int32]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Type[numpy.int8]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def obj2sctype(rep: Type[float]): + """ + usage.skimage: 1 + """ + ... + + +def obj2sctype( + rep: Union[ + type, Literal["int64", "uint16", "uint8", "float64", "float32"], numpy.dtype + ] +): + """ + usage.skimage: 17 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Type[numpy.bool_]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def ones(shape: Tuple[int, int]): + """ + usage.matplotlib: 4 + usage.sample-usage: 1 + usage.skimage: 76 + usage.sklearn: 81 + usage.xarray: 21 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Type[float]): + """ + usage.matplotlib: 1 + usage.skimage: 1 + usage.xarray: 3 + """ + ... + + +@overload +def ones(shape: List[int]): + """ + usage.skimage: 3 + usage.sklearn: 1 + usage.xarray: 8 + """ + ... + + +@overload +def ones(shape: Tuple[int, int, int]): + """ + usage.skimage: 17 + usage.sklearn: 2 + usage.xarray: 6 + """ + ... + + +@overload +def ones(shape: Tuple[int, int, int, int]): + """ + usage.skimage: 8 + usage.sklearn: 1 + usage.xarray: 1 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Type[numpy.uint8]): + """ + usage.matplotlib: 1 + usage.skimage: 41 + """ + ... + + +@overload +def ones(shape: int): + """ + usage.matplotlib: 40 + usage.skimage: 19 + usage.sklearn: 214 + usage.xarray: 21 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Type[numpy.float32]): + """ + usage.skimage: 3 + usage.sklearn: 5 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Type[numpy.int8]): + """ + usage.skimage: 6 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Type[numpy.float64]): + """ + usage.skimage: 2 + usage.sklearn: 4 + """ + ... + + +@overload +def ones(shape: int, dtype: Type[numpy.int64]): + """ + usage.skimage: 1 + usage.sklearn: 6 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Type[bool]): + """ + usage.matplotlib: 1 + usage.skimage: 29 + usage.sklearn: 2 + """ + ... + + +@overload +def ones(shape: Tuple[numpy.int64, numpy.int64], dtype: Type[numpy.uint8]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def ones(shape: Tuple[numpy.int64, numpy.int64], dtype: Type[numpy.uint16]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ones(shape: Tuple[int]): + """ + usage.matplotlib: 1 + usage.skimage: 5 + usage.sklearn: 21 + usage.xarray: 4 + """ + ... + + +@overload +def ones(shape: int, dtype: numpy.dtype): + """ + usage.matplotlib: 6 + usage.skimage: 2 + usage.sklearn: 13 + """ + ... + + +@overload +def ones(shape: Tuple[int], dtype: Type[float]): + """ + usage.skimage: 1 + usage.xarray: 1 + """ + ... + + +@overload +def ones(shape: int, dtype: Type[numpy.float64]): + """ + usage.matplotlib: 4 + usage.skimage: 1 + usage.sklearn: 11 + """ + ... + + +@overload +def ones(shape: Tuple[int, int, int], dtype: Type[numpy.uint8]): + """ + usage.skimage: 3 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Type[bool], order: Literal["F"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ones(shape: Tuple[int, int, int], dtype: Type[bool], order: Literal["F"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ones(shape: Tuple[int, int, int], dtype: Type[bool]): + """ + usage.skimage: 5 + """ + ... + + +@overload +def ones(shape: Tuple[int, int, int, int, int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ones(shape: List[int], dtype: Type[bool]): + """ + usage.skimage: 2 + usage.sklearn: 5 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Literal["uint8"]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def ones(shape: Tuple[int, int, int], dtype: Literal["uint8"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ones(shape: Tuple[int], dtype: Type[bool]): + """ + usage.skimage: 3 + usage.sklearn: 5 + usage.xarray: 1 + """ + ... + + +@overload +def ones(shape: Tuple[int, int, int, int], dtype: Type[bool]): + """ + usage.skimage: 3 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Type[int]): + """ + usage.skimage: 1 + usage.sklearn: 1 + usage.xarray: 4 + """ + ... + + +@overload +def ones(shape: Tuple[int], dtype: Type[numpy.float64]): + """ + usage.skimage: 1 + usage.sklearn: 5 + """ + ... + + +@overload +def ones(shape: numpy.ndarray): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ones(shape: int, dtype: Type[bool]): + """ + usage.skimage: 1 + usage.sklearn: 13 + """ + ... + + +@overload +def ones(shape: numpy.int64): + """ + usage.skimage: 2 + usage.sklearn: 2 + """ + ... + + +@overload +def ones(shape: Tuple[int, int, int, int, int, int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ones(shape: Tuple[int], dtype: Type[int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ones(shape: int, dtype: Literal[">f4"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ones( + shape: Union[int, Tuple[int, ...]], + dtype: Union[numpy.dtype, Literal["int64", "float64", "bool"], type] = ..., +): + """ + usage.pandas: 116 + """ + ... + + +@overload +def ones( + shape: object, dtype: Union[type, numpy.dtype, str] = ..., order: Literal["c"] = ... +): + """ + usage.scipy: 934 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Type[numpy.uint16]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def ones(shape: List[int], dtype: Type[numpy.int32]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def ones(shape: int, dtype: Type[numpy.int32]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def ones( + shape: Union[Tuple[Union[None, int], ...], int, numpy.ndarray, List[int]], + dtype: Union[ + numpy.dtype, + List[ + Tuple[ + Literal["a", "b", "c", "col1", "col2"], + Union[Literal["f8"], Tuple[Literal["f4"], Union[int, Tuple[int, int]]]], + ] + ], + type, + Literal["i4", "float32", "f8"], + ] = ..., + order: Literal["F", "C"] = ..., +): + """ + usage.dask: 186 + """ + ... + + +@overload +def ones(shape: int, dtype: Type[int]): + """ + usage.sklearn: 8 + """ + ... + + +@overload +def ones(shape: numpy.int64, dtype: Type[int]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def ones(shape: int, dtype: Type[numpy.float64], order: Literal["C"]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def ones(shape: int, dtype: None): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def ones(shape: Tuple[int], dtype: Type[numpy.float64], order: Literal["C"]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def ones(shape: int, dtype: Type[numpy.float32]): + """ + usage.sklearn: 16 + """ + ... + + +@overload +def ones(shape: Tuple[int], dtype: Type[numpy.float32]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def ones(shape: Tuple[int], dtype: numpy.dtype): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Type[numpy.int16]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def ones(shape: numpy.int64, dtype: Type[bool]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def ones(shape: numpy.int64, dtype: Type[numpy.float64]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def ones(shape: List[int], dtype: Type[int]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def ones(shape: int, dtype: Literal["int"]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: numpy.dtype): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def ones(shape: Tuple[int, int], dtype: Type[numpy.int32]): + """ + usage.sklearn: 1 + """ + ... + + +def ones( + shape: object, + dtype: Union[ + None, + str, + numpy.dtype, + type, + List[ + Tuple[ + Literal["a", "b", "c", "col1", "col2"], + Union[Literal["f8"], Tuple[Literal["f4"], Union[int, Tuple[int, int]]]], + ] + ], + ] = ..., + order: Literal["C", "F", "c"] = ..., +): + """ + usage.dask: 186 + usage.matplotlib: 62 + usage.pandas: 116 + usage.sample-usage: 1 + usage.scipy: 934 + usage.skimage: 248 + usage.sklearn: 435 + usage.xarray: 73 + """ + ... + + +@overload +def ones_like(a: numpy.ndarray): + """ + usage.matplotlib: 8 + usage.skimage: 17 + usage.sklearn: 29 + """ + ... + + +@overload +def ones_like(a: numpy.ndarray, dtype: Type[bool]): + """ + usage.skimage: 3 + usage.sklearn: 2 + """ + ... + + +@overload +def ones_like(a: numpy.ndarray, dtype: Type[numpy.uint8]): + """ + usage.skimage: 6 + """ + ... + + +@overload +def ones_like(a: object): + """ + usage.xarray: 11 + """ + ... + + +@overload +def ones_like(a: xarray.core.dataarray.DataArray): + """ + usage.xarray: 2 + """ + ... + + +@overload +def ones_like(a: xarray.core.variable.Variable): + """ + usage.xarray: 4 + """ + ... + + +@overload +def ones_like(a: Union[pandas.core.series.Series, numpy.ndarray]): + """ + usage.pandas: 6 + """ + ... + + +@overload +def ones_like( + a: Union[List[Union[float, int]], Tuple[int, ...], float, numpy.ndarray], + dtype: type = ..., +): + """ + usage.scipy: 62 + """ + ... + + +@overload +def ones_like(a: numpy.ma.core.MaskedArray, dtype: Type[numpy.float32]): + """ + usage.matplotlib: 6 + """ + ... + + +@overload +def ones_like(a: numpy.ma.core.MaskedArray): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def ones_like( + a: Union[numpy.ndarray, numpy.ma.core.MaskedArray], + order: Literal["F", "C"] = ..., + shape: Union[Tuple[int, ...], int, None] = ..., +): + """ + usage.dask: 11 + """ + ... + + +@overload +def ones_like(a: numpy.float64): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def ones_like(a: numpy.ndarray, dtype: Type[numpy.float32]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def ones_like(a: List[int]): + """ + usage.sklearn: 2 + """ + ... + + +def ones_like( + a: object, + dtype: type = ..., + order: Literal["F", "C"] = ..., + shape: Union[Tuple[int, ...], int, None] = ..., +): + """ + usage.dask: 11 + usage.matplotlib: 15 + usage.pandas: 6 + usage.scipy: 62 + usage.skimage: 26 + usage.sklearn: 37 + usage.xarray: 17 + """ + ... + + +@overload +def outer(a: numpy.ndarray, b: Union[numpy.ndarray, List[int]]): + """ + usage.scipy: 67 + """ + ... + + +@overload +def outer(a: numpy.ndarray, b: numpy.ndarray): + """ + usage.matplotlib: 1 + usage.sklearn: 14 + """ + ... + + +@overload +def outer( + a: Union[numpy.float64, numpy.ndarray], b: Union[numpy.float64, numpy.ndarray] +): + """ + usage.dask: 4 + """ + ... + + +def outer( + a: Union[numpy.ndarray, numpy.float64], + b: Union[numpy.ndarray, numpy.float64, List[int]], +): + """ + usage.dask: 4 + usage.matplotlib: 1 + usage.scipy: 67 + usage.sklearn: 14 + """ + ... + + +@overload +def pad(array: numpy.ndarray, pad_width: List[List[int]], mode: Literal["reflect"]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def pad(array: numpy.ndarray, pad_width: List[List[int]], mode: Literal["edge"]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def pad(array: numpy.ndarray, pad_width: int, mode: Literal["constant"]): + """ + usage.skimage: 13 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["constant"] +): + """ + usage.matplotlib: 1 + usage.skimage: 7 + usage.xarray: 16 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Tuple[Tuple[int, int], Tuple[int, int]], + mode: Literal["constant"], +): + """ + usage.skimage: 4 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], + mode: Literal["constant"], +): + """ + usage.skimage: 1 + usage.xarray: 5 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Tuple[Tuple[int, int], Tuple[int, int]], + mode: Literal["reflect"], +): + """ + usage.skimage: 2 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], + mode: Literal["reflect"], +): + """ + usage.skimage: 1 + usage.xarray: 2 + """ + ... + + +@overload +def pad(array: numpy.ndarray, pad_width: int, mode: Literal["edge"]): + """ + usage.skimage: 4 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: List[Tuple[numpy.int64, numpy.int64]], + mode: Literal["constant"], +): + """ + usage.skimage: 3 + """ + ... + + +@overload +def pad(array: numpy.ndarray, pad_width: Tuple[int, int], mode: Literal["constant"]): + """ + usage.sklearn: 1 + usage.xarray: 1 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["minimum"] +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], + mode: Literal["minimum"], +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["maximum"] +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], + mode: Literal["maximum"], +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def pad(array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["mean"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], + mode: Literal["mean"], +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["median"] +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], + mode: Literal["median"], +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["linear_ramp"] +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], + mode: Literal["linear_ramp"], +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["reflect"] +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["symmetric"] +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], + mode: Literal["symmetric"], +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def pad( + array: sparse._coo.core.COO, + pad_width: List[Tuple[int, int]], + mode: Literal["constant"], +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["constant"]): + """ + usage.xarray: 4 + """ + ... + + +@overload +def pad( + array: object, + pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], + mode: Literal["constant"], +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["mean"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["median"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["reflect"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["edge"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], + mode: Literal["edge"], +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["linear_ramp"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["maximum"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["minimum"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["symmetric"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad(array: object, pad_width: List[Tuple[int, int]], mode: Literal["wrap"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Tuple[Tuple[int, int], Tuple[int, int], Tuple[int, int]], + mode: Literal["wrap"], +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad(array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["edge"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad(array: numpy.ndarray, pad_width: List[Tuple[int, int]], mode: Literal["wrap"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: Union[int, Tuple[int, int], List[Tuple[int, int]]], + mode: Literal["reflect", "symmetric", "edge", "wrap", "constant"], +): + """ + usage.scipy: 42 + """ + ... + + +@overload +def pad( + array: Union[dask.array.core.Array, numpy.ndarray], + pad_width: Union[int, Tuple[Union[Tuple[int, int], int], ...]], + mode: Union[str, Callable], +): + """ + usage.dask: 142 + """ + ... + + +@overload +def pad( + array: numpy.ndarray, + pad_width: List[Tuple[int, Union[int, numpy.int64]]], + mode: Literal["constant"], +): + """ + usage.sklearn: 1 + """ + ... + + +def pad( + array: object, + pad_width: Union[ + Tuple[Union[Tuple[int, int], int], ...], + List[Union[List[int], Tuple[Union[int, numpy.int64], Union[int, numpy.int64]]]], + int, + ], + mode: Union[str, Callable], +): + """ + usage.dask: 142 + usage.matplotlib: 1 + usage.scipy: 42 + usage.skimage: 39 + usage.sklearn: 2 + usage.xarray: 63 + """ + ... + + +@overload +def partition(a: numpy.ndarray, kth: Tuple[int, int], axis: int): + """ + usage.scipy: 5 + """ + ... + + +@overload +def partition(a: numpy.ndarray, kth: int, axis: int): + """ + usage.dask: 1 + usage.sklearn: 4 + """ + ... + + +def partition(a: numpy.ndarray, kth: Union[int, Tuple[int, int]], axis: int): + """ + usage.dask: 1 + usage.scipy: 5 + usage.sklearn: 4 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: float): + """ + usage.skimage: 1 + usage.sklearn: 7 + usage.xarray: 2 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: List[int]): + """ + usage.matplotlib: 3 + usage.skimage: 5 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: int): + """ + usage.skimage: 3 + usage.sklearn: 5 + usage.xarray: 2 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: numpy.float64, axis: None): + """ + usage.xarray: 2 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: numpy.ndarray, axis: None): + """ + usage.xarray: 2 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: numpy.float64, axis: int): + """ + usage.xarray: 2 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: numpy.ndarray, axis: int): + """ + usage.xarray: 2 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: numpy.float64, axis: List[int]): + """ + usage.xarray: 4 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: numpy.ndarray, axis: List[int]): + """ + usage.xarray: 4 + """ + ... + + +@overload +def percentile( + _0: pandas.core.series.Series = ..., + /, + a: Union[ + numpy.ndarray, int, pandas.core.frame.DataFrame, pandas.core.series.Series + ] = ..., + q: Union[ + int, + numpy.ndarray, + pandas.core.frame.DataFrame, + pandas.core.series.Series, + float, + ] = ..., + axis: int = ..., + interpolation: Literal["linear", "midpoint", "nearest", "higher", "lower"] = ..., +): + """ + usage.pandas: 54 + """ + ... + + +@overload +def percentile( + a: Union[numpy.ndarray, List[float]], + q: Union[List[Union[float, int]], int], + axis: Union[Tuple[int, ...], int, None] = ..., + interpolation: str = ..., + keepdims: bool = ..., +): + """ + usage.scipy: 27 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: numpy.ndarray): + """ + usage.matplotlib: 1 + usage.sklearn: 5 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: List[float]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: Tuple[int, int]): + """ + usage.matplotlib: 1 + usage.sklearn: 1 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: list): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def percentile( + a: numpy.ndarray, q: numpy.ndarray, interpolation: Literal["linear", "nearest"] +): + """ + usage.dask: 8 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: numpy.ndarray, interpolation: Literal["midpoint"]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: float, axis: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: Tuple[int, int], axis: int): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def percentile(a: numpy.ndarray, q: int, axis: int): + """ + usage.sklearn: 1 + """ + ... + + +def percentile( + _0: pandas.core.series.Series = ..., + /, + a: Union[ + numpy.ndarray, + pandas.core.series.Series, + pandas.core.frame.DataFrame, + int, + List[float], + ] = ..., + q: object = ..., + axis: Union[int, List[int], Tuple[int, ...], None] = ..., + interpolation: str = ..., + keepdims: bool = ..., +): + """ + usage.dask: 8 + usage.matplotlib: 9 + usage.pandas: 54 + usage.scipy: 27 + usage.skimage: 9 + usage.sklearn: 25 + usage.xarray: 20 + """ + ... + + +@overload +def piecewise( + x: numpy.ndarray, + condlist: List[numpy.ndarray], + funclist: List[Union[int, Callable]], +): + """ + usage.scipy: 4 + """ + ... + + +@overload +def piecewise( + x: Union[int, numpy.ndarray], + condlist: Union[numpy.ndarray, List[numpy.ndarray]], + funclist: List[Union[Callable, int, numpy.ndarray]], + *args: Literal["v", "t"], +): + """ + usage.dask: 6 + """ + ... + + +def piecewise( + x: Union[numpy.ndarray, int], + condlist: Union[List[numpy.ndarray], numpy.ndarray], + funclist: List[Union[Callable, int, numpy.ndarray]], + *args: Literal["v", "t"], +): + """ + usage.dask: 6 + usage.scipy: 4 + """ + ... + + +@overload +def place(arr: numpy.ndarray, mask: numpy.ndarray, vals: object): + """ + usage.pandas: 19 + """ + ... + + +@overload +def place( + arr: Union[numpy.ma.core.MaskedArray, numpy.ndarray], mask: object, vals: object +): + """ + usage.scipy: 269 + """ + ... + + +def place( + arr: Union[numpy.ndarray, numpy.ma.core.MaskedArray], mask: object, vals: object +): + """ + usage.pandas: 19 + usage.scipy: 269 + """ + ... + + +def poly(seq_of_zeros: Union[List[int], numpy.ndarray]): + """ + usage.scipy: 24 + """ + ... + + +def polyadd(a1: Union[numpy.ndarray, int], a2: numpy.ndarray): + """ + usage.scipy: 14 + """ + ... + + +def polydiv(u: numpy.ndarray, v: numpy.ndarray): + """ + usage.scipy: 9 + """ + ... + + +def polyfit(x: numpy.ndarray, y: numpy.ndarray, deg: int): + """ + usage.scipy: 3 + usage.skimage: 1 + """ + ... + + +def polyint(p: Union[List[float], numpy.poly1d, numpy.ndarray]): + """ + usage.scipy: 3 + """ + ... + + +def polymul( + a1: Union[List[Union[float, int]], numpy.ndarray], + a2: Union[List[Union[int, float]], numpy.ndarray], +): + """ + usage.scipy: 26 + """ + ... + + +def polysub(a1: numpy.ndarray, a2: numpy.ndarray): + """ + usage.scipy: 2 + """ + ... + + +def polyval( + p: Union[List[Union[float, int]], numpy.ndarray, numpy.poly1d], + x: Union[float, numpy.ndarray, numpy.complex128, numpy.float64], +): + """ + usage.scipy: 36 + """ + ... + + +@overload +def prod(a: Tuple[int, int]): + """ + usage.matplotlib: 2 + usage.skimage: 2 + usage.sklearn: 3 + usage.xarray: 4 + """ + ... + + +@overload +def prod(a: Tuple[int, int, int]): + """ + usage.skimage: 3 + usage.sklearn: 1 + usage.xarray: 3 + """ + ... + + +@overload +def prod(a: List[int]): + """ + usage.skimage: 1 + usage.xarray: 1 + """ + ... + + +@overload +def prod(a: numpy.ndarray): + """ + usage.skimage: 3 + usage.sklearn: 2 + """ + ... + + +@overload +def prod(a: Tuple[int]): + """ + usage.sklearn: 1 + usage.xarray: 3 + """ + ... + + +@overload +def prod(a: Tuple[None, ...]): + """ + usage.matplotlib: 2 + usage.xarray: 1 + """ + ... + + +@overload +def prod(a: list): + """ + usage.xarray: 1 + """ + ... + + +@overload +def prod(a: Tuple[int, int, int, int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def prod(a: Tuple[int, int, int, int, int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def prod(a: numpy.ndarray, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def prod(a: object, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def prod(a: object): + """ + usage.xarray: 1 + """ + ... + + +@overload +def prod(a: xarray.core.dataarray.DataArray): + """ + usage.xarray: 1 + """ + ... + + +@overload +def prod(a: xarray.core.dataset.Dataset): + """ + usage.xarray: 1 + """ + ... + + +@overload +def prod(a: object, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def prod(a: numpy.ndarray, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def prod( + a: Union[numpy.ndarray, int, List[int], Tuple[Union[None, int], ...]], + dtype: Literal["i8"] = ..., + axis: Union[None, int] = ..., +): + """ + usage.pandas: 52 + """ + ... + + +@overload +def prod( + a: Union[ + Tuple[Union[float, int, numpy.int64, None], ...], int, numpy.ndarray, List[int] + ], + axis: int = ..., +): + """ + usage.scipy: 63 + """ + ... + + +@overload +def prod( + a: object, + axis: Union[None, Tuple[Union[None, int], ...], int] = ..., + out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., + keepdims: bool = ..., +): + """ + usage.dask: 100 + """ + ... + + +@overload +def prod(a: List[numpy.ndarray], axis: int, dtype: Type[numpy.float64]): + """ + usage.sklearn: 3 + """ + ... + + +def prod( + a: object, + axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., + dtype: Union[type, Literal["i8", "f8", "i4", "f4"]] = ..., + out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., + keepdims: bool = ..., +): + """ + usage.dask: 100 + usage.matplotlib: 4 + usage.pandas: 52 + usage.scipy: 63 + usage.skimage: 9 + usage.sklearn: 10 + usage.xarray: 22 + """ + ... + + +def product(*args: Literal["v", "t"]): + """ + usage.pandas: 1 + usage.skimage: 14 + usage.sklearn: 5 + """ + ... + + +@overload +def promote_types(_0: numpy.dtype, _1: numpy.dtype, /): + """ + usage.pandas: 55 + usage.skimage: 1 + """ + ... + + +@overload +def promote_types(_0: numpy.dtype, _1: Union[Literal["float64"], numpy.dtype], /): + """ + usage.scipy: 13 + """ + ... + + +@overload +def promote_types(_0: numpy.dtype, _1: Type[numpy.float32], /): + """ + usage.matplotlib: 9 + """ + ... + + +@overload +def promote_types(_0: numpy.dtype, _1: Union[numpy.dtype, Type[float]], /): + """ + usage.dask: 7 + """ + ... + + +def promote_types(_0: numpy.dtype, _1: Union[type, numpy.dtype, Literal["float64"]], /): + """ + usage.dask: 7 + usage.matplotlib: 9 + usage.pandas: 55 + usage.scipy: 13 + usage.skimage: 1 + """ + ... + + +@overload +def ptp(a: Union[numpy.ndarray, pandas.core.series.Series]): + """ + usage.pandas: 2 + """ + ... + + +@overload +def ptp(a: numpy.ndarray): + """ + usage.matplotlib: 8 + usage.sklearn: 1 + """ + ... + + +@overload +def ptp(a: numpy.ndarray, axis: int): + """ + usage.matplotlib: 1 + usage.sklearn: 1 + """ + ... + + +@overload +def ptp(a: numpy.ndarray, axis: Union[int, None]): + """ + usage.dask: 2 + """ + ... + + +@overload +def ptp(a: List[int]): + """ + usage.sklearn: 7 + """ + ... + + +def ptp( + a: Union[numpy.ndarray, pandas.core.series.Series, List[int]], + axis: Union[int, None] = ..., +): + """ + usage.dask: 2 + usage.matplotlib: 9 + usage.pandas: 2 + usage.sklearn: 9 + """ + ... + + +@overload +def putmask( + _0: Union[ + numpy.ndarray, + pandas.core.arrays.interval.IntervalArray, + pandas.core.arrays.categorical.Categorical, + ], + _1: Union[numpy.ndarray, pandas.core.series.Series, Literal["foo"]], + _2: object, + /, +): + """ + usage.pandas: 177 + """ + ... + + +@overload +def putmask( + _0: numpy.ndarray, _1: Union[numpy.ndarray, numpy.int64, bool], _2: float, / +): + """ + usage.scipy: 12 + """ + ... + + +@overload +def putmask(_0: numpy.ndarray, _1: numpy.ndarray, _2: numpy.ndarray, /): + """ + usage.matplotlib: 4 + """ + ... + + +def putmask( + _0: Union[ + numpy.ndarray, + pandas.core.arrays.interval.IntervalArray, + pandas.core.arrays.categorical.Categorical, + ], + _1: Union[ + numpy.ndarray, pandas.core.series.Series, numpy.int64, bool, Literal["foo"] + ], + _2: object, + /, +): + """ + usage.matplotlib: 4 + usage.pandas: 177 + usage.scipy: 12 + """ + ... + + +def quantile( + a: numpy.ndarray, + q: numpy.ndarray, + axis: numpy.ndarray, + interpolation: Literal["linear"], +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: Tuple[int, int, int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel(a: numpy.int64): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel(a: Tuple[int, int, int, int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel(a: int): + """ + usage.skimage: 1 + usage.sklearn: 2 + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: numpy.ndarray): + """ + usage.dask: 1 + usage.matplotlib: 22 + usage.skimage: 1 + usage.sklearn: 55 + usage.xarray: 27 + """ + ... + + +@overload +def ravel(a: Tuple[numpy.float64, numpy.float64]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel(a: float): + """ + usage.skimage: 1 + usage.sklearn: 2 + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: numpy.float64): + """ + usage.skimage: 1 + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: List[numpy.float64]): + """ + usage.skimage: 1 + usage.sklearn: 1 + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: numpy.matrix): + """ + usage.skimage: 3 + usage.sklearn: 8 + """ + ... + + +@overload +def ravel(a: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: numpy.float32): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: numpy.bytes_): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: numpy.uint8): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: numpy.int8): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: numpy.int16): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: bytes): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: numpy.int32): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: pandas.core.indexes.datetimes.DatetimeIndex): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: xarray.coding.cftimeindex.CFTimeIndex): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: pandas.core.indexes.numeric.Int64Index): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: pandas.core.indexes.numeric.Float64Index): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: pandas.core.indexes.base.Index): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: pandas.core.indexes.multi.MultiIndex): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: pandas.core.indexes.interval.IntervalIndex): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: xarray.core.variable.IndexVariable): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: pandas.core.indexes.range.RangeIndex): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: xarray.core.variable.Variable): + """ + usage.xarray: 1 + """ + ... + + +@overload +def ravel(a: Union[pandas.core.series.Series, pandas.core.frame.DataFrame]): + """ + usage.pandas: 2 + """ + ... + + +@overload +def ravel( + a: Union[ + numpy.ndarray, + numpy.matrix, + List[Union[None, float, int, List[float]]], + Tuple[int, int, int], + ] +): + """ + usage.scipy: 162 + """ + ... + + +@overload +def ravel(a: List[Union[float, None]]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def ravel(a: List[int]): + """ + usage.matplotlib: 1 + usage.sklearn: 3 + """ + ... + + +@overload +def ravel(a: List[List[int]]): + """ + usage.matplotlib: 1 + usage.sklearn: 1 + """ + ... + + +@overload +def ravel(a: numpy.ndarray, order: Literal["K"]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def ravel(a: List[numpy.ndarray]): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def ravel(a: List[Literal["spam", "egg"]]): + """ + usage.sklearn: 1 + """ + ... + + +def ravel(a: object, order: Literal["K"] = ...): + """ + usage.dask: 1 + usage.matplotlib: 25 + usage.pandas: 2 + usage.scipy: 162 + usage.skimage: 12 + usage.sklearn: 80 + usage.xarray: 49 + """ + ... + + +@overload +def ravel_multi_index(_0: List[int], _1: Tuple[int], /, *, order: Literal["F"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index( + _0: List[int], _1: Tuple[int, int, int], /, *, order: Literal["C"] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index(_0: List[int], _1: Tuple[int, int], /, *, order: Literal["C"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index( + _0: List[int], _1: Tuple[int, int, int, int], /, *, order: Literal["C"] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index( + _0: List[int], _1: Tuple[int, int, int, int, int], /, *, order: Literal["C"] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index(_0: List[int], _1: Tuple[int, int], /, *, order: Literal["F"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index(_0: Tuple[int], _1: Tuple[int], /, *, order: Literal["C"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index( + _0: Tuple[int, int], _1: Tuple[int, int], /, *, order: Literal["C"] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index( + _0: Tuple[int, int, int], _1: Tuple[int, int, int], /, *, order: Literal["C"] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index( + _0: Tuple[int, int, int, int], + _1: Tuple[int, int, int, int], + /, + *, + order: Literal["C"], +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index(_0: Tuple[int], _1: Tuple[int], /, *, order: Literal["F"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index( + _0: Tuple[int, int], _1: Tuple[int, int], /, *, order: Literal["F"] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index( + _0: Tuple[int, int, int], _1: Tuple[int, int, int], /, *, order: Literal["F"] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index( + _0: Tuple[int, int, int, int], + _1: Tuple[int, int, int, int], + /, + *, + order: Literal["F"], +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index(_0: Tuple[numpy.ndarray, numpy.ndarray], _1: Tuple[int, int], /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index(_0: numpy.ndarray, _1: Tuple[int, int], /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index( + _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], _1: Tuple[int, int, int], / +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index(_0: numpy.ndarray, _1: Tuple[int, int, int], /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def ravel_multi_index(_0: List[numpy.ndarray], _1: numpy.ndarray, /): + """ + usage.scipy: 3 + """ + ... + + +@overload +def ravel_multi_index(_0: List[Tuple[int, int]], _1: Tuple[int, int], /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def ravel_multi_index(_0: Tuple[numpy.int64, ...], _1: Tuple[int, ...], /): + """ + usage.dask: 3 + """ + ... + + +def ravel_multi_index( + _0: Union[ + Tuple[Union[int, numpy.ndarray, numpy.int64], ...], + numpy.ndarray, + List[Union[Tuple[int, int], int, numpy.ndarray]], + ], + _1: Union[Tuple[int, ...], numpy.ndarray], + /, + *, + order: Literal["F", "C"] = ..., +): + """ + usage.dask: 3 + usage.matplotlib: 1 + usage.scipy: 3 + usage.skimage: 18 + """ + ... + + +@overload +def real(val: numpy.ndarray): + """ + usage.skimage: 7 + usage.sklearn: 5 + """ + ... + + +@overload +def real(val: float): + """ + usage.skimage: 1 + """ + ... + + +@overload +def real(val: xarray.core.dataarray.DataArray): + """ + usage.xarray: 1 + """ + ... + + +@overload +def real(val: numpy.complex128): + """ + usage.pandas: 1 + """ + ... + + +@overload +def real(val: object): + """ + usage.dask: 32 + usage.scipy: 106 + """ + ... + + +def real(val: object): + """ + usage.dask: 32 + usage.pandas: 1 + usage.scipy: 106 + usage.skimage: 8 + usage.sklearn: 5 + usage.xarray: 1 + """ + ... + + +@overload +def repeat(a: numpy.ndarray, repeats: int, axis: int): + """ + usage.dask: 4 + usage.matplotlib: 16 + usage.sklearn: 3 + usage.xarray: 4 + """ + ... + + +@overload +def repeat( + _0: object = ..., + _1: int = ..., + /, + a: object = ..., + repeats: object = ..., + axis: int = ..., + *, + foo: Literal["bar"] = ..., +): + """ + usage.pandas: 168 + """ + ... + + +@overload +def repeat( + a: object, + repeats: Union[numpy.ndarray, int, Tuple[int], List[int]], + axis: int = ..., +): + """ + usage.scipy: 48 + """ + ... + + +@overload +def repeat(a: numpy.ndarray, repeats: int): + """ + usage.matplotlib: 8 + usage.sklearn: 6 + """ + ... + + +@overload +def repeat(a: List[int], repeats: int): + """ + usage.matplotlib: 1 + usage.sklearn: 1 + """ + ... + + +@overload +def repeat(a: float, repeats: int): + """ + usage.sklearn: 9 + """ + ... + + +@overload +def repeat(a: int, repeats: numpy.int64): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def repeat(a: int, repeats: int): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def repeat(a: numpy.ndarray, repeats: numpy.ndarray, axis: int): + """ + usage.sklearn: 13 + """ + ... + + +@overload +def repeat(a: numpy.ndarray, repeats: numpy.ndarray): + """ + usage.sklearn: 12 + """ + ... + + +@overload +def repeat(a: numpy.int64, repeats: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def repeat(a: numpy.float64, repeats: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def repeat(a: List[float], repeats: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def repeat(a: float, repeats: Tuple[int]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def repeat(a: List[int], repeats: float): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def repeat(a: range, repeats: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def repeat(a: numpy.str_, repeats: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def repeat(a: List[List[int]], repeats: numpy.ndarray, axis: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def repeat(a: List[int], repeats: numpy.ndarray, axis: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def repeat(a: List[numpy.ndarray], repeats: int, axis: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def repeat(a: List[float], repeats: numpy.ndarray): + """ + usage.sklearn: 2 + """ + ... + + +def repeat( + _0: object = ..., + _1: int = ..., + /, + a: object = ..., + repeats: object = ..., + axis: int = ..., + *, + foo: Literal["bar"] = ..., +): + """ + usage.dask: 4 + usage.matplotlib: 25 + usage.pandas: 168 + usage.scipy: 48 + usage.sklearn: 69 + usage.xarray: 4 + """ + ... + + +@overload +def require( + a: numpy.ndarray, dtype: Type[numpy.uint8], requirements: List[Literal["C"]] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def require(a: numpy.ndarray, dtype: numpy.dtype, requirements: Literal["C", "F"]): + """ + usage.scipy: 16 + """ + ... + + +@overload +def require(a: numpy.ndarray, requirements: Literal["W"]): + """ + usage.sklearn: 6 + """ + ... + + +def require( + a: numpy.ndarray, + requirements: Union[Literal["W", "C", "F"], List[Literal["C"]]], + dtype: Union[numpy.dtype, Type[numpy.uint8]] = ..., +): + """ + usage.scipy: 16 + usage.skimage: 1 + usage.sklearn: 6 + """ + ... + + +@overload +def reshape(a: List[int], newshape: List[int]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def reshape(a: numpy.ndarray, newshape: List[int]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def reshape(a: numpy.ndarray, newshape: Tuple[int, int]): + """ + usage.matplotlib: 2 + usage.skimage: 11 + usage.sklearn: 50 + usage.xarray: 3 + """ + ... + + +@overload +def reshape(a: numpy.ndarray, newshape: Tuple[int]): + """ + usage.skimage: 3 + usage.sklearn: 1 + """ + ... + + +@overload +def reshape(a: numpy.ndarray, newshape: Tuple[int, int, int]): + """ + usage.skimage: 5 + usage.sklearn: 1 + usage.xarray: 1 + """ + ... + + +@overload +def reshape(a: numpy.ndarray, newshape: Tuple[int, int, int, int]): + """ + usage.skimage: 4 + usage.sklearn: 1 + usage.xarray: 2 + """ + ... + + +@overload +def reshape(a: numpy.ndarray, newshape: Tuple[int, int, int, int, int]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def reshape(a: numpy.ndarray, newshape: Tuple[numpy.int64, numpy.int64]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def reshape(a: numpy.ndarray, newshape: Tuple[int, numpy.int64], order: Literal["F"]): + """ + usage.skimage: 3 + """ + ... + + +@overload +def reshape(a: List[float], newshape: Tuple[int, int]): + """ + usage.sklearn: 3 + usage.xarray: 2 + """ + ... + + +@overload +def reshape(a: List[float], newshape: Tuple[int]): + """ + usage.xarray: 7 + """ + ... + + +@overload +def reshape(a: List[float], newshape: Tuple[None, ...]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def reshape( + a: Union[numpy.ndarray, List[Literal["A2", "A0", "A4", "A3"]]], + newshape: Tuple[int, int], +): + """ + usage.pandas: 6 + """ + ... + + +@overload +def reshape( + a: Union[numpy.ndarray, List[int]], + newshape: Union[Tuple[Union[int, numpy.int64], ...], List[int]], +): + """ + usage.scipy: 50 + """ + ... + + +@overload +def reshape(a: numpy.ndarray, newshape: int): + """ + usage.matplotlib: 3 + """ + ... + + +@overload +def reshape(a: List[numpy.float64], newshape: Tuple[int, int]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def reshape(a: List[numpy.int64], newshape: Tuple[int, int]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def reshape(a: numpy.ndarray, newshape: Tuple[int, numpy.int64]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def reshape(a: List[numpy.ndarray], newshape: Tuple[int, int, int]): + """ + usage.sklearn: 6 + """ + ... + + +@overload +def reshape( + a: Tuple[numpy.ndarray, numpy.ndarray], newshape: Tuple[int, int, int, int] +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def reshape(a: Tuple[numpy.ndarray, numpy.ndarray], newshape: Tuple[int, int, int]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def reshape( + a: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], + newshape: Tuple[int, int, int, int], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def reshape( + a: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], + newshape: Tuple[int, int, int], +): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def reshape( + a: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray], + newshape: Tuple[int, int, int, int], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def reshape( + a: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray], + newshape: Tuple[int, int, int], +): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def reshape( + a: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + ], + newshape: Tuple[int, int, int, int], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def reshape( + a: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + ], + newshape: Tuple[int, int, int], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def reshape( + a: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + ], + newshape: Tuple[int, int, int, int], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def reshape( + a: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + ], + newshape: Tuple[int, int, int], +): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def reshape( + a: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + ], + newshape: Tuple[int, int, int, int], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def reshape( + a: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + ], + newshape: Tuple[int, int, int], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def reshape(a: List[int], newshape: Tuple[int, int]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def reshape(a: List[List[int]], newshape: Tuple[int, int]): + """ + usage.sklearn: 2 + """ + ... + + +def reshape( + a: Union[Tuple[numpy.ndarray, ...], list, numpy.ndarray], + newshape: Union[Tuple[Union[int, numpy.int64, None], ...], List[int], int], + order: Literal["F"] = ..., +): + """ + usage.matplotlib: 5 + usage.pandas: 6 + usage.scipy: 50 + usage.skimage: 33 + usage.sklearn: 97 + usage.xarray: 16 + """ + ... + + +@overload +def resize(a: List[bool], new_shape: int): + """ + usage.pandas: 3 + """ + ... + + +@overload +def resize(a: Union[numpy.ndarray, bool, int], new_shape: Union[Tuple[int, ...], int]): + """ + usage.scipy: 31 + """ + ... + + +@overload +def resize(a: numpy.ndarray, new_shape: Tuple[int]): + """ + usage.matplotlib: 8 + """ + ... + + +@overload +def resize(a: List[int], new_shape: Tuple[int]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def resize(a: numpy.ndarray, new_shape: Tuple[int, int]): + """ + usage.matplotlib: 2 + usage.sklearn: 11 + """ + ... + + +@overload +def resize(a: numpy.ndarray, new_shape: int): + """ + usage.matplotlib: 5 + usage.sklearn: 4 + """ + ... + + +def resize( + a: Union[numpy.ndarray, int, bool, List[Union[int, bool]]], + new_shape: Union[int, Tuple[int, ...]], +): + """ + usage.matplotlib: 17 + usage.pandas: 3 + usage.scipy: 31 + usage.sklearn: 15 + """ + ... + + +@overload +def result_type(_0: numpy.dtype, _1: numpy.dtype, _2: Type[numpy.float32], /): + """ + usage.skimage: 7 + """ + ... + + +@overload +def result_type(_0: numpy.ndarray, /): + """ + usage.xarray: 21 + """ + ... + + +@overload +def result_type(_0: numpy.ndarray, _1: numpy.ndarray, /): + """ + usage.xarray: 25 + """ + ... + + +@overload +def result_type(_0: dask.array.core.Array, /): + """ + usage.xarray: 2 + """ + ... + + +@overload +def result_type(_0: dask.array.core.Array, _1: dask.array.core.Array, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type( + _0: dask.array.core.Array, + _1: dask.array.core.Array, + _2: dask.array.core.Array, + _3: dask.array.core.Array, + _4: dask.array.core.Array, + _5: dask.array.core.Array, + _6: dask.array.core.Array, + _7: dask.array.core.Array, + _8: dask.array.core.Array, + _9: dask.array.core.Array, + _10: dask.array.core.Array, + _11: dask.array.core.Array, + _12: dask.array.core.Array, + _13: dask.array.core.Array, + _14: dask.array.core.Array, + _15: dask.array.core.Array, + _16: dask.array.core.Array, + _17: dask.array.core.Array, + _18: dask.array.core.Array, + _19: dask.array.core.Array, + /, +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: numpy.ndarray, _1: dask.array.core.Array, /): + """ + usage.xarray: 2 + """ + ... + + +@overload +def result_type( + _0: numpy.ndarray, + _1: numpy.ndarray, + _2: numpy.ndarray, + _3: numpy.ndarray, + _4: numpy.ndarray, + /, +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def result_type(_0: numpy.ndarray, _1: numpy.ndarray, _2: numpy.ndarray, /): + """ + usage.xarray: 6 + """ + ... + + +@overload +def result_type( + _0: numpy.ndarray, _1: numpy.ndarray, _2: numpy.ndarray, _3: numpy.ndarray, / +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def result_type( + _0: numpy.ndarray, + _1: numpy.ndarray, + _2: numpy.ndarray, + _3: numpy.ndarray, + _4: numpy.ndarray, + _5: numpy.ndarray, + _6: numpy.ndarray, + _7: numpy.ndarray, + _8: numpy.ndarray, + _9: numpy.ndarray, + /, +): + """ + usage.xarray: 3 + """ + ... + + +@overload +def result_type(_0: dask.array.core.Array, _1: numpy.ndarray, /): + """ + usage.xarray: 4 + """ + ... + + +@overload +def result_type( + _0: numpy.ndarray, + _1: numpy.ndarray, + _2: numpy.ndarray, + _3: numpy.ndarray, + _4: numpy.ndarray, + _5: numpy.ndarray, + _6: numpy.ndarray, + _7: numpy.ndarray, + /, +): + """ + usage.xarray: 5 + """ + ... + + +@overload +def result_type( + _0: numpy.ndarray, + _1: numpy.ndarray, + _2: numpy.ndarray, + _3: numpy.ndarray, + _4: numpy.ndarray, + _5: numpy.ndarray, + _6: numpy.ndarray, + _7: numpy.ndarray, + _8: numpy.ndarray, + /, +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def result_type( + _0: numpy.ndarray, + _1: numpy.ndarray, + _2: numpy.ndarray, + _3: numpy.ndarray, + _4: numpy.ndarray, + _5: numpy.ndarray, + _6: numpy.ndarray, + _7: numpy.ndarray, + _8: numpy.ndarray, + _9: numpy.ndarray, + _10: numpy.ndarray, + _11: numpy.ndarray, + _12: numpy.ndarray, + _13: numpy.ndarray, + _14: numpy.ndarray, + _15: numpy.ndarray, + _16: numpy.ndarray, + _17: numpy.ndarray, + _18: numpy.ndarray, + _19: numpy.ndarray, + /, +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def result_type( + _0: numpy.ndarray, + _1: numpy.ndarray, + _2: numpy.ndarray, + _3: numpy.ndarray, + _4: numpy.ndarray, + _5: numpy.ndarray, + _6: numpy.ndarray, + _7: numpy.ndarray, + _8: numpy.ndarray, + _9: numpy.ndarray, + _10: numpy.ndarray, + /, +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: sparse._coo.core.COO, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: sparse._coo.core.COO, _1: numpy.ndarray, /): + """ + usage.xarray: 2 + """ + ... + + +@overload +def result_type( + _0: numpy.ndarray, + _1: numpy.ndarray, + _2: numpy.ndarray, + _3: numpy.ndarray, + _4: numpy.ndarray, + _5: numpy.ndarray, + _6: numpy.ndarray, + _7: numpy.ndarray, + _8: numpy.ndarray, + _9: numpy.ndarray, + _10: numpy.ndarray, + _11: numpy.ndarray, + /, +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: Type[bool], /): + """ + usage.xarray: 2 + """ + ... + + +@overload +def result_type(_0: Type[numpy.bytes_], /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: Type[numpy.float32], /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: Type[numpy.float64], /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: Type[numpy.float32], _1: Type[numpy.float64], /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: Type[numpy.str_], /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: Type[numpy.int64], /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: Type[numpy.str_], _1: Type[numpy.str_], /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: float, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: numpy.ndarray, _1: float, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type( + _0: numpy.ndarray, + _1: numpy.ndarray, + _2: numpy.ndarray, + _3: numpy.ndarray, + _4: numpy.ndarray, + _5: numpy.ndarray, + _6: numpy.ndarray, + _7: numpy.ndarray, + _8: numpy.ndarray, + _9: numpy.ndarray, + _10: numpy.ndarray, + _11: numpy.ndarray, + _12: numpy.ndarray, + _13: numpy.ndarray, + /, +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: sparse._coo.core.COO, _1: sparse._coo.core.COO, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: numpy.ndarray, _1: sparse._coo.core.COO, /): + """ + usage.xarray: 2 + """ + ... + + +@overload +def result_type( + _0: sparse._coo.core.COO, + _1: sparse._coo.core.COO, + _2: sparse._coo.core.COO, + _3: sparse._coo.core.COO, + /, +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type( + _0: sparse._coo.core.COO, _1: sparse._coo.core.COO, _2: sparse._coo.core.COO, / +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: object, /): + """ + usage.xarray: 2 + """ + ... + + +@overload +def result_type(_0: object, _1: numpy.ndarray, /): + """ + usage.xarray: 2 + """ + ... + + +@overload +def result_type(_0: object, _1: object, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: numpy.ndarray, _1: object, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type( + _0: object, + _1: object, + _2: object, + _3: object, + _4: object, + _5: object, + _6: object, + _7: object, + _8: object, + _9: object, + /, +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: object, _1: object, _2: object, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: object, _1: object, _2: object, _3: object, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: object, _1: object, _2: object, _3: object, _4: object, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type( + _0: dask.array.core.Array, + _1: dask.array.core.Array, + _2: dask.array.core.Array, + _3: dask.array.core.Array, + _4: dask.array.core.Array, + _5: dask.array.core.Array, + _6: dask.array.core.Array, + _7: dask.array.core.Array, + _8: dask.array.core.Array, + _9: dask.array.core.Array, + /, +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: numpy.dtype, /): + """ + usage.sklearn: 2 + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: int, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type(_0: numpy.dtype, _1: int, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def result_type( + _0: object, + _1: object = ..., + _2: Union[numpy.dtype, numpy.float64, float, int, type] = ..., + _3: Union[int, Type[int]] = ..., + _4: Union[int, Type[int]] = ..., + _5: Union[int, Type[int]] = ..., + _6: Union[int, Type[int]] = ..., + _7: Union[int, Type[int]] = ..., + _8: Union[int, Type[int]] = ..., + _9: Union[int, Type[int]] = ..., + _10: Union[int, Type[int]] = ..., + _11: Union[int, Type[int]] = ..., + _12: Union[int, Type[int]] = ..., + _13: Union[int, Type[int]] = ..., + _14: Union[int, Type[int]] = ..., + _15: Union[int, Type[int]] = ..., + _16: Union[int, Type[int]] = ..., + _17: Union[int, Type[int]] = ..., + _18: Union[int, Type[int]] = ..., + _19: Union[int, Type[int]] = ..., + _20: Union[int, Type[int]] = ..., + _21: Union[int, Type[int]] = ..., + _22: Union[int, Type[int]] = ..., + _23: Union[int, Type[int]] = ..., + _24: Union[int, Type[int]] = ..., + _25: Union[int, Type[int]] = ..., + _26: Union[int, Type[int]] = ..., + _27: Union[int, Type[int]] = ..., + _28: Union[int, Type[int]] = ..., + _29: Union[int, Type[int]] = ..., + _30: Union[int, Type[int]] = ..., + _31: Union[int, Type[int]] = ..., + _32: Union[int, Type[int]] = ..., + /, +): + """ + usage.pandas: 128 + """ + ... + + +@overload +def result_type( + _0: object, + _1: Union[numpy.ndarray, numpy.dtype, type] = ..., + _2: Union[numpy.ndarray, type] = ..., + _3: object = ..., + /, +): + """ + usage.scipy: 778 + """ + ... + + +@overload +def result_type( + _0: object, + _1: object = ..., + _2: Union[Literal["f8"], Type[numpy.float64], numpy.dtype] = ..., + _3: numpy.dtype = ..., + _4: numpy.dtype = ..., + /, +): + """ + usage.dask: 80 + """ + ... + + +@overload +def result_type(_0: numpy.dtype, _1: numpy.dtype, /): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def result_type( + _0: numpy.dtype, + _1: numpy.dtype, + _2: numpy.dtype, + _3: numpy.dtype, + _4: numpy.dtype, + _5: numpy.dtype, + _6: numpy.dtype, + _7: numpy.dtype, + _8: numpy.dtype, + _9: numpy.dtype, + _10: numpy.dtype, + _11: numpy.dtype, + _12: numpy.dtype, + /, +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def result_type(_0: numpy.dtype, _1: numpy.dtype, _2: numpy.dtype, /): + """ + usage.sklearn: 7 + """ + ... + + +@overload +def result_type(_0: numpy.dtype, _1: numpy.dtype, _2: numpy.dtype, _3: numpy.dtype, /): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def result_type( + _0: numpy.dtype, + _1: numpy.dtype, + _2: numpy.dtype, + _3: numpy.dtype, + _4: numpy.dtype, + _5: numpy.dtype, + _6: numpy.dtype, + _7: numpy.dtype, + _8: numpy.dtype, + _9: numpy.dtype, + _10: numpy.dtype, + _11: numpy.dtype, + _12: numpy.dtype, + _13: numpy.dtype, + _14: numpy.dtype, + _15: numpy.dtype, + _16: numpy.dtype, + _17: numpy.dtype, + _18: numpy.dtype, + _19: numpy.dtype, + /, +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def result_type( + _0: numpy.dtype, + _1: numpy.dtype, + _2: numpy.dtype, + _3: numpy.dtype, + _4: numpy.dtype, + /, +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def result_type( + _0: numpy.dtype, + _1: numpy.dtype, + _2: numpy.dtype, + _3: numpy.dtype, + _4: numpy.dtype, + _5: numpy.dtype, + _6: numpy.dtype, + _7: numpy.dtype, + _8: numpy.dtype, + _9: numpy.dtype, + _10: numpy.dtype, + _11: numpy.dtype, + /, +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def result_type( + _0: numpy.dtype, + _1: numpy.dtype, + _2: numpy.dtype, + _3: numpy.dtype, + _4: numpy.dtype, + _5: numpy.dtype, + _6: numpy.dtype, + _7: numpy.dtype, + _8: numpy.dtype, + _9: numpy.dtype, + _10: numpy.dtype, + _11: numpy.dtype, + _12: numpy.dtype, + _13: numpy.dtype, + _14: numpy.dtype, + _15: numpy.dtype, + _16: numpy.dtype, + _17: numpy.dtype, + _18: numpy.dtype, + _19: numpy.dtype, + _20: numpy.dtype, + _21: numpy.dtype, + _22: numpy.dtype, + _23: numpy.dtype, + _24: numpy.dtype, + _25: numpy.dtype, + _26: numpy.dtype, + _27: numpy.dtype, + _28: numpy.dtype, + _29: numpy.dtype, + _30: numpy.dtype, + _31: numpy.dtype, + _32: numpy.dtype, + _33: numpy.dtype, + _34: numpy.dtype, + _35: numpy.dtype, + _36: numpy.dtype, + _37: numpy.dtype, + _38: numpy.dtype, + _39: numpy.dtype, + _40: numpy.dtype, + _41: numpy.dtype, + _42: numpy.dtype, + _43: numpy.dtype, + _44: numpy.dtype, + _45: numpy.dtype, + _46: numpy.dtype, + _47: numpy.dtype, + _48: numpy.dtype, + _49: numpy.dtype, + _50: numpy.dtype, + _51: numpy.dtype, + _52: numpy.dtype, + _53: numpy.dtype, + _54: numpy.dtype, + _55: numpy.dtype, + _56: numpy.dtype, + _57: numpy.dtype, + _58: numpy.dtype, + _59: numpy.dtype, + _60: numpy.dtype, + _61: numpy.dtype, + _62: numpy.dtype, + _63: numpy.dtype, + _64: numpy.dtype, + _65: numpy.dtype, + _66: numpy.dtype, + _67: numpy.dtype, + _68: numpy.dtype, + _69: numpy.dtype, + _70: numpy.dtype, + _71: numpy.dtype, + _72: numpy.dtype, + _73: numpy.dtype, + _74: numpy.dtype, + _75: numpy.dtype, + _76: numpy.dtype, + _77: numpy.dtype, + _78: numpy.dtype, + _79: numpy.dtype, + _80: numpy.dtype, + _81: numpy.dtype, + _82: numpy.dtype, + _83: numpy.dtype, + _84: numpy.dtype, + _85: numpy.dtype, + _86: numpy.dtype, + _87: numpy.dtype, + _88: numpy.dtype, + _89: numpy.dtype, + _90: numpy.dtype, + _91: numpy.dtype, + _92: numpy.dtype, + _93: numpy.dtype, + _94: numpy.dtype, + _95: numpy.dtype, + _96: numpy.dtype, + _97: numpy.dtype, + _98: numpy.dtype, + _99: numpy.dtype, + _100: numpy.dtype, + _101: numpy.dtype, + _102: numpy.dtype, + _103: numpy.dtype, + _104: numpy.dtype, + _105: numpy.dtype, + _106: numpy.dtype, + _107: numpy.dtype, + _108: numpy.dtype, + _109: numpy.dtype, + _110: numpy.dtype, + _111: numpy.dtype, + _112: numpy.dtype, + _113: numpy.dtype, + _114: numpy.dtype, + _115: numpy.dtype, + _116: numpy.dtype, + _117: numpy.dtype, + _118: numpy.dtype, + _119: numpy.dtype, + _120: numpy.dtype, + _121: numpy.dtype, + _122: numpy.dtype, + _123: numpy.dtype, + _124: numpy.dtype, + _125: numpy.dtype, + _126: numpy.dtype, + _127: numpy.dtype, + _128: numpy.dtype, + _129: numpy.dtype, + _130: numpy.dtype, + _131: numpy.dtype, + _132: numpy.dtype, + _133: numpy.dtype, + _134: numpy.dtype, + _135: numpy.dtype, + _136: numpy.dtype, + _137: numpy.dtype, + _138: numpy.dtype, + _139: numpy.dtype, + _140: numpy.dtype, + _141: numpy.dtype, + _142: numpy.dtype, + _143: numpy.dtype, + _144: numpy.dtype, + _145: numpy.dtype, + _146: numpy.dtype, + _147: numpy.dtype, + _148: numpy.dtype, + _149: numpy.dtype, + _150: numpy.dtype, + _151: numpy.dtype, + _152: numpy.dtype, + _153: numpy.dtype, + _154: numpy.dtype, + _155: numpy.dtype, + _156: numpy.dtype, + _157: numpy.dtype, + _158: numpy.dtype, + _159: numpy.dtype, + _160: numpy.dtype, + _161: numpy.dtype, + _162: numpy.dtype, + _163: numpy.dtype, + _164: numpy.dtype, + _165: numpy.dtype, + _166: numpy.dtype, + _167: numpy.dtype, + _168: numpy.dtype, + _169: numpy.dtype, + _170: numpy.dtype, + _171: numpy.dtype, + _172: numpy.dtype, + _173: numpy.dtype, + _174: numpy.dtype, + _175: numpy.dtype, + _176: numpy.dtype, + _177: numpy.dtype, + _178: numpy.dtype, + _179: numpy.dtype, + _180: numpy.dtype, + _181: numpy.dtype, + _182: numpy.dtype, + _183: numpy.dtype, + _184: numpy.dtype, + _185: numpy.dtype, + _186: numpy.dtype, + _187: numpy.dtype, + _188: numpy.dtype, + _189: numpy.dtype, + _190: numpy.dtype, + _191: numpy.dtype, + _192: numpy.dtype, + _193: numpy.dtype, + _194: numpy.dtype, + _195: numpy.dtype, + _196: numpy.dtype, + _197: numpy.dtype, + _198: numpy.dtype, + _199: numpy.dtype, + /, +): + """ + usage.sklearn: 1 + """ + ... + + +def result_type(_0: object, /, *_args: object): + """ + usage.dask: 80 + usage.pandas: 128 + usage.scipy: 778 + usage.skimage: 7 + usage.sklearn: 20 + usage.xarray: 114 + """ + ... + + +@overload +def roll(a: numpy.ndarray, shift: int, axis: int): + """ + usage.matplotlib: 3 + usage.skimage: 8 + usage.xarray: 1 + """ + ... + + +@overload +def roll(a: List[Union[float, int]], shift: int): + """ + usage.skimage: 2 + """ + ... + + +@overload +def roll(a: numpy.ndarray, shift: Tuple[int, int], axis: Tuple[int, int]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def roll(a: numpy.ndarray, shift: int): + """ + usage.matplotlib: 1 + usage.skimage: 2 + """ + ... + + +@overload +def roll(a: numpy.ndarray, shift: Union[numpy.ndarray, int], axis: int = ...): + """ + usage.pandas: 10 + """ + ... + + +@overload +def roll(a: numpy.ndarray, shift: int, axis: int = ...): + """ + usage.scipy: 8 + """ + ... + + +@overload +def roll( + a: numpy.ndarray, + shift: Union[Tuple[int, int], int], + axis: Union[Tuple[int, int], None, int] = ..., +): + """ + usage.dask: 5 + """ + ... + + +def roll( + a: Union[numpy.ndarray, List[Union[float, int]]], + shift: Union[int, numpy.ndarray, Tuple[int, int]], + axis: Union[int, None, Tuple[int, int]] = ..., +): + """ + usage.dask: 5 + usage.matplotlib: 4 + usage.pandas: 10 + usage.scipy: 8 + usage.skimage: 14 + usage.xarray: 1 + """ + ... + + +@overload +def rollaxis(a: numpy.ndarray, axis: int): + """ + usage.skimage: 12 + usage.sklearn: 4 + """ + ... + + +@overload +def rollaxis(a: numpy.ndarray, axis: int, start: int): + """ + usage.skimage: 2 + usage.sklearn: 1 + """ + ... + + +@overload +def rollaxis(a: numpy.ndarray, axis: int, start: int = ...): + """ + usage.scipy: 73 + """ + ... + + +@overload +def rollaxis( + a: Union[dask.array.core.Array, numpy.ndarray], axis: int, start: int = ... +): + """ + usage.dask: 5 + """ + ... + + +def rollaxis( + a: Union[numpy.ndarray, dask.array.core.Array], axis: int, start: int = ... +): + """ + usage.dask: 5 + usage.scipy: 73 + usage.skimage: 14 + usage.sklearn: 5 + """ + ... + + +def roots(p: Union[List[Union[float, int]], numpy.ndarray]): + """ + usage.scipy: 23 + """ + ... + + +@overload +def rot90(m: numpy.ndarray, k: int): + """ + usage.skimage: 2 + """ + ... + + +@overload +def rot90(m: numpy.ndarray): + """ + usage.scipy: 5 + usage.skimage: 5 + """ + ... + + +def rot90(m: numpy.ndarray, k: int = ...): + """ + usage.scipy: 5 + usage.skimage: 7 + """ + ... + + +@overload +def round_(a: float): + """ + usage.matplotlib: 1 + usage.skimage: 1 + """ + ... + + +@overload +def round_(a: List[Union[int, float]]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def round_(a: numpy.ndarray): + """ + usage.matplotlib: 4 + usage.skimage: 11 + usage.sklearn: 4 + """ + ... + + +@overload +def round_(a: float, decimals: int): + """ + usage.skimage: 1 + """ + ... + + +@overload +def round_(a: Tuple[numpy.float64, numpy.float64]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def round_(a: Tuple[int, int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def round_(a: numpy.ndarray, decimals: int): + """ + usage.matplotlib: 2 + usage.skimage: 5 + """ + ... + + +@overload +def round_(a: Tuple[int, int, int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def round_(a: Tuple[int, int, int, int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def round_(a: List[int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def round_(a: int): + """ + usage.skimage: 1 + """ + ... + + +@overload +def round_(a: numpy.float64): + """ + usage.matplotlib: 2 + usage.skimage: 1 + usage.sklearn: 5 + """ + ... + + +@overload +def round_(a: Tuple[numpy.int64, numpy.int64]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def round_(a: Tuple[numpy.int64, numpy.int64, numpy.int64]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def round_(a: Tuple[float, float]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def round_(a: object): + """ + usage.xarray: 1 + """ + ... + + +@overload +def round_(a: xarray.core.dataarray.DataArray): + """ + usage.xarray: 2 + """ + ... + + +@overload +def round_( + a: object, + decimals: int = ..., + out: Union[pandas.core.series.Series, pandas.core.frame.DataFrame] = ..., +): + """ + usage.pandas: 12 + """ + ... + + +@overload +def round_(a: object, decimals: int = ...): + """ + usage.scipy: 50 + """ + ... + + +@overload +def round_( + a: Union[numpy.ndarray, dask.array.core.Array, numpy.ma.core.MaskedArray], + decimals: int = ..., +): + """ + usage.dask: 9 + """ + ... + + +@overload +def round_(a: numpy.float64, decimals: int): + """ + usage.sklearn: 2 + """ + ... + + +def round_( + a: object, + decimals: int = ..., + out: Union[pandas.core.series.Series, pandas.core.frame.DataFrame] = ..., +): + """ + usage.dask: 9 + usage.matplotlib: 9 + usage.pandas: 12 + usage.scipy: 50 + usage.skimage: 30 + usage.sklearn: 11 + usage.xarray: 3 + """ + ... + + +def save(file: str, arr: Union[numpy.ndarray, numpy.memmap]): + """ + usage.dask: 6 + """ + ... + + +def savez(file: Literal["/tmp/tmpj7_8czx9.npz"]): + """ + usage.scipy: 1 + """ + ... + + +def savez_compressed(file: str): + """ + usage.scipy: 15 + """ + ... + + +def sctype2char(sctype: numpy.dtype): + """ + usage.skimage: 4 + """ + ... + + +@overload +def searchsorted(a: numpy.ndarray, v: int): + """ + usage.skimage: 2 + usage.sklearn: 3 + """ + ... + + +@overload +def searchsorted(a: numpy.ndarray, v: numpy.float64): + """ + usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 3 + """ + ... + + +@overload +def searchsorted( + a: xarray.coding.cftimeindex.CFTimeIndex, + v: xarray.coding.cftimeindex.CFTimeIndex, + side: Literal["left"], +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def searchsorted( + a: xarray.coding.cftimeindex.CFTimeIndex, + v: xarray.coding.cftimeindex.CFTimeIndex, + side: Literal["right"], +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def searchsorted(a: numpy.ndarray, v: numpy.ndarray): + """ + usage.matplotlib: 2 + usage.sklearn: 51 + usage.xarray: 1 + """ + ... + + +@overload +def searchsorted( + a: object, v: object, sorter: range = ..., side: Literal["right"] = ... +): + """ + usage.pandas: 24 + """ + ... + + +@overload +def searchsorted( + a: Union[numpy.ma.core.MaskedArray, numpy.ndarray, List[Union[float, int]]], + v: object, + side: Literal["right", "left"] = ..., +): + """ + usage.scipy: 52 + """ + ... + + +@overload +def searchsorted( + a: Union[numpy.ndarray, Tuple[int, ...], List[int]], + v: Union[numpy.ndarray, int], + side: Literal["right", "left"], +): + """ + usage.dask: 17 + """ + ... + + +@overload +def searchsorted(a: numpy.ndarray, v: float, side: Literal["right"]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def searchsorted(a: numpy.ndarray, v: numpy.float64, side: Literal["right"]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def searchsorted(a: numpy.ndarray, v: Literal["bar"]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def searchsorted(a: numpy.ndarray, v: Literal["baz"]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def searchsorted(a: numpy.ndarray, v: Literal["foo"]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def searchsorted(a: numpy.ndarray, v: numpy.int64): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def searchsorted(a: numpy.ndarray, v: Literal["label_not_present"]): + """ + usage.sklearn: 1 + """ + ... + + +def searchsorted( + a: object, v: object, side: Literal["right", "left"] = ..., sorter: range = ... +): + """ + usage.dask: 17 + usage.matplotlib: 3 + usage.pandas: 24 + usage.scipy: 52 + usage.skimage: 3 + usage.sklearn: 66 + usage.xarray: 3 + """ + ... + + +def select( + condlist: List[Union[numpy.bool_, numpy.ndarray]], + choicelist: List[Union[float, numpy.float64, numpy.ndarray]], + default: int = ..., +): + """ + usage.scipy: 7 + """ + ... + + +@overload +def set_printoptions(precision: int): + """ + usage.skimage: 1 + """ + ... + + +@overload +def set_printoptions(precision: int, threshold: int, edgeitems: int, linewidth: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def set_printoptions( + precision: int, + threshold: int, + edgeitems: int, + linewidth: int, + suppress: bool, + nanstr: Literal["nan"], + infstr: Literal["inf"], + formatter: None, + sign: Literal["-"], + floatmode: Literal["maxprec"], + *, + legacy: bool, +): + """ + usage.sklearn: 1 + usage.xarray: 1 + """ + ... + + +@overload +def set_printoptions(threshold: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def set_printoptions(precision: int, threshold: int, edgeitems: int): + """ + usage.sklearn: 1 + """ + ... + + +def set_printoptions( + precision: int = ..., + threshold: int = ..., + edgeitems: int = ..., + linewidth: int = ..., + suppress: bool = ..., + nanstr: Literal["nan"] = ..., + infstr: Literal["inf"] = ..., + formatter: None = ..., + sign: Literal["-"] = ..., + floatmode: Literal["maxprec"] = ..., + *, + legacy: bool = ..., +): + """ + usage.skimage: 1 + usage.sklearn: 2 + usage.xarray: 3 + """ + ... + + +@overload +def setdiff1d(ar1: numpy.ndarray, ar2: numpy.ndarray, assume_unique: bool): + """ + usage.pandas: 3 + usage.scipy: 1 + usage.sklearn: 24 + """ + ... + + +@overload +def setdiff1d(ar1: numpy.ndarray, ar2: numpy.ndarray): + """ + usage.sklearn: 9 + """ + ... + + +@overload +def setdiff1d(ar1: numpy.ndarray, ar2: List[int], assume_unique: bool): + """ + usage.sklearn: 6 + """ + ... + + +@overload +def setdiff1d( + ar1: numpy.ndarray, ar2: List[Literal["1", "2", "0"]], assume_unique: bool +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def setdiff1d( + ar1: numpy.ndarray, ar2: List[Literal["3", "1", "2", "0"]], assume_unique: bool +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def setdiff1d( + ar1: numpy.ndarray, ar2: List[Literal["bird", "ant"]], assume_unique: bool +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def setdiff1d( + ar1: numpy.ndarray, ar2: List[Literal["cat", "ant"]], assume_unique: bool +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def setdiff1d( + ar1: numpy.ndarray, ar2: List[Literal["bird", "cat"]], assume_unique: bool +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def setdiff1d(ar1: numpy.ndarray, ar2: List[Literal["ant"]], assume_unique: bool): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def setdiff1d(ar1: numpy.ndarray, ar2: List[Literal["bird"]], assume_unique: bool): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def setdiff1d(ar1: numpy.ndarray, ar2: List[Literal["cat"]], assume_unique: bool): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def setdiff1d( + ar1: numpy.ndarray, ar2: List[Literal["spam", "eggs"]], assume_unique: bool +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def setdiff1d(ar1: numpy.ndarray, ar2: List[int]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def setdiff1d(ar1: List[int], ar2: numpy.ndarray): + """ + usage.sklearn: 2 + """ + ... + + +def setdiff1d( + ar1: Union[numpy.ndarray, List[int]], + ar2: Union[List[Union[int, str]], numpy.ndarray], + assume_unique: bool = ..., +): + """ + usage.pandas: 3 + usage.scipy: 1 + usage.sklearn: 52 + """ + ... + + +@overload +def seterr(invalid: Literal["ignore"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def seterr( + divide: Literal["warn"], + over: Literal["warn"], + under: Literal["ignore"], + invalid: Literal["warn"], +): + """ + usage.skimage: 1 + usage.sklearn: 3 + """ + ... + + +@overload +def seterr( + divide: Literal["warn"] = ..., + over: Literal["warn"] = ..., + under: Literal["ignore"] = ..., + invalid: Literal["warn"] = ..., +): + """ + usage.dask: 2 + """ + ... + + +@overload +def seterr(all: Literal["ignore"]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def seterr(all: Literal["raise"]): + """ + usage.sklearn: 1 + """ + ... + + +def seterr( + divide: Literal["warn"] = ..., + over: Literal["warn"] = ..., + under: Literal["ignore"] = ..., + invalid: Literal["warn", "ignore"] = ..., +): + """ + usage.dask: 2 + usage.skimage: 2 + usage.sklearn: 6 + """ + ... + + +@overload +def shape(a: numpy.ndarray): + """ + usage.matplotlib: 9 + usage.skimage: 1 + usage.sklearn: 22 + usage.xarray: 9 + """ + ... + + +@overload +def shape(a: numpy.ma.core.MaskedArray): + """ + usage.matplotlib: 1 + usage.xarray: 3 + """ + ... + + +@overload +def shape(a: object): + """ + usage.scipy: 242 + """ + ... + + +@overload +def shape(a: List[numpy.ndarray]): + """ + usage.matplotlib: 10 + """ + ... + + +@overload +def shape(a: List[numpy.float64]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def shape(a: List[Union[float, None]]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def shape(a: List[List[int]]): + """ + usage.matplotlib: 4 + """ + ... + + +@overload +def shape(a: List[list]): + """ + usage.matplotlib: 3 + """ + ... + + +@overload +def shape(a: List[float]): + """ + usage.matplotlib: 6 + """ + ... + + +@overload +def shape(a: List[int]): + """ + usage.matplotlib: 6 + """ + ... + + +@overload +def shape(a: list): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def shape(a: List[numpy.int64]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def shape(a: dask.array.core.Array): + """ + usage.dask: 1 + """ + ... + + +def shape(a: object): + """ + usage.dask: 1 + usage.matplotlib: 46 + usage.scipy: 242 + usage.skimage: 1 + usage.sklearn: 22 + usage.xarray: 12 + """ + ... + + +def shares_memory(_0: numpy.ndarray, _1: numpy.ndarray, /): + """ + usage.pandas: 11 + """ + ... + + +@overload +def sinc(x: Union[float, numpy.ndarray, List[int]]): + """ + usage.scipy: 8 + """ + ... + + +@overload +def sinc( + x: Union[ + dask.dataframe.core.DataFrame, + dask.dataframe.core.Series, + numpy.ndarray, + pandas.core.series.Series, + pandas.core.frame.DataFrame, + ] +): + """ + usage.dask: 17 + """ + ... + + +def sinc(x: object): + """ + usage.dask: 17 + usage.scipy: 8 + """ + ... + + +@overload +def size(a: Union[pandas.core.series.Series, numpy.ndarray]): + """ + usage.pandas: 3 + """ + ... + + +@overload +def size(a: object, axis: int = ...): + """ + usage.scipy: 56 + """ + ... + + +@overload +def size(a: List[int]): + """ + usage.matplotlib: 3 + usage.sklearn: 2 + """ + ... + + +@overload +def size(a: numpy.ndarray): + """ + usage.matplotlib: 6 + usage.sklearn: 19 + """ + ... + + +@overload +def size(a: list): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def size(a: List[List[Literal["2017-01-01T00:00:00", "2017-01-02T00:00:00"]]]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def size(a: List[List[str]]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def size(a: List[list]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def size(a: Tuple[numpy.ndarray, numpy.ndarray]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def size(a: List[List[int]]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def size(a: List[Union[range, list]]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def size(a: List[range]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def size(a: List[Union[float, int]]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def size(a: numpy.ndarray, axis: int): + """ + usage.matplotlib: 4 + usage.sklearn: 1 + """ + ... + + +def size(a: object, axis: int = ...): + """ + usage.matplotlib: 25 + usage.pandas: 3 + usage.scipy: 56 + usage.sklearn: 22 + """ + ... + + +@overload +def sort(a: numpy.ndarray): + """ + usage.matplotlib: 4 + usage.pandas: 33 + usage.skimage: 13 + usage.sklearn: 39 + usage.xarray: 2 + """ + ... + + +@overload +def sort(a: numpy.ndarray, axis: int): + """ + usage.skimage: 1 + usage.sklearn: 3 + """ + ... + + +@overload +def sort( + a: Union[ + List[Union[numpy.complex128, float, complex, int, numpy.float64]], + numpy.ndarray, + numpy.ma.core.MaskedArray, + ], + axis: Union[int, None] = ..., +): + """ + usage.scipy: 217 + """ + ... + + +@overload +def sort(a: Union[numpy.ndarray, dask.array.core.Array], axis: int = ...): + """ + usage.dask: 14 + """ + ... + + +@overload +def sort(a: List[float]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def sort(a: List[int]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def sort(a: List[numpy.float64]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def sort(a: List[Union[int, float]]): + """ + usage.sklearn: 1 + """ + ... + + +def sort( + a: Union[ + List[Union[numpy.complex128, float, complex, int, numpy.float64]], + numpy.ndarray, + numpy.ma.core.MaskedArray, + dask.array.core.Array, + ], + axis: Union[int, None] = ..., +): + """ + usage.dask: 14 + usage.matplotlib: 4 + usage.pandas: 33 + usage.scipy: 217 + usage.skimage: 14 + usage.sklearn: 48 + usage.xarray: 2 + """ + ... + + +def sort_complex(a: numpy.ndarray): + """ + usage.scipy: 2 + """ + ... + + +@overload +def split(ary: numpy.ndarray, indices_or_sections: int): + """ + usage.skimage: 1 + """ + ... + + +@overload +def split(ary: numpy.ndarray, indices_or_sections: List[int], axis: int): + """ + usage.pandas: 4 + usage.scipy: 8 + """ + ... + + +@overload +def split(ary: numpy.ndarray, indices_or_sections: numpy.ndarray): + """ + usage.matplotlib: 2 + usage.sklearn: 7 + """ + ... + + +def split( + ary: numpy.ndarray, + indices_or_sections: Union[numpy.ndarray, int, List[int]], + axis: int = ..., +): + """ + usage.matplotlib: 2 + usage.pandas: 4 + usage.scipy: 8 + usage.skimage: 1 + usage.sklearn: 7 + """ + ... + + +@overload +def squeeze(a: numpy.ndarray): + """ + usage.skimage: 11 + usage.sklearn: 16 + """ + ... + + +@overload +def squeeze(a: numpy.ndarray, axis: int): + """ + usage.skimage: 7 + """ + ... + + +@overload +def squeeze(a: numpy.ndarray, axis: Tuple[int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def squeeze(a: numpy.ndarray, axis: Tuple[int, int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def squeeze( + a: Union[pandas.core.frame.DataFrame, numpy.ndarray, pandas.core.series.Series] +): + """ + usage.pandas: 5 + """ + ... + + +@overload +def squeeze( + a: Union[numpy.ndarray, float, numpy.float64, int], + axis: Union[Tuple[int, int], int, None] = ..., +): + """ + usage.scipy: 35 + """ + ... + + +@overload +def squeeze(a: numpy.ndarray, axis: Union[Tuple[int, int], None, int]): + """ + usage.dask: 3 + """ + ... + + +@overload +def squeeze(a: numpy.float64): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def squeeze(a: float): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def squeeze(a: List[float]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def squeeze(a: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def squeeze(a: List[int]): + """ + usage.sklearn: 2 + """ + ... + + +def squeeze(a: object, axis: Union[int, None, Tuple[int, ...]] = ...): + """ + usage.dask: 3 + usage.pandas: 5 + usage.scipy: 35 + usage.skimage: 18 + usage.sklearn: 23 + usage.xarray: 2 + """ + ... + + +@overload +def stack(arrays: List[numpy.ndarray], axis: int): + """ + usage.matplotlib: 8 + usage.skimage: 12 + usage.xarray: 24 + """ + ... + + +@overload +def stack(arrays: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], axis: int): + """ + usage.skimage: 6 + """ + ... + + +@overload +def stack(arrays: List[numpy.float64], axis: int): + """ + usage.skimage: 2 + """ + ... + + +@overload +def stack(arrays: Tuple[numpy.ndarray, numpy.float64, numpy.float64], axis: int): + """ + usage.skimage: 2 + """ + ... + + +@overload +def stack(arrays: Tuple[numpy.float64, numpy.ndarray, numpy.float64], axis: int): + """ + usage.skimage: 2 + """ + ... + + +@overload +def stack(arrays: Tuple[numpy.float64, numpy.float64, numpy.ndarray], axis: int): + """ + usage.skimage: 2 + """ + ... + + +@overload +def stack(arrays: List[numpy.ndarray]): + """ + usage.skimage: 2 + usage.xarray: 2 + """ + ... + + +@overload +def stack(arrays: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def stack(arrays: List[xarray.core.dataarray.DataArray], axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def stack(arrays: List[float], axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def stack(arrays: List[sparse._coo.core.COO], axis: int): + """ + usage.xarray: 2 + """ + ... + + +@overload +def stack(arrays: list, axis: int): + """ + usage.xarray: 3 + """ + ... + + +@overload +def stack(arrays: Union[Tuple[numpy.ndarray, numpy.ndarray], List[numpy.ndarray]]): + """ + usage.scipy: 3 + """ + ... + + +@overload +def stack(arrays: Tuple[numpy.ndarray, numpy.ndarray], axis: int): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def stack(arrays: Union[Tuple[numpy.ndarray, ...], list], axis: int = ...): + """ + usage.dask: 32 + """ + ... + + +def stack( + arrays: Union[list, Tuple[Union[numpy.ndarray, numpy.float64], ...]], + axis: int = ..., +): + """ + usage.dask: 32 + usage.matplotlib: 10 + usage.scipy: 3 + usage.skimage: 28 + usage.xarray: 34 + """ + ... + + +@overload +def std(a: numpy.ndarray): + """ + usage.skimage: 1 + usage.sklearn: 8 + usage.xarray: 1 + """ + ... + + +@overload +def std(a: numpy.ndarray, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def std(a: object, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def std(a: numpy.ndarray, axis: int): + """ + usage.sklearn: 6 + usage.xarray: 3 + """ + ... + + +@overload +def std(a: object, axis: int): + """ + usage.xarray: 2 + """ + ... + + +@overload +def std(a: object): + """ + usage.xarray: 1 + """ + ... + + +@overload +def std(a: xarray.core.dataarray.DataArray): + """ + usage.xarray: 1 + """ + ... + + +@overload +def std(a: numpy.ndarray, axis: None, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def std(a: object, axis: None, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def std(a: xarray.core.dataset.Dataset): + """ + usage.xarray: 1 + """ + ... + + +@overload +def std(a: object, axis: int, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def std(a: numpy.ndarray, axis: int, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def std(a: numpy.ndarray, axis: Tuple[int, int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def std( + a: Union[numpy.ndarray, pandas.core.series.Series], + axis: Union[None, int] = ..., + ddof: int = ..., +): + """ + usage.pandas: 20 + """ + ... + + +@overload +def std( + a: Union[numpy.ma.core.MaskedArray, numpy.ndarray, list], + axis: Union[int, None, Tuple[int, int]] = ..., + ddof: int = ..., +): + """ + usage.scipy: 14 + """ + ... + + +@overload +def std( + a: object, + axis: Union[None, Tuple[Union[None, int], ...], int] = ..., + out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., + ddof: int = ..., + keepdims: bool = ..., +): + """ + usage.dask: 55 + """ + ... + + +def std( + a: object, + axis: Union[int, None, Tuple[Union[None, int], ...]] = ..., + out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., + keepdims: bool = ..., + dtype: Union[Literal["i8", "f8"], None] = ..., + ddof: int = ..., +): + """ + usage.dask: 55 + usage.pandas: 20 + usage.scipy: 14 + usage.skimage: 1 + usage.sklearn: 14 + usage.xarray: 16 + """ + ... + + +@overload +def sum(a: numpy.ndarray): + """ + usage.matplotlib: 13 + usage.skimage: 91 + usage.sklearn: 250 + usage.xarray: 2 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: int): + """ + usage.matplotlib: 14 + usage.skimage: 14 + usage.sklearn: 105 + usage.xarray: 9 + """ + ... + + +@overload +def sum(a: List[numpy.ndarray], axis: int): + """ + usage.skimage: 6 + """ + ... + + +@overload +def sum(a: Tuple[int, int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def sum(a: Tuple[int, int, int]): + """ + usage.skimage: 1 + usage.sklearn: 1 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: Tuple[int, int]): + """ + usage.skimage: 1 + usage.xarray: 4 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: Tuple[int]): + """ + usage.xarray: 2 + """ + ... + + +@overload +def sum(a: dask.array.core.Array, axis: Tuple[int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: None): + """ + usage.xarray: 6 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: Tuple[int, int], dtype: None): + """ + usage.xarray: 3 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: None, dtype: None): + """ + usage.xarray: 4 + """ + ... + + +@overload +def sum(a: xarray.core.dataarray.DataArray): + """ + usage.xarray: 3 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: int, dtype: None): + """ + usage.xarray: 4 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: Tuple[None, ...]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: int, keepdims: bool): + """ + usage.sklearn: 1 + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: numpy.bool_, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: List[Tuple[int, int]], axis: int): + """ + usage.xarray: 2 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: None, dtype: Type[float]): + """ + usage.xarray: 3 + """ + ... + + +@overload +def sum(a: dask.array.core.Array, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: None, dtype: Type[float], keepdims: bool): + """ + usage.xarray: 2 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: None, dtype: None, keepdims: bool): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: int, dtype: Type[float]): + """ + usage.xarray: 3 + """ + ... + + +@overload +def sum(a: dask.array.core.Array, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: int, dtype: Type[float], keepdims: bool): + """ + usage.xarray: 2 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: int, dtype: None, keepdims: bool): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: sparse._coo.core.COO, axis: Tuple[int]): + """ + usage.xarray: 2 + """ + ... + + +@overload +def sum(a: sparse._coo.core.COO, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: sparse._coo.core.COO, axis: int): + """ + usage.xarray: 2 + """ + ... + + +@overload +def sum(a: sparse._coo.core.COO, axis: None, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: xarray.core.dataarray.DataArray, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: sparse._coo.core.COO, axis: int, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: object, axis: None, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: object, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: object): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: object, axis: int): + """ + usage.xarray: 2 + """ + ... + + +@overload +def sum(a: xarray.core.dataset.Dataset): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: object, axis: int, dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: Tuple[int], dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: Tuple[int, int, int], dtype: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: Tuple[int, int, int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def sum( + a: object, + axis: Union[int, None] = ..., + dtype: Type[numpy.int64] = ..., + out: numpy.float64 = ..., + initial: int = ..., + keepdims: bool = ..., +): + """ + usage.pandas: 38 + """ + ... + + +@overload +def sum( + a: object, + axis: Union[int, Tuple[int, int], None] = ..., + keepdims: bool = ..., + dtype: Union[type, None] = ..., +): + """ + usage.scipy: 442 + """ + ... + + +@overload +def sum(a: List[float]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def sum(a: numpy.ma.core.MaskedArray): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def sum( + a: object, + axis: Union[None, Tuple[Union[None, int], ...], int] = ..., + dtype: Union[numpy.dtype, Literal["f8", "i8", "i4", "f4", "u4"]] = ..., + keepdims: bool = ..., + out: Union[ + dask.dataframe.core.Scalar, dask.array.core.Array, dask.dataframe.core.Series + ] = ..., +): + """ + usage.dask: 216 + """ + ... + + +@overload +def sum(a: float): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def sum(a: numpy.float64): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def sum(a: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: None, dtype: Type[numpy.float64]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def sum(a: Tuple[float]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def sum(a: numpy.ndarray, dtype: Type[numpy.float64]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def sum(a: Tuple[float, float, float, float, float, float, float, float, float, float]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def sum(a: Tuple[float, float, float, float, float, float]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def sum(a: Tuple[float, float, float]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: int, out: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def sum(a: List[int]): + """ + usage.sklearn: 6 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: int, dtype: Type[numpy.float64]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def sum(a: List[List[float]], axis: int): + """ + usage.sklearn: 10 + """ + ... + + +@overload +def sum(a: List[numpy.float64]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def sum(a: numpy.ndarray, axis: int, dtype: Type[int]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def sum(a: numpy.float32): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def sum(a: Tuple[float, float]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def sum(a: List[int], axis: None, dtype: Type[numpy.float64]): + """ + usage.sklearn: 1 + """ + ... + + +def sum( + a: object, + axis: Union[None, int, Tuple[Union[None, int], ...]] = ..., + out: Union[ + numpy.ndarray, + numpy.float64, + dask.dataframe.core.Scalar, + dask.array.core.Array, + dask.dataframe.core.Series, + ] = ..., + dtype: Union[type, None, numpy.dtype, Literal["f8", "i8", "i4", "f4", "u4"]] = ..., + keepdims: bool = ..., +): + """ + usage.dask: 216 + usage.matplotlib: 30 + usage.pandas: 38 + usage.scipy: 442 + usage.skimage: 114 + usage.sklearn: 397 + usage.xarray: 75 + """ + ... + + +@overload +def swapaxes(a: numpy.ndarray, axis1: int, axis2: int): + """ + usage.dask: 4 + usage.scipy: 48 + usage.skimage: 2 + usage.sklearn: 2 + usage.xarray: 8 + """ + ... + + +@overload +def swapaxes(a: object, axis1: int, axis2: int): + """ + usage.xarray: 1 + """ + ... + + +def swapaxes(a: object, axis1: int, axis2: int): + """ + usage.dask: 4 + usage.scipy: 48 + usage.skimage: 2 + usage.sklearn: 2 + usage.xarray: 9 + """ + ... + + +@overload +def take(a: numpy.ndarray, indices: numpy.ndarray): + """ + usage.skimage: 1 + usage.sklearn: 3 + """ + ... + + +@overload +def take(a: numpy.ndarray, indices: int, axis: int): + """ + usage.xarray: 2 + """ + ... + + +@overload +def take(a: numpy.ndarray, indices: List[int], axis: int): + """ + usage.xarray: 10 + """ + ... + + +@overload +def take(a: numpy.ndarray, indices: numpy.ndarray, axis: int): + """ + usage.sklearn: 1 + usage.xarray: 1 + """ + ... + + +@overload +def take( + a: Union[ + pandas.core.indexes.numeric.Float64Index, + numpy.ndarray, + pandas.core.indexes.base.Index, + pandas.core.indexes.numeric.Int64Index, + List[Literal["#2ca02c", "#ff7f0e", "#1f77b4"]], + ], + indices: Union[numpy.ndarray, int, List[int]], + axis: int = ..., +): + """ + usage.pandas: 52 + """ + ... + + +@overload +def take( + a: Union[numpy.ndarray, Tuple[int, ...]], + indices: Union[int, numpy.ndarray, Tuple[int, ...]], + axis: int = ..., +): + """ + usage.scipy: 59 + """ + ... + + +@overload +def take(a: List[float], indices: List[int]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def take(a: numpy.ndarray, indices: List[int]): + """ + usage.matplotlib: 3 + """ + ... + + +@overload +def take(a: List[int], indices: List[int]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def take(a: numpy.ndarray, indices: Union[numpy.ndarray, int, List[int]], axis: int): + """ + usage.dask: 3 + """ + ... + + +@overload +def take(a: numpy.ndarray, indices: numpy.ndarray, mode: Literal["clip"]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def take(a: List[Literal["three", "two", "one"]], indices: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def take(a: List[Literal["two", "one"]], indices: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def take(a: List[int], indices: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def take(a: Tuple[int, int, int], indices: numpy.ndarray): + """ + usage.sklearn: 2 + """ + ... + + +def take( + a: object, + indices: Union[numpy.ndarray, int, Tuple[int, ...], List[int]], + axis: int = ..., + mode: Literal["clip"] = ..., +): + """ + usage.dask: 3 + usage.matplotlib: 5 + usage.pandas: 52 + usage.scipy: 59 + usage.skimage: 1 + usage.sklearn: 10 + usage.xarray: 13 + """ + ... + + +def take_along_axis(arr: numpy.ndarray, indices: numpy.ndarray, axis: int): + """ + usage.dask: 3 + usage.skimage: 1 + """ + ... + + +@overload +def tensordot(a: numpy.ndarray, b: numpy.ndarray, axes: Tuple[int, int]): + """ + usage.matplotlib: 1 + usage.skimage: 1 + """ + ... + + +@overload +def tensordot(a: numpy.ndarray, b: range, axes: List[int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def tensordot(a: numpy.ndarray, b: numpy.ndarray, axes: Tuple[List[int], List[int]]): + """ + usage.xarray: 3 + """ + ... + + +@overload +def tensordot(a: numpy.ndarray, b: numpy.ndarray, axes: List[int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def tensordot(a: numpy.ndarray, b: numpy.ndarray, axes: int): + """ + usage.scipy: 2 + """ + ... + + +@overload +def tensordot( + a: object, + b: object, + axes: Union[ + Tuple[ + Union[int, Tuple[Union[int, None], ...]], + Union[int, Tuple[Union[int, None], ...]], + ], + int, + ], +): + """ + usage.dask: 23 + """ + ... + + +def tensordot( + a: object, + b: object, + axes: Union[ + int, + Tuple[ + Union[List[int], int, Tuple[Union[None, int], ...]], + Union[List[int], int, Tuple[Union[None, int], ...]], + ], + List[int], + ], +): + """ + usage.dask: 23 + usage.matplotlib: 1 + usage.scipy: 2 + usage.skimage: 1 + usage.xarray: 5 + """ + ... + + +@overload +def tile(A: List[int], reps: Tuple[int, int]): + """ + usage.skimage: 2 + usage.sklearn: 2 + """ + ... + + +@overload +def tile(A: numpy.ndarray, reps: Tuple[int, int]): + """ + usage.matplotlib: 15 + usage.skimage: 8 + usage.sklearn: 8 + usage.xarray: 1 + """ + ... + + +@overload +def tile(A: numpy.ndarray, reps: Tuple[int, int, int]): + """ + usage.skimage: 2 + usage.xarray: 5 + """ + ... + + +@overload +def tile(A: numpy.ndarray, reps: List[int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def tile(A: numpy.ndarray, reps: Tuple[int, int, int, int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def tile(A: Tuple[int, int], reps: List[int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def tile( + A: Union[ + numpy.ndarray, + range, + pandas.core.indexes.base.Index, + List[Union[numpy.int8, int, Literal["c", "b", "a"]]], + ], + reps: Union[int, numpy.int64, Tuple[int, ...], List[int]], +): + """ + usage.pandas: 49 + """ + ... + + +@overload +def tile( + A: Union[Tuple[Union[numpy.int64, numpy.float64], ...], numpy.ndarray], + reps: Union[Tuple[int, int], List[int], int], +): + """ + usage.scipy: 51 + """ + ... + + +@overload +def tile(A: numpy.ndarray, reps: int): + """ + usage.matplotlib: 9 + usage.sklearn: 3 + """ + ... + + +@overload +def tile(A: List[Union[float, int]], reps: int): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def tile(A: Tuple[float, numpy.float64], reps: Tuple[int, int]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def tile(A: Union[numpy.ndarray, List[List[int]]], reps: Union[Tuple[int, ...], int]): + """ + usage.dask: 18 + """ + ... + + +@overload +def tile(A: numpy.float64, reps: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def tile(A: List[numpy.int64], reps: List[int]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def tile(A: List[float], reps: Tuple[int, int]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def tile( + A: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray], + reps: Tuple[int, int, int], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def tile(A: List[numpy.float64], reps: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def tile(A: List[None], reps: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def tile(A: Tuple[numpy.ndarray, numpy.ndarray], reps: Tuple[int, int, int]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def tile( + A: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + ], + reps: Tuple[int, int, int], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def tile( + A: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], reps: Tuple[int, int, int] +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def tile(A: int, reps: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def tile(A: range, reps: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def tile(A: List[numpy.str_], reps: List[int]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def tile(A: int, reps: Tuple[int, int]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def tile(A: Literal["one"], reps: Tuple[int, int]): + """ + usage.sklearn: 1 + """ + ... + + +def tile(A: object, reps: Union[List[int], int, numpy.int64, Tuple[int, ...]]): + """ + usage.dask: 18 + usage.matplotlib: 26 + usage.pandas: 49 + usage.scipy: 51 + usage.skimage: 15 + usage.sklearn: 31 + usage.xarray: 6 + """ + ... + + +@overload +def trace(a: numpy.ndarray): + """ + usage.scipy: 2 + usage.sklearn: 7 + """ + ... + + +@overload +def trace(a: numpy.ndarray, dtype: Type[numpy.float64]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def trace(a: numpy.ndarray, axis1: int, axis2: int): + """ + usage.sklearn: 2 + """ + ... + + +def trace(a: numpy.ndarray, axis1: int = ..., axis2: int = ...): + """ + usage.scipy: 2 + usage.sklearn: 11 + """ + ... + + +@overload +def transpose(a: List[numpy.ndarray]): + """ + usage.matplotlib: 2 + usage.skimage: 2 + usage.sklearn: 18 + """ + ... + + +@overload +def transpose(a: numpy.ndarray, axes: numpy.ndarray): + """ + usage.skimage: 3 + """ + ... + + +@overload +def transpose(a: numpy.ndarray): + """ + usage.matplotlib: 3 + usage.skimage: 3 + usage.sklearn: 11 + """ + ... + + +@overload +def transpose(a: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): + """ + usage.skimage: 4 + """ + ... + + +@overload +def transpose(a: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def transpose(a: Tuple[numpy.ndarray, numpy.ndarray]): + """ + usage.skimage: 3 + """ + ... + + +@overload +def transpose(a: numpy.ndarray, axes: Tuple[int, int, int, int]): + """ + usage.skimage: 2 + usage.sklearn: 2 + """ + ... + + +@overload +def transpose(a: numpy.ndarray, axes: List[int]): + """ + usage.matplotlib: 5 + usage.skimage: 1 + """ + ... + + +@overload +def transpose(a: numpy.ndarray, axes: Tuple[int, int, int]): + """ + usage.skimage: 1 + usage.sklearn: 1 + usage.xarray: 3 + """ + ... + + +@overload +def transpose(a: Tuple[numpy.ndarray]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def transpose(a: numpy.ndarray, axes: Tuple[int, int]): + """ + usage.xarray: 3 + """ + ... + + +@overload +def transpose(a: sparse._coo.core.COO, axes: Tuple[int, int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def transpose(a: object, axes: Tuple[int, int]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def transpose(a: object, axes: int = ...): + """ + usage.pandas: 18 + """ + ... + + +@overload +def transpose( + a: object, axes: Union[List[int], Tuple[Union[numpy.int64, int], ...]] = ... +): + """ + usage.scipy: 119 + """ + ... + + +@overload +def transpose(a: numpy.ma.core.MaskedArray): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def transpose(a: object, axes: Tuple[Union[None, int], ...]): + """ + usage.dask: 25 + """ + ... + + +@overload +def transpose(a: List[List[int]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def transpose(a: List[List[Union[int, float]]]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def transpose(a: List[Union[numpy.ndarray, List[int]]]): + """ + usage.sklearn: 1 + """ + ... + + +def transpose( + a: object, + axes: Union[ + Tuple[Union[None, numpy.int64, int], ...], List[int], int, numpy.ndarray + ] = ..., +): + """ + usage.dask: 25 + usage.matplotlib: 12 + usage.pandas: 18 + usage.scipy: 119 + usage.skimage: 21 + usage.sklearn: 37 + usage.xarray: 8 + """ + ... + + +@overload +def trapz( + y: xarray.core.dataarray.DataArray, x: xarray.core.dataarray.DataArray, axis: int +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def trapz(y: dask.array.core.Array, x: numpy.ndarray, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def trapz(y: numpy.ndarray, x: numpy.ndarray, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def trapz(y: numpy.ndarray, x: numpy.ndarray): + """ + usage.scipy: 1 + usage.sklearn: 2 + """ + ... + + +def trapz( + y: Union[numpy.ndarray, xarray.core.dataarray.DataArray, dask.array.core.Array], + x: Union[numpy.ndarray, xarray.core.dataarray.DataArray], + axis: int = ..., +): + """ + usage.scipy: 1 + usage.sklearn: 2 + usage.xarray: 4 + """ + ... + + +@overload +def tri(N: int): + """ + usage.skimage: 2 + usage.sklearn: 1 + """ + ... + + +@overload +def tri(N: int, M: int, k: int): + """ + usage.skimage: 6 + """ + ... + + +@overload +def tri(N: int, dtype: Type[numpy.int32]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def tri(N: int, M: int, k: int, dtype: Type[numpy.bool_]): + """ + usage.scipy: 1 + """ + ... + + +def tri(N: int, M: int = ..., k: int = ..., dtype: type = ...): + """ + usage.scipy: 1 + usage.skimage: 9 + usage.sklearn: 1 + """ + ... + + +@overload +def tril(m: numpy.ndarray): + """ + usage.matplotlib: 1 + usage.skimage: 1 + """ + ... + + +@overload +def tril(m: numpy.ndarray, k: int = ...): + """ + usage.dask: 7 + usage.scipy: 88 + """ + ... + + +@overload +def tril(m: numpy.ndarray, k: int): + """ + usage.sklearn: 1 + """ + ... + + +def tril(m: numpy.ndarray, k: int = ...): + """ + usage.dask: 7 + usage.matplotlib: 1 + usage.scipy: 88 + usage.skimage: 1 + usage.sklearn: 1 + """ + ... + + +@overload +def tril_indices(n: int, k: int = ...): + """ + usage.scipy: 15 + """ + ... + + +@overload +def tril_indices(n: int, k: int): + """ + usage.sklearn: 1 + """ + ... + + +def tril_indices(n: int, k: int = ...): + """ + usage.scipy: 15 + usage.sklearn: 1 + """ + ... + + +def tril_indices_from(arr: numpy.ndarray, k: int): + """ + usage.scipy: 2 + """ + ... + + +def trim_zeros(filt: numpy.ndarray, trim: Literal["b", "f"]): + """ + usage.scipy: 13 + """ + ... + + +@overload +def triu(m: numpy.ndarray): + """ + usage.skimage: 1 + """ + ... + + +@overload +def triu(m: numpy.ndarray, k: int = ...): + """ + usage.dask: 8 + usage.scipy: 205 + """ + ... + + +def triu(m: numpy.ndarray, k: int = ...): + """ + usage.dask: 8 + usage.scipy: 205 + usage.skimage: 1 + """ + ... + + +@overload +def triu_indices(n: int, k: int = ...): + """ + usage.scipy: 2 + """ + ... + + +@overload +def triu_indices(n: int, k: int): + """ + usage.sklearn: 2 + """ + ... + + +def triu_indices(n: int, k: int = ...): + """ + usage.scipy: 2 + usage.sklearn: 2 + """ + ... + + +def triu_indices_from(arr: numpy.ndarray, k: int = ...): + """ + usage.scipy: 6 + """ + ... + + +@overload +def union1d(ar1: List[Union[int, float, complex]], ar2: numpy.ndarray): + """ + usage.scipy: 25 + """ + ... + + +@overload +def union1d( + ar1: Union[numpy.ndarray, dask.array.core.Array], + ar2: Union[numpy.ndarray, dask.array.core.Array], +): + """ + usage.dask: 2 + """ + ... + + +@overload +def union1d(ar1: numpy.ndarray, ar2: numpy.ndarray): + """ + usage.sklearn: 12 + """ + ... + + +def union1d( + ar1: Union[numpy.ndarray, dask.array.core.Array, List[Union[int, float, complex]]], + ar2: Union[numpy.ndarray, dask.array.core.Array], +): + """ + usage.dask: 2 + usage.scipy: 25 + usage.sklearn: 12 + """ + ... + + +@overload +def unique(ar: numpy.ndarray, return_inverse: bool): + """ + usage.skimage: 4 + usage.sklearn: 125 + usage.xarray: 3 + """ + ... + + +@overload +def unique(ar: numpy.ndarray): + """ + usage.matplotlib: 6 + usage.skimage: 46 + usage.sklearn: 383 + usage.xarray: 10 + """ + ... + + +@overload +def unique(ar: numpy.ndarray, return_inverse: bool, return_counts: bool): + """ + usage.skimage: 3 + usage.sklearn: 2 + """ + ... + + +@overload +def unique(ar: numpy.ndarray, return_counts: bool): + """ + usage.skimage: 7 + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: numpy.ndarray, return_index: bool): + """ + usage.skimage: 4 + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: xarray.core.dataarray.DataArray): + """ + usage.xarray: 2 + """ + ... + + +@overload +def unique( + ar: Union[ + pandas.core.series.Series, + numpy.ndarray, + pandas.core.arrays.categorical.Categorical, + List[str], + ], + return_inverse: bool = ..., + return_index: bool = ..., +): + """ + usage.pandas: 22 + """ + ... + + +@overload +def unique( + ar: Union[numpy.ndarray, List[float]], + return_inverse: bool = ..., + return_index: bool = ..., + axis: int = ..., + return_counts: bool = ..., +): + """ + usage.scipy: 66 + """ + ... + + +@overload +def unique(ar: numpy.ma.core.MaskedArray): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def unique(ar: numpy.ndarray, return_index: bool, return_inverse: bool): + """ + usage.matplotlib: 1 + usage.sklearn: 6 + """ + ... + + +@overload +def unique( + ar: Union[ + pandas.core.series.Series, + numpy.ndarray, + List[ + Union[ + numpy.float32, numpy.bool_, numpy.int64, numpy.float64, numpy.complex128 + ] + ], + ], + return_index: bool = ..., + return_inverse: bool = ..., + return_counts: bool = ..., +): + """ + usage.dask: 30 + """ + ... + + +@overload +def unique(ar: List[int]): + """ + usage.sklearn: 26 + """ + ... + + +@overload +def unique(ar: numpy.memmap): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def unique(ar: List[Literal["copyright", "beer", "pizza", "the"]]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def unique(ar: List[Literal["copyright", "beer", "burger", "pizza", "the"]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: List[Literal["copyright", "beer", "burger", "the"]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: List[Literal["copyright", "coke", "burger", "the"]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: List[Literal["burger", "coke", "the"]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: List[Literal["copyright", "celeri", "salad", "the"]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: List[Literal["copyright", "water", "sparkling", "salad", "the"]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: List[Literal["copyright", "celeri", "the"]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: List[Literal["water", "salad", "tomato", "the"]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: List[Literal["copyright", "water", "salad", "tomato", "the"]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: pandas.core.series.Series): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: List[Literal["three", "two", "one"]]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def unique(ar: List[Union[float, int]], return_inverse: bool): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: List[Literal["b", "a"]]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def unique(ar: List[Literal["b", "c", "a"]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unique(ar: List[Literal["3", "2", "1"]]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def unique(ar: List[numpy.float64]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def unique(ar: List[float]): + """ + usage.sklearn: 3 + """ + ... + + +def unique( + ar: object, + return_index: bool = ..., + return_inverse: bool = ..., + return_counts: bool = ..., +): + """ + usage.dask: 30 + usage.matplotlib: 8 + usage.pandas: 22 + usage.scipy: 66 + usage.skimage: 64 + usage.sklearn: 572 + usage.xarray: 15 + """ + ... + + +def unpackbits(_0: numpy.ndarray, /): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def unravel_index(_0: numpy.int64, _1: Tuple[int, int], /): + """ + usage.skimage: 8 + usage.sklearn: 1 + """ + ... + + +@overload +def unravel_index(_0: numpy.ndarray, _1: Tuple[int, int], /): + """ + usage.skimage: 2 + """ + ... + + +@overload +def unravel_index(_0: numpy.int64, _1: Tuple[int, int, int], /): + """ + usage.skimage: 4 + """ + ... + + +@overload +def unravel_index(_0: numpy.int64, _1: Tuple[int, int, int, int], /): + """ + usage.skimage: 2 + """ + ... + + +@overload +def unravel_index(_0: int, _1: Tuple[int, int], /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def unravel_index(_0: int, _1: Tuple[int, int, int], /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def unravel_index( + _0: Union[numpy.ndarray, int], + _1: Union[numpy.ndarray, Tuple[int, int], List[int]], + _2: Literal["F"] = ..., + /, +): + """ + usage.scipy: 4 + """ + ... + + +@overload +def unravel_index(_0: List[numpy.int64], _1: Tuple[int, int], /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def unravel_index(_0: List[int], _1: Tuple[int, int], /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def unravel_index( + _0: Union[numpy.ndarray, numpy.int64], + _1: Tuple[Union[int, None], ...] = ..., + _2: Literal["F", "C"] = ..., + /, + *, + order: Literal["F", "C"] = ..., + shape: Tuple[int, ...] = ..., +): + """ + usage.dask: 12 + """ + ... + + +def unravel_index( + _0: Union[numpy.int64, int, numpy.ndarray, List[Union[int, numpy.int64]]], + _1: Union[Tuple[Union[None, int], ...], numpy.ndarray, List[int]] = ..., + _2: Literal["F", "C"] = ..., + /, + *, + order: Literal["F", "C"] = ..., + shape: Tuple[int, ...] = ..., +): + """ + usage.dask: 12 + usage.matplotlib: 2 + usage.scipy: 4 + usage.skimage: 18 + usage.sklearn: 1 + """ + ... + + +@overload +def unwrap(p: numpy.ndarray): + """ + usage.scipy: 5 + """ + ... + + +@overload +def unwrap(p: numpy.ndarray, axis: int): + """ + usage.matplotlib: 3 + """ + ... + + +def unwrap(p: numpy.ndarray, axis: int = ...): + """ + usage.matplotlib: 3 + usage.scipy: 5 + """ + ... + + +@overload +def vander(x: numpy.ndarray, N: int = ...): + """ + usage.scipy: 2 + """ + ... + + +@overload +def vander(x: numpy.ndarray, N: int): + """ + usage.sklearn: 1 + """ + ... + + +def vander(x: numpy.ndarray, N: int = ...): + """ + usage.scipy: 2 + usage.sklearn: 1 + """ + ... + + +@overload +def var(a: numpy.ndarray, axis: int): + """ + usage.sklearn: 30 + usage.xarray: 4 + """ + ... + + +@overload +def var(a: numpy.ndarray, axis: None): + """ + usage.xarray: 5 + """ + ... + + +@overload +def var(a: numpy.ndarray, axis: Tuple[None, ...]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def var(a: numpy.ndarray, axis: None, ddof: int): + """ + usage.xarray: 3 + """ + ... + + +@overload +def var(a: numpy.ndarray, axis: None, dtype: Type[float]): + """ + usage.xarray: 3 + """ + ... + + +@overload +def var(a: numpy.ndarray, axis: int, ddof: int): + """ + usage.sklearn: 13 + usage.xarray: 3 + """ + ... + + +@overload +def var(a: numpy.ndarray, axis: int, dtype: Type[float]): + """ + usage.xarray: 3 + """ + ... + + +@overload +def var(a: object, axis: None): + """ + usage.xarray: 1 + """ + ... + + +@overload +def var(a: object): + """ + usage.xarray: 1 + """ + ... + + +@overload +def var(a: xarray.core.dataarray.DataArray): + """ + usage.xarray: 1 + """ + ... + + +@overload +def var(a: numpy.ndarray, axis: None, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def var(a: object, axis: None, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def var(a: xarray.core.dataset.Dataset): + """ + usage.xarray: 1 + """ + ... + + +@overload +def var(a: object, axis: int, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def var(a: numpy.ndarray, axis: int, dtype: None, ddof: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def var(a: object, axis: int): + """ + usage.xarray: 1 + """ + ... + + +@overload +def var( + a: Union[numpy.ndarray, pandas.core.series.Series], + axis: Union[None, int] = ..., + ddof: int = ..., +): + """ + usage.pandas: 13 + """ + ... + + +@overload +def var(a: Union[numpy.ndarray, List[float]], axis: int = ..., ddof: int = ...): + """ + usage.scipy: 19 + """ + ... + + +@overload +def var( + a: object, + axis: Union[None, Tuple[Union[None, int], ...], int] = ..., + out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., + ddof: int = ..., + keepdims: bool = ..., +): + """ + usage.dask: 59 + """ + ... + + +@overload +def var(a: numpy.ndarray): + """ + usage.sklearn: 8 + """ + ... + + +@overload +def var(a: numpy.ndarray, ddof: int): + """ + usage.sklearn: 4 + """ + ... + + +def var( + a: object, + axis: Union[int, None, Tuple[Union[int, None], ...]] = ..., + out: Union[dask.dataframe.core.Scalar, dask.dataframe.core.Series] = ..., + keepdims: bool = ..., + dtype: Union[Literal["i8", "f8"], Type[float], None] = ..., + ddof: int = ..., +): + """ + usage.dask: 59 + usage.pandas: 13 + usage.scipy: 19 + usage.sklearn: 55 + usage.xarray: 31 + """ + ... + + +def vdot(_0: numpy.ndarray, _1: numpy.ndarray, /): + """ + usage.dask: 2 + usage.scipy: 14 + """ + ... + + +def vsplit(ary: numpy.ndarray, indices_or_sections: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def vstack(tup: Tuple[List[int], List[int]]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def vstack(tup: Tuple[List[float], List[Union[int, float]]]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def vstack(tup: Tuple[numpy.ndarray, numpy.ndarray]): + """ + usage.matplotlib: 9 + usage.skimage: 4 + usage.sklearn: 35 + """ + ... + + +@overload +def vstack(tup: Tuple[List[numpy.int64], List[numpy.int64]]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def vstack(tup: List[numpy.ndarray]): + """ + usage.matplotlib: 70 + usage.pandas: 49 + usage.skimage: 23 + usage.sklearn: 101 + """ + ... + + +@overload +def vstack(tup: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]): + """ + usage.matplotlib: 3 + usage.skimage: 2 + usage.sklearn: 6 + """ + ... + + +@overload +def vstack( + tup: Union[ + Tuple[ + Union[List[Union[List[int], float, int]], numpy.ndarray, numpy.float64], ... + ], + List[ + Union[ + Tuple[Union[numpy.float64, int], ...], + numpy.ndarray, + List[Union[int, float]], + ] + ], + ] +): + """ + usage.scipy: 199 + """ + ... + + +@overload +def vstack(tup: Tuple[numpy.ndarray, numpy.ndarray, List[float]]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def vstack(tup: Tuple[numpy.ma.core.MaskedArray, numpy.ma.core.MaskedArray]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def vstack( + tup: List[Union[List[Union[numpy.ndarray, Tuple[int, int]]], numpy.ndarray]] +): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def vstack(tup: List[Union[Tuple[int, int], numpy.ndarray]]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def vstack(tup: Tuple[numpy.ndarray]): + """ + usage.matplotlib: 1 + usage.sklearn: 5 + """ + ... + + +@overload +def vstack(tup: List[Tuple[float, float, float, float]]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def vstack(tup: List[Union[List[numpy.float64], numpy.ndarray]]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def vstack(tup: List[Union[numpy.ndarray, List[numpy.float64]]]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def vstack( + tup: Tuple[ + List[Union[int, numpy.float64]], numpy.ndarray, List[Union[int, numpy.float64]] + ] +): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def vstack( + tup: Tuple[ + List[Union[float, numpy.float64]], + numpy.ndarray, + List[Union[float, numpy.float64]], + List[Union[float, numpy.float64]], + ] +): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def vstack( + tup: Tuple[ + List[Union[float, numpy.float64]], + numpy.ndarray, + List[Union[float, numpy.float64]], + ] +): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def vstack(tup: Tuple[List[int]]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def vstack(tup: List[numpy.flatiter]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def vstack(tup: object): + """ + usage.dask: 10 + """ + ... + + +@overload +def vstack( + tup: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + ] +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def vstack(tup: Tuple[List[List[Union[int, float]]], List[List[int]], List[List[int]]]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def vstack(tup: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def vstack(tup: List[pandas.core.series.Series]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def vstack(tup: Tuple[List[int], numpy.ndarray]): + """ + usage.sklearn: 5 + """ + ... + + +@overload +def vstack(tup: List[float]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def vstack(tup: List[Union[List[List[int]], numpy.ndarray]]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def vstack( + tup: Tuple[ + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + numpy.ndarray, + ] +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def vstack(tup: Tuple[List[List[int]], numpy.ndarray]): + """ + usage.sklearn: 1 + """ + ... + + +def vstack(tup: object): + """ + usage.dask: 10 + usage.matplotlib: 98 + usage.pandas: 49 + usage.scipy: 199 + usage.skimage: 32 + usage.sklearn: 164 + """ + ... + + +@overload +def where(_0: numpy.bool_, _1: float, _2: int, /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: float, _2: int, /): + """ + usage.skimage: 3 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: numpy.ndarray, _2: numpy.ndarray, /): + """ + usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 1 + usage.xarray: 9 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: float, _2: numpy.ndarray, /): + """ + usage.skimage: 1 + usage.xarray: 7 + """ + ... + + +@overload +def where(_0: numpy.bool_, _1: numpy.float64, _2: numpy.float64, /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def where(_0: numpy.bool_, _1: float, _2: numpy.float64, /): + """ + usage.skimage: 1 + usage.xarray: 2 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: numpy.ndarray, _2: int, /): + """ + usage.skimage: 4 + usage.xarray: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, /): + """ + usage.matplotlib: 1 + usage.skimage: 12 + usage.sklearn: 63 + """ + ... + + +@overload +def where(_0: dask.array.core.Array, /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: int, _2: int, /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def where(_0: numpy.bool_, _1: numpy.ndarray, _2: numpy.ndarray, /): + """ + usage.xarray: 3 + """ + ... + + +@overload +def where(_0: bool, _1: float, _2: numpy.float64, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def where(_0: List[bool], _1: numpy.ndarray, _2: numpy.ndarray, /): + """ + usage.xarray: 2 + """ + ... + + +@overload +def where(_0: sparse._coo.core.COO, _1: sparse._coo.core.COO, _2: numpy.ndarray, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def where(_0: bool, _1: numpy.ndarray, _2: numpy.ndarray, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def where(_0: dask.array.core.Array, _1: float, _2: dask.array.core.Array, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def where( + _0: numpy.ndarray, _1: float, _2: pandas.core.indexes.numeric.Float64Index, / +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def where(_0: sparse._coo.core.COO, _1: numpy.ndarray, _2: sparse._coo.core.COO, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def where(_0: sparse._coo.core.COO, _1: numpy.ndarray, _2: numpy.ndarray, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def where( + _0: sparse._coo.core.COO, _1: sparse._coo.core.COO, _2: sparse._coo.core.COO, / +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: numpy.ndarray, _2: sparse._coo.core.COO, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: sparse._coo.core.COO, _2: numpy.ndarray, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: object, _2: numpy.ndarray, /): + """ + usage.xarray: 2 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: object, _2: object, /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: numpy.ndarray, _2: object, /): + """ + usage.xarray: 2 + """ + ... + + +@overload +def where(_0: object, _1: object = ..., _2: object = ..., /): + """ + usage.pandas: 175 + """ + ... + + +@overload +def where( + _0: Union[numpy.ndarray, numpy.bool_, bool], _1: object = ..., _2: object = ..., / +): + """ + usage.scipy: 193 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: numpy.ndarray, _2: numpy.ma.core.MaskedArray, /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: numpy.ndarray, _2: float, /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def where( + _0: numpy.ndarray, _1: numpy.ma.core.MaskedArray, _2: numpy.ma.core.MaskedArray, / +): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: numpy.float32, _2: numpy.float32, /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: int, _2: numpy.ndarray, /): + """ + usage.matplotlib: 1 + usage.sklearn: 4 + """ + ... + + +@overload +def where(_0: numpy.ma.core.MaskedArray, _1: numpy.ma.core.MaskedArray, _2: float, /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def where( + _0: Union[numpy.ndarray, bool, int, numpy.bool_, dask.array.core.Array], + _1: Union[numpy.ndarray, numpy.float64, int, numpy.int32, float] = ..., + _2: Union[float, int, numpy.float64, dask.array.core.Array, numpy.ndarray] = ..., + /, +): + """ + usage.dask: 58 + """ + ... + + +@overload +def where(_0: List[bool], /): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def where(_0: numpy.matrix, /): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: numpy.int64, _2: numpy.int64, /): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: numpy.bool_, _2: numpy.bool_, /): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: Literal["a"], _2: Literal["b"], /): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: numpy.int64, _2: numpy.ndarray, /): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: numpy.str_, _2: numpy.str_, /): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def where(_0: numpy.ndarray, _1: Literal["two"], _2: Literal["one"], /): + """ + usage.sklearn: 1 + """ + ... + + +def where(_0: object, _1: object = ..., _2: object = ..., /): + """ + usage.dask: 58 + usage.matplotlib: 8 + usage.pandas: 175 + usage.scipy: 193 + usage.skimage: 26 + usage.sklearn: 77 + usage.xarray: 39 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[float]): + """ + usage.skimage: 6 + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[numpy.float64]): + """ + usage.skimage: 11 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.int64]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /): + """ + usage.matplotlib: 25 + usage.sample-usage: 1 + usage.skimage: 228 + usage.sklearn: 164 + usage.xarray: 26 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.float64]): + """ + usage.skimage: 50 + usage.sklearn: 11 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], /): + """ + usage.matplotlib: 6 + usage.skimage: 73 + usage.sklearn: 12 + usage.xarray: 13 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], _1: Literal["uint8"], /): + """ + usage.skimage: 39 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], _1: Literal["uint8"], /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint8]): + """ + usage.skimage: 55 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: Type[int]): + """ + usage.skimage: 3 + usage.sklearn: 15 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[int]): + """ + usage.skimage: 35 + usage.sklearn: 10 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[numpy.uint8]): + """ + usage.matplotlib: 2 + usage.skimage: 3 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[bool]): + """ + usage.matplotlib: 3 + usage.skimage: 28 + usage.sklearn: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[bool]): + """ + usage.skimage: 6 + """ + ... + + +@overload +def zeros(_0: int, /): + """ + usage.matplotlib: 38 + usage.skimage: 15 + usage.sklearn: 133 + usage.xarray: 10 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.float32]): + """ + usage.skimage: 5 + usage.sklearn: 3 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[bool], order: Literal["C"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Literal["float32"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Literal["float64"]): + """ + usage.skimage: 5 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Literal["uint8"]): + """ + usage.skimage: 5 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Literal["int"]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: Type[bool]): + """ + usage.matplotlib: 5 + usage.skimage: 2 + usage.sklearn: 24 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], _1: Type[numpy.uint8], /): + """ + usage.skimage: 6 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int, int, int], /): + """ + usage.skimage: 3 + """ + ... + + +@overload +def zeros(_0: Tuple[int], /, *, dtype: Type[numpy.float64]): + """ + usage.skimage: 2 + usage.sklearn: 4 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Literal["float"]): + """ + usage.skimage: 2 + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int, int], /): + """ + usage.skimage: 18 + usage.sklearn: 1 + usage.xarray: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: numpy.dtype): + """ + usage.skimage: 7 + usage.sklearn: 38 + """ + ... + + +@overload +def zeros(_0: Tuple[int], /, *, dtype: numpy.dtype): + """ + usage.skimage: 6 + usage.sklearn: 3 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], _1: Type[float], /): + """ + usage.matplotlib: 6 + usage.skimage: 4 + """ + ... + + +@overload +def zeros(_0: Tuple[numpy.int64], _1: numpy.dtype, /): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], _1: Type[int], /): + """ + usage.skimage: 8 + """ + ... + + +@overload +def zeros( + _0: Tuple[int, int, int, int], /, *, dtype: Type[numpy.uint32], order: Literal["C"] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint32]): + """ + usage.skimage: 3 + """ + ... + + +@overload +def zeros(_0: List[int], /, *, dtype: numpy.dtype): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint16]): + """ + usage.skimage: 7 + """ + ... + + +@overload +def zeros(_0: List[int], /): + """ + usage.matplotlib: 2 + usage.skimage: 3 + usage.sklearn: 6 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[float]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[complex]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int], /): + """ + usage.skimage: 5 + usage.sklearn: 16 + usage.xarray: 4 + """ + ... + + +@overload +def zeros(_0: List[int], /, *, dtype: Type[numpy.uint8]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], _1: Literal["double"], /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: Type[numpy.uint8]): + """ + usage.skimage: 2 + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[numpy.float32]): + """ + usage.skimage: 3 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int, int], /, *, dtype: Type[numpy.float32]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int, int, int], /, *, dtype: Type[numpy.float32]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: numpy.int64, /, *, dtype: numpy.dtype): + """ + usage.skimage: 5 + usage.sklearn: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], _1: Type[bool], /): + """ + usage.skimage: 7 + """ + ... + + +@overload +def zeros(_0: int, _1: Literal["bool"], /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: Type[numpy.float64]): + """ + usage.matplotlib: 7 + usage.skimage: 1 + usage.sklearn: 18 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint64]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[int]): + """ + usage.skimage: 4 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int, int], /, *, dtype: Type[int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.int8]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], /, *, dtype: Type[numpy.int8]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int], _1: Type[bool], /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], _1: Type[bool], /): + """ + usage.skimage: 4 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], _1: Type[numpy.uint16], /): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], _1: Type[numpy.uint16], /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], _1: Type[int], /): + """ + usage.skimage: 6 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.bool_]): + """ + usage.skimage: 4 + """ + ... + + +@overload +def zeros(_0: Tuple[int], /, *, dtype: Type[bool]): + """ + usage.skimage: 1 + usage.sklearn: 2 + usage.xarray: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int], /, *, dtype: Type[numpy.uint8], order: Literal["F"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros( + _0: Tuple[int, int, int], /, *, dtype: Type[numpy.uint8], order: Literal["C"] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint8], order: Literal["C"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros( + _0: Tuple[int, int, int, int], /, *, dtype: Type[numpy.uint8], order: Literal["C"] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros( + _0: Tuple[int, int, int, int, int], + /, + *, + dtype: Type[numpy.uint8], + order: Literal["C"], +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.uint8], order: Literal["F"]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int, int], /, *, dtype: Type[numpy.uint8]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], /, *, dtype: numpy.dtype): + """ + usage.matplotlib: 3 + usage.skimage: 4 + usage.xarray: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int, int], /, *, dtype: numpy.dtype): + """ + usage.skimage: 4 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int, int, int], /, *, dtype: numpy.dtype): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], _1: numpy.dtype, /): + """ + usage.matplotlib: 3 + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], /, *, dtype: Literal["float64"]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], /, *, dtype: Literal["float32"]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: numpy.ndarray, /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: list, /): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: List[int], _1: numpy.dtype, /): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros(_0: int, _1: Type[int], /): + """ + usage.skimage: 4 + """ + ... + + +@overload +def zeros(_0: Tuple[int, numpy.int64], /): + """ + usage.skimage: 1 + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int, int], /, *, dtype: Type[numpy.float64]): + """ + usage.skimage: 3 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int, int, int], /, *, dtype: Type[numpy.float64]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int, int, int, int], /, *, dtype: Type[numpy.float64]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros( + _0: Tuple[int, int, int, int, int, int, int], /, *, dtype: Type[numpy.float64] +): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], _1: Type[numpy.bool_], /): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Literal["bool"]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Literal["int8"]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros(_0: numpy.int64, /): + """ + usage.matplotlib: 2 + usage.skimage: 2 + usage.sklearn: 2 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: Type[numpy.int64]): + """ + usage.sklearn: 6 + usage.xarray: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Literal["S1"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int], /, *, dtype: Type[numpy.bytes_]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[None, ...], /): + """ + usage.xarray: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int], /, *, dtype: Literal["bool"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def zeros(*, dtype: Type[float], shape: Tuple[int, int]): + """ + usage.xarray: 8 + """ + ... + + +@overload +def zeros(*, dtype: Type[int], shape: Tuple[int, int]): + """ + usage.xarray: 8 + """ + ... + + +@overload +def zeros( + _0: Union[int, numpy.int64, Tuple[int, ...]] = ..., + _1: Union[numpy.dtype, Literal["float64", "float32"]] = ..., + /, + *, + dtype: Union[ + Literal["uint64", "float64", "bool", "i4,f4,a10", "int64"], numpy.dtype, type + ] = ..., + shape: Tuple[int, int] = ..., +): + """ + usage.pandas: 125 + """ + ... + + +@overload +def zeros( + _0: Union[ + List[Union[numpy.int64, int]], + Tuple[Union[numpy.int64, int, None], ...], + int, + numpy.int64, + numpy.ndarray, + ], + _1: Union[ + str, + numpy.dtype, + List[Tuple[Literal["a", "junk"], Union[Literal["S1"], numpy.dtype]]], + type, + ] = ..., + _2: Literal["F"] = ..., + /, + *, + dtype: Union[ + numpy.dtype, + type, + str, + List[Tuple[Union[str, Type[object], numpy.dtype, int], ...]], + ] = ..., + order: Union[None, Literal["f", "C", "c", "F"]] = ..., +): + """ + usage.scipy: 2106 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], _1: numpy.dtype, /): + """ + usage.matplotlib: 3 + """ + ... + + +@overload +def zeros(_0: int, _1: Type[bool], /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def zeros(_0: int, _1: Literal["d"], /): + """ + usage.matplotlib: 3 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: Type[numpy.int8]): + """ + usage.matplotlib: 4 + """ + ... + + +@overload +def zeros(*, shape: int): + """ + usage.matplotlib: 1 + usage.sklearn: 4 + """ + ... + + +@overload +def zeros(_0: Tuple[int], /, *, dtype: Type[numpy.int16]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: Type[float]): + """ + usage.matplotlib: 1 + usage.sklearn: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.int16]): + """ + usage.matplotlib: 1 + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.float128]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[numpy.int64, numpy.int64], /): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def zeros(_0: List[int], /, *, dtype: Type[numpy.float64]): + """ + usage.matplotlib: 5 + """ + ... + + +@overload +def zeros( + _0: Union[Tuple[Union[None, int], ...], int, numpy.ndarray, List[int]] = ..., + _1: Type[numpy.float64] = ..., + /, + *, + dtype: Union[numpy.dtype, Literal["f8"], type] = ..., + shape: Tuple[int, ...] = ..., + order: Literal["F", "C"] = ..., +): + """ + usage.dask: 52 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: numpy.dtype): + """ + usage.sklearn: 44 + """ + ... + + +@overload +def zeros(_0: int, /, *, order: Literal["C"]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, order: Literal["C"]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: Type[numpy.int32]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def zeros(_0: int, _1: Type[numpy.float64], /): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def zeros(*, dtype: Type[numpy.float64], shape: Tuple[int, int]): + """ + usage.sklearn: 6 + """ + ... + + +@overload +def zeros(*, shape: Tuple[int, int]): + """ + usage.sklearn: 11 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: numpy.dtype, order: Literal["F"]): + """ + usage.sklearn: 6 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: Type[numpy.float64], order: Literal["C"]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def zeros(*, dtype: numpy.dtype, shape: Tuple[int, int]): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: Type[object]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.float64], order: Literal["C"]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def zeros(_0: Tuple[int, numpy.int64], /, *, dtype: Type[numpy.float64]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(*, dtype: Type[numpy.float64], shape: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: int, /, *, order: Literal["f"]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int, int], _1: Type[float], /): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int], _1: Type[float], /): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.float64], order: Literal["F"]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(*, dtype: Type[numpy.float64], order: Literal["C"], shape: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: numpy.dtype, order: Literal["C"]): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: Type[numpy.int32], order: Literal["C"]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int], /, *, dtype: numpy.dtype, order: Literal["F"]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def zeros(_0: Tuple[int, int], /, *, dtype: Type[numpy.float32], order: Literal["F"]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: Tuple[int], /, *, dtype: Type[numpy.float64], order: Literal["C"]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def zeros(*, shape: List[int]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: List[int], /, *, dtype: Type[int]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def zeros(_0: int, /, *, dtype: Literal["int"]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def zeros(*, dtype: numpy.dtype, shape: int): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def zeros(_0: Tuple[int, numpy.int64], /, *, dtype: numpy.dtype): + """ + usage.sklearn: 1 + """ + ... + + +def zeros( + *_args: object, + dtype: Union[ + type, + str, + numpy.dtype, + List[Tuple[Union[str, Type[object], numpy.dtype, int], ...]], + ] = ..., + order: Union[Literal["C", "F", "f", "c"], None] = ..., + shape: Union[List[int], Tuple[int, ...], int] = ..., +): + """ + usage.dask: 52 + usage.matplotlib: 124 + usage.pandas: 125 + usage.sample-usage: 1 + usage.scipy: 2106 + usage.skimage: 757 + usage.sklearn: 597 + usage.xarray: 77 + """ + ... + + +@overload +def zeros_like(a: numpy.ndarray): + """ + usage.matplotlib: 20 + usage.skimage: 46 + usage.sklearn: 64 + usage.xarray: 3 + """ + ... + + +@overload +def zeros_like(a: Tuple[int, int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros_like(a: Tuple[int, int, int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros_like(a: numpy.ndarray, dtype: Type[bool]): + """ + usage.skimage: 6 + usage.sklearn: 3 + usage.xarray: 40 + """ + ... + + +@overload +def zeros_like(a: numpy.ndarray, dtype: Type[numpy.float64]): + """ + usage.skimage: 8 + usage.sklearn: 1 + """ + ... + + +@overload +def zeros_like(a: numpy.ndarray, dtype: Type[numpy.float32]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros_like(a: numpy.ndarray, dtype: Type[numpy.uint8]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros_like(a: numpy.ndarray, dtype: Type[numpy.int32]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros_like(a: numpy.ndarray, dtype: Type[numpy.uint8], order: Literal["C"]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def zeros_like(a: numpy.ndarray, dtype: Type[numpy.bool_]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def zeros_like(a: sparse._coo.core.COO, dtype: Type[bool]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def zeros_like(a: object, dtype: Type[bool]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def zeros_like(a: object): + """ + usage.xarray: 2 + """ + ... + + +@overload +def zeros_like(a: xarray.core.dataarray.DataArray): + """ + usage.xarray: 2 + """ + ... + + +@overload +def zeros_like( + a: Union[pandas.core.indexes.datetimes.DatetimeIndex, numpy.ndarray], + dtype: Literal["int64"] = ..., +): + """ + usage.pandas: 20 + """ + ... + + +@overload +def zeros_like( + a: Union[numpy.ndarray, numpy.matrix, List[float]], + dtype: Union[numpy.dtype, type] = ..., +): + """ + usage.scipy: 135 + """ + ... + + +@overload +def zeros_like( + a: numpy.ndarray, + dtype: numpy.dtype = ..., + shape: Union[int, Tuple[int, ...], None] = ..., +): + """ + usage.dask: 14 + """ + ... + + +@overload +def zeros_like(a: numpy.ndarray, dtype: Type[numpy.uint8], order: Literal["F"]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def zeros_like(a: numpy.ndarray, dtype: Type[int]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def zeros_like(a: int): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def zeros_like(a: numpy.ndarray, dtype: Type[numpy.int64]): + """ + usage.sklearn: 1 + """ + ... + + +def zeros_like( + a: object, + dtype: Union[type, Literal["int64"], numpy.dtype] = ..., + shape: Union[int, Tuple[int, ...], None] = ..., + order: Literal["F", "C"] = ..., +): + """ + usage.dask: 14 + usage.matplotlib: 20 + usage.pandas: 20 + usage.scipy: 135 + usage.skimage: 68 + usage.sklearn: 76 + usage.xarray: 49 + """ + ... + + +class AxisError: pass -class bool_: +class bool_: + + # usage.dask: 1 + __module__: ClassVar[object] + + # usage.pandas: 2 + __name__: ClassVar[object] + + # usage.pandas: 2 + type: ClassVar[object] + + @overload + @classmethod + def __ne__(cls, _0: numpy.dtype, /): + """ + usage.skimage: 1 + """ + ... + + @overload + @classmethod + def __ne__( + cls, _0: Union[pandas._libs.missing.NAType, bool, numpy.dtype, numpy.bool_], / + ): + """ + usage.pandas: 16 + """ + ... + + @overload + @classmethod + def __ne__(cls, _0: Union[numpy.dtype, numpy.bool_], /): + """ + usage.scipy: 29 + """ + ... + + @overload + @classmethod + def __ne__(cls, _0: numpy.bool_, /): + """ + usage.matplotlib: 4 + """ + ... + + @overload + @classmethod + def __ne__(cls, _0: numpy.ndarray, /): + """ + usage.sklearn: 1 + """ + ... + + @classmethod + def __ne__( + cls, + _0: Union[ + numpy.ndarray, numpy.dtype, pandas._libs.missing.NAType, bool, numpy.bool_ + ], + /, + ): + """ + usage.matplotlib: 4 + usage.pandas: 16 + usage.scipy: 29 + usage.skimage: 1 + usage.sklearn: 1 + """ + ... + + # usage.dask: 4 + # usage.pandas: 3 + # usage.scipy: 2 + dtype: object + + # usage.dask: 5 + # usage.pandas: 3 + # usage.scipy: 3 + ndim: object + + # usage.dask: 7 + # usage.matplotlib: 1 + # usage.scipy: 2 + shape: object + + # usage.scipy: 1 + size: object + + # usage.pandas: 1 + values: object + + @overload + def __add__( + self, + _0: Union[ + numpy.bool_, pandas.core.arrays.boolean.BooleanArray, int, numpy.ndarray + ], + /, + ): + """ + usage.pandas: 7 + """ + ... + + @overload + def __add__(self, _0: object, /): + """ + usage.scipy: 18 + """ + ... + + @overload + def __add__(self, _0: numpy.float64, /): + """ + usage.matplotlib: 1 + """ + ... + + def __add__(self, _0: object, /): + """ + usage.matplotlib: 1 + usage.pandas: 7 + usage.scipy: 18 + """ + ... + + @overload + def __and__(self, _0: numpy.bool_, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __and__(self, _0: numpy.ndarray, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __and__( + self, + _0: Union[pandas.core.arrays.boolean.BooleanArray, numpy.bool_, numpy.ndarray], + /, + ): + """ + usage.pandas: 9 + """ + ... + + @overload + def __and__(self, _0: Union[int, numpy.int64, numpy.bool_, numpy.ndarray, bool], /): + """ + usage.scipy: 135 + """ + ... + + def __and__(self, _0: object, /): + """ + usage.pandas: 9 + usage.scipy: 135 + usage.xarray: 2 + """ + ... + + def __bool__(self, /): + """ + usage.dask: 12 + usage.matplotlib: 14 + usage.pandas: 66 + usage.scipy: 40 + usage.skimage: 18 + usage.sklearn: 67 + usage.xarray: 3 + """ + ... + + @overload + def __eq__(self, _0: numpy.bool_, /): + """ + usage.dask: 2 + usage.matplotlib: 10 + usage.xarray: 2 + """ + ... + + @overload + def __eq__(self, _0: bool, /): + """ + usage.matplotlib: 1 + usage.sklearn: 3 + usage.xarray: 2 + """ + ... + + @overload + def __eq__(self, _0: object, /): + """ + usage.pandas: 94 + """ + ... + + @overload + def __eq__( + self, _0: Union[bool, numpy.ndarray, numpy.bool_, Literal["force accept"]], / + ): + """ + usage.scipy: 26 + """ + ... + + @overload + def __eq__(self, _0: numpy.ndarray, /): + """ + usage.sklearn: 7 + """ + ... + + def __eq__(self, _0: object, /): + """ + usage.dask: 2 + usage.matplotlib: 11 + usage.pandas: 94 + usage.scipy: 26 + usage.sklearn: 10 + usage.xarray: 4 + """ + ... + + def __floordiv__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.boolean.BooleanArray], / + ): + """ + usage.pandas: 2 + """ + ... + + def __ge__(self, _0: pandas._libs.missing.NAType, /): + """ + usage.pandas: 2 + """ + ... + + def __getitem__(self, _0: Tuple[Union[None, ellipsis], ...], /): + """ + usage.dask: 2 + """ + ... + + @overload + def __gt__(self, _0: pandas._libs.missing.NAType, /): + """ + usage.pandas: 1 + """ + ... + + @overload + def __gt__(self, _0: numpy.float64, /): + """ + usage.matplotlib: 1 + """ + ... + + @overload + def __gt__(self, _0: float, /): + """ + usage.sklearn: 1 + """ + ... + + def __gt__(self, _0: Union[float, pandas._libs.missing.NAType, numpy.float64], /): + """ + usage.matplotlib: 1 + usage.pandas: 1 + usage.sklearn: 1 + """ + ... + + def __iand__(self, _0: numpy.bool_, /): + """ + usage.scipy: 1 + """ + ... + + def __invert__(self, /): + """ + usage.dask: 1 + usage.matplotlib: 1 + usage.pandas: 1 + usage.scipy: 12 + usage.skimage: 3 + usage.xarray: 1 + """ + ... + + def __ior__(self, _0: numpy.bool_, /): + """ + usage.scipy: 2 + """ + ... + + @overload + def __le__(self, _0: pandas._libs.missing.NAType, /): + """ + usage.pandas: 2 + """ + ... + + @overload + def __le__(self, _0: int, /): + """ + usage.sklearn: 1 + """ + ... + + def __le__(self, _0: Union[int, pandas._libs.missing.NAType], /): + """ + usage.pandas: 2 + usage.sklearn: 1 + """ + ... + + @overload + def __lt__(self, _0: pandas._libs.missing.NAType, /): + """ + usage.pandas: 1 + """ + ... + + @overload + def __lt__(self, _0: numpy.float64, /): + """ + usage.matplotlib: 1 + """ + ... + + def __lt__(self, _0: Union[numpy.float64, pandas._libs.missing.NAType], /): + """ + usage.matplotlib: 1 + usage.pandas: 1 + """ + ... + + def __mod__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.boolean.BooleanArray], / + ): + """ + usage.pandas: 2 + """ + ... + + @overload + def __mul__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.boolean.BooleanArray], / + ): + """ + usage.pandas: 2 + """ + ... + + @overload + def __mul__(self, _0: Union[bool, numpy.int64, int, numpy.bool_], /): + """ + usage.scipy: 10 + """ + ... + + def __mul__(self, _0: object, /): + """ + usage.pandas: 2 + usage.scipy: 10 + """ + ... + + @overload + def __or__(self, _0: numpy.bool_, /): + """ + usage.xarray: 2 + """ + ... + + @overload + def __or__(self, _0: numpy.ndarray, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __or__(self, _0: bool, /): + """ + usage.xarray: 2 + """ + ... + + @overload + def __or__( + self, _0: Union[pandas.core.arrays.boolean.BooleanArray, numpy.bool_], / + ): + """ + usage.pandas: 3 + """ + ... + + @overload + def __or__(self, _0: Union[numpy.ndarray, numpy.bool_], /): + """ + usage.scipy: 10 + """ + ... + + def __or__( + self, + _0: Union[ + numpy.bool_, numpy.ndarray, bool, pandas.core.arrays.boolean.BooleanArray + ], + /, + ): + """ + usage.pandas: 3 + usage.scipy: 10 + usage.xarray: 5 + """ + ... + + def __pow__( + self, + _0: Union[ + pandas._libs.missing.NAType, + pandas.core.arrays.boolean.BooleanArray, + numpy.ndarray, + ], + /, + ): + """ + usage.pandas: 3 + """ + ... + + @overload + def __radd__(self, _0: Union[pandas._libs.missing.NAType, numpy.bool_], /): + """ + usage.pandas: 6 + """ + ... + + @overload + def __radd__(self, _0: object, /): + """ + usage.scipy: 41 + """ + ... + + def __radd__(self, _0: object, /): + """ + usage.pandas: 6 + usage.scipy: 41 + """ + ... + + @overload + def __rand__(self, _0: numpy.bool_, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __rand__(self, _0: numpy.ndarray, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __rand__(self, _0: dask.array.core.Array, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __rand__( + self, _0: Union[pandas.core.arrays.boolean.BooleanArray, numpy.bool_], / + ): + """ + usage.pandas: 9 + """ + ... + + @overload + def __rand__( + self, _0: Union[bool, numpy.int64, numpy.bool_, int, numpy.ndarray], / + ): + """ + usage.scipy: 189 + """ + ... + + def __rand__(self, _0: object, /): + """ + usage.pandas: 9 + usage.scipy: 189 + usage.xarray: 3 + """ + ... + + def __rmul__( + self, + _0: Union[numpy.bool_, numpy.int64, numpy.float64, float, numpy.ndarray], + /, + ): + """ + usage.scipy: 11 + """ + ... + + @overload + def __ror__(self, _0: numpy.bool_, /): + """ + usage.xarray: 2 + """ + ... + + @overload + def __ror__(self, _0: numpy.ndarray, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __ror__(self, _0: bool, /): + """ + usage.xarray: 2 + """ + ... + + @overload + def __ror__( + self, _0: Union[pandas.core.arrays.boolean.BooleanArray, numpy.bool_], / + ): + """ + usage.pandas: 3 + """ + ... + + @overload + def __ror__(self, _0: Union[numpy.ndarray, numpy.bool_, bool], /): + """ + usage.scipy: 7 + """ + ... + + def __ror__( + self, + _0: Union[ + bool, numpy.bool_, numpy.ndarray, pandas.core.arrays.boolean.BooleanArray + ], + /, + ): + """ + usage.pandas: 3 + usage.scipy: 7 + usage.xarray: 5 + """ + ... + + @overload + def __rpow__(self, _0: int, /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __rpow__(self, _0: pandas._libs.missing.NAType, /): + """ + usage.pandas: 1 + """ + ... + + def __rpow__(self, _0: Union[pandas._libs.missing.NAType, int], /): + """ + usage.pandas: 1 + usage.skimage: 1 + """ + ... + + def __rsub__(self, _0: int, /): + """ + usage.scipy: 13 + """ + ... + + @overload + def __rxor__(self, _0: pandas.core.arrays.boolean.BooleanArray, /): + """ + usage.pandas: 1 + """ + ... + + @overload + def __rxor__(self, _0: numpy.bool_, /): + """ + usage.matplotlib: 3 + """ + ... + + def __rxor__( + self, _0: Union[numpy.bool_, pandas.core.arrays.boolean.BooleanArray], / + ): + """ + usage.matplotlib: 3 + usage.pandas: 1 + """ + ... + + def __sub__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.boolean.BooleanArray], / + ): + """ + usage.pandas: 2 + """ + ... + + def __truediv__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.boolean.BooleanArray], / + ): + """ + usage.pandas: 2 + """ + ... + + @overload + def __xor__(self, _0: pandas.core.arrays.boolean.BooleanArray, /): + """ + usage.pandas: 1 + """ + ... + + @overload + def __xor__(self, _0: numpy.bool_, /): + """ + usage.matplotlib: 3 + """ + ... + + def __xor__( + self, _0: Union[numpy.bool_, pandas.core.arrays.boolean.BooleanArray], / + ): + """ + usage.matplotlib: 3 + usage.pandas: 1 + """ + ... + + def all(self, /): + """ + usage.dask: 1 + usage.pandas: 4 + usage.scipy: 3 + usage.sklearn: 1 + usage.xarray: 9 + """ + ... + + def any(self, /): + """ + usage.dask: 1 + usage.pandas: 6 + usage.scipy: 10 + usage.xarray: 2 + """ + ... + + @overload + def astype(self, _0: numpy.dtype, /): + """ + usage.pandas: 1 + """ + ... + + @overload + def astype(self, _0: Type[numpy.float64], /): + """ + usage.matplotlib: 1 + """ + ... + + @overload + def astype(self, _0: Type[numpy.float32], /): + """ + usage.matplotlib: 2 + """ + ... + + def astype(self, _0: Union[type, numpy.dtype], /): + """ + usage.matplotlib: 3 + usage.pandas: 1 + """ + ... + + def item(self, /): + """ + usage.pandas: 12 + """ + ... + + def squeeze(self, /): + """ + usage.pandas: 1 + """ + ... + + def sum(self, /): + """ + usage.scipy: 2 + """ + ... + + +class broadcast: + + # usage.scipy: 2 + # usage.xarray: 4 + shape: object + + +class bytes_: + + # usage.matplotlib: 1 + __mro__: ClassVar[object] + + # usage.pandas: 2 + __name__: ClassVar[object] + + # usage.dask: 1 + ndim: object + + def __add__(self, _0: float, /): + """ + usage.matplotlib: 1 + """ + ... + + @overload + def __eq__(self, _0: numpy.ndarray, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __eq__(self, _0: numpy.bytes_, /): + """ + usage.pandas: 2 + """ + ... + + def __eq__(self, _0: Union[numpy.bytes_, numpy.ndarray], /): + """ + usage.pandas: 2 + usage.xarray: 1 + """ + ... + + @overload + def __getitem__(self, _0: slice[int, int, int], /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __getitem__(self, _0: slice[None, None, None], /): + """ + usage.xarray: 1 + """ + ... + + def __getitem__( + self, _0: slice[Union[int, None], Union[int, None], Union[int, None]], / + ): + """ + usage.xarray: 2 + """ + ... + + @overload + def __iadd__(self, _0: bytes, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __iadd__(self, _0: numpy.bytes_, /): + """ + usage.xarray: 1 + """ + ... + + def __iadd__(self, _0: Union[numpy.bytes_, bytes], /): + """ + usage.xarray: 2 + """ + ... + + def __radd__(self, _0: bytes, /): + """ + usage.xarray: 1 + """ + ... + + def decode(self, /, encoding: Literal["utf-8"]): + """ + usage.matplotlib: 1 + """ + ... + + @overload + def find(self, _0: numpy.bytes_, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def find(self, _0: numpy.bytes_, _1: int, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def find(self, _0: numpy.bytes_, _1: int, _2: int, /): + """ + usage.xarray: 1 + """ + ... + + def find(self, _0: numpy.bytes_, _1: int = ..., _2: int = ..., /): + """ + usage.xarray: 3 + """ + ... + + @overload + def rfind(self, _0: numpy.bytes_, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def rfind(self, _0: numpy.bytes_, _1: int, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def rfind(self, _0: numpy.bytes_, _1: int, _2: int, /): + """ + usage.xarray: 1 + """ + ... + + def rfind(self, _0: numpy.bytes_, _1: int = ..., _2: int = ..., /): + """ + usage.xarray: 3 + """ + ... + + def upper(self, /): + """ + usage.xarray: 1 + """ + ... + + +class complex128: + + # usage.pandas: 2 + __name__: ClassVar[object] + + # usage.scipy: 1 + __class__: object + + # usage.dask: 1 + # usage.pandas: 4 + # usage.scipy: 16 + dtype: object + + # usage.scipy: 2 + flags: object + + # usage.scipy: 25 + # usage.skimage: 1 + imag: object + + # usage.dask: 5 + # usage.pandas: 1 + ndim: object + + # usage.matplotlib: 2 + # usage.scipy: 32 + # usage.skimage: 1 + real: object + + # usage.dask: 5 + shape: object + + # usage.scipy: 1 + size: object + + def __add__(self, _0: object, /): + """ + usage.scipy: 46 + """ + ... + + def __bool__(self, /): + """ + usage.scipy: 1 + """ + ... + + @overload + def __eq__(self, _0: numpy.ndarray, /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __eq__( + self, _0: Union[numpy.ndarray, float, complex, int, numpy.complex128], / + ): + """ + usage.pandas: 6 + """ + ... + + @overload + def __eq__(self, _0: Union[numpy.ndarray, int, numpy.complex128], /): + """ + usage.scipy: 13 + """ + ... + + def __eq__( + self, _0: Union[numpy.complex128, int, numpy.ndarray, float, complex], / + ): + """ + usage.pandas: 6 + usage.scipy: 13 + usage.skimage: 1 + """ + ... + + def __ge__(self, _0: numpy.complex128, /): + """ + usage.scipy: 1 + """ + ... + + def __getitem__(self, _0: Tuple[Union[None, ellipsis], ...], /): + """ + usage.dask: 2 + """ + ... + + def __gt__(self, _0: Union[float, numpy.float64], /): + """ + usage.scipy: 2 + """ + ... + + def __iadd__( + self, _0: Union[complex, numpy.complex64, numpy.float64, numpy.complex128], / + ): + """ + usage.scipy: 10 + """ + ... + + def __isub__(self, _0: numpy.complex128, /): + """ + usage.scipy: 1 + """ + ... + + @overload + def __itruediv__(self, _0: numpy.float64, /): + """ + usage.skimage: 2 + """ + ... + + @overload + def __itruediv__(self, _0: numpy.complex128, /): + """ + usage.scipy: 1 + """ + ... + + def __itruediv__(self, _0: Union[numpy.complex128, numpy.float64], /): + """ + usage.scipy: 1 + usage.skimage: 2 + """ + ... + + def __le__(self, _0: Union[int, numpy.complex128], /): + """ + usage.scipy: 2 + """ + ... + + def __lt__(self, _0: numpy.ndarray, /): + """ + usage.scipy: 1 + """ + ... + + @overload + def __mul__(self, _0: numpy.complex128, /): + """ + usage.skimage: 2 + """ + ... + + @overload + def __mul__(self, _0: object, /): + """ + usage.scipy: 106 + """ + ... + + def __mul__(self, _0: object, /): + """ + usage.scipy: 106 + usage.skimage: 2 + """ + ... + + @overload + def __ne__(self, _0: complex, /): + """ + usage.pandas: 1 + """ + ... + + @overload + def __ne__(self, _0: Union[numpy.complex64, int, numpy.complex128, float], /): + """ + usage.scipy: 16 + """ + ... + + def __ne__( + self, _0: Union[float, numpy.complex128, int, numpy.complex64, complex], / + ): + """ + usage.pandas: 1 + usage.scipy: 16 + """ + ... + + def __neg__(self, /): + """ + usage.scipy: 17 + """ + ... + + def __pow__(self, _0: Union[float, numpy.float64, int], /): + """ + usage.scipy: 15 + """ + ... + + def __radd__(self, _0: object, /): + """ + usage.scipy: 64 + """ + ... + + @overload + def __rmul__(self, _0: numpy.complex128, /): + """ + usage.skimage: 2 + """ + ... + + @overload + def __rmul__(self, _0: object, /): + """ + usage.scipy: 106 + """ + ... + + def __rmul__(self, _0: object, /): + """ + usage.scipy: 106 + usage.skimage: 2 + """ + ... + + @overload + def __rsub__(self, _0: float, /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __rsub__(self, _0: object, /): + """ + usage.scipy: 53 + """ + ... + + def __rsub__(self, _0: object, /): + """ + usage.scipy: 53 + usage.skimage: 1 + """ + ... + + @overload + def __rtruediv__(self, _0: numpy.complex128, /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __rtruediv__(self, _0: object, /): + """ + usage.scipy: 50 + """ + ... + + def __rtruediv__(self, _0: object, /): + """ + usage.scipy: 50 + usage.skimage: 1 + """ + ... + + def __sub__(self, _0: Union[numpy.ndarray, numpy.complex128, int, complex], /): + """ + usage.scipy: 48 + """ + ... + + @overload + def __truediv__(self, _0: numpy.float64, /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __truediv__(self, _0: numpy.complex128, /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __truediv__(self, _0: numpy.ndarray, /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __truediv__(self, _0: object, /): + """ + usage.scipy: 46 + """ + ... + + def __truediv__(self, _0: object, /): + """ + usage.scipy: 46 + usage.skimage: 3 + """ + ... + + def conj(self, /): + """ + usage.scipy: 10 + usage.skimage: 1 + """ + ... + + def conjugate(self, /): + """ + usage.scipy: 1 + """ + ... + + +class complex256: + + # usage.scipy: 8 + dtype: object + + # usage.dask: 1 + ndim: object + + # usage.scipy: 1 + size: object + + def __add__(self, _0: object, /): + """ + usage.scipy: 18 + """ + ... + + def __bool__(self, /): + """ + usage.scipy: 1 + """ + ... + + def __iadd__(self, _0: numpy.complex256, /): + """ + usage.scipy: 2 + """ + ... + + def __mul__(self, _0: Union[float, int, numpy.complex256], /): + """ + usage.scipy: 6 + """ + ... + + def __ne__(self, _0: numpy.complex256, /): + """ + usage.scipy: 2 + """ + ... + + def __radd__(self, _0: object, /): + """ + usage.scipy: 20 + """ + ... + + def __rmul__(self, _0: Union[numpy.complex256, int, numpy.ndarray], /): + """ + usage.scipy: 4 + """ + ... + + def __rsub__(self, _0: numpy.ndarray, /): + """ + usage.scipy: 1 + """ + ... + + def __sub__(self, _0: numpy.ndarray, /): + """ + usage.scipy: 1 + """ + ... + + def __truediv__(self, _0: Union[numpy.ndarray, int], /): + """ + usage.scipy: 3 + """ + ... + + def conj(self, /): + """ + usage.scipy: 1 + """ + ... + + +class complex64: + + # usage.pandas: 2 + __name__: ClassVar[object] + + # usage.scipy: 9 + dtype: object + + # usage.scipy: 2 + imag: object + + # usage.dask: 2 + ndim: object + + # usage.scipy: 10 + real: object + + # usage.scipy: 1 + size: object + + def __add__(self, _0: object, /): + """ + usage.scipy: 17 + """ + ... + + def __bool__(self, /): + """ + usage.scipy: 1 + """ + ... + + @overload + def __eq__(self, _0: numpy.complex64, /): + """ + usage.pandas: 2 + """ + ... + + @overload + def __eq__(self, _0: int, /): + """ + usage.scipy: 1 + """ + ... + + def __eq__(self, _0: Union[int, numpy.complex64], /): + """ + usage.pandas: 2 + usage.scipy: 1 + """ + ... + + def __getitem__(self, _0: Tuple[ellipsis, None, None], /): + """ + usage.dask: 1 + """ + ... + + def __iadd__(self, _0: Union[numpy.complex64, complex, numpy.complex128], /): + """ + usage.scipy: 4 + """ + ... + + def __mul__(self, _0: Union[int, numpy.complex64, numpy.ndarray, float], /): + """ + usage.scipy: 29 + """ + ... + + def __ne__(self, _0: Union[numpy.complex128, numpy.complex64], /): + """ + usage.scipy: 3 + """ + ... + + def __pow__(self, _0: int, /): + """ + usage.scipy: 1 + """ + ... + + def __radd__(self, _0: object, /): + """ + usage.scipy: 21 + """ + ... + + def __rmul__( + self, _0: Union[numpy.complex128, float, numpy.ndarray, numpy.complex64, int], / + ): + """ + usage.scipy: 14 + """ + ... + + def __rsub__(self, _0: float, /): + """ + usage.scipy: 1 + """ + ... + + def __truediv__(self, _0: numpy.ndarray, /): + """ + usage.scipy: 1 + """ + ... + + def conj(self, /): + """ + usage.scipy: 2 + """ + ... + + +class datetime64: + + # usage.matplotlib: 1 + __mro__: ClassVar[object] + + # usage.pandas: 8 + __name__: ClassVar[object] + + @classmethod + def __ne__( + cls, + _0: Union[ + pandas._libs.tslibs.nattype.NaTType, + pandas._libs.tslibs.timestamps.Timestamp, + Type[numpy.datetime64], + Literal["unix", "2020-07-13T05:05:56.369928+00:00"], + ], + /, + ): + """ + usage.pandas: 6 + """ + ... + + # usage.dask: 1 + # usage.pandas: 8 + # usage.xarray: 3 + dtype: object + + # usage.dask: 1 + # usage.pandas: 1 + ndim: object + + # usage.matplotlib: 1 + tzinfo: object + + @overload + def __add__(self, _0: numpy.ndarray, /): + """ + usage.xarray: 4 + """ + ... + + @overload + def __add__(self, _0: object, /): + """ + usage.pandas: 32 + """ + ... + + def __add__(self, _0: object, /): + """ + usage.pandas: 32 + usage.xarray: 4 + """ + ... + + @overload + def __eq__(self, _0: xarray.core.dataarray.DataArray, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __eq__(self, _0: xarray.core.variable.Variable, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __eq__(self, _0: numpy.ndarray, /): + """ + usage.xarray: 4 + """ + ... + + @overload + def __eq__(self, _0: numpy.datetime64, /): + """ + usage.xarray: 8 + """ + ... + + @overload + def __eq__(self, _0: object, /): + """ + usage.pandas: 32 + """ + ... + + def __eq__(self, _0: object, /): + """ + usage.pandas: 32 + usage.xarray: 14 + """ + ... + + def __ge__( + self, _0: Union[numpy.ndarray, pandas.core.indexes.datetimes.DatetimeIndex], / + ): + """ + usage.pandas: 5 + """ + ... + + @overload + def __le__(self, _0: pandas.core.indexes.datetimes.DatetimeIndex, /): + """ + usage.pandas: 3 + """ + ... + + @overload + def __le__(self, _0: pandas._libs.tslibs.timestamps.Timestamp, /): + """ + usage.dask: 1 + """ + ... + + def __le__( + self, + _0: Union[ + pandas._libs.tslibs.timestamps.Timestamp, + pandas.core.indexes.datetimes.DatetimeIndex, + ], + /, + ): + """ + usage.dask: 1 + usage.pandas: 3 + """ + ... + + def __lt__(self, _0: pandas.core.indexes.datetimes.DatetimeIndex, /): + """ + usage.pandas: 2 + """ + ... + + def __radd__(self, _0: object, /): + """ + usage.pandas: 24 + """ + ... + + @overload + def __rsub__(self, _0: numpy.ndarray, /): + """ + usage.matplotlib: 1 + usage.xarray: 2 + """ + ... + + @overload + def __rsub__(self, _0: object, /): + """ + usage.pandas: 28 + """ + ... + + @overload + def __rsub__(self, _0: numpy.datetime64, /): + """ + usage.matplotlib: 2 + """ + ... + + def __rsub__(self, _0: object, /): + """ + usage.matplotlib: 3 + usage.pandas: 28 + usage.xarray: 2 + """ + ... + + @overload + def __sub__(self, _0: object, /): + """ + usage.pandas: 25 + """ + ... + + @overload + def __sub__(self, _0: numpy.datetime64, /): + """ + usage.matplotlib: 2 + """ + ... + + def __sub__(self, _0: object, /): + """ + usage.matplotlib: 2 + usage.pandas: 25 + """ + ... + + @overload + def astype(self, _0: Union[str, type], /): + """ + usage.pandas: 25 + """ + ... + + @overload + def astype(self, _0: Type[numpy.int64], /): + """ + usage.matplotlib: 2 + """ + ... + + @overload + def astype(self, _0: Literal["datetime64[s]"], /): + """ + usage.matplotlib: 2 + """ + ... + + @overload + def astype(self, _0: numpy.dtype, /): + """ + usage.dask: 3 + """ + ... + + def astype(self, _0: Union[numpy.dtype, str, type], /): + """ + usage.dask: 3 + usage.matplotlib: 4 + usage.pandas: 25 + """ + ... + + def item(self, /): + """ + usage.pandas: 1 + """ + ... + + def view(self, _0: Union[Literal["i8"], Type[numpy.int64]], /): + """ + usage.pandas: 7 + """ + ... + + +class dtype: # usage.dask: 1 __module__: ClassVar[object] - # usage.pandas: 2 - __name__: ClassVar[object] + # usage.scipy: 7 + alignment: object - # usage.pandas: 2 - type: ClassVar[object] + # usage.dask: 2 + # usage.pandas: 1 + base: object + + # usage.scipy: 69 + # usage.xarray: 14 + byteorder: object + + # usage.pandas: 1 + # usage.scipy: 389 + # usage.skimage: 12 + # usage.sklearn: 2 + # usage.xarray: 3 + char: object + + # usage.pandas: 6 + # usage.scipy: 5 + descr: object + + # usage.dask: 15 + # usage.pandas: 48 + # usage.scipy: 43 + fields: object + + # usage.dask: 33 + # usage.scipy: 44 + hasobject: object + + # usage.matplotlib: 19 + # usage.scipy: 6 + # usage.xarray: 26 + isnative: object + + # usage.dask: 38 + # usage.pandas: 130 + # usage.scipy: 61 + # usage.skimage: 41 + # usage.sklearn: 11 + # usage.xarray: 37 + itemsize: object + + # usage.dask: 75 + # usage.matplotlib: 42 + # usage.pandas: 643 + # usage.scipy: 263 + # usage.skimage: 60 + # usage.sklearn: 285 + # usage.xarray: 550 + kind: object + + # usage.xarray: 5 + metadata: object + + # usage.dask: 6 + # usage.pandas: 263 + # usage.scipy: 32 + # usage.skimage: 6 + # usage.sklearn: 6 + name: object + + # usage.dask: 6 + # usage.pandas: 125 + # usage.scipy: 4 + names: object + + # usage.dask: 21 + # usage.pandas: 25 + # usage.scipy: 17 + shape: object + + # usage.scipy: 51 + str: object + + # usage.dask: 9 + # usage.matplotlib: 5 + # usage.pandas: 1037 + # usage.scipy: 195 + # usage.skimage: 65 + # usage.sklearn: 16 + # usage.xarray: 75 + type: object + + @overload + def __eq__(self, _0: numpy.dtype, /): + """ + usage.skimage: 80 + usage.sklearn: 266 + usage.xarray: 192 + """ + ... + + @overload + def __eq__(self, _0: Type[bool], /): + """ + usage.skimage: 39 + usage.sklearn: 8 + usage.xarray: 24 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.uint8], /): + """ + usage.matplotlib: 6 + usage.skimage: 5 + usage.sklearn: 3 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.float64], /): + """ + usage.matplotlib: 1 + usage.skimage: 13 + usage.sklearn: 86 + usage.xarray: 8 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.uint16], /): + """ + usage.matplotlib: 1 + usage.skimage: 8 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.float32], /): + """ + usage.skimage: 3 + usage.sklearn: 89 + usage.xarray: 9 + """ + ... + + @overload + def __eq__(self, _0: Literal["bool"], /): + """ + usage.matplotlib: 1 + usage.skimage: 14 + usage.xarray: 2 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.float16], /): + """ + usage.skimage: 1 + usage.xarray: 2 + """ + ... + + @overload + def __eq__(self, _0: Type[float], /): + """ + usage.skimage: 6 + usage.sklearn: 8 + usage.xarray: 10 + """ + ... + + @overload + def __eq__(self, _0: Literal["float64"], /): + """ + usage.skimage: 4 + usage.sklearn: 2 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.int8], /): + """ + usage.skimage: 4 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.int16], /): + """ + usage.skimage: 2 + usage.sklearn: 1 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.int32], /): + """ + usage.skimage: 1 + usage.sklearn: 12 + usage.xarray: 2 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.int64], /): + """ + usage.skimage: 1 + usage.sklearn: 12 + usage.xarray: 4 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.longlong], /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.uint32], /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.uint64], /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __eq__(self, _0: Type[numpy.ulonglong], /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __eq__(self, _0: Literal["float32"], /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __eq__(self, _0: Literal["O"], /): + """ + usage.xarray: 5 + """ + ... @overload - @classmethod - def __ne__(cls, _0: numpy.dtype, /): + def __eq__(self, _0: Literal["datetime64[ns]"], /): """ - usage.skimage: 1 + usage.xarray: 9 """ ... @overload - @classmethod - def __ne__( - cls, _0: Union[pandas._libs.missing.NAType, bool, numpy.dtype, numpy.bool_], / - ): + def __eq__(self, _0: Literal["S1"], /): """ - usage.pandas: 16 + usage.xarray: 23 """ ... @overload - @classmethod - def __ne__(cls, _0: Union[numpy.dtype, numpy.bool_], /): + def __eq__(self, _0: Literal["timedelta64[ns]"], /): """ - usage.scipy: 29 + usage.xarray: 3 """ ... @overload - @classmethod - def __ne__(cls, _0: numpy.bool_, /): + def __eq__(self, _0: Literal["f4"], /): """ - usage.matplotlib: 4 + usage.xarray: 1 """ ... @overload - @classmethod - def __ne__(cls, _0: numpy.ndarray, /): + def __eq__(self, _0: Type[object], /): """ - usage.sklearn: 1 + usage.sklearn: 55 + usage.xarray: 6 """ ... - @classmethod - def __ne__( - cls, - _0: Union[ - numpy.ndarray, numpy.dtype, pandas._libs.missing.NAType, bool, numpy.bool_ - ], - /, - ): + @overload + def __eq__(self, _0: Literal["int64"], /): """ - usage.matplotlib: 4 - usage.pandas: 16 - usage.scipy: 29 - usage.skimage: 1 - usage.sklearn: 1 + usage.sklearn: 3 + usage.xarray: 5 """ ... - # usage.dask: 4 - # usage.pandas: 3 - # usage.scipy: 2 - dtype: object - - # usage.dask: 5 - # usage.pandas: 3 - # usage.scipy: 3 - ndim: object - - # usage.dask: 7 - # usage.matplotlib: 1 - # usage.scipy: 2 - shape: object - - # usage.scipy: 1 - size: object - - # usage.pandas: 1 - values: object - @overload - def __add__( - self, - _0: Union[ - numpy.bool_, pandas.core.arrays.boolean.BooleanArray, int, numpy.ndarray - ], - /, - ): + def __eq__(self, _0: Literal["S3"], /): """ - usage.pandas: 7 + usage.xarray: 1 """ ... @overload - def __add__(self, _0: object, /): + def __eq__(self, _0: Type[int], /): """ - usage.scipy: 18 + usage.sklearn: 3 + usage.xarray: 5 """ ... @overload - def __add__(self, _0: numpy.float64, /): + def __eq__(self, _0: Type[numpy.bool_], /): """ - usage.matplotlib: 1 + usage.xarray: 3 """ ... - def __add__(self, _0: object, /): + @overload + def __eq__(self, _0: Type[numpy.object_], /): """ - usage.matplotlib: 1 - usage.pandas: 7 - usage.scipy: 18 + usage.sklearn: 2 + usage.xarray: 1 """ ... @overload - def __and__(self, _0: numpy.bool_, /): + def __eq__(self, _0: Type[numpy.str_], /): """ usage.xarray: 1 """ ... @overload - def __and__(self, _0: numpy.ndarray, /): + def __eq__(self, _0: Type[numpy.complex128], /): """ usage.xarray: 1 """ ... @overload - def __and__( - self, - _0: Union[pandas.core.arrays.boolean.BooleanArray, numpy.bool_, numpy.ndarray], - /, - ): + def __eq__(self, _0: Type[numpy.complex64], /): """ - usage.pandas: 9 + usage.xarray: 1 """ ... @overload - def __and__(self, _0: Union[int, numpy.int64, numpy.bool_, numpy.ndarray, bool], /): + def __eq__(self, _0: Type[numpy.timedelta64], /): """ - usage.scipy: 135 + usage.xarray: 1 """ ... - def __and__(self, _0: object, /): + @overload + def __eq__(self, _0: Type[numpy.datetime64], /): """ - usage.pandas: 9 - usage.scipy: 135 - usage.xarray: 2 + usage.xarray: 1 """ ... - def __bool__(self, /): + @overload + def __eq__(self, _0: Literal["object"], /): """ - usage.dask: 12 - usage.matplotlib: 14 - usage.pandas: 66 - usage.scipy: 40 - usage.skimage: 18 - usage.sklearn: 67 - usage.xarray: 3 + usage.sklearn: 1 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: numpy.bool_, /): + def __eq__(self, _0: object, /): """ - usage.dask: 2 - usage.matplotlib: 10 - usage.xarray: 2 + usage.pandas: 2263 """ ... @overload - def __eq__(self, _0: bool, /): + def __eq__(self, _0: Union[type, numpy.dtype, Literal["float32", "float"]], /): """ - usage.matplotlib: 1 - usage.xarray: 2 + usage.scipy: 341 """ ... @overload + def __eq__(self, _0: Union[str, type, numpy.dtype], /): + """ + usage.dask: 329 + """ + ... + def __eq__(self, _0: object, /): """ - usage.pandas: 94 + usage.dask: 329 + usage.matplotlib: 9 + usage.pandas: 2263 + usage.scipy: 341 + usage.skimage: 186 + usage.sklearn: 551 + usage.xarray: 321 + """ + ... + + def __ge__(self, _0: numpy.dtype, /): + """ + usage.scipy: 2 + """ + ... + + def __getitem__(self, _0: str, /): + """ + usage.dask: 17 + usage.pandas: 58 """ ... @overload - def __eq__( - self, _0: Union[bool, numpy.ndarray, numpy.bool_, Literal["force accept"]], / - ): + def __gt__(self, _0: numpy.dtype, /): """ - usage.scipy: 26 + usage.pandas: 17 """ ... @overload - def __eq__(self, _0: Union[bool, numpy.ndarray], /): + def __gt__(self, _0: Union[Type[numpy.int32], numpy.dtype], /): """ - usage.sklearn: 10 + usage.scipy: 4 """ ... - def __eq__(self, _0: object, /): + def __gt__(self, _0: Union[numpy.dtype, Type[numpy.int32]], /): """ - usage.dask: 2 - usage.matplotlib: 11 - usage.pandas: 94 - usage.scipy: 26 - usage.sklearn: 10 - usage.xarray: 4 + usage.pandas: 17 + usage.scipy: 4 """ ... - def __floordiv__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.boolean.BooleanArray], / - ): + def __le__(self, _0: numpy.dtype, /): """ - usage.pandas: 2 + usage.scipy: 2 """ ... - def __ge__(self, _0: pandas._libs.missing.NAType, /): + def __lt__(self, _0: numpy.dtype, /): """ - usage.pandas: 2 + usage.pandas: 17 + usage.scipy: 2 """ ... - def __getitem__(self, _0: Tuple[Union[None, ellipsis], ...], /): + @overload + def __ne__(self, _0: numpy.dtype, /): """ - usage.dask: 2 + usage.skimage: 22 + usage.sklearn: 30 + usage.xarray: 38 """ ... @overload - def __gt__(self, _0: pandas._libs.missing.NAType, /): + def __ne__(self, _0: Type[bool], /): """ - usage.pandas: 1 + usage.skimage: 4 + usage.sklearn: 4 """ ... @overload - def __gt__(self, _0: numpy.float64, /): + def __ne__(self, _0: Type[numpy.bool_], /): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload - def __gt__(self, _0: float, /): + def __ne__(self, _0: Literal["S1"], /): """ - usage.sklearn: 1 + usage.xarray: 2 """ ... - def __gt__(self, _0: Union[float, pandas._libs.missing.NAType, numpy.float64], /): + @overload + def __ne__(self, _0: Type[object], /): """ - usage.matplotlib: 1 - usage.pandas: 1 - usage.sklearn: 1 + usage.sklearn: 7 + usage.xarray: 13 """ ... - def __iand__(self, _0: numpy.bool_, /): + @overload + def __ne__(self, _0: object, /): """ - usage.scipy: 1 + usage.pandas: 457 """ ... - def __invert__(self, /): + @overload + def __ne__(self, _0: Union[type, Literal["i"], numpy.dtype], /): """ - usage.dask: 1 - usage.matplotlib: 1 - usage.pandas: 1 - usage.scipy: 12 - usage.skimage: 3 - usage.xarray: 1 + usage.scipy: 290 """ ... - def __ior__(self, _0: numpy.bool_, /): + @overload + def __ne__(self, _0: Type[numpy.uint8], /): """ - usage.scipy: 2 + usage.matplotlib: 19 + usage.sklearn: 2 """ ... - def __le__(self, _0: pandas._libs.missing.NAType, /): + @overload + def __ne__(self, _0: Type[numpy.float64], /): """ - usage.pandas: 2 + usage.matplotlib: 2 + usage.sklearn: 13 """ ... @overload - def __lt__(self, _0: pandas._libs.missing.NAType, /): + def __ne__( + self, + _0: Union[ + numpy.dtype, + None, + List[ + Tuple[ + Literal["values", "indices", "inverse", "counts"], + Union[numpy.dtype, Type[numpy.int64]], + ] + ], + type, + str, + ], + /, + ): """ - usage.pandas: 1 + usage.dask: 272 """ ... @overload - def __lt__(self, _0: numpy.float64, /): + def __ne__(self, _0: Type[float], /): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... - def __lt__(self, _0: Union[numpy.float64, pandas._libs.missing.NAType], /): + @overload + def __ne__(self, _0: Type[int], /): """ - usage.matplotlib: 1 - usage.pandas: 1 + usage.sklearn: 2 """ ... - def __mod__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.boolean.BooleanArray], / - ): + @overload + def __ne__(self, _0: Type[numpy.float32], /): """ - usage.pandas: 2 + usage.sklearn: 3 """ ... @overload - def __mul__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.boolean.BooleanArray], / - ): + def __ne__(self, _0: Type[numpy.int32], /): """ - usage.pandas: 2 + usage.sklearn: 5 """ ... @overload - def __mul__(self, _0: Union[bool, numpy.int64, int, numpy.bool_], /): + def __ne__(self, _0: Literal["bool"], /): """ - usage.scipy: 10 + usage.sklearn: 2 """ ... - def __mul__(self, _0: object, /): + def __ne__(self, _0: object, /): """ - usage.pandas: 2 - usage.scipy: 10 + usage.dask: 272 + usage.matplotlib: 21 + usage.pandas: 457 + usage.scipy: 290 + usage.skimage: 27 + usage.sklearn: 71 + usage.xarray: 53 """ ... @overload - def __or__(self, _0: numpy.bool_, /): + def __rmod__(self, _0: str, /): """ - usage.xarray: 2 + usage.scipy: 4 + usage.skimage: 1 + usage.sklearn: 3 + usage.xarray: 1 """ ... @overload - def __or__(self, _0: numpy.ndarray, /): + def __rmod__(self, _0: Literal[", dtype=%s"], /): + """ + usage.dask: 2 + """ + ... + + def __rmod__(self, _0: str, /): + """ + usage.dask: 2 + usage.scipy: 4 + usage.skimage: 1 + usage.sklearn: 3 + usage.xarray: 1 + """ + ... + + @overload + def newbyteorder(self, _0: Literal["="], /): """ usage.xarray: 1 """ ... @overload - def __or__(self, _0: bool, /): + def newbyteorder(self, _0: str = ..., /): + """ + usage.scipy: 64 + """ + ... + + def newbyteorder(self, _0: str = ..., /): + """ + usage.scipy: 64 + usage.xarray: 1 + """ + ... + + +class errstate: + pass + + +class finfo: + + # usage.dask: 1 + # usage.pandas: 4 + # usage.scipy: 119 + # usage.skimage: 10 + # usage.sklearn: 46 + eps: object + + # usage.pandas: 7 + # usage.scipy: 3 + # usage.skimage: 2 + # usage.sklearn: 4 + max: object + + # usage.scipy: 1 + maxexp: object + + # usage.pandas: 3 + # usage.skimage: 2 + # usage.sklearn: 5 + min: object + + # usage.scipy: 1 + minexp: object + + # usage.scipy: 2 + nmant: object + + # usage.scipy: 5 + precision: object + + # usage.matplotlib: 1 + # usage.scipy: 1 + # usage.sklearn: 5 + resolution: object + + # usage.matplotlib: 1 + # usage.scipy: 4 + # usage.sklearn: 2 + tiny: object + + +class flagsobj: + + # usage.scipy: 26 + # usage.skimage: 2 + c_contiguous: object + + # usage.scipy: 20 + # usage.sklearn: 2 + contiguous: object + + # usage.pandas: 4 + # usage.scipy: 14 + # usage.skimage: 1 + # usage.sklearn: 2 + f_contiguous: object + + # usage.scipy: 1 + fortran: object + + # usage.scipy: 1 + # usage.xarray: 1 + owndata: object + + # usage.matplotlib: 2 + # usage.pandas: 14 + # usage.scipy: 13 + # usage.skimage: 3 + # usage.sklearn: 7 + # usage.xarray: 4 + writeable: bool + + @overload + def __getitem__(self, _0: Literal["C_CONTIGUOUS"], /): """ + usage.sklearn: 11 usage.xarray: 2 """ ... @overload - def __or__( - self, _0: Union[pandas.core.arrays.boolean.BooleanArray, numpy.bool_], / + def __getitem__(self, _0: Literal["F_CONTIGUOUS"], /): + """ + usage.sklearn: 8 + usage.xarray: 2 + """ + ... + + @overload + def __getitem__( + self, _0: Literal["CONTIGUOUS", "C_CONTIGUOUS", "FORTRAN", "ALIGNED"], / ): """ - usage.pandas: 3 + usage.scipy: 7 """ ... @overload - def __or__(self, _0: Union[numpy.ndarray, numpy.bool_], /): + def __getitem__(self, _0: Literal["WRITEABLE"], /): """ - usage.scipy: 10 + usage.sklearn: 2 """ ... - def __or__( - self, - _0: Union[ - numpy.bool_, numpy.ndarray, bool, pandas.core.arrays.boolean.BooleanArray - ], - /, - ): + def __getitem__(self, _0: str, /): """ - usage.pandas: 3 - usage.scipy: 10 - usage.xarray: 5 + usage.scipy: 7 + usage.sklearn: 21 + usage.xarray: 4 """ ... - def __pow__( - self, - _0: Union[ - pandas._libs.missing.NAType, - pandas.core.arrays.boolean.BooleanArray, - numpy.ndarray, - ], - /, - ): + def __setitem__(self, _0: Literal["WRITEABLE"], _1: bool, /): """ - usage.pandas: 3 + usage.scipy: 1 """ ... + +class flatiter: @overload - def __radd__(self, _0: Union[pandas._libs.missing.NAType, numpy.bool_], /): + def __eq__(self, _0: int, /): """ - usage.pandas: 6 + usage.skimage: 2 + """ + ... + + @overload + def __eq__(self, _0: numpy.int64, /): + """ + usage.skimage: 2 + """ + ... + + @overload + def __eq__(self, _0: numpy.float64, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __eq__(self, _0: numpy.float32, /): + """ + usage.sklearn: 1 + """ + ... + + def __eq__(self, _0: Union[numpy.float64, numpy.float32, int, numpy.int64], /): + """ + usage.skimage: 4 + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: int, /): + """ + usage.dask: 1 + usage.matplotlib: 4 + usage.skimage: 3 + usage.sklearn: 5 + usage.xarray: 6 """ ... @overload - def __radd__(self, _0: object, /): + def __getitem__(self, _0: numpy.int64, /): """ - usage.scipy: 41 + usage.skimage: 2 """ ... - def __radd__(self, _0: object, /): + @overload + def __getitem__(self, _0: slice[None, int, None], /): """ - usage.pandas: 6 - usage.scipy: 41 + usage.xarray: 2 """ ... @overload - def __rand__(self, _0: numpy.bool_, /): + def __getitem__(self, _0: slice[int, None, int], /): """ - usage.xarray: 1 + usage.xarray: 2 """ ... @overload - def __rand__(self, _0: numpy.ndarray, /): + def __getitem__(self, _0: numpy.ndarray, /): """ - usage.xarray: 1 + usage.pandas: 1 """ ... @overload - def __rand__(self, _0: dask.array.core.Array, /): + def __getitem__(self, _0: Union[slice[None, None, None], int], /): """ - usage.xarray: 1 + usage.scipy: 5 """ ... @overload - def __rand__( - self, _0: Union[pandas.core.arrays.boolean.BooleanArray, numpy.bool_], / - ): + def __getitem__(self, _0: slice[int, None, int], /): """ - usage.pandas: 9 + usage.matplotlib: 1 """ ... @overload - def __rand__( - self, _0: Union[bool, numpy.int64, numpy.bool_, int, numpy.ndarray], / - ): + def __getitem__(self, _0: slice[None, None, None], /): """ - usage.scipy: 189 + usage.sklearn: 28 """ ... - def __rand__(self, _0: object, /): + def __getitem__( + self, + _0: Union[ + int, + numpy.ndarray, + numpy.int64, + slice[Union[int, None], Union[None, int], Union[int, None]], + ], + /, + ): """ - usage.pandas: 9 - usage.scipy: 189 - usage.xarray: 3 + usage.dask: 1 + usage.matplotlib: 5 + usage.pandas: 1 + usage.scipy: 5 + usage.skimage: 5 + usage.sklearn: 33 + usage.xarray: 10 """ ... - def __rmul__( - self, - _0: Union[numpy.bool_, numpy.int64, numpy.float64, float, numpy.ndarray], - /, - ): + def __iter__(self, /): """ - usage.scipy: 11 + usage.matplotlib: 14 + usage.scipy: 3 + usage.skimage: 2 + usage.sklearn: 4 + usage.xarray: 8 """ ... @overload - def __ror__(self, _0: numpy.bool_, /): + def __setitem__(self, _0: List[int], _1: numpy.ndarray, /): """ - usage.xarray: 2 + usage.skimage: 2 """ ... @overload - def __ror__(self, _0: numpy.ndarray, /): + def __setitem__(self, _0: numpy.ndarray, _1: float, /): """ usage.xarray: 1 """ ... @overload - def __ror__(self, _0: bool, /): + def __setitem__(self, _0: numpy.ndarray, _1: numpy.ndarray, /): """ - usage.xarray: 2 + usage.pandas: 11 """ ... @overload - def __ror__( - self, _0: Union[pandas.core.arrays.boolean.BooleanArray, numpy.bool_], / + def __setitem__( + self, + _0: Union[numpy.ndarray, slice[None, None, None]], + _1: Union[float, int, numpy.ndarray], + /, ): """ - usage.pandas: 3 + usage.scipy: 9 """ ... @overload - def __ror__(self, _0: Union[numpy.ndarray, numpy.bool_, bool], /): + def __setitem__(self, _0: int, _1: Tuple[Union[str, int], ...], /): """ - usage.scipy: 7 + usage.dask: 6 """ ... - def __ror__( - self, - _0: Union[ - bool, numpy.bool_, numpy.ndarray, pandas.core.arrays.boolean.BooleanArray - ], - /, - ): + @overload + def __setitem__(self, _0: slice[None, None, None], _1: numpy.ndarray, /): """ - usage.pandas: 3 - usage.scipy: 7 - usage.xarray: 5 + usage.sklearn: 37 """ ... @overload - def __rpow__(self, _0: int, /): + def __setitem__(self, _0: slice[int, None, int], _1: int, /): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload - def __rpow__(self, _0: pandas._libs.missing.NAType, /): + def __setitem__(self, _0: slice[None, None, None], _1: int, /): """ - usage.pandas: 1 + usage.sklearn: 6 """ ... - def __rpow__(self, _0: Union[pandas._libs.missing.NAType, int], /): + @overload + def __setitem__(self, _0: slice[None, None, None], _1: float, /): """ - usage.pandas: 1 - usage.skimage: 1 + usage.sklearn: 1 """ ... - def __rsub__(self, _0: int, /): + def __setitem__( + self, + _0: Union[ + slice[Union[None, int], None, Union[None, int]], + List[int], + numpy.ndarray, + int, + ], + _1: Union[numpy.ndarray, int, float, Tuple[Union[str, int], ...]], + /, + ): """ - usage.scipy: 13 + usage.dask: 6 + usage.pandas: 11 + usage.scipy: 9 + usage.skimage: 2 + usage.sklearn: 45 + usage.xarray: 1 """ ... + +class float128: + + # usage.pandas: 1 + # usage.scipy: 10 + dtype: object + + # usage.dask: 1 + ndim: object + + # usage.scipy: 2 + real: object + + # usage.scipy: 1 + size: object + @overload - def __rxor__(self, _0: pandas.core.arrays.boolean.BooleanArray, /): + def __add__(self, _0: int, /): """ usage.pandas: 1 """ ... @overload - def __rxor__(self, _0: numpy.bool_, /): + def __add__(self, _0: object, /): """ - usage.matplotlib: 3 + usage.scipy: 20 """ ... - def __rxor__( - self, _0: Union[numpy.bool_, pandas.core.arrays.boolean.BooleanArray], / - ): + @overload + def __add__(self, _0: numpy.float64, /): """ - usage.matplotlib: 3 - usage.pandas: 1 + usage.matplotlib: 2 """ ... - def __sub__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.boolean.BooleanArray], / - ): + def __add__(self, _0: object, /): """ - usage.pandas: 2 + usage.matplotlib: 2 + usage.pandas: 1 + usage.scipy: 20 """ ... - def __truediv__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.boolean.BooleanArray], / - ): + def __bool__(self, /): """ - usage.pandas: 2 + usage.scipy: 1 """ ... @overload - def __xor__(self, _0: pandas.core.arrays.boolean.BooleanArray, /): + def __eq__(self, _0: int, /): """ usage.pandas: 1 """ ... @overload - def __xor__(self, _0: numpy.bool_, /): + def __eq__(self, _0: Union[Literal["silverman", "scott"], numpy.ndarray], /): """ - usage.matplotlib: 3 + usage.scipy: 3 """ ... - def __xor__( - self, _0: Union[numpy.bool_, pandas.core.arrays.boolean.BooleanArray], / - ): + @overload + def __eq__(self, _0: numpy.float128, /): """ - usage.matplotlib: 3 - usage.pandas: 1 + usage.matplotlib: 2 """ ... - def all(self, /): + @overload + def __eq__(self, _0: numpy.float64, /): """ - usage.dask: 1 - usage.pandas: 4 - usage.scipy: 3 - usage.sklearn: 1 - usage.xarray: 9 + usage.matplotlib: 1 """ ... - def any(self, /): + def __eq__( + self, + _0: Union[ + numpy.float128, + numpy.float64, + int, + numpy.ndarray, + Literal["silverman", "scott"], + ], + /, + ): """ - usage.dask: 1 - usage.pandas: 6 - usage.scipy: 10 - usage.xarray: 2 + usage.matplotlib: 3 + usage.pandas: 1 + usage.scipy: 3 """ ... - @overload - def astype(self, _0: numpy.dtype, /): + def __ge__(self, _0: Union[numpy.float128, numpy.ndarray, numpy.float64], /): """ - usage.pandas: 1 + usage.scipy: 4 """ ... @overload - def astype(self, _0: Type[numpy.float64], /): + def __gt__(self, _0: int, /): """ usage.matplotlib: 1 + usage.pandas: 1 """ ... @overload - def astype(self, _0: Type[numpy.float32], /): + def __gt__(self, _0: numpy.float128, /): """ - usage.matplotlib: 2 + usage.matplotlib: 3 + usage.scipy: 1 """ ... - def astype(self, _0: Union[type, numpy.dtype], /): + def __gt__(self, _0: Union[int, numpy.float128], /): """ - usage.matplotlib: 3 + usage.matplotlib: 4 usage.pandas: 1 + usage.scipy: 1 """ ... - def item(self, /): + def __iadd__(self, _0: numpy.float128, /): """ - usage.pandas: 12 + usage.scipy: 1 """ ... - def squeeze(self, /): + def __imul__(self, _0: Union[int, numpy.float128], /): """ - usage.pandas: 1 + usage.scipy: 3 """ ... - def sum(self, /): + @overload + def __le__(self, _0: Union[float, int], /): """ - usage.scipy: 2 + usage.pandas: 3 """ ... - -class broadcast: - - # usage.scipy: 2 - # usage.xarray: 4 - shape: object - - -class bytes_: - - # usage.matplotlib: 1 - __mro__: ClassVar[object] - - # usage.pandas: 2 - __name__: ClassVar[object] - - # usage.dask: 1 - ndim: object - - def __add__(self, _0: float, /): + @overload + def __le__(self, _0: Union[numpy.float128, float], /): """ - usage.matplotlib: 1 + usage.scipy: 2 """ ... - @overload - def __eq__(self, _0: numpy.ndarray, /): + def __le__(self, _0: Union[float, numpy.float128, int], /): """ - usage.xarray: 1 + usage.pandas: 3 + usage.scipy: 2 """ ... @overload - def __eq__(self, _0: numpy.bytes_, /): + def __lt__(self, _0: Union[numpy.float128, numpy.ndarray, numpy.float64], /): """ - usage.pandas: 2 + usage.scipy: 4 """ ... - def __eq__(self, _0: Union[numpy.bytes_, numpy.ndarray], /): + @overload + def __lt__(self, _0: numpy.float128, /): """ - usage.pandas: 2 - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload - def __getitem__(self, _0: slice[int, int, int], /): + def __lt__(self, _0: numpy.float64, /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __getitem__(self, _0: slice[None, None, None], /): + def __lt__(self, _0: int, /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... - def __getitem__( - self, _0: slice[Union[int, None], Union[int, None], Union[int, None]], / - ): + def __lt__(self, _0: Union[int, numpy.float64, numpy.float128, numpy.ndarray], /): """ - usage.xarray: 2 + usage.matplotlib: 5 + usage.scipy: 4 """ ... @overload - def __iadd__(self, _0: bytes, /): + def __mul__(self, _0: Union[numpy.float64, numpy.float128], /): """ - usage.xarray: 1 + usage.pandas: 8 """ ... @overload - def __iadd__(self, _0: numpy.bytes_, /): - """ - usage.xarray: 1 - """ - ... - - def __iadd__(self, _0: Union[numpy.bytes_, bytes], /): + def __mul__( + self, _0: Union[numpy.float128, int, float, numpy.float64, numpy.ndarray], / + ): """ - usage.xarray: 2 + usage.scipy: 20 """ ... - def __radd__(self, _0: bytes, /): + def __mul__( + self, _0: Union[numpy.ndarray, numpy.float64, float, int, numpy.float128], / + ): """ - usage.xarray: 1 + usage.pandas: 8 + usage.scipy: 20 """ ... - def decode(self, /, encoding: Literal["utf-8"]): + def __ne__(self, _0: numpy.float128, /): """ - usage.matplotlib: 1 + usage.scipy: 4 """ ... - @overload - def find(self, _0: numpy.bytes_, /): + def __neg__(self, /): """ - usage.xarray: 1 + usage.scipy: 1 """ ... @overload - def find(self, _0: numpy.bytes_, _1: int, /): + def __pow__(self, _0: Union[int, float], /): """ - usage.xarray: 1 + usage.pandas: 2 """ ... @overload - def find(self, _0: numpy.bytes_, _1: int, _2: int, /): + def __pow__(self, _0: int, /): """ - usage.xarray: 1 + usage.scipy: 2 """ ... - def find(self, _0: numpy.bytes_, _1: int = ..., _2: int = ..., /): + def __pow__(self, _0: Union[int, float], /): """ - usage.xarray: 3 + usage.pandas: 2 + usage.scipy: 2 """ ... @overload - def rfind(self, _0: numpy.bytes_, /): + def __radd__(self, _0: object, /): """ - usage.xarray: 1 + usage.scipy: 23 """ ... @overload - def rfind(self, _0: numpy.bytes_, _1: int, /): + def __radd__(self, _0: int, /): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... - @overload - def rfind(self, _0: numpy.bytes_, _1: int, _2: int, /): + def __radd__(self, _0: object, /): """ - usage.xarray: 1 + usage.matplotlib: 2 + usage.scipy: 23 """ ... - def rfind(self, _0: numpy.bytes_, _1: int = ..., _2: int = ..., /): + @overload + def __rmul__(self, _0: Union[int, numpy.float128], /): """ - usage.xarray: 3 + usage.pandas: 8 """ ... - def upper(self, /): + @overload + def __rmul__(self, _0: object, /): """ - usage.xarray: 1 + usage.scipy: 15 """ ... - -class complex128: - - # usage.pandas: 2 - __name__: ClassVar[object] - - # usage.scipy: 1 - __class__: object - - # usage.dask: 1 - # usage.pandas: 4 - # usage.scipy: 16 - dtype: object - - # usage.scipy: 2 - flags: object - - # usage.scipy: 25 - # usage.skimage: 1 - imag: object - - # usage.dask: 5 - # usage.pandas: 1 - ndim: object - - # usage.matplotlib: 2 - # usage.scipy: 32 - # usage.skimage: 1 - real: object - - # usage.dask: 5 - shape: object - - # usage.scipy: 1 - size: object - - def __add__(self, _0: object, /): + @overload + def __rmul__(self, _0: int, /): """ - usage.scipy: 46 + usage.matplotlib: 2 """ ... - def __bool__(self, /): + def __rmul__(self, _0: object, /): """ - usage.scipy: 1 + usage.matplotlib: 2 + usage.pandas: 8 + usage.scipy: 15 """ ... @overload - def __eq__(self, _0: numpy.ndarray, /): + def __rsub__(self, _0: Union[numpy.ndarray, numpy.float128], /): """ - usage.skimage: 1 + usage.pandas: 9 """ ... @overload - def __eq__( - self, _0: Union[numpy.ndarray, float, complex, int, numpy.complex128], / - ): + def __rsub__(self, _0: float, /): """ - usage.pandas: 6 + usage.scipy: 4 """ ... @overload - def __eq__(self, _0: Union[numpy.ndarray, int, numpy.complex128], /): - """ - usage.scipy: 13 - """ - ... - - def __eq__( - self, _0: Union[numpy.complex128, int, numpy.ndarray, float, complex], / - ): - """ - usage.pandas: 6 - usage.scipy: 13 - usage.skimage: 1 - """ - ... - - def __ge__(self, _0: numpy.complex128, /): + def __rsub__(self, _0: numpy.float128, /): """ - usage.scipy: 1 + usage.matplotlib: 1 """ ... - def __getitem__(self, _0: Tuple[Union[None, ellipsis], ...], /): + def __rsub__(self, _0: Union[numpy.float128, numpy.ndarray, float], /): """ - usage.dask: 2 + usage.matplotlib: 1 + usage.pandas: 9 + usage.scipy: 4 """ ... - def __gt__(self, _0: Union[float, numpy.float64], /): + @overload + def __rtruediv__(self, _0: Union[numpy.float64, numpy.float128], /): """ - usage.scipy: 2 + usage.pandas: 8 """ ... - def __iadd__( - self, _0: Union[complex, numpy.complex64, numpy.float64, numpy.complex128], / - ): + @overload + def __rtruediv__(self, _0: numpy.ndarray, /): """ - usage.scipy: 10 + usage.scipy: 1 """ ... - def __isub__(self, _0: numpy.complex128, /): + def __rtruediv__(self, _0: Union[numpy.ndarray, numpy.float128, numpy.float64], /): """ + usage.pandas: 8 usage.scipy: 1 """ ... @overload - def __itruediv__(self, _0: numpy.float64, /): + def __sub__(self, _0: Union[int, numpy.float128, numpy.ndarray], /): """ - usage.skimage: 2 + usage.pandas: 9 """ ... @overload - def __itruediv__(self, _0: numpy.complex128, /): + def __sub__(self, _0: Union[numpy.float64, numpy.ndarray], /): """ - usage.scipy: 1 + usage.scipy: 2 """ ... - def __itruediv__(self, _0: Union[numpy.complex128, numpy.float64], /): + @overload + def __sub__(self, _0: numpy.float128, /): """ - usage.scipy: 1 - usage.skimage: 2 + usage.matplotlib: 1 """ ... - def __le__(self, _0: Union[int, numpy.complex128], /): + @overload + def __sub__(self, _0: numpy.float64, /): """ - usage.scipy: 2 + usage.matplotlib: 2 """ ... - def __lt__(self, _0: numpy.ndarray, /): + def __sub__(self, _0: Union[numpy.float128, numpy.float64, int, numpy.ndarray], /): """ - usage.scipy: 1 + usage.matplotlib: 3 + usage.pandas: 9 + usage.scipy: 2 """ ... @overload - def __mul__(self, _0: numpy.complex128, /): + def __truediv__(self, _0: numpy.float128, /): """ - usage.skimage: 2 + usage.pandas: 8 """ ... @overload - def __mul__(self, _0: object, /): + def __truediv__(self, _0: Union[int, numpy.ndarray], /): """ - usage.scipy: 106 + usage.scipy: 3 """ ... - def __mul__(self, _0: object, /): + def __truediv__(self, _0: Union[numpy.ndarray, int, numpy.float128], /): """ - usage.scipy: 106 - usage.skimage: 2 + usage.pandas: 8 + usage.scipy: 3 """ ... @overload - def __ne__(self, _0: complex, /): + def astype(self, _0: numpy.dtype, /): """ - usage.pandas: 1 + usage.pandas: 3 """ ... @overload - def __ne__(self, _0: Union[numpy.complex64, int, numpy.complex128, float], /): + def astype(self, _0: Type[numpy.complex256], /): """ - usage.scipy: 16 + usage.scipy: 1 """ ... - def __ne__( - self, _0: Union[float, numpy.complex128, int, numpy.complex64, complex], / - ): + def astype(self, _0: Union[Type[numpy.complex256], numpy.dtype], /): """ - usage.pandas: 1 - usage.scipy: 16 + usage.pandas: 3 + usage.scipy: 1 """ ... - def __neg__(self, /): + def item(self, /): """ - usage.scipy: 17 + usage.matplotlib: 1 """ ... - def __pow__(self, _0: Union[float, numpy.float64, int], /): - """ - usage.scipy: 15 - """ - ... - def __radd__(self, _0: object, /): +class float16: + + # usage.scipy: 1 + dtype: object + + # usage.dask: 1 + ndim: object + + # usage.scipy: 1 + real: object + + def __eq__(self, _0: int, /): """ - usage.scipy: 64 + usage.skimage: 1 """ ... - @overload - def __rmul__(self, _0: numpy.complex128, /): + def __ge__(self, _0: numpy.ndarray, /): """ - usage.skimage: 2 + usage.scipy: 3 """ ... - @overload - def __rmul__(self, _0: object, /): + def __gt__(self, _0: numpy.float64, /): """ - usage.scipy: 106 + usage.skimage: 1 """ ... - def __rmul__(self, _0: object, /): + def __lt__(self, _0: numpy.ndarray, /): """ - usage.scipy: 106 - usage.skimage: 2 + usage.scipy: 3 """ ... - @overload - def __rsub__(self, _0: float, /): + def __mul__(self, _0: int, /): """ - usage.skimage: 1 + usage.scipy: 1 """ ... - @overload - def __rsub__(self, _0: object, /): + def __ne__(self, _0: numpy.float64, /): """ - usage.scipy: 53 + usage.scipy: 1 """ ... - def __rsub__(self, _0: object, /): + def __pow__(self, _0: int, /): """ - usage.scipy: 53 usage.skimage: 1 """ ... @overload - def __rtruediv__(self, _0: numpy.complex128, /): + def __rmul__(self, _0: float, /): """ usage.skimage: 1 """ ... @overload - def __rtruediv__(self, _0: object, /): + def __rmul__(self, _0: numpy.ndarray, /): """ - usage.scipy: 50 + usage.skimage: 1 """ ... - def __rtruediv__(self, _0: object, /): + def __rmul__(self, _0: Union[numpy.ndarray, float], /): """ - usage.scipy: 50 - usage.skimage: 1 + usage.skimage: 2 """ ... - def __sub__(self, _0: Union[numpy.ndarray, numpy.complex128, int, complex], /): + @overload + def __rsub__(self, _0: numpy.float16, /): """ - usage.scipy: 48 + usage.skimage: 2 """ ... @overload - def __truediv__(self, _0: numpy.float64, /): + def __rsub__(self, _0: numpy.ndarray, /): """ usage.skimage: 1 """ ... - @overload - def __truediv__(self, _0: numpy.complex128, /): + def __rsub__(self, _0: Union[numpy.ndarray, numpy.float16], /): """ - usage.skimage: 1 + usage.skimage: 3 """ ... - @overload - def __truediv__(self, _0: numpy.ndarray, /): + def __sub__(self, _0: numpy.float16, /): """ - usage.skimage: 1 + usage.skimage: 2 """ ... + +class float32: + + # usage.dask: 1 + __module__: ClassVar[object] + + # usage.pandas: 4 + __name__: ClassVar[object] + + # usage.dask: 6 + shape: ClassVar[object] + @overload - def __truediv__(self, _0: object, /): + @classmethod + def __ne__(cls, _0: numpy.dtype, /): """ - usage.scipy: 46 + usage.dask: 2 + usage.sklearn: 3 """ ... - def __truediv__(self, _0: object, /): + @overload + @classmethod + def __ne__(cls, _0: Union[numpy.float64, numpy.float32, int], /): """ - usage.scipy: 46 - usage.skimage: 3 + usage.scipy: 9 """ ... - def conj(self, /): + @overload + @classmethod + def __ne__(cls, _0: int, /): """ - usage.scipy: 10 - usage.skimage: 1 + usage.matplotlib: 1 + usage.sklearn: 1 """ ... - def conjugate(self, /): + @classmethod + def __ne__(cls, _0: Union[numpy.dtype, int, numpy.float32, numpy.float64], /): """ - usage.scipy: 1 + usage.dask: 2 + usage.matplotlib: 1 + usage.scipy: 9 + usage.sklearn: 4 """ ... - -class complex256: - - # usage.scipy: 8 + # usage.dask: 2 + # usage.pandas: 4 + # usage.scipy: 13 + # usage.sklearn: 2 + # usage.xarray: 1 dtype: object - # usage.dask: 1 + # usage.dask: 4 + # usage.pandas: 1 ndim: object + # usage.scipy: 12 + real: object + # usage.scipy: 1 size: object - def __add__(self, _0: object, /): + # usage.pandas: 1 + values: object + + @overload + def __add__(self, _0: int, /): """ - usage.scipy: 18 + usage.skimage: 1 + usage.sklearn: 2 """ ... - def __bool__(self, /): + @overload + def __add__(self, _0: numpy.float64, /): """ - usage.scipy: 1 + usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 3 """ ... - def __iadd__(self, _0: numpy.complex256, /): + @overload + def __add__( + self, + _0: Union[ + pandas.core.arrays.timedeltas.TimedeltaArray, int, pandas.core.series.Series + ], + /, + ): """ - usage.scipy: 2 + usage.pandas: 3 """ ... - def __mul__(self, _0: Union[float, int, numpy.complex256], /): + @overload + def __add__(self, _0: object, /): """ - usage.scipy: 6 + usage.scipy: 22 """ ... - def __ne__(self, _0: numpy.complex256, /): + @overload + def __add__(self, _0: Union[numpy.float32, numpy.float64], /): """ - usage.scipy: 2 + usage.dask: 3 """ ... - def __radd__(self, _0: object, /): + @overload + def __add__(self, _0: numpy.float32, /): """ - usage.scipy: 20 + usage.sklearn: 3 """ ... - def __rmul__(self, _0: Union[numpy.complex256, int, numpy.ndarray], /): + @overload + def __add__(self, _0: numpy.ndarray, /): """ - usage.scipy: 4 + usage.sklearn: 1 """ ... - def __rsub__(self, _0: numpy.ndarray, /): + def __add__(self, _0: object, /): """ - usage.scipy: 1 + usage.dask: 3 + usage.matplotlib: 1 + usage.pandas: 3 + usage.scipy: 22 + usage.skimage: 2 + usage.sklearn: 9 """ ... - def __sub__(self, _0: numpy.ndarray, /): + def __bool__(self, /): """ usage.scipy: 1 """ ... - def __truediv__(self, _0: Union[numpy.ndarray, int], /): + @overload + def __eq__(self, _0: numpy.ndarray, /): """ - usage.scipy: 3 + usage.skimage: 1 + usage.sklearn: 5 + usage.xarray: 2 """ ... - def conj(self, /): + @overload + def __eq__(self, _0: numpy.float32, /): """ - usage.scipy: 1 + usage.matplotlib: 4 + usage.skimage: 2 + usage.sklearn: 4 + usage.xarray: 2 """ ... + @overload + def __eq__(self, _0: float, /): + """ + usage.skimage: 1 + """ + ... -class complex64: - - # usage.pandas: 2 - __name__: ClassVar[object] - - # usage.scipy: 9 - dtype: object - - # usage.scipy: 2 - imag: object - - # usage.dask: 2 - ndim: object - - # usage.scipy: 10 - real: object - - # usage.scipy: 1 - size: object - - def __add__(self, _0: object, /): + @overload + def __eq__(self, _0: int, /): """ - usage.scipy: 17 + usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 4 """ ... - def __bool__(self, /): + @overload + def __eq__(self, _0: Union[numpy.float64, numpy.float32, int], /): """ - usage.scipy: 1 + usage.pandas: 9 """ ... @overload - def __eq__(self, _0: numpy.complex64, /): + def __eq__( + self, + _0: Union[Literal["silverman", "scott"], int, numpy.float32, numpy.ndarray], + /, + ): """ - usage.pandas: 2 + usage.scipy: 18 """ ... @overload - def __eq__(self, _0: int, /): + def __eq__(self, _0: numpy.float64, /): """ - usage.scipy: 1 + usage.matplotlib: 1 """ ... - def __eq__(self, _0: Union[int, numpy.complex64], /): + @overload + def __eq__(self, _0: Union[int, numpy.float32], /): """ - usage.pandas: 2 - usage.scipy: 1 + usage.dask: 3 """ ... - def __getitem__(self, _0: Tuple[ellipsis, None, None], /): + @overload + def __eq__(self, _0: numpy.flatiter, /): """ - usage.dask: 1 + usage.sklearn: 1 """ ... - def __iadd__(self, _0: Union[numpy.complex64, complex, numpy.complex128], /): + def __eq__(self, _0: object, /): """ - usage.scipy: 4 + usage.dask: 3 + usage.matplotlib: 6 + usage.pandas: 9 + usage.scipy: 18 + usage.skimage: 6 + usage.sklearn: 14 + usage.xarray: 4 """ ... - def __mul__(self, _0: Union[int, numpy.complex64, numpy.ndarray, float], /): + @overload + def __ge__(self, _0: int, /): """ - usage.scipy: 29 + usage.pandas: 1 + usage.skimage: 1 + usage.sklearn: 4 """ ... - def __ne__(self, _0: Union[numpy.complex128, numpy.complex64], /): + @overload + def __ge__(self, _0: numpy.ndarray, /): """ usage.scipy: 3 + usage.sklearn: 1 """ ... - def __pow__(self, _0: int, /): + @overload + def __ge__(self, _0: numpy.float32, /): """ - usage.scipy: 1 + usage.sklearn: 1 """ ... - def __radd__(self, _0: object, /): + @overload + def __ge__(self, _0: numpy.float64, /): """ - usage.scipy: 21 + usage.sklearn: 2 """ ... - def __rmul__( - self, _0: Union[numpy.complex128, float, numpy.ndarray, numpy.complex64, int], / + @overload + def __ge__(self, _0: float, /): + """ + usage.sklearn: 1 + """ + ... + + def __ge__( + self, _0: Union[float, numpy.float64, numpy.float32, int, numpy.ndarray], / ): """ - usage.scipy: 14 + usage.pandas: 1 + usage.scipy: 3 + usage.skimage: 1 + usage.sklearn: 9 """ ... - def __rsub__(self, _0: float, /): + def __getitem__(self, _0: Tuple[Union[ellipsis, None], ...], /): """ - usage.scipy: 1 + usage.dask: 3 """ ... - def __truediv__(self, _0: numpy.ndarray, /): + @overload + def __gt__(self, _0: int, /): """ + usage.matplotlib: 2 + usage.pandas: 1 usage.scipy: 1 + usage.skimage: 2 + usage.sklearn: 4 """ ... - def conj(self, /): + @overload + def __gt__(self, _0: float, /): """ - usage.scipy: 2 + usage.skimage: 3 """ ... - -class datetime64: - - # usage.matplotlib: 1 - __mro__: ClassVar[object] - - # usage.pandas: 8 - __name__: ClassVar[object] - - @classmethod - def __ne__( - cls, - _0: Union[ - pandas._libs.tslibs.nattype.NaTType, - pandas._libs.tslibs.timestamps.Timestamp, - Type[numpy.datetime64], - Literal["unix", "2020-07-13T05:05:56.369928+00:00"], - ], - /, - ): + @overload + def __gt__(self, _0: numpy.float64, /): """ - usage.pandas: 6 + usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 2 + usage.xarray: 1 """ ... - # usage.dask: 1 - # usage.pandas: 8 - # usage.xarray: 3 - dtype: object - - # usage.dask: 1 - # usage.pandas: 1 - ndim: object + @overload + def __gt__(self, _0: numpy.float32, /): + """ + usage.matplotlib: 1 + usage.sklearn: 1 + """ + ... - # usage.matplotlib: 1 - tzinfo: object + def __gt__(self, _0: Union[numpy.float64, int, numpy.float32, float], /): + """ + usage.matplotlib: 4 + usage.pandas: 1 + usage.scipy: 1 + usage.skimage: 6 + usage.sklearn: 7 + usage.xarray: 1 + """ + ... @overload - def __add__(self, _0: numpy.ndarray, /): + def __iadd__(self, _0: int, /): """ - usage.xarray: 4 + usage.xarray: 2 """ ... @overload - def __add__(self, _0: object, /): + def __iadd__(self, _0: Union[numpy.float64, numpy.float32], /): """ - usage.pandas: 32 + usage.scipy: 2 """ ... - def __add__(self, _0: object, /): + @overload + def __iadd__(self, _0: numpy.float32, /): """ - usage.pandas: 32 - usage.xarray: 4 + usage.sklearn: 2 """ ... - @overload - def __eq__(self, _0: xarray.core.dataarray.DataArray, /): + def __iadd__(self, _0: Union[numpy.float32, int, numpy.float64], /): """ - usage.xarray: 1 + usage.scipy: 2 + usage.sklearn: 2 + usage.xarray: 2 """ ... @overload - def __eq__(self, _0: xarray.core.variable.Variable, /): + def __itruediv__(self, _0: float, /): """ - usage.xarray: 1 + usage.scipy: 1 """ ... @overload - def __eq__(self, _0: numpy.ndarray, /): + def __itruediv__(self, _0: int, /): """ - usage.xarray: 4 + usage.sklearn: 1 """ ... - @overload - def __eq__(self, _0: numpy.datetime64, /): + def __itruediv__(self, _0: Union[int, float], /): """ - usage.xarray: 8 + usage.scipy: 1 + usage.sklearn: 1 """ ... @overload - def __eq__(self, _0: object, /): + def __le__(self, _0: int, /): """ - usage.pandas: 32 + usage.pandas: 2 + usage.sklearn: 2 """ ... - def __eq__(self, _0: object, /): + @overload + def __le__(self, _0: Union[float, numpy.float64], /): """ - usage.pandas: 32 - usage.xarray: 14 + usage.scipy: 3 """ ... - def __ge__( - self, _0: Union[numpy.ndarray, pandas.core.indexes.datetimes.DatetimeIndex], / - ): + @overload + def __le__(self, _0: numpy.float64, /): """ - usage.pandas: 5 + usage.sklearn: 4 """ ... @overload - def __le__(self, _0: pandas.core.indexes.datetimes.DatetimeIndex, /): + def __le__(self, _0: float, /): """ - usage.pandas: 3 + usage.sklearn: 1 """ ... @overload - def __le__(self, _0: pandas._libs.tslibs.timestamps.Timestamp, /): + def __le__(self, _0: numpy.float32, /): """ - usage.dask: 1 + usage.sklearn: 1 """ ... - def __le__( - self, - _0: Union[ - pandas._libs.tslibs.timestamps.Timestamp, - pandas.core.indexes.datetimes.DatetimeIndex, - ], - /, - ): + def __le__(self, _0: Union[numpy.float32, numpy.float64, float, int], /): """ - usage.dask: 1 - usage.pandas: 3 + usage.pandas: 2 + usage.scipy: 3 + usage.sklearn: 8 """ ... - def __lt__(self, _0: pandas.core.indexes.datetimes.DatetimeIndex, /): + @overload + def __lt__(self, _0: numpy.ndarray, /): """ - usage.pandas: 2 + usage.scipy: 3 + usage.skimage: 1 + usage.sklearn: 1 """ ... - def __radd__(self, _0: object, /): + @overload + def __lt__(self, _0: float, /): """ - usage.pandas: 24 + usage.skimage: 5 + usage.sklearn: 3 """ ... @overload - def __rsub__(self, _0: numpy.ndarray, /): + def __lt__(self, _0: numpy.float64, /): """ usage.matplotlib: 1 - usage.xarray: 2 + usage.skimage: 1 + usage.sklearn: 4 + usage.xarray: 1 """ ... @overload - def __rsub__(self, _0: object, /): + def __lt__(self, _0: int, /): """ - usage.pandas: 28 + usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 4 + usage.xarray: 1 """ ... @overload - def __rsub__(self, _0: numpy.datetime64, /): + def __lt__(self, _0: numpy.float32, /): """ - usage.matplotlib: 2 + usage.matplotlib: 1 + usage.sklearn: 1 """ ... - def __rsub__(self, _0: object, /): + def __lt__( + self, _0: Union[numpy.float64, int, numpy.ndarray, float, numpy.float32], / + ): """ usage.matplotlib: 3 - usage.pandas: 28 + usage.scipy: 3 + usage.skimage: 8 + usage.sklearn: 13 usage.xarray: 2 """ ... @overload - def __sub__(self, _0: object, /): + def __mul__(self, _0: int, /): """ - usage.pandas: 25 + usage.dask: 1 + usage.skimage: 1 + usage.sklearn: 4 """ ... @overload - def __sub__(self, _0: numpy.datetime64, /): + def __mul__( + self, + _0: Union[ + pandas.core.arrays.timedeltas.TimedeltaArray, + numpy.float64, + pandas.core.series.Series, + ], + /, + ): """ - usage.matplotlib: 2 + usage.pandas: 4 """ ... - def __sub__(self, _0: object, /): + @overload + def __mul__(self, _0: object, /): """ - usage.matplotlib: 2 - usage.pandas: 25 + usage.scipy: 57 """ ... @overload - def astype(self, _0: Union[str, type], /): + def __mul__(self, _0: float, /): """ - usage.pandas: 25 + usage.sklearn: 2 """ ... @overload - def astype(self, _0: Type[numpy.int64], /): + def __mul__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 2 + usage.sklearn: 10 """ ... @overload - def astype(self, _0: Literal["datetime64[s]"], /): + def __mul__(self, _0: numpy.float32, /): """ - usage.matplotlib: 2 + usage.sklearn: 2 """ ... - @overload - def astype(self, _0: numpy.dtype, /): + def __mul__(self, _0: object, /): """ - usage.dask: 3 + usage.dask: 1 + usage.pandas: 4 + usage.scipy: 57 + usage.skimage: 1 + usage.sklearn: 18 """ ... - def astype(self, _0: Union[numpy.dtype, str, type], /): + def __neg__(self, /): """ - usage.dask: 3 - usage.matplotlib: 4 - usage.pandas: 25 + usage.scipy: 3 + usage.sklearn: 2 """ ... - def item(self, /): + @overload + def __pow__(self, _0: int, /): """ usage.pandas: 1 + usage.skimage: 1 + usage.sklearn: 2 """ ... - def view(self, _0: Union[Literal["i8"], Type[numpy.int64]], /): + @overload + def __pow__(self, _0: Union[int, float], /): """ - usage.pandas: 7 + usage.scipy: 6 """ ... - -class dtype: - - # usage.dask: 1 - __module__: ClassVar[object] - - # usage.scipy: 7 - alignment: object - - # usage.dask: 2 - # usage.pandas: 1 - base: object - - # usage.scipy: 69 - # usage.xarray: 14 - byteorder: object - - # usage.pandas: 1 - # usage.scipy: 389 - # usage.skimage: 12 - # usage.sklearn: 2 - # usage.xarray: 3 - char: object - - # usage.pandas: 6 - # usage.scipy: 5 - descr: object - - # usage.dask: 15 - # usage.pandas: 48 - # usage.scipy: 43 - fields: object - - # usage.dask: 33 - # usage.scipy: 44 - hasobject: object - - # usage.matplotlib: 19 - # usage.scipy: 6 - # usage.xarray: 26 - isnative: object - - # usage.dask: 38 - # usage.pandas: 130 - # usage.scipy: 61 - # usage.skimage: 41 - # usage.sklearn: 11 - # usage.xarray: 37 - itemsize: object - - # usage.dask: 75 - # usage.matplotlib: 42 - # usage.pandas: 643 - # usage.scipy: 263 - # usage.skimage: 60 - # usage.sklearn: 285 - # usage.xarray: 550 - kind: object - - # usage.xarray: 5 - metadata: object - - # usage.dask: 6 - # usage.pandas: 263 - # usage.scipy: 32 - # usage.skimage: 6 - # usage.sklearn: 6 - name: object - - # usage.dask: 6 - # usage.pandas: 125 - # usage.scipy: 4 - names: object - - # usage.dask: 21 - # usage.pandas: 25 - # usage.scipy: 17 - shape: object - - # usage.scipy: 51 - str: object - - # usage.dask: 9 - # usage.matplotlib: 5 - # usage.pandas: 1037 - # usage.scipy: 195 - # usage.skimage: 65 - # usage.sklearn: 16 - # usage.xarray: 75 - type: object + def __pow__(self, _0: Union[int, float], /): + """ + usage.pandas: 1 + usage.scipy: 6 + usage.skimage: 1 + usage.sklearn: 2 + """ + ... @overload - def __eq__(self, _0: numpy.dtype, /): + def __radd__(self, _0: pandas.core.arrays.timedeltas.TimedeltaArray, /): """ - usage.skimage: 80 - usage.xarray: 192 + usage.pandas: 1 """ ... @overload - def __eq__(self, _0: Type[bool], /): + def __radd__(self, _0: object, /): """ - usage.skimage: 39 - usage.xarray: 24 + usage.scipy: 26 """ ... @overload - def __eq__(self, _0: Type[numpy.uint8], /): + def __radd__(self, _0: float, /): """ - usage.matplotlib: 6 - usage.skimage: 5 + usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload - def __eq__(self, _0: Type[numpy.float64], /): + def __radd__(self, _0: numpy.float64, /): """ usage.matplotlib: 1 - usage.skimage: 13 - usage.xarray: 8 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: Type[numpy.uint16], /): + def __radd__(self, _0: int, /): """ usage.matplotlib: 1 - usage.skimage: 8 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: Type[numpy.float32], /): + def __radd__(self, _0: numpy.float32, /): """ - usage.skimage: 3 - usage.xarray: 9 + usage.dask: 1 + usage.sklearn: 3 """ ... @overload - def __eq__(self, _0: Literal["bool"], /): + def __radd__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 1 - usage.skimage: 14 - usage.xarray: 2 + usage.sklearn: 8 """ ... - @overload - def __eq__(self, _0: Type[numpy.float16], /): + def __radd__(self, _0: object, /): """ - usage.skimage: 1 - usage.xarray: 2 + usage.dask: 1 + usage.matplotlib: 3 + usage.pandas: 1 + usage.scipy: 26 + usage.sklearn: 18 """ ... @overload - def __eq__(self, _0: Type[float], /): + def __rmod__(self, _0: Literal["%.7e\n", "%.8e\n"], /): """ - usage.skimage: 6 - usage.xarray: 10 + usage.scipy: 2 """ ... @overload - def __eq__(self, _0: Literal["float64"], /): + def __rmod__(self, _0: str, /): """ - usage.skimage: 4 + usage.sklearn: 2 """ ... - @overload - def __eq__(self, _0: Type[numpy.int8], /): + def __rmod__(self, _0: str, /): """ - usage.skimage: 4 + usage.scipy: 2 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: Type[numpy.int16], /): + def __rmul__(self, _0: float, /): """ + usage.matplotlib: 1 usage.skimage: 2 + usage.sklearn: 10 """ ... @overload - def __eq__(self, _0: Type[numpy.int32], /): + def __rmul__(self, _0: int, /): """ + usage.dask: 2 usage.skimage: 1 - usage.xarray: 2 + usage.sklearn: 4 """ ... @overload - def __eq__(self, _0: Type[numpy.int64], /): + def __rmul__(self, _0: numpy.ndarray, /): """ usage.skimage: 1 - usage.xarray: 4 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: Type[numpy.longlong], /): + def __rmul__(self, _0: Union[pandas.core.series.Series, numpy.ndarray], /): """ - usage.skimage: 1 + usage.pandas: 3 """ ... @overload - def __eq__(self, _0: Type[numpy.uint32], /): + def __rmul__(self, _0: object, /): """ - usage.skimage: 1 + usage.scipy: 119 """ ... @overload - def __eq__(self, _0: Type[numpy.uint64], /): + def __rmul__(self, _0: numpy.float32, /): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: Type[numpy.ulonglong], /): + def __rmul__(self, _0: numpy.float64, /): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... - @overload - def __eq__(self, _0: Literal["float32"], /): + def __rmul__(self, _0: object, /): """ - usage.skimage: 1 + usage.dask: 2 + usage.matplotlib: 1 + usage.pandas: 3 + usage.scipy: 119 + usage.skimage: 4 + usage.sklearn: 20 """ ... @overload - def __eq__(self, _0: Literal["O"], /): + def __rsub__(self, _0: numpy.float32, /): """ - usage.xarray: 5 + usage.dask: 1 + usage.matplotlib: 1 + usage.pandas: 7 + usage.skimage: 2 + usage.sklearn: 3 """ ... @overload - def __eq__(self, _0: Literal["datetime64[ns]"], /): + def __rsub__(self, _0: numpy.ndarray, /): """ - usage.xarray: 9 + usage.skimage: 1 + usage.sklearn: 3 """ ... @overload - def __eq__(self, _0: Literal["S1"], /): + def __rsub__(self, _0: Union[numpy.float32, float, numpy.ndarray], /): """ - usage.xarray: 23 + usage.scipy: 3 """ ... @overload - def __eq__(self, _0: Literal["timedelta64[ns]"], /): + def __rsub__(self, _0: int, /): """ - usage.xarray: 3 + usage.sklearn: 4 """ ... @overload - def __eq__(self, _0: Literal["f4"], /): + def __rsub__(self, _0: float, /): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: Type[object], /): + def __rsub__(self, _0: numpy.float64, /): """ - usage.xarray: 6 + usage.sklearn: 2 """ ... - @overload - def __eq__(self, _0: Literal["int64"], /): + def __rsub__( + self, _0: Union[float, int, numpy.float32, numpy.ndarray, numpy.float64], / + ): """ - usage.xarray: 5 + usage.dask: 1 + usage.matplotlib: 1 + usage.pandas: 7 + usage.scipy: 3 + usage.skimage: 3 + usage.sklearn: 14 """ ... @overload - def __eq__(self, _0: Literal["S3"], /): + def __rtruediv__(self, _0: numpy.float64, /): """ - usage.xarray: 1 + usage.skimage: 1 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: Type[int], /): + def __rtruediv__( + self, + _0: Union[ + numpy.ndarray, + pandas._libs.tslibs.timedeltas.Timedelta, + numpy.float32, + numpy.float64, + pandas._libs.tslibs.nattype.NaTType, + ], + /, + ): """ - usage.xarray: 5 + usage.pandas: 14 """ ... @overload - def __eq__(self, _0: Type[numpy.bool_], /): + def __rtruediv__(self, _0: object, /): """ - usage.xarray: 3 + usage.scipy: 18 """ ... @overload - def __eq__(self, _0: Type[numpy.object_], /): + def __rtruediv__(self, _0: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.sklearn: 4 """ ... @overload - def __eq__(self, _0: Type[numpy.str_], /): + def __rtruediv__(self, _0: numpy.float32, /): """ - usage.xarray: 1 + usage.dask: 1 + usage.sklearn: 4 """ ... @overload - def __eq__(self, _0: Type[numpy.complex128], /): + def __rtruediv__(self, _0: int, /): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: Type[numpy.complex64], /): + def __rtruediv__(self, _0: float, /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... - @overload - def __eq__(self, _0: Type[numpy.timedelta64], /): + def __rtruediv__(self, _0: object, /): """ - usage.xarray: 1 + usage.dask: 1 + usage.matplotlib: 1 + usage.pandas: 14 + usage.scipy: 18 + usage.skimage: 1 + usage.sklearn: 13 """ ... @overload - def __eq__(self, _0: Type[numpy.datetime64], /): + def __sub__(self, _0: numpy.ndarray, /): """ - usage.xarray: 1 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload - def __eq__(self, _0: Literal["object"], /): + def __sub__(self, _0: numpy.float64, /): """ - usage.xarray: 1 + usage.skimage: 2 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: object, /): + def __sub__(self, _0: numpy.float32, /): """ - usage.pandas: 2263 + usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 3 """ ... @overload - def __eq__(self, _0: Union[type, numpy.dtype, Literal["float32", "float"]], /): + def __sub__( + self, + _0: Union[ + pandas.core.arrays.timedeltas.TimedeltaArray, + int, + numpy.float32, + pandas.core.series.Series, + ], + /, + ): """ - usage.scipy: 341 + usage.pandas: 7 """ ... @overload - def __eq__(self, _0: Union[str, type, numpy.dtype], /): + def __sub__(self, _0: Union[numpy.float32, int], /): """ - usage.dask: 329 + usage.scipy: 2 """ ... @overload - def __eq__( - self, _0: Union[type, Literal["int64", "object", "float64"], numpy.dtype], / - ): + def __sub__(self, _0: Union[int, numpy.float32], /): """ - usage.sklearn: 551 + usage.dask: 2 """ ... - def __eq__(self, _0: object, /): + @overload + def __sub__(self, _0: float, /): """ - usage.dask: 329 - usage.matplotlib: 9 - usage.pandas: 2263 - usage.scipy: 341 - usage.skimage: 186 - usage.sklearn: 551 - usage.xarray: 321 + usage.sklearn: 1 """ ... - def __ge__(self, _0: numpy.dtype, /): + def __sub__(self, _0: object, /): """ + usage.dask: 2 + usage.matplotlib: 1 + usage.pandas: 7 usage.scipy: 2 + usage.skimage: 5 + usage.sklearn: 7 """ ... - def __getitem__(self, _0: str, /): + @overload + def __truediv__(self, _0: float, /): """ - usage.dask: 17 - usage.pandas: 58 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload - def __gt__(self, _0: numpy.dtype, /): + def __truediv__(self, _0: numpy.float64, /): """ - usage.pandas: 17 + usage.skimage: 1 + usage.sklearn: 4 """ ... @overload - def __gt__(self, _0: Union[Type[numpy.int32], numpy.dtype], /): + def __truediv__( + self, + _0: Union[ + int, + pandas.core.arrays.timedeltas.TimedeltaArray, + numpy.float32, + pandas.core.series.Series, + float, + ], + /, + ): """ - usage.scipy: 4 + usage.pandas: 14 """ ... - def __gt__(self, _0: Union[numpy.dtype, Type[numpy.int32]], /): + @overload + def __truediv__( + self, + _0: Union[ + numpy.ndarray, numpy.float32, scipy.signal.ltisys.StateSpaceContinuous + ], + /, + ): """ - usage.pandas: 17 - usage.scipy: 4 + usage.scipy: 5 """ ... - def __le__(self, _0: numpy.dtype, /): + @overload + def __truediv__(self, _0: numpy.float32, /): """ - usage.scipy: 2 + usage.dask: 1 + usage.sklearn: 4 """ ... - def __lt__(self, _0: numpy.dtype, /): + @overload + def __truediv__(self, _0: int, /): """ - usage.pandas: 17 - usage.scipy: 2 + usage.sklearn: 7 """ ... - @overload - def __ne__(self, _0: numpy.dtype, /): + def __truediv__(self, _0: object, /): """ - usage.skimage: 22 - usage.xarray: 38 + usage.dask: 1 + usage.pandas: 14 + usage.scipy: 5 + usage.skimage: 2 + usage.sklearn: 16 """ ... @overload - def __ne__(self, _0: Type[bool], /): + def astype(self, _0: type, /): """ - usage.skimage: 4 + usage.scipy: 6 """ ... @overload - def __ne__(self, _0: Type[numpy.bool_], /): + def astype(self, _0: numpy.dtype, /): """ - usage.skimage: 1 + usage.dask: 1 + usage.matplotlib: 2 """ ... - @overload - def __ne__(self, _0: Literal["S1"], /): + def astype(self, _0: Union[numpy.dtype, type], /): """ - usage.xarray: 2 + usage.dask: 1 + usage.matplotlib: 2 + usage.scipy: 6 """ ... - @overload - def __ne__(self, _0: Type[object], /): + def conj(self, /): """ - usage.xarray: 13 + usage.scipy: 1 + """ + ... + + def item(self, /): + """ + usage.matplotlib: 2 + usage.xarray: 1 """ ... + +class float64: + + # usage.dask: 2 + __module__: ClassVar[object] + + # usage.matplotlib: 1 + __mro__: ClassVar[object] + + # usage.pandas: 4 + __name__: ClassVar[object] + + # usage.pandas: 1 + type: ClassVar[object] + @overload - def __ne__(self, _0: object, /): + @classmethod + def __ne__(cls, _0: object, /): """ - usage.pandas: 457 + usage.pandas: 31 + usage.scipy: 116 """ ... @overload - def __ne__(self, _0: Union[type, Literal["i"], numpy.dtype], /): + @classmethod + def __ne__(cls, _0: float, /): """ - usage.scipy: 290 + usage.matplotlib: 15 + usage.skimage: 1 + usage.sklearn: 2 + usage.xarray: 2 """ ... @overload - def __ne__(self, _0: Type[numpy.uint8], /): + @classmethod + def __ne__(cls, _0: numpy.float64, /): """ - usage.matplotlib: 19 + usage.matplotlib: 14 + usage.skimage: 2 + usage.sklearn: 16 + usage.xarray: 4 """ ... @overload - def __ne__(self, _0: Type[numpy.float64], /): + @classmethod + def __ne__(cls, _0: int, /): """ - usage.matplotlib: 2 + usage.matplotlib: 14 + usage.skimage: 4 + usage.sklearn: 6 """ ... @overload - def __ne__( - self, - _0: Union[ - numpy.dtype, - None, - List[ - Tuple[ - Literal["values", "indices", "inverse", "counts"], - Union[numpy.dtype, Type[numpy.int64]], - ] - ], - type, - str, - ], - /, - ): + @classmethod + def __ne__(cls, _0: numpy.dtype, /): """ - usage.dask: 272 + usage.matplotlib: 2 + usage.sklearn: 13 """ ... @overload - def __ne__(self, _0: Union[type, Literal["bool"], numpy.dtype], /): - """ - usage.sklearn: 71 - """ - ... - - def __ne__(self, _0: object, /): + @classmethod + def __ne__(cls, _0: Union[float, numpy.dtype, int, numpy.float64], /): """ - usage.dask: 272 - usage.matplotlib: 21 - usage.pandas: 457 - usage.scipy: 290 - usage.skimage: 27 - usage.sklearn: 71 - usage.xarray: 53 + usage.dask: 13 """ ... @overload - def __rmod__(self, _0: str, /): + @classmethod + def __ne__(cls, _0: _pytest.python_api.ApproxScalar, /): """ - usage.scipy: 4 - usage.skimage: 1 - usage.sklearn: 3 - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload - def __rmod__(self, _0: Literal[", dtype=%s"], /): - """ - usage.dask: 2 - """ - ... - - def __rmod__(self, _0: str, /): + @classmethod + def __ne__(cls, _0: numpy.int64, /): """ - usage.dask: 2 - usage.scipy: 4 - usage.skimage: 1 - usage.sklearn: 3 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload - def newbyteorder(self, _0: Literal["="], /): + @classmethod + def __ne__(cls, _0: numpy.ndarray, /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload - def newbyteorder(self, _0: str = ..., /): + @classmethod + def __ne__(cls, _0: Type[inspect._empty], /): """ - usage.scipy: 64 + usage.sklearn: 2 """ ... - def newbyteorder(self, _0: str = ..., /): + @classmethod + def __ne__(cls, _0: object, /): """ - usage.scipy: 64 - usage.xarray: 1 + usage.dask: 13 + usage.matplotlib: 45 + usage.pandas: 31 + usage.scipy: 116 + usage.skimage: 7 + usage.sklearn: 43 + usage.xarray: 6 """ ... - -class errstate: - pass - - -class finfo: - - # usage.dask: 1 - # usage.pandas: 4 - # usage.scipy: 119 - # usage.skimage: 10 - # usage.sklearn: 46 - eps: object - - # usage.pandas: 7 - # usage.scipy: 3 - # usage.skimage: 2 - # usage.sklearn: 4 - max: object - # usage.scipy: 1 - maxexp: object - - # usage.pandas: 3 - # usage.skimage: 2 - # usage.sklearn: 5 - min: object + __class__: object - # usage.scipy: 1 - minexp: object + # usage.dask: 12 + # usage.pandas: 9 + # usage.scipy: 26 + # usage.sklearn: 10 + # usage.xarray: 4 + dtype: object # usage.scipy: 2 - nmant: object + flags: object - # usage.scipy: 5 - precision: object + # usage.scipy: 1 + imag: object - # usage.matplotlib: 1 # usage.scipy: 1 - # usage.sklearn: 5 - resolution: object + itemsize: object + # usage.dask: 6 # usage.matplotlib: 1 - # usage.scipy: 4 - # usage.sklearn: 2 - tiny: object - - -class flagsobj: - - # usage.scipy: 26 - # usage.skimage: 2 - c_contiguous: object - - # usage.scipy: 20 - # usage.sklearn: 2 - contiguous: object - - # usage.pandas: 4 - # usage.scipy: 14 + # usage.pandas: 2 + # usage.scipy: 24 # usage.skimage: 1 - # usage.sklearn: 2 - f_contiguous: object + ndim: object - # usage.scipy: 1 - fortran: object + # usage.scipy: 15 + real: object - # usage.scipy: 1 + # usage.dask: 12 + # usage.matplotlib: 1 + # usage.scipy: 10 + # usage.sklearn: 5 # usage.xarray: 1 - owndata: object + shape: object - # usage.matplotlib: 2 - # usage.pandas: 14 - # usage.scipy: 13 - # usage.skimage: 3 - # usage.sklearn: 7 - # usage.xarray: 4 - writeable: bool + # usage.scipy: 5 + size: object - @overload - def __getitem__(self, _0: Literal["C_CONTIGUOUS"], /): - """ - usage.xarray: 2 - """ - ... + # usage.pandas: 1 + values: object @overload - def __getitem__(self, _0: Literal["F_CONTIGUOUS"], /): + def __add__(self, _0: numpy.float64, /): """ - usage.xarray: 2 + usage.matplotlib: 180 + usage.skimage: 42 + usage.sklearn: 92 + usage.xarray: 5 """ ... @overload - def __getitem__( - self, _0: Literal["CONTIGUOUS", "C_CONTIGUOUS", "FORTRAN", "ALIGNED"], / - ): + def __add__(self, _0: numpy.ndarray, /): """ - usage.scipy: 7 + usage.matplotlib: 3 + usage.skimage: 4 + usage.sklearn: 10 """ ... @overload - def __getitem__(self, _0: Literal["WRITEABLE", "C_CONTIGUOUS", "F_CONTIGUOUS"], /): - """ - usage.sklearn: 21 - """ - ... - - def __getitem__(self, _0: str, /): - """ - usage.scipy: 7 - usage.sklearn: 21 - usage.xarray: 4 - """ - ... - - def __setitem__(self, _0: Literal["WRITEABLE"], _1: bool, /): + def __add__(self, _0: int, /): """ - usage.scipy: 1 + usage.matplotlib: 36 + usage.skimage: 19 + usage.sklearn: 18 + usage.xarray: 2 """ ... - -class flatiter: @overload - def __eq__(self, _0: int, /): + def __add__(self, _0: float, /): """ - usage.skimage: 2 + usage.matplotlib: 95 + usage.skimage: 7 + usage.sklearn: 43 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: numpy.int64, /): + def __add__(self, _0: numpy.int64, /): """ - usage.skimage: 2 + usage.matplotlib: 1 + usage.skimage: 3 """ ... @overload - def __eq__(self, _0: Union[numpy.float32, numpy.float64], /): + def __add__(self, _0: numpy.uint8, /): """ - usage.sklearn: 2 + usage.skimage: 1 """ ... - def __eq__(self, _0: Union[numpy.float64, numpy.float32, int, numpy.int64], /): + @overload + def __add__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.skimage: 4 - usage.sklearn: 2 + usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: int, /): + def __add__(self, _0: object, /): """ - usage.dask: 1 - usage.matplotlib: 4 - usage.skimage: 3 - usage.xarray: 6 + usage.pandas: 36 + usage.scipy: 871 """ ... @overload - def __getitem__(self, _0: numpy.int64, /): + def __add__(self, _0: numpy.float32, /): """ - usage.skimage: 2 + usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload - def __getitem__(self, _0: slice[None, int, None], /): + def __add__(self, _0: List[int], /): """ - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload - def __getitem__(self, _0: slice[int, None, int], /): + def __add__(self, _0: numpy.ma.core.MaskedConstant, /): """ - usage.xarray: 2 + usage.matplotlib: 3 """ ... @overload - def __getitem__(self, _0: numpy.ndarray, /): + def __add__(self, _0: Union[float, numpy.float64, int], /): """ - usage.pandas: 1 + usage.dask: 21 """ ... @overload - def __getitem__(self, _0: Union[slice[None, None, None], int], /): + def __add__(self, _0: bool, /): """ - usage.scipy: 5 - usage.sklearn: 33 + usage.sklearn: 2 """ ... - @overload - def __getitem__(self, _0: slice[int, None, int], /): + def __add__(self, _0: object, /): """ - usage.matplotlib: 1 + usage.dask: 21 + usage.matplotlib: 321 + usage.pandas: 36 + usage.scipy: 871 + usage.skimage: 76 + usage.sklearn: 167 + usage.xarray: 9 """ ... - def __getitem__( - self, - _0: Union[ - int, - numpy.ndarray, - numpy.int64, - slice[Union[int, None], Union[None, int], Union[int, None]], - ], - /, - ): + def __bool__(self, /): """ - usage.dask: 1 - usage.matplotlib: 5 - usage.pandas: 1 - usage.scipy: 5 - usage.skimage: 5 - usage.sklearn: 33 - usage.xarray: 10 + usage.scipy: 1 """ ... - def __iter__(self, /): + @overload + def __eq__(self, _0: int, /): """ - usage.matplotlib: 14 - usage.scipy: 3 - usage.skimage: 2 - usage.sklearn: 4 + usage.matplotlib: 75 + usage.skimage: 45 + usage.sklearn: 106 usage.xarray: 8 """ ... @overload - def __setitem__(self, _0: List[int], _1: numpy.ndarray, /): + def __eq__(self, _0: float, /): """ - usage.skimage: 2 + usage.matplotlib: 30 + usage.skimage: 20 + usage.sklearn: 89 + usage.xarray: 17 """ ... @overload - def __setitem__(self, _0: numpy.ndarray, _1: float, /): + def __eq__(self, _0: numpy.float64, /): """ - usage.xarray: 1 + usage.matplotlib: 74 + usage.skimage: 14 + usage.sklearn: 146 + usage.xarray: 24 """ ... @overload - def __setitem__(self, _0: numpy.ndarray, _1: numpy.ndarray, /): + def __eq__(self, _0: numpy.ndarray, /): """ - usage.pandas: 11 + usage.matplotlib: 3 + usage.skimage: 9 + usage.sklearn: 42 + usage.xarray: 4 """ ... @overload - def __setitem__( - self, - _0: Union[numpy.ndarray, slice[None, None, None]], - _1: Union[float, int, numpy.ndarray], - /, - ): + def __eq__(self, _0: numpy.int64, /): """ - usage.scipy: 9 + usage.skimage: 8 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: int, _1: Tuple[Union[str, int], ...], /): + def __eq__(self, _0: object, /): """ - usage.dask: 6 + usage.dask: 35 + usage.pandas: 381 + usage.scipy: 272 """ ... @overload - def __setitem__( - self, - _0: slice[Union[int, None], None, Union[int, None]], - _1: Union[float, int, numpy.ndarray], - /, - ): + def __eq__(self, _0: _pytest.python_api.ApproxScalar, /): """ - usage.sklearn: 45 + usage.matplotlib: 3 + usage.sklearn: 42 """ ... - def __setitem__( - self, - _0: Union[ - slice[Union[None, int], None, Union[None, int]], - List[int], - numpy.ndarray, - int, - ], - _1: Union[numpy.ndarray, int, float, Tuple[Union[str, int], ...]], - /, - ): + @overload + def __eq__(self, _0: numpy.float128, /): """ - usage.dask: 6 - usage.pandas: 11 - usage.scipy: 9 - usage.skimage: 2 - usage.sklearn: 45 - usage.xarray: 1 + usage.matplotlib: 1 """ ... - -class float128: - - # usage.pandas: 1 - # usage.scipy: 10 - dtype: object - - # usage.dask: 1 - ndim: object - - # usage.scipy: 2 - real: object - - # usage.scipy: 1 - size: object - @overload - def __add__(self, _0: int, /): + def __eq__(self, _0: numpy.float32, /): """ - usage.pandas: 1 + usage.matplotlib: 1 """ ... @overload - def __add__(self, _0: object, /): + def __eq__(self, _0: numpy.flatiter, /): """ - usage.scipy: 20 + usage.sklearn: 1 """ ... @overload - def __add__(self, _0: numpy.float64, /): + def __eq__(self, _0: Literal["mle"], /): """ - usage.matplotlib: 2 + usage.sklearn: 2 """ ... - def __add__(self, _0: object, /): + @overload + def __eq__(self, _0: Literal["auto"], /): """ - usage.matplotlib: 2 - usage.pandas: 1 - usage.scipy: 20 + usage.sklearn: 1 """ ... - def __bool__(self, /): + def __eq__(self, _0: object, /): """ - usage.scipy: 1 + usage.dask: 35 + usage.matplotlib: 187 + usage.pandas: 381 + usage.scipy: 272 + usage.skimage: 96 + usage.sklearn: 430 + usage.xarray: 53 """ ... @overload - def __eq__(self, _0: int, /): + def __floordiv__(self, _0: object, /): """ - usage.pandas: 1 + usage.pandas: 7 """ ... @overload - def __eq__(self, _0: Union[Literal["silverman", "scott"], numpy.ndarray], /): + def __floordiv__(self, _0: Union[numpy.float64, int], /): """ - usage.scipy: 3 + usage.scipy: 2 """ ... @overload - def __eq__(self, _0: numpy.float128, /): + def __floordiv__(self, _0: numpy.float64, /): """ usage.matplotlib: 2 """ ... @overload - def __eq__(self, _0: numpy.float64, /): - """ - usage.matplotlib: 1 - """ - ... - - def __eq__( - self, - _0: Union[ - numpy.float128, - numpy.float64, - int, - numpy.ndarray, - Literal["silverman", "scott"], - ], - /, - ): + def __floordiv__(self, _0: int, /): """ - usage.matplotlib: 3 - usage.pandas: 1 - usage.scipy: 3 + usage.dask: 1 + usage.matplotlib: 2 """ ... - def __ge__(self, _0: Union[numpy.float128, numpy.ndarray, numpy.float64], /): + def __floordiv__(self, _0: object, /): """ - usage.scipy: 4 + usage.dask: 1 + usage.matplotlib: 4 + usage.pandas: 7 + usage.scipy: 2 """ ... @overload - def __gt__(self, _0: int, /): + def __ge__(self, _0: int, /): """ - usage.matplotlib: 1 - usage.pandas: 1 + usage.matplotlib: 16 + usage.skimage: 10 + usage.sklearn: 23 """ ... @overload - def __gt__(self, _0: numpy.float128, /): + def __ge__(self, _0: numpy.float64, /): """ - usage.matplotlib: 3 - usage.scipy: 1 + usage.matplotlib: 18 + usage.skimage: 2 + usage.sklearn: 32 """ ... - def __gt__(self, _0: Union[int, numpy.float128], /): + @overload + def __ge__(self, _0: float, /): """ - usage.matplotlib: 4 - usage.pandas: 1 - usage.scipy: 1 + usage.matplotlib: 5 + usage.skimage: 3 + usage.sklearn: 20 """ ... - def __iadd__(self, _0: numpy.float128, /): + @overload + def __ge__(self, _0: xarray.core.dataarray.DataArray, /): """ - usage.scipy: 1 + usage.xarray: 1 """ ... - def __imul__(self, _0: Union[int, numpy.float128], /): + @overload + def __ge__( + self, + _0: Union[numpy.ndarray, float, int, numpy.float64, pandas.core.series.Series], + /, + ): """ - usage.scipy: 3 + usage.pandas: 8 """ ... @overload - def __le__(self, _0: Union[float, int], /): + def __ge__(self, _0: object, /): """ - usage.pandas: 3 + usage.dask: 29 + usage.scipy: 319 """ ... @overload - def __le__(self, _0: Union[numpy.float128, float], /): + def __ge__(self, _0: numpy.ndarray, /): """ - usage.scipy: 2 + usage.matplotlib: 15 + usage.sklearn: 11 """ ... - def __le__(self, _0: Union[float, numpy.float128, int], /): + @overload + def __ge__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.pandas: 3 - usage.scipy: 2 + usage.matplotlib: 1 """ ... @overload - def __lt__(self, _0: Union[numpy.float128, numpy.ndarray, numpy.float64], /): + def __ge__(self, _0: numpy.float32, /): """ - usage.scipy: 4 + usage.sklearn: 4 """ ... - @overload - def __lt__(self, _0: numpy.float128, /): + def __ge__(self, _0: object, /): """ - usage.matplotlib: 3 + usage.dask: 29 + usage.matplotlib: 55 + usage.pandas: 8 + usage.scipy: 319 + usage.skimage: 15 + usage.sklearn: 90 + usage.xarray: 1 """ ... @overload - def __lt__(self, _0: numpy.float64, /): + def __getitem__(self, _0: Tuple[ellipsis, None], /): """ - usage.matplotlib: 1 + usage.skimage: 2 """ ... @overload - def __lt__(self, _0: int, /): + def __getitem__(self, _0: Tuple[None, None], /): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... - def __lt__(self, _0: Union[int, numpy.float64, numpy.float128, numpy.ndarray], /): + @overload + def __getitem__(self, _0: Union[Tuple[None, ...], int], /): """ - usage.matplotlib: 5 - usage.scipy: 4 + usage.scipy: 10 """ ... @overload - def __mul__(self, _0: Union[numpy.float64, numpy.float128], /): + def __getitem__(self, _0: int, /): """ - usage.pandas: 8 + usage.matplotlib: 1 """ ... @overload - def __mul__( - self, _0: Union[numpy.float128, int, float, numpy.float64, numpy.ndarray], / - ): + def __getitem__(self, _0: Tuple[Union[ellipsis, None], ...], /): """ - usage.scipy: 20 + usage.dask: 7 """ ... - def __mul__( - self, _0: Union[numpy.ndarray, numpy.float64, float, int, numpy.float128], / - ): + def __getitem__(self, _0: Union[Tuple[Union[None, ellipsis], ...], int], /): """ - usage.pandas: 8 - usage.scipy: 20 + usage.dask: 7 + usage.matplotlib: 1 + usage.scipy: 10 + usage.skimage: 2 + usage.xarray: 1 """ ... - def __ne__(self, _0: numpy.float128, /): + @overload + def __gt__(self, _0: float, /): """ - usage.scipy: 4 + usage.matplotlib: 24 + usage.skimage: 35 + usage.sklearn: 150 + usage.xarray: 3 """ ... - def __neg__(self, /): + @overload + def __gt__(self, _0: numpy.ndarray, /): """ - usage.scipy: 1 + usage.matplotlib: 5 + usage.skimage: 3 + usage.sklearn: 22 """ ... @overload - def __pow__(self, _0: Union[int, float], /): + def __gt__(self, _0: numpy.float64, /): """ - usage.pandas: 2 + usage.matplotlib: 102 + usage.skimage: 64 + usage.sklearn: 93 + usage.xarray: 6 """ ... @overload - def __pow__(self, _0: int, /): + def __gt__(self, _0: int, /): """ - usage.scipy: 2 + usage.matplotlib: 33 + usage.skimage: 19 + usage.sklearn: 34 + usage.xarray: 2 """ ... - def __pow__(self, _0: Union[int, float], /): + @overload + def __gt__(self, _0: numpy.float32, /): """ - usage.pandas: 2 - usage.scipy: 2 + usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 4 + usage.xarray: 1 """ ... @overload - def __radd__(self, _0: object, /): + def __gt__(self, _0: numpy.int64, /): """ - usage.scipy: 23 + usage.matplotlib: 4 + usage.sklearn: 2 + usage.xarray: 1 """ ... @overload - def __radd__(self, _0: int, /): + def __gt__(self, _0: xarray.core.dataarray.DataArray, /): """ - usage.matplotlib: 2 + usage.xarray: 1 """ ... - def __radd__(self, _0: object, /): + @overload + def __gt__( + self, _0: Union[float, pandas.core.series.Series, int, numpy.float64], / + ): """ - usage.matplotlib: 2 - usage.scipy: 23 + usage.pandas: 15 """ ... @overload - def __rmul__(self, _0: Union[int, numpy.float128], /): + def __gt__(self, _0: object, /): """ - usage.pandas: 8 + usage.scipy: 253 """ ... @overload - def __rmul__(self, _0: object, /): + def __gt__(self, _0: numpy.bool_, /): """ - usage.scipy: 15 + usage.matplotlib: 1 """ ... @overload - def __rmul__(self, _0: int, /): + def __gt__(self, _0: numpy.float128, /): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... - def __rmul__(self, _0: object, /): + @overload + def __gt__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.matplotlib: 2 - usage.pandas: 8 - usage.scipy: 15 + usage.matplotlib: 1 """ ... @overload - def __rsub__(self, _0: Union[numpy.ndarray, numpy.float128], /): + def __gt__(self, _0: numpy.uint16, /): """ - usage.pandas: 9 + usage.matplotlib: 1 """ ... @overload - def __rsub__(self, _0: float, /): + def __gt__(self, _0: numpy.uint64, /): """ - usage.scipy: 4 + usage.matplotlib: 1 """ ... @overload - def __rsub__(self, _0: numpy.float128, /): + def __gt__(self, _0: Union[numpy.float64, int, float], /): """ - usage.matplotlib: 1 + usage.dask: 10 """ ... - def __rsub__(self, _0: Union[numpy.float128, numpy.ndarray, float], /): + def __gt__(self, _0: object, /): """ - usage.matplotlib: 1 - usage.pandas: 9 - usage.scipy: 4 + usage.dask: 10 + usage.matplotlib: 174 + usage.pandas: 15 + usage.scipy: 253 + usage.skimage: 122 + usage.sklearn: 305 + usage.xarray: 14 """ ... @overload - def __rtruediv__(self, _0: Union[numpy.float64, numpy.float128], /): + def __iadd__(self, _0: numpy.ndarray, /): """ - usage.pandas: 8 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload - def __rtruediv__(self, _0: numpy.ndarray, /): + def __iadd__(self, _0: numpy.float64, /): """ - usage.scipy: 1 + usage.dask: 1 + usage.matplotlib: 14 + usage.skimage: 7 + usage.sklearn: 57 + usage.xarray: 1 """ ... - def __rtruediv__(self, _0: Union[numpy.ndarray, numpy.float128, numpy.float64], /): + @overload + def __iadd__(self, _0: float, /): """ - usage.pandas: 8 - usage.scipy: 1 + usage.matplotlib: 3 + usage.pandas: 1 + usage.skimage: 1 + usage.sklearn: 2 + usage.xarray: 2 """ ... @overload - def __sub__(self, _0: Union[int, numpy.float128, numpy.ndarray], /): + def __iadd__(self, _0: int, /): """ - usage.pandas: 9 + usage.matplotlib: 8 + usage.sklearn: 6 + usage.xarray: 2 """ ... @overload - def __sub__(self, _0: Union[numpy.float64, numpy.ndarray], /): + def __iadd__(self, _0: bool, /): """ - usage.scipy: 2 + usage.xarray: 2 """ ... @overload - def __sub__(self, _0: numpy.float128, /): + def __iadd__(self, _0: object, /): """ - usage.matplotlib: 1 + usage.scipy: 99 """ ... @overload - def __sub__(self, _0: numpy.float64, /): + def __iadd__(self, _0: numpy.int64, /): """ - usage.matplotlib: 2 + usage.sklearn: 3 """ ... - def __sub__(self, _0: Union[numpy.float128, numpy.float64, int, numpy.ndarray], /): + def __iadd__(self, _0: object, /): + """ + usage.dask: 1 + usage.matplotlib: 25 + usage.pandas: 1 + usage.scipy: 99 + usage.skimage: 9 + usage.sklearn: 69 + usage.xarray: 7 + """ + ... + + def __ifloordiv__(self, _0: int, /): """ - usage.matplotlib: 3 - usage.pandas: 9 usage.scipy: 2 """ ... @overload - def __truediv__(self, _0: numpy.float128, /): + def __imod__(self, _0: float, /): """ - usage.pandas: 8 + usage.skimage: 2 """ ... @overload - def __truediv__(self, _0: Union[int, numpy.ndarray], /): + def __imod__(self, _0: numpy.float64, /): """ - usage.scipy: 3 + usage.matplotlib: 1 """ ... - def __truediv__(self, _0: Union[numpy.ndarray, int, numpy.float128], /): + def __imod__(self, _0: Union[numpy.float64, float], /): """ - usage.pandas: 8 - usage.scipy: 3 + usage.matplotlib: 1 + usage.skimage: 2 """ ... @overload - def astype(self, _0: numpy.dtype, /): + def __imul__(self, _0: numpy.float64, /): """ - usage.pandas: 3 + usage.skimage: 1 + usage.sklearn: 6 """ ... @overload - def astype(self, _0: Type[numpy.complex256], /): + def __imul__(self, _0: int, /): """ - usage.scipy: 1 + usage.dask: 1 + usage.matplotlib: 4 + usage.skimage: 4 + usage.sklearn: 3 """ ... - def astype(self, _0: Union[Type[numpy.complex256], numpy.dtype], /): + @overload + def __imul__( + self, _0: Union[numpy.float128, float, numpy.float64, int, numpy.int64], / + ): """ - usage.pandas: 3 - usage.scipy: 1 + usage.scipy: 43 """ ... - def item(self, /): + @overload + def __imul__(self, _0: float, /): """ - usage.matplotlib: 1 + usage.matplotlib: 7 + usage.sklearn: 3 """ ... - -class float16: - - # usage.scipy: 1 - dtype: object - - # usage.dask: 1 - ndim: object - - # usage.scipy: 1 - real: object - - def __eq__(self, _0: int, /): + @overload + def __imul__(self, _0: numpy.int64, /): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... - def __ge__(self, _0: numpy.ndarray, /): + def __imul__( + self, _0: Union[float, numpy.float64, int, numpy.int64, numpy.float128], / + ): """ - usage.scipy: 3 + usage.dask: 1 + usage.matplotlib: 11 + usage.scipy: 43 + usage.skimage: 5 + usage.sklearn: 13 """ ... - def __gt__(self, _0: numpy.float64, /): + @overload + def __isub__(self, _0: numpy.float64, /): """ + usage.matplotlib: 5 usage.skimage: 1 + usage.sklearn: 17 """ ... - def __lt__(self, _0: numpy.ndarray, /): + @overload + def __isub__(self, _0: float, /): """ - usage.scipy: 3 + usage.matplotlib: 6 + usage.pandas: 2 + usage.sklearn: 1 """ ... - def __mul__(self, _0: int, /): + @overload + def __isub__(self, _0: Union[float, numpy.float64, numpy.ndarray], /): """ - usage.scipy: 1 + usage.scipy: 19 """ ... - def __ne__(self, _0: numpy.float64, /): + @overload + def __isub__(self, _0: int, /): """ - usage.scipy: 1 + usage.matplotlib: 2 + usage.sklearn: 1 """ ... - def __pow__(self, _0: int, /): + @overload + def __isub__(self, _0: numpy.ndarray, /): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... - @overload - def __rmul__(self, _0: float, /): + def __isub__(self, _0: Union[numpy.ndarray, numpy.float64, int, float], /): """ + usage.matplotlib: 13 + usage.pandas: 2 + usage.scipy: 19 usage.skimage: 1 + usage.sklearn: 20 """ ... @overload - def __rmul__(self, _0: numpy.ndarray, /): + def __itruediv__(self, _0: numpy.float64, /): """ - usage.skimage: 1 + usage.skimage: 5 + usage.sklearn: 2 """ ... - def __rmul__(self, _0: Union[numpy.ndarray, float], /): + @overload + def __itruediv__(self, _0: float, /): """ - usage.skimage: 2 + usage.matplotlib: 1 + usage.skimage: 4 + usage.sklearn: 6 """ ... @overload - def __rsub__(self, _0: numpy.float16, /): + def __itruediv__(self, _0: numpy.float16, /): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload - def __rsub__(self, _0: numpy.ndarray, /): + def __itruediv__(self, _0: numpy.float32, /): """ usage.skimage: 1 """ ... - def __rsub__(self, _0: Union[numpy.ndarray, numpy.float16], /): + @overload + def __itruediv__(self, _0: numpy.int64, /): """ - usage.skimage: 3 + usage.skimage: 1 + usage.sklearn: 1 """ ... - def __sub__(self, _0: numpy.float16, /): + @overload + def __itruediv__( + self, _0: Union[int, numpy.ndarray, float, numpy.float64, numpy.int64], / + ): """ - usage.skimage: 2 + usage.scipy: 44 """ ... - -class float32: - - # usage.dask: 1 - __module__: ClassVar[object] - - # usage.pandas: 4 - __name__: ClassVar[object] - - # usage.dask: 6 - shape: ClassVar[object] - @overload - @classmethod - def __ne__(cls, _0: numpy.dtype, /): + def __itruediv__(self, _0: int, /): """ - usage.dask: 2 + usage.matplotlib: 1 + usage.sklearn: 7 """ ... - @overload - @classmethod - def __ne__(cls, _0: Union[numpy.float64, numpy.float32, int], /): + def __itruediv__(self, _0: object, /): """ - usage.scipy: 9 + usage.matplotlib: 2 + usage.scipy: 44 + usage.skimage: 12 + usage.sklearn: 16 """ ... @overload - @classmethod - def __ne__(cls, _0: int, /): + def __le__(self, _0: int, /): """ - usage.matplotlib: 1 + usage.matplotlib: 29 + usage.skimage: 62 + usage.sklearn: 19 """ ... @overload - @classmethod - def __ne__(cls, _0: Union[int, numpy.dtype], /): + def __le__(self, _0: numpy.float64, /): """ - usage.sklearn: 4 + usage.matplotlib: 18 + usage.skimage: 2 + usage.sklearn: 32 """ ... - @classmethod - def __ne__(cls, _0: Union[numpy.dtype, int, numpy.float32, numpy.float64], /): + @overload + def __le__(self, _0: float, /): """ - usage.dask: 2 - usage.matplotlib: 1 - usage.scipy: 9 - usage.sklearn: 4 + usage.matplotlib: 9 + usage.skimage: 4 + usage.sklearn: 17 """ ... - # usage.dask: 2 - # usage.pandas: 4 - # usage.scipy: 13 - # usage.sklearn: 2 - # usage.xarray: 1 - dtype: object - - # usage.dask: 4 - # usage.pandas: 1 - ndim: object - - # usage.scipy: 12 - real: object - - # usage.scipy: 1 - size: object - - # usage.pandas: 1 - values: object + @overload + def __le__(self, _0: Union[numpy.ndarray, float, numpy.float64, int], /): + """ + usage.pandas: 13 + """ + ... @overload - def __add__(self, _0: int, /): + def __le__(self, _0: object, /): """ - usage.skimage: 1 + usage.scipy: 418 """ ... @overload - def __add__(self, _0: numpy.float64, /): + def __le__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.matplotlib: 16 + usage.sklearn: 5 """ ... @overload - def __add__( + def __le__( self, - _0: Union[ - pandas.core.arrays.timedeltas.TimedeltaArray, int, pandas.core.series.Series - ], + _0: Union[pandas.core.series.Series, numpy.float64, float, int, numpy.ndarray], /, ): """ - usage.pandas: 3 + usage.dask: 25 """ ... @overload - def __add__(self, _0: object, /): + def __le__(self, _0: numpy.int64, /): """ - usage.scipy: 22 + usage.sklearn: 4 """ ... @overload - def __add__(self, _0: Union[numpy.float32, numpy.float64], /): + def __le__(self, _0: numpy.float32, /): """ - usage.dask: 3 + usage.sklearn: 2 """ ... - @overload - def __add__(self, _0: Union[numpy.ndarray, numpy.float32, int, numpy.float64], /): + def __le__(self, _0: object, /): """ - usage.sklearn: 9 + usage.dask: 25 + usage.matplotlib: 72 + usage.pandas: 13 + usage.scipy: 418 + usage.skimage: 68 + usage.sklearn: 79 """ ... - def __add__(self, _0: object, /): + @overload + def __lt__(self, _0: float, /): """ - usage.dask: 3 - usage.matplotlib: 1 - usage.pandas: 3 - usage.scipy: 22 - usage.skimage: 2 - usage.sklearn: 9 + usage.matplotlib: 55 + usage.skimage: 58 + usage.sklearn: 92 + usage.xarray: 2 """ ... - def __bool__(self, /): + @overload + def __lt__(self, _0: int, /): """ - usage.scipy: 1 + usage.matplotlib: 45 + usage.skimage: 37 + usage.sklearn: 34 + usage.xarray: 5 """ ... @overload - def __eq__(self, _0: numpy.ndarray, /): + def __lt__(self, _0: numpy.ndarray, /): """ - usage.skimage: 1 - usage.xarray: 2 + usage.matplotlib: 4 + usage.skimage: 14 + usage.sklearn: 20 """ ... @overload - def __eq__(self, _0: numpy.float32, /): + def __lt__(self, _0: numpy.float64, /): """ - usage.matplotlib: 4 - usage.skimage: 2 - usage.xarray: 2 + usage.matplotlib: 102 + usage.skimage: 64 + usage.sklearn: 93 + usage.xarray: 6 """ ... @overload - def __eq__(self, _0: float, /): + def __lt__(self, _0: numpy.uint8, /): """ usage.skimage: 1 """ ... @overload - def __eq__(self, _0: int, /): + def __lt__(self, _0: numpy.float32, /): """ usage.matplotlib: 1 - usage.skimage: 2 + usage.skimage: 1 + usage.sklearn: 2 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: Union[numpy.float64, numpy.float32, int], /): + def __lt__(self, _0: numpy.float16, /): """ - usage.pandas: 9 + usage.skimage: 1 """ ... - @overload - def __eq__( - self, - _0: Union[Literal["silverman", "scott"], int, numpy.float32, numpy.ndarray], - /, - ): + @overload + def __lt__(self, _0: numpy.uint64, /): """ - usage.scipy: 18 + usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload - def __eq__(self, _0: numpy.float64, /): + def __lt__(self, _0: numpy.int64, /): """ - usage.matplotlib: 1 + usage.matplotlib: 2 + usage.sklearn: 1 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: Union[int, numpy.float32], /): + def __lt__(self, _0: xarray.core.dataarray.DataArray, /): """ - usage.dask: 3 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: Union[numpy.float32, numpy.ndarray, int, numpy.flatiter], /): + def __lt__(self, _0: Union[float, numpy.ndarray, numpy.float64], /): """ - usage.sklearn: 14 + usage.pandas: 5 """ ... - def __eq__(self, _0: object, /): + @overload + def __lt__(self, _0: object, /): """ - usage.dask: 3 - usage.matplotlib: 6 - usage.pandas: 9 - usage.scipy: 18 - usage.skimage: 6 - usage.sklearn: 14 - usage.xarray: 4 + usage.scipy: 141 """ ... @overload - def __ge__(self, _0: int, /): + def __lt__(self, _0: numpy.bool_, /): """ - usage.pandas: 1 - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload - def __ge__(self, _0: numpy.ndarray, /): + def __lt__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.scipy: 3 + usage.matplotlib: 1 """ ... @overload - def __ge__(self, _0: Union[numpy.float64, int], /): + def __lt__(self, _0: numpy.uint16, /): """ - usage.sklearn: 5 + usage.matplotlib: 1 """ ... - def __ge__(self, _0: Union[int, numpy.float64, numpy.ndarray], /): + @overload + def __lt__(self, _0: Union[numpy.float64, pandas.core.series.Series], /): """ - usage.pandas: 1 - usage.scipy: 3 - usage.skimage: 1 - usage.sklearn: 5 + usage.dask: 3 """ ... - def __getitem__(self, _0: Tuple[Union[ellipsis, None], ...], /): + def __lt__(self, _0: object, /): """ usage.dask: 3 + usage.matplotlib: 213 + usage.pandas: 5 + usage.scipy: 141 + usage.skimage: 177 + usage.sklearn: 242 + usage.xarray: 16 """ ... @overload - def __gt__(self, _0: int, /): + def __mod__(self, _0: float, /): """ - usage.matplotlib: 2 - usage.pandas: 1 - usage.scipy: 1 + usage.matplotlib: 1 usage.skimage: 2 """ ... @overload - def __gt__(self, _0: float, /): + def __mod__(self, _0: int, /): """ - usage.skimage: 3 + usage.matplotlib: 10 + usage.skimage: 5 + usage.sklearn: 1 """ ... @overload - def __gt__(self, _0: numpy.float64, /): + def __mod__(self, _0: object, /): """ - usage.matplotlib: 1 - usage.skimage: 1 - usage.xarray: 1 + usage.pandas: 11 """ ... @overload - def __gt__(self, _0: numpy.float32, /): + def __mod__(self, _0: Union[int, numpy.float64], /): """ - usage.matplotlib: 1 + usage.scipy: 4 """ ... - @overload - def __gt__(self, _0: Union[numpy.float32, int], /): + def __mod__(self, _0: object, /): """ - usage.sklearn: 5 + usage.matplotlib: 11 + usage.pandas: 11 + usage.scipy: 4 + usage.skimage: 7 + usage.sklearn: 1 """ ... - def __gt__(self, _0: Union[int, numpy.float32, float, numpy.float64], /): + @overload + def __mul__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 4 - usage.pandas: 1 - usage.scipy: 1 - usage.skimage: 6 - usage.sklearn: 5 - usage.xarray: 1 + usage.matplotlib: 30 + usage.skimage: 28 + usage.sklearn: 115 + usage.xarray: 2 """ ... @overload - def __iadd__(self, _0: int, /): + def __mul__(self, _0: numpy.float64, /): """ + usage.matplotlib: 81 + usage.skimage: 30 + usage.sklearn: 75 usage.xarray: 2 """ ... @overload - def __iadd__(self, _0: Union[numpy.float64, numpy.float32], /): + def __mul__(self, _0: int, /): """ - usage.scipy: 2 + usage.matplotlib: 35 + usage.skimage: 12 + usage.sklearn: 38 + usage.xarray: 1 """ ... @overload - def __iadd__(self, _0: numpy.float32, /): + def __mul__(self, _0: float, /): """ - usage.sklearn: 2 + usage.matplotlib: 66 + usage.skimage: 7 + usage.sklearn: 39 """ ... - def __iadd__(self, _0: Union[numpy.float32, int, numpy.float64], /): + @overload + def __mul__(self, _0: object, /): """ - usage.scipy: 2 - usage.sklearn: 2 + usage.pandas: 71 + usage.scipy: 1498 usage.xarray: 2 """ ... @overload - def __itruediv__(self, _0: float, /): + def __mul__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.scipy: 1 + usage.matplotlib: 1 """ ... @overload - def __itruediv__(self, _0: int, /): + def __mul__(self, _0: numpy.int64, /): """ - usage.sklearn: 1 + usage.matplotlib: 1 + usage.sklearn: 6 """ ... - def __itruediv__(self, _0: Union[int, float], /): + @overload + def __mul__(self, _0: Union[int, numpy.float64], /): """ - usage.scipy: 1 - usage.sklearn: 1 + usage.dask: 8 """ ... @overload - def __le__(self, _0: int, /): + def __mul__(self, _0: scipy.sparse.csr.csr_matrix, /): """ - usage.pandas: 2 + usage.sklearn: 1 """ ... @overload - def __le__(self, _0: Union[float, numpy.float64], /): + def __mul__(self, _0: numpy.float32, /): """ - usage.scipy: 3 + usage.sklearn: 2 """ ... - @overload - def __le__(self, _0: Union[numpy.float64, int, float], /): + def __mul__(self, _0: object, /): """ - usage.sklearn: 10 + usage.dask: 8 + usage.matplotlib: 214 + usage.pandas: 71 + usage.scipy: 1498 + usage.skimage: 77 + usage.sklearn: 276 + usage.xarray: 7 """ ... - def __le__(self, _0: Union[float, int, numpy.float64], /): + def __neg__(self, /): """ + usage.matplotlib: 81 usage.pandas: 2 - usage.scipy: 3 - usage.sklearn: 10 + usage.scipy: 311 + usage.skimage: 22 + usage.sklearn: 54 + usage.xarray: 2 + """ + ... + + def __pos__(self, /): + """ + usage.scipy: 16 """ ... @overload - def __lt__(self, _0: numpy.ndarray, /): + def __pow__(self, _0: int, /): """ - usage.scipy: 3 - usage.skimage: 1 + usage.matplotlib: 9 + usage.skimage: 32 + usage.sklearn: 36 """ ... @overload - def __lt__(self, _0: float, /): + def __pow__(self, _0: float, /): """ - usage.skimage: 5 + usage.matplotlib: 4 + usage.skimage: 4 + usage.sklearn: 6 """ ... @overload - def __lt__(self, _0: numpy.float64, /): + def __pow__(self, _0: object, /): """ - usage.matplotlib: 1 - usage.skimage: 1 - usage.xarray: 1 + usage.pandas: 13 """ ... @overload - def __lt__(self, _0: int, /): + def __pow__(self, _0: Union[numpy.ndarray, numpy.float64, int, float], /): """ - usage.matplotlib: 1 - usage.skimage: 1 - usage.xarray: 1 + usage.scipy: 427 """ ... @overload - def __lt__(self, _0: numpy.float32, /): + def __pow__(self, _0: Union[int, float], /): """ - usage.matplotlib: 1 + usage.dask: 3 """ ... @overload - def __lt__(self, _0: Union[numpy.float32, numpy.float64, numpy.ndarray], /): + def __pow__(self, _0: numpy.float64, /): """ - usage.sklearn: 3 + usage.sklearn: 1 """ ... - def __lt__( - self, _0: Union[numpy.ndarray, numpy.float64, numpy.float32, float, int], / - ): + def __pow__(self, _0: object, /): """ - usage.matplotlib: 3 - usage.scipy: 3 - usage.skimage: 8 - usage.sklearn: 3 - usage.xarray: 2 + usage.dask: 3 + usage.matplotlib: 13 + usage.pandas: 13 + usage.scipy: 427 + usage.skimage: 36 + usage.sklearn: 43 """ ... @overload - def __mul__(self, _0: int, /): + def __radd__(self, _0: numpy.float64, /): """ - usage.dask: 1 - usage.skimage: 1 + usage.matplotlib: 180 + usage.skimage: 42 + usage.sklearn: 92 + usage.xarray: 5 """ ... @overload - def __mul__( - self, - _0: Union[ - pandas.core.arrays.timedeltas.TimedeltaArray, - numpy.float64, - pandas.core.series.Series, - ], - /, - ): + def __radd__(self, _0: numpy.ndarray, /): """ - usage.pandas: 4 + usage.matplotlib: 10 + usage.skimage: 18 + usage.sklearn: 51 + usage.xarray: 1 """ ... @overload - def __mul__(self, _0: object, /): + def __radd__(self, _0: float, /): """ - usage.scipy: 57 + usage.matplotlib: 42 + usage.skimage: 4 + usage.sklearn: 32 """ ... @overload - def __mul__(self, _0: Union[numpy.float32, numpy.ndarray, float, int], /): + def __radd__(self, _0: int, /): """ - usage.sklearn: 18 + usage.matplotlib: 29 + usage.skimage: 13 + usage.sklearn: 14 + usage.xarray: 1 """ ... - def __mul__(self, _0: object, /): + @overload + def __radd__(self, _0: numpy.int64, /): """ - usage.dask: 1 - usage.pandas: 4 - usage.scipy: 57 - usage.skimage: 1 - usage.sklearn: 18 + usage.matplotlib: 9 + usage.skimage: 2 + usage.sklearn: 1 + usage.xarray: 2 """ ... - def __neg__(self, /): + @overload + def __radd__(self, _0: numpy.float32, /): """ - usage.scipy: 3 - usage.sklearn: 2 + usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 3 """ ... @overload - def __pow__(self, _0: int, /): + def __radd__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.pandas: 1 + usage.matplotlib: 1 usage.skimage: 1 - usage.sklearn: 2 """ ... @overload - def __pow__(self, _0: Union[int, float], /): + def __radd__(self, _0: object, /): """ - usage.scipy: 6 + usage.dask: 26 + usage.pandas: 36 + usage.scipy: 861 """ ... - def __pow__(self, _0: Union[int, float], /): + @overload + def __radd__(self, _0: numpy.bool_, /): """ - usage.pandas: 1 - usage.scipy: 6 - usage.skimage: 1 - usage.sklearn: 2 + usage.matplotlib: 1 """ ... @overload - def __radd__(self, _0: pandas.core.arrays.timedeltas.TimedeltaArray, /): + def __radd__(self, _0: numpy.uint8, /): """ - usage.pandas: 1 + usage.matplotlib: 1 """ ... @overload - def __radd__(self, _0: object, /): + def __radd__(self, _0: numpy.uint16, /): """ - usage.scipy: 26 + usage.matplotlib: 1 """ ... @overload - def __radd__(self, _0: float, /): + def __radd__(self, _0: numpy.float128, /): """ - usage.matplotlib: 1 + usage.matplotlib: 2 """ ... @overload - def __radd__(self, _0: numpy.float64, /): + def __radd__(self, _0: numpy.ma.core.MaskedConstant, /): """ - usage.matplotlib: 1 + usage.matplotlib: 5 """ ... @overload - def __radd__(self, _0: int, /): + def __radd__(self, _0: numpy.int32, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 + """ + ... + + def __radd__(self, _0: object, /): + """ + usage.dask: 26 + usage.matplotlib: 282 + usage.pandas: 36 + usage.scipy: 861 + usage.skimage: 81 + usage.sklearn: 194 + usage.xarray: 9 """ ... @overload - def __radd__(self, _0: numpy.float32, /): + def __rfloordiv__(self, _0: numpy.ndarray, /): """ - usage.dask: 1 + usage.skimage: 1 """ ... @overload - def __radd__( - self, _0: Union[float, numpy.float64, int, numpy.ndarray, numpy.float32], / + def __rfloordiv__( + self, + _0: Union[ + pandas._libs.tslibs.timedeltas.Timedelta, + float, + pandas.core.indexes.numeric.Int64Index, + numpy.float64, + int, + ], + /, ): """ - usage.sklearn: 18 + usage.pandas: 6 """ ... - def __radd__(self, _0: object, /): + @overload + def __rfloordiv__(self, _0: numpy.float64, /): """ - usage.dask: 1 - usage.matplotlib: 3 - usage.pandas: 1 - usage.scipy: 26 - usage.sklearn: 18 + usage.matplotlib: 2 + usage.scipy: 1 """ ... @overload - def __rmod__(self, _0: Literal["%.7e\n", "%.8e\n"], /): + def __rfloordiv__(self, _0: float, /): """ - usage.scipy: 2 + usage.matplotlib: 6 """ ... - @overload - def __rmod__(self, _0: str, /): + def __rfloordiv__(self, _0: object, /): """ - usage.sklearn: 2 + usage.matplotlib: 8 + usage.pandas: 6 + usage.scipy: 1 + usage.skimage: 1 """ ... - def __rmod__(self, _0: str, /): + @overload + def __rmod__(self, _0: Union[int, numpy.float64, float], /): """ - usage.scipy: 2 - usage.sklearn: 2 + usage.pandas: 3 """ ... @overload - def __rmul__(self, _0: float, /): + def __rmod__(self, _0: Union[str, numpy.float64, numpy.ndarray], /): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.scipy: 29 """ ... @overload - def __rmul__(self, _0: int, /): + def __rmod__(self, _0: Literal["%1.1f"], /): """ - usage.dask: 2 - usage.skimage: 1 + usage.matplotlib: 3 """ ... @overload - def __rmul__(self, _0: numpy.ndarray, /): + def __rmod__(self, _0: Literal["%1.0f"], /): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload - def __rmul__(self, _0: Union[pandas.core.series.Series, numpy.ndarray], /): + def __rmod__(self, _0: bytes, /): """ - usage.pandas: 3 + usage.matplotlib: 2 """ ... @overload - def __rmul__(self, _0: object, /): + def __rmod__(self, _0: Literal["%1.2f"], /): """ - usage.scipy: 119 + usage.matplotlib: 1 """ ... @overload - def __rmul__( - self, _0: Union[numpy.float64, numpy.float32, int, float, numpy.ndarray], / - ): + def __rmod__(self, _0: Literal["%05.2lf"], /): """ - usage.sklearn: 20 + usage.matplotlib: 1 """ ... - def __rmul__(self, _0: object, /): + @overload + def __rmod__(self, _0: Literal["%08.2lf"], /): """ - usage.dask: 2 usage.matplotlib: 1 - usage.pandas: 3 - usage.scipy: 119 - usage.skimage: 4 - usage.sklearn: 20 """ ... @overload - def __rsub__(self, _0: numpy.float32, /): + def __rmod__(self, _0: Literal["%1.1f%%"], /): """ - usage.dask: 1 usage.matplotlib: 1 - usage.pandas: 7 - usage.skimage: 2 """ ... @overload - def __rsub__(self, _0: numpy.ndarray, /): + def __rmod__(self, _0: Literal["%.2f"], /): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload - def __rsub__(self, _0: Union[numpy.float32, float, numpy.ndarray], /): + def __rmod__(self, _0: Literal["%1.10e"], /): """ - usage.scipy: 3 + usage.matplotlib: 1 """ ... @overload - def __rsub__( - self, _0: Union[numpy.float64, numpy.ndarray, numpy.float32, int, float], / - ): + def __rmod__(self, _0: Literal["%1.3f"], /): """ - usage.sklearn: 14 + usage.matplotlib: 3 """ ... - def __rsub__( - self, _0: Union[float, int, numpy.float32, numpy.ndarray, numpy.float64], / - ): + @overload + def __rmod__(self, _0: Literal["%1.3f setgray\n"], /): """ - usage.dask: 1 usage.matplotlib: 1 - usage.pandas: 7 - usage.scipy: 3 - usage.skimage: 3 - usage.sklearn: 14 """ ... @overload - def __rtruediv__(self, _0: numpy.float64, /): + def __rmod__(self, _0: Literal["%1.3f setgray"], /): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload - def __rtruediv__( - self, - _0: Union[ - numpy.ndarray, - pandas._libs.tslibs.timedeltas.Timedelta, - numpy.float32, - numpy.float64, - pandas._libs.tslibs.nattype.NaTType, - ], - /, - ): + def __rmod__(self, _0: str, /): """ - usage.pandas: 14 + usage.dask: 5 + usage.matplotlib: 1 + usage.sklearn: 12 """ ... @overload - def __rtruediv__(self, _0: object, /): + def __rmod__(self, _0: Literal["%4.2e"], /): """ - usage.scipy: 18 + usage.matplotlib: 1 """ ... @overload - def __rtruediv__(self, _0: numpy.ndarray, /): + def __rmod__(self, _0: Literal["%-12g"], /): """ usage.matplotlib: 1 """ ... @overload - def __rtruediv__(self, _0: numpy.float32, /): + def __rmod__(self, _0: Literal["%2.0f"], /): """ - usage.dask: 1 + usage.matplotlib: 1 """ ... @overload - def __rtruediv__( - self, _0: Union[float, numpy.float32, int, numpy.ndarray, numpy.float64], / - ): + def __rmod__(self, _0: Literal["%G"], /): """ - usage.sklearn: 13 + usage.matplotlib: 1 """ ... - def __rtruediv__(self, _0: object, /): + @overload + def __rmod__(self, _0: Literal["%1.4f"], /): """ - usage.dask: 1 usage.matplotlib: 1 - usage.pandas: 14 - usage.scipy: 18 - usage.skimage: 1 - usage.sklearn: 13 """ ... @overload - def __sub__(self, _0: numpy.ndarray, /): + def __rmod__(self, _0: Literal["%1.7f"], /): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... @overload - def __sub__(self, _0: numpy.float64, /): + def __rmod__(self, _0: Literal["%1.0e"], /): """ - usage.skimage: 2 + usage.matplotlib: 2 """ ... @overload - def __sub__(self, _0: numpy.float32, /): + def __rmod__(self, _0: Literal["%d"], /): """ usage.matplotlib: 1 - usage.skimage: 2 """ ... @overload - def __sub__( - self, - _0: Union[ - pandas.core.arrays.timedeltas.TimedeltaArray, - int, - numpy.float32, - pandas.core.series.Series, - ], - /, - ): + def __rmod__(self, _0: Literal["%.16g"], /): """ - usage.pandas: 7 + usage.sklearn: 2 """ ... @overload - def __sub__(self, _0: Union[numpy.float32, int], /): + def __rmod__(self, _0: Literal["not %s"], /): """ - usage.scipy: 2 + usage.sklearn: 1 + """ + ... + + def __rmod__(self, _0: object, /): + """ + usage.dask: 5 + usage.matplotlib: 27 + usage.pandas: 3 + usage.scipy: 29 + usage.sklearn: 15 """ ... @overload - def __sub__(self, _0: Union[int, numpy.float32], /): + def __rmul__(self, _0: float, /): """ - usage.dask: 2 + usage.matplotlib: 80 + usage.skimage: 67 + usage.sklearn: 137 """ ... @overload - def __sub__(self, _0: Union[float, numpy.ndarray, numpy.float32, numpy.float64], /): + def __rmul__(self, _0: numpy.ndarray, /): """ - usage.sklearn: 7 + usage.matplotlib: 26 + usage.skimage: 29 + usage.sklearn: 38 """ ... - def __sub__(self, _0: object, /): + @overload + def __rmul__(self, _0: int, /): """ - usage.dask: 2 - usage.matplotlib: 1 - usage.pandas: 7 - usage.scipy: 2 - usage.skimage: 5 - usage.sklearn: 7 + usage.matplotlib: 42 + usage.skimage: 46 + usage.sklearn: 79 """ ... @overload - def __truediv__(self, _0: float, /): + def __rmul__(self, _0: numpy.float64, /): """ - usage.skimage: 1 + usage.matplotlib: 81 + usage.skimage: 30 + usage.sklearn: 75 + usage.xarray: 2 """ ... @overload - def __truediv__(self, _0: numpy.float64, /): + def __rmul__(self, _0: numpy.int64, /): """ usage.skimage: 1 + usage.sklearn: 2 """ ... @overload - def __truediv__( - self, - _0: Union[ - int, - pandas.core.arrays.timedeltas.TimedeltaArray, - numpy.float32, - pandas.core.series.Series, - float, - ], - /, - ): + def __rmul__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.pandas: 14 + usage.matplotlib: 3 + usage.xarray: 1 """ ... @overload - def __truediv__( - self, - _0: Union[ - numpy.ndarray, numpy.float32, scipy.signal.ltisys.StateSpaceContinuous - ], - /, - ): + def __rmul__(self, _0: object, /): """ - usage.scipy: 5 + usage.pandas: 22 + usage.scipy: 2141 """ ... @overload - def __truediv__(self, _0: numpy.float32, /): + def __rmul__(self, _0: kiwisolver.Variable, /): """ - usage.dask: 1 + usage.matplotlib: 14 """ ... @overload - def __truediv__(self, _0: Union[numpy.float64, int, float, numpy.float32], /): + def __rmul__(self, _0: numpy.ma.core.MaskedConstant, /): """ - usage.sklearn: 16 + usage.matplotlib: 3 """ ... - def __truediv__(self, _0: object, /): + @overload + def __rmul__( + self, + _0: Union[numpy.int64, float, numpy.float64, int, dask.array.core.Array], + /, + ): """ - usage.dask: 1 - usage.pandas: 14 - usage.scipy: 5 - usage.skimage: 2 - usage.sklearn: 16 + usage.dask: 14 """ ... - @overload - def astype(self, _0: type, /): + def __rmul__(self, _0: object, /): """ - usage.scipy: 6 + usage.dask: 14 + usage.matplotlib: 249 + usage.pandas: 22 + usage.scipy: 2141 + usage.skimage: 173 + usage.sklearn: 331 + usage.xarray: 3 """ ... @overload - def astype(self, _0: numpy.dtype, /): + def __rpow__(self, _0: float, /): """ - usage.dask: 1 - usage.matplotlib: 2 + usage.matplotlib: 4 + usage.skimage: 1 """ ... - def astype(self, _0: Union[numpy.dtype, type], /): + @overload + def __rpow__(self, _0: int, /): """ - usage.dask: 1 - usage.matplotlib: 2 - usage.scipy: 6 + usage.matplotlib: 26 + usage.skimage: 2 """ ... - def conj(self, /): + @overload + def __rpow__( + self, + _0: Union[pandas._libs.missing.NAType, float, numpy.float64, numpy.int64], + /, + ): """ - usage.scipy: 1 + usage.pandas: 4 """ ... - def item(self, /): + @overload + def __rpow__( + self, _0: Union[numpy.ndarray, int, numpy.complex128, numpy.float64, float], / + ): """ - usage.matplotlib: 2 - usage.xarray: 1 + usage.scipy: 72 """ ... - -class float64: - - # usage.dask: 2 - __module__: ClassVar[object] - - # usage.matplotlib: 1 - __mro__: ClassVar[object] - - # usage.pandas: 4 - __name__: ClassVar[object] - - # usage.pandas: 1 - type: ClassVar[object] - @overload - @classmethod - def __ne__(cls, _0: object, /): + def __rpow__(self, _0: numpy.ndarray, /): """ - usage.pandas: 31 - usage.scipy: 116 - usage.sklearn: 43 + usage.sklearn: 3 """ ... @overload - @classmethod - def __ne__(cls, _0: float, /): + def __rpow__(self, _0: numpy.float64, /): """ - usage.matplotlib: 15 - usage.skimage: 1 - usage.xarray: 2 + usage.sklearn: 1 """ ... - @overload - @classmethod - def __ne__(cls, _0: numpy.float64, /): + def __rpow__(self, _0: object, /): """ - usage.matplotlib: 14 - usage.skimage: 2 - usage.xarray: 4 + usage.matplotlib: 30 + usage.pandas: 4 + usage.scipy: 72 + usage.skimage: 3 + usage.sklearn: 4 """ ... @overload - @classmethod - def __ne__(cls, _0: int, /): + def __rsub__(self, _0: int, /): """ - usage.matplotlib: 14 - usage.skimage: 4 + usage.matplotlib: 34 + usage.skimage: 8 + usage.sklearn: 55 + usage.xarray: 2 """ ... @overload - @classmethod - def __ne__(cls, _0: numpy.dtype, /): + def __rsub__(self, _0: numpy.float64, /): """ - usage.matplotlib: 2 + usage.matplotlib: 222 + usage.skimage: 63 + usage.sklearn: 94 + usage.xarray: 6 """ ... @overload - @classmethod - def __ne__(cls, _0: Union[float, numpy.dtype, int, numpy.float64], /): + def __rsub__(self, _0: numpy.ndarray, /): """ - usage.dask: 13 + usage.matplotlib: 11 + usage.skimage: 31 + usage.sklearn: 51 + usage.xarray: 1 """ ... - @classmethod - def __ne__(cls, _0: object, /): + @overload + def __rsub__(self, _0: float, /): """ - usage.dask: 13 - usage.matplotlib: 45 - usage.pandas: 31 - usage.scipy: 116 - usage.skimage: 7 - usage.sklearn: 43 - usage.xarray: 6 + usage.matplotlib: 37 + usage.skimage: 1 + usage.sklearn: 29 + usage.xarray: 1 """ ... - # usage.scipy: 1 - __class__: object - - # usage.dask: 12 - # usage.pandas: 9 - # usage.scipy: 26 - # usage.sklearn: 10 - # usage.xarray: 4 - dtype: object - - # usage.scipy: 2 - flags: object - - # usage.scipy: 1 - imag: object - - # usage.scipy: 1 - itemsize: object - - # usage.dask: 6 - # usage.matplotlib: 1 - # usage.pandas: 2 - # usage.scipy: 24 - # usage.skimage: 1 - ndim: object - - # usage.scipy: 15 - real: object - - # usage.dask: 12 - # usage.matplotlib: 1 - # usage.scipy: 10 - # usage.sklearn: 5 - # usage.xarray: 1 - shape: object - - # usage.scipy: 5 - size: object - - # usage.pandas: 1 - values: object - @overload - def __add__(self, _0: numpy.float64, /): + def __rsub__(self, _0: numpy.float32, /): """ - usage.matplotlib: 180 - usage.skimage: 42 - usage.xarray: 5 + usage.skimage: 2 + usage.sklearn: 2 """ ... @overload - def __add__(self, _0: numpy.ndarray, /): + def __rsub__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.matplotlib: 3 - usage.skimage: 4 + usage.matplotlib: 1 + usage.skimage: 2 """ ... @overload - def __add__(self, _0: int, /): + def __rsub__(self, _0: numpy.int64, /): """ - usage.matplotlib: 36 - usage.skimage: 19 + usage.matplotlib: 5 + usage.sklearn: 1 usage.xarray: 2 """ ... @overload - def __add__(self, _0: float, /): + def __rsub__(self, _0: xarray.core.dataarray.DataArray, /): """ - usage.matplotlib: 95 - usage.skimage: 7 usage.xarray: 1 """ ... @overload - def __add__(self, _0: numpy.int64, /): + def __rsub__(self, _0: object, /): """ - usage.matplotlib: 1 - usage.skimage: 3 + usage.pandas: 26 + usage.scipy: 1100 """ ... @overload - def __add__(self, _0: numpy.uint8, /): + def __rsub__(self, _0: numpy.float128, /): """ - usage.skimage: 1 + usage.matplotlib: 2 """ ... @overload - def __add__(self, _0: numpy.ma.core.MaskedArray, /): + def __rsub__(self, _0: kiwisolver.Variable, /): """ usage.matplotlib: 1 - usage.xarray: 1 """ ... @overload - def __add__(self, _0: object, /): + def __rsub__(self, _0: numpy.ma.core.MaskedConstant, /): """ - usage.pandas: 36 - usage.scipy: 871 - usage.sklearn: 167 + usage.matplotlib: 1 """ ... @overload - def __add__(self, _0: numpy.float32, /): + def __rsub__(self, _0: Union[pandas.core.series.Series, int, numpy.float64], /): """ - usage.matplotlib: 1 + usage.dask: 13 """ ... @overload - def __add__(self, _0: List[int], /): + def __rsub__(self, _0: numpy.memmap, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... - @overload - def __add__(self, _0: numpy.ma.core.MaskedConstant, /): + def __rsub__(self, _0: object, /): """ - usage.matplotlib: 3 + usage.dask: 13 + usage.matplotlib: 314 + usage.pandas: 26 + usage.scipy: 1100 + usage.skimage: 107 + usage.sklearn: 233 + usage.xarray: 13 """ ... @overload - def __add__(self, _0: Union[float, numpy.float64, int], /): + def __rtruediv__(self, _0: numpy.ndarray, /): """ - usage.dask: 21 + usage.matplotlib: 24 + usage.skimage: 35 + usage.sklearn: 86 """ ... - def __add__(self, _0: object, /): + @overload + def __rtruediv__(self, _0: numpy.float64, /): """ - usage.dask: 21 - usage.matplotlib: 321 - usage.pandas: 36 - usage.scipy: 871 - usage.skimage: 76 - usage.sklearn: 167 - usage.xarray: 9 + usage.matplotlib: 91 + usage.skimage: 49 + usage.sklearn: 94 + usage.xarray: 1 """ ... - def __bool__(self, /): + @overload + def __rtruediv__(self, _0: int, /): """ - usage.scipy: 1 + usage.matplotlib: 9 + usage.skimage: 12 + usage.sklearn: 23 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: int, /): + def __rtruediv__(self, _0: float, /): """ - usage.matplotlib: 75 - usage.skimage: 45 - usage.xarray: 8 + usage.matplotlib: 39 + usage.skimage: 5 + usage.sklearn: 42 """ ... @overload - def __eq__(self, _0: float, /): + def __rtruediv__(self, _0: numpy.complex128, /): """ - usage.matplotlib: 30 - usage.skimage: 20 - usage.xarray: 17 + usage.skimage: 1 """ ... @overload - def __eq__(self, _0: numpy.float64, /): + def __rtruediv__(self, _0: numpy.float32, /): """ - usage.matplotlib: 74 - usage.skimage: 14 - usage.xarray: 24 + usage.skimage: 1 + usage.sklearn: 4 """ ... @overload - def __eq__(self, _0: numpy.ndarray, /): + def __rtruediv__(self, _0: numpy.int64, /): """ - usage.matplotlib: 3 - usage.skimage: 9 - usage.xarray: 4 + usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 3 """ ... @overload - def __eq__(self, _0: numpy.int64, /): + def __rtruediv__(self, _0: xarray.core.dataarray.DataArray, /): """ - usage.skimage: 8 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: object, /): + def __rtruediv__(self, _0: object, /): """ - usage.dask: 35 - usage.pandas: 381 - usage.scipy: 272 - usage.sklearn: 430 + usage.pandas: 61 + usage.scipy: 868 """ ... @overload - def __eq__(self, _0: _pytest.python_api.ApproxScalar, /): + def __rtruediv__(self, _0: kiwisolver.Term, /): """ - usage.matplotlib: 3 + usage.matplotlib: 10 """ ... @overload - def __eq__(self, _0: numpy.float128, /): + def __rtruediv__(self, _0: numpy.ma.core.MaskedArray, /): """ usage.matplotlib: 1 """ ... @overload - def __eq__(self, _0: numpy.float32, /): + def __rtruediv__( + self, _0: Union[int, numpy.float64, float, dask.array.core.Array], / + ): """ - usage.matplotlib: 1 + usage.dask: 18 """ ... - def __eq__(self, _0: object, /): + @overload + def __rtruediv__(self, _0: scipy.sparse.csr.csr_matrix, /): """ - usage.dask: 35 - usage.matplotlib: 187 - usage.pandas: 381 - usage.scipy: 272 - usage.skimage: 96 - usage.sklearn: 430 - usage.xarray: 53 + usage.sklearn: 1 """ ... @overload - def __floordiv__(self, _0: object, /): + def __rtruediv__(self, _0: numpy.memmap, /): """ - usage.pandas: 7 + usage.sklearn: 2 """ ... - @overload - def __floordiv__(self, _0: Union[numpy.float64, int], /): + def __rtruediv__(self, _0: object, /): """ - usage.scipy: 2 + usage.dask: 18 + usage.matplotlib: 175 + usage.pandas: 61 + usage.scipy: 868 + usage.skimage: 104 + usage.sklearn: 255 + usage.xarray: 3 """ ... - @overload - def __floordiv__(self, _0: numpy.float64, /): + def __setitem__(self, _0: numpy.bool_, _1: float, /): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload - def __floordiv__(self, _0: int, /): + def __sub__(self, _0: numpy.float64, /): """ - usage.dask: 1 - usage.matplotlib: 2 + usage.matplotlib: 222 + usage.skimage: 63 + usage.sklearn: 94 + usage.xarray: 6 """ ... - def __floordiv__(self, _0: object, /): + @overload + def __sub__(self, _0: float, /): """ - usage.dask: 1 - usage.matplotlib: 4 - usage.pandas: 7 - usage.scipy: 2 + usage.matplotlib: 90 + usage.skimage: 8 + usage.sklearn: 24 + usage.xarray: 2 """ ... @overload - def __ge__(self, _0: int, /): + def __sub__(self, _0: int, /): """ - usage.matplotlib: 16 - usage.skimage: 10 + usage.matplotlib: 29 + usage.skimage: 78 + usage.sklearn: 18 + usage.xarray: 6 """ ... @overload - def __ge__(self, _0: numpy.float64, /): + def __sub__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 18 - usage.skimage: 2 + usage.matplotlib: 11 + usage.skimage: 4 + usage.sklearn: 12 """ ... @overload - def __ge__(self, _0: float, /): + def __sub__(self, _0: numpy.uint8, /): """ - usage.matplotlib: 5 - usage.skimage: 3 + usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload - def __ge__(self, _0: xarray.core.dataarray.DataArray, /): + def __sub__(self, _0: object, /): """ - usage.xarray: 1 + usage.pandas: 28 + usage.scipy: 1106 """ ... @overload - def __ge__( - self, - _0: Union[numpy.ndarray, float, int, numpy.float64, pandas.core.series.Series], - /, - ): + def __sub__(self, _0: numpy.int64, /): """ - usage.pandas: 8 + usage.matplotlib: 3 + usage.sklearn: 3 """ ... @overload - def __ge__(self, _0: object, /): + def __sub__(self, _0: List[int], /): """ - usage.dask: 29 - usage.scipy: 319 - usage.sklearn: 122 + usage.matplotlib: 1 """ ... @overload - def __ge__(self, _0: numpy.ndarray, /): + def __sub__(self, _0: Union[numpy.float64, float, int, dask.array.core.Array], /): """ - usage.matplotlib: 15 + usage.dask: 9 """ ... @overload - def __ge__(self, _0: numpy.ma.core.MaskedArray, /): + def __sub__(self, _0: numpy.float32, /): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... - def __ge__(self, _0: object, /): + def __sub__(self, _0: object, /): """ - usage.dask: 29 - usage.matplotlib: 55 - usage.pandas: 8 - usage.scipy: 319 - usage.skimage: 15 - usage.sklearn: 122 - usage.xarray: 1 + usage.dask: 9 + usage.matplotlib: 357 + usage.pandas: 28 + usage.scipy: 1106 + usage.skimage: 154 + usage.sklearn: 153 + usage.xarray: 14 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis, None], /): + def __truediv__(self, _0: float, /): """ - usage.skimage: 2 + usage.matplotlib: 75 + usage.skimage: 25 + usage.sklearn: 21 + usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: Tuple[None, None], /): + def __truediv__(self, _0: numpy.float64, /): """ + usage.matplotlib: 91 + usage.skimage: 49 + usage.sklearn: 94 usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Union[Tuple[None, ...], int], /): + def __truediv__(self, _0: int, /): """ - usage.scipy: 10 + usage.matplotlib: 40 + usage.skimage: 16 + usage.sklearn: 85 + usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: int, /): + def __truediv__(self, _0: numpy.float32, /): """ - usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 2 """ ... @overload - def __getitem__(self, _0: Tuple[Union[ellipsis, None], ...], /): + def __truediv__(self, _0: numpy.int16, /): """ - usage.dask: 7 + usage.skimage: 1 """ ... - def __getitem__(self, _0: Union[Tuple[Union[None, ellipsis], ...], int], /): + @overload + def __truediv__(self, _0: numpy.uint8, /): """ - usage.dask: 7 - usage.matplotlib: 1 - usage.scipy: 10 usage.skimage: 2 - usage.xarray: 1 """ ... @overload - def __gt__(self, _0: float, /): + def __truediv__(self, _0: numpy.ndarray, /): + """ + usage.skimage: 1 + usage.sklearn: 4 + """ + ... + + @overload + def __truediv__(self, _0: numpy.int64, /): """ - usage.matplotlib: 24 - usage.skimage: 35 - usage.xarray: 3 + usage.skimage: 1 + usage.sklearn: 5 + usage.xarray: 1 """ ... @overload - def __gt__(self, _0: numpy.ndarray, /): + def __truediv__(self, _0: object, /): """ - usage.matplotlib: 5 - usage.skimage: 3 + usage.pandas: 61 + usage.scipy: 878 """ ... @overload - def __gt__(self, _0: numpy.float64, /): + def __truediv__( + self, _0: Union[dask.array.core.Array, numpy.float64, int, numpy.int64], / + ): """ - usage.matplotlib: 102 - usage.skimage: 64 - usage.xarray: 6 + usage.dask: 23 """ ... @overload - def __gt__(self, _0: int, /): + def __truediv__(self, _0: numpy.memmap, /): """ - usage.matplotlib: 33 - usage.skimage: 19 - usage.xarray: 2 + usage.sklearn: 1 """ ... - @overload - def __gt__(self, _0: numpy.float32, /): + def __truediv__(self, _0: object, /): """ - usage.matplotlib: 1 - usage.skimage: 1 - usage.xarray: 1 + usage.dask: 23 + usage.matplotlib: 206 + usage.pandas: 61 + usage.scipy: 878 + usage.skimage: 96 + usage.sklearn: 212 + usage.xarray: 8 """ ... @overload - def __gt__(self, _0: numpy.int64, /): + def astype(self, _0: Type[numpy.int64], /): """ - usage.matplotlib: 4 - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __gt__(self, _0: xarray.core.dataarray.DataArray, /): + def astype(self, _0: Literal["timedelta64[ns]"], /): """ usage.xarray: 1 """ ... @overload - def __gt__( - self, _0: Union[float, pandas.core.series.Series, int, numpy.float64], / - ): + def astype(self, _0: Type[float], /): """ - usage.pandas: 15 + usage.sklearn: 1 + usage.xarray: 2 """ ... @overload - def __gt__(self, _0: object, /): + def astype(self, _0: Type[numpy.float64], /): """ - usage.scipy: 253 + usage.matplotlib: 2 + usage.xarray: 1 """ ... @overload - def __gt__(self, _0: numpy.bool_, /): + def astype(self, _0: Union[Type[int], numpy.dtype], /): """ - usage.matplotlib: 1 + usage.pandas: 8 """ ... @overload - def __gt__(self, _0: numpy.float128, /): + def astype(self, _0: Union[type, numpy.dtype, Literal["l", "d"]], /): """ - usage.matplotlib: 1 + usage.scipy: 14 """ ... @overload - def __gt__(self, _0: numpy.ma.core.MaskedArray, /): + def astype(self, _0: Type[int], /): """ usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __gt__(self, _0: numpy.uint16, /): + def astype(self, _0: Type[numpy.float32], /): """ - usage.matplotlib: 1 + usage.matplotlib: 2 """ ... @overload - def __gt__(self, _0: numpy.uint64, /): + def astype(self, _0: Union[numpy.dtype, Literal["i8", "f8"]], /): """ - usage.matplotlib: 1 + usage.dask: 5 """ ... - @overload - def __gt__(self, _0: Union[numpy.float64, int, float], /): + def astype( + self, + _0: Union[type, numpy.dtype, Literal["i8", "f8", "l", "d", "timedelta64[ns]"]], + /, + ): """ - usage.dask: 10 + usage.dask: 5 + usage.matplotlib: 5 + usage.pandas: 8 + usage.scipy: 14 + usage.skimage: 1 + usage.sklearn: 2 + usage.xarray: 4 """ ... - @overload - def __gt__( - self, _0: Union[numpy.ndarray, numpy.float64, int, float, numpy.float32], / - ): + def byteswap(self, /): """ - usage.sklearn: 237 + usage.scipy: 1 """ ... - def __gt__(self, _0: object, /): + def clip(self, _0: float, _1: float, /): """ - usage.dask: 10 - usage.matplotlib: 174 - usage.pandas: 15 - usage.scipy: 253 - usage.skimage: 122 - usage.sklearn: 237 - usage.xarray: 14 + usage.scipy: 1 """ ... - @overload - def __iadd__(self, _0: numpy.ndarray, /): + def conj(self, /): """ - usage.skimage: 1 + usage.scipy: 1 """ ... - @overload - def __iadd__(self, _0: numpy.float64, /): + def copy(self, /): """ - usage.dask: 1 - usage.matplotlib: 14 - usage.skimage: 7 - usage.xarray: 1 + usage.scipy: 2 """ ... - @overload - def __iadd__(self, _0: float, /): + def item(self, /): """ usage.matplotlib: 3 usage.pandas: 1 - usage.skimage: 1 - usage.xarray: 2 + usage.scipy: 2 + usage.sklearn: 4 + usage.xarray: 3 """ ... - @overload - def __iadd__(self, _0: int, /): + def max(self, /): """ - usage.matplotlib: 8 - usage.xarray: 2 + usage.scipy: 2 """ ... - @overload - def __iadd__(self, _0: bool, /): + def mean(self, /): """ - usage.xarray: 2 + usage.sklearn: 2 """ ... - @overload - def __iadd__(self, _0: object, /): + def ravel(self, /): """ - usage.scipy: 99 + usage.matplotlib: 1 + usage.scipy: 2 + usage.xarray: 1 """ ... @overload - def __iadd__( - self, _0: Union[float, numpy.int64, numpy.float64, int, numpy.ndarray], / - ): + def reshape(self, _0: Union[Tuple[Union[int, None], ...], List[int]], /): """ - usage.sklearn: 69 + usage.scipy: 5 """ ... - def __iadd__(self, _0: object, /): + @overload + def reshape(self, _0: Tuple[int, ...], /): """ - usage.dask: 1 - usage.matplotlib: 25 - usage.pandas: 1 - usage.scipy: 99 - usage.skimage: 9 - usage.sklearn: 69 - usage.xarray: 7 + usage.dask: 2 """ ... - def __ifloordiv__(self, _0: int, /): + def reshape(self, _0: Union[Tuple[Union[None, int], ...], List[int]], /): """ - usage.scipy: 2 + usage.dask: 2 + usage.scipy: 5 """ ... - @overload - def __imod__(self, _0: float, /): + def squeeze(self, /): """ - usage.skimage: 2 + usage.scipy: 3 + usage.xarray: 1 """ ... - @overload - def __imod__(self, _0: numpy.float64, /): + def sum(self, /, *, axis: None): """ - usage.matplotlib: 1 + usage.scipy: 2 """ ... - def __imod__(self, _0: Union[numpy.float64, float], /): + def tobytes(self, /): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.scipy: 1 """ ... + +class iinfo: + + # usage.dask: 2 + # usage.pandas: 25 + # usage.scipy: 12 + # usage.skimage: 7 + # usage.sklearn: 13 + max: object + + # usage.pandas: 10 + # usage.scipy: 2 + # usage.skimage: 3 + min: object + + +class int16: + + # usage.pandas: 3 + __name__: ClassVar[object] + @overload - def __imul__(self, _0: numpy.float64, /): + @classmethod + def __ne__(cls, _0: Union[numpy.int16, Type[numpy.int64]], /): """ - usage.skimage: 1 + usage.pandas: 9 """ ... @overload - def __imul__(self, _0: int, /): + @classmethod + def __ne__(cls, _0: numpy.int16, /): """ - usage.dask: 1 - usage.matplotlib: 4 - usage.skimage: 4 + usage.scipy: 2 """ ... @overload - def __imul__( - self, _0: Union[numpy.float128, float, numpy.float64, int, numpy.int64], / - ): + @classmethod + def __ne__(cls, _0: int, /): """ - usage.scipy: 43 + usage.sklearn: 1 """ ... - @overload - def __imul__(self, _0: float, /): + @classmethod + def __ne__(cls, _0: Union[int, numpy.int16, Type[numpy.int64]], /): """ - usage.matplotlib: 7 + usage.pandas: 9 + usage.scipy: 2 + usage.sklearn: 1 """ ... - @overload - def __imul__(self, _0: Union[numpy.int64, int, numpy.float64, float], /): + # usage.pandas: 4 + # usage.scipy: 2 + dtype: object + + # usage.dask: 2 + ndim: object + + # usage.scipy: 1 + size: object + + def __add__(self, _0: object, /): """ - usage.sklearn: 13 + usage.pandas: 9 + usage.scipy: 17 """ ... - def __imul__( - self, _0: Union[float, numpy.float64, int, numpy.int64, numpy.float128], / - ): + def __bool__(self, /): """ - usage.dask: 1 - usage.matplotlib: 11 - usage.scipy: 43 - usage.skimage: 5 - usage.sklearn: 13 + usage.scipy: 1 """ ... @overload - def __isub__(self, _0: numpy.float64, /): + def __eq__(self, _0: int, /): """ - usage.matplotlib: 5 usage.skimage: 1 """ ... @overload - def __isub__(self, _0: float, /): + def __eq__(self, _0: numpy.int64, /): """ - usage.matplotlib: 6 - usage.pandas: 2 + usage.skimage: 1 """ ... @overload - def __isub__(self, _0: Union[float, numpy.float64, numpy.ndarray], /): + def __eq__(self, _0: numpy.ndarray, /): """ - usage.scipy: 19 + usage.scipy: 2 + usage.xarray: 1 """ ... @overload - def __isub__(self, _0: int, /): + def __eq__(self, _0: object, /): """ - usage.matplotlib: 2 + usage.pandas: 81 """ ... - @overload - def __isub__(self, _0: Union[float, int, numpy.float64, numpy.ndarray], /): + def __eq__(self, _0: object, /): """ - usage.sklearn: 20 + usage.pandas: 81 + usage.scipy: 2 + usage.skimage: 2 + usage.xarray: 1 """ ... - def __isub__(self, _0: Union[numpy.ndarray, numpy.float64, int, float], /): + def __floordiv__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / + ): + """ + usage.pandas: 2 + """ + ... + + def __ge__(self, _0: int, /): """ - usage.matplotlib: 13 usage.pandas: 2 - usage.scipy: 19 usage.skimage: 1 - usage.sklearn: 20 """ ... - @overload - def __itruediv__(self, _0: numpy.float64, /): + def __getitem__(self, _0: Tuple[Union[None, ellipsis], ...], /): """ - usage.skimage: 5 + usage.dask: 2 """ ... - @overload - def __itruediv__(self, _0: float, /): + def __gt__(self, _0: numpy.int16, /): """ usage.matplotlib: 1 - usage.skimage: 4 """ ... - @overload - def __itruediv__(self, _0: numpy.float16, /): + def __le__(self, _0: int, /): """ - usage.skimage: 1 + usage.pandas: 2 """ ... @overload - def __itruediv__(self, _0: numpy.float32, /): + def __lt__(self, _0: int, /): """ - usage.skimage: 1 + usage.matplotlib: 1 + usage.skimage: 3 """ ... @overload - def __itruediv__(self, _0: numpy.int64, /): + def __lt__(self, _0: numpy.int16, /): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... - @overload - def __itruediv__( - self, _0: Union[int, numpy.ndarray, float, numpy.float64, numpy.int64], / + def __lt__(self, _0: Union[numpy.int16, int], /): + """ + usage.matplotlib: 2 + usage.skimage: 3 + """ + ... + + def __mod__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / ): """ - usage.scipy: 44 + usage.pandas: 2 """ ... @overload - def __itruediv__(self, _0: int, /): + def __mul__( + self, + _0: Union[ + numpy.ndarray, + pandas.core.arrays.timedeltas.TimedeltaArray, + pandas.core.series.Series, + pandas.core.arrays.integer.IntegerArray, + ], + /, + ): """ - usage.matplotlib: 1 + usage.pandas: 4 """ ... @overload - def __itruediv__(self, _0: Union[numpy.int64, float, int, numpy.float64], /): + def __mul__(self, _0: Union[numpy.int64, int], /): """ - usage.sklearn: 16 + usage.scipy: 4 """ ... - def __itruediv__(self, _0: object, /): + def __mul__(self, _0: object, /): """ - usage.matplotlib: 2 - usage.scipy: 44 - usage.skimage: 12 - usage.sklearn: 16 + usage.pandas: 4 + usage.scipy: 4 """ ... @overload - def __le__(self, _0: int, /): + def __pow__(self, _0: int, /): """ - usage.matplotlib: 29 - usage.skimage: 62 + usage.skimage: 1 """ ... @overload - def __le__(self, _0: numpy.float64, /): + def __pow__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / + ): """ - usage.matplotlib: 18 - usage.skimage: 2 + usage.pandas: 2 """ ... - @overload - def __le__(self, _0: float, /): + def __pow__( + self, _0: Union[pandas.core.arrays.integer.IntegerArray, numpy.ndarray, int], / + ): """ - usage.matplotlib: 9 - usage.skimage: 4 + usage.pandas: 2 + usage.skimage: 1 """ ... @overload - def __le__(self, _0: Union[numpy.ndarray, float, numpy.float64, int], /): + def __radd__(self, _0: Union[pandas._libs.missing.NAType, numpy.int16], /): """ - usage.pandas: 13 + usage.pandas: 6 """ ... @overload - def __le__(self, _0: object, /): + def __radd__(self, _0: object, /): """ - usage.scipy: 418 - usage.sklearn: 187 + usage.scipy: 18 """ ... - @overload - def __le__(self, _0: numpy.ndarray, /): + def __radd__(self, _0: object, /): """ - usage.matplotlib: 16 + usage.pandas: 6 + usage.scipy: 18 """ ... @overload - def __le__( - self, - _0: Union[pandas.core.series.Series, numpy.float64, float, int, numpy.ndarray], - /, - ): + def __rmul__(self, _0: numpy.ndarray, /): """ - usage.dask: 25 + usage.pandas: 1 """ ... - def __le__(self, _0: object, /): + @overload + def __rmul__(self, _0: float, /): """ - usage.dask: 25 - usage.matplotlib: 72 - usage.pandas: 13 - usage.scipy: 418 - usage.skimage: 68 - usage.sklearn: 187 + usage.scipy: 1 """ ... - @overload - def __lt__(self, _0: float, /): + def __rmul__(self, _0: Union[float, numpy.ndarray], /): """ - usage.matplotlib: 55 - usage.skimage: 58 - usage.xarray: 2 + usage.pandas: 1 + usage.scipy: 1 """ ... @overload - def __lt__(self, _0: int, /): + def __rsub__(self, _0: numpy.int16, /): """ - usage.matplotlib: 45 - usage.skimage: 37 - usage.xarray: 5 + usage.skimage: 2 """ ... @overload - def __lt__(self, _0: numpy.ndarray, /): + def __rsub__(self, _0: pandas.core.arrays.timedeltas.TimedeltaArray, /): """ - usage.matplotlib: 4 - usage.skimage: 14 + usage.pandas: 1 """ ... - @overload - def __lt__(self, _0: numpy.float64, /): + def __rsub__( + self, _0: Union[pandas.core.arrays.timedeltas.TimedeltaArray, numpy.int16], / + ): """ - usage.matplotlib: 102 - usage.skimage: 64 - usage.xarray: 6 + usage.pandas: 1 + usage.skimage: 2 """ ... @overload - def __lt__(self, _0: numpy.uint8, /): + def __rtruediv__(self, _0: numpy.float64, /): """ usage.skimage: 1 """ ... @overload - def __lt__(self, _0: numpy.float32, /): + def __rtruediv__( + self, + _0: Union[ + numpy.ndarray, + pandas._libs.tslibs.timedeltas.Timedelta, + pandas._libs.tslibs.nattype.NaTType, + ], + /, + ): """ - usage.matplotlib: 1 - usage.skimage: 1 - usage.xarray: 1 + usage.pandas: 5 """ ... @overload - def __lt__(self, _0: numpy.float16, /): + def __rtruediv__(self, _0: numpy.ndarray, /): """ - usage.skimage: 1 + usage.scipy: 2 """ ... - @overload - def __lt__(self, _0: numpy.uint64, /): + def __rtruediv__( + self, + _0: Union[ + numpy.ndarray, + numpy.float64, + pandas._libs.tslibs.timedeltas.Timedelta, + pandas._libs.tslibs.nattype.NaTType, + ], + /, + ): """ - usage.matplotlib: 1 + usage.pandas: 5 + usage.scipy: 2 usage.skimage: 1 """ ... @overload - def __lt__(self, _0: numpy.int64, /): + def __sub__(self, _0: numpy.int16, /): """ - usage.matplotlib: 2 - usage.xarray: 1 + usage.skimage: 2 """ ... @overload - def __lt__(self, _0: xarray.core.dataarray.DataArray, /): + def __sub__( + self, + _0: Union[ + int, + pandas.core.arrays.integer.IntegerArray, + pandas.core.series.Series, + pandas.core.arrays.timedeltas.TimedeltaArray, + numpy.ndarray, + ], + /, + ): + """ + usage.pandas: 5 + """ + ... + + def __sub__(self, _0: object, /): + """ + usage.pandas: 5 + usage.skimage: 2 + """ + ... + + def __truediv__( + self, + _0: Union[ + numpy.ndarray, + pandas.core.arrays.timedeltas.TimedeltaArray, + pandas.core.series.Series, + pandas.core.arrays.integer.IntegerArray, + ], + /, + ): """ - usage.xarray: 1 + usage.pandas: 4 """ ... - @overload - def __lt__(self, _0: Union[float, numpy.ndarray, numpy.float64], /): + def astype(self, _0: numpy.dtype, /): """ - usage.pandas: 5 + usage.pandas: 1 """ ... - @overload - def __lt__(self, _0: object, /): + def item(self, /): """ - usage.scipy: 141 + usage.matplotlib: 1 """ ... + +class int32: + + # usage.dask: 2 + __module__: ClassVar[object] + + # usage.pandas: 3 + __name__: ClassVar[object] + @overload - def __lt__(self, _0: numpy.bool_, /): + @classmethod + def __lt__(cls, _0: Union[numpy.dtype, numpy.int32], /): """ - usage.matplotlib: 1 + usage.scipy: 3 """ ... @overload - def __lt__(self, _0: numpy.ma.core.MaskedArray, /): + @classmethod + def __lt__(cls, _0: int, /): """ - usage.matplotlib: 1 + usage.matplotlib: 2 + usage.skimage: 2 + usage.sklearn: 6 """ ... @overload - def __lt__(self, _0: numpy.uint16, /): + @classmethod + def __lt__(cls, _0: numpy.ma.core.MaskedConstant, /): """ usage.matplotlib: 1 """ ... @overload - def __lt__(self, _0: Union[numpy.float64, pandas.core.series.Series], /): + @classmethod + def __lt__(cls, _0: float, /): """ - usage.dask: 3 + usage.sklearn: 1 """ ... - @overload + @classmethod def __lt__( - self, _0: Union[numpy.int64, numpy.ndarray, numpy.float64, float, int], / + cls, + _0: Union[int, float, numpy.int32, numpy.dtype, numpy.ma.core.MaskedConstant], + /, ): """ - usage.sklearn: 84 + usage.matplotlib: 3 + usage.scipy: 3 + usage.skimage: 2 + usage.sklearn: 7 """ ... - def __lt__(self, _0: object, /): + @overload + @classmethod + def __ne__(cls, _0: Union[numpy.int32, Type[numpy.int64]], /): """ - usage.dask: 3 - usage.matplotlib: 213 - usage.pandas: 5 - usage.scipy: 141 - usage.skimage: 177 - usage.sklearn: 84 - usage.xarray: 16 + usage.pandas: 9 """ ... @overload - def __mod__(self, _0: float, /): + @classmethod + def __ne__(cls, _0: Union[numpy.int64, int, numpy.int32, Type[numpy.int32]], /): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.scipy: 33 """ ... @overload - def __mod__(self, _0: int, /): + @classmethod + def __ne__(cls, _0: numpy.dtype, /): """ - usage.matplotlib: 10 - usage.skimage: 5 - usage.sklearn: 1 + usage.dask: 1 + usage.sklearn: 5 """ ... @overload - def __mod__(self, _0: object, /): + @classmethod + def __ne__(cls, _0: numpy.ndarray, /): """ - usage.pandas: 11 + usage.sklearn: 1 """ ... @overload - def __mod__(self, _0: Union[int, numpy.float64], /): + @classmethod + def __ne__(cls, _0: numpy.int32, /): """ - usage.scipy: 4 + usage.sklearn: 2 """ ... - def __mod__(self, _0: object, /): + @classmethod + def __ne__(cls, _0: object, /): """ - usage.matplotlib: 11 - usage.pandas: 11 - usage.scipy: 4 - usage.skimage: 7 - usage.sklearn: 1 + usage.dask: 1 + usage.pandas: 9 + usage.scipy: 33 + usage.sklearn: 8 """ ... - @overload - def __mul__(self, _0: numpy.ndarray, /): - """ - usage.matplotlib: 30 - usage.skimage: 28 - usage.xarray: 2 - """ - ... + # usage.dask: 1 + # usage.pandas: 4 + # usage.scipy: 5 + # usage.xarray: 1 + dtype: object + + # usage.dask: 3 + # usage.xarray: 1 + ndim: object + + # usage.dask: 5 + shape: object + + # usage.scipy: 1 + size: object @overload - def __mul__(self, _0: numpy.float64, /): + def __add__( + self, + _0: Union[ + numpy.int32, + pandas.core.arrays.integer.IntegerArray, + pandas.core.series.Series, + pandas.core.arrays.timedeltas.TimedeltaArray, + numpy.ndarray, + ], + /, + ): """ - usage.matplotlib: 81 - usage.skimage: 30 - usage.xarray: 2 + usage.pandas: 8 """ ... @overload - def __mul__(self, _0: int, /): + def __add__(self, _0: object, /): """ - usage.matplotlib: 35 - usage.skimage: 12 - usage.xarray: 1 + usage.scipy: 25 """ ... @overload - def __mul__(self, _0: float, /): + def __add__(self, _0: Union[int, numpy.int64], /): """ - usage.matplotlib: 66 - usage.skimage: 7 + usage.dask: 3 """ ... @overload - def __mul__(self, _0: object, /): + def __add__(self, _0: bool, /): """ - usage.pandas: 71 - usage.scipy: 1498 - usage.sklearn: 276 - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload - def __mul__(self, _0: numpy.ma.core.MaskedArray, /): + def __add__(self, _0: numpy.int64, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __mul__(self, _0: numpy.int64, /): + def __add__(self, _0: numpy.int32, /): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload - def __mul__(self, _0: Union[int, numpy.float64], /): + def __add__(self, _0: numpy.float64, /): """ - usage.dask: 8 + usage.sklearn: 1 """ ... - def __mul__(self, _0: object, /): + @overload + def __add__(self, _0: int, /): """ - usage.dask: 8 - usage.matplotlib: 214 - usage.pandas: 71 - usage.scipy: 1498 - usage.skimage: 77 - usage.sklearn: 276 - usage.xarray: 7 + usage.sklearn: 1 """ ... - def __neg__(self, /): + def __add__(self, _0: object, /): """ - usage.matplotlib: 81 - usage.pandas: 2 - usage.scipy: 311 - usage.skimage: 22 - usage.sklearn: 54 - usage.xarray: 2 + usage.dask: 3 + usage.pandas: 8 + usage.scipy: 25 + usage.sklearn: 6 """ ... - def __pos__(self, /): + def __and__(self, _0: int, /): """ - usage.scipy: 16 + usage.scipy: 8 """ ... - @overload - def __pow__(self, _0: int, /): + def __bool__(self, /): """ - usage.matplotlib: 9 - usage.skimage: 32 + usage.scipy: 1 """ ... @overload - def __pow__(self, _0: float, /): + def __eq__(self, _0: int, /): """ usage.matplotlib: 4 - usage.skimage: 4 + usage.skimage: 2 + usage.sklearn: 5 """ ... @overload - def __pow__(self, _0: object, /): + def __eq__(self, _0: numpy.int64, /): """ - usage.pandas: 13 + usage.skimage: 1 """ ... @overload - def __pow__(self, _0: Union[numpy.ndarray, numpy.float64, int, float], /): + def __eq__(self, _0: numpy.ndarray, /): """ - usage.scipy: 427 + usage.sklearn: 3 + usage.xarray: 1 """ ... @overload - def __pow__(self, _0: Union[int, float], /): + def __eq__(self, _0: numpy.int32, /): """ - usage.dask: 3 + usage.sklearn: 6 + usage.xarray: 2 """ ... @overload - def __pow__(self, _0: Union[numpy.float64, int, float], /): - """ - usage.sklearn: 43 - """ - ... - - def __pow__(self, _0: object, /): + def __eq__( + self, + _0: Union[ + numpy.int64, + pandas.core.series.Series, + numpy.int32, + int, + pandas.core.arrays.integer.IntegerArray, + ], + /, + ): """ - usage.dask: 3 - usage.matplotlib: 13 - usage.pandas: 13 - usage.scipy: 427 - usage.skimage: 36 - usage.sklearn: 43 + usage.pandas: 77 """ ... @overload - def __radd__(self, _0: numpy.float64, /): + def __eq__( + self, + _0: Union[Literal["silverman", "scott"], numpy.int32, int, numpy.ndarray], + /, + ): """ - usage.matplotlib: 180 - usage.skimage: 42 - usage.xarray: 5 + usage.scipy: 34 """ ... @overload - def __radd__(self, _0: numpy.ndarray, /): + def __eq__(self, _0: Union[numpy.int32, numpy.int64, int], /): """ - usage.matplotlib: 10 - usage.skimage: 18 - usage.xarray: 1 + usage.dask: 5 """ ... @overload - def __radd__(self, _0: float, /): + def __eq__(self, _0: Literal["mle"], /): """ - usage.matplotlib: 42 - usage.skimage: 4 + usage.sklearn: 2 """ ... - @overload - def __radd__(self, _0: int, /): + def __eq__(self, _0: object, /): """ - usage.matplotlib: 29 - usage.skimage: 13 - usage.xarray: 1 + usage.dask: 5 + usage.matplotlib: 4 + usage.pandas: 77 + usage.scipy: 34 + usage.skimage: 3 + usage.sklearn: 16 + usage.xarray: 3 """ ... - @overload - def __radd__(self, _0: numpy.int64, /): + def __floordiv__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / + ): """ - usage.matplotlib: 9 - usage.skimage: 2 - usage.xarray: 2 + usage.pandas: 2 """ ... @overload - def __radd__(self, _0: numpy.float32, /): + def __ge__(self, _0: int, /): """ usage.matplotlib: 1 + usage.pandas: 3 usage.skimage: 1 + usage.sklearn: 5 """ ... @overload - def __radd__(self, _0: numpy.ma.core.MaskedArray, /): + def __ge__(self, _0: Union[numpy.int64, int, numpy.int32], /): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.scipy: 12 """ ... @overload - def __radd__(self, _0: object, /): + def __ge__(self, _0: float, /): """ - usage.dask: 26 - usage.pandas: 36 - usage.scipy: 861 - usage.sklearn: 194 + usage.sklearn: 1 """ ... - @overload - def __radd__(self, _0: numpy.bool_, /): + def __ge__(self, _0: Union[float, int, numpy.int32, numpy.int64], /): """ usage.matplotlib: 1 + usage.pandas: 3 + usage.scipy: 12 + usage.skimage: 1 + usage.sklearn: 6 """ ... @overload - def __radd__(self, _0: numpy.uint8, /): + def __getitem__(self, _0: ellipsis, /): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload - def __radd__(self, _0: numpy.uint16, /): + def __getitem__(self, _0: Tuple[Union[None, ellipsis], ...], /): """ - usage.matplotlib: 1 + usage.dask: 4 """ ... - @overload - def __radd__(self, _0: numpy.float128, /): + def __getitem__(self, _0: Union[Tuple[Union[ellipsis, None], ...], ellipsis], /): """ - usage.matplotlib: 2 + usage.dask: 4 + usage.xarray: 1 """ ... @overload - def __radd__(self, _0: numpy.ma.core.MaskedConstant, /): + def __gt__(self, _0: int, /): """ - usage.matplotlib: 5 + usage.pandas: 2 + usage.sklearn: 2 """ ... - def __radd__(self, _0: object, /): + @overload + def __gt__(self, _0: Union[numpy.int32, int], /): """ - usage.dask: 26 - usage.matplotlib: 282 - usage.pandas: 36 - usage.scipy: 861 - usage.skimage: 81 - usage.sklearn: 194 - usage.xarray: 9 + usage.scipy: 10 """ ... @overload - def __rfloordiv__(self, _0: numpy.ndarray, /): + def __gt__(self, _0: numpy.ma.core.MaskedConstant, /): """ - usage.skimage: 1 + usage.matplotlib: 1 """ ... - @overload - def __rfloordiv__( - self, - _0: Union[ - pandas._libs.tslibs.timedeltas.Timedelta, - float, - pandas.core.indexes.numeric.Int64Index, - numpy.float64, - int, - ], - /, - ): + def __gt__(self, _0: Union[int, numpy.int32, numpy.ma.core.MaskedConstant], /): """ - usage.pandas: 6 + usage.matplotlib: 1 + usage.pandas: 2 + usage.scipy: 10 + usage.sklearn: 2 """ ... - @overload - def __rfloordiv__(self, _0: numpy.float64, /): + def __iadd__(self, _0: Union[int, numpy.int32], /): """ - usage.matplotlib: 2 - usage.scipy: 1 + usage.scipy: 2 """ ... @overload - def __rfloordiv__(self, _0: float, /): + def __le__(self, _0: int, /): """ - usage.matplotlib: 6 + usage.pandas: 3 + usage.sklearn: 6 """ ... - def __rfloordiv__(self, _0: object, /): + @overload + def __le__(self, _0: Union[numpy.int32, numpy.int64, int, float], /): """ - usage.matplotlib: 8 - usage.pandas: 6 - usage.scipy: 1 - usage.skimage: 1 + usage.scipy: 10 """ ... - @overload - def __rmod__(self, _0: Union[int, numpy.float64, float], /): + def __le__(self, _0: Union[int, numpy.int32, numpy.int64, float], /): """ usage.pandas: 3 + usage.scipy: 10 + usage.sklearn: 6 """ ... - @overload - def __rmod__(self, _0: Union[str, numpy.float64, numpy.ndarray], /): + def __mod__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / + ): """ - usage.scipy: 29 + usage.pandas: 2 """ ... @overload - def __rmod__(self, _0: Literal["%1.1f"], /): + def __mul__( + self, + _0: Union[ + numpy.ndarray, + pandas.core.arrays.timedeltas.TimedeltaArray, + pandas.core.series.Series, + pandas.core.arrays.integer.IntegerArray, + ], + /, + ): """ - usage.matplotlib: 3 + usage.pandas: 4 """ ... @overload - def __rmod__(self, _0: Literal["%1.0f"], /): + def __mul__(self, _0: Union[numpy.int64, int], /): """ - usage.matplotlib: 1 + usage.scipy: 7 """ ... @overload - def __rmod__(self, _0: bytes, /): + def __mul__(self, _0: int, /): """ - usage.matplotlib: 2 + usage.dask: 1 """ ... - @overload - def __rmod__(self, _0: Literal["%1.2f"], /): + def __mul__(self, _0: object, /): """ - usage.matplotlib: 1 + usage.dask: 1 + usage.pandas: 4 + usage.scipy: 7 """ ... - @overload - def __rmod__(self, _0: Literal["%05.2lf"], /): + def __neg__(self, /): """ - usage.matplotlib: 1 + usage.scipy: 3 """ ... @overload - def __rmod__(self, _0: Literal["%08.2lf"], /): + def __pow__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / + ): """ - usage.matplotlib: 1 + usage.pandas: 2 """ ... @overload - def __rmod__(self, _0: Literal["%1.1f%%"], /): + def __pow__(self, _0: int, /): """ - usage.matplotlib: 1 + usage.scipy: 2 """ ... - @overload - def __rmod__(self, _0: Literal["%.2f"], /): + def __pow__( + self, _0: Union[int, pandas.core.arrays.integer.IntegerArray, numpy.ndarray], / + ): """ - usage.matplotlib: 1 + usage.pandas: 2 + usage.scipy: 2 """ ... @overload - def __rmod__(self, _0: Literal["%1.10e"], /): + def __radd__(self, _0: Union[pandas._libs.missing.NAType, numpy.int32], /): """ - usage.matplotlib: 1 + usage.pandas: 6 """ ... @overload - def __rmod__(self, _0: Literal["%1.3f"], /): + def __radd__(self, _0: object, /): """ - usage.matplotlib: 3 + usage.scipy: 20 """ ... @overload - def __rmod__(self, _0: Literal["%1.3f setgray\n"], /): + def __radd__(self, _0: numpy.int32, /): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... - @overload - def __rmod__(self, _0: Literal["%1.3f setgray"], /): + def __radd__(self, _0: object, /): """ - usage.matplotlib: 1 + usage.pandas: 6 + usage.scipy: 20 + usage.sklearn: 2 """ ... - @overload - def __rmod__(self, _0: str, /): + def __rfloordiv__(self, _0: pandas._libs.tslibs.timedeltas.Timedelta, /): """ - usage.dask: 5 - usage.matplotlib: 1 - usage.sklearn: 15 + usage.pandas: 1 """ ... @overload - def __rmod__(self, _0: Literal["%4.2e"], /): + def __rmod__(self, _0: str, /): """ - usage.matplotlib: 1 + usage.scipy: 2 """ ... @overload - def __rmod__(self, _0: Literal["%-12g"], /): + def __rmod__(self, _0: Literal["%d"], /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... - @overload - def __rmod__(self, _0: Literal["%2.0f"], /): + def __rmod__(self, _0: str, /): """ - usage.matplotlib: 1 + usage.scipy: 2 + usage.sklearn: 1 """ ... @overload - def __rmod__(self, _0: Literal["%G"], /): + def __rmul__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 1 + usage.pandas: 1 + usage.sklearn: 1 """ ... @overload - def __rmod__(self, _0: Literal["%1.4f"], /): + def __rmul__(self, _0: Union[float, int], /): """ - usage.matplotlib: 1 + usage.scipy: 2 """ ... @overload - def __rmod__(self, _0: Literal["%1.7f"], /): + def __rmul__(self, _0: int, /): """ - usage.matplotlib: 1 + usage.dask: 2 """ ... - @overload - def __rmod__(self, _0: Literal["%1.0e"], /): + def __rmul__(self, _0: Union[numpy.ndarray, float, int], /): """ - usage.matplotlib: 2 + usage.dask: 2 + usage.pandas: 1 + usage.scipy: 2 + usage.sklearn: 1 """ ... @overload - def __rmod__(self, _0: Literal["%d"], /): + def __rsub__(self, _0: pandas.core.arrays.timedeltas.TimedeltaArray, /): """ - usage.matplotlib: 1 + usage.pandas: 1 """ ... - def __rmod__(self, _0: object, /): + @overload + def __rsub__(self, _0: Union[numpy.int32, int], /): """ - usage.dask: 5 - usage.matplotlib: 27 - usage.pandas: 3 - usage.scipy: 29 - usage.sklearn: 15 + usage.scipy: 3 """ ... @overload - def __rmul__(self, _0: float, /): + def __rsub__(self, _0: numpy.int32, /): """ - usage.matplotlib: 80 - usage.skimage: 67 + usage.dask: 1 + usage.sklearn: 4 """ ... @overload - def __rmul__(self, _0: numpy.ndarray, /): + def __rsub__(self, _0: int, /): """ - usage.matplotlib: 26 - usage.skimage: 29 + usage.sklearn: 1 """ ... - @overload - def __rmul__(self, _0: int, /): + def __rsub__( + self, + _0: Union[int, numpy.int32, pandas.core.arrays.timedeltas.TimedeltaArray], + /, + ): """ - usage.matplotlib: 42 - usage.skimage: 46 + usage.dask: 1 + usage.pandas: 1 + usage.scipy: 3 + usage.sklearn: 5 """ ... @overload - def __rmul__(self, _0: numpy.float64, /): + def __rtruediv__( + self, + _0: Union[ + numpy.ndarray, + pandas._libs.tslibs.timedeltas.Timedelta, + pandas._libs.tslibs.nattype.NaTType, + ], + /, + ): """ - usage.matplotlib: 81 - usage.skimage: 30 - usage.xarray: 2 + usage.pandas: 5 """ ... @overload - def __rmul__(self, _0: numpy.int64, /): + def __rtruediv__(self, _0: numpy.ndarray, /): """ - usage.skimage: 1 + usage.scipy: 2 """ ... @overload - def __rmul__(self, _0: numpy.ma.core.MaskedArray, /): + def __rtruediv__(self, _0: numpy.int32, /): """ - usage.matplotlib: 3 - usage.xarray: 1 + usage.dask: 1 """ ... - @overload - def __rmul__(self, _0: object, /): + def __rtruediv__( + self, + _0: Union[ + numpy.int32, + pandas._libs.tslibs.timedeltas.Timedelta, + pandas._libs.tslibs.nattype.NaTType, + numpy.ndarray, + ], + /, + ): """ - usage.pandas: 22 - usage.scipy: 2141 + usage.dask: 1 + usage.pandas: 5 + usage.scipy: 2 """ ... @overload - def __rmul__(self, _0: kiwisolver.Variable, /): + def __sub__( + self, + _0: Union[ + int, + pandas.core.arrays.integer.IntegerArray, + pandas.core.series.Series, + pandas.core.arrays.timedeltas.TimedeltaArray, + numpy.ndarray, + ], + /, + ): """ - usage.matplotlib: 14 + usage.pandas: 5 """ ... @overload - def __rmul__(self, _0: numpy.ma.core.MaskedConstant, /): + def __sub__(self, _0: Union[numpy.int32, int], /): """ - usage.matplotlib: 3 + usage.scipy: 18 """ ... @overload - def __rmul__( - self, - _0: Union[numpy.int64, float, numpy.float64, int, dask.array.core.Array], - /, - ): + def __sub__(self, _0: numpy.int32, /): """ - usage.dask: 14 + usage.dask: 1 + usage.sklearn: 4 """ ... @overload - def __rmul__( - self, _0: Union[float, numpy.ndarray, numpy.float64, int, numpy.int64], / - ): + def __sub__(self, _0: int, /): """ - usage.sklearn: 331 + usage.sklearn: 1 """ ... - def __rmul__(self, _0: object, /): + def __sub__(self, _0: object, /): """ - usage.dask: 14 - usage.matplotlib: 249 - usage.pandas: 22 - usage.scipy: 2141 - usage.skimage: 173 - usage.sklearn: 331 - usage.xarray: 3 + usage.dask: 1 + usage.pandas: 5 + usage.scipy: 18 + usage.sklearn: 5 """ ... @overload - def __rpow__(self, _0: float, /): + def __truediv__( + self, + _0: Union[ + numpy.ndarray, + pandas.core.arrays.timedeltas.TimedeltaArray, + pandas.core.series.Series, + pandas.core.arrays.integer.IntegerArray, + ], + /, + ): """ - usage.matplotlib: 4 - usage.skimage: 1 + usage.pandas: 4 """ ... @overload - def __rpow__(self, _0: int, /): + def __truediv__(self, _0: numpy.int32, /): """ - usage.matplotlib: 26 - usage.skimage: 2 + usage.dask: 1 """ ... - @overload - def __rpow__( + def __truediv__( self, - _0: Union[pandas._libs.missing.NAType, float, numpy.float64, numpy.int64], + _0: Union[ + numpy.int32, + pandas.core.arrays.integer.IntegerArray, + pandas.core.series.Series, + pandas.core.arrays.timedeltas.TimedeltaArray, + numpy.ndarray, + ], /, ): """ + usage.dask: 1 usage.pandas: 4 """ ... @overload - def __rpow__( - self, _0: Union[numpy.ndarray, int, numpy.complex128, numpy.float64, float], / - ): + def astype(self, _0: Type[numpy.int64], /): """ - usage.scipy: 72 + usage.skimage: 2 """ ... @overload - def __rpow__(self, _0: Union[numpy.float64, numpy.ndarray], /): + def astype(self, _0: numpy.dtype, /): """ - usage.sklearn: 4 + usage.pandas: 1 """ ... - def __rpow__(self, _0: object, /): + def astype(self, _0: Union[numpy.dtype, Type[numpy.int64]], /): """ - usage.matplotlib: 30 - usage.pandas: 4 - usage.scipy: 72 - usage.skimage: 3 - usage.sklearn: 4 + usage.pandas: 1 + usage.skimage: 2 """ ... + +class int64: + + # usage.dask: 1 + __module__: ClassVar[object] + + # usage.matplotlib: 1 + __mro__: ClassVar[object] + + # usage.pandas: 6 + # usage.scipy: 2 + __name__: ClassVar[object] + + # usage.pandas: 1 + type: ClassVar[object] + @overload - def __rsub__(self, _0: int, /): + @classmethod + def __ne__(cls, _0: object, /): """ - usage.matplotlib: 34 - usage.skimage: 8 - usage.xarray: 2 + usage.pandas: 74 + usage.scipy: 71 """ ... @overload - def __rsub__(self, _0: numpy.float64, /): + @classmethod + def __ne__(cls, _0: int, /): """ - usage.matplotlib: 222 - usage.skimage: 63 + usage.matplotlib: 12 + usage.skimage: 4 + usage.sklearn: 25 usage.xarray: 6 """ ... @overload - def __rsub__(self, _0: numpy.ndarray, /): + @classmethod + def __ne__(cls, _0: numpy.int64, /): """ - usage.matplotlib: 11 - usage.skimage: 31 - usage.xarray: 1 + usage.skimage: 2 + usage.sklearn: 2 """ ... @overload - def __rsub__(self, _0: float, /): + @classmethod + def __ne__(cls, _0: Literal[""], /): """ - usage.matplotlib: 37 - usage.skimage: 1 - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __rsub__(self, _0: numpy.float32, /): + @classmethod + def __ne__(cls, _0: Literal["1"], /): """ - usage.skimage: 2 + usage.matplotlib: 1 """ ... @overload - def __rsub__(self, _0: numpy.ma.core.MaskedArray, /): + @classmethod + def __ne__(cls, _0: Literal["2"], /): """ usage.matplotlib: 1 - usage.skimage: 2 """ ... @overload - def __rsub__(self, _0: numpy.int64, /): + @classmethod + def __ne__(cls, _0: Literal["3"], /): """ - usage.matplotlib: 5 - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload - def __rsub__(self, _0: xarray.core.dataarray.DataArray, /): + @classmethod + def __ne__(cls, _0: Literal["4"], /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __rsub__(self, _0: object, /): + @classmethod + def __ne__(cls, _0: Literal["5"], /): """ - usage.pandas: 26 - usage.scipy: 1100 - usage.sklearn: 233 + usage.matplotlib: 1 """ ... @overload - def __rsub__(self, _0: numpy.float128, /): + @classmethod + def __ne__(cls, _0: None, /): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload - def __rsub__(self, _0: kiwisolver.Variable, /): + @classmethod + def __ne__(cls, _0: float, /): """ usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __rsub__(self, _0: numpy.ma.core.MaskedConstant, /): + @classmethod + def __ne__(cls, _0: Union[float, int, numpy.dtype, numpy.int64], /): """ - usage.matplotlib: 1 + usage.dask: 14 """ ... @overload - def __rsub__(self, _0: Union[pandas.core.series.Series, int, numpy.float64], /): + @classmethod + def __ne__(cls, _0: Type[inspect._empty], /): """ - usage.dask: 13 + usage.sklearn: 1 """ ... - def __rsub__(self, _0: object, /): + @overload + @classmethod + def __ne__(cls, _0: numpy.ndarray, /): """ - usage.dask: 13 - usage.matplotlib: 314 - usage.pandas: 26 - usage.scipy: 1100 - usage.skimage: 107 - usage.sklearn: 233 - usage.xarray: 13 + usage.sklearn: 6 """ ... @overload - def __rtruediv__(self, _0: numpy.ndarray, /): + @classmethod + def __ne__(cls, _0: numpy.float64, /): """ - usage.matplotlib: 24 - usage.skimage: 35 + usage.sklearn: 1 """ ... - @overload - def __rtruediv__(self, _0: numpy.float64, /): + @classmethod + def __ne__(cls, _0: object, /): """ - usage.matplotlib: 91 - usage.skimage: 49 - usage.xarray: 1 + usage.dask: 14 + usage.matplotlib: 20 + usage.pandas: 74 + usage.scipy: 71 + usage.skimage: 6 + usage.sklearn: 36 + usage.xarray: 6 """ ... + # usage.xarray: 1 + coords: object + + # usage.dask: 11 + # usage.pandas: 5 + # usage.scipy: 9 + # usage.xarray: 1 + dtype: object + + # usage.scipy: 3 + itemsize: object + + # usage.dask: 8 + # usage.matplotlib: 1 + # usage.pandas: 3 + # usage.skimage: 1 + ndim: object + + # usage.dask: 10 + # usage.scipy: 1 + # usage.xarray: 1 + shape: object + + # usage.scipy: 1 + size: object + + # usage.pandas: 1 + values: object + + # usage.xarray: 1 + variable: object + + # usage.xarray: 1 + variables: object + @overload - def __rtruediv__(self, _0: int, /): + def __add__(self, _0: int, /): """ - usage.matplotlib: 9 - usage.skimage: 12 - usage.xarray: 1 + usage.matplotlib: 25 + usage.skimage: 25 + usage.sklearn: 35 + usage.xarray: 7 """ ... @overload - def __rtruediv__(self, _0: float, /): + def __add__(self, _0: numpy.float64, /): """ - usage.matplotlib: 39 - usage.skimage: 5 + usage.matplotlib: 9 + usage.skimage: 2 + usage.sklearn: 1 + usage.xarray: 2 """ ... @overload - def __rtruediv__(self, _0: numpy.complex128, /): + def __add__(self, _0: numpy.int64, /): """ - usage.skimage: 1 + usage.matplotlib: 10 + usage.skimage: 6 + usage.sklearn: 11 """ ... @overload - def __rtruediv__(self, _0: numpy.float32, /): + def __add__(self, _0: float, /): """ - usage.skimage: 1 + usage.matplotlib: 6 + usage.skimage: 2 + usage.sklearn: 1 + usage.xarray: 3 """ ... @overload - def __rtruediv__(self, _0: numpy.int64, /): + def __add__(self, _0: numpy.ndarray, /): """ usage.matplotlib: 1 - usage.skimage: 1 + usage.skimage: 2 """ ... @overload - def __rtruediv__(self, _0: xarray.core.dataarray.DataArray, /): + def __add__(self, _0: object, /): """ - usage.xarray: 1 + usage.pandas: 63 + usage.scipy: 282 """ ... @overload - def __rtruediv__(self, _0: object, /): + def __add__(self, _0: List[int], /): """ - usage.pandas: 61 - usage.scipy: 868 - usage.sklearn: 255 + usage.matplotlib: 1 """ ... @overload - def __rtruediv__(self, _0: kiwisolver.Term, /): + def __add__(self, _0: Union[float, numpy.ndarray, numpy.int64, int], /): """ - usage.matplotlib: 10 + usage.dask: 20 """ ... @overload - def __rtruediv__(self, _0: numpy.ma.core.MaskedArray, /): + def __add__(self, _0: bool, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 + """ + ... + + def __add__(self, _0: object, /): + """ + usage.dask: 20 + usage.matplotlib: 52 + usage.pandas: 63 + usage.scipy: 282 + usage.skimage: 37 + usage.sklearn: 49 + usage.xarray: 12 """ ... @overload - def __rtruediv__( - self, _0: Union[int, numpy.float64, float, dask.array.core.Array], / - ): + def __and__(self, _0: Union[numpy.ndarray, numpy.bool_], /): """ - usage.dask: 18 + usage.scipy: 36 """ ... - def __rtruediv__(self, _0: object, /): + @overload + def __and__(self, _0: Union[bool, numpy.int64], /): """ - usage.dask: 18 - usage.matplotlib: 175 - usage.pandas: 61 - usage.scipy: 868 - usage.skimage: 104 - usage.sklearn: 255 - usage.xarray: 3 + usage.dask: 2 """ ... - def __setitem__(self, _0: numpy.bool_, _1: float, /): + def __and__(self, _0: Union[numpy.int64, bool, numpy.bool_, numpy.ndarray], /): """ - usage.matplotlib: 1 + usage.dask: 2 + usage.scipy: 36 """ ... - @overload - def __sub__(self, _0: numpy.float64, /): + def __bool__(self, /): """ - usage.matplotlib: 222 - usage.skimage: 63 - usage.xarray: 6 + usage.dask: 1 + usage.scipy: 1 """ ... @overload - def __sub__(self, _0: float, /): + def __eq__(self, _0: numpy.flatiter, /): """ - usage.matplotlib: 90 - usage.skimage: 8 - usage.xarray: 2 + usage.skimage: 2 """ ... @overload - def __sub__(self, _0: int, /): + def __eq__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 29 - usage.skimage: 78 - usage.xarray: 6 + usage.matplotlib: 4 + usage.skimage: 7 + usage.sklearn: 38 + usage.xarray: 10 """ ... @overload - def __sub__(self, _0: numpy.ndarray, /): + def __eq__(self, _0: int, /): """ - usage.matplotlib: 11 - usage.skimage: 4 + usage.matplotlib: 26 + usage.skimage: 46 + usage.sklearn: 138 + usage.xarray: 22 """ ... @overload - def __sub__(self, _0: numpy.uint8, /): + def __eq__(self, _0: numpy.int64, /): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.matplotlib: 22 + usage.skimage: 16 + usage.sklearn: 34 + usage.xarray: 4 """ ... @overload - def __sub__(self, _0: object, /): + def __eq__(self, _0: numpy.float64, /): """ - usage.pandas: 28 - usage.scipy: 1106 - usage.sklearn: 153 + usage.skimage: 8 + usage.sklearn: 1 """ ... @overload - def __sub__(self, _0: numpy.int64, /): + def __eq__(self, _0: numpy.uint8, /): """ - usage.matplotlib: 3 + usage.skimage: 1 """ ... @overload - def __sub__(self, _0: List[int], /): + def __eq__(self, _0: numpy.uint64, /): """ - usage.matplotlib: 1 + usage.skimage: 2 """ ... @overload - def __sub__(self, _0: Union[numpy.float64, float, int, dask.array.core.Array], /): + def __eq__(self, _0: numpy.int32, /): """ - usage.dask: 9 + usage.skimage: 1 """ ... - def __sub__(self, _0: object, /): + @overload + def __eq__(self, _0: numpy.int8, /): """ - usage.dask: 9 - usage.matplotlib: 357 - usage.pandas: 28 - usage.scipy: 1106 - usage.skimage: 154 - usage.sklearn: 153 - usage.xarray: 14 + usage.skimage: 1 """ ... @overload - def __truediv__(self, _0: float, /): + def __eq__(self, _0: numpy.int16, /): """ - usage.matplotlib: 75 - usage.skimage: 25 - usage.xarray: 3 + usage.skimage: 1 """ ... @overload - def __truediv__(self, _0: numpy.float64, /): + def __eq__(self, _0: numpy.longlong, /): """ - usage.matplotlib: 91 - usage.skimage: 49 - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __truediv__(self, _0: int, /): + def __eq__(self, _0: numpy.uint16, /): """ - usage.matplotlib: 40 - usage.skimage: 16 - usage.xarray: 3 + usage.skimage: 1 """ ... @overload - def __truediv__(self, _0: numpy.float32, /): + def __eq__(self, _0: numpy.uint32, /): """ usage.skimage: 1 """ ... @overload - def __truediv__(self, _0: numpy.int16, /): + def __eq__(self, _0: numpy.ulonglong, /): """ usage.skimage: 1 """ ... @overload - def __truediv__(self, _0: numpy.uint8, /): + def __eq__(self, _0: dask.array.core.Array, /): """ - usage.skimage: 2 + usage.xarray: 3 """ ... @overload - def __truediv__(self, _0: numpy.ndarray, /): + def __eq__(self, _0: xarray.core.dataarray.DataArray, /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __truediv__(self, _0: numpy.int64, /): + def __eq__(self, _0: xarray.core.variable.Variable, /): """ - usage.skimage: 1 usage.xarray: 1 """ ... @overload - def __truediv__(self, _0: object, /): + def __eq__(self, _0: object, /): + """ + usage.dask: 82 + usage.pandas: 513 + usage.scipy: 175 + """ + ... + + @overload + def __eq__(self, _0: float, /): """ - usage.pandas: 61 - usage.scipy: 878 - usage.sklearn: 212 + usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload - def __truediv__( - self, _0: Union[dask.array.core.Array, numpy.float64, int, numpy.int64], / - ): + def __eq__(self, _0: numpy.str_, /): """ - usage.dask: 23 + usage.sklearn: 1 """ ... - def __truediv__(self, _0: object, /): + def __eq__(self, _0: object, /): """ - usage.dask: 23 - usage.matplotlib: 206 - usage.pandas: 61 - usage.scipy: 878 - usage.skimage: 96 - usage.sklearn: 212 - usage.xarray: 8 + usage.dask: 82 + usage.matplotlib: 54 + usage.pandas: 513 + usage.scipy: 175 + usage.skimage: 89 + usage.sklearn: 213 + usage.xarray: 41 """ ... @overload - def astype(self, _0: Type[numpy.int64], /): + def __floordiv__(self, _0: int, /): """ - usage.skimage: 1 + usage.matplotlib: 2 + usage.skimage: 5 + usage.sklearn: 2 + usage.xarray: 1 """ ... @overload - def astype(self, _0: Literal["timedelta64[ns]"], /): + def __floordiv__( + self, + _0: Union[ + pandas._libs.missing.NAType, + numpy.ndarray, + pandas.core.series.Series, + pandas.core.arrays.integer.IntegerArray, + int, + ], + /, + ): """ - usage.xarray: 1 + usage.pandas: 6 """ ... @overload - def astype(self, _0: Type[float], /): + def __floordiv__(self, _0: Union[numpy.ndarray, int], /): """ - usage.xarray: 2 + usage.scipy: 12 """ ... @overload - def astype(self, _0: Type[numpy.float64], /): + def __floordiv__(self, _0: Union[int, numpy.int64], /): """ - usage.matplotlib: 2 - usage.xarray: 1 + usage.dask: 2 """ ... - @overload - def astype(self, _0: Union[Type[int], numpy.dtype], /): + def __floordiv__(self, _0: object, /): """ - usage.pandas: 8 + usage.dask: 2 + usage.matplotlib: 2 + usage.pandas: 6 + usage.scipy: 12 + usage.skimage: 5 + usage.sklearn: 2 + usage.xarray: 1 """ ... @overload - def astype(self, _0: Union[type, numpy.dtype, Literal["l", "d"]], /): + def __ge__(self, _0: int, /): """ - usage.scipy: 14 + usage.matplotlib: 6 + usage.skimage: 3 + usage.sklearn: 13 + usage.xarray: 2 """ ... @overload - def astype(self, _0: Type[int], /): + def __ge__(self, _0: numpy.ndarray, /): """ usage.matplotlib: 1 + usage.skimage: 6 """ ... @overload - def astype(self, _0: Type[numpy.float32], /): + def __ge__(self, _0: float, /): """ - usage.matplotlib: 2 + usage.skimage: 1 + usage.sklearn: 2 """ ... @overload - def astype(self, _0: Union[numpy.dtype, Literal["i8", "f8"]], /): + def __ge__(self, _0: numpy.int64, /): """ - usage.dask: 5 + usage.matplotlib: 3 + usage.skimage: 1 + usage.sklearn: 3 """ ... @overload - def astype(self, _0: type, /): + def __ge__( + self, _0: Union[numpy.ndarray, int, numpy.int64, pandas._libs.missing.NAType], / + ): """ - usage.sklearn: 2 + usage.pandas: 19 """ ... - def astype( - self, - _0: Union[type, numpy.dtype, Literal["i8", "f8", "l", "d", "timedelta64[ns]"]], - /, + @overload + def __ge__( + self, _0: Union[numpy.ndarray, numpy.int32, numpy.int64, int, numpy.float64], / ): """ - usage.dask: 5 - usage.matplotlib: 5 - usage.pandas: 8 - usage.scipy: 14 - usage.skimage: 1 - usage.sklearn: 2 - usage.xarray: 4 + usage.scipy: 51 """ ... - def byteswap(self, /): + @overload + def __ge__(self, _0: object, /): """ - usage.scipy: 1 + usage.dask: 39 """ ... - def clip(self, _0: float, _1: float, /): + @overload + def __ge__(self, _0: numpy.float64, /): """ - usage.scipy: 1 + usage.sklearn: 4 """ ... - def conj(self, /): + def __ge__(self, _0: object, /): """ - usage.scipy: 1 + usage.dask: 39 + usage.matplotlib: 10 + usage.pandas: 19 + usage.scipy: 51 + usage.skimage: 11 + usage.sklearn: 22 + usage.xarray: 2 """ ... - def copy(self, /): + @overload + def __getitem__(self, _0: Tuple[None, ellipsis], /): """ - usage.scipy: 2 + usage.xarray: 1 """ ... - def item(self, /): + @overload + def __getitem__(self, _0: int, /): """ - usage.matplotlib: 3 - usage.pandas: 1 - usage.scipy: 2 - usage.sklearn: 4 - usage.xarray: 3 + usage.matplotlib: 1 """ ... - def max(self, /): + @overload + def __getitem__(self, _0: Tuple[Union[ellipsis, None], ...], /): """ - usage.scipy: 2 + usage.dask: 9 """ ... - def mean(self, /): + def __getitem__(self, _0: Union[Tuple[Union[ellipsis, None], ...], int], /): """ - usage.sklearn: 2 + usage.dask: 9 + usage.matplotlib: 1 + usage.xarray: 1 """ ... - def ravel(self, /): + @overload + def __gt__(self, _0: numpy.ndarray, /): """ usage.matplotlib: 1 - usage.scipy: 2 - usage.xarray: 1 + usage.skimage: 3 + usage.sklearn: 1 """ ... @overload - def reshape(self, _0: Union[Tuple[Union[int, None], ...], List[int]], /): + def __gt__(self, _0: int, /): """ - usage.scipy: 5 + usage.matplotlib: 7 + usage.skimage: 12 + usage.sklearn: 37 """ ... @overload - def reshape(self, _0: Tuple[int, ...], /): + def __gt__(self, _0: numpy.int64, /): """ - usage.dask: 2 + usage.matplotlib: 16 + usage.skimage: 5 + usage.sklearn: 13 + usage.xarray: 2 """ ... - def reshape(self, _0: Union[Tuple[Union[None, int], ...], List[int]], /): + @overload + def __gt__(self, _0: dask.array.core.Array, /): """ - usage.dask: 2 - usage.scipy: 5 + usage.skimage: 1 """ ... - def squeeze(self, /): + @overload + def __gt__(self, _0: float, /): """ - usage.scipy: 3 - usage.xarray: 1 + usage.skimage: 1 + usage.sklearn: 4 """ ... - def sum(self, /, *, axis: None): + @overload + def __gt__(self, _0: numpy.float64, /): """ - usage.scipy: 2 + usage.matplotlib: 2 + usage.sklearn: 1 + usage.xarray: 1 """ ... - def tobytes(self, /): + @overload + def __gt__(self, _0: numpy.uint8, /): """ - usage.scipy: 1 + usage.xarray: 1 """ ... - -class iinfo: - - # usage.dask: 2 - # usage.pandas: 25 - # usage.scipy: 12 - # usage.skimage: 7 - # usage.sklearn: 13 - max: object - - # usage.pandas: 10 - # usage.scipy: 2 - # usage.skimage: 3 - min: object - - -class int16: - - # usage.pandas: 3 - __name__: ClassVar[object] - @overload - @classmethod - def __ne__(cls, _0: Union[numpy.int16, Type[numpy.int64]], /): + def __gt__( + self, + _0: Union[ + pandas._libs.missing.NAType, + numpy.int64, + int, + pandas.core.series.Series, + float, + ], + /, + ): """ - usage.pandas: 9 + usage.pandas: 24 """ ... @overload - @classmethod - def __ne__(cls, _0: numpy.int16, /): + def __gt__(self, _0: Union[numpy.int64, int, float, numpy.float64], /): """ - usage.scipy: 2 + usage.scipy: 50 """ ... @overload - @classmethod - def __ne__(cls, _0: int, /): + def __gt__(self, _0: Union[numpy.int64, int, float], /): """ - usage.sklearn: 1 + usage.dask: 20 """ ... - @classmethod - def __ne__(cls, _0: Union[int, numpy.int16, Type[numpy.int64]], /): + def __gt__(self, _0: object, /): """ - usage.pandas: 9 - usage.scipy: 2 - usage.sklearn: 1 + usage.dask: 20 + usage.matplotlib: 26 + usage.pandas: 24 + usage.scipy: 50 + usage.skimage: 22 + usage.sklearn: 56 + usage.xarray: 4 """ ... - # usage.pandas: 4 - # usage.scipy: 2 - dtype: object - - # usage.dask: 2 - ndim: object - - # usage.scipy: 1 - size: object - - def __add__(self, _0: object, /): + @overload + def __iadd__(self, _0: numpy.int64, /): """ - usage.pandas: 9 - usage.scipy: 17 + usage.dask: 1 + usage.matplotlib: 1 + usage.skimage: 1 """ ... - def __bool__(self, /): + @overload + def __iadd__(self, _0: numpy.float64, /): """ - usage.scipy: 1 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: int, /): + def __iadd__(self, _0: int, /): """ - usage.skimage: 1 + usage.matplotlib: 2 + usage.sklearn: 4 + usage.xarray: 2 """ ... @overload - def __eq__(self, _0: numpy.int64, /): + def __iadd__(self, _0: Union[int, numpy.int64], /): """ - usage.skimage: 1 + usage.pandas: 3 """ ... @overload - def __eq__(self, _0: numpy.ndarray, /): + def __iadd__(self, _0: Union[numpy.longlong, int, numpy.int64, numpy.float64], /): """ - usage.scipy: 2 - usage.xarray: 1 + usage.scipy: 29 """ ... - @overload - def __eq__(self, _0: object, /): + def __iadd__(self, _0: Union[int, numpy.longlong, numpy.float64, numpy.int64], /): """ - usage.pandas: 81 + usage.dask: 1 + usage.matplotlib: 3 + usage.pandas: 3 + usage.scipy: 29 + usage.skimage: 1 + usage.sklearn: 4 + usage.xarray: 3 """ ... - def __eq__(self, _0: object, /): + def __ifloordiv__(self, _0: int, /): """ - usage.pandas: 81 - usage.scipy: 2 - usage.skimage: 2 - usage.xarray: 1 + usage.pandas: 1 """ ... - def __floordiv__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / - ): + def __imod__(self, _0: int, /): """ - usage.pandas: 2 + usage.skimage: 1 """ ... - def __ge__(self, _0: int, /): + @overload + def __imul__(self, _0: numpy.int64, /): """ - usage.pandas: 2 - usage.skimage: 1 + usage.pandas: 1 """ ... - def __getitem__(self, _0: Tuple[Union[None, ellipsis], ...], /): + @overload + def __imul__(self, _0: Union[int, numpy.ndarray], /): """ - usage.dask: 2 + usage.scipy: 2 """ ... - def __gt__(self, _0: numpy.int16, /): + @overload + def __imul__(self, _0: int, /): """ - usage.matplotlib: 1 + usage.dask: 1 """ ... - def __le__(self, _0: int, /): + def __imul__(self, _0: Union[int, numpy.int64, numpy.ndarray], /): """ - usage.pandas: 2 + usage.dask: 1 + usage.pandas: 1 + usage.scipy: 2 """ ... @overload - def __lt__(self, _0: int, /): + def __isub__(self, _0: numpy.int64, /): """ - usage.matplotlib: 1 - usage.skimage: 3 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload - def __lt__(self, _0: numpy.int16, /): + def __isub__(self, _0: int, /): """ + usage.dask: 1 usage.matplotlib: 1 + usage.scipy: 1 + usage.skimage: 1 + usage.sklearn: 3 """ ... - def __lt__(self, _0: Union[numpy.int16, int], /): + @overload + def __isub__(self, _0: Union[numpy.int64, int], /): """ - usage.matplotlib: 2 - usage.skimage: 3 + usage.pandas: 6 """ ... - def __mod__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / - ): + def __isub__(self, _0: Union[int, numpy.int64], /): """ - usage.pandas: 2 + usage.dask: 1 + usage.matplotlib: 1 + usage.pandas: 6 + usage.scipy: 1 + usage.skimage: 2 + usage.sklearn: 4 """ ... @overload - def __mul__( - self, - _0: Union[ - numpy.ndarray, - pandas.core.arrays.timedeltas.TimedeltaArray, - pandas.core.series.Series, - pandas.core.arrays.integer.IntegerArray, - ], - /, - ): + def __itruediv__(self, _0: numpy.float64, /): """ - usage.pandas: 4 + usage.skimage: 2 """ ... @overload - def __mul__(self, _0: Union[numpy.int64, int], /): + def __itruediv__(self, _0: float, /): """ - usage.scipy: 4 + usage.pandas: 1 """ ... - def __mul__(self, _0: object, /): + def __itruediv__(self, _0: Union[float, numpy.float64], /): """ - usage.pandas: 4 - usage.scipy: 4 + usage.pandas: 1 + usage.skimage: 2 """ ... @overload - def __pow__(self, _0: int, /): + def __le__(self, _0: numpy.ndarray, /): """ + usage.matplotlib: 2 usage.skimage: 1 """ ... @overload - def __pow__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / - ): + def __le__(self, _0: int, /): """ - usage.pandas: 2 + usage.matplotlib: 3 + usage.skimage: 3 + usage.sklearn: 17 """ ... - def __pow__( - self, _0: Union[pandas.core.arrays.integer.IntegerArray, numpy.ndarray, int], / - ): + @overload + def __le__(self, _0: numpy.int64, /): """ - usage.pandas: 2 + usage.matplotlib: 3 usage.skimage: 1 + usage.sklearn: 3 """ ... @overload - def __radd__(self, _0: Union[pandas._libs.missing.NAType, numpy.int16], /): + def __le__( + self, + _0: Union[pandas._libs.missing.NAType, numpy.ndarray, int, float, numpy.int64], + /, + ): """ - usage.pandas: 6 + usage.pandas: 27 """ ... @overload - def __radd__(self, _0: object, /): + def __le__(self, _0: object, /): """ - usage.scipy: 18 + usage.scipy: 99 """ ... - def __radd__(self, _0: object, /): + @overload + def __le__(self, _0: Union[float, numpy.int64, pandas.core.series.Series, int], /): """ - usage.pandas: 6 - usage.scipy: 18 + usage.dask: 35 """ ... @overload - def __rmul__(self, _0: numpy.ndarray, /): + def __le__(self, _0: float, /): """ - usage.pandas: 1 + usage.sklearn: 2 """ ... - @overload - def __rmul__(self, _0: float, /): + def __le__(self, _0: object, /): """ - usage.scipy: 1 + usage.dask: 35 + usage.matplotlib: 8 + usage.pandas: 27 + usage.scipy: 99 + usage.skimage: 5 + usage.sklearn: 22 """ ... - def __rmul__(self, _0: Union[float, numpy.ndarray], /): + @overload + def __lt__(self, _0: numpy.int64, /): """ - usage.pandas: 1 - usage.scipy: 1 + usage.matplotlib: 16 + usage.skimage: 5 + usage.sklearn: 13 + usage.xarray: 2 """ ... @overload - def __rsub__(self, _0: numpy.int16, /): + def __lt__(self, _0: numpy.ndarray, /): """ - usage.skimage: 2 + usage.matplotlib: 1 + usage.skimage: 9 """ ... @overload - def __rsub__(self, _0: pandas.core.arrays.timedeltas.TimedeltaArray, /): + def __lt__(self, _0: int, /): """ - usage.pandas: 1 + usage.matplotlib: 13 + usage.skimage: 10 + usage.sklearn: 35 + usage.xarray: 3 """ ... - def __rsub__( - self, _0: Union[pandas.core.arrays.timedeltas.TimedeltaArray, numpy.int16], / - ): + @overload + def __lt__(self, _0: numpy.float64, /): """ - usage.pandas: 1 - usage.skimage: 2 + usage.matplotlib: 4 + usage.sklearn: 2 + usage.xarray: 1 """ ... @overload - def __rtruediv__(self, _0: numpy.float64, /): + def __lt__(self, _0: numpy.uint8, /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __rtruediv__( + def __lt__( self, _0: Union[ - numpy.ndarray, - pandas._libs.tslibs.timedeltas.Timedelta, - pandas._libs.tslibs.nattype.NaTType, + pandas._libs.missing.NAType, + numpy.int64, + pandas.core.arrays.categorical.Categorical, + int, ], /, ): """ - usage.pandas: 5 + usage.pandas: 10 """ ... @overload - def __rtruediv__(self, _0: numpy.ndarray, /): + def __lt__(self, _0: Union[float, numpy.int64, numpy.ndarray, numpy.float64], /): """ - usage.scipy: 2 + usage.scipy: 7 """ ... - def __rtruediv__( + @overload + def __lt__( self, _0: Union[ - numpy.ndarray, - numpy.float64, - pandas._libs.tslibs.timedeltas.Timedelta, - pandas._libs.tslibs.nattype.NaTType, + dask.dataframe.core.Series, + float, + numpy.int64, + int, + pandas.core.series.Series, ], /, ): """ - usage.pandas: 5 - usage.scipy: 2 - usage.skimage: 1 + usage.dask: 13 + """ + ... + + @overload + def __lt__(self, _0: float, /): + """ + usage.sklearn: 3 + """ + ... + + def __lt__(self, _0: object, /): + """ + usage.dask: 13 + usage.matplotlib: 34 + usage.pandas: 10 + usage.scipy: 7 + usage.skimage: 24 + usage.sklearn: 53 + usage.xarray: 7 """ ... @overload - def __sub__(self, _0: numpy.int16, /): + def __mod__(self, _0: int, /): """ - usage.skimage: 2 + usage.matplotlib: 3 + usage.scipy: 11 + usage.skimage: 1 + usage.sklearn: 2 """ ... @overload - def __sub__( + def __mod__( self, _0: Union[ - int, + pandas._libs.missing.NAType, pandas.core.arrays.integer.IntegerArray, - pandas.core.series.Series, - pandas.core.arrays.timedeltas.TimedeltaArray, + int, numpy.ndarray, ], /, ): """ - usage.pandas: 5 + usage.pandas: 10 """ ... - def __sub__(self, _0: object, /): + @overload + def __mod__(self, _0: Union[int, numpy.int64], /): """ - usage.pandas: 5 - usage.skimage: 2 + usage.dask: 2 """ ... - def __truediv__( + def __mod__( self, _0: Union[ + int, numpy.ndarray, - pandas.core.arrays.timedeltas.TimedeltaArray, - pandas.core.series.Series, pandas.core.arrays.integer.IntegerArray, + pandas._libs.missing.NAType, + numpy.int64, ], /, ): """ - usage.pandas: 4 + usage.dask: 2 + usage.matplotlib: 3 + usage.pandas: 10 + usage.scipy: 11 + usage.skimage: 1 + usage.sklearn: 2 """ ... - def astype(self, _0: numpy.dtype, /): + @overload + def __mul__(self, _0: numpy.ndarray, /): """ - usage.pandas: 1 + usage.skimage: 2 + usage.sklearn: 2 """ ... - def item(self, /): + @overload + def __mul__(self, _0: numpy.float64, /): """ - usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 2 """ ... - -class int32: - - # usage.dask: 2 - __module__: ClassVar[object] - - # usage.pandas: 3 - __name__: ClassVar[object] - @overload - @classmethod - def __lt__(cls, _0: Union[numpy.dtype, numpy.int32], /): + def __mul__(self, _0: numpy.int64, /): """ - usage.scipy: 3 + usage.skimage: 1 + usage.sklearn: 5 """ ... @overload - @classmethod - def __lt__(cls, _0: int, /): + def __mul__(self, _0: int, /): """ - usage.matplotlib: 2 - usage.skimage: 2 + usage.matplotlib: 6 + usage.sklearn: 2 + usage.xarray: 1 """ ... @overload - @classmethod - def __lt__(cls, _0: numpy.ma.core.MaskedConstant, /): + def __mul__(self, _0: object, /): """ - usage.matplotlib: 1 + usage.pandas: 59 + usage.scipy: 103 """ ... - @classmethod - def __lt__( - cls, _0: Union[int, numpy.ma.core.MaskedConstant, numpy.dtype, numpy.int32], / - ): + @overload + def __mul__(self, _0: float, /): """ usage.matplotlib: 3 - usage.scipy: 3 - usage.skimage: 2 + usage.sklearn: 8 """ ... @overload - @classmethod - def __ne__(cls, _0: Union[numpy.int32, Type[numpy.int64]], /): + def __mul__(self, _0: Union[numpy.float64, int, numpy.ndarray, numpy.int64], /): """ - usage.pandas: 9 + usage.dask: 9 """ ... - @overload - @classmethod - def __ne__(cls, _0: Union[numpy.int64, int, numpy.int32, Type[numpy.int32]], /): + def __mul__(self, _0: object, /): """ - usage.scipy: 33 + usage.dask: 9 + usage.matplotlib: 9 + usage.pandas: 59 + usage.scipy: 103 + usage.skimage: 4 + usage.sklearn: 19 + usage.xarray: 1 """ ... - @overload - @classmethod - def __ne__(cls, _0: numpy.dtype, /): + def __neg__(self, /): """ - usage.dask: 1 + usage.dask: 5 + usage.matplotlib: 1 + usage.pandas: 3 + usage.scipy: 20 + usage.skimage: 6 + usage.sklearn: 8 + usage.xarray: 1 """ ... - @overload - @classmethod - def __ne__(cls, _0: Union[numpy.dtype, numpy.int32, numpy.ndarray], /): + def __or__(self, _0: Union[bool, numpy.int64], /): """ - usage.sklearn: 8 + usage.dask: 2 """ ... - @classmethod - def __ne__(cls, _0: object, /): + @overload + def __pow__(self, _0: int, /): """ - usage.dask: 1 - usage.pandas: 9 - usage.scipy: 33 - usage.sklearn: 8 + usage.scipy: 13 + usage.skimage: 2 """ ... - # usage.dask: 1 - # usage.pandas: 4 - # usage.scipy: 5 - # usage.xarray: 1 - dtype: object - - # usage.dask: 3 - # usage.xarray: 1 - ndim: object - - # usage.dask: 5 - shape: object - - # usage.scipy: 1 - size: object - @overload - def __add__( + def __pow__( self, _0: Union[ - numpy.int32, + pandas._libs.missing.NAType, pandas.core.arrays.integer.IntegerArray, pandas.core.series.Series, - pandas.core.arrays.timedeltas.TimedeltaArray, + numpy.float64, numpy.ndarray, ], /, ): """ - usage.pandas: 8 + usage.pandas: 10 """ ... @overload - def __add__(self, _0: object, /): + def __pow__(self, _0: Union[numpy.int64, int], /): """ - usage.scipy: 25 + usage.dask: 3 """ ... - @overload - def __add__(self, _0: Union[int, numpy.int64], /): + def __pow__(self, _0: object, /): """ usage.dask: 3 + usage.pandas: 10 + usage.scipy: 13 + usage.skimage: 2 """ ... @overload - def __add__(self, _0: Union[int, numpy.int32, bool, numpy.int64, numpy.float64], /): + def __radd__(self, _0: numpy.ndarray, /): """ - usage.sklearn: 6 + usage.matplotlib: 2 + usage.skimage: 2 + usage.sklearn: 1 """ ... - def __add__(self, _0: object, /): + @overload + def __radd__(self, _0: int, /): """ - usage.dask: 3 - usage.pandas: 8 - usage.scipy: 25 - usage.sklearn: 6 + usage.matplotlib: 8 + usage.skimage: 6 + usage.sklearn: 8 + usage.xarray: 1 """ ... - def __and__(self, _0: int, /): + @overload + def __radd__(self, _0: numpy.float64, /): """ - usage.scipy: 8 + usage.matplotlib: 1 + usage.skimage: 3 """ ... - def __bool__(self, /): + @overload + def __radd__(self, _0: numpy.int64, /): """ - usage.scipy: 1 + usage.matplotlib: 10 + usage.skimage: 6 + usage.sklearn: 11 """ ... @overload - def __eq__(self, _0: int, /): + def __radd__(self, _0: object, /): """ - usage.matplotlib: 4 - usage.skimage: 2 + usage.dask: 26 + usage.pandas: 63 + usage.scipy: 145 """ ... @overload - def __eq__(self, _0: numpy.int64, /): + def __radd__(self, _0: numpy.int32, /): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload - def __eq__(self, _0: numpy.ndarray, /): + def __radd__(self, _0: float, /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload - def __eq__(self, _0: numpy.int32, /): + def __radd__(self, _0: numpy.memmap, /): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... - @overload - def __eq__( - self, - _0: Union[ - numpy.int64, - pandas.core.series.Series, - numpy.int32, - int, - pandas.core.arrays.integer.IntegerArray, - ], - /, - ): + def __radd__(self, _0: object, /): """ - usage.pandas: 77 + usage.dask: 26 + usage.matplotlib: 21 + usage.pandas: 63 + usage.scipy: 145 + usage.skimage: 17 + usage.sklearn: 23 + usage.xarray: 1 """ ... @overload - def __eq__( - self, - _0: Union[Literal["silverman", "scott"], numpy.int32, int, numpy.ndarray], - /, - ): + def __rand__(self, _0: Union[numpy.bool_, numpy.ndarray], /): """ - usage.scipy: 34 + usage.scipy: 8 """ ... @overload - def __eq__(self, _0: Union[numpy.int32, numpy.int64, int], /): + def __rand__(self, _0: Union[bool, numpy.int64], /): """ - usage.dask: 5 + usage.dask: 2 """ ... - @overload - def __eq__(self, _0: Union[Literal["mle"], numpy.ndarray, numpy.int32, int], /): + def __rand__(self, _0: Union[numpy.int64, bool, numpy.ndarray, numpy.bool_], /): """ - usage.sklearn: 16 + usage.dask: 2 + usage.scipy: 8 """ ... - def __eq__(self, _0: object, /): + @overload + def __rfloordiv__(self, _0: int, /): """ - usage.dask: 5 - usage.matplotlib: 4 - usage.pandas: 77 - usage.scipy: 34 - usage.skimage: 3 - usage.sklearn: 16 - usage.xarray: 3 + usage.matplotlib: 1 + usage.scipy: 1 + usage.skimage: 1 + usage.sklearn: 2 """ ... - def __floordiv__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / + @overload + def __rfloordiv__( + self, _0: Union[pandas.core.indexes.numeric.Int64Index, numpy.ndarray], / ): """ usage.pandas: 2 @@ -43345,321 +56360,378 @@ def __floordiv__( ... @overload - def __ge__(self, _0: int, /): + def __rfloordiv__(self, _0: Union[int, numpy.int64], /): + """ + usage.dask: 2 + """ + ... + + @overload + def __rfloordiv__(self, _0: float, /): + """ + usage.sklearn: 1 + """ + ... + + def __rfloordiv__( + self, + _0: Union[ + int, + float, + numpy.ndarray, + pandas.core.indexes.numeric.Int64Index, + numpy.int64, + ], + /, + ): """ + usage.dask: 2 usage.matplotlib: 1 - usage.pandas: 3 + usage.pandas: 2 + usage.scipy: 1 usage.skimage: 1 + usage.sklearn: 3 """ ... @overload - def __ge__(self, _0: Union[numpy.int64, int, numpy.int32], /): + def __rmod__(self, _0: numpy.ndarray, /): """ - usage.scipy: 12 + usage.pandas: 52 """ ... @overload - def __ge__(self, _0: Union[int, float], /): + def __rmod__(self, _0: str, /): """ + usage.scipy: 14 usage.sklearn: 6 """ ... - def __ge__(self, _0: Union[float, int, numpy.int32, numpy.int64], /): + @overload + def __rmod__(self, _0: Literal["%-12g"], /): """ usage.matplotlib: 1 - usage.pandas: 3 - usage.scipy: 12 - usage.skimage: 1 - usage.sklearn: 6 """ ... @overload - def __getitem__(self, _0: ellipsis, /): + def __rmod__(self, _0: Union[int, numpy.int64, Literal["%d B"]], /): """ - usage.xarray: 1 + usage.dask: 3 """ ... @overload - def __getitem__(self, _0: Tuple[Union[None, ellipsis], ...], /): + def __rmod__(self, _0: Literal["x%d"], /): """ - usage.dask: 4 + usage.sklearn: 1 """ ... - def __getitem__(self, _0: Union[Tuple[Union[ellipsis, None], ...], ellipsis], /): + @overload + def __rmod__(self, _0: Literal["%d"], /): """ - usage.dask: 4 - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload - def __gt__(self, _0: int, /): + def __rmod__(self, _0: Literal["not %s"], /): """ - usage.pandas: 2 usage.sklearn: 1 """ ... @overload - def __gt__(self, _0: Union[numpy.int32, int], /): + def __rmod__(self, _0: Literal["%s"], /): """ - usage.scipy: 10 + usage.sklearn: 1 """ ... @overload - def __gt__(self, _0: numpy.ma.core.MaskedConstant, /): + def __rmod__(self, _0: Literal['%d [label="(...)"'], /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... - def __gt__(self, _0: Union[int, numpy.int32, numpy.ma.core.MaskedConstant], /): + @overload + def __rmod__(self, _0: Literal["] ;\n"], /): """ - usage.matplotlib: 1 - usage.pandas: 2 - usage.scipy: 10 usage.sklearn: 1 """ ... - def __iadd__(self, _0: Union[int, numpy.int32], /): + def __rmod__(self, _0: Union[str, numpy.ndarray, int, numpy.int64], /): """ - usage.scipy: 2 + usage.dask: 3 + usage.matplotlib: 1 + usage.pandas: 52 + usage.scipy: 14 + usage.sklearn: 13 """ ... @overload - def __le__(self, _0: int, /): + def __rmul__(self, _0: float, /): """ - usage.pandas: 3 + usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 6 """ ... @overload - def __le__(self, _0: Union[numpy.int32, numpy.int64, int, float], /): + def __rmul__(self, _0: int, /): """ - usage.scipy: 10 + usage.matplotlib: 3 + usage.skimage: 3 """ ... @overload - def __le__(self, _0: Union[int, float], /): + def __rmul__(self, _0: numpy.ndarray, /): """ - usage.sklearn: 8 + usage.matplotlib: 3 + usage.skimage: 2 + usage.sklearn: 1 """ ... - def __le__(self, _0: Union[float, int, numpy.int32, numpy.int64], /): + @overload + def __rmul__(self, _0: numpy.int64, /): """ - usage.pandas: 3 - usage.scipy: 10 - usage.sklearn: 8 + usage.skimage: 1 + usage.sklearn: 5 """ ... - def __mod__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / - ): + @overload + def __rmul__(self, _0: Tuple[int], /): """ - usage.pandas: 2 + usage.xarray: 1 """ ... @overload - def __mul__( - self, - _0: Union[ - numpy.ndarray, - pandas.core.arrays.timedeltas.TimedeltaArray, - pandas.core.series.Series, - pandas.core.arrays.integer.IntegerArray, - ], - /, - ): + def __rmul__(self, _0: Tuple[slice[None, None, None]], /): """ - usage.pandas: 4 + usage.xarray: 1 """ ... @overload - def __mul__(self, _0: Union[numpy.int64, int], /): + def __rmul__(self, _0: object, /): """ - usage.scipy: 7 + usage.pandas: 59 + usage.scipy: 193 """ ... @overload - def __mul__(self, _0: int, /): + def __rmul__(self, _0: numpy.float64, /): """ - usage.dask: 1 + usage.matplotlib: 1 + usage.sklearn: 6 """ ... - def __mul__(self, _0: object, /): + @overload + def __rmul__(self, _0: Union[numpy.int64, int], /): """ - usage.dask: 1 - usage.pandas: 4 - usage.scipy: 7 + usage.dask: 4 """ ... - def __neg__(self, /): + @overload + def __rmul__(self, _0: List[int], /): """ - usage.scipy: 3 + usage.sklearn: 1 """ ... - @overload - def __pow__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / - ): + def __rmul__(self, _0: object, /): """ - usage.pandas: 2 + usage.dask: 4 + usage.matplotlib: 8 + usage.pandas: 59 + usage.scipy: 193 + usage.skimage: 8 + usage.sklearn: 19 + usage.xarray: 2 """ ... - @overload - def __pow__(self, _0: int, /): + def __ror__(self, _0: Union[bool, numpy.int64], /): """ - usage.scipy: 2 + usage.dask: 2 """ ... - def __pow__( - self, _0: Union[int, pandas.core.arrays.integer.IntegerArray, numpy.ndarray], / - ): + @overload + def __rpow__(self, _0: pandas._libs.missing.NAType, /): """ - usage.pandas: 2 - usage.scipy: 2 + usage.pandas: 1 """ ... @overload - def __radd__(self, _0: Union[pandas._libs.missing.NAType, numpy.int32], /): + def __rpow__(self, _0: Union[float, int], /): """ - usage.pandas: 6 + usage.scipy: 11 """ ... @overload - def __radd__(self, _0: object, /): + def __rpow__(self, _0: float, /): """ - usage.scipy: 20 + usage.matplotlib: 1 """ ... @overload - def __radd__(self, _0: numpy.int32, /): + def __rpow__(self, _0: Union[int, numpy.int64], /): """ - usage.sklearn: 2 + usage.dask: 2 """ ... - def __radd__(self, _0: object, /): + def __rpow__( + self, _0: Union[numpy.int64, int, pandas._libs.missing.NAType, float], / + ): """ - usage.pandas: 6 - usage.scipy: 20 - usage.sklearn: 2 + usage.dask: 2 + usage.matplotlib: 1 + usage.pandas: 1 + usage.scipy: 11 """ ... - def __rfloordiv__(self, _0: pandas._libs.tslibs.timedeltas.Timedelta, /): + @overload + def __rsub__(self, _0: numpy.ndarray, /): """ - usage.pandas: 1 + usage.matplotlib: 1 + usage.skimage: 9 + usage.sklearn: 1 + usage.xarray: 3 """ ... @overload - def __rmod__(self, _0: str, /): + def __rsub__(self, _0: int, /): """ - usage.scipy: 2 + usage.matplotlib: 2 + usage.skimage: 10 + usage.sklearn: 8 + usage.xarray: 3 """ ... @overload - def __rmod__(self, _0: Literal["%d"], /): + def __rsub__(self, _0: numpy.int64, /): """ - usage.sklearn: 1 + usage.matplotlib: 8 + usage.skimage: 9 + usage.sklearn: 11 + usage.xarray: 4 """ ... - def __rmod__(self, _0: str, /): + @overload + def __rsub__(self, _0: object, /): """ - usage.scipy: 2 - usage.sklearn: 1 + usage.pandas: 32 + """ + ... + + @overload + def __rsub__( + self, _0: Union[numpy.ndarray, numpy.float64, numpy.int64, float, int], / + ): + """ + usage.scipy: 93 """ ... @overload - def __rmul__(self, _0: numpy.ndarray, /): + def __rsub__(self, _0: numpy.float64, /): """ - usage.pandas: 1 - usage.sklearn: 1 + usage.matplotlib: 3 + usage.sklearn: 3 """ ... @overload - def __rmul__(self, _0: Union[float, int], /): + def __rsub__(self, _0: float, /): """ - usage.scipy: 2 + usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload - def __rmul__(self, _0: int, /): + def __rsub__(self, _0: Union[numpy.int64, int], /): """ - usage.dask: 2 + usage.dask: 3 """ ... - def __rmul__(self, _0: Union[numpy.ndarray, float, int], /): + def __rsub__(self, _0: object, /): """ - usage.dask: 2 - usage.pandas: 1 - usage.scipy: 2 - usage.sklearn: 1 + usage.dask: 3 + usage.matplotlib: 15 + usage.pandas: 32 + usage.scipy: 93 + usage.skimage: 28 + usage.sklearn: 25 + usage.xarray: 10 """ ... @overload - def __rsub__(self, _0: pandas.core.arrays.timedeltas.TimedeltaArray, /): + def __rtruediv__(self, _0: numpy.ndarray, /): """ - usage.pandas: 1 + usage.matplotlib: 2 + usage.skimage: 6 + usage.sklearn: 15 + usage.xarray: 1 """ ... @overload - def __rsub__(self, _0: Union[numpy.int32, int], /): + def __rtruediv__(self, _0: int, /): """ - usage.scipy: 3 - usage.sklearn: 5 + usage.skimage: 2 + usage.sklearn: 2 """ ... @overload - def __rsub__(self, _0: numpy.int32, /): + def __rtruediv__(self, _0: numpy.int64, /): """ - usage.dask: 1 + usage.skimage: 2 + usage.sklearn: 7 """ ... - def __rsub__( - self, - _0: Union[int, numpy.int32, pandas.core.arrays.timedeltas.TimedeltaArray], - /, - ): + @overload + def __rtruediv__(self, _0: numpy.float64, /): """ - usage.dask: 1 - usage.pandas: 1 - usage.scipy: 3 + usage.skimage: 1 usage.sklearn: 5 + usage.xarray: 1 + """ + ... + + @overload + def __rtruediv__(self, _0: float, /): + """ + usage.matplotlib: 5 + usage.sklearn: 6 + usage.xarray: 1 """ ... @@ -43667,9 +56739,9 @@ def __rsub__( def __rtruediv__( self, _0: Union[ + pandas._libs.tslibs.nattype.NaTType, numpy.ndarray, pandas._libs.tslibs.timedeltas.Timedelta, - pandas._libs.tslibs.nattype.NaTType, ], /, ): @@ -43679,4645 +56751,4771 @@ def __rtruediv__( ... @overload - def __rtruediv__(self, _0: numpy.ndarray, /): + def __rtruediv__(self, _0: object, /): """ - usage.scipy: 2 + usage.dask: 10 + usage.scipy: 47 """ ... - @overload - def __rtruediv__(self, _0: numpy.int32, /): + def __rtruediv__(self, _0: object, /): """ - usage.dask: 1 + usage.dask: 10 + usage.matplotlib: 7 + usage.pandas: 5 + usage.scipy: 47 + usage.skimage: 11 + usage.sklearn: 35 + usage.xarray: 3 """ ... - def __rtruediv__( - self, - _0: Union[ - numpy.int32, - pandas._libs.tslibs.timedeltas.Timedelta, - pandas._libs.tslibs.nattype.NaTType, - numpy.ndarray, - ], - /, - ): + def __rxor__(self, _0: Union[bool, numpy.int64], /): """ - usage.dask: 1 - usage.pandas: 5 - usage.scipy: 2 + usage.dask: 2 """ ... @overload - def __sub__( - self, - _0: Union[ - int, - pandas.core.arrays.integer.IntegerArray, - pandas.core.series.Series, - pandas.core.arrays.timedeltas.TimedeltaArray, - numpy.ndarray, - ], - /, - ): + def __sub__(self, _0: int, /): """ - usage.pandas: 5 + usage.matplotlib: 12 + usage.skimage: 12 + usage.sklearn: 32 + usage.xarray: 2 """ ... @overload - def __sub__(self, _0: Union[numpy.int32, int], /): + def __sub__(self, _0: numpy.int64, /): """ - usage.scipy: 18 - usage.sklearn: 5 + usage.matplotlib: 8 + usage.skimage: 9 + usage.sklearn: 11 + usage.xarray: 4 """ ... @overload - def __sub__(self, _0: numpy.int32, /): + def __sub__(self, _0: float, /): """ - usage.dask: 1 + usage.matplotlib: 5 + usage.skimage: 1 + usage.xarray: 2 """ ... - def __sub__(self, _0: object, /): + @overload + def __sub__(self, _0: numpy.float64, /): """ - usage.dask: 1 - usage.pandas: 5 - usage.scipy: 18 - usage.sklearn: 5 + usage.matplotlib: 5 + usage.sklearn: 1 + usage.xarray: 2 """ ... @overload - def __truediv__( - self, - _0: Union[ - numpy.ndarray, - pandas.core.arrays.timedeltas.TimedeltaArray, - pandas.core.series.Series, - pandas.core.arrays.integer.IntegerArray, - ], - /, - ): + def __sub__(self, _0: object, /): """ - usage.pandas: 4 + usage.pandas: 26 + usage.scipy: 150 """ ... @overload - def __truediv__(self, _0: numpy.int32, /): + def __sub__(self, _0: numpy.ndarray, /): """ - usage.dask: 1 + usage.matplotlib: 2 + usage.sklearn: 2 """ ... - def __truediv__( - self, - _0: Union[ - numpy.int32, - pandas.core.arrays.integer.IntegerArray, - pandas.core.series.Series, - pandas.core.arrays.timedeltas.TimedeltaArray, - numpy.ndarray, - ], - /, - ): + @overload + def __sub__(self, _0: List[int], /): """ - usage.dask: 1 - usage.pandas: 4 + usage.matplotlib: 1 """ ... @overload - def astype(self, _0: Type[numpy.int64], /): + def __sub__(self, _0: Union[numpy.int64, int], /): """ - usage.skimage: 2 + usage.dask: 7 """ ... - @overload - def astype(self, _0: numpy.dtype, /): + def __sub__(self, _0: object, /): """ - usage.pandas: 1 + usage.dask: 7 + usage.matplotlib: 33 + usage.pandas: 26 + usage.scipy: 150 + usage.skimage: 22 + usage.sklearn: 46 + usage.xarray: 10 """ ... - def astype(self, _0: Union[numpy.dtype, Type[numpy.int64]], /): + @overload + def __truediv__(self, _0: int, /): """ - usage.pandas: 1 - usage.skimage: 2 + usage.matplotlib: 5 + usage.skimage: 7 + usage.sklearn: 10 """ ... - -class int64: - - # usage.dask: 1 - __module__: ClassVar[object] - - # usage.matplotlib: 1 - __mro__: ClassVar[object] - - # usage.pandas: 6 - # usage.scipy: 2 - __name__: ClassVar[object] - - # usage.pandas: 1 - type: ClassVar[object] - @overload - @classmethod - def __ne__(cls, _0: object, /): + def __truediv__(self, _0: numpy.int64, /): """ - usage.pandas: 74 - usage.scipy: 71 - usage.sklearn: 36 + usage.skimage: 2 + usage.sklearn: 7 """ ... @overload - @classmethod - def __ne__(cls, _0: int, /): + def __truediv__(self, _0: float, /): """ - usage.matplotlib: 12 + usage.matplotlib: 3 usage.skimage: 4 - usage.xarray: 6 + usage.sklearn: 5 + usage.xarray: 2 """ ... @overload - @classmethod - def __ne__(cls, _0: numpy.int64, /): + def __truediv__(self, _0: numpy.float64, /): """ - usage.skimage: 2 + usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 3 """ ... @overload - @classmethod - def __ne__(cls, _0: Literal[""], /): + def __truediv__(self, _0: object, /): """ - usage.matplotlib: 1 + usage.pandas: 22 """ ... @overload - @classmethod - def __ne__(cls, _0: Literal["1"], /): + def __truediv__( + self, _0: Union[numpy.int64, float, int, numpy.float64, numpy.ndarray], / + ): """ - usage.matplotlib: 1 + usage.scipy: 50 """ ... @overload - @classmethod - def __ne__(cls, _0: Literal["2"], /): + def __truediv__(self, _0: Union[numpy.int64, int, float], /): """ - usage.matplotlib: 1 + usage.dask: 6 """ ... - @overload - @classmethod - def __ne__(cls, _0: Literal["3"], /): + def __truediv__(self, _0: object, /): """ - usage.matplotlib: 1 + usage.dask: 6 + usage.matplotlib: 9 + usage.pandas: 22 + usage.scipy: 50 + usage.skimage: 14 + usage.sklearn: 25 + usage.xarray: 2 + """ + ... + + def __xor__(self, _0: Union[bool, numpy.int64], /): + """ + usage.dask: 2 """ ... @overload - @classmethod - def __ne__(cls, _0: Literal["4"], /): + def astype(self, _0: Type[numpy.int64], /): """ - usage.matplotlib: 1 + usage.skimage: 3 """ ... @overload - @classmethod - def __ne__(cls, _0: Literal["5"], /): + def astype(self, _0: numpy.dtype, /): """ - usage.matplotlib: 1 + usage.pandas: 3 """ ... @overload - @classmethod - def __ne__(cls, _0: None, /): + def astype(self, _0: Union[Type[float], Literal["d"]], /): """ - usage.matplotlib: 1 + usage.scipy: 3 """ ... @overload - @classmethod - def __ne__(cls, _0: float, /): + def astype(self, _0: Type[numpy.float64], /): """ usage.matplotlib: 1 """ ... @overload - @classmethod - def __ne__(cls, _0: Union[float, int, numpy.dtype, numpy.int64], /): + def astype(self, _0: Type[numpy.float32], /): """ - usage.dask: 14 + usage.matplotlib: 2 """ ... - @classmethod - def __ne__(cls, _0: object, /): + @overload + def astype(self, _0: Union[Literal["int64"], numpy.dtype], /): """ - usage.dask: 14 - usage.matplotlib: 20 - usage.pandas: 74 - usage.scipy: 71 - usage.skimage: 6 - usage.sklearn: 36 - usage.xarray: 6 + usage.dask: 2 """ ... - # usage.xarray: 1 - coords: object - - # usage.dask: 11 - # usage.pandas: 5 - # usage.scipy: 9 - # usage.xarray: 1 - dtype: object - - # usage.scipy: 3 - itemsize: object - - # usage.dask: 8 - # usage.matplotlib: 1 - # usage.pandas: 3 - # usage.skimage: 1 - ndim: object - - # usage.dask: 10 - # usage.scipy: 1 - # usage.xarray: 1 - shape: object - - # usage.scipy: 1 - size: object - - # usage.pandas: 1 - values: object - - # usage.xarray: 1 - variable: object + def astype(self, _0: Union[numpy.dtype, Literal["int64", "d"], type], /): + """ + usage.dask: 2 + usage.matplotlib: 3 + usage.pandas: 3 + usage.scipy: 3 + usage.skimage: 3 + """ + ... - # usage.xarray: 1 - variables: object + def item(self, /): + """ + usage.pandas: 1 + usage.sklearn: 2 + """ + ... - @overload - def __add__(self, _0: int, /): + def ravel(self, /): """ - usage.matplotlib: 25 - usage.skimage: 25 - usage.xarray: 7 + usage.dask: 1 """ ... @overload - def __add__(self, _0: numpy.float64, /): + def reshape(self, _0: List[int], /): """ - usage.matplotlib: 9 - usage.skimage: 2 - usage.xarray: 2 + usage.scipy: 2 """ ... @overload - def __add__(self, _0: numpy.int64, /): + def reshape(self, _0: Tuple[int], /): """ - usage.matplotlib: 10 - usage.skimage: 6 + usage.dask: 1 """ ... - @overload - def __add__(self, _0: float, /): + def reshape(self, _0: Union[Tuple[int], List[int]], /): """ - usage.matplotlib: 6 - usage.skimage: 2 - usage.xarray: 3 + usage.dask: 1 + usage.scipy: 2 """ ... - @overload - def __add__(self, _0: numpy.ndarray, /): + def tolist(self, /): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.xarray: 1 """ ... - @overload - def __add__(self, _0: object, /): + def view(self, _0: Literal["M8[ns]", "M8[us]"], /): """ - usage.pandas: 63 - usage.scipy: 282 + usage.pandas: 2 """ ... + +class int8: + + # usage.pandas: 3 + __name__: ClassVar[object] + @overload - def __add__(self, _0: List[int], /): + @classmethod + def __ne__(cls, _0: Union[numpy.int8, Type[numpy.int64]], /): """ - usage.matplotlib: 1 + usage.pandas: 9 """ ... @overload - def __add__(self, _0: Union[float, numpy.ndarray, numpy.int64, int], /): + @classmethod + def __ne__(cls, _0: numpy.int8, /): """ - usage.dask: 20 + usage.scipy: 2 """ ... @overload - def __add__(self, _0: Union[numpy.float64, bool, numpy.int64, int, float], /): + @classmethod + def __ne__(cls, _0: numpy.dtype, /): """ - usage.sklearn: 49 + usage.dask: 2 """ ... - def __add__(self, _0: object, /): + @classmethod + def __ne__(cls, _0: Union[numpy.dtype, numpy.int8, Type[numpy.int64]], /): """ - usage.dask: 20 - usage.matplotlib: 52 - usage.pandas: 63 - usage.scipy: 282 - usage.skimage: 37 - usage.sklearn: 49 - usage.xarray: 12 + usage.dask: 2 + usage.pandas: 9 + usage.scipy: 2 """ ... + # usage.pandas: 3 + # usage.scipy: 2 + dtype: object + + # usage.dask: 2 + ndim: object + + # usage.scipy: 1 + size: object + @overload - def __and__(self, _0: Union[numpy.ndarray, numpy.bool_], /): + def __add__(self, _0: object, /): """ - usage.scipy: 36 + usage.pandas: 13 + usage.scipy: 17 """ ... @overload - def __and__(self, _0: Union[bool, numpy.int64], /): + def __add__(self, _0: int, /): """ - usage.dask: 2 + usage.matplotlib: 6 """ ... - def __and__(self, _0: Union[numpy.int64, bool, numpy.bool_, numpy.ndarray], /): + def __add__(self, _0: object, /): """ - usage.dask: 2 - usage.scipy: 36 + usage.matplotlib: 6 + usage.pandas: 13 + usage.scipy: 17 """ ... def __bool__(self, /): """ - usage.dask: 1 usage.scipy: 1 """ ... @overload - def __eq__(self, _0: numpy.flatiter, /): + def __eq__(self, _0: int, /): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload - def __eq__(self, _0: numpy.ndarray, /): + def __eq__(self, _0: numpy.int64, /): """ - usage.matplotlib: 4 - usage.skimage: 7 - usage.xarray: 10 + usage.skimage: 1 """ ... @overload - def __eq__(self, _0: int, /): + def __eq__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 26 - usage.skimage: 46 - usage.xarray: 22 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: numpy.int64, /): + def __eq__(self, _0: object, /): """ - usage.matplotlib: 22 - usage.skimage: 16 - usage.xarray: 4 + usage.pandas: 75 """ ... @overload - def __eq__(self, _0: numpy.float64, /): + def __eq__(self, _0: Union[numpy.ndarray, int], /): """ - usage.skimage: 8 + usage.scipy: 3 """ ... @overload - def __eq__(self, _0: numpy.uint8, /): + def __eq__(self, _0: numpy.int8, /): """ - usage.skimage: 1 + usage.matplotlib: 8 """ ... @overload - def __eq__(self, _0: numpy.uint64, /): + def __eq__(self, _0: importlib._bootstrap.MonotonicConstraint, /): + """ + usage.sklearn: 2 """ + ... + + def __eq__(self, _0: object, /): + """ + usage.matplotlib: 8 + usage.pandas: 75 + usage.scipy: 3 usage.skimage: 2 + usage.sklearn: 2 + usage.xarray: 1 """ ... - @overload - def __eq__(self, _0: numpy.int32, /): + def __floordiv__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / + ): """ - usage.skimage: 1 + usage.pandas: 2 """ ... @overload - def __eq__(self, _0: numpy.int8, /): + def __ge__(self, _0: int, /): """ - usage.skimage: 1 + usage.pandas: 2 """ ... @overload - def __eq__(self, _0: numpy.int16, /): + def __ge__(self, _0: numpy.ndarray, /): """ - usage.skimage: 1 + usage.scipy: 1 """ ... @overload - def __eq__(self, _0: numpy.longlong, /): + def __ge__(self, _0: numpy.int8, /): """ - usage.skimage: 1 + usage.matplotlib: 4 """ ... - @overload - def __eq__(self, _0: numpy.uint16, /): + def __ge__(self, _0: Union[numpy.int8, int, numpy.ndarray], /): """ - usage.skimage: 1 + usage.matplotlib: 4 + usage.pandas: 2 + usage.scipy: 1 """ ... - @overload - def __eq__(self, _0: numpy.uint32, /): + def __getitem__(self, _0: Tuple[ellipsis, None], /): """ - usage.skimage: 1 + usage.dask: 1 """ ... - @overload - def __eq__(self, _0: numpy.ulonglong, /): + def __gt__(self, _0: int, /): """ - usage.skimage: 1 + usage.scipy: 1 """ ... @overload - def __eq__(self, _0: dask.array.core.Array, /): + def __le__(self, _0: int, /): """ - usage.xarray: 3 + usage.pandas: 2 + usage.scipy: 1 """ ... @overload - def __eq__(self, _0: xarray.core.dataarray.DataArray, /): + def __le__(self, _0: numpy.int8, /): """ - usage.xarray: 1 + usage.matplotlib: 4 """ ... - @overload - def __eq__(self, _0: xarray.core.variable.Variable, /): + def __le__(self, _0: Union[numpy.int8, int], /): """ - usage.xarray: 1 + usage.matplotlib: 4 + usage.pandas: 2 + usage.scipy: 1 """ ... @overload - def __eq__(self, _0: object, /): + def __lt__(self, _0: int, /): """ - usage.dask: 82 - usage.pandas: 513 - usage.scipy: 175 - usage.sklearn: 213 + usage.skimage: 1 """ ... @overload - def __eq__(self, _0: float, /): + def __lt__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 2 + usage.scipy: 1 """ ... - def __eq__(self, _0: object, /): + def __lt__(self, _0: Union[numpy.ndarray, int], /): """ - usage.dask: 82 - usage.matplotlib: 54 - usage.pandas: 513 - usage.scipy: 175 - usage.skimage: 89 - usage.sklearn: 213 - usage.xarray: 41 + usage.scipy: 1 + usage.skimage: 1 """ ... - @overload - def __floordiv__(self, _0: int, /): + def __mod__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / + ): """ - usage.matplotlib: 2 - usage.skimage: 5 - usage.sklearn: 2 - usage.xarray: 1 + usage.pandas: 2 """ ... @overload - def __floordiv__( + def __mul__( self, _0: Union[ - pandas._libs.missing.NAType, numpy.ndarray, + pandas.core.arrays.timedeltas.TimedeltaArray, pandas.core.series.Series, pandas.core.arrays.integer.IntegerArray, - int, ], /, ): """ - usage.pandas: 6 + usage.pandas: 4 """ ... @overload - def __floordiv__(self, _0: Union[numpy.ndarray, int], /): + def __mul__(self, _0: Union[numpy.int64, int], /): """ - usage.scipy: 12 + usage.scipy: 4 """ ... - @overload - def __floordiv__(self, _0: Union[int, numpy.int64], /): + def __mul__(self, _0: object, /): """ - usage.dask: 2 + usage.pandas: 4 + usage.scipy: 4 """ ... - def __floordiv__(self, _0: object, /): + def __pow__( + self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / + ): """ - usage.dask: 2 - usage.matplotlib: 2 - usage.pandas: 6 - usage.scipy: 12 - usage.skimage: 5 - usage.sklearn: 2 - usage.xarray: 1 + usage.pandas: 2 """ ... @overload - def __ge__(self, _0: int, /): + def __radd__(self, _0: Union[pandas._libs.missing.NAType, numpy.int8], /): """ - usage.matplotlib: 6 - usage.skimage: 3 - usage.xarray: 2 + usage.pandas: 6 """ ... @overload - def __ge__(self, _0: numpy.ndarray, /): + def __radd__(self, _0: object, /): """ - usage.matplotlib: 1 - usage.skimage: 6 + usage.scipy: 18 """ ... - @overload - def __ge__(self, _0: float, /): + def __radd__(self, _0: object, /): """ - usage.skimage: 1 + usage.pandas: 6 + usage.scipy: 18 """ ... @overload - def __ge__(self, _0: numpy.int64, /): + def __rmul__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 3 - usage.skimage: 1 + usage.pandas: 1 """ ... @overload - def __ge__( - self, _0: Union[numpy.ndarray, int, numpy.int64, pandas._libs.missing.NAType], / - ): + def __rmul__(self, _0: float, /): """ - usage.pandas: 19 + usage.scipy: 1 """ ... - @overload - def __ge__( - self, _0: Union[numpy.ndarray, numpy.int32, numpy.int64, int, numpy.float64], / - ): + def __rmul__(self, _0: Union[float, numpy.ndarray], /): """ - usage.scipy: 51 + usage.pandas: 1 + usage.scipy: 1 """ ... @overload - def __ge__(self, _0: object, /): + def __rsub__(self, _0: pandas.core.arrays.timedeltas.TimedeltaArray, /): """ - usage.dask: 39 + usage.pandas: 1 """ ... @overload - def __ge__( - self, _0: Union[float, numpy.int64, numpy.float64, int, numpy.ndarray], / - ): + def __rsub__(self, _0: numpy.int8, /): """ - usage.sklearn: 32 + usage.matplotlib: 4 """ ... - def __ge__(self, _0: object, /): + def __rsub__( + self, _0: Union[numpy.int8, pandas.core.arrays.timedeltas.TimedeltaArray], / + ): """ - usage.dask: 39 - usage.matplotlib: 10 - usage.pandas: 19 - usage.scipy: 51 - usage.skimage: 11 - usage.sklearn: 32 - usage.xarray: 2 + usage.matplotlib: 4 + usage.pandas: 1 """ ... @overload - def __getitem__(self, _0: Tuple[None, ellipsis], /): + def __rtruediv__( + self, + _0: Union[ + numpy.ndarray, + pandas._libs.tslibs.timedeltas.Timedelta, + pandas._libs.tslibs.nattype.NaTType, + ], + /, + ): """ - usage.xarray: 1 + usage.pandas: 5 """ ... @overload - def __getitem__(self, _0: int, /): + def __rtruediv__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 1 + usage.scipy: 2 """ ... - @overload - def __getitem__(self, _0: Tuple[Union[ellipsis, None], ...], /): + def __rtruediv__( + self, + _0: Union[ + numpy.ndarray, + pandas._libs.tslibs.nattype.NaTType, + pandas._libs.tslibs.timedeltas.Timedelta, + ], + /, + ): """ - usage.dask: 9 + usage.pandas: 5 + usage.scipy: 2 """ ... - def __getitem__(self, _0: Union[Tuple[Union[ellipsis, None], ...], int], /): + @overload + def __sub__( + self, + _0: Union[ + int, + pandas.core.arrays.integer.IntegerArray, + pandas.core.series.Series, + pandas.core.arrays.timedeltas.TimedeltaArray, + numpy.ndarray, + ], + /, + ): """ - usage.dask: 9 - usage.matplotlib: 1 - usage.xarray: 1 + usage.pandas: 5 """ ... @overload - def __gt__(self, _0: numpy.ndarray, /): + def __sub__(self, _0: int, /): """ - usage.matplotlib: 1 - usage.skimage: 3 + usage.scipy: 1 """ ... @overload - def __gt__(self, _0: int, /): + def __sub__(self, _0: numpy.int8, /): """ - usage.matplotlib: 7 - usage.skimage: 12 + usage.matplotlib: 4 """ ... - @overload - def __gt__(self, _0: numpy.int64, /): + def __sub__(self, _0: object, /): """ - usage.matplotlib: 16 - usage.skimage: 5 - usage.xarray: 2 + usage.matplotlib: 4 + usage.pandas: 5 + usage.scipy: 1 """ ... - @overload - def __gt__(self, _0: dask.array.core.Array, /): + def __truediv__( + self, + _0: Union[ + numpy.ndarray, + pandas.core.arrays.timedeltas.TimedeltaArray, + pandas.core.series.Series, + pandas.core.arrays.integer.IntegerArray, + ], + /, + ): """ - usage.skimage: 1 + usage.pandas: 4 """ ... @overload - def __gt__(self, _0: float, /): + def astype(self, _0: Type[numpy.int64], /): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload - def __gt__(self, _0: numpy.float64, /): + def astype(self, _0: numpy.dtype, /): """ - usage.matplotlib: 2 - usage.xarray: 1 + usage.pandas: 1 """ ... - @overload - def __gt__(self, _0: numpy.uint8, /): + def astype(self, _0: Union[numpy.dtype, Type[numpy.int64]], /): """ - usage.xarray: 1 + usage.pandas: 1 + usage.skimage: 2 """ ... - @overload - def __gt__( - self, - _0: Union[ - pandas._libs.missing.NAType, - numpy.int64, - int, - pandas.core.series.Series, - float, - ], - /, - ): + def item(self, /): """ - usage.pandas: 24 + usage.matplotlib: 1 """ ... + +class longlong: + + # usage.scipy: 4 + dtype: object + + # usage.dask: 1 + ndim: object + + # usage.scipy: 1 + size: object + @overload - def __gt__(self, _0: Union[numpy.int64, int, float, numpy.float64], /): + def __add__(self, _0: int, /): """ - usage.scipy: 50 + usage.pandas: 1 + usage.sklearn: 1 """ ... @overload - def __gt__(self, _0: Union[numpy.int64, int, float], /): + def __add__(self, _0: object, /): """ - usage.dask: 20 + usage.scipy: 23 """ ... - @overload - def __gt__(self, _0: Union[numpy.float64, float, int, numpy.int64], /): + def __add__(self, _0: object, /): """ - usage.sklearn: 42 + usage.pandas: 1 + usage.scipy: 23 + usage.sklearn: 1 """ ... - def __gt__(self, _0: object, /): + def __bool__(self, /): """ - usage.dask: 20 - usage.matplotlib: 26 - usage.pandas: 24 - usage.scipy: 50 - usage.skimage: 22 - usage.sklearn: 42 - usage.xarray: 4 + usage.scipy: 1 """ ... @overload - def __iadd__(self, _0: numpy.int64, /): + def __eq__(self, _0: int, /): """ - usage.dask: 1 - usage.matplotlib: 1 usage.skimage: 1 """ ... @overload - def __iadd__(self, _0: numpy.float64, /): + def __eq__(self, _0: numpy.int64, /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __iadd__(self, _0: int, /): + def __eq__(self, _0: Union[numpy.int64, int], /): """ - usage.matplotlib: 2 - usage.sklearn: 4 - usage.xarray: 2 + usage.pandas: 8 """ ... - @overload - def __iadd__(self, _0: Union[int, numpy.int64], /): + def __eq__(self, _0: Union[int, numpy.int64], /): """ - usage.pandas: 3 + usage.pandas: 8 + usage.skimage: 2 """ ... - @overload - def __iadd__(self, _0: Union[numpy.longlong, int, numpy.int64, numpy.float64], /): + def __ge__(self, _0: numpy.longlong, /): """ - usage.scipy: 29 + usage.scipy: 1 """ ... - def __iadd__(self, _0: Union[int, numpy.longlong, numpy.float64, numpy.int64], /): + def __gt__(self, _0: int, /): """ - usage.dask: 1 - usage.matplotlib: 3 - usage.pandas: 3 - usage.scipy: 29 - usage.skimage: 1 - usage.sklearn: 4 - usage.xarray: 3 + usage.scipy: 1 + usage.sklearn: 2 """ ... - def __ifloordiv__(self, _0: int, /): + def __le__(self, _0: Union[int, numpy.longlong], /): """ - usage.pandas: 1 + usage.scipy: 2 """ ... - def __imod__(self, _0: int, /): + def __lt__(self, _0: int, /): """ usage.skimage: 1 """ ... - @overload - def __imul__(self, _0: numpy.int64, /): + def __mul__(self, _0: Union[numpy.int64, int], /): """ - usage.pandas: 1 + usage.scipy: 4 """ ... - @overload - def __imul__(self, _0: Union[int, numpy.ndarray], /): + def __ne__(self, _0: numpy.longlong, /): """ usage.scipy: 2 """ ... - @overload - def __imul__(self, _0: int, /): + def __radd__(self, _0: object, /): """ - usage.dask: 1 + usage.scipy: 18 """ ... - def __imul__(self, _0: Union[int, numpy.int64, numpy.ndarray], /): + def __rmul__(self, _0: float, /): """ - usage.dask: 1 usage.pandas: 1 - usage.scipy: 2 + usage.scipy: 1 """ ... - @overload - def __isub__(self, _0: numpy.int64, /): + def __rsub__(self, _0: numpy.longlong, /): """ - usage.skimage: 1 + usage.scipy: 1 """ ... - @overload - def __isub__(self, _0: int, /): + def __sub__(self, _0: numpy.longlong, /): """ - usage.dask: 1 - usage.matplotlib: 1 usage.scipy: 1 - usage.skimage: 1 """ ... - @overload - def __isub__(self, _0: Union[numpy.int64, int], /): - """ - usage.pandas: 6 - usage.sklearn: 4 - """ - ... - def __isub__(self, _0: Union[int, numpy.int64], /): +class matrix: + + # usage.dask: 2 + __module__: ClassVar[object] + + # usage.dask: 1 + __name__: ClassVar[object] + + # usage.scipy: 43 + # usage.skimage: 2 + # usage.sklearn: 2 + A: object + + # usage.scipy: 41 + T: object + + # usage.dask: 1 + # usage.scipy: 8 + __class__: object + + # usage.scipy: 1 + base: object + + # usage.dask: 3 + # usage.scipy: 158 + dtype: object + + # usage.scipy: 9 + flags: object + + # usage.scipy: 1 + flat: object + + # usage.scipy: 3 + imag: numpy.matrix + + # usage.dask: 4 + # usage.scipy: 53 + ndim: object + + # usage.scipy: 4 + real: object + + # usage.dask: 4 + # usage.scipy: 62 + # usage.sklearn: 2 + shape: object + + # usage.scipy: 3 + size: object + + @overload + def __add__(self, _0: object, /): """ - usage.dask: 1 - usage.matplotlib: 1 - usage.pandas: 6 - usage.scipy: 1 - usage.skimage: 2 - usage.sklearn: 4 + usage.scipy: 869 """ ... @overload - def __itruediv__(self, _0: numpy.float64, /): + def __add__(self, _0: int, /): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... - @overload - def __itruediv__(self, _0: float, /): + def __add__(self, _0: object, /): """ - usage.pandas: 1 + usage.scipy: 869 + usage.sklearn: 1 """ ... - def __itruediv__(self, _0: Union[float, numpy.float64], /): + @overload + def __eq__(self, _0: object, /): """ - usage.pandas: 1 - usage.skimage: 2 + usage.scipy: 325 """ ... @overload - def __le__(self, _0: numpy.ndarray, /): + def __eq__(self, _0: float, /): """ - usage.matplotlib: 2 - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload - def __le__(self, _0: int, /): + def __eq__(self, _0: int, /): """ - usage.matplotlib: 3 - usage.skimage: 3 + usage.sklearn: 2 """ ... - @overload - def __le__(self, _0: numpy.int64, /): + def __eq__(self, _0: object, /): """ - usage.matplotlib: 3 - usage.skimage: 1 + usage.scipy: 325 + usage.sklearn: 3 """ ... - @overload - def __le__( + def __ge__( self, - _0: Union[pandas._libs.missing.NAType, numpy.ndarray, int, float, numpy.int64], + _0: Union[ + scipy.sparse.bsr.bsr_matrix, + scipy.sparse.csc.csc_matrix, + scipy.sparse.csr.csr_matrix, + numpy.matrix, + int, + ], /, ): """ - usage.pandas: 27 + usage.scipy: 676 """ ... @overload - def __le__(self, _0: object, /): + def __getitem__(self, _0: object, /): """ - usage.scipy: 99 + usage.scipy: 266 """ ... @overload - def __le__(self, _0: Union[float, numpy.int64, pandas.core.series.Series, int], /): + def __getitem__( + self, _0: Union[Tuple[slice[int, int, int], slice[int, int, int]], int], / + ): """ - usage.dask: 35 + usage.dask: 2 """ ... @overload - def __le__(self, _0: Union[numpy.float64, numpy.int64, int, float], /): - """ - usage.sklearn: 53 - """ - ... - - def __le__(self, _0: object, /): - """ - usage.dask: 35 - usage.matplotlib: 8 - usage.pandas: 27 - usage.scipy: 99 - usage.skimage: 5 - usage.sklearn: 53 + def __getitem__(self, _0: Tuple[int, slice[None, None, None]], /): + """ + usage.sklearn: 2 """ ... - @overload - def __lt__(self, _0: numpy.int64, /): + def __getitem__(self, _0: object, /): """ - usage.matplotlib: 16 - usage.skimage: 5 - usage.xarray: 2 + usage.dask: 2 + usage.scipy: 266 + usage.sklearn: 2 """ ... - @overload - def __lt__(self, _0: numpy.ndarray, /): + def __gt__(self, _0: object, /): """ - usage.matplotlib: 1 - usage.skimage: 9 + usage.scipy: 319 """ ... - @overload - def __lt__(self, _0: int, /): + def __imul__(self, _0: Union[float, int], /): """ - usage.matplotlib: 13 - usage.skimage: 10 - usage.xarray: 3 + usage.scipy: 5 """ ... - @overload - def __lt__(self, _0: numpy.float64, /): + def __itruediv__(self, _0: Union[float, int], /): """ - usage.matplotlib: 4 - usage.xarray: 1 + usage.scipy: 4 """ ... - @overload - def __lt__(self, _0: numpy.uint8, /): + def __le__(self, _0: object, /): """ - usage.xarray: 1 + usage.scipy: 690 """ ... - @overload def __lt__( self, _0: Union[ - pandas._libs.missing.NAType, - numpy.int64, - pandas.core.arrays.categorical.Categorical, + scipy.sparse.bsr.bsr_matrix, int, + numpy.matrix, + scipy.sparse.csr.csr_matrix, + scipy.sparse.csc.csc_matrix, ], /, ): """ - usage.pandas: 10 + usage.scipy: 390 """ ... @overload - def __lt__(self, _0: Union[float, numpy.int64, numpy.ndarray, numpy.float64], /): + def __matmul__(self, _0: numpy.ndarray, /): """ - usage.scipy: 7 + usage.skimage: 1 """ ... @overload - def __lt__( - self, - _0: Union[ - dask.dataframe.core.Series, - float, - numpy.int64, - int, - pandas.core.series.Series, - ], - /, - ): + def __matmul__(self, _0: Union[numpy.ndarray, numpy.matrix], /): """ - usage.dask: 13 + usage.scipy: 242 """ ... - @overload - def __lt__(self, _0: Union[int, numpy.int64], /): + def __matmul__(self, _0: Union[numpy.matrix, numpy.ndarray], /): """ - usage.sklearn: 7 + usage.scipy: 242 + usage.skimage: 1 """ ... - def __lt__(self, _0: object, /): + def __mul__(self, _0: object, /): """ - usage.dask: 13 - usage.matplotlib: 34 - usage.pandas: 10 - usage.scipy: 7 - usage.skimage: 24 - usage.sklearn: 7 - usage.xarray: 7 + usage.scipy: 69 """ ... - @overload - def __mod__(self, _0: int, /): + def __ne__(self, _0: object, /): """ - usage.matplotlib: 3 - usage.scipy: 11 - usage.skimage: 1 - usage.sklearn: 2 + usage.scipy: 345 """ ... - @overload - def __mod__( - self, - _0: Union[ - pandas._libs.missing.NAType, - pandas.core.arrays.integer.IntegerArray, - int, - numpy.ndarray, - ], - /, - ): + def __neg__(self, /): """ - usage.pandas: 10 + usage.scipy: 6 + usage.skimage: 2 """ ... - @overload - def __mod__(self, _0: Union[int, numpy.int64], /): + def __pow__(self, _0: int, /): """ - usage.dask: 2 + usage.scipy: 1 """ ... - def __mod__( - self, - _0: Union[ - int, - numpy.ndarray, - pandas.core.arrays.integer.IntegerArray, - pandas._libs.missing.NAType, - numpy.int64, - ], - /, - ): + def __radd__(self, _0: object, /): """ - usage.dask: 2 - usage.matplotlib: 3 - usage.pandas: 10 - usage.scipy: 11 - usage.skimage: 1 - usage.sklearn: 2 + usage.scipy: 888 """ ... @overload - def __mul__(self, _0: numpy.ndarray, /): + def __rmatmul__(self, _0: numpy.ndarray, /): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload - def __mul__(self, _0: numpy.float64, /): + def __rmatmul__(self, _0: Union[numpy.ndarray, numpy.matrix], /): """ - usage.skimage: 1 + usage.scipy: 237 """ ... - @overload - def __mul__(self, _0: numpy.int64, /): + def __rmatmul__(self, _0: Union[numpy.matrix, numpy.ndarray], /): """ + usage.scipy: 237 usage.skimage: 1 """ ... @overload - def __mul__(self, _0: int, /): + def __rmul__(self, _0: object, /): """ - usage.matplotlib: 6 - usage.xarray: 1 + usage.scipy: 213 """ ... @overload - def __mul__(self, _0: object, /): + def __rmul__(self, _0: int, /): """ - usage.pandas: 59 - usage.scipy: 103 + usage.sklearn: 1 """ ... - @overload - def __mul__(self, _0: float, /): + def __rmul__(self, _0: object, /): """ - usage.matplotlib: 3 + usage.scipy: 213 + usage.sklearn: 1 """ ... - @overload - def __mul__(self, _0: Union[numpy.float64, int, numpy.ndarray, numpy.int64], /): + def __rsub__(self, _0: object, /): """ - usage.dask: 9 + usage.scipy: 598 """ ... @overload - def __mul__( - self, _0: Union[numpy.ndarray, int, numpy.int64, float, numpy.float64], / - ): + def __rtruediv__(self, _0: Union[int, float], /): """ - usage.sklearn: 19 + usage.scipy: 2 """ ... - def __mul__(self, _0: object, /): + @overload + def __rtruediv__(self, _0: float, /): """ - usage.dask: 9 - usage.matplotlib: 9 - usage.pandas: 59 - usage.scipy: 103 - usage.skimage: 4 - usage.sklearn: 19 - usage.xarray: 1 + usage.sklearn: 2 """ ... - def __neg__(self, /): + @overload + def __rtruediv__(self, _0: numpy.ndarray, /): """ - usage.dask: 5 - usage.matplotlib: 1 - usage.pandas: 3 - usage.scipy: 20 - usage.skimage: 6 - usage.sklearn: 8 - usage.xarray: 1 + usage.sklearn: 1 """ ... - def __or__(self, _0: Union[bool, numpy.int64], /): + def __rtruediv__(self, _0: Union[float, numpy.ndarray, int], /): """ - usage.dask: 2 + usage.scipy: 2 + usage.sklearn: 3 """ ... @overload - def __pow__(self, _0: int, /): + def __setitem__(self, _0: object, _1: object, /): """ - usage.scipy: 13 - usage.skimage: 2 + usage.scipy: 303 """ ... @overload - def __pow__( - self, - _0: Union[ - pandas._libs.missing.NAType, - pandas.core.arrays.integer.IntegerArray, - pandas.core.series.Series, - numpy.float64, - numpy.ndarray, - ], - /, - ): + def __setitem__(self, _0: slice[None, int, None], _1: int, /): """ - usage.pandas: 10 + usage.dask: 1 """ ... - @overload - def __pow__(self, _0: Union[numpy.int64, int], /): + def __setitem__(self, _0: object, _1: object, /): """ - usage.dask: 3 + usage.dask: 1 + usage.scipy: 303 """ ... - def __pow__(self, _0: object, /): + @overload + def __sub__(self, _0: object, /): """ - usage.dask: 3 - usage.pandas: 10 - usage.scipy: 13 - usage.skimage: 2 + usage.scipy: 610 """ ... @overload - def __radd__(self, _0: numpy.ndarray, /): + def __sub__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 2 - usage.skimage: 2 + usage.sklearn: 2 """ ... - @overload - def __radd__(self, _0: int, /): + def __sub__(self, _0: object, /): """ - usage.matplotlib: 8 - usage.skimage: 6 - usage.xarray: 1 + usage.scipy: 610 + usage.sklearn: 2 """ ... @overload - def __radd__(self, _0: numpy.float64, /): + def __truediv__(self, _0: Union[int, complex], /): """ - usage.matplotlib: 1 - usage.skimage: 3 + usage.scipy: 2 """ ... @overload - def __radd__(self, _0: numpy.int64, /): + def __truediv__(self, _0: float, /): """ - usage.matplotlib: 10 - usage.skimage: 6 + usage.sklearn: 1 """ ... - @overload - def __radd__(self, _0: object, /): + def __truediv__(self, _0: Union[float, complex, int], /): """ - usage.dask: 26 - usage.pandas: 63 - usage.scipy: 145 - usage.sklearn: 23 + usage.scipy: 2 + usage.sklearn: 1 """ ... - def __radd__(self, _0: object, /): + def all(self, /): """ - usage.dask: 26 - usage.matplotlib: 21 - usage.pandas: 63 - usage.scipy: 145 - usage.skimage: 17 - usage.sklearn: 23 - usage.xarray: 1 + usage.dask: 2 + usage.scipy: 2 """ ... - @overload - def __rand__(self, _0: Union[numpy.bool_, numpy.ndarray], /): + def any(self, /): """ - usage.scipy: 8 + usage.sklearn: 1 """ ... - @overload - def __rand__(self, _0: Union[bool, numpy.int64], /): + def astype(self, _0: Union[type, Literal["int32", "int16"]], /): """ - usage.dask: 2 + usage.scipy: 151 """ ... - def __rand__(self, _0: Union[numpy.int64, bool, numpy.ndarray, numpy.bool_], /): + def conj(self, /): """ - usage.dask: 2 - usage.scipy: 8 + usage.scipy: 4 """ ... - @overload - def __rfloordiv__(self, _0: int, /): + def copy(self, /): """ - usage.matplotlib: 1 - usage.scipy: 1 - usage.skimage: 1 + usage.dask: 1 + usage.scipy: 168 """ ... @overload - def __rfloordiv__( - self, _0: Union[pandas.core.indexes.numeric.Int64Index, numpy.ndarray], / - ): + def max(self, /, axis: int): """ - usage.pandas: 2 + usage.scipy: 7 """ ... @overload - def __rfloordiv__(self, _0: Union[int, numpy.int64], /): + def max(self, /): """ - usage.dask: 2 + usage.sklearn: 3 """ ... - @overload - def __rfloordiv__(self, _0: Union[float, int], /): + def max(self, /, axis: int = ...): """ + usage.scipy: 7 usage.sklearn: 3 """ ... - def __rfloordiv__( - self, - _0: Union[ - int, - float, - numpy.ndarray, - pandas.core.indexes.numeric.Int64Index, - numpy.int64, - ], - /, - ): + def mean(self, /, axis: Union[int, None] = ..., out: numpy.matrix = ...): """ - usage.dask: 2 - usage.matplotlib: 1 - usage.pandas: 2 - usage.scipy: 1 - usage.skimage: 1 - usage.sklearn: 3 + usage.scipy: 187 """ ... - @overload - def __rmod__(self, _0: numpy.ndarray, /): + def min(self, /, axis: int): """ - usage.pandas: 52 + usage.scipy: 7 + """ + ... + + def nonzero(self, /): + """ + usage.scipy: 1 + usage.sklearn: 2 """ ... @overload - def __rmod__(self, _0: str, /): + def reshape( + self, + _0: Union[int, Tuple[int, int]], + _1: int = ..., + /, + *, + order: Literal["F", "C"] = ..., + ): """ - usage.scipy: 14 - usage.sklearn: 13 + usage.scipy: 21 """ ... @overload - def __rmod__(self, _0: Literal["%-12g"], /): + def reshape(self, _0: Tuple[int], /): """ - usage.matplotlib: 1 + usage.dask: 1 """ ... @overload - def __rmod__(self, _0: Union[int, numpy.int64, Literal["%d B"]], /): + def reshape(self, _0: int, _1: int, /): """ - usage.dask: 3 + usage.sklearn: 2 """ ... - def __rmod__(self, _0: Union[str, numpy.ndarray, int, numpy.int64], /): + def reshape( + self, + _0: Union[int, Tuple[int, ...]], + _1: int = ..., + /, + *, + order: Literal["F", "C"] = ..., + ): """ - usage.dask: 3 - usage.matplotlib: 1 - usage.pandas: 52 - usage.scipy: 14 - usage.sklearn: 13 + usage.dask: 1 + usage.scipy: 21 + usage.sklearn: 2 """ ... - @overload - def __rmul__(self, _0: float, /): + def sum( + self, + /, + axis: Union[Tuple[None, ...], int, None] = ..., + dtype: Union[type, None] = ..., + out: Union[None, numpy.matrix] = ..., + ): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.scipy: 271 """ ... - @overload - def __rmul__(self, _0: int, /): + def transpose(self, /): """ - usage.matplotlib: 3 - usage.skimage: 3 + usage.scipy: 33 """ ... @overload - def __rmul__(self, _0: numpy.ndarray, /): + def view(self, _0: Type[numpy.ndarray], /): """ - usage.matplotlib: 3 - usage.skimage: 2 + usage.scipy: 1 """ ... @overload - def __rmul__(self, _0: numpy.int64, /): + def view(self, /, *, type: Type[numpy.ndarray]): """ - usage.skimage: 1 + usage.dask: 2 """ ... - @overload - def __rmul__(self, _0: Tuple[int], /): + def view( + self, _0: Type[numpy.ndarray] = ..., /, *, type: Type[numpy.ndarray] = ... + ): """ - usage.xarray: 1 + usage.dask: 2 + usage.scipy: 1 """ ... - @overload - def __rmul__(self, _0: Tuple[slice[None, None, None]], /): + +class memmap: + + # usage.dask: 1 + __module__: ClassVar[object] + + # usage.sklearn: 1 + __name__: ClassVar[object] + + @classmethod + def __rmod__(cls, _0: str, /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... + # usage.sklearn: 15 + T: object + + # usage.sklearn: 1 + __class__: object + + # usage.dask: 1 + _mmap: object + + # usage.dask: 5 + base: object + + # usage.dask: 4 + ctypes: object + + # usage.dask: 4 + # usage.scipy: 13 + # usage.sklearn: 7 + dtype: object + + # usage.dask: 6 + # usage.sklearn: 3 + filename: object + + # usage.sklearn: 3 + flags: object + + # usage.dask: 1 + # usage.sklearn: 1 + ndim: object + + # usage.dask: 8 + # usage.scipy: 5 + # usage.sklearn: 21 + shape: object + + # usage.sklearn: 2 + size: object + + # usage.dask: 3 + strides: object + @overload - def __rmul__(self, _0: object, /): + def __add__(self, _0: numpy.memmap, /): """ - usage.pandas: 59 - usage.scipy: 193 + usage.dask: 1 """ ... @overload - def __rmul__(self, _0: numpy.float64, /): + def __add__(self, _0: numpy.int64, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... - @overload - def __rmul__(self, _0: Union[numpy.int64, int], /): + def __add__(self, _0: Union[numpy.int64, numpy.memmap], /): """ - usage.dask: 4 + usage.dask: 1 + usage.sklearn: 1 """ ... @overload - def __rmul__( - self, _0: Union[numpy.float64, numpy.ndarray, numpy.int64, float, List[int]], / + def __getitem__( + self, + _0: Union[ + Tuple[ + Union[slice[Union[None, int], Union[None, int], Union[None, int]], int], + Union[slice[None, None, None], int], + ], + slice[int, int, int], + ], + /, ): """ - usage.sklearn: 19 + usage.dask: 7 """ ... - def __rmul__(self, _0: object, /): + @overload + def __getitem__(self, _0: numpy.ndarray, /): """ - usage.dask: 4 - usage.matplotlib: 8 - usage.pandas: 59 - usage.scipy: 193 - usage.skimage: 8 - usage.sklearn: 19 - usage.xarray: 2 + usage.sklearn: 2 """ ... - def __ror__(self, _0: Union[bool, numpy.int64], /): + @overload + def __getitem__(self, _0: slice[None, int, None], /): """ - usage.dask: 2 + usage.sklearn: 4 """ ... @overload - def __rpow__(self, _0: pandas._libs.missing.NAType, /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, int, None]], / + ): """ - usage.pandas: 1 + usage.sklearn: 1 """ ... @overload - def __rpow__(self, _0: Union[float, int], /): + def __getitem__(self, _0: Tuple[slice[None, None, None], numpy.ndarray], /): """ - usage.scipy: 11 + usage.sklearn: 1 """ ... @overload - def __rpow__(self, _0: float, /): + def __getitem__(self, _0: numpy.int64, /): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload - def __rpow__(self, _0: Union[int, numpy.int64], /): + def __getitem__(self, _0: int, /): """ - usage.dask: 2 + usage.sklearn: 4 """ ... - def __rpow__( - self, _0: Union[numpy.int64, int, pandas._libs.missing.NAType, float], / - ): + @overload + def __getitem__(self, _0: slice[int, None, int], /): """ - usage.dask: 2 - usage.matplotlib: 1 - usage.pandas: 1 - usage.scipy: 11 + usage.sklearn: 2 """ ... @overload - def __rsub__(self, _0: numpy.ndarray, /): + def __getitem__(self, _0: Tuple[slice[None, None, None], None], /): """ - usage.matplotlib: 1 - usage.skimage: 9 - usage.xarray: 3 + usage.sklearn: 1 """ ... - @overload - def __rsub__(self, _0: int, /): + def __getitem__( + self, + _0: Union[ + slice[Union[int, None], Union[int, None], Union[int, None]], + numpy.int64, + numpy.ndarray, + int, + Tuple[ + Union[int, slice[Union[None, int], Union[None, int], Union[None, int]]], + Union[int, numpy.ndarray, slice[None, Union[int, None], None], None], + ], + ], + /, + ): """ - usage.matplotlib: 2 - usage.skimage: 10 - usage.xarray: 3 + usage.dask: 7 + usage.sklearn: 18 """ ... @overload - def __rsub__(self, _0: numpy.int64, /): + def __isub__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 8 - usage.skimage: 9 - usage.xarray: 4 + usage.sklearn: 2 """ ... @overload - def __rsub__(self, _0: object, /): + def __isub__(self, _0: numpy.float64, /): """ - usage.pandas: 32 + usage.sklearn: 2 """ ... - @overload - def __rsub__( - self, _0: Union[numpy.ndarray, numpy.float64, numpy.int64, float, int], / - ): + def __isub__(self, _0: Union[numpy.float64, numpy.ndarray], /): """ - usage.scipy: 93 + usage.sklearn: 4 """ ... - @overload - def __rsub__(self, _0: numpy.float64, /): + def __pow__(self, _0: int, /): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... - @overload - def __rsub__(self, _0: float, /): + def __radd__(self, _0: numpy.memmap, /): """ - usage.matplotlib: 1 + usage.dask: 1 """ ... - @overload - def __rsub__(self, _0: Union[numpy.int64, int], /): + def __rsub__(self, _0: numpy.ndarray, /): """ - usage.dask: 3 + usage.sklearn: 1 """ ... @overload - def __rsub__( - self, _0: Union[int, float, numpy.int64, numpy.float64, numpy.ndarray], / - ): + def __rtruediv__(self, _0: numpy.ndarray, /): """ - usage.sklearn: 25 + usage.sklearn: 1 """ ... - def __rsub__(self, _0: object, /): + @overload + def __rtruediv__(self, _0: numpy.float64, /): """ - usage.dask: 3 - usage.matplotlib: 15 - usage.pandas: 32 - usage.scipy: 93 - usage.skimage: 28 - usage.sklearn: 25 - usage.xarray: 10 + usage.sklearn: 1 """ ... - @overload - def __rtruediv__(self, _0: numpy.ndarray, /): + def __rtruediv__(self, _0: Union[numpy.float64, numpy.ndarray], /): """ - usage.matplotlib: 2 - usage.skimage: 6 - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload - def __rtruediv__(self, _0: int, /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], numpy.ndarray], _1: numpy.ndarray, / + ): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload - def __rtruediv__(self, _0: numpy.int64, /): + def __setitem__(self, _0: numpy.int64, _1: numpy.ndarray, /): """ - usage.skimage: 2 + usage.sklearn: 2 """ ... @overload - def __rtruediv__(self, _0: numpy.float64, /): + def __setitem__(self, _0: int, _1: numpy.ndarray, /): """ - usage.skimage: 1 - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload - def __rtruediv__(self, _0: float, /): + def __setitem__(self, _0: slice[None, None, None], _1: int, /): """ - usage.matplotlib: 5 - usage.xarray: 1 + usage.sklearn: 3 """ ... - @overload - def __rtruediv__( + def __setitem__( self, _0: Union[ - pandas._libs.tslibs.nattype.NaTType, - numpy.ndarray, - pandas._libs.tslibs.timedeltas.Timedelta, + slice[None, None, None], + Tuple[slice[None, None, None], numpy.ndarray], + numpy.int64, + int, ], + _1: Union[int, numpy.ndarray], /, ): """ - usage.pandas: 5 + usage.sklearn: 8 """ ... @overload - def __rtruediv__(self, _0: object, /): + def __sub__(self, _0: numpy.ndarray, /): """ - usage.dask: 10 - usage.scipy: 47 + usage.sklearn: 1 """ ... @overload - def __rtruediv__( - self, _0: Union[numpy.ndarray, numpy.float64, int, numpy.int64, float], / - ): + def __sub__(self, _0: numpy.float64, /): """ - usage.sklearn: 35 + usage.sklearn: 1 """ ... - def __rtruediv__(self, _0: object, /): + def __sub__(self, _0: Union[numpy.float64, numpy.ndarray], /): """ - usage.dask: 10 - usage.matplotlib: 7 - usage.pandas: 5 - usage.scipy: 47 - usage.skimage: 11 - usage.sklearn: 35 - usage.xarray: 3 + usage.sklearn: 2 """ ... - def __rxor__(self, _0: Union[bool, numpy.int64], /): + @overload + def __truediv__(self, _0: numpy.ndarray, /): """ - usage.dask: 2 + usage.sklearn: 2 """ ... @overload - def __sub__(self, _0: int, /): + def __truediv__(self, _0: numpy.float64, /): """ - usage.matplotlib: 12 - usage.skimage: 12 - usage.xarray: 2 + usage.sklearn: 2 """ ... - @overload - def __sub__(self, _0: numpy.int64, /): + def __truediv__(self, _0: Union[numpy.float64, numpy.ndarray], /): """ - usage.matplotlib: 8 - usage.skimage: 9 - usage.xarray: 4 + usage.sklearn: 4 """ ... @overload - def __sub__(self, _0: float, /): + def copy(self, /): """ - usage.matplotlib: 5 - usage.skimage: 1 - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload - def __sub__(self, _0: numpy.float64, /): + def copy(self, _0: Literal["C"], /): """ - usage.matplotlib: 5 - usage.xarray: 2 + usage.sklearn: 1 """ ... - @overload - def __sub__(self, _0: object, /): + def copy(self, _0: Literal["C"] = ..., /): """ - usage.pandas: 26 - usage.scipy: 150 + usage.sklearn: 2 """ ... - @overload - def __sub__(self, _0: numpy.ndarray, /): + def item(self, /): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... - @overload - def __sub__(self, _0: List[int], /): + def mean(self, /, *, axis: int): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... - @overload - def __sub__(self, _0: Union[numpy.int64, int], /): + def min(self, /): """ - usage.dask: 7 + usage.sklearn: 1 """ ... - @overload - def __sub__(self, _0: Union[numpy.float64, int, numpy.int64, numpy.ndarray], /): + def ravel(self, /): """ - usage.sklearn: 46 + usage.sklearn: 1 """ ... - def __sub__(self, _0: object, /): + def reshape(self, _0: int, _1: int, /): """ - usage.dask: 7 - usage.matplotlib: 33 - usage.pandas: 26 - usage.scipy: 150 - usage.skimage: 22 - usage.sklearn: 46 - usage.xarray: 10 + usage.scipy: 7 """ ... - @overload - def __truediv__(self, _0: int, /): + def tolist(self, /): """ - usage.matplotlib: 5 - usage.skimage: 7 + usage.sklearn: 7 """ ... + +class ndarray: + + # usage.pandas: 2 + __array_ufunc__: ClassVar[object] + + # usage.dask: 8 + __module__: ClassVar[object] + + # usage.matplotlib: 1 + __mro__: ClassVar[object] + + # usage.dask: 6 + # usage.pandas: 11 + # usage.sklearn: 1 + __name__: ClassVar[object] + @overload - def __truediv__(self, _0: numpy.int64, /): + @classmethod + def __ne__(cls, _0: Type[numpy.ndarray], /): """ usage.skimage: 2 """ ... @overload - def __truediv__(self, _0: float, /): + @classmethod + def __ne__(cls, _0: int, /): """ - usage.matplotlib: 3 - usage.skimage: 4 - usage.xarray: 2 + usage.matplotlib: 2 + usage.skimage: 9 + usage.sklearn: 82 + usage.xarray: 3 """ ... @overload - def __truediv__(self, _0: numpy.float64, /): + @classmethod + def __ne__(cls, _0: float, /): """ - usage.matplotlib: 1 usage.skimage: 1 + usage.sklearn: 9 + usage.xarray: 2 """ ... @overload - def __truediv__(self, _0: object, /): + @classmethod + def __ne__(cls, _0: numpy.ndarray, /): """ - usage.pandas: 22 + usage.matplotlib: 18 + usage.skimage: 30 + usage.sklearn: 74 + usage.xarray: 4 """ ... @overload - def __truediv__( - self, _0: Union[numpy.int64, float, int, numpy.float64, numpy.ndarray], / - ): + @classmethod + def __ne__(cls, _0: numpy.timedelta64, /): """ - usage.scipy: 50 + usage.xarray: 1 """ ... @overload - def __truediv__(self, _0: Union[numpy.int64, int, float], /): + @classmethod + def __ne__(cls, _0: Literal["z"], /): """ - usage.dask: 6 + usage.xarray: 5 """ ... @overload - def __truediv__(self, _0: Union[numpy.float64, int, numpy.int64, float], /): + @classmethod + def __ne__(cls, _0: object, /): """ - usage.sklearn: 25 + usage.pandas: 142 + usage.scipy: 273 """ ... - def __truediv__(self, _0: object, /): + @overload + @classmethod + def __ne__(cls, _0: Tuple[numpy.float64, numpy.float64, numpy.float64, int], /): """ - usage.dask: 6 - usage.matplotlib: 9 - usage.pandas: 22 - usage.scipy: 50 - usage.skimage: 14 - usage.sklearn: 25 - usage.xarray: 2 + usage.matplotlib: 2 """ ... - def __xor__(self, _0: Union[bool, numpy.int64], /): + @overload + @classmethod + def __ne__(cls, _0: Tuple[float, float, float, float], /): """ - usage.dask: 2 + usage.matplotlib: 1 """ ... @overload - def astype(self, _0: Type[numpy.int64], /): + @classmethod + def __ne__(cls, _0: Union[int, numpy.ndarray], /): """ - usage.skimage: 3 + usage.dask: 5 """ ... @overload - def astype(self, _0: numpy.dtype, /): + @classmethod + def __ne__(cls, _0: numpy.int64, /): """ - usage.pandas: 3 + usage.sklearn: 6 """ ... @overload - def astype(self, _0: Union[Type[float], Literal["d"]], /): + @classmethod + def __ne__(cls, _0: numpy.str_, /): """ - usage.scipy: 3 + usage.sklearn: 6 """ ... @overload - def astype(self, _0: Type[numpy.float64], /): + @classmethod + def __ne__(cls, _0: numpy.float64, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def astype(self, _0: Type[numpy.float32], /): + @classmethod + def __ne__(cls, _0: numpy.int32, /): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload - def astype(self, _0: Union[Literal["int64"], numpy.dtype], /): + @classmethod + def __ne__(cls, _0: numpy.bool_, /): """ - usage.dask: 2 + usage.sklearn: 1 """ ... - def astype(self, _0: Union[numpy.dtype, Literal["int64", "d"], type], /): + @overload + @classmethod + def __ne__(cls, _0: List[int], /): """ - usage.dask: 2 - usage.matplotlib: 3 - usage.pandas: 3 - usage.scipy: 3 - usage.skimage: 3 + usage.sklearn: 1 """ ... - def item(self, /): + @overload + @classmethod + def __ne__(cls, _0: List[Union[int, float]], /): """ - usage.pandas: 1 - usage.sklearn: 2 + usage.sklearn: 1 """ ... - def ravel(self, /): + @classmethod + def __ne__(cls, _0: object, /): """ - usage.dask: 1 + usage.dask: 5 + usage.matplotlib: 23 + usage.pandas: 142 + usage.scipy: 273 + usage.skimage: 42 + usage.sklearn: 182 + usage.xarray: 15 """ ... - @overload - def reshape(self, _0: List[int], /): + @classmethod + def __rmod__(cls, _0: object, /): """ - usage.scipy: 2 + usage.dask: 2 + usage.pandas: 57 + usage.sample-usage: 1 + usage.scipy: 12 + usage.skimage: 1 + usage.sklearn: 11 + usage.xarray: 1 """ ... - @overload - def reshape(self, _0: Tuple[int], /): - """ - usage.dask: 1 - """ - ... + # usage.dask: 28 + # usage.matplotlib: 93 + # usage.pandas: 211 + # usage.sample-usage: 1 + # usage.scipy: 1342 + # usage.skimage: 84 + # usage.sklearn: 765 + # usage.xarray: 34 + T: object - def reshape(self, _0: Union[Tuple[int], List[int]], /): - """ - usage.dask: 1 - usage.scipy: 2 - """ - ... + # usage.scipy: 18 + __array_interface__: object + + # usage.dask: 4 + # usage.pandas: 2 + __array_priority__: object + + # usage.dask: 29 + # usage.scipy: 5 + # usage.sklearn: 19 + __class__: object + + # usage.xarray: 1 + attrs: object + + # usage.matplotlib: 2 + # usage.pandas: 87 + # usage.scipy: 37 + # usage.sklearn: 2 + # usage.xarray: 8 + base: object + + # usage.sklearn: 1 + columns: object + + # usage.xarray: 4 + coords: object + + # usage.scipy: 1 + ctypes: object + + # usage.scipy: 3 + # usage.sklearn: 6 + data: object + + # usage.xarray: 2 + dims: object + + # usage.dask: 451 + # usage.matplotlib: 79 + # usage.pandas: 3135 + # usage.sample-usage: 1 + # usage.scipy: 2777 + # usage.skimage: 550 + # usage.sklearn: 1008 + # usage.xarray: 844 + dtype: object + + # usage.matplotlib: 2 + # usage.pandas: 86 + # usage.scipy: 237 + # usage.skimage: 13 + # usage.sklearn: 62 + # usage.xarray: 21 + flags: object + + # usage.dask: 7 + # usage.matplotlib: 33 + # usage.pandas: 14 + # usage.scipy: 31 + # usage.skimage: 21 + # usage.sklearn: 67 + # usage.xarray: 46 + flat: numpy.ndarray + + # usage.dask: 1 + # usage.matplotlib: 2 + # usage.scipy: 144 + # usage.xarray: 3 + imag: numpy.ndarray + + # usage.matplotlib: 2 + index: object + + # usage.pandas: 11 + # usage.scipy: 64 + # usage.skimage: 4 + # usage.sklearn: 2 + itemsize: object + + # usage.dask: 2 + keys: object + + # usage.xarray: 5 + magnitude: object + + # usage.matplotlib: 2 + name: object + + # usage.dask: 11 + # usage.pandas: 46 + # usage.scipy: 19 + # usage.sklearn: 3 + nbytes: object + + # usage.dask: 360 + # usage.matplotlib: 141 + # usage.pandas: 741 + # usage.sample-usage: 1 + # usage.scipy: 1935 + # usage.skimage: 462 + # usage.sklearn: 467 + # usage.xarray: 309 + ndim: object + + # usage.dask: 1 + # usage.matplotlib: 6 + # usage.pandas: 1 + # usage.scipy: 235 + # usage.xarray: 3 + real: object + + # usage.dask: 445 + # usage.matplotlib: 323 + # usage.pandas: 695 + # usage.sample-usage: 2 + # usage.scipy: 4734 + # usage.skimage: 1138 + # usage.sklearn: 3271 + # usage.xarray: 350 + shape: Union[Tuple[Union[int, None], ...], List[int], numpy.ndarray] + + # usage.dask: 16 + # usage.matplotlib: 84 + # usage.pandas: 147 + # usage.sample-usage: 1 + # usage.scipy: 975 + # usage.skimage: 88 + # usage.sklearn: 240 + # usage.xarray: 64 + size: object - def tolist(self, /): - """ - usage.xarray: 1 - """ - ... + # usage.dask: 29 + # usage.matplotlib: 8 + # usage.pandas: 1 + # usage.scipy: 30 + # usage.skimage: 12 + # usage.sklearn: 7 + # usage.xarray: 4 + strides: Union[Tuple[int, int, int], int] - def view(self, _0: Literal["M8[ns]", "M8[us]"], /): - """ - usage.pandas: 2 - """ - ... + # usage.matplotlib: 1 + tzinfo: object + # usage.xarray: 6 + units: object -class int8: + # usage.pandas: 4 + values: object - # usage.pandas: 3 - __name__: ClassVar[object] + # usage.xarray: 3 + variable: object - @overload - @classmethod - def __ne__(cls, _0: Union[numpy.int8, Type[numpy.int64]], /): - """ - usage.pandas: 9 - """ - ... + # usage.xarray: 4 + variables: object @overload - @classmethod - def __ne__(cls, _0: numpy.int8, /): + def __add__(self, _0: float, /): """ - usage.scipy: 2 + usage.matplotlib: 54 + usage.skimage: 28 + usage.sklearn: 61 + usage.xarray: 21 """ ... @overload - @classmethod - def __ne__(cls, _0: numpy.dtype, /): - """ - usage.dask: 2 - """ - ... - - @classmethod - def __ne__(cls, _0: Union[numpy.dtype, numpy.int8, Type[numpy.int64]], /): + def __add__(self, _0: numpy.ndarray, /): """ - usage.dask: 2 - usage.pandas: 9 - usage.scipy: 2 + usage.matplotlib: 213 + usage.sample-usage: 1 + usage.skimage: 177 + usage.sklearn: 274 + usage.xarray: 26 """ ... - # usage.pandas: 3 - # usage.scipy: 2 - dtype: object - - # usage.dask: 2 - ndim: object - - # usage.scipy: 1 - size: object - @overload - def __add__(self, _0: object, /): + def __add__(self, _0: numpy.float64, /): """ - usage.pandas: 13 - usage.scipy: 17 + usage.matplotlib: 10 + usage.skimage: 18 + usage.sklearn: 51 + usage.xarray: 1 """ ... @overload def __add__(self, _0: int, /): """ - usage.matplotlib: 6 - """ - ... - - def __add__(self, _0: object, /): - """ - usage.matplotlib: 6 - usage.pandas: 13 - usage.scipy: 17 + usage.matplotlib: 94 + usage.sample-usage: 1 + usage.skimage: 41 + usage.sklearn: 94 + usage.xarray: 22 """ ... - def __bool__(self, /): + @overload + def __add__(self, _0: numpy.int64, /): """ - usage.scipy: 1 + usage.matplotlib: 2 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload - def __eq__(self, _0: int, /): + def __add__(self, _0: dask.array.core.Array, /): """ usage.skimage: 1 """ ... @overload - def __eq__(self, _0: numpy.int64, /): + def __add__(self, _0: bool, /): """ usage.skimage: 1 """ ... @overload - def __eq__(self, _0: numpy.ndarray, /): + def __add__(self, _0: datetime.timedelta, /): """ usage.xarray: 1 """ ... @overload - def __eq__(self, _0: object, /): - """ - usage.pandas: 75 - """ - ... - - @overload - def __eq__(self, _0: Union[numpy.ndarray, int], /): + def __add__(self, _0: xarray.coding.cftime_offsets.Day, /): """ - usage.scipy: 3 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: numpy.int8, /): + def __add__(self, _0: xarray.coding.cftime_offsets.Hour, /): """ - usage.matplotlib: 8 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: importlib._bootstrap.MonotonicConstraint, /): - """ - usage.sklearn: 2 - """ - ... - - def __eq__(self, _0: object, /): + def __add__(self, _0: xarray.core.dataarray.DataArray, /): """ - usage.matplotlib: 8 - usage.pandas: 75 - usage.scipy: 3 - usage.skimage: 2 - usage.sklearn: 2 usage.xarray: 1 """ ... - def __floordiv__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / - ): - """ - usage.pandas: 2 - """ - ... - @overload - def __ge__(self, _0: int, /): + def __add__(self, _0: xarray.core.dataset.Dataset, /): """ - usage.pandas: 2 + usage.xarray: 2 """ ... @overload - def __ge__(self, _0: numpy.ndarray, /): + def __add__(self, _0: object, /): """ - usage.scipy: 1 + usage.dask: 194 + usage.pandas: 272 + usage.scipy: 2298 """ ... @overload - def __ge__(self, _0: numpy.int8, /): - """ - usage.matplotlib: 4 - """ - ... - - def __ge__(self, _0: Union[numpy.int8, int, numpy.ndarray], /): + def __add__(self, _0: Tuple[numpy.float64, numpy.float64], /): """ usage.matplotlib: 4 - usage.pandas: 2 - usage.scipy: 1 - """ - ... - - def __getitem__(self, _0: Tuple[ellipsis, None], /): - """ - usage.dask: 1 - """ - ... - - def __gt__(self, _0: int, /): - """ - usage.scipy: 1 """ ... @overload - def __le__(self, _0: int, /): + def __add__(self, _0: Tuple[int, int], /): """ - usage.pandas: 2 - usage.scipy: 1 + usage.matplotlib: 1 """ ... @overload - def __le__(self, _0: numpy.int8, /): - """ - usage.matplotlib: 4 - """ - ... - - def __le__(self, _0: Union[numpy.int8, int], /): + def __add__(self, _0: List[numpy.float64], /): """ - usage.matplotlib: 4 - usage.pandas: 2 - usage.scipy: 1 + usage.matplotlib: 5 """ ... @overload - def __lt__(self, _0: int, /): + def __add__(self, _0: Tuple[numpy.float64, float], /): """ - usage.skimage: 1 + usage.matplotlib: 2 """ ... @overload - def __lt__(self, _0: numpy.ndarray, /): - """ - usage.scipy: 1 - """ - ... - - def __lt__(self, _0: Union[numpy.ndarray, int], /): - """ - usage.scipy: 1 - usage.skimage: 1 - """ - ... - - def __mod__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / - ): + def __add__(self, _0: List[List[float]], /): """ - usage.pandas: 2 + usage.matplotlib: 1 """ ... @overload - def __mul__( - self, - _0: Union[ - numpy.ndarray, - pandas.core.arrays.timedeltas.TimedeltaArray, - pandas.core.series.Series, - pandas.core.arrays.integer.IntegerArray, - ], - /, - ): + def __add__(self, _0: range, /): """ - usage.pandas: 4 + usage.matplotlib: 2 """ ... @overload - def __mul__(self, _0: Union[numpy.int64, int], /): - """ - usage.scipy: 4 - """ - ... - - def __mul__(self, _0: object, /): + def __add__(self, _0: List[float], /): """ - usage.pandas: 4 - usage.scipy: 4 + usage.matplotlib: 8 """ ... - def __pow__( - self, _0: Union[numpy.ndarray, pandas.core.arrays.integer.IntegerArray], / - ): + @overload + def __add__(self, _0: List[List[numpy.int64]], /): """ - usage.pandas: 2 + usage.matplotlib: 1 """ ... @overload - def __radd__(self, _0: Union[pandas._libs.missing.NAType, numpy.int8], /): + def __add__(self, _0: List[Union[int, float]], /): """ - usage.pandas: 6 + usage.matplotlib: 1 """ ... @overload - def __radd__(self, _0: object, /): + def __add__(self, _0: List[int], /): """ - usage.scipy: 18 + usage.matplotlib: 2 + usage.sklearn: 5 """ ... - def __radd__(self, _0: object, /): + @overload + def __add__(self, _0: List[Union[numpy.float64, float]], /): """ - usage.pandas: 6 - usage.scipy: 18 + usage.matplotlib: 2 """ ... @overload - def __rmul__(self, _0: numpy.ndarray, /): + def __add__(self, _0: List[Union[float, numpy.float64]], /): """ - usage.pandas: 1 + usage.matplotlib: 2 """ ... @overload - def __rmul__(self, _0: float, /): + def __add__(self, _0: numpy.float32, /): """ - usage.scipy: 1 + usage.sklearn: 8 """ ... - def __rmul__(self, _0: Union[float, numpy.ndarray], /): + def __add__(self, _0: object, /): """ - usage.pandas: 1 - usage.scipy: 1 + usage.dask: 194 + usage.matplotlib: 404 + usage.pandas: 272 + usage.sample-usage: 2 + usage.scipy: 2298 + usage.skimage: 268 + usage.sklearn: 494 + usage.xarray: 76 """ ... @overload - def __rsub__(self, _0: pandas.core.arrays.timedeltas.TimedeltaArray, /): + def __and__(self, _0: numpy.ndarray, /): """ - usage.pandas: 1 + usage.matplotlib: 46 + usage.skimage: 12 + usage.sklearn: 11 + usage.xarray: 1 """ ... @overload - def __rsub__(self, _0: numpy.int8, /): + def __and__(self, _0: int, /): """ - usage.matplotlib: 4 + usage.sample-usage: 1 + usage.skimage: 2 """ ... - def __rsub__( - self, _0: Union[numpy.int8, pandas.core.arrays.timedeltas.TimedeltaArray], / - ): + @overload + def __and__(self, _0: dask.array.core.Array, /): """ - usage.matplotlib: 4 - usage.pandas: 1 + usage.xarray: 1 """ ... @overload - def __rtruediv__( - self, - _0: Union[ - numpy.ndarray, - pandas._libs.tslibs.timedeltas.Timedelta, - pandas._libs.tslibs.nattype.NaTType, - ], - /, - ): + def __and__(self, _0: numpy.bool_, /): """ - usage.pandas: 5 + usage.xarray: 1 """ ... @overload - def __rtruediv__(self, _0: numpy.ndarray, /): + def __and__(self, _0: sparse._coo.core.COO, /): """ - usage.scipy: 2 + usage.xarray: 1 """ ... - def __rtruediv__( - self, - _0: Union[ - numpy.ndarray, - pandas._libs.tslibs.nattype.NaTType, - pandas._libs.tslibs.timedeltas.Timedelta, - ], - /, - ): + @overload + def __and__(self, _0: Union[int, bool, numpy.ndarray], /): """ - usage.pandas: 5 - usage.scipy: 2 + usage.pandas: 88 """ ... @overload - def __sub__( - self, - _0: Union[ - int, - pandas.core.arrays.integer.IntegerArray, - pandas.core.series.Series, - pandas.core.arrays.timedeltas.TimedeltaArray, - numpy.ndarray, - ], - /, - ): + def __and__(self, _0: object, /): """ - usage.pandas: 5 + usage.scipy: 296 """ ... @overload - def __sub__(self, _0: int, /): + def __and__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.scipy: 1 + usage.matplotlib: 2 """ ... @overload - def __sub__(self, _0: numpy.int8, /): + def __and__(self, _0: Union[numpy.ndarray, bool], /): """ - usage.matplotlib: 4 + usage.dask: 5 """ ... - def __sub__(self, _0: object, /): + def __and__(self, _0: object, /): """ - usage.matplotlib: 4 - usage.pandas: 5 - usage.scipy: 1 + usage.dask: 5 + usage.matplotlib: 48 + usage.pandas: 88 + usage.sample-usage: 1 + usage.scipy: 296 + usage.skimage: 14 + usage.sklearn: 11 + usage.xarray: 4 """ ... - def __truediv__( + def __array_function__( self, - _0: Union[ - numpy.ndarray, - pandas.core.arrays.timedeltas.TimedeltaArray, - pandas.core.series.Series, - pandas.core.arrays.integer.IntegerArray, + _0: Callable, + _1: Tuple[Union[Type[numpy.ndarray], None], ...], + _2: Tuple[Union[numpy.ndarray, numpy.int64, list], ...], + _3: Dict[ + str, + Union[ + Type[numpy.float64], + int, + bool, + numpy.dtype, + Tuple[Union[int, Tuple[int]], ...], + ], ], /, ): """ - usage.pandas: 4 - """ - ... - - @overload - def astype(self, _0: Type[numpy.int64], /): - """ - usage.skimage: 2 - """ - ... - - @overload - def astype(self, _0: numpy.dtype, /): - """ - usage.pandas: 1 - """ - ... - - def astype(self, _0: Union[numpy.dtype, Type[numpy.int64]], /): - """ - usage.pandas: 1 - usage.skimage: 2 - """ - ... - - def item(self, /): - """ - usage.matplotlib: 1 - """ - ... - - -class longlong: - - # usage.scipy: 4 - dtype: object - - # usage.dask: 1 - ndim: object - - # usage.scipy: 1 - size: object - - @overload - def __add__(self, _0: int, /): - """ - usage.pandas: 1 - usage.sklearn: 1 - """ - ... - - @overload - def __add__(self, _0: object, /): - """ - usage.scipy: 23 + usage.dask: 30 """ ... - def __add__(self, _0: object, /): + def __array_wrap__(self, _0: numpy.ndarray, /): """ - usage.pandas: 1 - usage.scipy: 23 - usage.sklearn: 1 + usage.dask: 2 + usage.scipy: 2 """ ... def __bool__(self, /): """ - usage.scipy: 1 - """ - ... - - @overload - def __eq__(self, _0: int, /): - """ - usage.skimage: 1 - """ - ... - - @overload - def __eq__(self, _0: numpy.int64, /): - """ - usage.skimage: 1 - """ - ... - - @overload - def __eq__(self, _0: Union[numpy.int64, int], /): - """ - usage.pandas: 8 - """ - ... - - def __eq__(self, _0: Union[int, numpy.int64], /): - """ - usage.pandas: 8 - usage.skimage: 2 - """ - ... - - @overload - def __ge__(self, _0: numpy.longlong, /): - """ - usage.scipy: 1 + usage.pandas: 2 + usage.sample-usage: 1 + usage.scipy: 3 """ ... @overload - def __ge__(self, _0: int, /): - """ - usage.sklearn: 1 - """ - ... - - def __ge__(self, _0: Union[int, numpy.longlong], /): - """ - usage.scipy: 1 - usage.sklearn: 1 - """ - ... - - def __gt__(self, _0: int, /): + def __contains__(self, _0: int, /): """ - usage.scipy: 1 - usage.sklearn: 1 + usage.matplotlib: 1 + usage.sample-usage: 1 + usage.skimage: 5 + usage.sklearn: 26 + usage.xarray: 3 """ ... - def __le__(self, _0: Union[int, numpy.longlong], /): + @overload + def __contains__(self, _0: Tuple[int, int], /): """ - usage.scipy: 2 + usage.skimage: 6 """ ... - def __lt__(self, _0: int, /): + @overload + def __contains__(self, _0: numpy.int64, /): """ - usage.skimage: 1 + usage.skimage: 2 + usage.sklearn: 1 """ ... - def __mul__(self, _0: Union[numpy.int64, int], /): + @overload + def __contains__( + self, _0: Union[numpy.uint64, numpy.int64, int, Literal["one", "bar"]], / + ): """ - usage.scipy: 4 + usage.pandas: 13 """ ... - def __ne__(self, _0: numpy.longlong, /): + @overload + def __contains__(self, _0: Union[numpy.int64, int, numpy.complex128], /): """ - usage.scipy: 2 + usage.scipy: 11 """ ... - def __radd__(self, _0: object, /): + @overload + def __contains__(self, _0: float, /): """ - usage.scipy: 18 + usage.matplotlib: 2 + usage.sklearn: 2 """ ... - def __rmul__(self, _0: float, /): + @overload + def __contains__(self, _0: numpy.str_, /): """ - usage.pandas: 1 - usage.scipy: 1 + usage.sklearn: 2 """ ... - def __rsub__(self, _0: numpy.longlong, /): + @overload + def __contains__(self, _0: Literal["spam"], /): """ - usage.scipy: 1 + usage.sklearn: 2 """ ... - def __sub__(self, _0: numpy.longlong, /): + @overload + def __contains__(self, _0: numpy.ndarray, /): """ - usage.scipy: 1 + usage.sklearn: 1 """ ... - -class matrix: - - # usage.dask: 2 - __module__: ClassVar[object] - - # usage.dask: 1 - __name__: ClassVar[object] - - # usage.scipy: 43 - # usage.skimage: 2 - # usage.sklearn: 2 - A: object - - # usage.scipy: 41 - T: object - - # usage.dask: 1 - # usage.scipy: 8 - __class__: object - - # usage.scipy: 1 - base: object - - # usage.dask: 3 - # usage.scipy: 158 - dtype: object - - # usage.scipy: 9 - flags: object - - # usage.scipy: 1 - flat: object - - # usage.scipy: 3 - imag: numpy.matrix - - # usage.dask: 4 - # usage.scipy: 53 - ndim: object - - # usage.scipy: 4 - real: object - - # usage.dask: 4 - # usage.scipy: 62 - # usage.sklearn: 2 - shape: object - - # usage.scipy: 3 - size: object - @overload - def __add__(self, _0: object, /): + def __contains__(self, _0: Literal["c"], /): """ - usage.scipy: 869 + usage.sklearn: 1 """ ... @overload - def __add__(self, _0: int, /): + def __contains__(self, _0: Literal["b"], /): """ usage.sklearn: 1 """ ... - def __add__(self, _0: object, /): + @overload + def __contains__(self, _0: Literal["def"], /): """ - usage.scipy: 869 usage.sklearn: 1 """ ... @overload - def __eq__(self, _0: object, /): + def __contains__(self, _0: Literal["ghi"], /): """ - usage.scipy: 325 + usage.sklearn: 1 """ ... @overload - def __eq__(self, _0: Union[int, float], /): + def __contains__(self, _0: Literal["a"], /): """ - usage.sklearn: 3 + usage.sklearn: 1 """ ... - def __eq__(self, _0: object, /): + @overload + def __contains__(self, _0: numpy.float64, /): """ - usage.scipy: 325 - usage.sklearn: 3 + usage.sklearn: 2 """ ... - def __ge__( - self, - _0: Union[ - scipy.sparse.bsr.bsr_matrix, - scipy.sparse.csc.csc_matrix, - scipy.sparse.csr.csr_matrix, - numpy.matrix, - int, - ], - /, - ): + def __contains__(self, _0: object, /): """ - usage.scipy: 676 + usage.matplotlib: 3 + usage.pandas: 13 + usage.sample-usage: 1 + usage.scipy: 11 + usage.skimage: 13 + usage.sklearn: 41 + usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: object, /): + def __eq__(self, _0: numpy.ndarray, /): """ - usage.scipy: 266 + usage.matplotlib: 46 + usage.sample-usage: 2 + usage.skimage: 104 + usage.sklearn: 214 + usage.xarray: 256 """ ... @overload - def __getitem__( - self, _0: Union[Tuple[slice[int, int, int], slice[int, int, int]], int], / - ): + def __eq__(self, _0: float, /): """ - usage.dask: 2 + usage.matplotlib: 4 + usage.skimage: 9 + usage.sklearn: 31 + usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[None, None, None]], /): + def __eq__(self, _0: numpy.int64, /): """ - usage.sklearn: 2 + usage.matplotlib: 4 + usage.skimage: 7 + usage.sklearn: 38 + usage.xarray: 10 """ ... - def __getitem__(self, _0: object, /): + @overload + def __eq__(self, _0: int, /): """ - usage.dask: 2 - usage.scipy: 266 - usage.sklearn: 2 + usage.matplotlib: 28 + usage.skimage: 111 + usage.sklearn: 269 + usage.xarray: 11 """ ... - def __gt__(self, _0: object, /): + @overload + def __eq__(self, _0: numpy.uint8, /): """ - usage.scipy: 319 + usage.matplotlib: 1 + usage.skimage: 3 + usage.xarray: 1 """ ... - def __imul__(self, _0: Union[float, int], /): + @overload + def __eq__(self, _0: numpy.float64, /): """ - usage.scipy: 5 + usage.matplotlib: 3 + usage.skimage: 9 + usage.sklearn: 42 + usage.xarray: 4 """ ... - def __itruediv__(self, _0: Union[float, int], /): + @overload + def __eq__(self, _0: numpy.float32, /): """ - usage.scipy: 4 + usage.skimage: 1 + usage.sklearn: 5 + usage.xarray: 2 """ ... - def __le__(self, _0: object, /): + @overload + def __eq__(self, _0: Literal["type-2-x"], /): """ - usage.scipy: 690 + usage.skimage: 2 """ ... - def __lt__( - self, - _0: Union[ - scipy.sparse.bsr.bsr_matrix, - int, - numpy.matrix, - scipy.sparse.csr.csr_matrix, - scipy.sparse.csc.csc_matrix, - ], - /, - ): + @overload + def __eq__(self, _0: Literal["type-2-y"], /): """ - usage.scipy: 390 + usage.skimage: 2 """ ... @overload - def __matmul__(self, _0: numpy.ndarray, /): + def __eq__(self, _0: Literal["type-3-x"], /): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload - def __matmul__(self, _0: Union[numpy.ndarray, numpy.matrix], /): + def __eq__(self, _0: Literal["type-3-y"], /): """ - usage.scipy: 242 + usage.skimage: 2 """ ... - def __matmul__(self, _0: Union[numpy.matrix, numpy.ndarray], /): + @overload + def __eq__(self, _0: Literal["type-4"], /): """ - usage.scipy: 242 - usage.skimage: 1 + usage.skimage: 2 """ ... - def __mul__(self, _0: object, /): + @overload + def __eq__(self, _0: Tuple[int, int], /): """ - usage.scipy: 69 + usage.skimage: 2 """ ... - def __ne__(self, _0: object, /): + @overload + def __eq__(self, _0: bool, /): """ - usage.scipy: 345 + usage.skimage: 5 """ ... - def __neg__(self, /): + @overload + def __eq__(self, _0: List[int], /): """ - usage.scipy: 6 - usage.skimage: 2 + usage.matplotlib: 1 + usage.skimage: 4 + usage.sklearn: 12 + usage.xarray: 2 """ ... - def __pow__(self, _0: int, /): + @overload + def __eq__(self, _0: Tuple[int, int, int], /): """ - usage.scipy: 1 + usage.skimage: 1 + usage.sklearn: 1 """ ... - def __radd__(self, _0: object, /): + @overload + def __eq__(self, _0: numpy.complex128, /): """ - usage.scipy: 888 + usage.skimage: 1 """ ... @overload - def __rmatmul__(self, _0: numpy.ndarray, /): + def __eq__(self, _0: numpy.uint64, /): """ usage.skimage: 1 """ ... @overload - def __rmatmul__(self, _0: Union[numpy.ndarray, numpy.matrix], /): + def __eq__(self, _0: numpy.bytes_, /): """ - usage.scipy: 237 + usage.xarray: 1 """ ... - def __rmatmul__(self, _0: Union[numpy.matrix, numpy.ndarray], /): + @overload + def __eq__(self, _0: numpy.int8, /): """ - usage.scipy: 237 - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __rmul__(self, _0: object, /): + def __eq__(self, _0: numpy.int16, /): """ - usage.scipy: 213 + usage.xarray: 1 """ ... @overload - def __rmul__(self, _0: int, /): + def __eq__(self, _0: dask.array.core.Array, /): """ - usage.sklearn: 1 + usage.xarray: 35 """ ... - def __rmul__(self, _0: object, /): + @overload + def __eq__(self, _0: numpy.int32, /): """ - usage.scipy: 213 - usage.sklearn: 1 + usage.sklearn: 3 + usage.xarray: 1 """ ... - def __rsub__(self, _0: object, /): + @overload + def __eq__(self, _0: Literal["float32"], /): """ - usage.scipy: 598 + usage.xarray: 1 """ ... @overload - def __rtruediv__(self, _0: Union[int, float], /): + def __eq__(self, _0: cftime._cftime.DatetimeGregorian, /): """ - usage.scipy: 2 + usage.xarray: 1 """ ... @overload - def __rtruediv__(self, _0: Union[numpy.ndarray, float], /): + def __eq__(self, _0: Literal["_not_supplied"], /): """ - usage.sklearn: 3 + usage.xarray: 1 """ ... - def __rtruediv__(self, _0: Union[float, numpy.ndarray, int], /): + @overload + def __eq__(self, _0: Literal["dim2"], /): """ - usage.scipy: 2 - usage.sklearn: 3 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: object, _1: object, /): + def __eq__(self, _0: Literal["dim1"], /): """ - usage.scipy: 303 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: slice[None, int, None], _1: int, /): + def __eq__(self, _0: Literal["foo"], /): """ - usage.dask: 1 + usage.sklearn: 1 + usage.xarray: 3 """ ... - def __setitem__(self, _0: object, _1: object, /): + @overload + def __eq__(self, _0: numpy.datetime64, /): """ - usage.dask: 1 - usage.scipy: 303 + usage.xarray: 4 """ ... @overload - def __sub__(self, _0: object, /): + def __eq__(self, _0: numpy.str_, /): """ - usage.scipy: 610 + usage.sklearn: 23 + usage.xarray: 1 """ ... @overload - def __sub__(self, _0: numpy.ndarray, /): + def __eq__(self, _0: sparse._coo.core.COO, /): """ - usage.sklearn: 2 + usage.xarray: 3 """ ... - def __sub__(self, _0: object, /): + @overload + def __eq__(self, _0: List[Literal["d", "b", "a"]], /): """ - usage.scipy: 610 - usage.sklearn: 2 + usage.xarray: 2 """ ... @overload - def __truediv__(self, _0: Union[int, complex], /): + def __eq__(self, _0: List[Literal["e", "d", "c", "b", "a"]], /): """ - usage.scipy: 2 + usage.xarray: 2 """ ... @overload - def __truediv__(self, _0: float, /): + def __eq__(self, _0: object, /): """ - usage.sklearn: 1 + usage.dask: 192 + usage.pandas: 895 + usage.scipy: 616 + usage.xarray: 5 """ ... - def __truediv__(self, _0: Union[float, complex, int], /): + @overload + def __eq__(self, _0: xarray.core.variable.Variable, /): """ - usage.scipy: 2 - usage.sklearn: 1 + usage.xarray: 2 """ ... - def all(self, /): + @overload + def __eq__(self, _0: pandas._libs.tslibs.period.Period, /): """ - usage.dask: 2 - usage.scipy: 2 + usage.xarray: 1 """ ... - def any(self, /): + @overload + def __eq__(self, _0: Literal["a"], /): """ - usage.sklearn: 1 + usage.sklearn: 2 + usage.xarray: 4 """ ... - def astype(self, _0: Union[type, Literal["int32", "int16"]], /): + @overload + def __eq__(self, _0: Literal["z"], /): """ - usage.scipy: 151 + usage.xarray: 5 """ ... - def conj(self, /): + @overload + def __eq__(self, _0: bytes, /): """ - usage.scipy: 4 + usage.xarray: 1 """ ... - def copy(self, /): + @overload + def __eq__(self, _0: Tuple[numpy.float64, numpy.float64, numpy.float64, int], /): """ - usage.dask: 1 - usage.scipy: 168 + usage.matplotlib: 1 """ ... @overload - def max(self, /, axis: int): + def __eq__( + self, _0: sklearn.ensemble._hist_gradient_boosting.splitting._memoryviewslice, / + ): """ - usage.scipy: 7 + usage.sklearn: 1 """ ... @overload - def max(self, /): + def __eq__(self, _0: numpy.bool_, /): """ - usage.sklearn: 3 + usage.sklearn: 7 """ ... - def max(self, /, axis: int = ...): + @overload + def __eq__(self, _0: None, /): """ - usage.scipy: 7 - usage.sklearn: 3 + usage.sklearn: 1 """ ... - def mean(self, /, axis: Union[int, None] = ..., out: numpy.matrix = ...): + @overload + def __eq__(self, _0: Literal["NAN"], /): """ - usage.scipy: 187 + usage.sklearn: 1 """ ... - def min(self, /, axis: int): + @overload + def __eq__(self, _0: Literal[""], /): """ - usage.scipy: 7 + usage.sklearn: 1 """ ... - def nonzero(self, /): + @overload + def __eq__(self, _0: List[numpy.int64], /): """ - usage.scipy: 1 usage.sklearn: 2 """ ... @overload - def reshape( - self, - _0: Union[int, Tuple[int, int]], - _1: int = ..., - /, - *, - order: Literal["F", "C"] = ..., - ): + def __eq__(self, _0: Literal["bar"], /): """ - usage.scipy: 21 + usage.sklearn: 1 """ ... @overload - def reshape(self, _0: Tuple[int], /): + def __eq__(self, _0: Literal["spam"], /): """ - usage.dask: 1 + usage.sklearn: 4 """ ... @overload - def reshape(self, _0: int, _1: int, /): + def __eq__(self, _0: _pytest.python_api.ApproxNumpy, /): """ - usage.sklearn: 2 + usage.sklearn: 12 """ ... - def reshape( - self, - _0: Union[int, Tuple[int, ...]], - _1: int = ..., - /, - *, - order: Literal["F", "C"] = ..., - ): + @overload + def __eq__(self, _0: _pytest.python_api.ApproxSequencelike, /): """ - usage.dask: 1 - usage.scipy: 21 - usage.sklearn: 2 + usage.sklearn: 4 """ ... - def sum( - self, - /, - axis: Union[Tuple[None, ...], int, None] = ..., - dtype: Union[type, None] = ..., - out: Union[None, numpy.matrix] = ..., - ): + @overload + def __eq__(self, _0: Literal["c"], /): """ - usage.scipy: 271 + usage.sklearn: 1 """ ... - def transpose(self, /): + @overload + def __eq__(self, _0: Literal["b"], /): """ - usage.scipy: 33 + usage.sklearn: 1 """ ... @overload - def view(self, _0: Type[numpy.ndarray], /): + def __eq__(self, _0: Literal["def"], /): """ - usage.scipy: 1 + usage.sklearn: 1 """ ... @overload - def view(self, /, *, type: Type[numpy.ndarray]): + def __eq__(self, _0: _pytest.python_api.ApproxScalar, /): """ - usage.dask: 2 + usage.sklearn: 2 """ ... - def view( - self, _0: Type[numpy.ndarray] = ..., /, *, type: Type[numpy.ndarray] = ... - ): + @overload + def __eq__(self, _0: Literal["one"], /): """ - usage.dask: 2 - usage.scipy: 1 + usage.sklearn: 4 """ ... - -class memmap: - - # usage.dask: 1 - __module__: ClassVar[object] - - # usage.sklearn: 1 - __name__: ClassVar[object] - - @classmethod - def __rmod__(cls, _0: str, /): + @overload + def __eq__(self, _0: Literal["two"], /): """ - usage.sklearn: 1 + usage.sklearn: 4 """ ... - # usage.sklearn: 15 - T: object - - # usage.sklearn: 1 - __class__: object - - # usage.dask: 1 - _mmap: object - - # usage.dask: 5 - base: object - - # usage.dask: 4 - ctypes: object - - # usage.dask: 4 - # usage.scipy: 13 - # usage.sklearn: 7 - dtype: object - - # usage.dask: 6 - # usage.sklearn: 3 - filename: object - - # usage.sklearn: 3 - flags: object - - # usage.dask: 1 - # usage.sklearn: 1 - ndim: object - - # usage.dask: 8 - # usage.scipy: 5 - # usage.sklearn: 21 - shape: object - - # usage.sklearn: 2 - size: object - - # usage.dask: 3 - strides: object + @overload + def __eq__(self, _0: Literal["three"], /): + """ + usage.sklearn: 4 + """ + ... - @overload - def __add__(self, _0: numpy.memmap, /): + def __eq__(self, _0: object, /): """ - usage.dask: 1 + usage.dask: 192 + usage.matplotlib: 88 + usage.pandas: 895 + usage.sample-usage: 2 + usage.scipy: 616 + usage.skimage: 268 + usage.sklearn: 692 + usage.xarray: 366 """ ... @overload - def __add__(self, _0: numpy.int64, /): + def __floordiv__(self, _0: int, /): """ - usage.sklearn: 1 + usage.sample-usage: 1 + usage.skimage: 8 + usage.sklearn: 7 + usage.xarray: 1 """ ... - def __add__(self, _0: Union[numpy.int64, numpy.memmap], /): + @overload + def __floordiv__(self, _0: numpy.ndarray, /): """ - usage.dask: 1 + usage.skimage: 2 usage.sklearn: 1 """ ... @overload - def __getitem__( - self, - _0: Union[ - Tuple[ - Union[slice[Union[None, int], Union[None, int], Union[None, int]], int], - Union[slice[None, None, None], int], - ], - slice[int, int, int], - ], - /, - ): + def __floordiv__(self, _0: numpy.float64, /): """ - usage.dask: 7 + usage.skimage: 1 """ ... @overload - def __getitem__( - self, - _0: Union[ - Tuple[ - slice[None, None, None], - Union[slice[None, int, None], numpy.ndarray, None], - ], - int, - numpy.ndarray, - numpy.int64, - slice[Union[int, None], Union[None, int], Union[int, None]], - ], - /, - ): + def __floordiv__(self, _0: object, /): """ - usage.sklearn: 18 + usage.pandas: 58 """ ... - def __getitem__( - self, - _0: Union[ - slice[Union[int, None], Union[int, None], Union[int, None]], - numpy.int64, - numpy.ndarray, - int, - Tuple[ - Union[int, slice[Union[None, int], Union[None, int], Union[None, int]]], - Union[int, numpy.ndarray, slice[None, Union[int, None], None], None], - ], - ], - /, - ): + @overload + def __floordiv__(self, _0: Union[int, numpy.ndarray], /): """ - usage.dask: 7 - usage.sklearn: 18 + usage.scipy: 18 """ ... - def __isub__(self, _0: Union[numpy.float64, numpy.ndarray], /): + @overload + def __floordiv__(self, _0: Union[float, numpy.ndarray, int], /): """ - usage.sklearn: 4 + usage.dask: 5 """ ... - def __pow__(self, _0: int, /): + @overload + def __floordiv__(self, _0: List[int], /): """ usage.sklearn: 1 """ ... - def __radd__(self, _0: numpy.memmap, /): + @overload + def __floordiv__(self, _0: List[float], /): """ - usage.dask: 1 + usage.sklearn: 3 """ ... - def __rsub__(self, _0: numpy.ndarray, /): + def __floordiv__(self, _0: object, /): """ - usage.sklearn: 1 + usage.dask: 5 + usage.pandas: 58 + usage.sample-usage: 1 + usage.scipy: 18 + usage.skimage: 11 + usage.sklearn: 12 + usage.xarray: 1 """ ... - def __rtruediv__(self, _0: Union[numpy.float64, numpy.ndarray], /): + @overload + def __ge__(self, _0: float, /): """ - usage.sklearn: 2 + usage.matplotlib: 9 + usage.skimage: 1 + usage.sklearn: 17 """ ... - def __setitem__( - self, - _0: Union[ - slice[None, None, None], - Tuple[slice[None, None, None], numpy.ndarray], - numpy.int64, - int, - ], - _1: Union[int, numpy.ndarray], - /, - ): + @overload + def __ge__(self, _0: int, /): """ - usage.sklearn: 8 + usage.matplotlib: 9 + usage.skimage: 43 + usage.sklearn: 47 + usage.xarray: 3 """ ... - def __sub__(self, _0: Union[numpy.float64, numpy.ndarray], /): + @overload + def __ge__(self, _0: numpy.int64, /): """ - usage.sklearn: 2 + usage.matplotlib: 2 + usage.skimage: 1 """ ... - def __truediv__(self, _0: Union[numpy.float64, numpy.ndarray], /): + @overload + def __ge__(self, _0: numpy.ndarray, /): """ - usage.sklearn: 4 + usage.matplotlib: 2 + usage.skimage: 5 + usage.sklearn: 7 + usage.xarray: 7 """ ... - def copy(self, _0: Literal["C"] = ..., /): + @overload + def __ge__(self, _0: object, /): """ - usage.sklearn: 2 + usage.pandas: 90 + usage.scipy: 464 """ ... - def item(self, /): + @overload + def __ge__(self, _0: numpy.float64, /): """ - usage.sklearn: 1 + usage.matplotlib: 16 + usage.sklearn: 5 """ ... - def mean(self, /, *, axis: int): + @overload + def __ge__(self, _0: numpy.ma.core.MaskedArray, /): """ + usage.matplotlib: 1 usage.sklearn: 2 """ ... - def min(self, /): + @overload + def __ge__(self, _0: Union[numpy.float64, int, numpy.ndarray, float], /): """ - usage.sklearn: 1 + usage.dask: 17 """ ... - def ravel(self, /): + def __ge__(self, _0: object, /): """ - usage.sklearn: 1 + usage.dask: 17 + usage.matplotlib: 39 + usage.pandas: 90 + usage.scipy: 464 + usage.skimage: 50 + usage.sklearn: 78 + usage.xarray: 10 """ ... - def reshape(self, _0: int, _1: int, /): + @overload + def __getitem__(self, _0: slice[None, int, None], /): """ - usage.scipy: 7 + usage.matplotlib: 53 + usage.skimage: 64 + usage.sklearn: 412 + usage.xarray: 22 """ ... - def tolist(self, /): + @overload + def __getitem__(self, _0: slice[int, None, int], /): """ - usage.sklearn: 7 + usage.matplotlib: 45 + usage.skimage: 25 + usage.sklearn: 184 + usage.xarray: 8 """ ... + @overload + def __getitem__(self, _0: int, /): + """ + usage.matplotlib: 433 + usage.sample-usage: 2 + usage.skimage: 360 + usage.sklearn: 1005 + usage.xarray: 105 + """ + ... -class ndarray: + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], int], /): + """ + usage.matplotlib: 130 + usage.skimage: 109 + usage.sklearn: 483 + usage.xarray: 16 + """ + ... - # usage.pandas: 2 - __array_ufunc__: ClassVar[object] + @overload + def __getitem__(self, _0: numpy.ndarray, /): + """ + usage.matplotlib: 182 + usage.sample-usage: 1 + usage.skimage: 244 + usage.sklearn: 927 + usage.xarray: 22 + """ + ... - # usage.dask: 8 - __module__: ClassVar[object] + @overload + def __getitem__(self, _0: Tuple[slice[int, int, int], slice[int, int, int]], /): + """ + usage.matplotlib: 4 + usage.skimage: 92 + usage.sklearn: 3 + """ + ... - # usage.matplotlib: 1 - __mro__: ClassVar[object] + @overload + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, None, None], int], / + ): + """ + usage.matplotlib: 9 + usage.skimage: 21 + usage.sklearn: 6 + usage.xarray: 1 + """ + ... - # usage.dask: 6 - # usage.pandas: 11 - # usage.sklearn: 1 - __name__: ClassVar[object] + @overload + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[None, int, None] + ], + /, + ): + """ + usage.matplotlib: 3 + usage.skimage: 5 + usage.sklearn: 3 + usage.xarray: 2 + """ + ... @overload - @classmethod - def __ne__(cls, _0: Type[numpy.ndarray], /): + def __getitem__(self, _0: Tuple[ellipsis, int], /): """ - usage.skimage: 2 + usage.matplotlib: 16 + usage.skimage: 100 + usage.xarray: 6 """ ... @overload - @classmethod - def __ne__(cls, _0: int, /): + def __getitem__(self, _0: Tuple[numpy.ndarray, int], /): """ - usage.matplotlib: 2 - usage.skimage: 9 - usage.xarray: 3 + usage.matplotlib: 6 + usage.skimage: 5 + usage.sklearn: 23 """ ... @overload - @classmethod - def __ne__(cls, _0: float, /): + def __getitem__(self, _0: Tuple[ellipsis, None], /): """ - usage.skimage: 1 + usage.matplotlib: 8 + usage.skimage: 37 + usage.sklearn: 4 usage.xarray: 2 """ ... @overload - @classmethod - def __ne__(cls, _0: numpy.ndarray, /): + def __getitem__(self, _0: Tuple[int, int, slice[None, None, None]], /): """ - usage.matplotlib: 18 - usage.skimage: 30 - usage.xarray: 4 + usage.skimage: 6 """ ... @overload - @classmethod - def __ne__(cls, _0: numpy.timedelta64, /): + def __getitem__(self, _0: Tuple[ellipsis, slice[None, int, None]], /): """ - usage.xarray: 1 + usage.matplotlib: 5 + usage.skimage: 13 + usage.sklearn: 2 + usage.xarray: 4 """ ... @overload - @classmethod - def __ne__(cls, _0: Literal["z"], /): + def __getitem__(self, _0: Tuple[ellipsis, int, None], /): """ - usage.xarray: 5 + usage.matplotlib: 1 + usage.skimage: 2 """ ... @overload - @classmethod - def __ne__(cls, _0: object, /): + def __getitem__(self, _0: Tuple[int, int, int], /): """ - usage.pandas: 142 - usage.scipy: 273 - usage.sklearn: 182 + usage.matplotlib: 1 + usage.skimage: 24 + usage.xarray: 3 """ ... @overload - @classmethod - def __ne__(cls, _0: Tuple[numpy.float64, numpy.float64, numpy.float64, int], /): + def __getitem__(self, _0: Tuple[None, ellipsis], /): """ - usage.matplotlib: 2 + usage.skimage: 11 + usage.xarray: 2 """ ... @overload - @classmethod - def __ne__(cls, _0: Tuple[float, float, float, float], /): + def __getitem__(self, _0: numpy.bool_, /): """ - usage.matplotlib: 1 + usage.skimage: 2 """ ... @overload - @classmethod - def __ne__(cls, _0: Union[int, numpy.ndarray], /): + def __getitem__(self, _0: Tuple[None, ellipsis, slice[None, int, None]], /): """ - usage.dask: 5 + usage.skimage: 1 """ ... - @classmethod - def __ne__(cls, _0: object, /): + @overload + def __getitem__(self, _0: Tuple[int, int], /): """ - usage.dask: 5 - usage.matplotlib: 23 - usage.pandas: 142 - usage.scipy: 273 - usage.skimage: 42 - usage.sklearn: 182 + usage.matplotlib: 82 + usage.skimage: 128 + usage.sklearn: 61 usage.xarray: 15 """ ... - @classmethod - def __rmod__(cls, _0: object, /): + @overload + def __getitem__(self, _0: Tuple[int, slice[int, None, int]], /): """ - usage.dask: 2 - usage.pandas: 57 - usage.sample-usage: 1 - usage.scipy: 12 + usage.matplotlib: 2 usage.skimage: 1 - usage.sklearn: 11 - usage.xarray: 1 + usage.sklearn: 5 """ ... - # usage.dask: 28 - # usage.matplotlib: 93 - # usage.pandas: 211 - # usage.sample-usage: 1 - # usage.scipy: 1342 - # usage.skimage: 84 - # usage.sklearn: 765 - # usage.xarray: 34 - T: object - - # usage.scipy: 18 - __array_interface__: object - - # usage.dask: 4 - # usage.pandas: 2 - __array_priority__: object - - # usage.dask: 29 - # usage.scipy: 5 - # usage.sklearn: 19 - __class__: object - - # usage.xarray: 1 - attrs: object - - # usage.matplotlib: 2 - # usage.pandas: 87 - # usage.scipy: 37 - # usage.sklearn: 2 - # usage.xarray: 8 - base: object - - # usage.sklearn: 1 - columns: object - - # usage.xarray: 4 - coords: object - - # usage.scipy: 1 - ctypes: object - - # usage.scipy: 3 - # usage.sklearn: 6 - data: object - - # usage.xarray: 2 - dims: object - - # usage.dask: 451 - # usage.matplotlib: 79 - # usage.pandas: 3135 - # usage.sample-usage: 1 - # usage.scipy: 2777 - # usage.skimage: 550 - # usage.sklearn: 1008 - # usage.xarray: 844 - dtype: object - - # usage.matplotlib: 2 - # usage.pandas: 86 - # usage.scipy: 237 - # usage.skimage: 13 - # usage.sklearn: 62 - # usage.xarray: 21 - flags: object - - # usage.dask: 7 - # usage.matplotlib: 33 - # usage.pandas: 14 - # usage.scipy: 31 - # usage.skimage: 21 - # usage.sklearn: 67 - # usage.xarray: 46 - flat: numpy.ndarray - - # usage.dask: 1 - # usage.matplotlib: 2 - # usage.scipy: 144 - # usage.xarray: 3 - imag: numpy.ndarray - - # usage.matplotlib: 2 - index: object - - # usage.pandas: 11 - # usage.scipy: 64 - # usage.skimage: 4 - # usage.sklearn: 2 - itemsize: object - - # usage.dask: 2 - keys: object - - # usage.xarray: 5 - magnitude: object - - # usage.matplotlib: 2 - name: object - - # usage.dask: 11 - # usage.pandas: 46 - # usage.scipy: 19 - # usage.sklearn: 3 - nbytes: object - - # usage.dask: 360 - # usage.matplotlib: 141 - # usage.pandas: 741 - # usage.sample-usage: 1 - # usage.scipy: 1935 - # usage.skimage: 462 - # usage.sklearn: 467 - # usage.xarray: 309 - ndim: object - - # usage.dask: 1 - # usage.matplotlib: 6 - # usage.pandas: 1 - # usage.scipy: 235 - # usage.xarray: 3 - real: object - - # usage.dask: 445 - # usage.matplotlib: 323 - # usage.pandas: 695 - # usage.sample-usage: 2 - # usage.scipy: 4734 - # usage.skimage: 1138 - # usage.sklearn: 3271 - # usage.xarray: 350 - shape: Union[Tuple[Union[int, None], ...], List[int], numpy.ndarray] - - # usage.dask: 16 - # usage.matplotlib: 84 - # usage.pandas: 147 - # usage.sample-usage: 1 - # usage.scipy: 975 - # usage.skimage: 88 - # usage.sklearn: 240 - # usage.xarray: 64 - size: object + @overload + def __getitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray], /): + """ + usage.matplotlib: 10 + usage.skimage: 27 + usage.sklearn: 51 + usage.xarray: 25 + """ + ... - # usage.dask: 29 - # usage.matplotlib: 8 - # usage.pandas: 1 - # usage.scipy: 30 - # usage.skimage: 12 - # usage.sklearn: 7 - # usage.xarray: 4 - strides: Union[Tuple[int, int, int], int] + @overload + def __getitem__(self, _0: Literal["L1"], /): + """ + usage.skimage: 4 + """ + ... - # usage.matplotlib: 1 - tzinfo: object + @overload + def __getitem__(self, _0: Literal["a1"], /): + """ + usage.skimage: 4 + """ + ... - # usage.xarray: 6 - units: object + @overload + def __getitem__(self, _0: Literal["b1"], /): + """ + usage.skimage: 4 + """ + ... - # usage.pandas: 4 - values: object + @overload + def __getitem__(self, _0: Literal["L2"], /): + """ + usage.skimage: 4 + """ + ... - # usage.xarray: 3 - variable: object + @overload + def __getitem__(self, _0: Literal["a2"], /): + """ + usage.skimage: 4 + """ + ... - # usage.xarray: 4 - variables: object + @overload + def __getitem__(self, _0: Literal["b2"], /): + """ + usage.skimage: 4 + """ + ... @overload - def __add__(self, _0: float, /): + def __getitem__(self, _0: Literal["dE"], /): """ - usage.matplotlib: 54 - usage.skimage: 28 - usage.xarray: 21 + usage.skimage: 1 """ ... @overload - def __add__(self, _0: numpy.ndarray, /): + def __getitem__(self, _0: slice[int, int, int], /): """ - usage.matplotlib: 213 + usage.matplotlib: 44 usage.sample-usage: 1 - usage.skimage: 177 - usage.xarray: 26 + usage.skimage: 10 + usage.sklearn: 101 + usage.xarray: 10 """ ... @overload - def __add__(self, _0: numpy.float64, /): + def __getitem__(self, _0: Tuple[None, slice[None, None, None]], /): """ usage.matplotlib: 10 - usage.skimage: 18 - usage.xarray: 1 + usage.skimage: 4 + usage.sklearn: 24 + usage.xarray: 9 """ ... @overload - def __add__(self, _0: int, /): + def __getitem__(self, _0: Tuple[numpy.int64, numpy.int64], /): """ - usage.matplotlib: 94 - usage.sample-usage: 1 - usage.skimage: 41 - usage.xarray: 22 + usage.skimage: 10 + usage.sklearn: 4 """ ... @overload - def __add__(self, _0: numpy.int64, /): + def __getitem__(self, _0: Tuple[slice[int, int, int], slice[None, None, None]], /): """ - usage.matplotlib: 2 - usage.skimage: 2 + usage.matplotlib: 8 + usage.skimage: 5 + usage.sklearn: 14 """ ... @overload - def __add__(self, _0: dask.array.core.Array, /): + def __getitem__(self, _0: Tuple[slice[None, None, None], None], /): """ - usage.skimage: 1 + usage.matplotlib: 42 + usage.skimage: 12 + usage.sklearn: 266 + usage.xarray: 14 """ ... @overload - def __add__(self, _0: bool, /): + def __getitem__( + self, + _0: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], + /, + ): """ - usage.skimage: 1 + usage.skimage: 20 + usage.sklearn: 1 + usage.xarray: 1 """ ... @overload - def __add__(self, _0: datetime.timedelta, /): + def __getitem__( + self, + _0: Tuple[ + slice[numpy.int64, numpy.int64, numpy.int64], + slice[numpy.int64, numpy.int64, numpy.int64], + ], + /, + ): """ - usage.xarray: 1 + usage.skimage: 9 """ ... @overload - def __add__(self, _0: xarray.coding.cftime_offsets.Day, /): + def __getitem__(self, _0: slice[int, None, int], /): """ - usage.xarray: 1 + usage.matplotlib: 6 + usage.skimage: 2 + usage.sklearn: 17 + usage.xarray: 2 """ ... @overload - def __add__(self, _0: xarray.coding.cftime_offsets.Hour, /): + def __getitem__(self, _0: slice[int, None, int], /): """ - usage.xarray: 1 + usage.skimage: 2 """ ... @overload - def __add__(self, _0: xarray.core.dataarray.DataArray, /): + def __getitem__(self, _0: Tuple[slice[int, int, int], slice[int, int, int]], /): """ + usage.skimage: 1 usage.xarray: 1 """ ... @overload - def __add__(self, _0: xarray.core.dataset.Dataset, /): + def __getitem__(self, _0: Tuple[int, slice[None, None, None]], /): """ - usage.xarray: 2 + usage.matplotlib: 11 + usage.skimage: 25 + usage.sklearn: 70 + usage.xarray: 6 """ ... @overload - def __add__(self, _0: object, /): + def __getitem__(self, _0: numpy.int64, /): """ - usage.dask: 194 - usage.pandas: 272 - usage.scipy: 2298 - usage.sklearn: 494 + usage.matplotlib: 13 + usage.skimage: 25 + usage.sklearn: 155 + usage.xarray: 5 """ ... @overload - def __add__(self, _0: Tuple[numpy.float64, numpy.float64], /): + def __getitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], /): + """ + usage.matplotlib: 5 + usage.skimage: 11 + usage.sklearn: 6 + usage.xarray: 9 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], slice[int, int, int]], /): """ usage.matplotlib: 4 + usage.skimage: 6 + usage.sklearn: 28 + usage.xarray: 4 """ ... @overload - def __add__(self, _0: Tuple[int, int], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, int, None]], / + ): """ - usage.matplotlib: 1 + usage.matplotlib: 3 + usage.skimage: 23 + usage.sklearn: 106 + usage.xarray: 7 """ ... @overload - def __add__(self, _0: List[numpy.float64], /): + def __getitem__(self, _0: Tuple[slice[None, None, None], slice[int, None, int]], /): """ - usage.matplotlib: 5 + usage.matplotlib: 4 + usage.skimage: 25 + usage.sklearn: 43 + usage.xarray: 8 """ ... @overload - def __add__(self, _0: Tuple[numpy.float64, float], /): + def __getitem__( + self, _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray], / + ): """ - usage.matplotlib: 2 + usage.skimage: 3 + usage.xarray: 2 """ ... @overload - def __add__(self, _0: List[List[float]], /): + def __getitem__(self, _0: slice[None, None, None], /): """ usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 17 + usage.xarray: 4 """ ... @overload - def __add__(self, _0: range, /): + def __getitem__(self, _0: Tuple[numpy.ndarray, slice[None, None, None]], /): """ - usage.matplotlib: 2 + usage.matplotlib: 5 + usage.skimage: 9 + usage.sklearn: 43 + usage.xarray: 6 """ ... @overload - def __add__(self, _0: List[float], /): + def __getitem__(self, _0: Tuple[ellipsis, slice[int, int, int]], /): """ - usage.matplotlib: 8 + usage.skimage: 2 """ ... @overload - def __add__(self, _0: List[List[numpy.int64]], /): + def __getitem__(self, _0: Tuple[slice[None, None, None], List[int]], /): """ - usage.matplotlib: 1 + usage.matplotlib: 2 + usage.skimage: 2 + usage.sklearn: 36 + usage.xarray: 3 """ ... @overload - def __add__(self, _0: List[Union[int, float]], /): + def __getitem__(self, _0: Tuple[int, slice[None, int, None]], /): """ usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 15 """ ... @overload - def __add__(self, _0: List[int], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], None, slice[None, None, None]], / + ): """ - usage.matplotlib: 2 + usage.skimage: 2 + usage.sklearn: 7 + usage.xarray: 2 """ ... @overload - def __add__(self, _0: List[Union[numpy.float64, float]], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], int, slice[None, None, None]], / + ): """ - usage.matplotlib: 2 + usage.matplotlib: 20 + usage.skimage: 8 + usage.xarray: 1 """ ... @overload - def __add__(self, _0: List[Union[float, numpy.float64]], /): + def __getitem__(self, _0: Tuple[ellipsis, slice[None, None, None]], /): """ - usage.matplotlib: 2 + usage.skimage: 1 + usage.xarray: 1 """ ... - def __add__(self, _0: object, /): + @overload + def __getitem__(self, _0: Tuple[slice[None, int, None], slice[None, int, None]], /): """ - usage.dask: 194 - usage.matplotlib: 404 - usage.pandas: 272 - usage.sample-usage: 2 - usage.scipy: 2298 - usage.skimage: 268 - usage.sklearn: 494 - usage.xarray: 76 + usage.matplotlib: 14 + usage.skimage: 42 + usage.sklearn: 16 + usage.xarray: 3 """ ... @overload - def __and__(self, _0: numpy.ndarray, /): + def __getitem__( + self, _0: Tuple[int, slice[None, None, None], slice[None, None, None]], / + ): """ - usage.matplotlib: 46 - usage.skimage: 12 - usage.sklearn: 11 + usage.skimage: 3 usage.xarray: 1 """ ... @overload - def __and__(self, _0: int, /): + def __getitem__( + self, + _0: Tuple[ + int, slice[None, None, None], slice[int, int, int], slice[int, int, int] + ], + /, + ): """ - usage.sample-usage: 1 usage.skimage: 2 """ ... @overload - def __and__(self, _0: dask.array.core.Array, /): + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[None, None, None] + ], + /, + ): """ + usage.skimage: 3 usage.xarray: 1 """ ... @overload - def __and__(self, _0: numpy.bool_, /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, None, None], None], / + ): + """ + usage.matplotlib: 1 + usage.skimage: 5 + usage.sklearn: 17 + usage.xarray: 4 + """ + ... + + @overload + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[int, int, int] + ], + /, + ): + """ + usage.matplotlib: 1 + usage.skimage: 3 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[int, int, slice[int, int, int]], /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[slice[None, int, None], slice[None, None, None]], / + ): + """ + usage.matplotlib: 11 + usage.skimage: 33 + usage.sklearn: 30 + usage.xarray: 2 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[int, None, int], slice[None, None, None]], /): """ + usage.matplotlib: 3 + usage.skimage: 18 + usage.sklearn: 5 usage.xarray: 1 """ ... @overload - def __and__(self, _0: sparse._coo.core.COO, /): + def __getitem__( + self, + _0: Tuple[slice[int, int, int], slice[int, int, int], slice[None, None, None]], + /, + ): + """ + usage.skimage: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[int, int, numpy.int64], /): + """ + usage.skimage: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[List[int], slice[None, None, None]], /): + """ + usage.skimage: 1 + usage.xarray: 2 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[int, slice[int, int, int]], /): """ + usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 8 usage.xarray: 1 """ ... @overload - def __and__(self, _0: Union[int, bool, numpy.ndarray], /): + def __getitem__(self, _0: slice[None, None, None], /): """ - usage.pandas: 88 + usage.matplotlib: 43 + usage.skimage: 27 + usage.sklearn: 80 + usage.xarray: 8 """ ... @overload - def __and__(self, _0: object, /): + def __getitem__(self, _0: None, /): """ - usage.scipy: 296 + usage.matplotlib: 13 + usage.skimage: 5 + usage.sklearn: 1 """ ... @overload - def __and__(self, _0: numpy.ma.core.MaskedArray, /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, None, None]], / + ): """ usage.matplotlib: 2 + usage.skimage: 16 + usage.sklearn: 4 + usage.xarray: 1 """ ... @overload - def __and__(self, _0: Union[numpy.ndarray, bool], /): + def __getitem__(self, _0: Tuple[slice[int, int, int], slice[None, int, None]], /): """ - usage.dask: 5 + usage.skimage: 13 + usage.sklearn: 1 """ ... - def __and__(self, _0: object, /): + @overload + def __getitem__(self, _0: Tuple[slice[int, int, int], slice[int, None, int]], /): """ - usage.dask: 5 - usage.matplotlib: 48 - usage.pandas: 88 - usage.sample-usage: 1 - usage.scipy: 296 - usage.skimage: 14 - usage.sklearn: 11 - usage.xarray: 4 + usage.matplotlib: 1 + usage.skimage: 11 + usage.sklearn: 2 """ ... - def __array_function__( + @overload + def __getitem__( self, - _0: Callable, - _1: Tuple[Union[Type[numpy.ndarray], None], ...], - _2: Tuple[Union[numpy.ndarray, numpy.int64, list], ...], - _3: Dict[ - str, - Union[ - Type[numpy.float64], - int, - bool, - numpy.dtype, - Tuple[Union[int, Tuple[int]], ...], - ], + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[None, None, None] ], /, ): """ - usage.dask: 30 + usage.skimage: 5 + usage.sklearn: 3 """ ... - def __array_wrap__(self, _0: numpy.ndarray, /): + @overload + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, None, None], int, int], / + ): """ - usage.dask: 2 - usage.scipy: 2 + usage.skimage: 17 """ ... - def __bool__(self, /): + @overload + def __getitem__(self, _0: Tuple[numpy.int64, int], /): """ - usage.pandas: 2 - usage.sample-usage: 1 - usage.scipy: 3 + usage.skimage: 2 + usage.sklearn: 2 """ ... @overload - def __contains__(self, _0: int, /): + def __getitem__(self, _0: List[int], /): """ - usage.matplotlib: 1 - usage.sample-usage: 1 - usage.skimage: 5 - usage.xarray: 3 + usage.matplotlib: 11 + usage.skimage: 1 + usage.sklearn: 9 + usage.xarray: 12 """ ... @overload - def __contains__(self, _0: Tuple[int, int], /): + def __getitem__(self, _0: slice[None, numpy.int64, None], /): """ - usage.skimage: 6 + usage.matplotlib: 3 + usage.skimage: 2 + usage.sklearn: 15 """ ... @overload - def __contains__(self, _0: numpy.int64, /): + def __getitem__(self, _0: slice[numpy.int64, None, numpy.int64], /): """ - usage.skimage: 2 + usage.matplotlib: 3 + usage.skimage: 4 + usage.sklearn: 3 """ ... @overload - def __contains__( - self, _0: Union[numpy.uint64, numpy.int64, int, Literal["one", "bar"]], / + def __getitem__(self, _0: dask.array.core.Array, /): + """ + usage.skimage: 4 + """ + ... + + @overload + def __getitem__( + self, + _0: Tuple[ + slice[numpy.int64, None, numpy.int64], + slice[numpy.int64, None, numpy.int64], + slice[numpy.int64, None, numpy.int64], + ], + /, ): """ - usage.pandas: 13 + usage.skimage: 3 """ ... @overload - def __contains__(self, _0: Union[numpy.int64, int, numpy.complex128], /): + def __getitem__(self, _0: Literal["data"], /): """ - usage.scipy: 11 + usage.skimage: 2 """ ... @overload - def __contains__(self, _0: float, /): + def __getitem__(self, _0: Literal["row"], /): """ - usage.matplotlib: 2 + usage.skimage: 2 """ ... @overload - def __contains__(self, _0: object, /): + def __getitem__(self, _0: Literal["column"], /): """ - usage.sklearn: 41 + usage.skimage: 2 """ ... - def __contains__(self, _0: object, /): + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], numpy.ndarray], /): """ - usage.matplotlib: 3 - usage.pandas: 13 - usage.sample-usage: 1 - usage.scipy: 11 - usage.skimage: 13 - usage.sklearn: 41 - usage.xarray: 3 + usage.skimage: 8 + usage.sklearn: 130 + usage.xarray: 5 """ ... @overload - def __eq__(self, _0: numpy.ndarray, /): + def __getitem__(self, _0: Tuple[numpy.ndarray, slice[None, None, None]], /): """ - usage.matplotlib: 46 - usage.sample-usage: 2 - usage.skimage: 104 - usage.xarray: 256 + usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: float, /): + def __getitem__(self, _0: Tuple[numpy.int64, numpy.int64, numpy.int64], /): """ - usage.matplotlib: 4 - usage.skimage: 9 - usage.xarray: 3 + usage.skimage: 5 """ ... @overload - def __eq__(self, _0: numpy.int64, /): + def __getitem__(self, _0: Tuple[slice[None, None, None], numpy.int64], /): """ - usage.matplotlib: 4 - usage.skimage: 7 - usage.xarray: 10 + usage.skimage: 4 + usage.sklearn: 17 """ ... @overload - def __eq__(self, _0: int, /): + def __getitem__(self, _0: Tuple[numpy.int64, slice[None, None, None]], /): """ - usage.matplotlib: 28 - usage.skimage: 111 - usage.xarray: 11 + usage.skimage: 2 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: numpy.uint8, /): + def __getitem__( + self, _0: Tuple[slice[int, numpy.int64, int], slice[None, None, None]], / + ): """ usage.matplotlib: 1 - usage.skimage: 3 - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __eq__(self, _0: numpy.float64, /): + def __getitem__(self, _0: slice[int, numpy.int64, int], /): """ - usage.matplotlib: 3 - usage.skimage: 9 - usage.xarray: 4 + usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 7 """ ... @overload - def __eq__(self, _0: numpy.float32, /): + def __getitem__( + self, + _0: Tuple[ + slice[numpy.int64, numpy.int64, numpy.int64], slice[None, None, None] + ], + /, + ): """ + usage.matplotlib: 1 usage.skimage: 1 - usage.xarray: 2 + usage.sklearn: 4 """ ... @overload - def __eq__(self, _0: Literal["type-2-x"], /): + def __getitem__(self, _0: slice[numpy.int64, numpy.int64, numpy.int64], /): """ - usage.skimage: 2 + usage.matplotlib: 5 + usage.skimage: 1 + usage.sklearn: 13 """ ... @overload - def __eq__(self, _0: Literal["type-2-y"], /): + def __getitem__( + self, + _0: Tuple[slice[numpy.int64, int, numpy.int64], slice[None, None, None]], + /, + ): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload - def __eq__(self, _0: Literal["type-3-x"], /): + def __getitem__(self, _0: slice[numpy.int64, int, numpy.int64], /): """ - usage.skimage: 2 + usage.skimage: 1 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: Literal["type-3-y"], /): + def __getitem__( + self, _0: Tuple[Tuple[int, int, int, int, int], slice[None, None, None]], / + ): """ + usage.matplotlib: 1 usage.skimage: 2 """ ... @overload - def __eq__(self, _0: Literal["type-4"], /): + def __getitem__(self, _0: Tuple[Tuple[int, int, int], slice[None, None, None]], /): """ - usage.skimage: 2 + usage.matplotlib: 3 + usage.skimage: 1 """ ... @overload - def __eq__(self, _0: Tuple[int, int], /): + def __getitem__( + self, + _0: Tuple[ + Tuple[int, int, int, int, int, int, int, int, int, int], + slice[None, None, None], + ], + /, + ): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload - def __eq__(self, _0: bool, /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, None, None]], / + ): """ - usage.skimage: 5 + usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 1 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: List[int], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, None, None]], / + ): """ usage.matplotlib: 1 - usage.skimage: 4 - usage.xarray: 2 + usage.skimage: 3 + usage.sklearn: 14 + usage.xarray: 3 """ ... @overload - def __eq__(self, _0: Tuple[int, int, int], /): + def __getitem__(self, _0: Tuple[slice[None, int, None], int], /): """ - usage.skimage: 1 + usage.skimage: 13 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: numpy.complex128, /): + def __getitem__(self, _0: Tuple[slice[int, int, int], int, None], /): """ usage.skimage: 1 """ ... @overload - def __eq__(self, _0: numpy.uint64, /): + def __getitem__(self, _0: Tuple[slice[None, None, None], int, None], /): """ - usage.skimage: 1 + usage.matplotlib: 6 + usage.skimage: 3 """ ... @overload - def __eq__(self, _0: numpy.bytes_, /): + def __getitem__( + self, + _0: Tuple[ + slice[numpy.int64, numpy.int64, numpy.int64], + slice[numpy.int64, numpy.int64, numpy.int64], + slice[numpy.int64, numpy.int64, numpy.int64], + slice[numpy.int64, numpy.int64, numpy.int64], + slice[numpy.int64, numpy.int64, numpy.int64], + ], + /, + ): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __eq__(self, _0: numpy.int8, /): + def __getitem__(self, _0: Tuple[numpy.ndarray], /): """ - usage.xarray: 1 + usage.skimage: 6 + usage.sklearn: 3 + usage.xarray: 14 """ ... @overload - def __eq__(self, _0: numpy.int16, /): + def __getitem__(self, _0: Tuple[None, ...], /): """ - usage.xarray: 1 + usage.skimage: 1 + usage.xarray: 5 """ ... @overload - def __eq__(self, _0: dask.array.core.Array, /): + def __getitem__(self, _0: Tuple[slice[int, int, int]], /): """ - usage.xarray: 35 + usage.skimage: 1 + usage.sklearn: 1 + usage.xarray: 1 """ ... @overload - def __eq__(self, _0: numpy.int32, /): + def __getitem__(self, _0: Tuple[int], /): """ - usage.xarray: 1 + usage.matplotlib: 5 + usage.skimage: 1 + usage.sklearn: 1 + usage.xarray: 2 """ ... @overload - def __eq__(self, _0: Literal["float32"], /): + def __getitem__(self, _0: Tuple[int, int, int, int], /): """ - usage.xarray: 1 + usage.skimage: 2 """ ... @overload - def __eq__(self, _0: cftime._cftime.DatetimeGregorian, /): + def __getitem__( + self, + _0: Tuple[ + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + ], + /, + ): """ + usage.skimage: 1 usage.xarray: 1 """ ... @overload - def __eq__(self, _0: Literal["_not_supplied"], /): + def __getitem__(self, _0: Tuple[int, int, int, int, int], /): """ + usage.skimage: 1 usage.xarray: 1 """ ... @overload - def __eq__(self, _0: Literal["dim2"], /): + def __getitem__( + self, + _0: Tuple[ + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + ], + /, + ): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __eq__(self, _0: Literal["dim1"], /): + def __getitem__(self, _0: Tuple[slice[numpy.int64, numpy.int64, numpy.int64]], /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __eq__(self, _0: Literal["foo"], /): + def __getitem__(self, _0: Tuple[slice[int, None, int], slice[int, None, int]], /): """ + usage.matplotlib: 4 + usage.skimage: 12 + usage.sklearn: 2 usage.xarray: 3 """ ... @overload - def __eq__(self, _0: numpy.datetime64, /): + def __getitem__(self, _0: Tuple[slice[int, None, int], slice[None, int, None]], /): """ - usage.xarray: 4 + usage.matplotlib: 3 + usage.skimage: 11 """ ... @overload - def __eq__(self, _0: numpy.str_, /): + def __getitem__(self, _0: Tuple[slice[None, int, None], slice[int, None, int]], /): """ - usage.xarray: 1 + usage.matplotlib: 3 + usage.skimage: 11 + usage.sklearn: 2 """ ... @overload - def __eq__(self, _0: sparse._coo.core.COO, /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, None, None]], / + ): """ - usage.xarray: 3 + usage.matplotlib: 1 + usage.skimage: 4 + usage.sklearn: 5 + usage.xarray: 9 """ ... @overload - def __eq__(self, _0: List[Literal["d", "b", "a"]], /): + def __getitem__( + self, + _0: Tuple[ + slice[numpy.int64, numpy.int64, numpy.int64], + slice[numpy.int64, numpy.int64, numpy.int64], + slice[numpy.int64, numpy.int64, numpy.int64], + ], + /, + ): """ - usage.xarray: 2 + usage.skimage: 3 """ ... @overload - def __eq__(self, _0: List[Literal["e", "d", "c", "b", "a"]], /): + def __getitem__(self, _0: Tuple[slice[None, int, None], ellipsis], /): """ - usage.xarray: 2 + usage.skimage: 1 + usage.xarray: 7 """ ... @overload - def __eq__(self, _0: object, /): + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[None, None, None] + ], + /, + ): """ - usage.dask: 192 - usage.pandas: 895 - usage.scipy: 616 - usage.sklearn: 692 - usage.xarray: 5 + usage.skimage: 2 """ ... @overload - def __eq__(self, _0: xarray.core.variable.Variable, /): + def __getitem__( + self, + _0: Tuple[ + slice[numpy.int64, numpy.int64, numpy.int64], + slice[numpy.int64, numpy.int64, numpy.int64], + slice[None, None, None], + ], + /, + ): """ - usage.xarray: 2 + usage.skimage: 2 """ ... @overload - def __eq__(self, _0: pandas._libs.tslibs.period.Period, /): + def __getitem__( + self, _0: Tuple[numpy.int64, numpy.int64, numpy.int64, numpy.int64], / + ): """ - usage.xarray: 1 + usage.skimage: 2 """ ... @overload - def __eq__(self, _0: Literal["a"], /): + def __getitem__( + self, _0: Tuple[int, int, slice[int, int, int], slice[None, None, None]], / + ): """ - usage.xarray: 4 + usage.skimage: 2 """ ... @overload - def __eq__(self, _0: Literal["z"], /): + def __getitem__( + self, _0: Tuple[int, int, slice[None, None, None], slice[int, int, int]], / + ): """ - usage.xarray: 5 + usage.skimage: 2 """ ... @overload - def __eq__(self, _0: bytes, /): + def __getitem__( + self, + _0: Tuple[ + slice[int, None, int], slice[None, None, None], slice[None, None, None] + ], + /, + ): """ + usage.skimage: 2 usage.xarray: 1 """ ... @overload - def __eq__(self, _0: Tuple[numpy.float64, numpy.float64, numpy.float64, int], /): + def __getitem__( + self, + _0: Tuple[ + int, + int, + slice[int, int, int], + slice[None, None, None], + slice[None, None, None], + ], + /, + ): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... - def __eq__(self, _0: object, /): + @overload + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[int, None, int], slice[None, None, None] + ], + /, + ): """ - usage.dask: 192 - usage.matplotlib: 88 - usage.pandas: 895 - usage.sample-usage: 2 - usage.scipy: 616 - usage.skimage: 268 - usage.sklearn: 692 - usage.xarray: 366 + usage.skimage: 2 """ ... @overload - def __floordiv__(self, _0: int, /): + def __getitem__( + self, + _0: Tuple[ + int, + int, + slice[None, None, None], + slice[int, int, int], + slice[None, None, None], + ], + /, + ): """ - usage.sample-usage: 1 - usage.skimage: 8 - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __floordiv__(self, _0: numpy.ndarray, /): + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[int, None, int] + ], + /, + ): """ + usage.matplotlib: 1 usage.skimage: 2 + usage.sklearn: 1 """ ... @overload - def __floordiv__(self, _0: numpy.float64, /): + def __getitem__( + self, + _0: Tuple[ + int, + int, + slice[None, None, None], + slice[None, None, None], + slice[int, int, int], + ], + /, + ): """ usage.skimage: 1 """ ... @overload - def __floordiv__(self, _0: object, /): + def __getitem__( + self, _0: Tuple[int, slice[int, int, int], slice[None, None, None]], / + ): """ - usage.pandas: 58 + usage.skimage: 1 """ ... @overload - def __floordiv__(self, _0: Union[int, numpy.ndarray], /): + def __getitem__( + self, _0: Tuple[int, slice[None, None, None], slice[int, int, int]], / + ): """ - usage.scipy: 18 + usage.skimage: 1 """ ... @overload - def __floordiv__(self, _0: Union[float, numpy.ndarray, int], /): + def __getitem__( + self, + _0: Tuple[ + slice[None, int, None], + slice[None, int, None], + None, + slice[None, None, None], + ], + /, + ): """ - usage.dask: 5 + usage.skimage: 1 """ ... @overload - def __floordiv__(self, _0: Union[numpy.ndarray, int, List[Union[float, int]]], /): - """ - usage.sklearn: 12 - """ - ... - - def __floordiv__(self, _0: object, /): + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + int, + slice[None, None, None], + ], + /, + ): """ - usage.dask: 5 - usage.pandas: 58 - usage.sample-usage: 1 - usage.scipy: 18 - usage.skimage: 11 - usage.sklearn: 12 - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __ge__(self, _0: float, /): + def __getitem__( + self, + _0: Tuple[ + int, slice[int, int, int], slice[None, None, None], slice[None, None, None] + ], + /, + ): """ - usage.matplotlib: 9 usage.skimage: 1 """ ... @overload - def __ge__(self, _0: int, /): + def __getitem__( + self, + _0: Tuple[ + int, slice[None, None, None], slice[int, int, int], slice[None, None, None] + ], + /, + ): """ - usage.matplotlib: 9 - usage.skimage: 43 - usage.xarray: 3 + usage.skimage: 1 """ ... @overload - def __ge__(self, _0: numpy.int64, /): + def __getitem__( + self, + _0: Tuple[ + int, slice[None, None, None], slice[None, None, None], slice[int, int, int] + ], + /, + ): """ - usage.matplotlib: 2 usage.skimage: 1 """ ... @overload - def __ge__(self, _0: numpy.ndarray, /): + def __getitem__(self, _0: Tuple[slice[int, None, int]], /): """ - usage.matplotlib: 2 - usage.skimage: 5 - usage.xarray: 7 + usage.skimage: 1 + usage.xarray: 9 """ ... @overload - def __ge__(self, _0: object, /): + def __getitem__( + self, + _0: Tuple[ + slice[int, None, int], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ], + /, + ): """ - usage.pandas: 90 - usage.scipy: 464 + usage.skimage: 1 """ ... @overload - def __ge__(self, _0: numpy.float64, /): + def __getitem__( + self, + _0: Tuple[ + int, + slice[int, int, int], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ], + /, + ): """ - usage.matplotlib: 16 + usage.skimage: 1 """ ... @overload - def __ge__(self, _0: numpy.ma.core.MaskedArray, /): + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[int, None, int], + slice[None, None, None], + slice[None, None, None], + ], + /, + ): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload - def __ge__(self, _0: Union[numpy.float64, int, numpy.ndarray, float], /): + def __getitem__( + self, + _0: Tuple[ + int, + slice[None, None, None], + slice[int, int, int], + slice[None, None, None], + slice[None, None, None], + ], + /, + ): """ - usage.dask: 17 + usage.skimage: 1 """ ... @overload - def __ge__( + def __getitem__( self, - _0: Union[int, numpy.ndarray, numpy.float64, numpy.ma.core.MaskedArray, float], + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[int, None, int], + slice[None, None, None], + ], /, ): """ - usage.sklearn: 85 + usage.skimage: 1 """ ... - def __ge__(self, _0: object, /): + @overload + def __getitem__( + self, + _0: Tuple[ + int, + slice[None, None, None], + slice[None, None, None], + slice[int, int, int], + slice[None, None, None], + ], + /, + ): """ - usage.dask: 17 - usage.matplotlib: 39 - usage.pandas: 90 - usage.scipy: 464 - usage.skimage: 50 - usage.sklearn: 85 - usage.xarray: 10 + usage.skimage: 1 """ ... @overload - def __getitem__(self, _0: slice[None, int, None], /): + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[int, None, int], + ], + /, + ): """ - usage.matplotlib: 53 - usage.skimage: 64 - usage.xarray: 22 + usage.skimage: 1 """ ... @overload - def __getitem__(self, _0: slice[int, None, int], /): + def __getitem__( + self, + _0: Tuple[ + int, + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[int, int, int], + ], + /, + ): """ - usage.matplotlib: 45 - usage.skimage: 25 - usage.xarray: 8 + usage.skimage: 1 """ ... @overload - def __getitem__(self, _0: int, /): + def __getitem__(self, _0: Tuple[ellipsis, None, None], /): """ - usage.matplotlib: 433 - usage.sample-usage: 2 - usage.skimage: 360 - usage.xarray: 105 + usage.skimage: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], int], /): + def __getitem__(self, _0: Tuple[slice[None, int, None]], /): """ - usage.matplotlib: 130 - usage.skimage: 109 - usage.xarray: 16 + usage.skimage: 2 + usage.xarray: 9 """ ... @overload - def __getitem__(self, _0: numpy.ndarray, /): + def __getitem__( + self, + _0: Tuple[ + slice[None, int, None], slice[None, int, None], slice[None, int, None] + ], + /, + ): """ - usage.matplotlib: 182 - usage.sample-usage: 1 - usage.skimage: 244 - usage.xarray: 22 + usage.skimage: 5 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], slice[int, int, int]], /): + def __getitem__( + self, + _0: Tuple[ + slice[None, int, None], + slice[None, int, None], + slice[None, int, None], + slice[None, int, None], + ], + /, + ): """ - usage.matplotlib: 4 - usage.skimage: 92 + usage.skimage: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None], int], / + self, + _0: Tuple[ + slice[numpy.int64, None, numpy.int64], slice[numpy.int64, None, numpy.int64] + ], + /, ): """ - usage.matplotlib: 9 - usage.skimage: 21 - usage.xarray: 1 + usage.skimage: 3 """ ... @@ -48325,224 +61523,271 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, int, None] + slice[numpy.int64, None, numpy.int64], + slice[numpy.int64, None, numpy.int64], + slice[numpy.int64, None, numpy.int64], ], /, ): """ - usage.matplotlib: 3 - usage.skimage: 5 - usage.xarray: 2 + usage.skimage: 2 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis, int], /): + def __getitem__(self, _0: Tuple[slice[int, None, int], slice[int, int, int]], /): """ - usage.matplotlib: 16 - usage.skimage: 100 - usage.xarray: 6 + usage.matplotlib: 1 + usage.skimage: 10 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.ndarray, int], /): + def __getitem__(self, _0: Tuple[slice[None, int, None], slice[int, int, int]], /): """ - usage.matplotlib: 6 - usage.skimage: 5 + usage.skimage: 12 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis, None], /): + def __getitem__(self, _0: Tuple[ellipsis, slice[int, None, int]], /): """ - usage.matplotlib: 8 - usage.skimage: 37 - usage.xarray: 2 + usage.skimage: 2 + usage.sklearn: 2 """ ... @overload - def __getitem__(self, _0: Tuple[int, int, slice[None, None, None]], /): + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + None, + slice[None, None, None], + ], + /, + ): """ - usage.skimage: 6 + usage.skimage: 1 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis, slice[None, int, None]], /): + def __getitem__(self, _0: Tuple[None, ellipsis, None], /): """ - usage.matplotlib: 5 - usage.skimage: 13 - usage.xarray: 4 + usage.skimage: 2 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis, int, None], /): + def __getitem__( + self, + _0: Tuple[ + slice[None, int, None], slice[None, int, None], slice[int, None, int] + ], + /, + ): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.skimage: 4 """ ... @overload - def __getitem__(self, _0: Tuple[int, int, int], /): + def __getitem__( + self, + _0: Tuple[ + slice[None, int, None], slice[int, None, int], slice[None, int, None] + ], + /, + ): """ - usage.matplotlib: 1 - usage.skimage: 24 - usage.xarray: 3 + usage.skimage: 4 """ ... @overload - def __getitem__(self, _0: Tuple[None, ellipsis], /): + def __getitem__( + self, + _0: Tuple[slice[None, int, None], slice[int, None, int], slice[int, None, int]], + /, + ): """ - usage.skimage: 11 - usage.xarray: 2 + usage.skimage: 4 """ ... @overload - def __getitem__(self, _0: numpy.bool_, /): + def __getitem__( + self, + _0: Tuple[ + slice[int, None, int], slice[None, int, None], slice[None, int, None] + ], + /, + ): """ - usage.skimage: 2 + usage.skimage: 4 """ ... @overload - def __getitem__(self, _0: Tuple[None, ellipsis, slice[None, int, None]], /): + def __getitem__( + self, + _0: Tuple[slice[int, None, int], slice[None, int, None], slice[int, None, int]], + /, + ): """ - usage.skimage: 1 + usage.skimage: 4 """ ... @overload - def __getitem__(self, _0: Tuple[int, int], /): + def __getitem__( + self, + _0: Tuple[slice[int, None, int], slice[int, None, int], slice[None, int, None]], + /, + ): """ - usage.matplotlib: 82 - usage.skimage: 128 - usage.xarray: 15 + usage.skimage: 4 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[int, None, int]], /): + def __getitem__( + self, + _0: Tuple[slice[int, None, int], slice[int, None, int], slice[int, None, int]], + /, + ): """ - usage.matplotlib: 2 - usage.skimage: 1 + usage.skimage: 4 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray], /): + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[None, None, None] + ], + /, + ): """ - usage.matplotlib: 10 - usage.skimage: 27 - usage.xarray: 25 + usage.skimage: 3 + usage.xarray: 5 """ ... @overload - def __getitem__(self, _0: Literal["L1"], /): + def __getitem__( + self, + _0: Tuple[ + slice[numpy.int64, None, numpy.int64], slice[numpy.int64, None, numpy.int64] + ], + /, + ): """ - usage.skimage: 4 + usage.skimage: 2 """ ... @overload - def __getitem__(self, _0: Literal["a1"], /): + def __getitem__(self, _0: Tuple[slice[int, int, int], int], /): """ - usage.skimage: 4 + usage.matplotlib: 4 + usage.skimage: 2 + usage.sklearn: 22 """ ... @overload - def __getitem__(self, _0: Literal["b1"], /): + def __getitem__(self, _0: Tuple[int, numpy.ndarray], /): """ - usage.skimage: 4 + usage.skimage: 1 + usage.sklearn: 16 """ ... @overload - def __getitem__(self, _0: Literal["L2"], /): + def __getitem__(self, _0: List[numpy.int64], /): """ - usage.skimage: 4 + usage.skimage: 10 + usage.sklearn: 28 """ ... @overload - def __getitem__(self, _0: Literal["a2"], /): + def __getitem__(self, _0: Tuple[int, ellipsis], /): """ - usage.skimage: 4 + usage.skimage: 2 + usage.xarray: 12 """ ... @overload - def __getitem__(self, _0: Literal["b2"], /): + def __getitem__(self, _0: Tuple[slice[int, None, int], int, int], /): """ - usage.skimage: 4 + usage.skimage: 1 """ ... @overload - def __getitem__(self, _0: Literal["dE"], /): + def __getitem__(self, _0: Tuple[slice[None, None, None]], /): """ usage.skimage: 1 + usage.sklearn: 1 + usage.xarray: 4 """ ... @overload - def __getitem__(self, _0: slice[int, int, int], /): + def __getitem__(self, _0: Tuple[slice[None, None, None]], /): """ - usage.matplotlib: 44 - usage.sample-usage: 1 - usage.skimage: 10 - usage.xarray: 10 + usage.xarray: 15 """ ... @overload - def __getitem__(self, _0: Tuple[None, slice[None, None, None]], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, None, None], ellipsis], / + ): """ - usage.matplotlib: 10 - usage.skimage: 4 usage.xarray: 9 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.int64, numpy.int64], /): + def __getitem__(self, _0: Tuple[slice[None, None, None], ellipsis], /): """ - usage.skimage: 10 + usage.xarray: 17 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], slice[None, None, None]], /): + def __getitem__(self, _0: Tuple[ellipsis], /): """ - usage.matplotlib: 8 - usage.skimage: 5 + usage.xarray: 16 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], None], /): + def __getitem__(self, _0: Tuple[slice[int, None, int], ellipsis], /): """ - usage.matplotlib: 42 - usage.skimage: 12 - usage.xarray: 14 + usage.xarray: 7 """ ... @overload def __getitem__( self, - _0: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ], /, ): """ - usage.skimage: 20 usage.xarray: 1 """ ... @@ -48551,193 +61796,176 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[numpy.int64, numpy.int64, numpy.int64], - slice[numpy.int64, numpy.int64, numpy.int64], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ellipsis, ], /, ): """ - usage.skimage: 9 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: slice[int, None, int], /): + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ellipsis, + ], + /, + ): """ - usage.matplotlib: 6 - usage.skimage: 2 - usage.xarray: 2 + usage.xarray: 4 """ ... @overload - def __getitem__(self, _0: slice[int, None, int], /): + def __getitem__(self, _0: ellipsis, /): """ - usage.skimage: 2 + usage.xarray: 9 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], slice[int, int, int]], /): + def __getitem__(self, _0: Tuple[slice[None, int, None], numpy.ndarray], /): """ - usage.skimage: 1 - usage.xarray: 1 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[None, None, None]], /): + def __getitem__(self, _0: Tuple[int, slice[None, None, None], ellipsis], /): """ - usage.matplotlib: 11 - usage.skimage: 25 - usage.xarray: 6 + usage.xarray: 4 """ ... @overload - def __getitem__(self, _0: numpy.int64, /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, None, None], ellipsis], / + ): """ - usage.matplotlib: 13 - usage.skimage: 25 - usage.xarray: 5 + usage.xarray: 4 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], /): + def __getitem__(self, _0: Tuple[int, int, ellipsis], /): """ - usage.matplotlib: 5 - usage.skimage: 11 - usage.xarray: 9 + usage.xarray: 7 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], slice[int, int, int]], /): + def __getitem__(self, _0: slice[int, int, int], /): """ - usage.matplotlib: 4 - usage.skimage: 6 - usage.xarray: 4 + usage.matplotlib: 2 + usage.sklearn: 3 + usage.xarray: 5 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, int, None]], / - ): + def __getitem__(self, _0: Tuple[slice[int, int, int], ellipsis], /): """ - usage.matplotlib: 3 - usage.skimage: 23 - usage.xarray: 7 + usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], slice[int, None, int]], /): + def __getitem__(self, _0: Tuple[slice[int, int, int], numpy.ndarray], /): """ - usage.matplotlib: 4 - usage.skimage: 25 - usage.xarray: 8 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray], / + self, _0: Tuple[slice[None, None, None], slice[None, None, None], ellipsis], / ): """ - usage.skimage: 3 - usage.xarray: 2 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: slice[None, None, None], /): + def __getitem__(self, _0: Tuple[None, None], /): """ - usage.matplotlib: 1 - usage.skimage: 1 usage.xarray: 4 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.ndarray, slice[None, None, None]], /): + def __getitem__( + self, _0: Tuple[slice[int, int, int], slice[None, None, None], ellipsis], / + ): """ - usage.matplotlib: 5 - usage.skimage: 9 - usage.xarray: 6 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis, slice[int, int, int]], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[int, int, int], ellipsis], / + ): """ - usage.skimage: 2 + usage.xarray: 6 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], List[int]], /): + def __getitem__(self, _0: Tuple[slice[None, None, None], int, ellipsis], /): """ - usage.matplotlib: 2 - usage.skimage: 2 - usage.xarray: 3 + usage.xarray: 5 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[None, int, None]], /): + def __getitem__(self, _0: Tuple[slice[int, int, int], ellipsis], /): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.xarray: 4 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], None, slice[None, None, None]], / + self, _0: Tuple[slice[None, int, None], slice[None, int, None], ellipsis], / ): """ - usage.skimage: 2 - usage.xarray: 2 + usage.xarray: 3 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], int, slice[None, None, None]], / + self, _0: Tuple[slice[int, int, int], slice[None, int, None], ellipsis], / ): """ - usage.matplotlib: 20 - usage.skimage: 8 usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis, slice[None, None, None]], /): + def __getitem__( + self, _0: Tuple[slice[None, int, None], slice[int, int, int], ellipsis], / + ): """ - usage.skimage: 1 usage.xarray: 1 """ ... - @overload - def __getitem__(self, _0: Tuple[slice[None, int, None], slice[None, int, None]], /): - """ - usage.matplotlib: 14 - usage.skimage: 42 - usage.xarray: 3 - """ - ... - @overload def __getitem__( - self, _0: Tuple[int, slice[None, None, None], slice[None, None, None]], / + self, _0: Tuple[slice[int, int, int], slice[int, int, int], ellipsis], / ): """ - usage.skimage: 3 - usage.xarray: 1 + usage.xarray: 2 """ ... @@ -48745,12 +61973,15 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - int, slice[None, None, None], slice[int, int, int], slice[int, int, int] + slice[None, None, None], + slice[int, int, int], + slice[int, int, int], + ellipsis, ], /, ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @@ -48758,353 +61989,320 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, None, None] + slice[int, int, int], slice[int, int, int], slice[int, int, int], ellipsis ], /, ): """ - usage.skimage: 3 - usage.xarray: 1 + usage.xarray: 2 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None], None], / + self, _0: Tuple[slice[int, int, int], int, slice[int, int, int], ellipsis], / ): """ - usage.matplotlib: 1 - usage.skimage: 5 - usage.xarray: 4 + usage.xarray: 1 """ ... @overload def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[int, int, int] - ], - /, + self, _0: Tuple[slice[None, None, None], numpy.ndarray, numpy.ndarray], / ): """ - usage.matplotlib: 1 - usage.skimage: 3 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[int, int, slice[int, int, int]], /): + def __getitem__(self, _0: Tuple[slice[int, int, int], int, int, ellipsis], /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, int, None], slice[None, None, None]], / - ): + def __getitem__(self, _0: Tuple[None], /): """ - usage.matplotlib: 11 - usage.skimage: 33 - usage.xarray: 2 + usage.xarray: 6 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, None, int], slice[None, None, None]], /): + def __getitem__( + self, + _0: Tuple[numpy.ndarray, slice[None, None, None], slice[None, None, None]], + /, + ): """ - usage.matplotlib: 3 - usage.skimage: 18 - usage.xarray: 1 + usage.xarray: 6 """ ... @overload def __getitem__( self, - _0: Tuple[slice[int, int, int], slice[int, int, int], slice[None, None, None]], + _0: Tuple[int, slice[None, None, None], slice[None, None, None], ellipsis], /, ): """ - usage.skimage: 1 + usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: Tuple[int, int, numpy.int64], /): + def __getitem__(self, _0: Tuple[slice[None, None, None], int, int, ellipsis], /): """ - usage.skimage: 1 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[List[int], slice[None, None, None]], /): + def __getitem__(self, _0: Tuple[int, int, int, ellipsis], /): """ - usage.skimage: 1 usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[int, int, int]], /): + def __getitem__(self, _0: Tuple[int, slice[int, int, int], int, ellipsis], /): """ - usage.matplotlib: 1 - usage.skimage: 2 usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: slice[None, None, None], /): - """ - usage.matplotlib: 43 - usage.skimage: 27 - usage.xarray: 8 - """ - ... - - @overload - def __getitem__(self, _0: None, /): - """ - usage.matplotlib: 13 - usage.skimage: 5 - """ - ... - - @overload - def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None]], / - ): + def __getitem__(self, _0: Tuple[int, int, slice[int, int, int], ellipsis], /): """ - usage.matplotlib: 2 - usage.skimage: 16 usage.xarray: 1 """ ... - @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], slice[None, int, None]], /): - """ - usage.skimage: 13 - """ - ... - - @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], slice[int, None, int]], /): - """ - usage.matplotlib: 1 - usage.skimage: 11 - """ - ... - @overload def __getitem__( self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, None, None] - ], + _0: Tuple[slice[None, None, None], numpy.ndarray, slice[None, None, None]], /, ): """ - usage.skimage: 5 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None], int, int], / + self, + _0: Tuple[slice[None, None, None], slice[None, None, None], numpy.ndarray], + /, ): """ - usage.skimage: 17 + usage.xarray: 5 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.int64, int], /): + def __getitem__( + self, _0: Tuple[slice[None, int, None], slice[None, None, None], ellipsis], / + ): """ - usage.skimage: 2 + usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: List[int], /): + def __getitem__(self, _0: Tuple[int, slice[None, int, None], ellipsis], /): """ - usage.matplotlib: 11 - usage.skimage: 1 - usage.xarray: 12 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: slice[None, numpy.int64, None], /): + def __getitem__( + self, _0: Tuple[slice[int, None, int], slice[int, None, int], ellipsis], / + ): """ - usage.matplotlib: 3 - usage.skimage: 2 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: slice[numpy.int64, None, numpy.int64], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, int, None], ellipsis], / + ): """ - usage.matplotlib: 3 - usage.skimage: 4 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: dask.array.core.Array, /): + def __getitem__( + self, + _0: Tuple[int, slice[None, int, None], slice[None, None, None], ellipsis], + /, + ): """ - usage.skimage: 4 + usage.xarray: 2 """ ... @overload def __getitem__( self, - _0: Tuple[ - slice[numpy.int64, None, numpy.int64], - slice[numpy.int64, None, numpy.int64], - slice[numpy.int64, None, numpy.int64], - ], + _0: Tuple[int, slice[int, None, int], slice[None, None, None], ellipsis], /, ): """ - usage.skimage: 3 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Literal["data"], /): + def __getitem__(self, _0: Tuple[int, slice[int, None, int], ellipsis], /): """ - usage.skimage: 2 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Literal["row"], /): + def __getitem__( + self, _0: Tuple[None, slice[None, None, None], slice[None, None, None]], / + ): """ - usage.skimage: 2 + usage.sklearn: 7 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Literal["column"], /): + def __getitem__( + self, _0: Tuple[slice[int, None, int], slice[None, None, None], ellipsis], / + ): """ - usage.skimage: 2 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], numpy.ndarray], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[int, None, int], ellipsis], / + ): """ - usage.skimage: 8 - usage.xarray: 5 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.ndarray, slice[None, None, None]], /): + def __getitem__(self, _0: Tuple[slice[None, int, None], int, ellipsis], /): """ - usage.skimage: 1 usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.int64, numpy.int64, numpy.int64], /): + def __getitem__(self, _0: Tuple[List[List[int]], slice[None, None, None]], /): """ - usage.skimage: 5 + usage.xarray: 5 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], numpy.int64], /): + def __getitem__(self, _0: Tuple[None, None, None], /): """ - usage.skimage: 4 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.int64, slice[None, None, None]], /): + def __getitem__(self, _0: Tuple[slice[None, None, None], List[int], List[int]], /): """ - usage.skimage: 2 + usage.xarray: 3 """ ... @overload def __getitem__( - self, _0: Tuple[slice[int, numpy.int64, int], slice[None, None, None]], / + self, _0: Tuple[slice[None, None, None], slice[None, None, None], ellipsis], / ): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: slice[int, numpy.int64, int], /): + def __getitem__(self, _0: Tuple[slice[int, int, int], numpy.ndarray], /): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.xarray: 1 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[numpy.ndarray, numpy.ndarray, slice[None, None, None]], / + ): + """ + usage.xarray: 2 """ ... @overload def __getitem__( self, - _0: Tuple[ - slice[numpy.int64, numpy.int64, numpy.int64], slice[None, None, None] - ], + _0: Tuple[int, int, slice[None, None, None], slice[None, None, None], ellipsis], /, ): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: slice[numpy.int64, numpy.int64, numpy.int64], /): + def __getitem__( + self, + _0: Tuple[slice[None, None, None], int, int, slice[None, None, None], ellipsis], + /, + ): """ - usage.matplotlib: 5 - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __getitem__( self, - _0: Tuple[slice[numpy.int64, int, numpy.int64], slice[None, None, None]], + _0: Tuple[slice[None, None, None], slice[None, None, None], int, int, ellipsis], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: slice[numpy.int64, int, numpy.int64], /): + def __getitem__( + self, + _0: Tuple[int, slice[None, None, None], int, slice[None, None, None], ellipsis], + /, + ): """ - usage.skimage: 1 + usage.xarray: 2 """ ... @overload def __getitem__( - self, _0: Tuple[Tuple[int, int, int, int, int], slice[None, None, None]], / + self, _0: Tuple[slice[int, None, int], slice[None, None, None], ellipsis], / ): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[Tuple[int, int, int], slice[None, None, None]], /): + def __getitem__(self, _0: Tuple[numpy.int64], /): """ - usage.matplotlib: 3 - usage.skimage: 1 + usage.xarray: 2 """ ... @@ -49112,57 +62310,58 @@ def __getitem__(self, _0: Tuple[Tuple[int, int, int], slice[None, None, None]], def __getitem__( self, _0: Tuple[ - Tuple[int, int, int, int, int, int, int, int, int, int], slice[None, None, None], + slice[None, None, None], + slice[int, int, int], + ellipsis, ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None]], / + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[int, None, int], + ellipsis, + ], + /, ): """ - usage.matplotlib: 1 - usage.skimage: 2 usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None]], / + self, + _0: Tuple[ + slice[int, int, int], + slice[None, None, None], + slice[None, None, None], + ellipsis, + ], + /, ): """ - usage.matplotlib: 1 - usage.skimage: 3 - usage.xarray: 3 - """ - ... - - @overload - def __getitem__(self, _0: Tuple[slice[None, int, None], int], /): - """ - usage.skimage: 13 - """ - ... - - @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], int, None], /): - """ - usage.skimage: 1 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], int, None], /): + def __getitem__( + self, + _0: Tuple[slice[None, None, None], slice[None, None, None], int, ellipsis], + /, + ): """ - usage.matplotlib: 6 - usage.skimage: 3 + usage.xarray: 4 """ ... @@ -49170,141 +62369,141 @@ def __getitem__(self, _0: Tuple[slice[None, None, None], int, None], /): def __getitem__( self, _0: Tuple[ - slice[numpy.int64, numpy.int64, numpy.int64], - slice[numpy.int64, numpy.int64, numpy.int64], - slice[numpy.int64, numpy.int64, numpy.int64], - slice[numpy.int64, numpy.int64, numpy.int64], - slice[numpy.int64, numpy.int64, numpy.int64], + slice[None, None, None], + slice[int, int, int], + slice[None, None, None], + ellipsis, ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.ndarray], /): + def __getitem__( + self, + _0: Tuple[slice[None, None, None], int, slice[None, None, None], ellipsis], + /, + ): """ - usage.skimage: 6 - usage.xarray: 14 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[None, ...], /): + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ellipsis, + ], + /, + ): """ - usage.skimage: 1 - usage.xarray: 5 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int]], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[int, int, int], ellipsis], / + ): """ - usage.skimage: 1 usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[int], /): - """ - usage.matplotlib: 5 - usage.skimage: 1 - usage.xarray: 2 - """ - ... - - @overload - def __getitem__(self, _0: Tuple[int, int, int, int], /): + def __getitem__(self, _0: Tuple[slice[None, None, None], slice[int, int, int]], /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __getitem__( - self, - _0: Tuple[ - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - ], - /, + self, _0: Tuple[slice[None, None, None], slice[None, int, None], ellipsis], / ): """ - usage.skimage: 1 usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[int, int, int, int, int], /): + def __getitem__( + self, _0: Tuple[slice[None, int, None], slice[None, None, None], ellipsis], / + ): """ - usage.skimage: 1 usage.xarray: 1 """ ... @overload def __getitem__( - self, - _0: Tuple[ - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - ], - /, + self, _0: Tuple[slice[int, int, int], slice[None, None, None], ellipsis], / ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[numpy.int64, numpy.int64, numpy.int64]], /): + def __getitem__(self, _0: Tuple[slice[None, None, None], ellipsis], /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, None, int], slice[int, None, int]], /): + def __getitem__( + self, _0: Tuple[slice[None, int, None], slice[int, None, int], ellipsis], / + ): """ - usage.matplotlib: 4 - usage.skimage: 12 - usage.xarray: 3 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, None, int], slice[None, int, None]], /): + def __getitem__(self, _0: Tuple[int, slice[None, None, None], int, ellipsis], /): """ - usage.matplotlib: 3 - usage.skimage: 11 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, int, None], slice[int, None, int]], /): + def __getitem__( + self, + _0: Tuple[ + slice[int, None, int], + slice[None, None, None], + slice[None, None, None], + ellipsis, + ], + /, + ): """ - usage.matplotlib: 3 - usage.skimage: 11 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None]], / + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ellipsis, + ], + /, ): """ - usage.matplotlib: 1 - usage.skimage: 4 - usage.xarray: 9 + usage.xarray: 2 """ ... @@ -49312,35 +62511,30 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[numpy.int64, numpy.int64, numpy.int64], - slice[numpy.int64, numpy.int64, numpy.int64], - slice[numpy.int64, numpy.int64, numpy.int64], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ellipsis, ], /, ): """ - usage.skimage: 3 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, int, None], ellipsis], /): + def __getitem__(self, _0: Tuple[slice[int, None, int], None], /): """ - usage.skimage: 1 - usage.xarray: 7 + usage.xarray: 2 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, None, None] - ], - /, - ): + def __getitem__(self, _0: Tuple[slice[None, int, None], None], /): """ - usage.skimage: 2 + usage.sklearn: 4 + usage.xarray: 2 """ ... @@ -49348,41 +62542,43 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[numpy.int64, numpy.int64, numpy.int64], - slice[numpy.int64, numpy.int64, numpy.int64], - slice[None, None, None], + slice[None, None, None], slice[None, None, None], slice[None, None, None] ], /, ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[numpy.int64, numpy.int64, numpy.int64, numpy.int64], / + self, _0: Tuple[int, slice[None, int, None], slice[None, None, None]], / ): """ - usage.skimage: 2 + usage.xarray: 2 """ ... @overload def __getitem__( - self, _0: Tuple[int, int, slice[int, int, int], slice[None, None, None]], / + self, + _0: Tuple[ + slice[None, int, None], slice[None, None, None], slice[None, None, None] + ], + /, ): """ - usage.skimage: 2 + usage.xarray: 3 """ ... @overload def __getitem__( - self, _0: Tuple[int, int, slice[None, None, None], slice[int, int, int]], / + self, _0: Tuple[int, slice[int, None, int], slice[None, None, None]], / ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @@ -49390,12 +62586,14 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[int, None, int], slice[None, None, None], slice[None, None, None] + slice[int, None, int], + slice[int, None, int], + slice[int, None, int], + slice[int, None, int], ], /, ): """ - usage.skimage: 2 usage.xarray: 1 """ ... @@ -49404,29 +62602,23 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - int, - int, - slice[int, int, int], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], slice[None, None, None], slice[None, None, None], ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[int, None, int], slice[None, None, None] - ], - /, - ): + def __getitem__(self, _0: Tuple[int, slice[None, None, None], int], /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @@ -49435,15 +62627,15 @@ def __getitem__( self, _0: Tuple[ int, + slice[None, None, None], int, slice[None, None, None], - slice[int, int, int], slice[None, None, None], ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @@ -49451,13 +62643,23 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[int, None, int] + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + int, ], /, ): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.xarray: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, int, None], ellipsis, int], /): + """ + usage.xarray: 1 """ ... @@ -49465,34 +62667,40 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - int, - int, + slice[None, int, None], slice[None, None, None], slice[None, None, None], - slice[int, int, int], + slice[None, None, None], + int, ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[int, slice[int, int, int], slice[None, None, None]], / - ): + def __getitem__(self, _0: Tuple[ellipsis, int, slice[None, None, None]], /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[int, slice[None, None, None], slice[int, int, int]], / + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + int, + slice[None, None, None], + ], + /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @@ -49500,15 +62708,16 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[None, int, None], - slice[None, int, None], - None, + numpy.ndarray, + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], slice[None, None, None], ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @@ -49516,28 +62725,23 @@ def __getitem__( def __getitem__( self, _0: Tuple[ + numpy.ndarray, + numpy.ndarray, slice[None, None, None], slice[None, None, None], - int, slice[None, None, None], ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - int, slice[int, int, int], slice[None, None, None], slice[None, None, None] - ], - /, - ): + def __getitem__(self, _0: Tuple[ellipsis, numpy.ndarray, numpy.ndarray], /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @@ -49545,12 +62749,45 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - int, slice[None, None, None], slice[int, int, int], slice[None, None, None] + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + numpy.ndarray, + numpy.ndarray, ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[ellipsis, int, int, int, int, int], /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __getitem__(self, _0: slice[numpy.int64, numpy.int64, numpy.int64], /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __getitem__(self, _0: slice[numpy.int64, None, numpy.int64], /): + """ + usage.sklearn: 2 + usage.xarray: 1 + """ + ... + + @overload + def __getitem__(self, _0: slice[None, int, None], /): + """ + usage.xarray: 4 """ ... @@ -49558,20 +62795,15 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - int, slice[None, None, None], slice[None, None, None], slice[int, int, int] + slice[None, None, None], + slice[None, None, None], + slice[None, int, None], + ellipsis, ], /, ): """ - usage.skimage: 1 - """ - ... - - @overload - def __getitem__(self, _0: Tuple[slice[int, None, int]], /): - """ - usage.skimage: 1 - usage.xarray: 9 + usage.xarray: 1 """ ... @@ -49579,32 +62811,35 @@ def __getitem__(self, _0: Tuple[slice[int, None, int]], /): def __getitem__( self, _0: Tuple[ - slice[int, None, int], - slice[None, None, None], slice[None, None, None], slice[None, None, None], + slice[int, int, int], + ellipsis, ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __getitem__( self, - _0: Tuple[ - int, - slice[int, int, int], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ], + _0: Tuple[slice[None, None, None], int, slice[None, int, None], ellipsis], /, ): """ - usage.skimage: 1 + usage.xarray: 1 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[slice[None, None, None], int, slice[int, int, int], ellipsis], / + ): + """ + usage.xarray: 1 """ ... @@ -49613,14 +62848,14 @@ def __getitem__( self, _0: Tuple[ slice[None, None, None], - slice[int, None, int], - slice[None, None, None], + slice[None, int, None], slice[None, None, None], + ellipsis, ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @@ -49628,49 +62863,35 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - int, slice[None, None, None], slice[int, int, int], slice[None, None, None], - slice[None, None, None], + ellipsis, ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __getitem__( self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[int, None, int], - slice[None, None, None], - ], + _0: Tuple[slice[None, None, None], slice[None, int, None], int, ellipsis], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __getitem__( - self, - _0: Tuple[ - int, - slice[None, None, None], - slice[None, None, None], - slice[int, int, int], - slice[None, None, None], - ], - /, + self, _0: Tuple[slice[None, None, None], slice[int, int, int], int, ellipsis], / ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @@ -49679,14 +62900,14 @@ def __getitem__( self, _0: Tuple[ slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[int, None, int], + slice[None, int, None], + slice[None, int, None], + ellipsis, ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @@ -49694,171 +62915,150 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - int, - slice[None, None, None], - slice[None, None, None], slice[None, None, None], slice[int, int, int], + slice[int, int, int], + ellipsis, ], /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis, None, None], /): + def __getitem__( + self, + _0: Tuple[slice[None, None, None], slice[None, int, None], numpy.ndarray], + /, + ): """ - usage.skimage: 1 + usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, int, None]], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[int, int, int], numpy.ndarray], / + ): """ - usage.skimage: 2 - usage.xarray: 9 + usage.xarray: 2 """ ... @overload def __getitem__( self, - _0: Tuple[ - slice[None, int, None], slice[None, int, None], slice[None, int, None] - ], + _0: Tuple[slice[None, None, None], numpy.ndarray, slice[None, None, None]], /, ): """ - usage.skimage: 5 - usage.xarray: 2 + usage.xarray: 4 """ ... @overload def __getitem__( self, - _0: Tuple[ - slice[None, int, None], - slice[None, int, None], - slice[None, int, None], - slice[None, int, None], - ], + _0: Tuple[slice[None, None, None], numpy.ndarray, slice[None, int, None]], /, ): """ - usage.skimage: 1 + usage.xarray: 3 """ ... @overload def __getitem__( - self, - _0: Tuple[ - slice[numpy.int64, None, numpy.int64], slice[numpy.int64, None, numpy.int64] - ], - /, + self, _0: Tuple[slice[None, None, None], numpy.ndarray, slice[int, int, int]], / ): """ - usage.skimage: 3 + usage.xarray: 2 """ ... @overload def __getitem__( self, - _0: Tuple[ - slice[numpy.int64, None, numpy.int64], - slice[numpy.int64, None, numpy.int64], - slice[numpy.int64, None, numpy.int64], - ], + _0: Tuple[int, slice[None, None, None], slice[None, int, None], ellipsis], /, ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, None, int], slice[int, int, int]], /): + def __getitem__( + self, _0: Tuple[int, slice[None, None, None], slice[int, int, int], ellipsis], / + ): """ - usage.matplotlib: 1 - usage.skimage: 10 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, int, None], slice[int, int, int]], /): + def __getitem__(self, _0: Tuple[int, int, slice[None, None, None], ellipsis], /): """ - usage.skimage: 12 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis, slice[int, None, int]], /): + def __getitem__(self, _0: Tuple[int, int, slice[None, int, None], ellipsis], /): """ - usage.skimage: 2 + usage.xarray: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[int, int, slice[int, int, int], ellipsis], /): + """ + usage.xarray: 1 """ ... @overload def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - None, - slice[None, None, None], - ], - /, + self, _0: Tuple[int, slice[int, int, int], slice[None, None, None], ellipsis], / ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[None, ellipsis, None], /): + def __getitem__(self, _0: Tuple[int, slice[None, int, None], int, ellipsis], /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, int, None], slice[None, int, None], slice[int, None, int] - ], - /, - ): + def __getitem__(self, _0: Tuple[int, slice[int, int, int], int, ellipsis], /): """ - usage.skimage: 4 + usage.xarray: 1 """ ... @overload def __getitem__( self, - _0: Tuple[ - slice[None, int, None], slice[int, None, int], slice[None, int, None] - ], + _0: Tuple[int, slice[None, int, None], slice[None, int, None], ellipsis], /, ): """ - usage.skimage: 4 + usage.xarray: 1 """ ... @overload def __getitem__( - self, - _0: Tuple[slice[None, int, None], slice[int, None, int], slice[int, None, int]], - /, + self, _0: Tuple[int, slice[int, int, int], slice[int, int, int], ellipsis], / ): """ - usage.skimage: 4 + usage.xarray: 1 """ ... @@ -49866,45 +63066,51 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[int, None, int], slice[None, int, None], slice[None, int, None] + slice[None, int, None], + slice[None, None, None], + slice[None, None, None], + ellipsis, ], /, ): """ - usage.skimage: 4 + usage.xarray: 2 """ ... @overload def __getitem__( self, - _0: Tuple[slice[int, None, int], slice[None, int, None], slice[int, None, int]], + _0: Tuple[ + slice[int, int, int], + slice[None, None, None], + slice[None, None, None], + ellipsis, + ], /, ): """ - usage.skimage: 4 + usage.xarray: 1 """ ... @overload def __getitem__( self, - _0: Tuple[slice[int, None, int], slice[int, None, int], slice[None, int, None]], + _0: Tuple[slice[None, int, None], slice[None, None, None], int, ellipsis], /, ): """ - usage.skimage: 4 + usage.xarray: 1 """ ... @overload def __getitem__( - self, - _0: Tuple[slice[int, None, int], slice[int, None, int], slice[int, None, int]], - /, + self, _0: Tuple[slice[int, int, int], slice[None, None, None], int, ellipsis], / ): """ - usage.skimage: 4 + usage.xarray: 1 """ ... @@ -49912,13 +63118,15 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, None, None] + slice[None, int, None], + slice[None, None, None], + slice[None, int, None], + ellipsis, ], /, ): """ - usage.skimage: 3 - usage.xarray: 5 + usage.xarray: 1 """ ... @@ -49926,107 +63134,138 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[numpy.int64, None, numpy.int64], slice[numpy.int64, None, numpy.int64] + slice[int, int, int], + slice[None, None, None], + slice[int, int, int], + ellipsis, ], /, ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], int], /): + def __getitem__( + self, + _0: Tuple[slice[None, int, None], slice[None, None, None], numpy.ndarray], + /, + ): """ - usage.matplotlib: 4 - usage.skimage: 2 + usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: Tuple[int, numpy.ndarray], /): + def __getitem__( + self, _0: Tuple[slice[int, int, int], slice[None, None, None], numpy.ndarray], / + ): """ - usage.skimage: 1 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: List[numpy.int64], /): + def __getitem__( + self, + _0: Tuple[slice[None, int, None], int, slice[None, None, None], ellipsis], + /, + ): """ - usage.skimage: 10 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[int, ellipsis], /): + def __getitem__( + self, _0: Tuple[slice[int, int, int], int, slice[None, None, None], ellipsis], / + ): """ - usage.skimage: 2 - usage.xarray: 12 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, None, int], int, int], /): + def __getitem__(self, _0: Tuple[slice[None, int, None], int, int, ellipsis], /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None]], /): + def __getitem__(self, _0: Tuple[slice[int, int, int], int, int, ellipsis], /): """ - usage.skimage: 1 - usage.xarray: 4 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None]], /): + def __getitem__( + self, + _0: Tuple[slice[None, int, None], int, slice[None, int, None], ellipsis], + /, + ): """ - usage.xarray: 15 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None], ellipsis], / + self, _0: Tuple[slice[int, int, int], int, slice[int, int, int], ellipsis], / ): """ - usage.xarray: 9 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], ellipsis], /): + def __getitem__( + self, + _0: Tuple[ + slice[None, int, None], + slice[None, int, None], + slice[None, None, None], + ellipsis, + ], + /, + ): """ - usage.xarray: 17 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis], /): + def __getitem__( + self, + _0: Tuple[ + slice[int, int, int], + slice[int, int, int], + slice[None, None, None], + ellipsis, + ], + /, + ): """ - usage.xarray: 16 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, None, int], ellipsis], /): + def __getitem__( + self, + _0: Tuple[slice[None, int, None], slice[None, int, None], int, ellipsis], + /, + ): """ - usage.xarray: 7 + usage.xarray: 1 """ ... @overload def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ], - /, + self, _0: Tuple[slice[int, int, int], slice[int, int, int], int, ellipsis], / ): """ usage.xarray: 1 @@ -50037,16 +63276,15 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], + slice[None, int, None], + slice[None, int, None], + slice[None, int, None], ellipsis, ], /, ): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @@ -50054,137 +63292,165 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ellipsis, + slice[int, int, int], slice[int, int, int], slice[int, int, int], ellipsis ], /, ): """ - usage.xarray: 4 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: ellipsis, /): + def __getitem__( + self, + _0: Tuple[slice[None, int, None], slice[None, int, None], numpy.ndarray], + /, + ): """ - usage.xarray: 9 + usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, int, None], numpy.ndarray], /): + def __getitem__( + self, _0: Tuple[slice[int, int, int], slice[int, int, int], numpy.ndarray], / + ): """ usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[None, None, None], ellipsis], /): + def __getitem__( + self, + _0: Tuple[slice[None, int, None], numpy.ndarray, slice[None, None, None]], + /, + ): """ - usage.xarray: 4 + usage.xarray: 3 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None], ellipsis], / + self, _0: Tuple[slice[int, int, int], numpy.ndarray, slice[None, None, None]], / ): """ - usage.xarray: 4 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[int, int, ellipsis], /): + def __getitem__( + self, + _0: Tuple[slice[None, int, None], numpy.ndarray, slice[None, int, None]], + /, + ): """ - usage.xarray: 7 + usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: slice[int, int, int], /): + def __getitem__( + self, _0: Tuple[slice[int, int, int], numpy.ndarray, slice[int, int, int]], / + ): """ - usage.matplotlib: 2 - usage.xarray: 5 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], ellipsis], /): + def __getitem__( + self, + _0: Tuple[numpy.ndarray, slice[None, None, None], slice[None, int, None]], + /, + ): """ usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], numpy.ndarray], /): + def __getitem__( + self, _0: Tuple[numpy.ndarray, slice[None, None, None], slice[int, int, int]], / + ): """ - usage.xarray: 1 + usage.xarray: 2 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None], ellipsis], / + self, + _0: Tuple[numpy.ndarray, slice[None, int, None], slice[None, None, None]], + /, ): """ - usage.xarray: 1 + usage.xarray: 3 """ ... @overload - def __getitem__(self, _0: Tuple[None, None], /): + def __getitem__( + self, _0: Tuple[numpy.ndarray, slice[int, int, int], slice[None, None, None]], / + ): """ - usage.xarray: 4 + usage.xarray: 2 """ ... @overload def __getitem__( - self, _0: Tuple[slice[int, int, int], slice[None, None, None], ellipsis], / + self, + _0: Tuple[numpy.ndarray, slice[None, int, None], slice[None, int, None]], + /, ): """ - usage.xarray: 2 + usage.xarray: 3 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[int, int, int], ellipsis], / + self, _0: Tuple[numpy.ndarray, slice[int, int, int], slice[int, int, int]], / ): """ - usage.xarray: 6 + usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], int, ellipsis], /): + def __getitem__(self, _0: Tuple[slice[int, int, int], int, ellipsis], /): """ - usage.xarray: 5 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], ellipsis], /): + def __getitem__( + self, + _0: Tuple[slice[None, None, None], int, slice[None, int, None], ellipsis], + /, + ): """ - usage.xarray: 4 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, int, None], slice[None, int, None], ellipsis], / + self, _0: Tuple[numpy.ndarray, numpy.ndarray, slice[int, int, int]], / ): """ - usage.xarray: 3 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[int, int, int], slice[None, int, None], ellipsis], / + self, _0: Tuple[numpy.ndarray, slice[int, int, int], numpy.ndarray], / ): """ usage.xarray: 1 @@ -50193,7 +63459,7 @@ def __getitem__( @overload def __getitem__( - self, _0: Tuple[slice[None, int, None], slice[int, int, int], ellipsis], / + self, _0: Tuple[numpy.ndarray, slice[None, None, None], numpy.ndarray], / ): """ usage.xarray: 1 @@ -50202,22 +63468,17 @@ def __getitem__( @overload def __getitem__( - self, _0: Tuple[slice[int, int, int], slice[int, int, int], ellipsis], / + self, _0: Tuple[slice[int, int, int], numpy.ndarray, numpy.ndarray], / ): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload def __getitem__( self, - _0: Tuple[ - slice[None, None, None], - slice[int, int, int], - slice[int, int, int], - ellipsis, - ], + _0: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], /, ): """ @@ -50228,9 +63489,7 @@ def __getitem__( @overload def __getitem__( self, - _0: Tuple[ - slice[int, int, int], slice[int, int, int], slice[int, int, int], ellipsis - ], + _0: Tuple[slice[int, int, int], slice[int, int, int], slice[None, None, None]], /, ): """ @@ -50240,7 +63499,9 @@ def __getitem__( @overload def __getitem__( - self, _0: Tuple[slice[int, int, int], int, slice[int, int, int], ellipsis], / + self, + _0: Tuple[slice[int, int, int], slice[None, None, None], slice[int, int, int]], + /, ): """ usage.xarray: 1 @@ -50249,72 +63510,90 @@ def __getitem__( @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], numpy.ndarray, numpy.ndarray], / + self, + _0: Tuple[ + slice[int, int, int], slice[None, None, None], slice[None, None, None] + ], + /, ): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], int, int, ellipsis], /): + def __getitem__( + self, + _0: Tuple[slice[None, None, None], slice[int, int, int], slice[int, int, int]], + /, + ): """ usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[None], /): + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[int, int, int], slice[None, None, None] + ], + /, + ): """ - usage.xarray: 6 + usage.xarray: 1 """ ... @overload def __getitem__( self, - _0: Tuple[numpy.ndarray, slice[None, None, None], slice[None, None, None]], + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[int, int, int] + ], /, ): """ - usage.xarray: 6 + usage.xarray: 1 """ ... @overload def __getitem__( - self, - _0: Tuple[int, slice[None, None, None], slice[None, None, None], ellipsis], - /, + self, _0: Tuple[slice[int, int, int], int, slice[None, None, None], ellipsis], / ): """ - usage.xarray: 3 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], int, int, ellipsis], /): + def __getitem__(self, _0: Tuple[slice[int, int, int], int, ellipsis], /): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[int, int, int, ellipsis], /): + def __getitem__( + self, _0: Tuple[slice[int, int, int], numpy.ndarray, slice[int, int, int]], / + ): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[int, int, int], int, ellipsis], /): + def __getitem__( + self, _0: Tuple[int, slice[int, int, int], slice[int, int, int], ellipsis], / + ): """ usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[int, int, slice[int, int, int], ellipsis], /): + def __getitem__(self, _0: Tuple[int, slice[int, int, int], ellipsis], /): """ usage.xarray: 1 """ @@ -50323,7 +63602,9 @@ def __getitem__(self, _0: Tuple[int, int, slice[int, int, int], ellipsis], /): @overload def __getitem__( self, - _0: Tuple[slice[None, None, None], numpy.ndarray, slice[None, None, None]], + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[None, None, None] + ], /, ): """ @@ -50333,43 +63614,54 @@ def __getitem__( @overload def __getitem__( - self, - _0: Tuple[slice[None, None, None], slice[None, None, None], numpy.ndarray], - /, + self, _0: Tuple[slice[int, int, int], slice[int, int, int], ellipsis], / ): """ - usage.xarray: 5 + usage.xarray: 1 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[int, slice[int, int, int], slice[int, int, int]], / + ): + """ + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, int, None], slice[None, None, None], ellipsis], / + self, _0: Tuple[numpy.ndarray, slice[int, int, int], slice[int, int, int]], / ): """ - usage.xarray: 3 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[None, int, None], ellipsis], /): + def __getitem__(self, _0: Tuple[numpy.ndarray, slice[int, int, int]], /): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[int, None, int], slice[int, None, int], ellipsis], / + self, _0: Tuple[slice[None, None, None], int, slice[None, int, None]], / ): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, int, None], ellipsis], / + self, + _0: Tuple[ + slice[None, None, None], slice[None, int, None], slice[None, None, None] + ], + /, ): """ usage.xarray: 2 @@ -50378,28 +63670,21 @@ def __getitem__( @overload def __getitem__( - self, - _0: Tuple[int, slice[None, int, None], slice[None, None, None], ellipsis], - /, + self, _0: Tuple[slice[None, None, None], slice[None, int, None], int], / ): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload def __getitem__( self, - _0: Tuple[int, slice[int, None, int], slice[None, None, None], ellipsis], + _0: Tuple[ + slice[None, None, None], slice[None, int, None], slice[None, int, None] + ], /, ): - """ - usage.xarray: 1 - """ - ... - - @overload - def __getitem__(self, _0: Tuple[int, slice[int, None, int], ellipsis], /): """ usage.xarray: 2 """ @@ -50407,7 +63692,7 @@ def __getitem__(self, _0: Tuple[int, slice[int, None, int], ellipsis], /): @overload def __getitem__( - self, _0: Tuple[None, slice[None, None, None], slice[None, None, None]], / + self, _0: Tuple[int, slice[None, None, None], slice[None, int, None]], / ): """ usage.xarray: 1 @@ -50416,53 +63701,65 @@ def __getitem__( @overload def __getitem__( - self, _0: Tuple[slice[int, None, int], slice[None, None, None], ellipsis], / + self, _0: Tuple[numpy.ndarray, numpy.ndarray, slice[None, int, None]], / ): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[int, None, int], ellipsis], / + self, _0: Tuple[numpy.ndarray, slice[None, int, None], numpy.ndarray], / ): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, int, None], int, ellipsis], /): + def __getitem__( + self, _0: Tuple[int, slice[None, int, None], slice[None, int, None]], / + ): """ usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[List[List[int]], slice[None, None, None]], /): + def __getitem__( + self, _0: Tuple[slice[None, int, None], slice[None, None, None], int], / + ): """ - usage.xarray: 5 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[None, None, None], /): + def __getitem__( + self, + _0: Tuple[ + slice[None, int, None], slice[None, None, None], slice[None, int, None] + ], + /, + ): """ usage.xarray: 2 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], List[int], List[int]], /): + def __getitem__( + self, _0: Tuple[slice[None, int, None], int, slice[None, None, None]], / + ): """ - usage.xarray: 3 + usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None], ellipsis], / + self, _0: Tuple[slice[None, int, None], numpy.ndarray, numpy.ndarray], / ): """ usage.xarray: 1 @@ -50470,7 +63767,9 @@ def __getitem__( ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], numpy.ndarray], /): + def __getitem__( + self, _0: Tuple[slice[None, int, None], int, slice[None, int, None]], / + ): """ usage.xarray: 1 """ @@ -50478,7 +63777,11 @@ def __getitem__(self, _0: Tuple[slice[int, int, int], numpy.ndarray], /): @overload def __getitem__( - self, _0: Tuple[numpy.ndarray, numpy.ndarray, slice[None, None, None]], / + self, + _0: Tuple[ + slice[None, int, None], slice[None, int, None], slice[None, None, None] + ], + /, ): """ usage.xarray: 2 @@ -50487,19 +63790,31 @@ def __getitem__( @overload def __getitem__( - self, - _0: Tuple[int, int, slice[None, None, None], slice[None, None, None], ellipsis], - /, + self, _0: Tuple[slice[None, int, None], slice[None, int, None], int], / ): """ usage.xarray: 1 """ ... + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], None, None], /): + """ + usage.matplotlib: 1 + usage.sklearn: 3 + usage.xarray: 4 + """ + ... + @overload def __getitem__( self, - _0: Tuple[slice[None, None, None], int, int, slice[None, None, None], ellipsis], + _0: Tuple[ + slice[int, int, int], + slice[int, int, int], + slice[None, None, None], + ellipsis, + ], /, ): """ @@ -50510,7 +63825,12 @@ def __getitem__( @overload def __getitem__( self, - _0: Tuple[slice[None, None, None], slice[None, None, None], int, int, ellipsis], + _0: Tuple[ + slice[int, int, int], + slice[None, None, None], + slice[int, int, int], + ellipsis, + ], /, ): """ @@ -50519,29 +63839,23 @@ def __getitem__( ... @overload - def __getitem__( - self, - _0: Tuple[int, slice[None, None, None], int, slice[None, None, None], ellipsis], - /, - ): + def __getitem__(self, _0: Tuple[List[int], List[int]], /): """ - usage.xarray: 2 + usage.xarray: 4 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[int, None, int], slice[None, None, None], ellipsis], / - ): + def __getitem__(self, _0: Tuple[List[int], List[int], slice[None, None, None]], /): """ usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.int64], /): + def __getitem__(self, _0: Tuple[List[int], slice[None, None, None], List[int]], /): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @@ -50549,9 +63863,11 @@ def __getitem__(self, _0: Tuple[numpy.int64], /): def __getitem__( self, _0: Tuple[ + int, + int, + slice[None, None, None], slice[None, None, None], slice[None, None, None], - slice[int, int, int], ellipsis, ], /, @@ -50561,13 +63877,25 @@ def __getitem__( """ ... + @overload + def __getitem__( + self, + _0: Tuple[int, slice[None, None, None], slice[None, None, None], int, ellipsis], + /, + ): + """ + usage.xarray: 1 + """ + ... + @overload def __getitem__( self, _0: Tuple[ slice[None, None, None], slice[None, None, None], - slice[int, None, int], + slice[None, None, None], + int, ellipsis, ], /, @@ -50581,38 +63909,32 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[int, int, int], slice[None, None, None], + int, + slice[None, None, None], + int, + int, slice[None, None, None], ellipsis, ], /, ): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[slice[None, None, None], slice[None, None, None], int, ellipsis], - /, - ): + def __getitem__(self, _0: Tuple[Tuple[List[int], List[int]], ellipsis], /): """ - usage.xarray: 4 + usage.xarray: 1 """ ... @overload def __getitem__( self, - _0: Tuple[ - slice[None, None, None], - slice[int, int, int], - slice[None, None, None], - ellipsis, - ], + _0: Tuple[numpy.ndarray, slice[None, None, None], numpy.ndarray, numpy.ndarray], /, ): """ @@ -50623,7 +63945,13 @@ def __getitem__( @overload def __getitem__( self, - _0: Tuple[slice[None, None, None], int, slice[None, None, None], ellipsis], + _0: Tuple[ + int, + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ellipsis, + ], /, ): """ @@ -50637,7 +63965,7 @@ def __getitem__( _0: Tuple[ slice[None, None, None], slice[None, None, None], - slice[None, None, None], + int, slice[None, None, None], ellipsis, ], @@ -50650,7 +63978,7 @@ def __getitem__( @overload def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[int, int, int], ellipsis], / + self, _0: Tuple[slice[None, None, None], Tuple[List[int], List[int]]], / ): """ usage.xarray: 1 @@ -50658,49 +63986,46 @@ def __getitem__( ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], slice[int, int, int]], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], xarray.core.variable.Variable], / + ): """ usage.xarray: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, int, None], ellipsis], / - ): + def __getitem__(self, _0: Tuple[slice[None, None, None], List[List[int]]], /): """ usage.xarray: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, int, None], slice[None, None, None], ellipsis], / - ): + def __getitem__(self, _0: Tuple[numpy.ndarray, slice[None, int, None]], /): """ usage.xarray: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], slice[None, None, None], ellipsis], / - ): + def __getitem__(self, _0: Tuple[List[List[int]], slice[None, int, None]], /): """ usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], ellipsis], /): + def __getitem__(self, _0: list, /): """ + usage.sklearn: 4 usage.xarray: 1 """ ... @overload def __getitem__( - self, _0: Tuple[slice[None, int, None], slice[int, None, int], ellipsis], / + self, _0: Tuple[xarray.core.variable.Variable, xarray.core.variable.Variable], / ): """ usage.xarray: 1 @@ -50708,93 +64033,67 @@ def __getitem__( ... @overload - def __getitem__(self, _0: Tuple[int, slice[None, None, None], int, ellipsis], /): + def __getitem__(self, _0: object, /): """ - usage.xarray: 2 + usage.dask: 654 + usage.pandas: 2206 + usage.scipy: 9038 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[int, None, int], - slice[None, None, None], - slice[None, None, None], - ellipsis, - ], - /, - ): + def __getitem__(self, _0: Literal["ones"], /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ellipsis, - ], - /, - ): + def __getitem__(self, _0: Literal["twos"], /): """ - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ellipsis, - ], - /, - ): + def __getitem__(self, _0: Literal["r"], /): """ - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, None, int], None], /): + def __getitem__(self, _0: Tuple[int, slice[int, int, int]], /): """ - usage.xarray: 2 + usage.matplotlib: 2 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, int, None], None], /): + def __getitem__(self, _0: Tuple[slice[int, None, int], slice[int, None, int]], /): """ - usage.xarray: 2 + usage.matplotlib: 2 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, None, None] - ], - /, - ): + def __getitem__(self, _0: Tuple[int, slice[int, None, int]], /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[int, slice[None, int, None], slice[None, None, None]], / - ): + def __getitem__(self, _0: Tuple[slice[int, None, int], slice[int, int, int]], /): """ - usage.xarray: 2 + usage.matplotlib: 1 + """ + ... + + @overload + def __getitem__(self, _0: slice[numpy.int8, numpy.int64, numpy.int8], /): + """ + usage.matplotlib: 2 """ ... @@ -50802,4252 +64101,4208 @@ def __getitem__( def __getitem__( self, _0: Tuple[ - slice[None, int, None], slice[None, None, None], slice[None, None, None] + slice[None, None, None], slice[None, None, None], slice[None, int, None] ], /, ): """ - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload def __getitem__( - self, _0: Tuple[int, slice[int, None, int], slice[None, None, None]], / + self, _0: Tuple[Tuple[int, int, int, int], slice[None, None, None]], / ): """ - usage.xarray: 1 + usage.matplotlib: 3 + usage.sklearn: 2 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[int, None, int], - slice[int, None, int], - slice[int, None, int], - slice[int, None, int], - ], - /, - ): + def __getitem__(self, _0: Tuple[slice[int, None, int], int], /): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ], - /, - ): + def __getitem__(self, _0: Tuple[ellipsis, None, slice[None, None, None]], /): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[None, None, None], int], /): + def __getitem__(self, _0: Tuple[slice[None, None, None], int, int], /): """ - usage.xarray: 1 + usage.matplotlib: 46 + usage.sklearn: 13 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - int, - slice[None, None, None], - int, - slice[None, None, None], - slice[None, None, None], - ], - /, - ): + def __getitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray, int], /): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - int, - ], - /, - ): + def __getitem__(self, _0: Tuple[numpy.int32, numpy.int32], /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, int, None], ellipsis, int], /): + def __getitem__(self, _0: numpy.int32, /): """ - usage.xarray: 1 + usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, int, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - int, - ], - /, - ): + def __getitem__(self, _0: Tuple[numpy.ndarray, int, int], /): """ - usage.xarray: 1 + usage.matplotlib: 9 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis, int, slice[None, None, None]], /): + def __getitem__(self, _0: Tuple[slice[None, None, None], slice[int, None, int]], /): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - int, - slice[None, None, None], - ], - /, - ): + def __getitem__(self, _0: Tuple[slice[int, int, int], int], /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - numpy.ndarray, - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ], - /, - ): + def __getitem__(self, _0: Tuple[Tuple[int, int, int], Tuple[int, int, int]], /): """ - usage.xarray: 1 + usage.sample-usage: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[numpy.ndarray, range], /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[int, None], /): + """ + usage.sklearn: 6 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[numpy.ndarray, None], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: slice[numpy.int32, numpy.int32, numpy.int32], /): + """ + usage.sklearn: 42 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[Tuple[numpy.int64, numpy.int64]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[ellipsis, numpy.ndarray], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[numpy.int64, numpy.ndarray], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[range, numpy.ndarray], /): + """ + usage.sklearn: 6 + """ + ... + + @overload + def __getitem__(self, _0: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: List[bool], /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], List[bool]], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], list], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[None, int], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[numpy.int64, slice[None, int, None]], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[int, slice[None, None, None], None], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Literal["depth"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["is_leaf"], /): + """ + usage.sklearn: 2 """ ... @overload def __getitem__( - self, - _0: Tuple[ - numpy.ndarray, - numpy.ndarray, - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ], - /, + self, _0: sklearn.ensemble._hist_gradient_boosting.splitting._memoryviewslice, / ): """ - usage.xarray: 1 + usage.sklearn: 6 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[int, numpy.uint8], /): + """ + usage.sklearn: 1 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis, numpy.ndarray, numpy.ndarray], /): + def __getitem__(self, _0: numpy.uint32, /): """ - usage.xarray: 1 + usage.sklearn: 5 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - numpy.ndarray, - numpy.ndarray, - ], - /, - ): + def __getitem__(self, _0: Literal["count"], /): """ - usage.xarray: 1 + usage.sklearn: 7 """ ... @overload - def __getitem__(self, _0: Tuple[ellipsis, int, int, int, int, int], /): + def __getitem__(self, _0: Literal["sum_gradients"], /): """ - usage.xarray: 1 + usage.sklearn: 8 """ ... @overload - def __getitem__(self, _0: slice[numpy.int64, numpy.int64, numpy.int64], /): + def __getitem__(self, _0: Literal["sum_hessians"], /): """ - usage.xarray: 1 + usage.sklearn: 8 """ ... @overload - def __getitem__(self, _0: slice[numpy.int64, None, numpy.int64], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, numpy.int64, None]], / + ): """ - usage.xarray: 1 + usage.sklearn: 4 """ ... @overload - def __getitem__(self, _0: slice[None, int, None], /): + def __getitem__(self, _0: Tuple[int, numpy.int64], /): """ - usage.xarray: 4 + usage.sklearn: 4 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, int, None], - ellipsis, - ], - /, - ): + def __getitem__(self, _0: Tuple[slice[None, None, None], range], /): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[int, int, int], - ellipsis, - ], - /, + self, _0: Tuple[slice[None, None, None], int, slice[None, numpy.int64, None]], / ): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[slice[None, None, None], int, slice[None, int, None], ellipsis], - /, - ): + def __getitem__(self, _0: Tuple[int, None, None], /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, None, None], int, slice[int, int, int], ellipsis], / - ): + def __getitem__(self, _0: Tuple[int, None, None, int, None, None], /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, int, None], - slice[None, None, None], - ellipsis, - ], - /, + self, _0: Tuple[int, None, None, int, None, None, int, None, None], / ): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[int, int, int], - slice[None, None, None], - ellipsis, - ], - /, - ): + def __getitem__(self, _0: Tuple[slice[None, None, None], Tuple[int, int, int]], /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload def __getitem__( - self, - _0: Tuple[slice[None, None, None], slice[None, int, None], int, ellipsis], - /, + self, _0: Tuple[int, slice[numpy.int64, numpy.int64, numpy.int64]], / ): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[int, int, int], int, ellipsis], / - ): + def __getitem__(self, _0: Tuple[List[int], slice[int, None, int]], /): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, int, None], - slice[None, int, None], - ellipsis, - ], - /, - ): + def __getitem__(self, _0: Tuple[List[int], int], /): """ - usage.xarray: 1 + usage.sklearn: 11 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[int, int, int], - slice[int, int, int], - ellipsis, - ], - /, - ): + def __getitem__(self, _0: Tuple[int, List[int]], /): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload - def __getitem__( - self, - _0: Tuple[slice[None, None, None], slice[None, int, None], numpy.ndarray], - /, - ): + def __getitem__(self, _0: Tuple[int, Tuple[int, int, int, int, int]], /): """ - usage.xarray: 3 + usage.sklearn: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[int, int, int], numpy.ndarray], / - ): + def __getitem__(self, _0: Tuple[Tuple[int, int, int, int, int], int], /): """ - usage.xarray: 2 + usage.sklearn: 2 """ ... @overload def __getitem__( self, - _0: Tuple[slice[None, None, None], numpy.ndarray, slice[None, None, None]], + _0: Tuple[slice[None, None, None], numpy.int64, slice[None, None, None]], /, ): """ - usage.xarray: 4 + usage.sklearn: 1 """ ... @overload def __getitem__( self, - _0: Tuple[slice[None, None, None], numpy.ndarray, slice[None, int, None]], + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + numpy.int64, + slice[None, None, None], + ], /, ): """ - usage.xarray: 3 + usage.sklearn: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, None, None], numpy.ndarray, slice[int, int, int]], / - ): + def __getitem__(self, _0: Tuple[int, numpy.int64, slice[None, None, None]], /): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload def __getitem__( self, - _0: Tuple[int, slice[None, None, None], slice[None, int, None], ellipsis], + _0: Tuple[slice[None, None, None], int, numpy.int64, slice[None, None, None]], /, ): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[int, slice[None, None, None], slice[int, int, int], ellipsis], / - ): + def __getitem__(self, _0: Tuple[int, slice[None, None, None]], /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload - def __getitem__(self, _0: Tuple[int, int, slice[None, None, None], ellipsis], /): + def __getitem__( + self, + _0: Tuple[int, slice[None, None, None], slice[numpy.int64, None, numpy.int64]], + /, + ): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload - def __getitem__(self, _0: Tuple[int, int, slice[None, int, None], ellipsis], /): + def __getitem__( + self, _0: Tuple[slice[None, None, None], Tuple[int, int, int, int]], / + ): """ - usage.xarray: 1 + usage.sklearn: 4 """ ... @overload - def __getitem__(self, _0: Tuple[int, int, slice[int, int, int], ellipsis], /): + def __getitem__(self, _0: Tuple[numpy.ndarray, numpy.int64], /): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload - def __getitem__( - self, _0: Tuple[int, slice[int, int, int], slice[None, None, None], ellipsis], / - ): + def __getitem__(self, _0: slice[numpy.int32, None, numpy.int32], /): """ - usage.xarray: 1 + usage.sklearn: 4 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[None, int, None], int, ellipsis], /): + def __getitem__(self, _0: slice[None, numpy.int32, None], /): """ - usage.xarray: 1 + usage.sklearn: 7 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[int, int, int], int, ellipsis], /): + def __getitem__(self, _0: Tuple[slice[None, None, None], Tuple[int]], /): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload - def __getitem__( - self, - _0: Tuple[int, slice[None, int, None], slice[None, int, None], ellipsis], - /, - ): + def __getitem__(self, _0: Tuple[slice[None, None, None], int], /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[int, slice[int, int, int], slice[int, int, int], ellipsis], / - ): + def __getitem__(self, _0: Tuple[slice[int, None, int], slice[None, None, None]], /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, int, None], - slice[None, None, None], - slice[None, None, None], - ellipsis, - ], - /, - ): + def __getitem__(self, _0: Tuple[range, int], /): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload def __getitem__( - self, - _0: Tuple[ - slice[int, int, int], - slice[None, None, None], - slice[None, None, None], - ellipsis, - ], - /, + self, _0: Tuple[slice[None, None, None], pandas.core.series.Series], / ): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... - @overload - def __getitem__( - self, - _0: Tuple[slice[None, int, None], slice[None, None, None], int, ellipsis], - /, - ): + def __getitem__(self, _0: object, /): """ - usage.xarray: 1 + usage.dask: 654 + usage.matplotlib: 1402 + usage.pandas: 2206 + usage.sample-usage: 5 + usage.scipy: 9038 + usage.skimage: 1989 + usage.sklearn: 4815 + usage.xarray: 888 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], slice[None, None, None], int, ellipsis], / - ): + def __gt__(self, _0: float, /): """ + usage.matplotlib: 10 + usage.skimage: 13 + usage.sklearn: 58 usage.xarray: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, int, None], - slice[None, None, None], - slice[None, int, None], - ellipsis, - ], - /, - ): + def __gt__(self, _0: int, /): """ - usage.xarray: 1 + usage.matplotlib: 26 + usage.sample-usage: 1 + usage.skimage: 90 + usage.sklearn: 77 + usage.xarray: 9 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[int, int, int], - slice[None, None, None], - slice[int, int, int], - ellipsis, - ], - /, - ): + def __gt__(self, _0: numpy.float64, /): """ - usage.xarray: 1 + usage.matplotlib: 4 + usage.skimage: 14 + usage.sklearn: 20 """ ... @overload - def __getitem__( - self, - _0: Tuple[slice[None, int, None], slice[None, None, None], numpy.ndarray], - /, - ): + def __gt__(self, _0: numpy.ndarray, /): """ - usage.xarray: 3 + usage.matplotlib: 9 + usage.skimage: 18 + usage.sklearn: 18 + usage.xarray: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], slice[None, None, None], numpy.ndarray], / - ): + def __gt__(self, _0: numpy.float32, /): """ - usage.xarray: 2 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[slice[None, int, None], int, slice[None, None, None], ellipsis], - /, - ): + def __gt__(self, _0: numpy.uint8, /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], int, slice[None, None, None], ellipsis], / - ): + def __gt__(self, _0: numpy.int64, /): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.skimage: 9 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, int, None], int, int, ellipsis], /): + def __gt__(self, _0: object, /): """ - usage.xarray: 1 + usage.pandas: 82 + usage.scipy: 477 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], int, int, ellipsis], /): + def __gt__(self, _0: Union[numpy.ndarray, int, float], /): """ - usage.xarray: 1 + usage.dask: 27 """ ... - @overload - def __getitem__( - self, - _0: Tuple[slice[None, int, None], int, slice[None, int, None], ellipsis], - /, - ): + def __gt__(self, _0: object, /): """ - usage.xarray: 1 + usage.dask: 27 + usage.matplotlib: 50 + usage.pandas: 82 + usage.sample-usage: 1 + usage.scipy: 477 + usage.skimage: 146 + usage.sklearn: 174 + usage.xarray: 11 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], int, slice[int, int, int], ellipsis], / - ): + def __iadd__(self, _0: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 38 + usage.skimage: 78 + usage.sklearn: 161 + usage.xarray: 3 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, int, None], - slice[None, int, None], - slice[None, None, None], - ellipsis, - ], - /, - ): + def __iadd__(self, _0: int, /): """ - usage.xarray: 1 + usage.matplotlib: 8 + usage.sample-usage: 1 + usage.skimage: 27 + usage.sklearn: 33 + usage.xarray: 2 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[int, int, int], - slice[int, int, int], - slice[None, None, None], - ellipsis, - ], - /, - ): + def __iadd__(self, _0: float, /): """ - usage.xarray: 1 + usage.matplotlib: 14 + usage.skimage: 9 + usage.sklearn: 28 """ ... @overload - def __getitem__( - self, - _0: Tuple[slice[None, int, None], slice[None, int, None], int, ellipsis], - /, - ): + def __iadd__(self, _0: numpy.int64, /): """ + usage.matplotlib: 2 + usage.skimage: 3 + usage.sklearn: 6 usage.xarray: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], slice[int, int, int], int, ellipsis], / - ): + def __iadd__(self, _0: numpy.float64, /): """ - usage.xarray: 1 + usage.matplotlib: 4 + usage.skimage: 3 + usage.sklearn: 29 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, int, None], - slice[None, int, None], - slice[None, int, None], - ellipsis, - ], - /, - ): + def __iadd__(self, _0: numpy.float16, /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[int, int, int], slice[int, int, int], slice[int, int, int], ellipsis - ], - /, - ): + def __iadd__(self, _0: numpy.float32, /): """ - usage.xarray: 1 + usage.skimage: 1 + usage.sklearn: 3 """ ... @overload - def __getitem__( - self, - _0: Tuple[slice[None, int, None], slice[None, int, None], numpy.ndarray], - /, - ): + def __iadd__(self, _0: List[numpy.float64], /): """ - usage.xarray: 3 + usage.skimage: 2 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], slice[int, int, int], numpy.ndarray], / - ): + def __iadd__(self, _0: List[int], /): """ - usage.xarray: 2 + usage.matplotlib: 2 + usage.skimage: 1 + usage.sklearn: 5 """ ... @overload - def __getitem__( - self, - _0: Tuple[slice[None, int, None], numpy.ndarray, slice[None, None, None]], - /, - ): + def __iadd__(self, _0: List[Union[numpy.float64, int]], /): """ - usage.xarray: 3 + usage.skimage: 3 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], numpy.ndarray, slice[None, None, None]], / - ): + def __iadd__(self, _0: numpy.int32, /): """ - usage.xarray: 2 + usage.sklearn: 1 + usage.xarray: 1 """ ... @overload - def __getitem__( + def __iadd__( self, - _0: Tuple[slice[None, int, None], numpy.ndarray, slice[None, int, None]], + _0: Union[ + numpy.int64, + pandas.core.arrays.sparse.array.SparseArray, + numpy.ndarray, + numpy.uint64, + int, + ], /, ): """ - usage.xarray: 3 + usage.pandas: 13 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], numpy.ndarray, slice[int, int, int]], / - ): + def __iadd__(self, _0: object, /): """ - usage.xarray: 2 + usage.scipy: 368 """ ... @overload - def __getitem__( - self, - _0: Tuple[numpy.ndarray, slice[None, None, None], slice[None, int, None]], - /, - ): + def __iadd__(self, _0: List[Union[int, float]], /): """ - usage.xarray: 3 + usage.matplotlib: 2 """ ... @overload - def __getitem__( - self, _0: Tuple[numpy.ndarray, slice[None, None, None], slice[int, int, int]], / - ): + def __iadd__(self, _0: Union[numpy.float64, int, numpy.ndarray], /): """ - usage.xarray: 2 + usage.dask: 12 """ ... - @overload - def __getitem__( - self, - _0: Tuple[numpy.ndarray, slice[None, int, None], slice[None, None, None]], - /, - ): + def __iadd__(self, _0: object, /): """ - usage.xarray: 3 + usage.dask: 12 + usage.matplotlib: 70 + usage.pandas: 13 + usage.sample-usage: 1 + usage.scipy: 368 + usage.skimage: 128 + usage.sklearn: 266 + usage.xarray: 7 """ ... @overload - def __getitem__( - self, _0: Tuple[numpy.ndarray, slice[int, int, int], slice[None, None, None]], / - ): + def __iand__(self, _0: numpy.ndarray, /): """ - usage.xarray: 2 + usage.skimage: 2 + usage.sklearn: 4 """ ... @overload - def __getitem__( - self, - _0: Tuple[numpy.ndarray, slice[None, int, None], slice[None, int, None]], - /, - ): + def __iand__(self, _0: Union[int, pandas.core.series.Series, numpy.ndarray], /): """ - usage.xarray: 3 + usage.pandas: 4 """ ... @overload - def __getitem__( - self, _0: Tuple[numpy.ndarray, slice[int, int, int], slice[int, int, int]], / - ): + def __iand__(self, _0: Union[List[numpy.bool_], numpy.ndarray], /): """ - usage.xarray: 2 + usage.scipy: 20 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], int, ellipsis], /): + def __iand__(self, _0: int, /): """ - usage.xarray: 1 + usage.sample-usage: 1 """ ... - @overload - def __getitem__( + def __iand__( self, - _0: Tuple[slice[None, None, None], int, slice[None, int, None], ellipsis], + _0: Union[numpy.ndarray, pandas.core.series.Series, int, List[numpy.bool_]], /, ): """ - usage.xarray: 1 + usage.pandas: 4 + usage.sample-usage: 1 + usage.scipy: 20 + usage.skimage: 2 + usage.sklearn: 4 """ ... - @overload - def __getitem__( - self, _0: Tuple[numpy.ndarray, numpy.ndarray, slice[int, int, int]], / - ): + def __ifloordiv__(self, _0: int, /): """ - usage.xarray: 1 + usage.sample-usage: 1 + usage.scipy: 8 + usage.skimage: 6 """ ... @overload - def __getitem__( - self, _0: Tuple[numpy.ndarray, slice[int, int, int], numpy.ndarray], / - ): + def __ilshift__(self, _0: numpy.ndarray, /): """ - usage.xarray: 1 + usage.pandas: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[numpy.ndarray, slice[None, None, None], numpy.ndarray], / - ): + def __ilshift__(self, _0: int, /): """ - usage.xarray: 1 + usage.sample-usage: 1 """ ... - @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], numpy.ndarray, numpy.ndarray], / - ): + def __ilshift__(self, _0: Union[int, numpy.ndarray], /): """ - usage.xarray: 1 + usage.pandas: 1 + usage.sample-usage: 1 """ ... - @overload - def __getitem__( - self, - _0: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], - /, - ): + def __imod__(self, _0: int, /): """ - usage.xarray: 1 + usage.dask: 2 + usage.sample-usage: 1 + usage.scipy: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[slice[int, int, int], slice[int, int, int], slice[None, None, None]], - /, - ): + def __imul__(self, _0: numpy.ndarray, /): """ - usage.xarray: 2 + usage.matplotlib: 2 + usage.skimage: 16 + usage.sklearn: 77 """ ... @overload - def __getitem__( - self, - _0: Tuple[slice[int, int, int], slice[None, None, None], slice[int, int, int]], - /, - ): + def __imul__(self, _0: float, /): """ + usage.matplotlib: 12 + usage.skimage: 10 + usage.sklearn: 46 usage.xarray: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[int, int, int], slice[None, None, None], slice[None, None, None] - ], - /, - ): + def __imul__(self, _0: numpy.float64, /): """ + usage.matplotlib: 4 + usage.skimage: 4 + usage.sklearn: 19 usage.xarray: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[slice[None, None, None], slice[int, int, int], slice[int, int, int]], - /, - ): + def __imul__(self, _0: int, /): """ + usage.dask: 1 + usage.matplotlib: 9 + usage.sample-usage: 1 + usage.skimage: 4 + usage.sklearn: 35 usage.xarray: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[int, int, int], slice[None, None, None] - ], - /, - ): + def __imul__(self, _0: numpy.uint8, /): """ - usage.xarray: 1 + usage.skimage: 2 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[int, int, int] - ], - /, - ): + def __imul__(self, _0: numpy.float32, /): """ + usage.skimage: 1 + usage.sklearn: 1 usage.xarray: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], int, slice[None, None, None], ellipsis], / - ): + def __imul__(self, _0: numpy.int16, /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], int, ellipsis], /): + def __imul__(self, _0: numpy.uint16, /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], numpy.ndarray, slice[int, int, int]], / - ): + def __imul__(self, _0: Union[int, numpy.uint64], /): """ - usage.xarray: 1 + usage.pandas: 4 """ ... @overload - def __getitem__( - self, _0: Tuple[int, slice[int, int, int], slice[int, int, int], ellipsis], / - ): + def __imul__(self, _0: object, /): """ - usage.xarray: 1 + usage.scipy: 244 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[int, int, int], ellipsis], /): + def __imul__(self, _0: List[int], /): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... - @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, None, None] - ], - /, - ): + def __imul__(self, _0: object, /): """ - usage.xarray: 1 + usage.dask: 1 + usage.matplotlib: 29 + usage.pandas: 4 + usage.sample-usage: 1 + usage.scipy: 244 + usage.skimage: 39 + usage.sklearn: 178 + usage.xarray: 4 """ ... - @overload - def __getitem__( - self, _0: Tuple[slice[int, int, int], slice[int, int, int], ellipsis], / - ): + def __invert__(self, /): """ - usage.xarray: 1 + usage.dask: 9 + usage.matplotlib: 25 + usage.pandas: 122 + usage.sample-usage: 1 + usage.scipy: 158 + usage.skimage: 26 + usage.sklearn: 84 + usage.xarray: 9 """ ... @overload - def __getitem__( - self, _0: Tuple[int, slice[int, int, int], slice[int, int, int]], / - ): + def __ior__(self, _0: numpy.ndarray, /): """ + usage.matplotlib: 1 + usage.scipy: 4 + usage.skimage: 3 usage.xarray: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[numpy.ndarray, slice[int, int, int], slice[int, int, int]], / - ): + def __ior__(self, _0: Union[numpy.ndarray, bool], /): """ - usage.xarray: 1 + usage.pandas: 9 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.ndarray, slice[int, int, int]], /): + def __ior__(self, _0: int, /): """ - usage.xarray: 1 + usage.sample-usage: 1 """ ... - @overload - def __getitem__( - self, _0: Tuple[slice[None, None, None], int, slice[None, int, None]], / - ): + def __ior__(self, _0: Union[int, bool, numpy.ndarray], /): """ + usage.matplotlib: 1 + usage.pandas: 9 + usage.sample-usage: 1 + usage.scipy: 4 + usage.skimage: 3 usage.xarray: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[None, int, None], slice[None, None, None] - ], - /, - ): + def __ipow__(self, _0: Union[int, float], /): """ - usage.xarray: 2 + usage.scipy: 6 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, None, None], slice[None, int, None], int], / - ): + def __ipow__(self, _0: int, /): """ - usage.xarray: 1 + usage.sample-usage: 1 + usage.sklearn: 20 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[None, int, None], slice[None, int, None] - ], - /, - ): + def __ipow__(self, _0: float, /): """ - usage.xarray: 2 + usage.sklearn: 10 """ ... - @overload - def __getitem__( - self, _0: Tuple[int, slice[None, None, None], slice[None, int, None]], / - ): + def __ipow__(self, _0: Union[float, int], /): """ - usage.xarray: 1 + usage.sample-usage: 1 + usage.scipy: 6 + usage.sklearn: 30 """ ... @overload - def __getitem__( - self, _0: Tuple[numpy.ndarray, numpy.ndarray, slice[None, int, None]], / - ): + def __irshift__(self, _0: numpy.ndarray, /): """ - usage.xarray: 1 + usage.pandas: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[numpy.ndarray, slice[None, int, None], numpy.ndarray], / - ): + def __irshift__(self, _0: int, /): """ - usage.xarray: 1 + usage.sample-usage: 1 + """ + ... + + def __irshift__(self, _0: Union[int, numpy.ndarray], /): + """ + usage.pandas: 1 + usage.sample-usage: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[int, slice[None, int, None], slice[None, int, None]], / - ): + def __isub__(self, _0: int, /): """ + usage.dask: 2 + usage.matplotlib: 10 + usage.sample-usage: 1 + usage.skimage: 16 + usage.sklearn: 3 usage.xarray: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, int, None], slice[None, None, None], int], / - ): + def __isub__(self, _0: float, /): """ - usage.xarray: 1 + usage.matplotlib: 3 + usage.skimage: 4 + usage.sklearn: 2 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, int, None], slice[None, None, None], slice[None, int, None] - ], - /, - ): + def __isub__(self, _0: numpy.ndarray, /): """ - usage.xarray: 2 + usage.matplotlib: 1 + usage.skimage: 17 + usage.sklearn: 94 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, int, None], int, slice[None, None, None]], / - ): + def __isub__(self, _0: numpy.int64, /): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 2 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, int, None], numpy.ndarray, numpy.ndarray], / - ): + def __isub__(self, _0: numpy.float64, /): """ - usage.xarray: 1 + usage.matplotlib: 14 + usage.skimage: 3 + usage.sklearn: 36 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, int, None], int, slice[None, int, None]], / - ): + def __isub__(self, _0: numpy.uint8, /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __getitem__( + def __isub__( self, - _0: Tuple[ - slice[None, int, None], slice[None, int, None], slice[None, None, None] - ], + _0: Union[int, pandas.core.indexes.datetimes.DatetimeIndex, numpy.ndarray], /, ): """ - usage.xarray: 2 + usage.pandas: 8 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, int, None], slice[None, int, None], int], / - ): + def __isub__(self, _0: object, /): """ - usage.xarray: 1 + usage.scipy: 109 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], None, None], /): + def __isub__(self, _0: numpy.bool_, /): """ usage.matplotlib: 1 - usage.xarray: 4 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[int, int, int], - slice[int, int, int], - slice[None, None, None], - ellipsis, - ], - /, - ): + def __isub__(self, _0: numpy.float32, /): """ - usage.xarray: 1 + usage.matplotlib: 2 + usage.sklearn: 4 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[int, int, int], - slice[None, None, None], - slice[int, int, int], - ellipsis, - ], - /, - ): + def __isub__(self, _0: numpy.float128, /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __getitem__(self, _0: Tuple[List[int], List[int]], /): + def __isub__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.xarray: 4 + usage.matplotlib: 1 """ ... @overload - def __getitem__(self, _0: Tuple[List[int], List[int], slice[None, None, None]], /): + def __isub__(self, _0: numpy.uint64, /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __getitem__(self, _0: Tuple[List[int], slice[None, None, None], List[int]], /): + def __isub__(self, _0: List[int], /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - int, - int, - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ellipsis, - ], - /, - ): + def __isub__(self, _0: numpy.matrix, /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... - @overload - def __getitem__( - self, - _0: Tuple[int, slice[None, None, None], slice[None, None, None], int, ellipsis], - /, - ): + def __isub__(self, _0: object, /): """ + usage.dask: 2 + usage.matplotlib: 36 + usage.pandas: 8 + usage.sample-usage: 1 + usage.scipy: 109 + usage.skimage: 42 + usage.sklearn: 142 usage.xarray: 1 """ ... - @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - int, - ellipsis, - ], - /, - ): + def __iter__(self, /): """ - usage.xarray: 1 + usage.dask: 6 + usage.matplotlib: 363 + usage.pandas: 181 + usage.sample-usage: 2 + usage.scipy: 302 + usage.skimage: 134 + usage.sklearn: 254 + usage.xarray: 92 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - int, - slice[None, None, None], - int, - int, - slice[None, None, None], - ellipsis, - ], - /, - ): + def __itruediv__(self, _0: float, /): """ - usage.xarray: 1 + usage.dask: 1 + usage.matplotlib: 6 + usage.skimage: 5 + usage.sklearn: 10 """ ... @overload - def __getitem__(self, _0: Tuple[Tuple[List[int], List[int]], ellipsis], /): + def __itruediv__(self, _0: numpy.float64, /): """ - usage.xarray: 1 + usage.matplotlib: 15 + usage.skimage: 15 + usage.sklearn: 29 """ ... @overload - def __getitem__( - self, - _0: Tuple[numpy.ndarray, slice[None, None, None], numpy.ndarray, numpy.ndarray], - /, - ): + def __itruediv__(self, _0: numpy.ndarray, /): """ + usage.matplotlib: 2 + usage.skimage: 14 + usage.sklearn: 102 usage.xarray: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - int, - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ellipsis, - ], - /, - ): + def __itruediv__(self, _0: numpy.float32, /): """ - usage.xarray: 2 + usage.matplotlib: 1 + usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - int, - slice[None, None, None], - ellipsis, - ], - /, - ): + def __itruediv__(self, _0: numpy.float16, /): """ - usage.xarray: 2 + usage.skimage: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, None, None], Tuple[List[int], List[int]]], / - ): + def __itruediv__(self, _0: int, /): """ + usage.matplotlib: 2 + usage.skimage: 1 + usage.sklearn: 11 usage.xarray: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[slice[None, None, None], xarray.core.variable.Variable], / - ): + def __itruediv__(self, _0: object, /): """ - usage.xarray: 1 + usage.scipy: 155 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], List[List[int]]], /): + def __itruediv__(self, _0: numpy.float128, /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.ndarray, slice[None, int, None]], /): + def __itruediv__(self, _0: List[numpy.float64], /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __getitem__(self, _0: Tuple[List[List[int]], slice[None, int, None]], /): + def __itruediv__(self, _0: numpy.int64, /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __getitem__(self, _0: list, /): + def __itruediv__(self, _0: numpy.matrix, /): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... - @overload - def __getitem__( - self, _0: Tuple[xarray.core.variable.Variable, xarray.core.variable.Variable], / - ): + def __itruediv__(self, _0: object, /): """ - usage.xarray: 1 + usage.dask: 1 + usage.matplotlib: 29 + usage.scipy: 155 + usage.skimage: 38 + usage.sklearn: 153 + usage.xarray: 3 """ ... - @overload - def __getitem__(self, _0: object, /): + def __ixor__(self, _0: numpy.ndarray, /): """ - usage.dask: 654 - usage.pandas: 2206 - usage.scipy: 9038 - usage.sklearn: 4815 + usage.pandas: 4 """ ... @overload - def __getitem__(self, _0: Literal["ones"], /): + def __le__(self, _0: int, /): """ - usage.matplotlib: 1 + usage.matplotlib: 14 + usage.skimage: 16 + usage.sklearn: 22 """ ... @overload - def __getitem__(self, _0: Literal["twos"], /): + def __le__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 1 + usage.matplotlib: 2 + usage.skimage: 5 + usage.sklearn: 7 + usage.xarray: 7 """ ... @overload - def __getitem__(self, _0: Literal["r"], /): + def __le__(self, _0: numpy.int64, /): """ usage.matplotlib: 1 + usage.skimage: 6 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[int, int, int]], /): + def __le__(self, _0: float, /): """ - usage.matplotlib: 2 + usage.matplotlib: 7 + usage.skimage: 1 + usage.sklearn: 24 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, None, int], slice[int, None, int]], /): + def __le__(self, _0: numpy.timedelta64, /): """ - usage.matplotlib: 2 + usage.xarray: 15 """ ... @overload - def __getitem__(self, _0: Tuple[int, slice[int, None, int]], /): + def __le__(self, _0: object, /): """ - usage.matplotlib: 1 + usage.pandas: 134 + usage.scipy: 561 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, None, int], slice[int, int, int]], /): + def __le__(self, _0: numpy.float64, /): """ - usage.matplotlib: 1 + usage.matplotlib: 15 + usage.sklearn: 11 """ ... @overload - def __getitem__(self, _0: slice[numpy.int8, numpy.int64, numpy.int8], /): + def __le__(self, _0: numpy.uint8, /): """ - usage.matplotlib: 2 + usage.matplotlib: 1 """ ... @overload - def __getitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, int, None] - ], - /, - ): + def __le__(self, _0: numpy.ma.core.MaskedArray, /): """ usage.matplotlib: 1 """ ... @overload - def __getitem__( - self, _0: Tuple[Tuple[int, int, int, int], slice[None, None, None]], / - ): + def __le__(self, _0: Union[numpy.float64, int, numpy.ndarray, float], /): """ - usage.matplotlib: 3 + usage.dask: 35 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, None, int], int], /): + def __le__(self, _0: numpy.float32, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... - @overload - def __getitem__(self, _0: Tuple[ellipsis, None, slice[None, None, None]], /): + def __le__(self, _0: object, /): """ - usage.matplotlib: 2 + usage.dask: 35 + usage.matplotlib: 41 + usage.pandas: 134 + usage.scipy: 561 + usage.skimage: 28 + usage.sklearn: 65 + usage.xarray: 22 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], int, int], /): + def __lshift__(self, _0: Union[int, numpy.ndarray], /): """ - usage.matplotlib: 46 + usage.pandas: 4 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray, int], /): + def __lshift__(self, _0: int, /): """ - usage.matplotlib: 1 + usage.sample-usage: 1 """ ... - @overload - def __getitem__(self, _0: Tuple[numpy.int32, numpy.int32], /): + def __lshift__(self, _0: Union[int, numpy.ndarray], /): """ - usage.matplotlib: 1 + usage.pandas: 4 + usage.sample-usage: 1 """ ... @overload - def __getitem__(self, _0: numpy.int32, /): + def __lt__(self, _0: float, /): """ - usage.matplotlib: 2 + usage.matplotlib: 17 + usage.skimage: 17 + usage.sklearn: 74 + usage.xarray: 1 """ ... @overload - def __getitem__(self, _0: Tuple[numpy.ndarray, int, int], /): + def __lt__(self, _0: int, /): """ - usage.matplotlib: 9 + usage.matplotlib: 29 + usage.sample-usage: 1 + usage.skimage: 62 + usage.sklearn: 92 + usage.xarray: 12 """ ... @overload - def __getitem__(self, _0: Tuple[slice[None, None, None], slice[int, None, int]], /): + def __lt__(self, _0: numpy.int64, /): """ - usage.matplotlib: 3 + usage.matplotlib: 1 + usage.skimage: 3 + usage.sklearn: 1 """ ... @overload - def __getitem__(self, _0: Tuple[slice[int, int, int], int], /): + def __lt__(self, _0: numpy.float64, /): """ - usage.matplotlib: 1 + usage.matplotlib: 5 + usage.skimage: 3 + usage.sklearn: 22 """ ... @overload - def __getitem__(self, _0: Tuple[Tuple[int, int, int], Tuple[int, int, int]], /): + def __lt__(self, _0: numpy.ndarray, /): """ - usage.sample-usage: 1 + usage.matplotlib: 9 + usage.skimage: 18 + usage.sklearn: 18 + usage.xarray: 1 """ ... - def __getitem__(self, _0: object, /): + @overload + def __lt__(self, _0: object, /): """ - usage.dask: 654 - usage.matplotlib: 1402 - usage.pandas: 2206 - usage.sample-usage: 5 - usage.scipy: 9038 - usage.skimage: 1989 - usage.sklearn: 4815 - usage.xarray: 888 + usage.pandas: 48 """ ... @overload - def __gt__(self, _0: float, /): + def __lt__(self, _0: Union[numpy.ndarray, int, float, numpy.float64], /): """ - usage.matplotlib: 10 - usage.skimage: 13 - usage.xarray: 1 + usage.scipy: 75 """ ... @overload - def __gt__(self, _0: int, /): + def __lt__(self, _0: Union[numpy.ndarray, int], /): """ - usage.matplotlib: 26 - usage.sample-usage: 1 - usage.skimage: 90 - usage.xarray: 9 + usage.dask: 3 """ ... - @overload - def __gt__(self, _0: numpy.float64, /): + def __lt__(self, _0: object, /): """ - usage.matplotlib: 4 - usage.skimage: 14 + usage.dask: 3 + usage.matplotlib: 61 + usage.pandas: 48 + usage.sample-usage: 1 + usage.scipy: 75 + usage.skimage: 103 + usage.sklearn: 207 + usage.xarray: 14 """ ... @overload - def __gt__(self, _0: numpy.ndarray, /): + def __matmul__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 9 - usage.skimage: 18 - usage.xarray: 1 + usage.sample-usage: 1 + usage.skimage: 47 + usage.sklearn: 23 """ ... @overload - def __gt__(self, _0: numpy.float32, /): + def __matmul__(self, _0: numpy.matrix, /): """ usage.skimage: 1 """ ... @overload - def __gt__(self, _0: numpy.uint8, /): + def __matmul__( + self, + _0: Union[ + numpy.matrix, + numpy.ndarray, + List[Union[complex, int, List[Union[complex, int, numpy.int64]]]], + ], + /, + ): """ - usage.skimage: 1 + usage.scipy: 423 """ ... @overload - def __gt__(self, _0: numpy.int64, /): + def __matmul__(self, _0: scipy.sparse.csc.csc_matrix, /): """ - usage.matplotlib: 1 - usage.skimage: 9 + usage.sklearn: 5 """ ... @overload - def __gt__(self, _0: object, /): + def __matmul__(self, _0: scipy.sparse.csr.csr_matrix, /): """ - usage.pandas: 82 - usage.scipy: 477 + usage.sklearn: 5 """ ... @overload - def __gt__(self, _0: Union[numpy.ndarray, int, float], /): + def __matmul__(self, _0: scipy.sparse.lil.lil_matrix, /): """ - usage.dask: 27 + usage.sklearn: 1 """ ... @overload - def __gt__( - self, _0: Union[int, numpy.ndarray, numpy.float32, numpy.float64, float], / - ): + def __matmul__(self, _0: scipy.sparse.dok.dok_matrix, /): """ - usage.sklearn: 160 + usage.sklearn: 1 """ ... - def __gt__(self, _0: object, /): + def __matmul__(self, _0: object, /): """ - usage.dask: 27 - usage.matplotlib: 50 - usage.pandas: 82 usage.sample-usage: 1 - usage.scipy: 477 - usage.skimage: 146 - usage.sklearn: 160 - usage.xarray: 11 + usage.scipy: 423 + usage.skimage: 48 + usage.sklearn: 35 """ ... @overload - def __iadd__(self, _0: numpy.ndarray, /): + def __mod__(self, _0: int, /): """ - usage.matplotlib: 38 - usage.skimage: 78 + usage.matplotlib: 10 + usage.sample-usage: 1 + usage.skimage: 3 + usage.sklearn: 16 usage.xarray: 3 """ ... @overload - def __iadd__(self, _0: int, /): + def __mod__(self, _0: float, /): """ - usage.matplotlib: 8 - usage.sample-usage: 1 - usage.skimage: 27 - usage.xarray: 2 + usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload - def __iadd__(self, _0: float, /): + def __mod__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 14 - usage.skimage: 9 + usage.skimage: 1 """ ... @overload - def __iadd__(self, _0: numpy.int64, /): + def __mod__(self, _0: object, /): """ - usage.matplotlib: 2 - usage.skimage: 3 - usage.xarray: 1 + usage.pandas: 52 """ ... @overload - def __iadd__(self, _0: numpy.float64, /): + def __mod__(self, _0: Union[numpy.ndarray, numpy.float64, int], /): """ - usage.matplotlib: 4 - usage.skimage: 3 + usage.scipy: 23 """ ... @overload - def __iadd__(self, _0: numpy.float16, /): + def __mod__(self, _0: Union[int, numpy.ndarray], /): """ - usage.skimage: 1 + usage.dask: 12 """ ... - @overload - def __iadd__(self, _0: numpy.float32, /): + def __mod__(self, _0: object, /): """ - usage.skimage: 1 + usage.dask: 12 + usage.matplotlib: 12 + usage.pandas: 52 + usage.sample-usage: 1 + usage.scipy: 23 + usage.skimage: 5 + usage.sklearn: 16 + usage.xarray: 3 """ ... @overload - def __iadd__(self, _0: List[numpy.float64], /): + def __mul__(self, _0: numpy.ndarray, /): """ - usage.skimage: 2 + usage.matplotlib: 154 + usage.skimage: 215 + usage.sklearn: 355 + usage.xarray: 17 """ ... @overload - def __iadd__(self, _0: List[int], /): + def __mul__(self, _0: int, /): """ - usage.matplotlib: 2 - usage.skimage: 1 + usage.matplotlib: 58 + usage.sample-usage: 1 + usage.skimage: 83 + usage.sklearn: 59 + usage.xarray: 148 """ ... @overload - def __iadd__(self, _0: List[Union[numpy.float64, int]], /): + def __mul__(self, _0: numpy.float64, /): """ - usage.skimage: 3 + usage.matplotlib: 26 + usage.skimage: 29 + usage.sklearn: 38 """ ... @overload - def __iadd__(self, _0: numpy.int32, /): + def __mul__(self, _0: float, /): """ - usage.xarray: 1 + usage.matplotlib: 83 + usage.skimage: 36 + usage.sklearn: 60 + usage.xarray: 16 """ ... @overload - def __iadd__( - self, - _0: Union[ - numpy.int64, - pandas.core.arrays.sparse.array.SparseArray, - numpy.ndarray, - numpy.uint64, - int, - ], - /, - ): + def __mul__(self, _0: Tuple[int, int], /): """ - usage.pandas: 13 + usage.skimage: 4 """ ... @overload - def __iadd__(self, _0: object, /): + def __mul__(self, _0: Tuple[int, int, int, int], /): """ - usage.scipy: 368 - usage.sklearn: 266 + usage.skimage: 3 """ ... @overload - def __iadd__(self, _0: List[Union[int, float]], /): + def __mul__(self, _0: numpy.int64, /): """ - usage.matplotlib: 2 + usage.matplotlib: 3 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload - def __iadd__(self, _0: Union[numpy.float64, int, numpy.ndarray], /): + def __mul__(self, _0: numpy.float16, /): """ - usage.dask: 12 + usage.skimage: 1 """ ... - def __iadd__(self, _0: object, /): + @overload + def __mul__(self, _0: numpy.float32, /): """ - usage.dask: 12 - usage.matplotlib: 70 - usage.pandas: 13 - usage.sample-usage: 1 - usage.scipy: 368 - usage.skimage: 128 - usage.sklearn: 266 - usage.xarray: 7 + usage.skimage: 1 + usage.sklearn: 2 """ ... @overload - def __iand__(self, _0: numpy.ndarray, /): + def __mul__(self, _0: List[bool], /): """ usage.skimage: 2 - usage.sklearn: 4 """ ... @overload - def __iand__(self, _0: Union[int, pandas.core.series.Series, numpy.ndarray], /): + def __mul__(self, _0: dask.array.core.Array, /): """ - usage.pandas: 4 + usage.xarray: 1 """ ... @overload - def __iand__(self, _0: Union[List[numpy.bool_], numpy.ndarray], /): + def __mul__(self, _0: numpy.timedelta64, /): """ - usage.scipy: 20 + usage.xarray: 4 """ ... @overload - def __iand__(self, _0: int, /): + def __mul__(self, _0: sparse._coo.core.COO, /): """ - usage.sample-usage: 1 + usage.xarray: 1 """ ... - def __iand__( - self, - _0: Union[numpy.ndarray, pandas.core.series.Series, int, List[numpy.bool_]], - /, - ): + @overload + def __mul__(self, _0: object, /): """ - usage.pandas: 4 - usage.sample-usage: 1 - usage.scipy: 20 - usage.skimage: 2 - usage.sklearn: 4 + usage.dask: 69 + usage.pandas: 256 + usage.scipy: 2345 + usage.xarray: 561 """ ... - def __ifloordiv__(self, _0: int, /): + @overload + def __mul__(self, _0: xarray.core.variable.Variable, /): """ - usage.sample-usage: 1 - usage.scipy: 8 - usage.skimage: 6 + usage.xarray: 1 """ ... @overload - def __ilshift__(self, _0: numpy.ndarray, /): + def __mul__(self, _0: xarray.core.variable.IndexVariable, /): """ - usage.pandas: 1 + usage.xarray: 1 """ ... @overload - def __ilshift__(self, _0: int, /): + def __mul__(self, _0: List[int], /): """ - usage.sample-usage: 1 + usage.matplotlib: 1 + usage.sklearn: 5 """ ... - def __ilshift__(self, _0: Union[int, numpy.ndarray], /): + @overload + def __mul__(self, _0: complex, /): """ - usage.pandas: 1 - usage.sample-usage: 1 + usage.matplotlib: 3 """ ... - def __imod__(self, _0: int, /): + @overload + def __mul__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.dask: 2 - usage.sample-usage: 1 - usage.scipy: 1 + usage.matplotlib: 1 """ ... @overload - def __imul__(self, _0: numpy.ndarray, /): + def __mul__(self, _0: numpy.int32, /): """ - usage.matplotlib: 2 - usage.skimage: 16 + usage.sklearn: 1 """ ... @overload - def __imul__(self, _0: float, /): + def __mul__(self, _0: List[float], /): """ - usage.matplotlib: 12 - usage.skimage: 10 - usage.xarray: 1 + usage.sklearn: 2 """ ... - @overload - def __imul__(self, _0: numpy.float64, /): + def __mul__(self, _0: object, /): """ - usage.matplotlib: 4 - usage.skimage: 4 - usage.xarray: 1 + usage.dask: 69 + usage.matplotlib: 329 + usage.pandas: 256 + usage.sample-usage: 1 + usage.scipy: 2345 + usage.skimage: 376 + usage.sklearn: 523 + usage.xarray: 750 """ ... - @overload - def __imul__(self, _0: int, /): + def __neg__(self, /): """ - usage.dask: 1 - usage.matplotlib: 9 + usage.dask: 7 + usage.matplotlib: 68 + usage.pandas: 25 usage.sample-usage: 1 - usage.skimage: 4 - usage.xarray: 1 + usage.scipy: 785 + usage.skimage: 41 + usage.sklearn: 141 + usage.xarray: 24 """ ... @overload - def __imul__(self, _0: numpy.uint8, /): + def __or__(self, _0: numpy.ndarray, /): """ - usage.skimage: 2 + usage.matplotlib: 6 + usage.skimage: 4 + usage.sklearn: 7 + usage.xarray: 3 """ ... @overload - def __imul__(self, _0: numpy.float32, /): + def __or__(self, _0: numpy.bool_, /): """ - usage.skimage: 1 usage.xarray: 1 """ ... @overload - def __imul__(self, _0: numpy.int16, /): + def __or__(self, _0: dask.array.core.Array, /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __imul__(self, _0: numpy.uint16, /): + def __or__(self, _0: sparse._coo.core.COO, /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __imul__(self, _0: Union[int, numpy.uint64], /): + def __or__(self, _0: Union[numpy.ndarray, bool, pandas.core.series.Series], /): """ - usage.pandas: 4 + usage.pandas: 61 """ ... @overload - def __imul__(self, _0: object, /): + def __or__(self, _0: Union[numpy.bool_, numpy.ndarray], /): """ - usage.scipy: 244 + usage.scipy: 35 """ ... @overload - def __imul__(self, _0: List[int], /): + def __or__(self, _0: int, /): """ - usage.matplotlib: 2 + usage.sample-usage: 1 """ ... @overload - def __imul__( - self, _0: Union[numpy.float64, float, int, numpy.float32, numpy.ndarray], / - ): + def __or__(self, _0: Union[numpy.ndarray, bool], /): """ - usage.sklearn: 178 + usage.dask: 3 """ ... - def __imul__(self, _0: object, /): + def __or__(self, _0: object, /): """ - usage.dask: 1 - usage.matplotlib: 29 - usage.pandas: 4 + usage.dask: 3 + usage.matplotlib: 6 + usage.pandas: 61 usage.sample-usage: 1 - usage.scipy: 244 - usage.skimage: 39 - usage.sklearn: 178 - usage.xarray: 4 + usage.scipy: 35 + usage.skimage: 4 + usage.sklearn: 7 + usage.xarray: 6 """ ... - def __invert__(self, /): + def __pos__(self, /): """ - usage.dask: 9 - usage.matplotlib: 25 - usage.pandas: 122 + usage.dask: 1 usage.sample-usage: 1 - usage.scipy: 158 - usage.skimage: 26 - usage.sklearn: 84 - usage.xarray: 9 + usage.scipy: 2 """ ... @overload - def __ior__(self, _0: numpy.ndarray, /): + def __pow__(self, _0: int, /): """ - usage.matplotlib: 1 - usage.scipy: 4 - usage.skimage: 3 - usage.xarray: 1 + usage.matplotlib: 69 + usage.sample-usage: 1 + usage.skimage: 154 + usage.sklearn: 274 + usage.xarray: 11 """ ... @overload - def __ior__(self, _0: Union[numpy.ndarray, bool], /): + def __pow__(self, _0: numpy.ndarray, /): """ - usage.pandas: 9 + usage.skimage: 2 """ ... @overload - def __ior__(self, _0: int, /): + def __pow__(self, _0: float, /): """ - usage.sample-usage: 1 + usage.matplotlib: 16 + usage.skimage: 11 + usage.sklearn: 25 """ ... - def __ior__(self, _0: Union[int, bool, numpy.ndarray], /): + @overload + def __pow__(self, _0: object, /): """ - usage.matplotlib: 1 - usage.pandas: 9 - usage.sample-usage: 1 - usage.scipy: 4 - usage.skimage: 3 - usage.xarray: 1 + usage.pandas: 63 + """ + ... + + @overload + def __pow__(self, _0: Union[numpy.float64, float, complex, int, numpy.ndarray], /): + """ + usage.scipy: 872 """ ... @overload - def __ipow__(self, _0: Union[int, float], /): + def __pow__(self, _0: Union[int, numpy.ndarray], /): """ - usage.scipy: 6 - usage.sklearn: 30 + usage.dask: 12 """ ... @overload - def __ipow__(self, _0: int, /): + def __pow__(self, _0: numpy.float64, /): """ - usage.sample-usage: 1 + usage.sklearn: 3 """ ... - def __ipow__(self, _0: Union[float, int], /): + def __pow__(self, _0: object, /): """ + usage.dask: 12 + usage.matplotlib: 85 + usage.pandas: 63 usage.sample-usage: 1 - usage.scipy: 6 - usage.sklearn: 30 + usage.scipy: 872 + usage.skimage: 167 + usage.sklearn: 302 + usage.xarray: 11 """ ... @overload - def __irshift__(self, _0: numpy.ndarray, /): + def __radd__(self, _0: float, /): """ - usage.pandas: 1 + usage.matplotlib: 26 + usage.skimage: 18 + usage.sklearn: 33 + usage.xarray: 2 """ ... @overload - def __irshift__(self, _0: int, /): + def __radd__(self, _0: numpy.ndarray, /): """ + usage.matplotlib: 213 usage.sample-usage: 1 + usage.skimage: 177 + usage.sklearn: 274 + usage.xarray: 26 """ ... - def __irshift__(self, _0: Union[int, numpy.ndarray], /): + @overload + def __radd__(self, _0: numpy.float64, /): """ - usage.pandas: 1 - usage.sample-usage: 1 + usage.matplotlib: 3 + usage.skimage: 4 + usage.sklearn: 10 """ ... @overload - def __isub__(self, _0: int, /): + def __radd__(self, _0: int, /): """ - usage.dask: 2 - usage.matplotlib: 10 + usage.matplotlib: 13 usage.sample-usage: 1 - usage.skimage: 16 - usage.xarray: 1 + usage.skimage: 34 + usage.sklearn: 22 + usage.xarray: 13 """ ... @overload - def __isub__(self, _0: float, /): + def __radd__(self, _0: Tuple[int, int], /): """ - usage.matplotlib: 3 - usage.skimage: 4 + usage.skimage: 1 """ ... @overload - def __isub__(self, _0: numpy.ndarray, /): + def __radd__(self, _0: List[numpy.ndarray], /): """ - usage.matplotlib: 1 - usage.skimage: 17 + usage.skimage: 3 """ ... @overload - def __isub__(self, _0: numpy.int64, /): + def __radd__(self, _0: numpy.int64, /): """ usage.matplotlib: 1 - usage.skimage: 1 + usage.skimage: 2 """ ... @overload - def __isub__(self, _0: numpy.float64, /): + def __radd__(self, _0: numpy.datetime64, /): """ - usage.matplotlib: 14 - usage.skimage: 3 + usage.xarray: 4 """ ... @overload - def __isub__(self, _0: numpy.uint8, /): + def __radd__(self, _0: datetime.timedelta, /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __isub__( - self, - _0: Union[int, pandas.core.indexes.datetimes.DatetimeIndex, numpy.ndarray], - /, - ): + def __radd__(self, _0: xarray.core.dataarray.DataArray, /): """ - usage.pandas: 8 + usage.xarray: 2 """ ... @overload - def __isub__(self, _0: object, /): + def __radd__(self, _0: xarray.core.dataset.Dataset, /): """ - usage.scipy: 109 - usage.sklearn: 142 + usage.xarray: 2 """ ... @overload - def __isub__(self, _0: numpy.bool_, /): + def __radd__(self, _0: numpy.timedelta64, /): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload - def __isub__(self, _0: numpy.float32, /): + def __radd__(self, _0: xarray.core.variable.Variable, /): """ - usage.matplotlib: 2 + usage.xarray: 1 """ ... @overload - def __isub__(self, _0: numpy.float128, /): + def __radd__(self, _0: object, /): """ - usage.matplotlib: 1 + usage.dask: 78 + usage.pandas: 289 + usage.scipy: 2001 """ ... @overload - def __isub__(self, _0: numpy.ma.core.MaskedArray, /): + def __radd__(self, _0: List[int], /): """ - usage.matplotlib: 1 + usage.sklearn: 12 """ ... @overload - def __isub__(self, _0: numpy.uint64, /): + def __radd__(self, _0: scipy.sparse.csr.csr_matrix, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __isub__(self, _0: List[int], /): + def __radd__(self, _0: numpy.float32, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... - def __isub__(self, _0: object, /): + def __radd__(self, _0: object, /): """ - usage.dask: 2 - usage.matplotlib: 36 - usage.pandas: 8 - usage.sample-usage: 1 - usage.scipy: 109 - usage.skimage: 42 - usage.sklearn: 142 - usage.xarray: 1 + usage.dask: 78 + usage.matplotlib: 256 + usage.pandas: 289 + usage.sample-usage: 2 + usage.scipy: 2001 + usage.skimage: 239 + usage.sklearn: 353 + usage.xarray: 52 """ ... - def __iter__(self, /): + @overload + def __rand__(self, _0: numpy.ndarray, /): """ - usage.dask: 6 - usage.matplotlib: 363 - usage.pandas: 181 - usage.sample-usage: 2 - usage.scipy: 302 - usage.skimage: 134 - usage.sklearn: 254 - usage.xarray: 92 + usage.matplotlib: 46 + usage.skimage: 12 + usage.sklearn: 11 + usage.xarray: 1 """ ... @overload - def __itruediv__(self, _0: float, /): + def __rand__(self, _0: dask.array.core.Array, /): """ - usage.dask: 1 - usage.matplotlib: 6 - usage.skimage: 5 + usage.xarray: 1 """ ... @overload - def __itruediv__(self, _0: numpy.float64, /): + def __rand__(self, _0: numpy.bool_, /): """ - usage.matplotlib: 15 - usage.skimage: 15 + usage.xarray: 1 """ ... @overload - def __itruediv__(self, _0: numpy.ndarray, /): + def __rand__(self, _0: sparse._coo.core.COO, /): """ - usage.matplotlib: 2 - usage.skimage: 14 usage.xarray: 1 """ ... @overload - def __itruediv__(self, _0: numpy.float32, /): + def __rand__( + self, + _0: Union[ + pandas.core.series.Series, + numpy.ndarray, + numpy.bool_, + pandas.core.arrays.sparse.array.SparseArray, + ], + /, + ): """ - usage.matplotlib: 1 - usage.skimage: 2 - usage.xarray: 1 + usage.pandas: 88 """ ... @overload - def __itruediv__(self, _0: numpy.float16, /): + def __rand__( + self, _0: Union[numpy.ndarray, int, numpy.bool_, numpy.int64, bool], / + ): """ - usage.skimage: 1 + usage.scipy: 293 """ ... @overload - def __itruediv__(self, _0: int, /): + def __rand__(self, _0: bool, /): """ usage.matplotlib: 2 - usage.skimage: 1 - usage.xarray: 1 """ ... @overload - def __itruediv__(self, _0: object, /): + def __rand__(self, _0: int, /): """ - usage.scipy: 155 + usage.sample-usage: 1 """ ... @overload - def __itruediv__(self, _0: numpy.float128, /): + def __rand__(self, _0: Union[numpy.ndarray, bool], /): """ - usage.matplotlib: 1 + usage.dask: 5 """ ... - @overload - def __itruediv__(self, _0: List[numpy.float64], /): + def __rand__(self, _0: object, /): """ - usage.matplotlib: 1 + usage.dask: 5 + usage.matplotlib: 48 + usage.pandas: 88 + usage.sample-usage: 1 + usage.scipy: 293 + usage.skimage: 12 + usage.sklearn: 11 + usage.xarray: 4 """ ... @overload - def __itruediv__(self, _0: numpy.int64, /): + def __rfloordiv__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload - def __itruediv__( - self, _0: Union[numpy.ndarray, numpy.float64, int, numpy.matrix, float], / - ): + def __rfloordiv__(self, _0: object, /): """ - usage.sklearn: 153 + usage.pandas: 107 """ ... - def __itruediv__(self, _0: object, /): + @overload + def __rfloordiv__(self, _0: Union[numpy.ndarray, numpy.int64], /): """ - usage.dask: 1 - usage.matplotlib: 29 - usage.scipy: 155 - usage.skimage: 38 - usage.sklearn: 153 - usage.xarray: 3 + usage.scipy: 8 """ ... - def __ixor__(self, _0: numpy.ndarray, /): + @overload + def __rfloordiv__(self, _0: int, /): """ - usage.pandas: 4 + usage.sample-usage: 1 """ ... @overload - def __le__(self, _0: int, /): + def __rfloordiv__(self, _0: Union[int, numpy.ndarray], /): """ - usage.matplotlib: 14 - usage.skimage: 16 + usage.dask: 2 """ ... - @overload - def __le__(self, _0: numpy.ndarray, /): + def __rfloordiv__(self, _0: object, /): """ - usage.matplotlib: 2 - usage.skimage: 5 - usage.xarray: 7 + usage.dask: 2 + usage.pandas: 107 + usage.sample-usage: 1 + usage.scipy: 8 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload - def __le__(self, _0: numpy.int64, /): + def __rlshift__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 1 - usage.skimage: 6 + usage.pandas: 4 """ ... @overload - def __le__(self, _0: float, /): + def __rlshift__(self, _0: int, /): """ - usage.matplotlib: 7 - usage.skimage: 1 + usage.dask: 1 + usage.sample-usage: 1 """ ... - @overload - def __le__(self, _0: numpy.timedelta64, /): + def __rlshift__(self, _0: Union[int, numpy.ndarray], /): """ - usage.xarray: 15 + usage.dask: 1 + usage.pandas: 4 + usage.sample-usage: 1 """ ... @overload - def __le__(self, _0: object, /): + def __rmatmul__(self, _0: numpy.ndarray, /): """ - usage.pandas: 134 - usage.scipy: 561 + usage.sample-usage: 1 + usage.skimage: 47 + usage.sklearn: 23 """ ... @overload - def __le__(self, _0: numpy.float64, /): + def __rmatmul__(self, _0: List[int], /): """ - usage.matplotlib: 15 + usage.skimage: 2 """ ... @overload - def __le__(self, _0: numpy.uint8, /): + def __rmatmul__(self, _0: numpy.matrix, /): """ - usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload - def __le__(self, _0: numpy.ma.core.MaskedArray, /): + def __rmatmul__( + self, + _0: Union[ + numpy.matrix, + numpy.ndarray, + scipy.sparse.dia.dia_matrix, + scipy.sparse.csc.csc_matrix, + List[Union[List[int], complex, int]], + ], + /, + ): """ - usage.matplotlib: 1 + usage.scipy: 432 """ ... @overload - def __le__(self, _0: Union[numpy.float64, int, numpy.ndarray, float], /): + def __rmatmul__(self, _0: scipy.sparse.csr.csr_matrix, /): """ - usage.dask: 35 + usage.sklearn: 6 """ ... @overload - def __le__( - self, _0: Union[numpy.float64, int, float, numpy.ndarray, numpy.int64], / - ): + def __rmatmul__(self, _0: scipy.sparse.csc.csc_matrix, /): """ - usage.sklearn: 192 + usage.sklearn: 6 """ ... - def __le__(self, _0: object, /): + @overload + def __rmatmul__(self, _0: scipy.sparse.dia.dia_matrix, /): """ - usage.dask: 35 - usage.matplotlib: 41 - usage.pandas: 134 - usage.scipy: 561 - usage.skimage: 28 - usage.sklearn: 192 - usage.xarray: 22 + usage.sklearn: 2 """ ... @overload - def __lshift__(self, _0: Union[int, numpy.ndarray], /): + def __rmatmul__(self, _0: scipy.sparse.coo.coo_matrix, /): """ - usage.pandas: 4 + usage.sklearn: 2 """ ... @overload - def __lshift__(self, _0: int, /): + def __rmatmul__(self, _0: scipy.sparse.lil.lil_matrix, /): """ - usage.sample-usage: 1 + usage.sklearn: 1 """ ... - def __lshift__(self, _0: Union[int, numpy.ndarray], /): + @overload + def __rmatmul__(self, _0: scipy.sparse.dok.dok_matrix, /): """ - usage.pandas: 4 - usage.sample-usage: 1 + usage.sklearn: 1 """ ... @overload - def __lt__(self, _0: float, /): + def __rmatmul__(self, _0: scipy.sparse.bsr.bsr_matrix, /): """ - usage.matplotlib: 17 - usage.skimage: 17 - usage.xarray: 1 + usage.sklearn: 1 """ ... - @overload - def __lt__(self, _0: int, /): + def __rmatmul__(self, _0: object, /): """ - usage.matplotlib: 29 usage.sample-usage: 1 - usage.skimage: 62 - usage.xarray: 12 + usage.scipy: 432 + usage.skimage: 50 + usage.sklearn: 42 """ ... @overload - def __lt__(self, _0: numpy.int64, /): + def __rmul__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 1 - usage.skimage: 3 + usage.matplotlib: 154 + usage.skimage: 215 + usage.sklearn: 355 + usage.xarray: 17 """ ... @overload - def __lt__(self, _0: numpy.float64, /): + def __rmul__(self, _0: numpy.float64, /): """ - usage.matplotlib: 5 - usage.skimage: 3 + usage.matplotlib: 30 + usage.skimage: 28 + usage.sklearn: 115 + usage.xarray: 2 """ ... @overload - def __lt__(self, _0: numpy.ndarray, /): + def __rmul__(self, _0: float, /): """ - usage.matplotlib: 9 - usage.skimage: 18 - usage.xarray: 1 + usage.matplotlib: 177 + usage.skimage: 152 + usage.sklearn: 270 + usage.xarray: 22 """ ... @overload - def __lt__(self, _0: object, /): + def __rmul__(self, _0: int, /): """ - usage.pandas: 48 + usage.matplotlib: 82 + usage.sample-usage: 2 + usage.skimage: 120 + usage.sklearn: 178 + usage.xarray: 27 """ ... @overload - def __lt__(self, _0: Union[numpy.ndarray, int, float, numpy.float64], /): + def __rmul__(self, _0: complex, /): """ - usage.scipy: 75 + usage.matplotlib: 2 + usage.skimage: 6 + usage.sklearn: 2 + usage.xarray: 4 """ ... @overload - def __lt__(self, _0: Union[numpy.ndarray, int], /): + def __rmul__(self, _0: numpy.int64, /): """ - usage.dask: 3 + usage.skimage: 2 + usage.sklearn: 2 """ ... @overload - def __lt__(self, _0: Union[numpy.ndarray, numpy.float64, int], /): + def __rmul__(self, _0: dask.array.core.Array, /): """ - usage.sklearn: 17 + usage.skimage: 4 """ ... - def __lt__(self, _0: object, /): + @overload + def __rmul__(self, _0: Tuple[int, int], /): """ - usage.dask: 3 - usage.matplotlib: 61 - usage.pandas: 48 - usage.sample-usage: 1 - usage.scipy: 75 - usage.skimage: 103 - usage.sklearn: 17 - usage.xarray: 14 + usage.skimage: 1 """ ... @overload - def __matmul__(self, _0: numpy.ndarray, /): + def __rmul__(self, _0: Tuple[int], /): """ - usage.sample-usage: 1 - usage.skimage: 47 + usage.skimage: 1 """ ... @overload - def __matmul__(self, _0: numpy.matrix, /): + def __rmul__(self, _0: Tuple[int, int, int], /): """ usage.skimage: 1 """ ... @overload - def __matmul__( - self, - _0: Union[ - numpy.matrix, - numpy.ndarray, - List[Union[complex, int, List[Union[complex, int, numpy.int64]]]], - ], - /, - ): + def __rmul__(self, _0: xarray.core.variable.Variable, /): """ - usage.scipy: 423 + usage.xarray: 1 """ ... @overload - def __matmul__( - self, - _0: Union[ - scipy.sparse.dok.dok_matrix, - scipy.sparse.csc.csc_matrix, - numpy.ndarray, - scipy.sparse.csr.csr_matrix, - scipy.sparse.lil.lil_matrix, - ], - /, - ): + def __rmul__(self, _0: xarray.core.variable.IndexVariable, /): """ - usage.sklearn: 35 + usage.xarray: 1 """ ... - def __matmul__(self, _0: object, /): + @overload + def __rmul__(self, _0: object, /): """ - usage.sample-usage: 1 - usage.scipy: 423 - usage.skimage: 48 - usage.sklearn: 35 + usage.dask: 77 + usage.pandas: 243 + usage.scipy: 4244 """ ... @overload - def __mod__(self, _0: int, /): + def __rmul__(self, _0: List[float], /): """ - usage.matplotlib: 10 - usage.sample-usage: 1 - usage.skimage: 3 - usage.sklearn: 16 - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload - def __mod__(self, _0: float, /): + def __rmul__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.matplotlib: 2 - usage.skimage: 1 + usage.matplotlib: 5 """ ... @overload - def __mod__(self, _0: numpy.ndarray, /): + def __rmul__(self, _0: scipy.sparse.csr.csr_matrix, /): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload - def __mod__(self, _0: object, /): + def __rmul__(self, _0: scipy.sparse.csc.csc_matrix, /): """ - usage.pandas: 52 + usage.sklearn: 1 """ ... @overload - def __mod__(self, _0: Union[numpy.ndarray, numpy.float64, int], /): + def __rmul__(self, _0: numpy.float32, /): """ - usage.scipy: 23 + usage.sklearn: 10 """ ... @overload - def __mod__(self, _0: Union[int, numpy.ndarray], /): + def __rmul__(self, _0: scipy.sparse.dia.dia_matrix, /): """ - usage.dask: 12 + usage.sklearn: 1 """ ... - def __mod__(self, _0: object, /): + def __rmul__(self, _0: object, /): """ - usage.dask: 12 - usage.matplotlib: 12 - usage.pandas: 52 - usage.sample-usage: 1 - usage.scipy: 23 - usage.skimage: 5 - usage.sklearn: 16 + usage.dask: 77 + usage.matplotlib: 451 + usage.pandas: 243 + usage.sample-usage: 2 + usage.scipy: 4244 + usage.skimage: 530 + usage.sklearn: 936 + usage.xarray: 74 + """ + ... + + @overload + def __ror__(self, _0: numpy.ndarray, /): + """ + usage.matplotlib: 6 + usage.skimage: 4 + usage.sklearn: 7 usage.xarray: 3 """ ... @overload - def __mul__(self, _0: numpy.ndarray, /): + def __ror__(self, _0: bool, /): """ - usage.matplotlib: 154 - usage.skimage: 215 - usage.xarray: 17 + usage.xarray: 3 """ ... @overload - def __mul__(self, _0: int, /): + def __ror__(self, _0: dask.array.core.Array, /): """ - usage.matplotlib: 58 - usage.sample-usage: 1 - usage.skimage: 83 - usage.xarray: 148 + usage.xarray: 1 """ ... @overload - def __mul__(self, _0: numpy.float64, /): + def __ror__(self, _0: numpy.bool_, /): """ - usage.matplotlib: 26 - usage.skimage: 29 + usage.xarray: 1 """ ... @overload - def __mul__(self, _0: float, /): + def __ror__( + self, + _0: Union[ + pandas.core.series.Series, + pandas.core.arrays.sparse.array.SparseArray, + numpy.ndarray, + ], + /, + ): """ - usage.matplotlib: 83 - usage.skimage: 36 - usage.xarray: 16 + usage.pandas: 61 """ ... @overload - def __mul__(self, _0: Tuple[int, int], /): + def __ror__(self, _0: Union[bool, numpy.ndarray, numpy.bool_], /): """ - usage.skimage: 4 + usage.scipy: 40 """ ... @overload - def __mul__(self, _0: Tuple[int, int, int, int], /): + def __ror__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.skimage: 3 + usage.matplotlib: 1 """ ... @overload - def __mul__(self, _0: numpy.int64, /): + def __ror__(self, _0: int, /): """ - usage.matplotlib: 3 - usage.skimage: 2 + usage.sample-usage: 1 """ ... @overload - def __mul__(self, _0: numpy.float16, /): + def __ror__(self, _0: Union[numpy.ndarray, bool], /): """ - usage.skimage: 1 + usage.dask: 3 """ ... - @overload - def __mul__(self, _0: numpy.float32, /): + def __ror__(self, _0: object, /): """ - usage.skimage: 1 + usage.dask: 3 + usage.matplotlib: 7 + usage.pandas: 61 + usage.sample-usage: 1 + usage.scipy: 40 + usage.skimage: 4 + usage.sklearn: 7 + usage.xarray: 8 """ ... @overload - def __mul__(self, _0: List[bool], /): + def __rpow__(self, _0: numpy.ndarray, /): """ usage.skimage: 2 """ ... @overload - def __mul__(self, _0: dask.array.core.Array, /): + def __rpow__(self, _0: int, /): """ - usage.xarray: 1 + usage.matplotlib: 9 + usage.sample-usage: 2 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload - def __mul__(self, _0: numpy.timedelta64, /): + def __rpow__(self, _0: object, /): """ - usage.xarray: 4 + usage.pandas: 50 """ ... @overload - def __mul__(self, _0: sparse._coo.core.COO, /): + def __rpow__(self, _0: Union[numpy.float64, float, int, numpy.ndarray], /): """ - usage.xarray: 1 + usage.scipy: 192 """ ... @overload - def __mul__(self, _0: object, /): + def __rpow__(self, _0: float, /): """ - usage.dask: 69 - usage.pandas: 256 - usage.scipy: 2345 - usage.sklearn: 523 - usage.xarray: 561 + usage.matplotlib: 7 + usage.sklearn: 1 """ ... @overload - def __mul__(self, _0: xarray.core.variable.Variable, /): + def __rpow__(self, _0: Union[float, numpy.ndarray, int], /): """ - usage.xarray: 1 + usage.dask: 3 """ ... - @overload - def __mul__(self, _0: xarray.core.variable.IndexVariable, /): + def __rpow__(self, _0: object, /): """ - usage.xarray: 1 + usage.dask: 3 + usage.matplotlib: 16 + usage.pandas: 50 + usage.sample-usage: 2 + usage.scipy: 192 + usage.skimage: 3 + usage.sklearn: 2 """ ... @overload - def __mul__(self, _0: List[int], /): + def __rrshift__(self, _0: numpy.ndarray, /): """ - usage.matplotlib: 1 + usage.pandas: 1 """ ... @overload - def __mul__(self, _0: complex, /): + def __rrshift__(self, _0: numpy.uint64, /): """ - usage.matplotlib: 3 + usage.scipy: 1 """ ... @overload - def __mul__(self, _0: numpy.ma.core.MaskedArray, /): - """ - usage.matplotlib: 1 - """ - ... - - def __mul__(self, _0: object, /): + def __rrshift__(self, _0: int, /): """ - usage.dask: 69 - usage.matplotlib: 329 - usage.pandas: 256 usage.sample-usage: 1 - usage.scipy: 2345 - usage.skimage: 376 - usage.sklearn: 523 - usage.xarray: 750 """ ... - def __neg__(self, /): + def __rrshift__(self, _0: Union[int, numpy.ndarray, numpy.uint64], /): """ - usage.dask: 7 - usage.matplotlib: 68 - usage.pandas: 25 + usage.pandas: 1 usage.sample-usage: 1 - usage.scipy: 785 - usage.skimage: 41 - usage.sklearn: 141 - usage.xarray: 24 + usage.scipy: 1 """ ... @overload - def __or__(self, _0: numpy.ndarray, /): + def __rshift__(self, _0: Union[int, numpy.ndarray], /): """ - usage.matplotlib: 6 - usage.skimage: 4 - usage.sklearn: 7 - usage.xarray: 3 + usage.pandas: 5 """ ... @overload - def __or__(self, _0: numpy.bool_, /): + def __rshift__(self, _0: int, /): """ - usage.xarray: 1 + usage.dask: 1 + usage.sample-usage: 1 + usage.scipy: 1 """ ... - @overload - def __or__(self, _0: dask.array.core.Array, /): + def __rshift__(self, _0: Union[int, numpy.ndarray], /): """ - usage.xarray: 1 + usage.dask: 1 + usage.pandas: 5 + usage.sample-usage: 1 + usage.scipy: 1 """ ... @overload - def __or__(self, _0: sparse._coo.core.COO, /): + def __rsub__(self, _0: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 133 + usage.skimage: 183 + usage.sklearn: 526 + usage.xarray: 35 """ ... @overload - def __or__(self, _0: Union[numpy.ndarray, bool, pandas.core.series.Series], /): + def __rsub__(self, _0: int, /): """ - usage.pandas: 61 + usage.matplotlib: 25 + usage.sample-usage: 1 + usage.skimage: 20 + usage.sklearn: 79 + usage.xarray: 2 """ ... @overload - def __or__(self, _0: Union[numpy.bool_, numpy.ndarray], /): + def __rsub__(self, _0: numpy.float64, /): """ - usage.scipy: 35 + usage.matplotlib: 11 + usage.skimage: 4 + usage.sklearn: 12 """ ... @overload - def __or__(self, _0: int, /): + def __rsub__(self, _0: float, /): """ - usage.sample-usage: 1 + usage.matplotlib: 19 + usage.skimage: 13 + usage.sklearn: 13 """ ... @overload - def __or__(self, _0: Union[numpy.ndarray, bool], /): - """ - usage.dask: 3 - """ - ... - - def __or__(self, _0: object, /): - """ - usage.dask: 3 - usage.matplotlib: 6 - usage.pandas: 61 - usage.sample-usage: 1 - usage.scipy: 35 - usage.skimage: 4 - usage.sklearn: 7 - usage.xarray: 6 - """ - ... - - def __pos__(self, /): + def __rsub__(self, _0: numpy.float32, /): """ - usage.dask: 1 - usage.sample-usage: 1 - usage.scipy: 2 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload - def __pow__(self, _0: int, /): + def __rsub__(self, _0: dask.array.core.Array, /): """ - usage.matplotlib: 69 - usage.sample-usage: 1 - usage.skimage: 154 - usage.xarray: 11 + usage.skimage: 1 """ ... @overload - def __pow__(self, _0: numpy.ndarray, /): + def __rsub__(self, _0: xarray.coding.cftimeindex.CFTimeIndex, /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __pow__(self, _0: float, /): + def __rsub__(self, _0: cftime._cftime.DatetimeNoLeap, /): """ - usage.matplotlib: 16 - usage.skimage: 11 + usage.xarray: 1 """ ... @overload - def __pow__(self, _0: object, /): + def __rsub__(self, _0: cftime._cftime.Datetime360Day, /): """ - usage.pandas: 63 + usage.xarray: 1 """ ... @overload - def __pow__(self, _0: Union[numpy.float64, float, complex, int, numpy.ndarray], /): + def __rsub__(self, _0: cftime._cftime.DatetimeJulian, /): """ - usage.scipy: 872 + usage.xarray: 1 """ ... @overload - def __pow__(self, _0: Union[int, numpy.ndarray], /): + def __rsub__(self, _0: cftime._cftime.DatetimeAllLeap, /): """ - usage.dask: 12 + usage.xarray: 1 """ ... @overload - def __pow__(self, _0: Union[float, numpy.float64, int], /): + def __rsub__(self, _0: cftime._cftime.DatetimeGregorian, /): """ - usage.sklearn: 302 + usage.xarray: 1 """ ... - def __pow__(self, _0: object, /): + @overload + def __rsub__(self, _0: cftime._cftime.DatetimeProlepticGregorian, /): """ - usage.dask: 12 - usage.matplotlib: 85 - usage.pandas: 63 - usage.sample-usage: 1 - usage.scipy: 872 - usage.skimage: 167 - usage.sklearn: 302 - usage.xarray: 11 + usage.xarray: 1 """ ... @overload - def __radd__(self, _0: float, /): + def __rsub__(self, _0: datetime.timedelta, /): """ - usage.matplotlib: 26 - usage.skimage: 18 - usage.xarray: 2 + usage.xarray: 1 """ ... @overload - def __radd__(self, _0: numpy.ndarray, /): + def __rsub__(self, _0: xarray.core.dataarray.DataArray, /): """ - usage.matplotlib: 213 - usage.sample-usage: 1 - usage.skimage: 177 - usage.xarray: 26 + usage.xarray: 1 """ ... @overload - def __radd__(self, _0: numpy.float64, /): + def __rsub__(self, _0: xarray.core.variable.Variable, /): """ - usage.matplotlib: 3 - usage.skimage: 4 + usage.xarray: 1 """ ... @overload - def __radd__(self, _0: int, /): + def __rsub__(self, _0: xarray.core.variable.IndexVariable, /): """ - usage.matplotlib: 13 - usage.sample-usage: 1 - usage.skimage: 34 - usage.xarray: 13 + usage.xarray: 1 """ ... @overload - def __radd__(self, _0: Tuple[int, int], /): + def __rsub__(self, _0: object, /): """ - usage.skimage: 1 + usage.pandas: 273 + usage.scipy: 2130 """ ... @overload - def __radd__(self, _0: List[numpy.ndarray], /): + def __rsub__(self, _0: numpy.int64, /): """ - usage.skimage: 3 + usage.matplotlib: 2 + usage.sklearn: 2 """ ... @overload - def __radd__(self, _0: numpy.int64, /): + def __rsub__(self, _0: numpy.ma.core.MaskedArray, /): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.matplotlib: 2 """ ... @overload - def __radd__(self, _0: numpy.datetime64, /): + def __rsub__(self, _0: Union[numpy.ndarray, int], /): """ - usage.xarray: 4 + usage.dask: 20 """ ... @overload - def __radd__(self, _0: datetime.timedelta, /): + def __rsub__(self, _0: numpy.matrix, /): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload - def __radd__(self, _0: xarray.core.dataarray.DataArray, /): + def __rsub__(self, _0: List[int], /): """ - usage.xarray: 2 + usage.sklearn: 3 """ ... @overload - def __radd__(self, _0: xarray.core.dataset.Dataset, /): + def __rsub__(self, _0: numpy.memmap, /): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... - @overload - def __radd__(self, _0: numpy.timedelta64, /): + def __rsub__(self, _0: object, /): """ - usage.xarray: 1 + usage.dask: 20 + usage.matplotlib: 192 + usage.pandas: 273 + usage.sample-usage: 1 + usage.scipy: 2130 + usage.skimage: 222 + usage.sklearn: 639 + usage.xarray: 48 """ ... @overload - def __radd__(self, _0: xarray.core.variable.Variable, /): + def __rtruediv__(self, _0: numpy.ndarray, /): """ + usage.matplotlib: 37 + usage.skimage: 68 + usage.sklearn: 204 usage.xarray: 1 """ ... @overload - def __radd__(self, _0: object, /): + def __rtruediv__(self, _0: int, /): """ - usage.dask: 78 - usage.pandas: 289 - usage.scipy: 2001 - usage.sklearn: 353 + usage.matplotlib: 7 + usage.skimage: 5 + usage.sklearn: 40 """ ... - def __radd__(self, _0: object, /): + @overload + def __rtruediv__(self, _0: float, /): """ - usage.dask: 78 - usage.matplotlib: 256 - usage.pandas: 289 - usage.sample-usage: 2 - usage.scipy: 2001 - usage.skimage: 239 - usage.sklearn: 353 - usage.xarray: 52 + usage.matplotlib: 6 + usage.skimage: 8 + usage.sklearn: 41 """ ... @overload - def __rand__(self, _0: numpy.ndarray, /): + def __rtruediv__(self, _0: numpy.complex128, /): """ - usage.matplotlib: 46 - usage.skimage: 12 - usage.sklearn: 11 - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __rand__(self, _0: dask.array.core.Array, /): + def __rtruediv__(self, _0: numpy.float64, /): """ - usage.xarray: 1 + usage.skimage: 1 + usage.sklearn: 4 """ ... @overload - def __rand__(self, _0: numpy.bool_, /): + def __rtruediv__(self, _0: object, /): """ - usage.xarray: 1 + usage.pandas: 420 + usage.scipy: 1157 """ ... @overload - def __rand__(self, _0: sparse._coo.core.COO, /): + def __rtruediv__(self, _0: List[numpy.float64], /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __rand__( - self, - _0: Union[ - pandas.core.series.Series, - numpy.ndarray, - numpy.bool_, - pandas.core.arrays.sparse.array.SparseArray, - ], - /, - ): + def __rtruediv__(self, _0: Union[numpy.ndarray, int], /): """ - usage.pandas: 88 + usage.dask: 13 """ ... @overload - def __rand__( - self, _0: Union[numpy.ndarray, int, numpy.bool_, numpy.int64, bool], / - ): + def __rtruediv__(self, _0: numpy.memmap, /): """ - usage.scipy: 293 + usage.sklearn: 2 """ ... - @overload - def __rand__(self, _0: bool, /): + def __rtruediv__(self, _0: object, /): """ - usage.matplotlib: 2 + usage.dask: 13 + usage.matplotlib: 51 + usage.pandas: 420 + usage.scipy: 1157 + usage.skimage: 83 + usage.sklearn: 291 + usage.xarray: 1 """ ... @overload - def __rand__(self, _0: int, /): + def __rxor__(self, _0: Union[pandas.core.series.Series, numpy.ndarray], /): """ - usage.sample-usage: 1 + usage.pandas: 8 """ ... @overload - def __rand__(self, _0: Union[numpy.ndarray, bool], /): + def __rxor__(self, _0: Union[bool, numpy.ndarray], /): """ - usage.dask: 5 + usage.dask: 2 """ ... - def __rand__(self, _0: object, /): + def __rxor__(self, _0: Union[numpy.ndarray, bool, pandas.core.series.Series], /): """ - usage.dask: 5 - usage.matplotlib: 48 - usage.pandas: 88 - usage.sample-usage: 1 - usage.scipy: 293 - usage.skimage: 12 - usage.sklearn: 11 - usage.xarray: 4 + usage.dask: 2 + usage.pandas: 8 """ ... @overload - def __rfloordiv__(self, _0: numpy.ndarray, /): + def __setitem__(self, _0: numpy.ndarray, _1: numpy.ndarray, /): """ - usage.skimage: 2 - usage.sklearn: 1 + usage.matplotlib: 45 + usage.skimage: 52 + usage.sklearn: 114 + usage.xarray: 4 """ ... @overload - def __rfloordiv__(self, _0: object, /): + def __setitem__(self, _0: numpy.ndarray, _1: int, /): """ - usage.pandas: 107 + usage.matplotlib: 20 + usage.skimage: 68 + usage.sklearn: 130 + usage.xarray: 1 """ ... @overload - def __rfloordiv__(self, _0: Union[numpy.ndarray, numpy.int64], /): + def __setitem__(self, _0: numpy.ndarray, _1: float, /): """ - usage.scipy: 8 + usage.matplotlib: 9 + usage.skimage: 14 + usage.sklearn: 65 + usage.xarray: 6 """ ... @overload - def __rfloordiv__(self, _0: int, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, int], _1: numpy.ndarray, /): """ - usage.sample-usage: 1 + usage.matplotlib: 3 + usage.skimage: 3 + usage.sklearn: 12 """ ... @overload - def __rfloordiv__(self, _0: Union[int, numpy.ndarray], /): + def __setitem__(self, _0: Tuple[ellipsis, int], _1: numpy.ndarray, /): """ - usage.dask: 2 + usage.matplotlib: 11 + usage.skimage: 49 + usage.sklearn: 2 """ ... - def __rfloordiv__(self, _0: object, /): + @overload + def __setitem__( + self, + _0: Tuple[slice[None, None, None], slice[None, None, None], int], + _1: numpy.ndarray, + /, + ): """ - usage.dask: 2 - usage.pandas: 107 - usage.sample-usage: 1 - usage.scipy: 8 - usage.skimage: 2 + usage.matplotlib: 8 + usage.skimage: 7 usage.sklearn: 1 """ ... @overload - def __rlshift__(self, _0: numpy.ndarray, /): + def __setitem__( + self, _0: Tuple[ellipsis, slice[None, int, None]], _1: numpy.ndarray, / + ): """ - usage.pandas: 4 + usage.matplotlib: 1 + usage.skimage: 4 """ ... @overload - def __rlshift__(self, _0: int, /): + def __setitem__(self, _0: Tuple[ellipsis, int], _1: int, /): """ - usage.dask: 1 - usage.sample-usage: 1 + usage.matplotlib: 1 + usage.skimage: 4 + usage.xarray: 1 """ ... - def __rlshift__(self, _0: Union[int, numpy.ndarray], /): + @overload + def __setitem__(self, _0: Tuple[ellipsis, int], _1: float, /): """ - usage.dask: 1 - usage.pandas: 4 - usage.sample-usage: 1 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload - def __rmatmul__(self, _0: numpy.ndarray, /): + def __setitem__(self, _0: numpy.bool_, _1: numpy.ndarray, /): """ - usage.sample-usage: 1 - usage.skimage: 47 + usage.skimage: 2 """ ... @overload - def __rmatmul__(self, _0: List[int], /): + def __setitem__(self, _0: Tuple[ellipsis, int], _1: numpy.float64, /): """ - usage.skimage: 2 + usage.skimage: 7 + usage.sklearn: 1 """ ... @overload - def __rmatmul__(self, _0: numpy.matrix, /): + def __setitem__( + self, _0: Tuple[slice[int, int, int], slice[int, int, int]], _1: int, / + ): """ - usage.skimage: 1 + usage.matplotlib: 1 + usage.skimage: 69 + usage.sklearn: 1 """ ... @overload - def __rmatmul__( - self, - _0: Union[ - numpy.matrix, - numpy.ndarray, - scipy.sparse.dia.dia_matrix, - scipy.sparse.csc.csc_matrix, - List[Union[List[int], complex, int]], - ], - /, + def __setitem__( + self, _0: Tuple[numpy.ndarray, numpy.ndarray], _1: numpy.ndarray, / ): """ - usage.scipy: 432 + usage.matplotlib: 1 + usage.skimage: 14 + usage.sklearn: 20 + usage.xarray: 3 """ ... @overload - def __rmatmul__(self, _0: object, /): + def __setitem__( + self, _0: Tuple[numpy.ndarray, numpy.ndarray], _1: Tuple[int, int, int], / + ): """ - usage.sklearn: 42 + usage.skimage: 1 """ ... - def __rmatmul__(self, _0: object, /): + @overload + def __setitem__( + self, _0: Tuple[slice[None, None, None], int], _1: numpy.ndarray, / + ): """ - usage.sample-usage: 1 - usage.scipy: 432 - usage.skimage: 50 - usage.sklearn: 42 + usage.matplotlib: 31 + usage.skimage: 34 + usage.sklearn: 146 + usage.xarray: 6 """ ... @overload - def __rmul__(self, _0: numpy.ndarray, /): + def __setitem__(self, _0: Tuple[int, int], _1: numpy.float64, /): """ - usage.matplotlib: 154 - usage.skimage: 215 - usage.xarray: 17 + usage.matplotlib: 9 + usage.skimage: 10 + usage.sklearn: 13 """ ... @overload - def __rmul__(self, _0: numpy.float64, /): + def __setitem__(self, _0: int, _1: numpy.float64, /): """ - usage.matplotlib: 30 - usage.skimage: 28 - usage.xarray: 2 + usage.matplotlib: 26 + usage.skimage: 14 + usage.sklearn: 73 """ ... @overload - def __rmul__(self, _0: float, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray], _1: int, /): """ - usage.matplotlib: 177 - usage.skimage: 152 - usage.xarray: 22 + usage.skimage: 125 + usage.sklearn: 7 + usage.xarray: 1 """ ... @overload - def __rmul__(self, _0: int, /): + def __setitem__( + self, _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], _1: int, / + ): """ - usage.matplotlib: 82 - usage.sample-usage: 2 - usage.skimage: 120 - usage.xarray: 27 + usage.skimage: 5 """ ... @overload - def __rmul__(self, _0: complex, /): + def __setitem__(self, _0: Tuple[int, slice[None, None, None]], _1: int, /): """ usage.matplotlib: 2 - usage.skimage: 6 - usage.xarray: 4 + usage.skimage: 32 + usage.xarray: 1 """ ... @overload - def __rmul__(self, _0: numpy.int64, /): + def __setitem__(self, _0: Tuple[slice[None, None, None], int], _1: int, /): """ - usage.skimage: 2 + usage.skimage: 12 + usage.sklearn: 17 + usage.xarray: 1 """ ... @overload - def __rmul__(self, _0: dask.array.core.Array, /): + def __setitem__( + self, _0: Tuple[slice[int, None, int], slice[None, None, None]], _1: int, / + ): """ - usage.skimage: 4 + usage.skimage: 1 """ ... @overload - def __rmul__(self, _0: Tuple[int, int], /): + def __setitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray, int], _1: int, /): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload - def __rmul__(self, _0: Tuple[int], /): + def __setitem__(self, _0: slice[None, None, None], _1: numpy.ndarray, /): """ - usage.skimage: 1 + usage.matplotlib: 2 + usage.skimage: 8 + usage.sklearn: 9 + usage.xarray: 2 """ ... @overload - def __rmul__(self, _0: Tuple[int, int, int], /): + def __setitem__(self, _0: List[int], _1: int, /): """ - usage.skimage: 1 + usage.skimage: 2 + usage.sklearn: 10 """ ... @overload - def __rmul__(self, _0: xarray.core.variable.Variable, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray], _1: bool, /): """ - usage.xarray: 1 + usage.skimage: 5 + usage.sklearn: 1 """ ... @overload - def __rmul__(self, _0: xarray.core.variable.IndexVariable, /): + def __setitem__( + self, + _0: Tuple[ + slice[numpy.int64, numpy.int64, numpy.int64], + slice[numpy.int64, numpy.int64, numpy.int64], + ], + _1: int, + /, + ): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __rmul__(self, _0: object, /): + def __setitem__(self, _0: int, _1: int, /): """ - usage.dask: 77 - usage.pandas: 243 - usage.scipy: 4244 - usage.sklearn: 936 + usage.matplotlib: 8 + usage.sample-usage: 1 + usage.skimage: 50 + usage.sklearn: 59 + usage.xarray: 2 """ ... @overload - def __rmul__(self, _0: List[float], /): + def __setitem__(self, _0: Tuple[int, int], _1: float, /): """ - usage.matplotlib: 1 + usage.matplotlib: 15 + usage.skimage: 30 + usage.sklearn: 34 + usage.xarray: 7 """ ... @overload - def __rmul__(self, _0: numpy.ma.core.MaskedArray, /): + def __setitem__(self, _0: Tuple[slice[None, int, None]], _1: bool, /): """ - usage.matplotlib: 5 + usage.skimage: 1 """ ... - def __rmul__(self, _0: object, /): + @overload + def __setitem__(self, _0: Tuple[slice[int, None, int]], _1: bool, /): """ - usage.dask: 77 - usage.matplotlib: 451 - usage.pandas: 243 - usage.sample-usage: 2 - usage.scipy: 4244 - usage.skimage: 530 - usage.sklearn: 936 - usage.xarray: 74 + usage.skimage: 1 """ ... @overload - def __ror__(self, _0: numpy.ndarray, /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], slice[None, int, None]], _1: bool, / + ): """ - usage.matplotlib: 6 - usage.skimage: 4 - usage.sklearn: 7 - usage.xarray: 3 + usage.skimage: 1 """ ... @overload - def __ror__(self, _0: bool, /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], slice[int, None, int]], _1: bool, / + ): """ - usage.xarray: 3 + usage.skimage: 1 """ ... @overload - def __ror__(self, _0: dask.array.core.Array, /): + def __setitem__(self, _0: Tuple[int, slice[int, int, int]], _1: int, /): """ - usage.xarray: 1 + usage.skimage: 9 + usage.sklearn: 2 """ ... @overload - def __ror__(self, _0: numpy.bool_, /): + def __setitem__(self, _0: numpy.ndarray, _1: bool, /): """ - usage.xarray: 1 + usage.skimage: 7 + usage.sklearn: 28 """ ... @overload - def __ror__( - self, - _0: Union[ - pandas.core.series.Series, - pandas.core.arrays.sparse.array.SparseArray, - numpy.ndarray, - ], - /, - ): + def __setitem__(self, _0: Tuple[slice[None, int, None], int], _1: numpy.ndarray, /): """ - usage.pandas: 61 + usage.matplotlib: 7 + usage.skimage: 8 + usage.sklearn: 3 """ ... @overload - def __ror__(self, _0: Union[bool, numpy.ndarray, numpy.bool_], /): + def __setitem__(self, _0: Tuple[slice[None, int, None], int], _1: int, /): """ - usage.scipy: 40 + usage.skimage: 1 """ ... @overload - def __ror__(self, _0: numpy.ma.core.MaskedArray, /): + def __setitem__(self, _0: Tuple[slice[int, None, int], int], _1: numpy.ndarray, /): """ - usage.matplotlib: 1 + usage.matplotlib: 10 + usage.skimage: 6 """ ... @overload - def __ror__(self, _0: int, /): + def __setitem__(self, _0: Tuple[slice[int, None, int], int], _1: int, /): """ - usage.sample-usage: 1 + usage.skimage: 1 + usage.sklearn: 3 """ ... @overload - def __ror__(self, _0: Union[numpy.ndarray, bool], /): + def __setitem__(self, _0: Tuple[int, int], _1: int, /): """ - usage.dask: 3 + usage.matplotlib: 9 + usage.skimage: 151 + usage.sklearn: 34 + usage.xarray: 1 """ ... - def __ror__(self, _0: object, /): + @overload + def __setitem__(self, _0: int, _1: Tuple[int, int, int], /): """ - usage.dask: 3 - usage.matplotlib: 7 - usage.pandas: 61 - usage.sample-usage: 1 - usage.scipy: 40 - usage.skimage: 4 - usage.sklearn: 7 - usage.xarray: 8 + usage.skimage: 2 """ ... @overload - def __rpow__(self, _0: numpy.ndarray, /): + def __setitem__( + self, + _0: Tuple[slice[int, int, int], slice[int, int, int]], + _1: numpy.ndarray, + /, + ): """ - usage.skimage: 2 + usage.skimage: 22 + usage.sklearn: 3 """ ... @overload - def __rpow__(self, _0: int, /): + def __setitem__(self, _0: slice[None, None, None], _1: int, /): """ - usage.matplotlib: 9 - usage.sample-usage: 2 usage.skimage: 1 + usage.sklearn: 9 + usage.xarray: 2 """ ... @overload - def __rpow__(self, _0: object, /): + def __setitem__(self, _0: Tuple[int, int, int], _1: int, /): """ - usage.pandas: 50 + usage.skimage: 30 """ ... @overload - def __rpow__(self, _0: Union[numpy.float64, float, int, numpy.ndarray], /): + def __setitem__(self, _0: Tuple[ellipsis, int, int], _1: numpy.ndarray, /): """ - usage.scipy: 192 + usage.skimage: 2 """ ... @overload - def __rpow__(self, _0: float, /): + def __setitem__( + self, _0: Tuple[slice[None, int, None], slice[None, int, None]], _1: float, / + ): """ - usage.matplotlib: 7 + usage.matplotlib: 1 + usage.skimage: 6 """ ... @overload - def __rpow__(self, _0: Union[float, numpy.ndarray, int], /): + def __setitem__( + self, _0: Tuple[slice[None, int, None], slice[None, int, None]], _1: int, / + ): """ - usage.dask: 3 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload - def __rpow__(self, _0: Union[int, float], /): + def __setitem__( + self, _0: Tuple[slice[int, None, int], slice[int, None, int]], _1: int, / + ): """ - usage.sklearn: 2 + usage.skimage: 3 + usage.sklearn: 1 """ ... - def __rpow__(self, _0: object, /): + @overload + def __setitem__( + self, _0: slice[None, None, None], _1: Tuple[numpy.float64, numpy.float64], / + ): """ - usage.dask: 3 - usage.matplotlib: 16 - usage.pandas: 50 - usage.sample-usage: 2 - usage.scipy: 192 - usage.skimage: 3 - usage.sklearn: 2 + usage.matplotlib: 1 + usage.skimage: 2 """ ... @overload - def __rrshift__(self, _0: numpy.ndarray, /): + def __setitem__( + self, + _0: Tuple[int, slice[None, None, None]], + _1: Tuple[numpy.float64, numpy.float64], + /, + ): """ - usage.pandas: 1 + usage.skimage: 2 """ ... @overload - def __rrshift__(self, _0: numpy.uint64, /): + def __setitem__( + self, _0: Tuple[int, slice[None, None, None]], _1: Tuple[float, float], / + ): """ - usage.scipy: 1 + usage.skimage: 2 """ ... @overload - def __rrshift__(self, _0: int, /): + def __setitem__( + self, + _0: Tuple[slice[None, None, None], slice[None, int, None]], + _1: numpy.ndarray, + /, + ): """ - usage.sample-usage: 1 + usage.skimage: 5 + usage.sklearn: 15 """ ... - def __rrshift__(self, _0: Union[int, numpy.ndarray, numpy.uint64], /): + @overload + def __setitem__( + self, + _0: Tuple[slice[None, int, None], slice[None, None, None]], + _1: numpy.ndarray, + /, + ): """ - usage.pandas: 1 - usage.sample-usage: 1 - usage.scipy: 1 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload - def __rshift__(self, _0: Union[int, numpy.ndarray], /): + def __setitem__( + self, + _0: Tuple[int, slice[None, None, None], slice[None, None, None]], + _1: numpy.ndarray, + /, + ): """ - usage.pandas: 5 + usage.skimage: 4 """ ... @overload - def __rshift__(self, _0: int, /): + def __setitem__( + self, + _0: Tuple[int, int, slice[None, None, None], slice[None, None, None]], + _1: numpy.ndarray, + /, + ): """ - usage.dask: 1 - usage.sample-usage: 1 - usage.scipy: 1 + usage.skimage: 1 """ ... - def __rshift__(self, _0: Union[int, numpy.ndarray], /): + @overload + def __setitem__( + self, + _0: Tuple[ + slice[None, int, None], slice[None, None, None], slice[None, None, None] + ], + _1: numpy.ndarray, + /, + ): """ - usage.dask: 1 - usage.pandas: 5 - usage.sample-usage: 1 - usage.scipy: 1 + usage.skimage: 1 """ ... @overload - def __rsub__(self, _0: numpy.ndarray, /): + def __setitem__( + self, + _0: Tuple[ + slice[int, int, int], slice[None, None, None], slice[None, None, None] + ], + _1: numpy.ndarray, + /, + ): """ - usage.matplotlib: 133 - usage.skimage: 183 - usage.xarray: 35 + usage.skimage: 1 """ ... @overload - def __rsub__(self, _0: int, /): + def __setitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[int, int, int] + ], + _1: numpy.ndarray, + /, + ): """ - usage.matplotlib: 25 - usage.sample-usage: 1 - usage.skimage: 20 - usage.xarray: 2 + usage.matplotlib: 1 + usage.skimage: 1 """ ... @overload - def __rsub__(self, _0: numpy.float64, /): + def __setitem__( + self, + _0: Tuple[slice[int, int, int], slice[None, None, None]], + _1: numpy.ndarray, + /, + ): """ - usage.matplotlib: 11 usage.skimage: 4 """ ... @overload - def __rsub__(self, _0: float, /): + def __setitem__( + self, + _0: Tuple[slice[None, None, None], slice[int, int, int]], + _1: numpy.ndarray, + /, + ): """ - usage.matplotlib: 19 - usage.skimage: 13 + usage.matplotlib: 1 + usage.skimage: 6 + usage.sklearn: 7 """ ... @overload - def __rsub__(self, _0: numpy.float32, /): + def __setitem__( + self, _0: Tuple[int, int, slice[None, None, None]], _1: numpy.ndarray, / + ): """ usage.skimage: 1 """ ... @overload - def __rsub__(self, _0: dask.array.core.Array, /): + def __setitem__(self, _0: slice[int, None, int], _1: int, /): + """ + usage.matplotlib: 5 + usage.skimage: 2 + usage.sklearn: 6 + """ + ... + + @overload + def __setitem__( + self, + _0: Tuple[slice[None, None, None], slice[None, None, None], Tuple[int, int]], + _1: int, + /, + ): """ usage.skimage: 1 """ ... @overload - def __rsub__(self, _0: xarray.coding.cftimeindex.CFTimeIndex, /): + def __setitem__(self, _0: Tuple[slice[int, int, int], int], _1: Tuple[int, int], /): """ - usage.xarray: 1 + usage.skimage: 3 """ ... @overload - def __rsub__(self, _0: cftime._cftime.DatetimeNoLeap, /): + def __setitem__(self, _0: int, _1: float, /): """ - usage.xarray: 1 + usage.matplotlib: 13 + usage.skimage: 3 + usage.sklearn: 40 + usage.xarray: 5 """ ... @overload - def __rsub__(self, _0: cftime._cftime.Datetime360Day, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray], _1: float, /): """ + usage.skimage: 1 + usage.sklearn: 8 usage.xarray: 1 """ ... @overload - def __rsub__(self, _0: cftime._cftime.DatetimeJulian, /): + def __setitem__(self, _0: numpy.ndarray, _1: numpy.float64, /): """ + usage.skimage: 1 + usage.sklearn: 11 usage.xarray: 1 """ ... @overload - def __rsub__(self, _0: cftime._cftime.DatetimeAllLeap, /): + def __setitem__( + self, + _0: Tuple[int, Tuple[int, int, int, int]], + _1: Tuple[int, int, int, int], + /, + ): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __rsub__(self, _0: cftime._cftime.DatetimeGregorian, /): + def __setitem__( + self, _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], _1: bool, / + ): """ - usage.xarray: 1 + usage.skimage: 2 """ ... @overload - def __rsub__(self, _0: cftime._cftime.DatetimeProlepticGregorian, /): + def __setitem__(self, _0: None, _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __rsub__(self, _0: datetime.timedelta, /): + def __setitem__( + self, _0: Tuple[int, int, slice[None, None, None]], _1: numpy.ndarray, / + ): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __rsub__(self, _0: xarray.core.dataarray.DataArray, /): + def __setitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[None, int, None] + ], + _1: bool, + /, + ): """ - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __rsub__(self, _0: xarray.core.variable.Variable, /): + def __setitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[int, None, int] + ], + _1: bool, + /, + ): """ - usage.xarray: 1 + usage.skimage: 2 """ ... @overload - def __rsub__(self, _0: xarray.core.variable.IndexVariable, /): + def __setitem__(self, _0: int, _1: numpy.int64, /): """ - usage.xarray: 1 + usage.matplotlib: 2 + usage.skimage: 4 + usage.sklearn: 17 """ ... @overload - def __rsub__(self, _0: object, /): + def __setitem__(self, _0: slice[int, None, int], _1: numpy.ndarray, /): """ - usage.pandas: 273 - usage.scipy: 2130 - usage.sklearn: 639 + usage.matplotlib: 9 + usage.skimage: 5 + usage.sklearn: 2 """ ... @overload - def __rsub__(self, _0: numpy.int64, /): + def __setitem__(self, _0: Tuple[List[int], List[int], List[int]], _1: int, /): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload - def __rsub__(self, _0: numpy.ma.core.MaskedArray, /): + def __setitem__(self, _0: Tuple[int, int, int], _1: bool, /): """ - usage.matplotlib: 2 + usage.skimage: 1 """ ... @overload - def __rsub__(self, _0: Union[numpy.ndarray, int], /): + def __setitem__(self, _0: Tuple[int, int, int, int], _1: int, /): """ - usage.dask: 20 + usage.skimage: 5 """ ... - def __rsub__(self, _0: object, /): + @overload + def __setitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[None, int, None], + ], + _1: bool, + /, + ): """ - usage.dask: 20 - usage.matplotlib: 192 - usage.pandas: 273 - usage.sample-usage: 1 - usage.scipy: 2130 - usage.skimage: 222 - usage.sklearn: 639 - usage.xarray: 48 + usage.skimage: 1 """ ... @overload - def __rtruediv__(self, _0: numpy.ndarray, /): + def __setitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[int, None, int], + ], + _1: bool, + /, + ): """ - usage.matplotlib: 37 - usage.skimage: 68 - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __rtruediv__(self, _0: int, /): + def __setitem__( + self, _0: Tuple[slice[None, int, None], slice[None, None, None]], _1: int, / + ): """ - usage.matplotlib: 7 - usage.skimage: 5 + usage.skimage: 1 + usage.sklearn: 1 """ ... @overload - def __rtruediv__(self, _0: float, /): + def __setitem__( + self, + _0: Tuple[slice[int, int, int], slice[None, int, None]], + _1: numpy.ndarray, + /, + ): """ - usage.matplotlib: 6 - usage.skimage: 8 + usage.skimage: 2 """ ... @overload - def __rtruediv__(self, _0: numpy.complex128, /): + def __setitem__( + self, + _0: Tuple[slice[int, int, int], slice[int, None, int]], + _1: numpy.ndarray, + /, + ): """ usage.skimage: 1 """ ... @overload - def __rtruediv__(self, _0: numpy.float64, /): + def __setitem__( + self, + _0: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], + _1: numpy.ndarray, + /, + ): """ - usage.skimage: 1 + usage.skimage: 6 """ ... @overload - def __rtruediv__(self, _0: object, /): + def __setitem__( + self, + _0: Tuple[slice[None, int, None], slice[None, int, None]], + _1: numpy.ndarray, + /, + ): """ - usage.pandas: 420 - usage.scipy: 1157 + usage.skimage: 8 + usage.sklearn: 1 """ ... @overload - def __rtruediv__(self, _0: List[numpy.float64], /): + def __setitem__( + self, _0: Tuple[slice[int, int, int], slice[int, int, int]], _1: float, / + ): """ usage.matplotlib: 1 + usage.skimage: 9 """ ... @overload - def __rtruediv__(self, _0: Union[numpy.ndarray, int], /): + def __setitem__(self, _0: Tuple[int, int], _1: numpy.ndarray, /): """ - usage.dask: 13 + usage.skimage: 1 + usage.sklearn: 4 """ ... @overload - def __rtruediv__( - self, _0: Union[numpy.ndarray, numpy.float64, float, int, numpy.memmap], / - ): + def __setitem__(self, _0: numpy.bool_, _1: float, /): """ - usage.sklearn: 291 + usage.matplotlib: 1 + usage.skimage: 1 """ ... - def __rtruediv__(self, _0: object, /): + @overload + def __setitem__(self, _0: int, _1: numpy.ndarray, /): """ - usage.dask: 13 - usage.matplotlib: 51 - usage.pandas: 420 - usage.scipy: 1157 - usage.skimage: 83 - usage.sklearn: 291 - usage.xarray: 1 + usage.matplotlib: 4 + usage.skimage: 31 + usage.sklearn: 101 """ ... @overload - def __rxor__(self, _0: Union[pandas.core.series.Series, numpy.ndarray], /): + def __setitem__(self, _0: Tuple[int, int], _1: numpy.int64, /): """ - usage.pandas: 8 + usage.skimage: 1 """ ... @overload - def __rxor__(self, _0: Union[bool, numpy.ndarray], /): + def __setitem__( + self, _0: Tuple[slice[int, int, int], slice[None, None, None]], _1: int, / + ): """ - usage.dask: 2 + usage.skimage: 2 """ ... - def __rxor__(self, _0: Union[numpy.ndarray, bool, pandas.core.series.Series], /): + @overload + def __setitem__( + self, _0: Tuple[slice[None, None, None], slice[int, int, int]], _1: int, / + ): """ - usage.dask: 2 - usage.pandas: 8 + usage.skimage: 4 """ ... @overload - def __setitem__(self, _0: numpy.ndarray, _1: numpy.ndarray, /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], slice[int, None, int]], _1: int, / + ): """ - usage.matplotlib: 45 - usage.skimage: 52 - usage.xarray: 4 + usage.skimage: 5 """ ... @overload - def __setitem__(self, _0: numpy.ndarray, _1: int, /): + def __setitem__(self, _0: Tuple[int, int, int], _1: numpy.int64, /): """ - usage.matplotlib: 20 - usage.skimage: 68 - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: numpy.ndarray, _1: float, /): + def __setitem__(self, _0: numpy.int64, _1: int, /): """ - usage.matplotlib: 9 - usage.skimage: 14 - usage.xarray: 6 + usage.skimage: 2 + usage.sklearn: 9 """ ... @overload - def __setitem__(self, _0: Tuple[numpy.ndarray, int], _1: numpy.ndarray, /): + def __setitem__(self, _0: List[numpy.int64], _1: numpy.int64, /): """ - usage.matplotlib: 3 - usage.skimage: 3 + usage.skimage: 2 """ ... @overload - def __setitem__(self, _0: Tuple[ellipsis, int], _1: numpy.ndarray, /): + def __setitem__(self, _0: slice[int, None, int], _1: float, /): """ - usage.matplotlib: 11 - usage.skimage: 49 + usage.skimage: 1 + usage.sklearn: 2 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, None, None], slice[None, None, None], int], - _1: numpy.ndarray, - /, - ): + def __setitem__(self, _0: slice[None, None, None], _1: int, /): """ - usage.matplotlib: 8 - usage.skimage: 7 + usage.matplotlib: 1 + usage.skimage: 3 + usage.sklearn: 4 """ ... @overload def __setitem__( - self, _0: Tuple[ellipsis, slice[None, int, None]], _1: numpy.ndarray, / + self, _0: Tuple[int, slice[None, None, None]], _1: Tuple[int, int], / ): """ - usage.matplotlib: 1 - usage.skimage: 4 + usage.skimage: 3 """ ... @overload - def __setitem__(self, _0: Tuple[ellipsis, int], _1: int, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, int], _1: numpy.float64, /): """ - usage.matplotlib: 1 - usage.skimage: 4 - usage.xarray: 1 + usage.skimage: 1 + usage.sklearn: 4 """ ... @overload - def __setitem__(self, _0: Tuple[ellipsis, int], _1: float, /): + def __setitem__(self, _0: int, _1: Tuple[int, int], /): """ - usage.skimage: 2 + usage.matplotlib: 4 + usage.skimage: 3 """ ... @overload - def __setitem__(self, _0: numpy.bool_, _1: numpy.ndarray, /): + def __setitem__(self, _0: Tuple[int, int, int], _1: float, /): """ usage.skimage: 2 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[ellipsis, int], _1: numpy.float64, /): + def __setitem__( + self, + _0: Tuple[ + slice[None, int, None], slice[None, None, None], slice[None, None, None] + ], + _1: bool, + /, + ): """ - usage.skimage: 7 + usage.skimage: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[slice[int, int, int], slice[int, int, int]], _1: int, / - ): + def __setitem__(self, _0: Tuple[int, int, int], _1: numpy.float64, /): """ - usage.matplotlib: 1 - usage.skimage: 69 + usage.skimage: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[numpy.ndarray, numpy.ndarray], _1: numpy.ndarray, / - ): + def __setitem__(self, _0: Tuple[int, slice[int, int, int]], _1: float, /): """ - usage.matplotlib: 1 - usage.skimage: 14 - usage.xarray: 3 + usage.skimage: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[numpy.ndarray, numpy.ndarray], _1: Tuple[int, int, int], / - ): + def __setitem__(self, _0: int, _1: bool, /): """ - usage.skimage: 1 + usage.matplotlib: 1 + usage.skimage: 6 + usage.sklearn: 4 """ ... @overload - def __setitem__( - self, _0: Tuple[slice[None, None, None], int], _1: numpy.ndarray, / - ): + def __setitem__(self, _0: numpy.int64, _1: bool, /): """ - usage.matplotlib: 31 - usage.skimage: 34 - usage.xarray: 6 + usage.skimage: 1 + usage.sklearn: 3 """ ... @overload - def __setitem__(self, _0: Tuple[int, int], _1: numpy.float64, /): + def __setitem__(self, _0: slice[int, None, int], _1: numpy.ndarray, /): """ - usage.matplotlib: 9 - usage.skimage: 10 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: int, _1: numpy.float64, /): + def __setitem__(self, _0: slice[None, None, None], _1: numpy.ndarray, /): """ - usage.matplotlib: 26 - usage.skimage: 14 + usage.skimage: 5 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray], _1: int, /): + def __setitem__(self, _0: List[int], _1: float, /): """ - usage.skimage: 125 - usage.xarray: 1 + usage.matplotlib: 1 + usage.skimage: 2 """ ... @overload def __setitem__( - self, _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], _1: int, / + self, _0: Tuple[slice[None, None, None], slice[None, None, None]], _1: int, / ): """ - usage.skimage: 5 + usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, slice[None, None, None]], _1: int, /): + def __setitem__(self, _0: slice[None, int, None], _1: int, /): """ - usage.matplotlib: 2 - usage.skimage: 32 + usage.matplotlib: 1 + usage.skimage: 1 + usage.sklearn: 20 usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[None, None, None], int], _1: int, /): + def __setitem__(self, _0: Tuple[numpy.ndarray], _1: numpy.ndarray, /): """ - usage.skimage: 12 - usage.xarray: 1 + usage.skimage: 2 """ ... @overload def __setitem__( - self, _0: Tuple[slice[int, None, int], slice[None, None, None]], _1: int, / + self, _0: Tuple[slice[int, int, int], slice[int, int, int]], _1: bool, / ): """ - usage.skimage: 1 + usage.matplotlib: 1 + usage.skimage: 10 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray, int], _1: int, /): + def __setitem__( + self, + _0: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], + _1: int, + /, + ): """ - usage.skimage: 2 + usage.skimage: 12 """ ... @overload - def __setitem__(self, _0: slice[None, None, None], _1: numpy.ndarray, /): + def __setitem__(self, _0: Tuple[List[int], List[int]], _1: bool, /): """ - usage.matplotlib: 2 - usage.skimage: 8 - usage.xarray: 2 + usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: List[int], _1: int, /): + def __setitem__(self, _0: Tuple[int, int], _1: bool, /): """ - usage.skimage: 2 + usage.matplotlib: 5 + usage.skimage: 4 """ ... @overload - def __setitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray], _1: bool, /): + def __setitem__(self, _0: Tuple[slice[int, int, int]], _1: numpy.ndarray, /): """ - usage.skimage: 5 + usage.skimage: 2 """ ... @overload - def __setitem__( - self, - _0: Tuple[ - slice[numpy.int64, numpy.int64, numpy.int64], - slice[numpy.int64, numpy.int64, numpy.int64], - ], - _1: int, - /, - ): + def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: numpy.float16, /): """ usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: int, _1: int, /): + def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: int, /): """ - usage.matplotlib: 8 - usage.sample-usage: 1 - usage.skimage: 50 - usage.xarray: 2 + usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, int], _1: float, /): + def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: numpy.uint8, /): """ - usage.matplotlib: 15 - usage.skimage: 30 - usage.xarray: 7 + usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[None, int, None]], _1: bool, /): + def __setitem__( + self, + _0: Tuple[slice[None, None, None], Tuple[int, int], ellipsis], + _1: numpy.uint8, + /, + ): """ usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[int, None, int]], _1: bool, /): + def __setitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], Tuple[int, int], ellipsis + ], + _1: numpy.uint8, + /, + ): """ usage.skimage: 1 """ @@ -55055,7 +68310,7 @@ def __setitem__(self, _0: Tuple[slice[int, None, int]], _1: bool, /): @overload def __setitem__( - self, _0: Tuple[slice[None, None, None], slice[None, int, None]], _1: bool, / + self, _0: Tuple[slice[None, None, None], Tuple[int, int], ellipsis], _1: int, / ): """ usage.skimage: 1 @@ -55064,7 +68319,12 @@ def __setitem__( @overload def __setitem__( - self, _0: Tuple[slice[None, None, None], slice[int, None, int]], _1: bool, / + self, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], Tuple[int, int], ellipsis + ], + _1: int, + /, ): """ usage.skimage: 1 @@ -55072,174 +68332,273 @@ def __setitem__( ... @overload - def __setitem__(self, _0: Tuple[int, slice[int, int, int]], _1: int, /): + def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: numpy.float32, /): """ - usage.skimage: 9 + usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: numpy.ndarray, _1: bool, /): + def __setitem__( + self, + _0: Tuple[slice[None, None, None], Tuple[int, int], ellipsis], + _1: numpy.float32, + /, + ): """ - usage.skimage: 7 + usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[None, int, None], int], _1: numpy.ndarray, /): + def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: numpy.int64, /): """ - usage.matplotlib: 7 - usage.skimage: 8 + usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[None, int, None], int], _1: int, /): + def __setitem__( + self, + _0: Tuple[slice[None, None, None], Tuple[int, int], ellipsis], + _1: numpy.int64, + /, + ): """ usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[int, None, int], int], _1: numpy.ndarray, /): + def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: numpy.float64, /): """ - usage.matplotlib: 10 - usage.skimage: 6 + usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[int, None, int], int], _1: int, /): + def __setitem__( + self, + _0: Tuple[slice[None, None, None], Tuple[int, int], ellipsis], + _1: numpy.float64, + /, + ): """ usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, int], _1: int, /): + def __setitem__( + self, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], Tuple[int, int], ellipsis + ], + _1: numpy.float64, + /, + ): """ - usage.matplotlib: 9 - usage.skimage: 151 - usage.xarray: 1 + usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: int, _1: Tuple[int, int, int], /): + def __setitem__( + self, + _0: Tuple[ + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + ], + _1: int, + /, + ): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload def __setitem__( self, - _0: Tuple[slice[int, int, int], slice[int, int, int]], + _0: Tuple[ + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + ], _1: numpy.ndarray, /, ): """ - usage.skimage: 22 + usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: slice[None, None, None], _1: int, /): + def __setitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + Tuple[int, int], + ellipsis, + ], + _1: numpy.float64, + /, + ): """ usage.skimage: 1 - usage.xarray: 2 """ ... @overload - def __setitem__(self, _0: Tuple[int, int, int], _1: int, /): + def __setitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + Tuple[int, int], + ellipsis, + ], + _1: int, + /, + ): """ - usage.skimage: 30 + usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[ellipsis, int, int], _1: numpy.ndarray, /): + def __setitem__( + self, + _0: Tuple[ + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + ], + _1: int, + /, + ): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, int, None], slice[None, int, None]], _1: float, / + self, + _0: Tuple[ + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + ], + _1: numpy.ndarray, + /, ): """ - usage.matplotlib: 1 - usage.skimage: 6 + usage.skimage: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, int, None], slice[None, int, None]], _1: int, / + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + Tuple[int, int], + ellipsis, + ], + _1: numpy.float64, + /, ): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[int, None, int], slice[int, None, int]], _1: int, / + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + Tuple[int, int], + ellipsis, + ], + _1: int, + /, ): """ - usage.skimage: 3 + usage.skimage: 1 """ ... @overload def __setitem__( - self, _0: slice[None, None, None], _1: Tuple[numpy.float64, numpy.float64], / + self, _0: Tuple[int, slice[int, int, int], slice[int, int, int]], _1: int, / ): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.skimage: 6 """ ... @overload def __setitem__( self, - _0: Tuple[int, slice[None, None, None]], - _1: Tuple[numpy.float64, numpy.float64], + _0: Tuple[ + slice[numpy.int64, numpy.int64, numpy.int64], + slice[numpy.int64, numpy.int64, numpy.int64], + ], + _1: bool, /, ): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload def __setitem__( - self, _0: Tuple[int, slice[None, None, None]], _1: Tuple[float, float], / + self, + _0: Tuple[ + int, + slice[numpy.int64, numpy.int64, numpy.int64], + slice[numpy.int64, numpy.int64, numpy.int64], + ], + _1: numpy.ndarray, + /, ): """ - usage.skimage: 2 + usage.skimage: 4 """ ... @overload def __setitem__( - self, - _0: Tuple[slice[None, None, None], slice[None, int, None]], - _1: numpy.ndarray, - /, + self, _0: Tuple[slice[numpy.int64, numpy.int64, numpy.int64]], _1: bool, / ): """ - usage.skimage: 5 + usage.skimage: 1 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, int, None], slice[None, None, None]], + _0: Tuple[int, slice[numpy.int64, numpy.int64, numpy.int64]], _1: numpy.ndarray, /, ): @@ -55248,50 +68607,59 @@ def __setitem__( """ ... + @overload + def __setitem__(self, _0: Tuple[slice[None, None, None], int], _1: bool, /): + """ + usage.skimage: 6 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[int, slice[int, int, int]], _1: bool, /): + """ + usage.skimage: 1 + """ + ... + @overload def __setitem__( - self, - _0: Tuple[int, slice[None, None, None], slice[None, None, None]], - _1: numpy.ndarray, - /, + self, _0: Tuple[slice[None, None, None], slice[int, int, int]], _1: bool, / ): """ - usage.skimage: 4 + usage.skimage: 1 """ ... @overload def __setitem__( self, - _0: Tuple[int, int, slice[None, None, None], slice[None, None, None]], + _0: Tuple[slice[int, None, int], slice[int, None, int]], _1: numpy.ndarray, /, ): """ usage.skimage: 1 + usage.sklearn: 1 """ ... @overload def __setitem__( self, - _0: Tuple[ - slice[None, int, None], slice[None, None, None], slice[None, None, None] - ], + _0: Tuple[slice[int, None, int], slice[None, None, None]], _1: numpy.ndarray, /, ): """ - usage.skimage: 1 + usage.matplotlib: 1 + usage.skimage: 4 """ ... @overload def __setitem__( self, - _0: Tuple[ - slice[int, int, int], slice[None, None, None], slice[None, None, None] - ], + _0: Tuple[slice[int, None, int], slice[None, int, None]], _1: numpy.ndarray, /, ): @@ -55303,46 +68671,47 @@ def __setitem__( @overload def __setitem__( self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[int, int, int] - ], + _0: Tuple[slice[None, None, None], slice[int, None, int]], _1: numpy.ndarray, /, ): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.skimage: 4 + usage.sklearn: 8 """ ... @overload def __setitem__( self, - _0: Tuple[slice[int, int, int], slice[None, None, None]], + _0: Tuple[slice[None, None, None], slice[None, None, None]], _1: numpy.ndarray, /, ): """ - usage.skimage: 4 + usage.skimage: 1 + usage.sklearn: 4 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, None, None], slice[int, int, int]], + _0: Tuple[slice[None, int, None], slice[int, None, int]], _1: numpy.ndarray, /, ): """ - usage.matplotlib: 1 - usage.skimage: 6 + usage.skimage: 1 """ ... @overload def __setitem__( - self, _0: Tuple[int, int, slice[None, None, None]], _1: numpy.ndarray, / + self, + _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray], + _1: int, + /, ): """ usage.skimage: 1 @@ -55350,72 +68719,62 @@ def __setitem__( ... @overload - def __setitem__(self, _0: slice[int, None, int], _1: int, /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], numpy.ndarray], _1: numpy.ndarray, / + ): """ - usage.matplotlib: 5 - usage.skimage: 2 + usage.skimage: 4 + usage.sklearn: 22 + usage.xarray: 2 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, None, None], slice[None, None, None], Tuple[int, int]], - _1: int, + _0: Tuple[int, slice[int, int, int], slice[None, None, None]], + _1: numpy.ndarray, /, ): - """ - usage.skimage: 1 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[slice[int, int, int], int], _1: Tuple[int, int], /): """ usage.skimage: 3 """ ... @overload - def __setitem__(self, _0: int, _1: float, /): + def __setitem__( + self, + _0: Tuple[int, slice[None, None, None], slice[int, int, int]], + _1: numpy.ndarray, + /, + ): """ - usage.matplotlib: 13 usage.skimage: 3 - usage.xarray: 5 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray], _1: float, /): - """ - usage.skimage: 1 - usage.xarray: 1 - """ - ... - - @overload - def __setitem__(self, _0: numpy.ndarray, _1: numpy.float64, /): - """ - usage.skimage: 1 - usage.xarray: 1 """ ... @overload def __setitem__( self, - _0: Tuple[int, Tuple[int, int, int, int]], - _1: Tuple[int, int, int, int], + _0: Tuple[ + int, slice[int, int, int], slice[None, None, None], slice[None, None, None] + ], + _1: numpy.ndarray, /, ): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload def __setitem__( - self, _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], _1: bool, / + self, + _0: Tuple[ + int, slice[None, None, None], slice[int, int, int], slice[None, None, None] + ], + _1: numpy.ndarray, + /, ): """ usage.skimage: 2 @@ -55423,18 +68782,30 @@ def __setitem__( ... @overload - def __setitem__(self, _0: None, _1: numpy.ndarray, /): + def __setitem__( + self, + _0: Tuple[ + int, slice[None, None, None], slice[None, None, None], slice[int, int, int] + ], + _1: numpy.ndarray, + /, + ): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload def __setitem__( - self, _0: Tuple[int, int, slice[None, None, None]], _1: numpy.ndarray, / + self, + _0: Tuple[ + slice[int, None, int], slice[None, None, None], slice[None, None, None] + ], + _1: numpy.ndarray, + /, ): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @@ -55442,13 +68813,13 @@ def __setitem__( def __setitem__( self, _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, int, None] + slice[None, None, None], slice[int, None, int], slice[None, None, None] ], - _1: bool, + _1: numpy.ndarray, /, ): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @@ -55458,7 +68829,7 @@ def __setitem__( _0: Tuple[ slice[None, None, None], slice[None, None, None], slice[int, None, int] ], - _1: bool, + _1: numpy.ndarray, /, ): """ @@ -55467,39 +68838,33 @@ def __setitem__( ... @overload - def __setitem__(self, _0: int, _1: numpy.int64, /): - """ - usage.matplotlib: 2 - usage.skimage: 4 - """ - ... - - @overload - def __setitem__(self, _0: slice[int, None, int], _1: numpy.ndarray, /): - """ - usage.matplotlib: 9 - usage.skimage: 5 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[List[int], List[int], List[int]], _1: int, /): + def __setitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + int, + slice[None, None, None], + ], + _1: numpy.ndarray, + /, + ): """ usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, int, int], _1: bool, /): + def __setitem__(self, _0: Tuple[int, slice[int, int, int]], _1: numpy.ndarray, /): """ usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, int, int, int], _1: int, /): + def __setitem__(self, _0: Tuple[slice[int, None, int]], _1: numpy.ndarray, /): """ - usage.skimage: 5 + usage.skimage: 1 """ ... @@ -55507,12 +68872,13 @@ def __setitem__(self, _0: Tuple[int, int, int, int], _1: int, /): def __setitem__( self, _0: Tuple[ + int, + slice[int, int, int], slice[None, None, None], slice[None, None, None], slice[None, None, None], - slice[None, int, None], ], - _1: bool, + _1: numpy.ndarray, /, ): """ @@ -55524,12 +68890,13 @@ def __setitem__( def __setitem__( self, _0: Tuple[ + int, slice[None, None, None], + slice[int, int, int], slice[None, None, None], slice[None, None, None], - slice[int, None, int], ], - _1: bool, + _1: numpy.ndarray, /, ): """ @@ -55539,7 +68906,16 @@ def __setitem__( @overload def __setitem__( - self, _0: Tuple[slice[None, int, None], slice[None, None, None]], _1: int, / + self, + _0: Tuple[ + int, + slice[None, None, None], + slice[None, None, None], + slice[int, int, int], + slice[None, None, None], + ], + _1: numpy.ndarray, + /, ): """ usage.skimage: 1 @@ -55549,19 +68925,30 @@ def __setitem__( @overload def __setitem__( self, - _0: Tuple[slice[int, int, int], slice[None, int, None]], + _0: Tuple[ + int, + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[int, int, int], + ], _1: numpy.ndarray, /, ): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload def __setitem__( self, - _0: Tuple[slice[int, int, int], slice[int, None, int]], + _0: Tuple[ + slice[int, None, int], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ], _1: numpy.ndarray, /, ): @@ -55573,62 +68960,32 @@ def __setitem__( @overload def __setitem__( self, - _0: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], + _0: Tuple[ + slice[None, None, None], + slice[int, None, int], + slice[None, None, None], + slice[None, None, None], + ], _1: numpy.ndarray, /, ): """ - usage.skimage: 6 + usage.skimage: 1 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, int, None], slice[None, int, None]], + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[int, None, int], + slice[None, None, None], + ], _1: numpy.ndarray, /, ): - """ - usage.skimage: 8 - """ - ... - - @overload - def __setitem__( - self, _0: Tuple[slice[int, int, int], slice[int, int, int]], _1: float, / - ): - """ - usage.matplotlib: 1 - usage.skimage: 9 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[int, int], _1: numpy.ndarray, /): - """ - usage.skimage: 1 - """ - ... - - @overload - def __setitem__(self, _0: numpy.bool_, _1: float, /): - """ - usage.matplotlib: 1 - usage.skimage: 1 - """ - ... - - @overload - def __setitem__(self, _0: int, _1: numpy.ndarray, /): - """ - usage.matplotlib: 4 - usage.skimage: 31 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[int, int], _1: numpy.int64, /): """ usage.skimage: 1 """ @@ -55636,105 +68993,59 @@ def __setitem__(self, _0: Tuple[int, int], _1: numpy.int64, /): @overload def __setitem__( - self, _0: Tuple[slice[int, int, int], slice[None, None, None]], _1: int, / - ): - """ - usage.skimage: 2 - """ - ... - - @overload - def __setitem__( - self, _0: Tuple[slice[None, None, None], slice[int, int, int]], _1: int, / + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[int, None, int], + ], + _1: numpy.ndarray, + /, ): """ - usage.skimage: 4 + usage.skimage: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, None, None], slice[int, None, int]], _1: int, / + self, + _0: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], + _1: float, + /, ): """ - usage.skimage: 5 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[int, int, int], _1: numpy.int64, /): - """ - usage.skimage: 1 - """ - ... - - @overload - def __setitem__(self, _0: numpy.int64, _1: int, /): - """ - usage.skimage: 2 - """ - ... - - @overload - def __setitem__(self, _0: List[numpy.int64], _1: numpy.int64, /): - """ - usage.skimage: 2 - """ - ... - - @overload - def __setitem__(self, _0: slice[int, None, int], _1: float, /): - """ - usage.skimage: 1 - """ - ... - - @overload - def __setitem__(self, _0: slice[None, None, None], _1: int, /): - """ - usage.matplotlib: 1 usage.skimage: 3 """ ... @overload def __setitem__( - self, _0: Tuple[int, slice[None, None, None]], _1: Tuple[int, int], / + self, _0: Tuple[int, slice[int, int, int], slice[int, int, int]], _1: float, / ): """ - usage.skimage: 3 + usage.skimage: 2 """ ... @overload - def __setitem__(self, _0: Tuple[numpy.ndarray, int], _1: numpy.float64, /): + def __setitem__(self, _0: Tuple[slice[int, int, int]], _1: float, /): """ usage.skimage: 1 """ ... - @overload - def __setitem__(self, _0: int, _1: Tuple[int, int], /): - """ - usage.matplotlib: 4 - usage.skimage: 3 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[int, int, int], _1: float, /): - """ - usage.skimage: 2 - """ - ... - @overload def __setitem__( self, _0: Tuple[ - slice[None, int, None], slice[None, None, None], slice[None, None, None] + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], + slice[int, int, int], ], - _1: bool, + _1: float, /, ): """ @@ -55743,77 +69054,61 @@ def __setitem__( ... @overload - def __setitem__(self, _0: Tuple[int, int, int], _1: numpy.float64, /): + def __setitem__(self, _0: Tuple[int, slice[int, None, int]], _1: int, /): """ - usage.skimage: 1 + usage.skimage: 4 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, slice[int, int, int]], _1: float, /): + def __setitem__(self, _0: Tuple[numpy.int64, numpy.int64], _1: int, /): """ usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: int, _1: bool, /): - """ - usage.matplotlib: 1 - usage.skimage: 6 - """ - ... - - @overload - def __setitem__(self, _0: numpy.int64, _1: bool, /): + def __setitem__(self, _0: Tuple[numpy.int64, numpy.int64], _1: numpy.float64, /): """ usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: slice[int, None, int], _1: numpy.ndarray, /): - """ - usage.skimage: 2 - """ - ... - - @overload - def __setitem__(self, _0: slice[None, None, None], _1: numpy.ndarray, /): + def __setitem__( + self, _0: Tuple[int, slice[int, None, int], slice[None, None, None]], _1: int, / + ): """ - usage.skimage: 5 + usage.skimage: 3 """ ... @overload - def __setitem__(self, _0: List[int], _1: float, /): + def __setitem__(self, _0: Tuple[numpy.int64, numpy.int64, numpy.int64], _1: int, /): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.skimage: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None]], _1: int, / + self, _0: Tuple[numpy.int64, numpy.int64, numpy.int64], _1: numpy.float64, / ): """ - usage.skimage: 2 - usage.xarray: 1 - """ - ... - - @overload - def __setitem__(self, _0: slice[None, int, None], _1: int, /): - """ - usage.matplotlib: 1 usage.skimage: 1 - usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[numpy.ndarray], _1: numpy.ndarray, /): + def __setitem__( + self, + _0: Tuple[ + slice[numpy.int64, None, numpy.int64], slice[numpy.int64, None, numpy.int64] + ], + _1: numpy.ndarray, + /, + ): """ usage.skimage: 2 """ @@ -55821,11 +69116,17 @@ def __setitem__(self, _0: Tuple[numpy.ndarray], _1: numpy.ndarray, /): @overload def __setitem__( - self, _0: Tuple[slice[int, int, int], slice[int, int, int]], _1: bool, / + self, + _0: Tuple[ + slice[numpy.int64, None, numpy.int64], + slice[numpy.int64, None, numpy.int64], + slice[numpy.int64, None, numpy.int64], + ], + _1: numpy.ndarray, + /, ): """ - usage.matplotlib: 1 - usage.skimage: 10 + usage.skimage: 2 """ ... @@ -55833,66 +69134,43 @@ def __setitem__( def __setitem__( self, _0: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], - _1: int, + _1: bool, /, ): - """ - usage.skimage: 12 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[List[int], List[int]], _1: bool, /): """ usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, int], _1: bool, /): + def __setitem__(self, _0: Tuple[int, slice[None, None, None]], _1: bool, /): """ - usage.matplotlib: 5 - usage.skimage: 4 + usage.skimage: 3 """ ... @overload - def __setitem__(self, _0: Tuple[slice[int, int, int]], _1: numpy.ndarray, /): + def __setitem__( + self, _0: Tuple[int, slice[None, None, None]], _1: numpy.ndarray, / + ): """ + usage.matplotlib: 7 usage.skimage: 2 + usage.sklearn: 18 """ ... @overload - def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: numpy.float16, /): - """ - usage.skimage: 1 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: int, /): - """ - usage.skimage: 1 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: numpy.uint8, /): + def __setitem__(self, _0: Tuple[int, slice[None, int, None]], _1: List[int], /): """ - usage.skimage: 1 + usage.skimage: 3 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, None, None], Tuple[int, int], ellipsis], - _1: numpy.uint8, - /, - ): + def __setitem__(self, _0: Tuple[int, slice[int, None, int]], _1: List[int], /): """ - usage.skimage: 1 + usage.skimage: 3 """ ... @@ -55900,9 +69178,9 @@ def __setitem__( def __setitem__( self, _0: Tuple[ - slice[None, None, None], slice[None, None, None], Tuple[int, int], ellipsis + slice[None, None, None], slice[None, None, None], slice[None, None, None] ], - _1: numpy.uint8, + _1: numpy.ndarray, /, ): """ @@ -55911,21 +69189,17 @@ def __setitem__( ... @overload - def __setitem__( - self, _0: Tuple[slice[None, None, None], Tuple[int, int], ellipsis], _1: int, / - ): + def __setitem__(self, _0: numpy.ndarray, _1: Tuple[int, int, int], /): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload def __setitem__( self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], Tuple[int, int], ellipsis - ], - _1: int, + _0: Tuple[slice[None, None, None], slice[None, None, None]], + _1: numpy.ndarray, /, ): """ @@ -55933,19 +69207,9 @@ def __setitem__( """ ... - @overload - def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: numpy.float32, /): - """ - usage.skimage: 1 - """ - ... - @overload def __setitem__( - self, - _0: Tuple[slice[None, None, None], Tuple[int, int], ellipsis], - _1: numpy.float32, - /, + self, _0: Tuple[slice[None, None, None], slice[None, None, None]], _1: bool, / ): """ usage.skimage: 1 @@ -55953,7 +69217,9 @@ def __setitem__( ... @overload - def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: numpy.int64, /): + def __setitem__( + self, _0: Tuple[slice[None, int, None], slice[None, int, None]], _1: bool, / + ): """ usage.skimage: 1 """ @@ -55961,10 +69227,7 @@ def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: numpy.int64, /): @overload def __setitem__( - self, - _0: Tuple[slice[None, None, None], Tuple[int, int], ellipsis], - _1: numpy.int64, - /, + self, _0: Tuple[slice[int, None, int], slice[None, int, None]], _1: bool, / ): """ usage.skimage: 1 @@ -55972,21 +69235,19 @@ def __setitem__( ... @overload - def __setitem__(self, _0: Tuple[Tuple[int, int], ellipsis], _1: numpy.float64, /): + def __setitem__( + self, _0: Tuple[slice[None, int, None], slice[int, None, int]], _1: bool, / + ): """ usage.skimage: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, None, None], Tuple[int, int], ellipsis], - _1: numpy.float64, - /, - ): + def __setitem__(self, _0: Tuple[slice[int, int, int], int], _1: int, /): """ - usage.skimage: 1 + usage.skimage: 2 + usage.sklearn: 1 """ ... @@ -55994,9 +69255,9 @@ def __setitem__( def __setitem__( self, _0: Tuple[ - slice[None, None, None], slice[None, None, None], Tuple[int, int], ellipsis + slice[None, int, None], slice[None, int, None], slice[None, int, None] ], - _1: numpy.float64, + _1: bool, /, ): """ @@ -56008,12 +69269,9 @@ def __setitem__( def __setitem__( self, _0: Tuple[ - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], + slice[int, None, int], slice[None, int, None], slice[None, int, None] ], - _1: int, + _1: bool, /, ): """ @@ -56025,12 +69283,9 @@ def __setitem__( def __setitem__( self, _0: Tuple[ - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], + slice[None, int, None], slice[int, None, int], slice[None, int, None] ], - _1: numpy.ndarray, + _1: bool, /, ): """ @@ -56042,13 +69297,9 @@ def __setitem__( def __setitem__( self, _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - Tuple[int, int], - ellipsis, + slice[None, int, None], slice[None, int, None], slice[int, None, int] ], - _1: numpy.float64, + _1: bool, /, ): """ @@ -56058,99 +69309,52 @@ def __setitem__( @overload def __setitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - Tuple[int, int], - ellipsis, - ], - _1: int, - /, + self, _0: Tuple[slice[None, int, None], slice[int, None, int]], _1: float, / ): """ - usage.skimage: 1 + usage.skimage: 3 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - ], - _1: int, - /, + self, _0: Tuple[slice[int, None, int], slice[None, int, None]], _1: float, / ): """ - usage.skimage: 1 + usage.skimage: 7 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - ], - _1: numpy.ndarray, - /, + self, _0: Tuple[slice[int, None, int], slice[int, None, int]], _1: float, / ): """ - usage.skimage: 1 + usage.skimage: 7 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - Tuple[int, int], - ellipsis, - ], - _1: numpy.float64, - /, + self, _0: Tuple[slice[None, int, None], slice[None, int, None], int], _1: int, / ): """ - usage.skimage: 1 + usage.skimage: 6 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - Tuple[int, int], - ellipsis, - ], - _1: int, - /, + self, _0: Tuple[slice[int, None, int], slice[None, int, None], int], _1: int, / ): """ - usage.skimage: 1 + usage.skimage: 6 """ ... @overload def __setitem__( - self, _0: Tuple[int, slice[int, int, int], slice[int, int, int]], _1: int, / + self, _0: Tuple[slice[int, None, int], slice[int, None, int], int], _1: int, / ): """ usage.skimage: 6 @@ -56161,10 +69365,11 @@ def __setitem__( def __setitem__( self, _0: Tuple[ + int, slice[numpy.int64, numpy.int64, numpy.int64], slice[numpy.int64, numpy.int64, numpy.int64], ], - _1: bool, + _1: int, /, ): """ @@ -56175,33 +69380,32 @@ def __setitem__( @overload def __setitem__( self, - _0: Tuple[ - int, - slice[numpy.int64, numpy.int64, numpy.int64], - slice[numpy.int64, numpy.int64, numpy.int64], - ], + _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], _1: numpy.ndarray, /, ): """ - usage.skimage: 4 + usage.skimage: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[numpy.int64, numpy.int64, numpy.int64]], _1: bool, / + self, + _0: Tuple[slice[None, int, None], slice[None, int, None], slice[int, int, int]], + _1: int, + /, ): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload def __setitem__( self, - _0: Tuple[int, slice[numpy.int64, numpy.int64, numpy.int64]], - _1: numpy.ndarray, + _0: Tuple[slice[None, int, None], slice[int, None, int], slice[int, int, int]], + _1: int, /, ): """ @@ -56210,144 +69414,158 @@ def __setitem__( ... @overload - def __setitem__(self, _0: Tuple[slice[None, None, None], int], _1: bool, /): - """ - usage.skimage: 6 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[int, slice[int, int, int]], _1: bool, /): + def __setitem__( + self, + _0: Tuple[slice[int, None, int], slice[None, int, None], slice[int, int, int]], + _1: int, + /, + ): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, None, None], slice[int, int, int]], _1: bool, / + self, + _0: Tuple[slice[int, None, int], slice[int, None, int], slice[int, int, int]], + _1: int, + /, ): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload def __setitem__( self, - _0: Tuple[slice[int, None, int], slice[int, None, int]], - _1: numpy.ndarray, + _0: Tuple[ + slice[None, int, None], slice[None, int, None], slice[None, int, None] + ], + _1: Tuple[int, int, int], /, ): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload def __setitem__( self, - _0: Tuple[slice[int, None, int], slice[None, None, None]], - _1: numpy.ndarray, + _0: Tuple[ + slice[None, int, None], slice[None, int, None], slice[int, None, int] + ], + _1: Tuple[int, int, int], /, ): """ - usage.matplotlib: 1 - usage.skimage: 4 + usage.skimage: 2 """ ... @overload def __setitem__( self, - _0: Tuple[slice[int, None, int], slice[None, int, None]], - _1: numpy.ndarray, + _0: Tuple[ + slice[None, int, None], slice[int, None, int], slice[None, int, None] + ], + _1: Tuple[int, int, int], /, ): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, None, None], slice[int, None, int]], - _1: numpy.ndarray, + _0: Tuple[slice[None, int, None], slice[int, None, int], slice[int, None, int]], + _1: Tuple[int, int, int], /, ): """ - usage.skimage: 4 + usage.skimage: 2 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, None, None], slice[None, None, None]], - _1: numpy.ndarray, + _0: Tuple[ + slice[int, None, int], slice[None, int, None], slice[None, int, None] + ], + _1: Tuple[int, int, int], /, ): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, int, None], slice[int, None, int]], - _1: numpy.ndarray, + _0: Tuple[slice[int, None, int], slice[None, int, None], slice[int, None, int]], + _1: Tuple[int, int, int], /, ): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload def __setitem__( self, - _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray], - _1: int, + _0: Tuple[slice[int, None, int], slice[int, None, int], slice[None, int, None]], + _1: Tuple[int, int, int], /, ): """ - usage.skimage: 1 + usage.skimage: 2 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, None, None], numpy.ndarray], _1: numpy.ndarray, / + self, + _0: Tuple[slice[int, None, int], slice[int, None, int], slice[int, None, int]], + _1: Tuple[int, int, int], + /, ): """ - usage.skimage: 4 - usage.xarray: 2 + usage.skimage: 2 """ ... @overload def __setitem__( self, - _0: Tuple[int, slice[int, int, int], slice[None, None, None]], - _1: numpy.ndarray, + _0: Tuple[ + slice[None, int, None], slice[None, int, None], slice[None, int, None] + ], + _1: numpy.float64, /, ): """ - usage.skimage: 3 + usage.skimage: 2 """ ... @overload def __setitem__( self, - _0: Tuple[int, slice[None, None, None], slice[int, int, int]], - _1: numpy.ndarray, + _0: Tuple[ + slice[None, int, None], slice[None, int, None], slice[int, None, int] + ], + _1: numpy.float64, /, ): """ - usage.skimage: 3 + usage.skimage: 2 """ ... @@ -56355,9 +69573,9 @@ def __setitem__( def __setitem__( self, _0: Tuple[ - int, slice[int, int, int], slice[None, None, None], slice[None, None, None] + slice[None, int, None], slice[int, None, int], slice[None, int, None] ], - _1: numpy.ndarray, + _1: numpy.float64, /, ): """ @@ -56368,10 +69586,8 @@ def __setitem__( @overload def __setitem__( self, - _0: Tuple[ - int, slice[None, None, None], slice[int, int, int], slice[None, None, None] - ], - _1: numpy.ndarray, + _0: Tuple[slice[None, int, None], slice[int, None, int], slice[int, None, int]], + _1: numpy.float64, /, ): """ @@ -56383,9 +69599,9 @@ def __setitem__( def __setitem__( self, _0: Tuple[ - int, slice[None, None, None], slice[None, None, None], slice[int, int, int] + slice[int, None, int], slice[None, int, None], slice[None, int, None] ], - _1: numpy.ndarray, + _1: numpy.float64, /, ): """ @@ -56396,10 +69612,8 @@ def __setitem__( @overload def __setitem__( self, - _0: Tuple[ - slice[int, None, int], slice[None, None, None], slice[None, None, None] - ], - _1: numpy.ndarray, + _0: Tuple[slice[int, None, int], slice[None, int, None], slice[int, None, int]], + _1: numpy.float64, /, ): """ @@ -56410,10 +69624,8 @@ def __setitem__( @overload def __setitem__( self, - _0: Tuple[ - slice[None, None, None], slice[int, None, int], slice[None, None, None] - ], - _1: numpy.ndarray, + _0: Tuple[slice[int, None, int], slice[int, None, int], slice[None, int, None]], + _1: numpy.float64, /, ): """ @@ -56424,10 +69636,8 @@ def __setitem__( @overload def __setitem__( self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[int, None, int] - ], - _1: numpy.ndarray, + _0: Tuple[slice[int, None, int], slice[int, None, int], slice[int, None, int]], + _1: numpy.float64, /, ): """ @@ -56439,10 +69649,7 @@ def __setitem__( def __setitem__( self, _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - int, - slice[None, None, None], + slice[numpy.int64, None, numpy.int64], slice[numpy.int64, None, numpy.int64] ], _1: numpy.ndarray, /, @@ -56453,101 +69660,104 @@ def __setitem__( ... @overload - def __setitem__(self, _0: Tuple[int, slice[int, int, int]], _1: numpy.ndarray, /): + def __setitem__( + self, _0: Tuple[slice[int, None, int], numpy.int64], _1: numpy.ndarray, / + ): """ usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[int, None, int]], _1: numpy.ndarray, /): + def __setitem__( + self, _0: Tuple[numpy.ndarray, slice[None, None, None]], _1: int, / + ): """ - usage.skimage: 1 + usage.matplotlib: 1 + usage.skimage: 2 + usage.sklearn: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[ - int, - slice[int, int, int], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ], - _1: numpy.ndarray, - /, + self, _0: Tuple[numpy.ndarray, slice[None, None, None]], _1: numpy.ndarray, / ): """ - usage.skimage: 1 + usage.skimage: 2 + usage.sklearn: 7 + usage.xarray: 3 """ ... @overload - def __setitem__( - self, - _0: Tuple[ - int, - slice[None, None, None], - slice[int, int, int], - slice[None, None, None], - slice[None, None, None], - ], - _1: numpy.ndarray, - /, - ): + def __setitem__(self, _0: Tuple[int, numpy.ndarray], _1: numpy.ndarray, /): """ usage.skimage: 1 + usage.sklearn: 4 """ ... @overload - def __setitem__( - self, - _0: Tuple[ - int, - slice[None, None, None], - slice[None, None, None], - slice[int, int, int], - slice[None, None, None], - ], - _1: numpy.ndarray, - /, - ): + def __setitem__(self, _0: Tuple[slice[None, None, None], int], _1: float, /): """ + usage.matplotlib: 5 usage.skimage: 1 + usage.sklearn: 13 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[slice[int, int, int], int], _1: bool, /): + """ + usage.skimage: 4 """ ... @overload def __setitem__( - self, - _0: Tuple[ - int, - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[int, int, int], - ], - _1: numpy.ndarray, - /, + self, _0: Tuple[slice[int, int, int], int], _1: Tuple[float, float], / ): """ usage.skimage: 1 """ ... + @overload + def __setitem__(self, _0: Tuple[int, int, int, int, int], _1: int, /): + """ + usage.skimage: 2 + usage.xarray: 1 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[int, int, int, int, int, int], _1: int, /): + """ + usage.skimage: 2 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[int, int, int, int, int, int, int], _1: int, /): + """ + usage.skimage: 2 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[slice[int, int, int], int], _1: numpy.ndarray, /): + """ + usage.matplotlib: 8 + usage.skimage: 2 + """ + ... + @overload def __setitem__( self, - _0: Tuple[ - slice[int, None, int], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ], - _1: numpy.ndarray, + _0: Tuple[slice[int, int, int], int], + _1: Tuple[numpy.float64, numpy.float64], /, ): """ @@ -56555,16 +69765,18 @@ def __setitem__( """ ... + @overload + def __setitem__(self, _0: Tuple[int, int, slice[None, None, None]], _1: int, /): + """ + usage.skimage: 2 + """ + ... + @overload def __setitem__( self, - _0: Tuple[ - slice[None, None, None], - slice[int, None, int], - slice[None, None, None], - slice[None, None, None], - ], - _1: numpy.ndarray, + _0: Tuple[slice[int, int, int], slice[int, int, int], slice[None, None, None]], + _1: int, /, ): """ @@ -56575,13 +69787,8 @@ def __setitem__( @overload def __setitem__( self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[int, None, int], - slice[None, None, None], - ], - _1: numpy.ndarray, + _0: Tuple[slice[int, int, int], slice[int, int, int], ellipsis], + _1: int, /, ): """ @@ -56592,15 +69799,17 @@ def __setitem__( @overload def __setitem__( self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[int, None, int], - ], - _1: numpy.ndarray, + _0: Tuple[slice[int, int, int], slice[int, int, int], slice[None, None, None]], + _1: float, /, ): + """ + usage.skimage: 4 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[int, ellipsis], _1: range, /): """ usage.skimage: 1 """ @@ -56609,8 +69818,8 @@ def __setitem__( @overload def __setitem__( self, - _0: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], - _1: float, + _0: Tuple[slice[int, int, int], slice[int, int, int], slice[None, None, None]], + _1: numpy.ndarray, /, ): """ @@ -56619,16 +69828,14 @@ def __setitem__( ... @overload - def __setitem__( - self, _0: Tuple[int, slice[int, int, int], slice[int, int, int]], _1: float, / - ): + def __setitem__(self, _0: Tuple[ellipsis, int], _1: numpy.float32, /): """ - usage.skimage: 2 + usage.skimage: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[int, int, int]], _1: float, /): + def __setitem__(self, _0: Tuple[ellipsis, int], _1: numpy.int64, /): """ usage.skimage: 1 """ @@ -56638,12 +69845,25 @@ def __setitem__(self, _0: Tuple[slice[int, int, int]], _1: float, /): def __setitem__( self, _0: Tuple[ - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], - slice[int, int, int], + slice[numpy.int64, None, numpy.int64], slice[numpy.int64, None, numpy.int64] ], - _1: float, + _1: int, + /, + ): + """ + usage.skimage: 2 + """ + ... + + @overload + def __setitem__( + self, + _0: Tuple[ + slice[numpy.int64, None, numpy.int64], + slice[numpy.int64, None, numpy.int64], + slice[numpy.int64, None, numpy.int64], + ], + _1: int, /, ): """ @@ -56652,121 +69872,126 @@ def __setitem__( ... @overload - def __setitem__(self, _0: Tuple[int, slice[int, None, int]], _1: int, /): + def __setitem__(self, _0: ellipsis, _1: numpy.ndarray, /): """ - usage.skimage: 4 + usage.sklearn: 1 + usage.xarray: 2 """ ... @overload - def __setitem__(self, _0: Tuple[numpy.int64, numpy.int64], _1: int, /): + def __setitem__(self, _0: numpy.ndarray, _1: bytes, /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[numpy.int64, numpy.int64], _1: numpy.float64, /): + def __setitem__(self, _0: numpy.ndarray, _1: Literal[""], /): """ - usage.skimage: 1 + usage.xarray: 1 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[int, ellipsis], _1: numpy.ndarray, /): + """ + usage.xarray: 4 """ ... @overload def __setitem__( - self, _0: Tuple[int, slice[int, None, int], slice[None, None, None]], _1: int, / + self, + _0: Tuple[slice[None, None, None], slice[None, None, None], ellipsis], + _1: numpy.ndarray, + /, ): """ - usage.skimage: 3 + usage.xarray: 3 """ ... @overload - def __setitem__(self, _0: Tuple[numpy.int64, numpy.int64, numpy.int64], _1: int, /): + def __setitem__(self, _0: Tuple[None, ...], _1: cftime._cftime.DatetimeNoLeap, /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[numpy.int64, numpy.int64, numpy.int64], _1: numpy.float64, / - ): + def __setitem__(self, _0: Tuple[None, ...], _1: cftime._cftime.Datetime360Day, /): """ - usage.skimage: 1 + usage.xarray: 1 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[None, ...], _1: cftime._cftime.DatetimeJulian, /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[None, ...], _1: cftime._cftime.DatetimeAllLeap, /): + """ + usage.xarray: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[numpy.int64, None, numpy.int64], slice[numpy.int64, None, numpy.int64] - ], - _1: numpy.ndarray, - /, + self, _0: Tuple[None, ...], _1: cftime._cftime.DatetimeGregorian, / ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[numpy.int64, None, numpy.int64], - slice[numpy.int64, None, numpy.int64], - slice[numpy.int64, None, numpy.int64], - ], - _1: numpy.ndarray, - /, + self, _0: Tuple[None, ...], _1: cftime._cftime.DatetimeProlepticGregorian, / ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], - _1: bool, - /, - ): + def __setitem__(self, _0: Tuple[int], _1: numpy.datetime64, /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, slice[None, None, None]], _1: bool, /): + def __setitem__(self, _0: Tuple[int, int], _1: numpy.datetime64, /): """ - usage.skimage: 3 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[int, slice[None, None, None]], _1: numpy.ndarray, / - ): + def __setitem__(self, _0: Tuple[None, ...], _1: numpy.datetime64, /): """ - usage.matplotlib: 7 - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, slice[None, int, None]], _1: List[int], /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], ellipsis], _1: numpy.ndarray, / + ): """ - usage.skimage: 3 + usage.xarray: 12 """ ... @overload - def __setitem__(self, _0: Tuple[int, slice[int, None, int]], _1: List[int], /): + def __setitem__(self, _0: Tuple[ellipsis], _1: numpy.ndarray, /): """ - usage.skimage: 3 + usage.xarray: 4 """ ... @@ -56774,379 +69999,294 @@ def __setitem__(self, _0: Tuple[int, slice[int, None, int]], _1: List[int], /): def __setitem__( self, _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, None, None] + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ellipsis, ], _1: numpy.ndarray, /, ): """ - usage.skimage: 1 - """ - ... - - @overload - def __setitem__(self, _0: numpy.ndarray, _1: Tuple[int, int, int], /): - """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, None, None], slice[None, None, None]], + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ellipsis, + ], _1: numpy.ndarray, /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[slice[None, None, None], slice[None, None, None]], _1: bool, / - ): + def __setitem__(self, _0: Tuple[int, int, ellipsis], _1: numpy.ndarray, /): """ - usage.skimage: 1 + usage.xarray: 4 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, int, None], slice[None, int, None]], _1: bool, / + self, _0: Tuple[int, slice[None, None, None], ellipsis], _1: numpy.ndarray, / ): """ - usage.skimage: 1 + usage.xarray: 4 """ ... @overload def __setitem__( - self, _0: Tuple[slice[int, None, int], slice[None, int, None]], _1: bool, / + self, _0: Tuple[slice[None, None, None], int, ellipsis], _1: numpy.ndarray, / ): """ - usage.skimage: 1 + usage.xarray: 2 """ ... @overload - def __setitem__( - self, _0: Tuple[slice[None, int, None], slice[int, None, int]], _1: bool, / - ): + def __setitem__(self, _0: Tuple[List[int], slice[None, None, None]], _1: int, /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[int, int, int], int], _1: int, /): + def __setitem__(self, _0: Tuple[slice[None, None, None], List[int]], _1: int, /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( self, - _0: Tuple[ - slice[None, int, None], slice[None, int, None], slice[None, int, None] - ], - _1: bool, + _0: Tuple[numpy.ndarray, slice[None, None, None], slice[None, None, None]], + _1: numpy.ndarray, /, ): """ - usage.skimage: 1 + usage.xarray: 2 """ ... @overload def __setitem__( self, - _0: Tuple[ - slice[int, None, int], slice[None, int, None], slice[None, int, None] - ], - _1: bool, + _0: Tuple[slice[int, int, int], slice[None, None, None], ellipsis], + _1: numpy.ndarray, /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[None, int, None], slice[int, None, int], slice[None, int, None] - ], - _1: bool, - /, + self, _0: Tuple[None, ...], _1: Tuple[Literal["a"], numpy.int64, numpy.int64], / ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[None, int, None], slice[None, int, None], slice[int, None, int] - ], - _1: bool, - /, + self, _0: Tuple[None, ...], _1: Tuple[Literal["b"], numpy.int64, numpy.int64], / ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, int, None], slice[int, None, int]], _1: float, / + self, + _0: slice[None, None, None], + _1: List[Tuple[Literal["a", "b"], int, int]], + /, ): """ - usage.skimage: 3 + usage.xarray: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[int, None, int], slice[None, int, None]], _1: float, / + self, + _0: Tuple[slice[None, int, None], slice[None, int, None], ellipsis], + _1: numpy.ndarray, + /, ): """ - usage.skimage: 7 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[slice[int, None, int], slice[int, None, int]], _1: float, / - ): + def __setitem__(self, _0: Tuple[slice[None, None, None], int], _1: float, /): """ - usage.skimage: 7 + usage.xarray: 2 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, int, None], slice[None, int, None], int], _1: int, / + self, _0: Tuple[slice[None, int, None], ellipsis], _1: numpy.ndarray, / ): """ - usage.skimage: 6 + usage.xarray: 2 """ ... @overload - def __setitem__( - self, _0: Tuple[slice[int, None, int], slice[None, int, None], int], _1: int, / - ): + def __setitem__(self, _0: Tuple[ellipsis, slice[int, None, int]], _1: float, /): """ - usage.skimage: 6 + usage.xarray: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[int, None, int], slice[int, None, int], int], _1: int, / + self, _0: Tuple[ellipsis, slice[int, None, int]], _1: numpy.ndarray, / ): """ - usage.skimage: 6 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[ - int, - slice[numpy.int64, numpy.int64, numpy.int64], - slice[numpy.int64, numpy.int64, numpy.int64], - ], - _1: int, - /, - ): + def __setitem__(self, _0: Tuple[None, ...], _1: Literal["2000-01-01"], /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], - _1: numpy.ndarray, - /, - ): + def __setitem__(self, _0: Tuple[None, ...], _1: Literal["2000-01-02"], /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, int, None], slice[None, int, None], slice[int, int, int]], - _1: int, - /, - ): + def __setitem__(self, _0: Tuple[None, ...], _1: Literal["2000-01-03"], /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, int, None], slice[int, None, int], slice[int, int, int]], - _1: int, + _0: Tuple[slice[None, int, None], slice[None, None, None], ellipsis], + _1: numpy.ndarray, /, ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( self, - _0: Tuple[slice[int, None, int], slice[None, int, None], slice[int, int, int]], - _1: int, + _0: Tuple[slice[int, None, int], slice[None, None, None], ellipsis], + _1: numpy.ndarray, /, ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[slice[int, None, int], slice[int, None, int], slice[int, int, int]], - _1: int, - /, + self, _0: Tuple[slice[int, None, int], ellipsis], _1: numpy.ndarray, / ): """ - usage.skimage: 2 + usage.xarray: 3 """ ... @overload - def __setitem__( - self, - _0: Tuple[ - slice[None, int, None], slice[None, int, None], slice[None, int, None] - ], - _1: Tuple[int, int, int], - /, - ): + def __setitem__(self, _0: Tuple[None, ...], _1: Literal["a"], /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[ - slice[None, int, None], slice[None, int, None], slice[int, None, int] - ], - _1: Tuple[int, int, int], - /, - ): + def __setitem__(self, _0: Tuple[None, ...], _1: Literal["b"], /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[ - slice[None, int, None], slice[int, None, int], slice[None, int, None] - ], - _1: Tuple[int, int, int], - /, - ): + def __setitem__(self, _0: Tuple[None, ...], _1: bytes, /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, int, None], slice[int, None, int], slice[int, None, int]], - _1: Tuple[int, int, int], - /, - ): + def __setitem__(self, _0: Tuple[None, ...], _1: bool, /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[ - slice[int, None, int], slice[None, int, None], slice[None, int, None] - ], - _1: Tuple[int, int, int], - /, - ): + def __setitem__(self, _0: Tuple[None, ...], _1: Literal["d"], /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[slice[int, None, int], slice[None, int, None], slice[int, None, int]], - _1: Tuple[int, int, int], - /, + self, _0: Tuple[None, ...], _1: Tuple[Literal["a"], numpy.int64], / ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[slice[int, None, int], slice[int, None, int], slice[None, int, None]], - _1: Tuple[int, int, int], - /, + self, _0: Tuple[None, ...], _1: Tuple[Literal["b"], numpy.int64], / ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[slice[int, None, int], slice[int, None, int], slice[int, None, int]], - _1: Tuple[int, int, int], - /, + self, _0: Tuple[None, ...], _1: Tuple[Literal["c"], numpy.int64], / ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[ - slice[None, int, None], slice[None, int, None], slice[None, int, None] - ], - _1: numpy.float64, - /, - ): + def __setitem__(self, _0: Tuple[int, int, slice[None, None, None]], _1: float, /): """ - usage.skimage: 2 + usage.xarray: 3 """ ... @@ -57154,39 +70294,31 @@ def __setitem__( def __setitem__( self, _0: Tuple[ - slice[None, int, None], slice[None, int, None], slice[int, None, int] + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], ], - _1: numpy.float64, + _1: int, /, ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[ - slice[None, int, None], slice[int, None, int], slice[None, int, None] - ], - _1: numpy.float64, - /, - ): + def __setitem__(self, _0: ellipsis, _1: int, /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, int, None], slice[int, None, int], slice[int, None, int]], - _1: numpy.float64, - /, - ): + def __setitem__(self, _0: Tuple[int, slice[None, None, None], int], _1: int, /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @@ -57194,49 +70326,69 @@ def __setitem__( def __setitem__( self, _0: Tuple[ - slice[int, None, int], slice[None, int, None], slice[None, int, None] + int, + slice[None, None, None], + int, + slice[None, None, None], + slice[None, None, None], ], - _1: numpy.float64, + _1: int, /, ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( self, - _0: Tuple[slice[int, None, int], slice[None, int, None], slice[int, None, int]], - _1: numpy.float64, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + int, + ], + _1: int, /, ): """ - usage.skimage: 2 + usage.xarray: 1 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[slice[None, int, None], ellipsis, int], _1: int, /): + """ + usage.xarray: 1 """ ... @overload def __setitem__( self, - _0: Tuple[slice[int, None, int], slice[int, None, int], slice[None, int, None]], - _1: numpy.float64, + _0: Tuple[ + slice[None, int, None], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + int, + ], + _1: int, /, ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[slice[int, None, int], slice[int, None, int], slice[int, None, int]], - _1: numpy.float64, - /, + self, _0: Tuple[ellipsis, int, slice[None, None, None]], _1: int, / ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @@ -57244,287 +70396,285 @@ def __setitem__( def __setitem__( self, _0: Tuple[ - slice[numpy.int64, None, numpy.int64], slice[numpy.int64, None, numpy.int64] + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + int, + slice[None, None, None], ], - _1: numpy.ndarray, + _1: int, /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[int, None, int], numpy.int64], _1: numpy.ndarray, / + self, + _0: Tuple[ + numpy.ndarray, + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ], + _1: int, + /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __setitem__( - self, _0: Tuple[numpy.ndarray, slice[None, None, None]], _1: int, / + self, + _0: Tuple[ + numpy.ndarray, + numpy.ndarray, + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ], + _1: int, + /, ): """ - usage.matplotlib: 1 - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( - self, _0: Tuple[numpy.ndarray, slice[None, None, None]], _1: numpy.ndarray, / + self, _0: Tuple[ellipsis, numpy.ndarray, numpy.ndarray], _1: int, / ): """ - usage.skimage: 2 - usage.xarray: 3 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[int, numpy.ndarray], _1: numpy.ndarray, /): - """ - usage.skimage: 1 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[slice[None, None, None], int], _1: float, /): - """ - usage.matplotlib: 5 - usage.skimage: 1 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[slice[int, int, int], int], _1: bool, /): - """ - usage.skimage: 4 + usage.xarray: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[int, int, int], int], _1: Tuple[float, float], / + self, + _0: Tuple[ + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + numpy.ndarray, + numpy.ndarray, + ], + _1: int, + /, ): """ - usage.skimage: 1 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[int, int, int, int, int], _1: int, /): - """ - usage.skimage: 2 usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, int, int, int, int, int], _1: int, /): + def __setitem__(self, _0: Tuple[ellipsis, int, int, int, int, int], _1: int, /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, int, int, int, int, int, int], _1: int, /): + def __setitem__(self, _0: Tuple[None, ...], _1: Tuple[Literal["a"], int], /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[int, int, int], int], _1: numpy.ndarray, /): + def __setitem__(self, _0: slice[None, None, None], _1: List[Tuple[int]], /): """ - usage.matplotlib: 8 - usage.skimage: 2 + usage.sklearn: 3 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[int, int, int], int], - _1: Tuple[numpy.float64, numpy.float64], - /, - ): + def __setitem__(self, _0: Tuple[None, ...], _1: Tuple[int, int], /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, int, slice[None, None, None]], _1: int, /): + def __setitem__(self, _0: Tuple[None, ...], _1: Tuple[numpy.int64, numpy.int64], /): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[int, int, int], slice[int, int, int], slice[None, None, None]], - _1: int, - /, - ): + def __setitem__(self, _0: Tuple[int, int, int, ellipsis], _1: numpy.ndarray, /): """ - usage.skimage: 1 + usage.xarray: 2 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[int, int, int], slice[int, int, int], ellipsis], - _1: int, - /, - ): + def __setitem__(self, _0: Tuple[slice[None, None, None], ellipsis], _1: int, /): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __setitem__( self, - _0: Tuple[slice[int, int, int], slice[int, int, int], slice[None, None, None]], - _1: float, + _0: Tuple[slice[None, None, None], slice[None, int, None], ellipsis], + _1: numpy.ndarray, /, ): """ - usage.skimage: 4 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[int, ellipsis], _1: range, /): - """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload def __setitem__( self, - _0: Tuple[slice[int, int, int], slice[int, int, int], slice[None, None, None]], + _0: Tuple[slice[None, None, None], slice[int, None, int], ellipsis], _1: numpy.ndarray, /, ): """ - usage.skimage: 3 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[ellipsis, int], _1: numpy.float32, /): - """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[ellipsis, int], _1: numpy.int64, /): + def __setitem__(self, _0: slice[None, None, None], _1: float, /): """ - usage.skimage: 1 + usage.matplotlib: 2 + usage.sklearn: 1 + usage.xarray: 1 """ ... @overload def __setitem__( self, - _0: Tuple[ - slice[numpy.int64, None, numpy.int64], slice[numpy.int64, None, numpy.int64] - ], - _1: int, + _0: Tuple[List[int], List[int], slice[None, None, None]], + _1: numpy.ndarray, /, ): """ - usage.skimage: 2 + usage.xarray: 1 """ ... @overload def __setitem__( self, - _0: Tuple[ - slice[numpy.int64, None, numpy.int64], - slice[numpy.int64, None, numpy.int64], - slice[numpy.int64, None, numpy.int64], - ], - _1: int, + _0: Tuple[List[int], slice[None, None, None], List[int]], + _1: numpy.ndarray, /, ): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: ellipsis, _1: numpy.ndarray, /): + def __setitem__( + self, + _0: Tuple[slice[None, None, None], List[int], List[int]], + _1: numpy.ndarray, + /, + ): """ - usage.xarray: 2 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: numpy.ndarray, _1: bytes, /): + def __setitem__(self, _0: slice[int, int, int], _1: float, /): """ - usage.xarray: 1 + usage.sklearn: 1 + usage.xarray: 2 """ ... @overload - def __setitem__(self, _0: numpy.ndarray, _1: Literal[""], /): + def __setitem__( + self, + _0: Tuple[slice[None, None, None], slice[None, None, None], int, ellipsis], + _1: numpy.ndarray, + /, + ): """ usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, ellipsis], _1: numpy.ndarray, /): + def __setitem__(self, _0: int, _1: List[int], /): """ - usage.xarray: 4 + usage.xarray: 1 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, None, None], slice[None, None, None], ellipsis], + _0: Tuple[ + int, + slice[None, None, None], + slice[None, None, None], + slice[None, None, None], + ellipsis, + ], _1: numpy.ndarray, /, ): """ - usage.xarray: 3 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: cftime._cftime.DatetimeNoLeap, /): + def __setitem__(self, _0: int, _1: Tuple[Literal["a"], int], /): """ usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: cftime._cftime.Datetime360Day, /): + def __setitem__( + self, + _0: Tuple[xarray.core.variable.Variable, xarray.core.variable.Variable], + _1: int, + /, + ): """ usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: cftime._cftime.DatetimeJulian, /): + def __setitem__( + self, + _0: Tuple[xarray.core.variable.Variable, xarray.core.variable.Variable], + _1: xarray.core.variable.Variable, + /, + ): """ usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: cftime._cftime.DatetimeAllLeap, /): + def __setitem__( + self, + _0: Tuple[slice[None, None, None], xarray.core.variable.Variable], + _1: xarray.core.variable.Variable, + /, + ): """ usage.xarray: 1 """ @@ -57532,16 +70682,22 @@ def __setitem__(self, _0: Tuple[None, ...], _1: cftime._cftime.DatetimeAllLeap, @overload def __setitem__( - self, _0: Tuple[None, ...], _1: cftime._cftime.DatetimeGregorian, / + self, + _0: Tuple[xarray.core.variable.Variable, xarray.core.variable.Variable], + _1: List[int], + /, ): """ - usage.xarray: 1 + usage.xarray: 2 """ ... @overload def __setitem__( - self, _0: Tuple[None, ...], _1: cftime._cftime.DatetimeProlepticGregorian, / + self, + _0: Tuple[numpy.ndarray, slice[None, None, None], numpy.ndarray], + _1: numpy.ndarray, + /, ): """ usage.xarray: 1 @@ -57549,21 +70705,26 @@ def __setitem__( ... @overload - def __setitem__(self, _0: Tuple[int], _1: numpy.datetime64, /): + def __setitem__( + self, + _0: Tuple[int, slice[None, None, None], int, ellipsis], + _1: numpy.ndarray, + /, + ): """ usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, int], _1: numpy.datetime64, /): + def __setitem__(self, _0: Tuple[None, ...], _1: object, /): """ usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: numpy.datetime64, /): + def __setitem__(self, _0: Tuple[None, ...], _1: List[int], /): """ usage.xarray: 1 """ @@ -57571,775 +70732,685 @@ def __setitem__(self, _0: Tuple[None, ...], _1: numpy.datetime64, /): @overload def __setitem__( - self, _0: Tuple[slice[None, None, None], ellipsis], _1: numpy.ndarray, / + self, _0: Tuple[None, ...], _1: pandas._libs.tslibs.period.Period, / ): """ - usage.xarray: 12 + usage.xarray: 1 """ ... @overload - def __setitem__(self, _0: Tuple[ellipsis], _1: numpy.ndarray, /): + def __setitem__(self, _0: object, _1: object, /): """ - usage.xarray: 4 + usage.dask: 134 + usage.pandas: 5607 + usage.scipy: 3994 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ellipsis, - ], - _1: numpy.ndarray, - /, + self, _0: Tuple[slice[None, None, None], int], _1: Tuple[float, float], / ): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ellipsis, - ], - _1: numpy.ndarray, - /, + self, _0: Tuple[int, int], _1: matplotlib.axes._subplots.AxesSubplot, / ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, int, ellipsis], _1: numpy.ndarray, /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], int, int], _1: numpy.ndarray, / + ): """ - usage.xarray: 4 + usage.matplotlib: 17 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[int, slice[None, None, None], ellipsis], _1: numpy.ndarray, / - ): + def __setitem__(self, _0: Tuple[slice[None, None, None], int, int], _1: float, /): """ - usage.xarray: 4 + usage.matplotlib: 2 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, None, None], int, ellipsis], _1: numpy.ndarray, / + self, + _0: Tuple[slice[None, None, None], int], + _1: Tuple[numpy.float64, numpy.float64], + /, ): """ - usage.xarray: 2 + usage.matplotlib: 2 """ ... @overload - def __setitem__(self, _0: Tuple[List[int], slice[None, None, None]], _1: int, /): + def __setitem__(self, _0: int, _1: Tuple[numpy.int64, numpy.float64], /): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload - def __setitem__(self, _0: Tuple[slice[None, None, None], List[int]], _1: int, /): + def __setitem__(self, _0: slice[None, None, None], _1: numpy.uint8, /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[numpy.ndarray, slice[None, None, None], slice[None, None, None]], - _1: numpy.ndarray, - /, - ): + def __setitem__(self, _0: int, _1: numpy.uint8, /): """ - usage.xarray: 2 + usage.matplotlib: 14 """ ... @overload def __setitem__( self, - _0: Tuple[slice[int, int, int], slice[None, None, None], ellipsis], - _1: numpy.ndarray, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[None, int, None] + ], + _1: Tuple[int, int, int], /, ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[None, ...], _1: Tuple[Literal["a"], numpy.int64, numpy.int64], / - ): + def __setitem__(self, _0: slice[None, int, None], _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 4 + usage.sklearn: 30 """ ... @overload - def __setitem__( - self, _0: Tuple[None, ...], _1: Tuple[Literal["b"], numpy.int64, numpy.int64], / - ): + def __setitem__(self, _0: int, _1: Tuple[float, float, float, float], /): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload def __setitem__( self, - _0: slice[None, None, None], - _1: List[Tuple[Literal["a", "b"], int, int]], + _0: Tuple[slice[None, None, None], slice[None, None, None], int], + _1: int, /, ): """ - usage.xarray: 1 + usage.matplotlib: 5 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, int, None], slice[None, int, None], ellipsis], - _1: numpy.ndarray, - /, - ): + def __setitem__(self, _0: slice[int, int, int], _1: numpy.uint8, /): """ - usage.xarray: 1 + usage.matplotlib: 4 """ ... @overload - def __setitem__(self, _0: Tuple[slice[None, None, None], int], _1: float, /): + def __setitem__(self, _0: slice[int, None, int], _1: float, /): """ - usage.xarray: 2 + usage.matplotlib: 3 """ ... @overload - def __setitem__( - self, _0: Tuple[slice[None, int, None], ellipsis], _1: numpy.ndarray, / - ): + def __setitem__(self, _0: slice[int, int, int], _1: numpy.ndarray, /): """ - usage.xarray: 2 + usage.matplotlib: 11 + usage.sklearn: 10 """ ... @overload - def __setitem__(self, _0: Tuple[ellipsis, slice[int, None, int]], _1: float, /): + def __setitem__(self, _0: Tuple[slice[int, None, int], int], _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 12 """ ... @overload - def __setitem__( - self, _0: Tuple[ellipsis, slice[int, None, int]], _1: numpy.ndarray, / - ): + def __setitem__(self, _0: slice[int, None, int], _1: numpy.uint8, /): """ - usage.xarray: 1 + usage.matplotlib: 8 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: Literal["2000-01-01"], /): + def __setitem__(self, _0: int, _1: List[numpy.float64], /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: Literal["2000-01-02"], /): + def __setitem__(self, _0: Tuple[slice[int, int, int], int], _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 6 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: Literal["2000-01-03"], /): + def __setitem__(self, _0: Tuple[int, int], _1: None, /): """ - usage.xarray: 1 + usage.matplotlib: 4 + usage.sklearn: 2 """ ... @overload def __setitem__( - self, - _0: Tuple[slice[None, int, None], slice[None, None, None], ellipsis], - _1: numpy.ndarray, - /, + self, _0: Tuple[int, int], _1: matplotlib.axes._subplots.PolarAxesSubplot, / ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[int, None, int], slice[None, None, None], ellipsis], - _1: numpy.ndarray, - /, - ): + def __setitem__(self, _0: slice[int, int, int], _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 4 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[slice[int, None, int], ellipsis], _1: numpy.ndarray, / - ): + def __setitem__(self, _0: int, _1: Tuple[numpy.float64, numpy.float64], /): """ - usage.xarray: 3 + usage.matplotlib: 4 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: Literal["a"], /): + def __setitem__(self, _0: slice[int, None, int], _1: bool, /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: Literal["b"], /): + def __setitem__( + self, _0: Tuple[int, int], _1: matplotlib.axes._subplots.AitoffAxesSubplot, / + ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: bytes, /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], int], _1: Tuple[int, int], / + ): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: bool, /): + def __setitem__(self, _0: Literal["flags"], _1: int, /): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: Literal["d"], /): + def __setitem__(self, _0: Literal["points"], _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload - def __setitem__( - self, _0: Tuple[None, ...], _1: Tuple[Literal["a"], numpy.int64], / - ): + def __setitem__(self, _0: Literal["colors"], _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload - def __setitem__( - self, _0: Tuple[None, ...], _1: Tuple[Literal["b"], numpy.int64], / - ): + def __setitem__(self, _0: Tuple[slice[None, int, None], int], _1: numpy.float64, /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[None, ...], _1: Tuple[Literal["c"], numpy.int64], / - ): + def __setitem__(self, _0: Tuple[slice[int, None, int], int], _1: float, /): """ - usage.xarray: 1 + usage.matplotlib: 4 """ ... @overload - def __setitem__(self, _0: Tuple[int, int, slice[None, None, None]], _1: float, /): + def __setitem__( + self, _0: int, _1: Tuple[numpy.float64, numpy.float64, numpy.float64, float], / + ): """ - usage.xarray: 3 + usage.matplotlib: 3 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ], - _1: int, - /, + self, _0: Tuple[slice[None, None, None], int], _1: Tuple[numpy.float64, int], / ): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload - def __setitem__(self, _0: ellipsis, _1: int, /): + def __setitem__(self, _0: List[int], _1: bool, /): """ - usage.xarray: 1 + usage.matplotlib: 6 """ ... @overload - def __setitem__(self, _0: Tuple[int, slice[None, None, None], int], _1: int, /): + def __setitem__(self, _0: Tuple[int, slice[int, None, int]], _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 7 """ ... @overload def __setitem__( self, - _0: Tuple[ - int, - slice[None, None, None], - int, - slice[None, None, None], - slice[None, None, None], - ], - _1: int, + _0: Tuple[slice[int, None, int], slice[int, None, int]], + _1: Tuple[numpy.ndarray], /, ): """ - usage.xarray: 1 + usage.matplotlib: 6 """ ... @overload def __setitem__( self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - int, - ], - _1: int, + _0: Tuple[slice[int, None, int], slice[int, None, int]], + _1: numpy.ndarray, /, ): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload - def __setitem__(self, _0: Tuple[slice[None, int, None], ellipsis, int], _1: int, /): + def __setitem__(self, _0: Tuple[int, slice[int, int, int]], _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[ - slice[None, int, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - int, - ], - _1: int, - /, - ): + def __setitem__(self, _0: Tuple[int, slice[None, int, None]], _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 2 + usage.sklearn: 9 """ ... @overload - def __setitem__( - self, _0: Tuple[ellipsis, int, slice[None, None, None]], _1: int, / - ): + def __setitem__(self, _0: Tuple[int, slice[int, None, int]], _1: numpy.ndarray, /): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload def __setitem__( self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - int, - slice[None, None, None], - ], - _1: int, + _0: Tuple[slice[None, None, None], int], + _1: Tuple[numpy.int64, numpy.int64], /, ): """ - usage.xarray: 1 + usage.matplotlib: 2 + """ + ... + + @overload + def __setitem__(self, _0: int, _1: Tuple[numpy.int64, numpy.int64], /): + """ + usage.matplotlib: 4 """ ... @overload def __setitem__( - self, - _0: Tuple[ - numpy.ndarray, - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ], - _1: int, - /, + self, _0: slice[numpy.int64, numpy.int64, numpy.int64], _1: bool, / ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload def __setitem__( self, - _0: Tuple[ - numpy.ndarray, - numpy.ndarray, - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ], - _1: int, + _0: Tuple[slice[int, None, int], slice[int, None, int]], + _1: Tuple[numpy.ndarray, numpy.ndarray], /, ): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload - def __setitem__( - self, _0: Tuple[ellipsis, numpy.ndarray, numpy.ndarray], _1: int, / - ): + def __setitem__(self, _0: Tuple[int, slice[int, None, int]], _1: list, /): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload def __setitem__( self, - _0: Tuple[ - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - numpy.ndarray, - numpy.ndarray, - ], - _1: int, + _0: Tuple[slice[int, None, int], slice[int, None, int]], + _1: Tuple[list], /, ): """ - usage.xarray: 1 - """ - ... - - @overload - def __setitem__(self, _0: Tuple[ellipsis, int, int, int, int, int], _1: int, /): - """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: Tuple[Literal["a"], int], /): + def __setitem__( + self, + _0: Tuple[slice[int, None, int], slice[int, None, int]], + _1: Tuple[Literal["a"]], + /, + ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__(self, _0: slice[None, None, None], _1: List[Tuple[int]], /): + def __setitem__( + self, _0: Tuple[slice[None, int, None], slice[int, None, int]], _1: int, / + ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: Tuple[int, int], /): + def __setitem__(self, _0: numpy.bool_, _1: int, /): """ - usage.xarray: 1 + usage.matplotlib: 5 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: Tuple[numpy.int64, numpy.int64], /): + def __setitem__(self, _0: slice[None, None, None], _1: List[float], /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, int, int, ellipsis], _1: numpy.ndarray, /): + def __setitem__(self, _0: slice[None, None, None], _1: List[numpy.float64], /): """ - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[None, None, None], ellipsis], _1: int, /): + def __setitem__( + self, _0: int, _1: Tuple[numpy.float64, numpy.float64, numpy.float64, int], / + ): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, None, None], slice[None, int, None], ellipsis], - _1: numpy.ndarray, + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[None, int, None] + ], + _1: numpy.ma.core.MaskedArray, /, ): """ - usage.xarray: 1 + usage.matplotlib: 4 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, None, None], slice[int, None, int], ellipsis], - _1: numpy.ndarray, + _0: Tuple[slice[None, None, None], slice[None, None, None], int], + _1: float, /, ): """ - usage.xarray: 1 + usage.matplotlib: 5 """ ... @overload - def __setitem__(self, _0: slice[None, None, None], _1: float, /): - """ - usage.matplotlib: 2 - usage.xarray: 1 + def __setitem__(self, _0: slice[int, int, int], _1: bool, /): + """ + usage.matplotlib: 1 """ ... @overload def __setitem__( self, - _0: Tuple[List[int], List[int], slice[None, None, None]], - _1: numpy.ndarray, + _0: Tuple[slice[None, None, None], slice[None, None, None], int], + _1: numpy.uint8, /, ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload def __setitem__( self, - _0: Tuple[List[int], slice[None, None, None], List[int]], + _0: Tuple[ + slice[None, None, None], slice[None, None, None], slice[None, int, None] + ], _1: numpy.ndarray, /, ): """ - usage.xarray: 1 + usage.matplotlib: 2 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, None, None], List[int], List[int]], - _1: numpy.ndarray, - /, - ): + def __setitem__(self, _0: slice[numpy.int64, numpy.int64, numpy.int64], _1: int, /): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: slice[int, int, int], _1: float, /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], int], _1: Tuple[float, int], / + ): """ - usage.xarray: 2 + usage.matplotlib: 2 """ ... @overload def __setitem__( - self, - _0: Tuple[slice[None, None, None], slice[None, None, None], int, ellipsis], - _1: numpy.ndarray, - /, + self, _0: Tuple[int, slice[None, None, None]], _1: List[numpy.float64], / ): """ - usage.xarray: 1 + usage.matplotlib: 5 """ ... @overload - def __setitem__(self, _0: int, _1: List[int], /): + def __setitem__(self, _0: slice[None, None, None], _1: float, /): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[ - int, - slice[None, None, None], - slice[None, None, None], - slice[None, None, None], - ellipsis, - ], - _1: numpy.ndarray, - /, + self, _0: Tuple[slice[None, None, None], int], _1: Tuple[int, float], / ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__(self, _0: int, _1: Tuple[Literal["a"], int], /): + def __setitem__(self, _0: slice[int, int, int], _1: int, /): """ - usage.xarray: 1 + usage.matplotlib: 2 + usage.sklearn: 6 """ ... @overload - def __setitem__( - self, - _0: Tuple[xarray.core.variable.Variable, xarray.core.variable.Variable], - _1: int, - /, - ): + def __setitem__(self, _0: Tuple[slice[None, None, None], int, int], _1: int, /): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload def __setitem__( self, - _0: Tuple[xarray.core.variable.Variable, xarray.core.variable.Variable], - _1: xarray.core.variable.Variable, + _0: Tuple[slice[None, None, None], slice[int, int, int], slice[int, int, int]], + _1: numpy.ndarray, /, ): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload def __setitem__( self, - _0: Tuple[slice[None, None, None], xarray.core.variable.Variable], - _1: xarray.core.variable.Variable, + _0: Tuple[slice[None, None, None], int, slice[None, None, None]], + _1: numpy.ndarray, /, ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload def __setitem__( self, - _0: Tuple[xarray.core.variable.Variable, xarray.core.variable.Variable], - _1: List[int], + _0: Tuple[slice[None, None, None], slice[int, int, int]], + _1: numpy.ndarray, /, ): """ - usage.xarray: 2 + usage.matplotlib: 3 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[numpy.int32, numpy.int32], _1: numpy.float64, /): + """ + usage.matplotlib: 1 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[numpy.ndarray, int, int], _1: numpy.ndarray, /): + """ + usage.matplotlib: 8 + usage.sklearn: 3 """ ... @overload def __setitem__( self, - _0: Tuple[numpy.ndarray, slice[None, None, None], numpy.ndarray], + _0: Tuple[slice[None, None, None], slice[int, None, int]], _1: numpy.ndarray, /, ): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload def __setitem__( self, - _0: Tuple[int, slice[None, None, None], int, ellipsis], + _0: Tuple[slice[int, None, int], slice[None, None, None]], _1: numpy.ndarray, /, ): """ - usage.xarray: 1 + usage.matplotlib: 4 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: object, /): + def __setitem__( + self, _0: Tuple[numpy.ma.core.MaskedArray, slice[None, None, None]], _1: int, / + ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__(self, _0: Tuple[None, ...], _1: List[int], /): + def __setitem__(self, _0: numpy.ma.core.MaskedArray, _1: int, /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[None, ...], _1: pandas._libs.tslibs.period.Period, / - ): + def __setitem__(self, _0: slice[int, None, int], _1: bool, /): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload - def __setitem__(self, _0: object, _1: object, /): + def __setitem__(self, _0: Tuple[slice[int, int, int], int], _1: List[float], /): """ - usage.dask: 134 - usage.pandas: 5607 - usage.scipy: 3994 - usage.sklearn: 1434 + usage.matplotlib: 2 """ ... @overload - def __setitem__( - self, _0: Tuple[slice[None, None, None], int], _1: Tuple[float, float], / - ): + def __setitem__(self, _0: int, _1: Tuple[float, int], /): """ usage.matplotlib: 2 """ @@ -58347,7 +71418,7 @@ def __setitem__( @overload def __setitem__( - self, _0: Tuple[int, int], _1: matplotlib.axes._subplots.AxesSubplot, / + self, _0: Tuple[slice[None, None, None], int], _1: Tuple[int, numpy.float64], / ): """ usage.matplotlib: 1 @@ -58356,659 +71427,603 @@ def __setitem__( @overload def __setitem__( - self, _0: Tuple[slice[None, None, None], int, int], _1: numpy.ndarray, / + self, _0: Tuple[int, int], _1: matplotlib.axes._subplots.Axes3DSubplot, / ): """ - usage.matplotlib: 17 + usage.matplotlib: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[None, None, None], int, int], _1: float, /): + def __setitem__(self, _0: numpy.int64, _1: numpy.ndarray, /): """ - usage.matplotlib: 2 + usage.sklearn: 16 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, None, None], int], - _1: Tuple[numpy.float64, numpy.float64], - /, - ): + def __setitem__(self, _0: numpy.int64, _1: numpy.float64, /): """ - usage.matplotlib: 2 + usage.sklearn: 8 """ ... @overload - def __setitem__(self, _0: int, _1: Tuple[numpy.int64, numpy.float64], /): + def __setitem__(self, _0: slice[None, None, None], _1: List[numpy.ndarray], /): """ - usage.matplotlib: 2 + usage.sklearn: 21 """ ... @overload - def __setitem__(self, _0: slice[None, None, None], _1: numpy.uint8, /): + def __setitem__( + self, _0: slice[numpy.int32, numpy.int32, numpy.int32], _1: numpy.ndarray, / + ): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload - def __setitem__(self, _0: int, _1: numpy.uint8, /): + def __setitem__(self, _0: int, _1: sklearn.utils._fast_dict.IntFloatDict, /): """ - usage.matplotlib: 14 + usage.sklearn: 2 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, int, None] - ], - _1: Tuple[int, int, int], - /, + self, _0: Tuple[slice[None, None, None], slice[None, None, None]], _1: int, / ): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload - def __setitem__(self, _0: slice[None, int, None], _1: numpy.ndarray, /): + def __setitem__(self, _0: int, _1: numpy.float32, /): """ - usage.matplotlib: 4 + usage.sklearn: 9 """ ... @overload - def __setitem__(self, _0: int, _1: Tuple[float, float, float, float], /): + def __setitem__(self, _0: int, _1: numpy.matrix, /): """ - usage.matplotlib: 3 + usage.sklearn: 2 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, None, None], slice[None, None, None], int], - _1: int, - /, - ): + def __setitem__(self, _0: List[int], _1: numpy.ndarray, /): """ - usage.matplotlib: 5 + usage.sklearn: 2 """ ... @overload - def __setitem__(self, _0: slice[int, int, int], _1: numpy.uint8, /): + def __setitem__(self, _0: numpy.ndarray, _1: numpy.int64, /): """ - usage.matplotlib: 4 + usage.sklearn: 5 """ ... @overload - def __setitem__(self, _0: slice[int, None, int], _1: float, /): + def __setitem__(self, _0: numpy.int64, _1: numpy.int64, /): """ - usage.matplotlib: 3 + usage.sklearn: 5 """ ... @overload - def __setitem__(self, _0: slice[int, int, int], _1: numpy.ndarray, /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], numpy.int64], _1: numpy.ndarray, / + ): """ - usage.matplotlib: 11 + usage.sklearn: 4 """ ... @overload - def __setitem__(self, _0: Tuple[slice[int, None, int], int], _1: numpy.ndarray, /): + def __setitem__(self, _0: List[numpy.int64], _1: numpy.ndarray, /): """ - usage.matplotlib: 12 + usage.sklearn: 10 """ ... @overload - def __setitem__(self, _0: slice[int, None, int], _1: numpy.uint8, /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], numpy.ndarray], _1: float, / + ): """ - usage.matplotlib: 8 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: int, _1: List[numpy.float64], /): + def __setitem__(self, _0: slice[None, None, None], _1: List[int], /): """ - usage.matplotlib: 1 + usage.sklearn: 8 """ ... @overload - def __setitem__(self, _0: Tuple[slice[int, int, int], int], _1: numpy.ndarray, /): + def __setitem__(self, _0: Tuple[int, slice[None, None, None]], _1: float, /): """ - usage.matplotlib: 6 + usage.sklearn: 11 """ ... @overload - def __setitem__(self, _0: Tuple[int, int], _1: None, /): + def __setitem__(self, _0: numpy.ndarray, _1: numpy.float32, /): """ - usage.matplotlib: 4 + usage.sklearn: 12 """ ... @overload def __setitem__( - self, _0: Tuple[int, int], _1: matplotlib.axes._subplots.PolarAxesSubplot, / + self, _0: Tuple[slice[None, None, None], numpy.ndarray], _1: int, / ): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload - def __setitem__(self, _0: slice[int, int, int], _1: numpy.ndarray, /): + def __setitem__(self, _0: slice[int, int, int], _1: numpy.float64, /): """ - usage.matplotlib: 4 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: int, _1: Tuple[numpy.float64, numpy.float64], /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], slice[int, int, int]], _1: float, / + ): """ - usage.matplotlib: 4 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: slice[int, None, int], _1: bool, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, int], _1: float, /): """ - usage.matplotlib: 1 + usage.sklearn: 5 """ ... @overload - def __setitem__( - self, _0: Tuple[int, int], _1: matplotlib.axes._subplots.AitoffAxesSubplot, / - ): + def __setitem__(self, _0: Tuple[int, numpy.uint8], _1: numpy.float64, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[slice[None, None, None], int], _1: Tuple[int, int], / - ): + def __setitem__(self, _0: Tuple[int, numpy.int64], _1: numpy.float64, /): """ - usage.matplotlib: 2 + usage.sklearn: 3 """ ... @overload - def __setitem__(self, _0: Literal["flags"], _1: int, /): + def __setitem__(self, _0: slice[None, numpy.int64, None], _1: numpy.ndarray, /): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Literal["points"], _1: numpy.ndarray, /): + def __setitem__(self, _0: numpy.int64, _1: float, /): """ - usage.matplotlib: 2 + usage.sklearn: 2 """ ... @overload - def __setitem__(self, _0: Literal["colors"], _1: numpy.ndarray, /): + def __setitem__(self, _0: Tuple[numpy.int64, int, int], _1: numpy.float64, /): """ - usage.matplotlib: 2 + usage.sklearn: 4 """ ... @overload - def __setitem__(self, _0: Tuple[slice[None, int, None], int], _1: numpy.float64, /): + def __setitem__( + self, _0: Tuple[int, int], _1: sklearn.tree._classes.DecisionTreeRegressor, / + ): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[int, None, int], int], _1: float, /): + def __setitem__(self, _0: Tuple[numpy.int64, int], _1: numpy.float64, /): """ - usage.matplotlib: 4 + usage.sklearn: 2 + """ + ... + + @overload + def __setitem__(self, _0: Tuple[numpy.int64, int, int], _1: float, /): + """ + usage.sklearn: 2 """ ... @overload def __setitem__( - self, _0: int, _1: Tuple[numpy.float64, numpy.float64, numpy.float64, float], / + self, _0: Tuple[int, int], _1: Dict[Literal["foo"], Literal["bar"]], / ): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, None, None], int], _1: Tuple[numpy.float64, int], / + self, _0: Tuple[numpy.int64, slice[None, None, None]], _1: numpy.ndarray, / ): """ - usage.matplotlib: 2 + usage.sklearn: 4 """ ... @overload - def __setitem__(self, _0: List[int], _1: bool, /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], numpy.int64], _1: float, / + ): """ - usage.matplotlib: 6 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, slice[int, None, int]], _1: numpy.ndarray, /): + def __setitem__(self, _0: Tuple[int, int], _1: numpy.float32, /): """ - usage.matplotlib: 7 + usage.sklearn: 2 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[int, None, int], slice[int, None, int]], - _1: Tuple[numpy.ndarray], - /, - ): + def __setitem__(self, _0: Tuple[int, int], _1: numpy.int16, /): """ - usage.matplotlib: 6 + usage.sklearn: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[slice[int, None, int], slice[int, None, int]], - _1: numpy.ndarray, - /, + self, _0: Tuple[slice[int, None, int], slice[int, None, int]], _1: bool, / ): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, slice[int, int, int]], _1: numpy.ndarray, /): + def __setitem__(self, _0: slice[None, None, None], _1: bool, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, slice[None, int, None]], _1: numpy.ndarray, /): + def __setitem__( + self, + _0: Tuple[ellipsis, int], + _1: sklearn.linear_model._cd_fast._memoryviewslice, + /, + ): """ - usage.matplotlib: 2 + usage.sklearn: 2 """ ... @overload - def __setitem__(self, _0: Tuple[int, slice[int, None, int]], _1: numpy.ndarray, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, numpy.int64], _1: numpy.ndarray, /): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, None, None], int], - _1: Tuple[numpy.int64, numpy.int64], - /, - ): + def __setitem__(self, _0: int, _1: Literal["c"], /): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: int, _1: Tuple[numpy.int64, numpy.int64], /): + def __setitem__(self, _0: int, _1: Literal["d"], /): """ - usage.matplotlib: 4 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, _0: slice[numpy.int64, numpy.int64, numpy.int64], _1: bool, / - ): + def __setitem__(self, _0: int, _1: Literal["a"], /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[int, None, int], slice[int, None, int]], - _1: Tuple[numpy.ndarray, numpy.ndarray], - /, - ): + def __setitem__(self, _0: int, _1: Literal["j"], /): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[int, slice[int, None, int]], _1: list, /): + def __setitem__(self, _0: int, _1: Literal["x"], /): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[int, None, int], slice[int, None, int]], - _1: Tuple[list], - /, - ): + def __setitem__(self, _0: int, _1: Literal["b"], /): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[int, None, int], slice[int, None, int]], - _1: Tuple[Literal["a"]], - /, - ): + def __setitem__(self, _0: Tuple[slice[None, int, None], int], _1: float, /): """ - usage.matplotlib: 1 + usage.sklearn: 4 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, int, None], slice[int, None, int]], _1: int, / + self, _0: Tuple[slice[None, None, None], numpy.int32], _1: numpy.float64, / ): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload - def __setitem__(self, _0: numpy.bool_, _1: int, /): + def __setitem__(self, _0: slice[None, None, None], _1: numpy.float64, /): """ - usage.matplotlib: 5 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: slice[None, None, None], _1: List[float], /): + def __setitem__(self, _0: int, _1: numpy.int32, /): """ - usage.matplotlib: 1 + usage.sklearn: 4 """ ... @overload - def __setitem__(self, _0: slice[None, None, None], _1: List[numpy.float64], /): + def __setitem__(self, _0: Tuple[range, numpy.ndarray], _1: int, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, _0: int, _1: Tuple[numpy.float64, numpy.float64, numpy.float64, int], / - ): + def __setitem__(self, _0: Tuple[numpy.ndarray], _1: int, /): """ - usage.matplotlib: 3 + usage.sklearn: 2 """ ... @overload def __setitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, int, None] - ], - _1: numpy.ma.core.MaskedArray, - /, + self, _0: Tuple[slice[None, None, None], int], _1: numpy.float64, / ): """ - usage.matplotlib: 4 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, None, None], slice[None, None, None], int], - _1: float, - /, - ): + def __setitem__(self, _0: Tuple[numpy.ndarray, numpy.ndarray], _1: List[float], /): """ - usage.matplotlib: 5 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: slice[int, int, int], _1: bool, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, int], _1: int, /): """ - usage.matplotlib: 1 + usage.sklearn: 4 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, None, None], slice[None, None, None], int], - _1: numpy.uint8, - /, - ): + def __setitem__(self, _0: Tuple[int, int], _1: scipy.sparse.csr.csr_matrix, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[ - slice[None, None, None], slice[None, None, None], slice[None, int, None] - ], - _1: numpy.ndarray, - /, - ): + def __setitem__(self, _0: slice[None, None, None], _1: List[bool], /): """ - usage.matplotlib: 2 + usage.sklearn: 6 """ ... @overload - def __setitem__(self, _0: slice[numpy.int64, numpy.int64, numpy.int64], _1: int, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, numpy.int64], _1: float, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, None, None], int], _1: Tuple[float, int], / + self, _0: slice[numpy.int32, numpy.int32, numpy.int32], _1: float, / ): """ - usage.matplotlib: 2 + usage.sklearn: 3 """ ... @overload - def __setitem__( - self, _0: Tuple[int, slice[None, None, None]], _1: List[numpy.float64], / - ): + def __setitem__(self, _0: numpy.ndarray, _1: numpy.str_, /): """ - usage.matplotlib: 5 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: slice[None, None, None], _1: float, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, int], _1: None, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, _0: Tuple[slice[None, None, None], int], _1: Tuple[int, float], / - ): + def __setitem__(self, _0: Tuple[slice[None, None, None], int], _1: None, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: slice[int, int, int], _1: int, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, int], _1: Literal["abc"], /): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[None, None, None], int, int], _1: int, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, int], _1: numpy.int64, /): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, None, None], slice[int, int, int], slice[int, int, int]], - _1: numpy.ndarray, - /, - ): + def __setitem__(self, _0: Tuple[slice[None, None, None], int], _1: numpy.int64, /): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, None, None], int, slice[None, None, None]], - _1: numpy.ndarray, - /, - ): + def __setitem__(self, _0: Tuple[numpy.ndarray, int], _1: Literal["Female"], /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, - _0: Tuple[slice[None, None, None], slice[int, int, int]], - _1: numpy.ndarray, - /, - ): + def __setitem__(self, _0: numpy.ndarray, _1: Literal["a"], /): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[numpy.int32, numpy.int32], _1: numpy.float64, /): + def __setitem__(self, _0: Tuple[numpy.ndarray, int], _1: Literal["def"], /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[numpy.ndarray, int, int], _1: numpy.ndarray, /): + def __setitem__( + self, _0: slice[None, None, None], _1: List[Literal["1", "3", "2"]], / + ): """ - usage.matplotlib: 8 + usage.sklearn: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[slice[None, None, None], slice[int, None, int]], - _1: numpy.ndarray, - /, + self, _0: slice[None, None, None], _1: List[Literal["3", "2", "1"]], / ): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload def __setitem__( - self, - _0: Tuple[slice[int, None, int], slice[None, None, None]], - _1: numpy.ndarray, - /, + self, _0: slice[None, None, None], _1: List[Literal["a", "c", "b"]], / ): """ - usage.matplotlib: 4 + usage.sklearn: 1 """ ... @overload def __setitem__( - self, _0: Tuple[numpy.ma.core.MaskedArray, slice[None, None, None]], _1: int, / + self, _0: slice[None, None, None], _1: List[Literal["c", "b", "a"]], / ): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: numpy.ma.core.MaskedArray, _1: int, /): + def __setitem__(self, _0: Tuple[int, numpy.int32], _1: numpy.int64, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: slice[int, None, int], _1: bool, /): + def __setitem__(self, _0: Tuple[int, numpy.int32], _1: numpy.float64, /): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Tuple[slice[int, int, int], int], _1: List[float], /): + def __setitem__(self, _0: numpy.int64, _1: numpy.float32, /): """ - usage.matplotlib: 2 + usage.sklearn: 2 """ ... @overload - def __setitem__(self, _0: int, _1: Tuple[float, int], /): + def __setitem__( + self, _0: Tuple[slice[None, None, None], int], _1: numpy.ndarray, / + ): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload def __setitem__( - self, _0: Tuple[slice[None, None, None], int], _1: Tuple[int, numpy.float64], / + self, _0: Tuple[slice[None, None, None], Tuple[numpy.ndarray]], _1: float, / ): """ - usage.matplotlib: 1 + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: slice[None, int, None], _1: float, /): + """ + usage.sklearn: 2 """ ... @overload def __setitem__( - self, _0: Tuple[int, int], _1: matplotlib.axes._subplots.Axes3DSubplot, / + self, _0: Tuple[slice[None, None, None], slice[None, int, None]], _1: int, / ): """ - usage.matplotlib: 1 + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: slice[None, None, None], _1: List[List[int]], /): + """ + usage.sklearn: 1 """ ... @@ -59020,7 +72035,7 @@ def __setitem__(self, _0: object, _1: object, /): usage.sample-usage: 1 usage.scipy: 3994 usage.skimage: 1348 - usage.sklearn: 1434 + usage.sklearn: 1435 usage.xarray: 178 """ ... @@ -59036,6 +72051,7 @@ def __sub__(self, _0: numpy.ndarray, /): """ usage.matplotlib: 133 usage.skimage: 183 + usage.sklearn: 526 usage.xarray: 35 """ ... @@ -59045,6 +72061,7 @@ def __sub__(self, _0: float, /): """ usage.matplotlib: 34 usage.skimage: 20 + usage.sklearn: 36 usage.xarray: 3 """ ... @@ -59055,6 +72072,7 @@ def __sub__(self, _0: int, /): usage.matplotlib: 34 usage.sample-usage: 1 usage.skimage: 54 + usage.sklearn: 85 usage.xarray: 9 """ ... @@ -59064,6 +72082,7 @@ def __sub__(self, _0: numpy.float64, /): """ usage.matplotlib: 11 usage.skimage: 31 + usage.sklearn: 51 usage.xarray: 1 """ ... @@ -59073,6 +72092,7 @@ def __sub__(self, _0: numpy.int64, /): """ usage.matplotlib: 1 usage.skimage: 9 + usage.sklearn: 1 usage.xarray: 3 """ ... @@ -59137,6 +72157,7 @@ def __sub__(self, _0: numpy.float16, /): def __sub__(self, _0: numpy.float32, /): """ usage.skimage: 1 + usage.sklearn: 3 """ ... @@ -59265,7 +72286,6 @@ def __sub__(self, _0: object, /): """ usage.pandas: 228 usage.scipy: 2435 - usage.sklearn: 705 """ ... @@ -59290,6 +72310,20 @@ def __sub__(self, _0: Union[numpy.ndarray, float, int], /): """ ... + @overload + def __sub__(self, _0: List[float], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __sub__(self, _0: numpy.memmap, /): + """ + usage.sklearn: 1 + """ + ... + def __sub__(self, _0: object, /): """ usage.dask: 45 @@ -59308,6 +72342,7 @@ def __truediv__(self, _0: numpy.float64, /): """ usage.matplotlib: 24 usage.skimage: 35 + usage.sklearn: 86 """ ... @@ -59316,6 +72351,7 @@ def __truediv__(self, _0: numpy.ndarray, /): """ usage.matplotlib: 37 usage.skimage: 68 + usage.sklearn: 204 usage.xarray: 1 """ ... @@ -59325,6 +72361,7 @@ def __truediv__(self, _0: float, /): """ usage.matplotlib: 48 usage.skimage: 62 + usage.sklearn: 72 """ ... @@ -59333,6 +72370,7 @@ def __truediv__(self, _0: int, /): """ usage.matplotlib: 53 usage.skimage: 44 + usage.sklearn: 88 usage.xarray: 4 """ ... @@ -59342,6 +72380,7 @@ def __truediv__(self, _0: numpy.int64, /): """ usage.matplotlib: 2 usage.skimage: 6 + usage.sklearn: 15 usage.xarray: 1 """ ... @@ -59358,7 +72397,6 @@ def __truediv__(self, _0: object, /): """ usage.pandas: 420 usage.scipy: 1657 - usage.sklearn: 471 """ ... @@ -59366,6 +72404,7 @@ def __truediv__(self, _0: object, /): def __truediv__(self, _0: numpy.float32, /): """ usage.matplotlib: 1 + usage.sklearn: 4 """ ... @@ -59378,6 +72417,20 @@ def __truediv__( """ ... + @overload + def __truediv__(self, _0: numpy.matrix, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __truediv__(self, _0: numpy.memmap, /): + """ + usage.sklearn: 1 + """ + ... + def __truediv__(self, _0: object, /): """ usage.dask: 23 @@ -59416,6 +72469,7 @@ def all(self, /): """ usage.matplotlib: 16 usage.skimage: 28 + usage.sklearn: 68 usage.xarray: 45 """ ... @@ -59438,6 +72492,7 @@ def all(self, /, *, axis: int = ..., keepdims: bool = ...): def all(self, /, *, axis: int): """ usage.matplotlib: 1 + usage.sklearn: 4 """ ... @@ -59445,7 +72500,6 @@ def all(self, /, *, axis: int): def all(self, /, *, axis: int = ...): """ usage.dask: 90 - usage.sklearn: 72 """ ... @@ -59469,6 +72523,7 @@ def any(self, /): usage.dask: 14 usage.matplotlib: 21 usage.skimage: 15 + usage.sklearn: 49 usage.xarray: 10 """ ... @@ -59484,7 +72539,6 @@ def any(self, _0: Union[None, int] = ..., /, *, axis: int = ...): def any(self, /, *, axis: int = ...): """ usage.scipy: 105 - usage.sklearn: 55 """ ... @@ -59492,6 +72546,7 @@ def any(self, /, *, axis: int = ...): def any(self, /, *, axis: int): """ usage.matplotlib: 1 + usage.sklearn: 6 """ ... @@ -59511,6 +72566,7 @@ def any(self, _0: Union[None, int] = ..., /, *, axis: int = ...): def argmax(self, /, *, axis: int): """ usage.skimage: 1 + usage.sklearn: 28 """ ... @@ -59519,6 +72575,7 @@ def argmax(self, /): """ usage.matplotlib: 2 usage.skimage: 5 + usage.sklearn: 11 usage.xarray: 2 """ ... @@ -59527,6 +72584,7 @@ def argmax(self, /): def argmax(self, _0: int, /): """ usage.skimage: 1 + usage.sklearn: 1 """ ... @@ -59544,13 +72602,6 @@ def argmax(self, /, *, axis: int = ...): """ ... - @overload - def argmax(self, _0: int = ..., /, *, axis: int = ...): - """ - usage.sklearn: 40 - """ - ... - def argmax(self, _0: Union[int, None] = ..., /, *, axis: int = ...): """ usage.matplotlib: 2 @@ -59567,6 +72618,7 @@ def argmin(self, /): """ usage.matplotlib: 3 usage.skimage: 1 + usage.sklearn: 4 usage.xarray: 2 """ ... @@ -59591,13 +72643,7 @@ def argmin(self, /, *, axis: int): usage.dask: 2 usage.matplotlib: 1 usage.scipy: 1 - """ - ... - - @overload - def argmin(self, /, *, axis: int = ...): - """ - usage.sklearn: 10 + usage.sklearn: 6 """ ... @@ -59668,6 +72714,7 @@ def argsort( def astype(self, _0: Literal["float32"], /): """ usage.skimage: 22 + usage.sklearn: 3 """ ... @@ -59676,6 +72723,7 @@ def astype(self, _0: Type[numpy.int64], /): """ usage.matplotlib: 5 usage.skimage: 7 + usage.sklearn: 21 usage.xarray: 3 """ ... @@ -59685,6 +72733,7 @@ def astype(self, _0: Type[numpy.uint8], /): """ usage.matplotlib: 4 usage.skimage: 70 + usage.sklearn: 3 """ ... @@ -59692,6 +72741,7 @@ def astype(self, _0: Type[numpy.uint8], /): def astype(self, _0: Type[numpy.uint16], /): """ usage.skimage: 17 + usage.sklearn: 1 """ ... @@ -59701,6 +72751,7 @@ def astype(self, _0: Type[int], /): usage.matplotlib: 17 usage.sample-usage: 1 usage.skimage: 20 + usage.sklearn: 54 usage.xarray: 154 """ ... @@ -59709,6 +72760,7 @@ def astype(self, _0: Type[int], /): def astype(self, _0: numpy.dtype, /): """ usage.skimage: 46 + usage.sklearn: 8 usage.xarray: 14 """ ... @@ -59718,6 +72770,7 @@ def astype(self, _0: Type[float], /): """ usage.matplotlib: 13 usage.skimage: 75 + usage.sklearn: 14 usage.xarray: 160 """ ... @@ -59727,6 +72780,7 @@ def astype(self, _0: Type[bool], /): """ usage.matplotlib: 8 usage.skimage: 29 + usage.sklearn: 12 """ ... @@ -59735,6 +72789,7 @@ def astype(self, _0: Type[numpy.float64], /): """ usage.matplotlib: 4 usage.skimage: 47 + usage.sklearn: 79 usage.xarray: 12 """ ... @@ -59743,6 +72798,7 @@ def astype(self, _0: Type[numpy.float64], /): def astype(self, _0: Literal["float64"], /): """ usage.skimage: 17 + usage.sklearn: 3 """ ... @@ -59751,6 +72807,7 @@ def astype(self, _0: Literal["uint8"], /): """ usage.matplotlib: 2 usage.skimage: 6 + usage.sklearn: 1 """ ... @@ -59765,6 +72822,7 @@ def astype(self, _0: Literal["int"], /): def astype(self, _0: Literal["float"], /): """ usage.skimage: 3 + usage.sklearn: 3 """ ... @@ -59772,6 +72830,7 @@ def astype(self, _0: Literal["float"], /): def astype(self, _0: Type[numpy.float32], /): """ usage.skimage: 33 + usage.sklearn: 89 usage.xarray: 11 """ ... @@ -59780,6 +72839,7 @@ def astype(self, _0: Type[numpy.float32], /): def astype(self, _0: Type[numpy.int32], /): """ usage.skimage: 12 + usage.sklearn: 17 """ ... @@ -59787,6 +72847,7 @@ def astype(self, _0: Type[numpy.int32], /): def astype(self, _0: Type[numpy.uint32], /): """ usage.skimage: 4 + usage.sklearn: 3 """ ... @@ -59794,6 +72855,7 @@ def astype(self, _0: Type[numpy.uint32], /): def astype(self, _0: Type[numpy.float16], /): """ usage.skimage: 3 + usage.sklearn: 2 """ ... @@ -59802,6 +72864,7 @@ def astype(self, _0: Type[numpy.int16], /): """ usage.matplotlib: 3 usage.skimage: 14 + usage.sklearn: 1 """ ... @@ -59816,6 +72879,7 @@ def astype(self, _0: Type[numpy.uint64], /): def astype(self, _0: Type[numpy.int8], /): """ usage.skimage: 5 + usage.sklearn: 2 """ ... @@ -59823,6 +72887,7 @@ def astype(self, _0: Type[numpy.int8], /): def astype(self, _0: Type[numpy.bool_], /): """ usage.skimage: 1 + usage.sklearn: 7 usage.xarray: 1 """ ... @@ -59831,6 +72896,7 @@ def astype(self, _0: Type[numpy.bool_], /): def astype(self, _0: Type[numpy.float64], /, *, copy: bool): """ usage.skimage: 1 + usage.sklearn: 55 """ ... @@ -59838,6 +72904,7 @@ def astype(self, _0: Type[numpy.float64], /, *, copy: bool): def astype(self, _0: numpy.dtype, /, *, copy: bool): """ usage.skimage: 27 + usage.sklearn: 29 usage.xarray: 35 """ ... @@ -59860,6 +72927,7 @@ def astype(self, _0: Literal["double"], /): def astype(self, _0: Literal["int64"], /): """ usage.skimage: 1 + usage.sklearn: 7 """ ... @@ -59908,6 +72976,7 @@ def astype(self, _0: Literal["int32"], /): @overload def astype(self, _0: Type[str], /): """ + usage.sklearn: 4 usage.xarray: 4 """ ... @@ -59965,6 +73034,7 @@ def astype(self, _0: Literal["bool"], /): @overload def astype(self, _0: Type[object], /): """ + usage.sklearn: 4 usage.xarray: 6 """ ... @@ -60069,6 +73139,7 @@ def astype( def astype(self, _0: Type[float], /, *, copy: bool): """ usage.matplotlib: 2 + usage.sklearn: 5 """ ... @@ -60107,18 +73178,159 @@ def astype( """ ... + @overload + def astype(self, _0: Type[numpy.int64], /, *, copy: bool): + """ + usage.sklearn: 18 + """ + ... + + @overload + def astype(self, _0: Type[int], /, *, copy: bool): + """ + usage.sklearn: 8 + """ + ... + + @overload + def astype(self, _0: Literal["float64"], /, *, copy: bool): + """ + usage.sklearn: 3 + """ + ... + + @overload + def astype(self, /, *, copy: bool, dtype: Type[bool]): + """ + usage.sklearn: 3 + """ + ... + + @overload + def astype(self, _0: Type[numpy.float32], /, *, copy: bool): + """ + usage.sklearn: 53 + """ + ... + + @overload + def astype(self, /, *, copy: bool, dtype: Literal[">u4"]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, _0: Type[numpy.int32], /, *, copy: bool): + """ + usage.sklearn: 4 + """ + ... + + @overload + def astype(self, _0: Literal["float"], /, *, copy: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, _0: Literal["O"], /): + """ + usage.sklearn: 8 + """ + ... + + @overload + def astype(self, _0: Literal["intp"], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def astype(self, _0: Literal["float32"], /, *, copy: bool): + """ + usage.sklearn: 2 + """ + ... + + @overload + def astype(self, _0: Type[numpy.bool_], /, *, copy: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, _0: Type[numpy.uint64], /, *, copy: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, _0: None, /, *, copy: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, _0: Type[object], /, *, copy: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, _0: Literal["str"], /): + """ + usage.sklearn: 5 + """ + ... + + @overload + def astype(self, /, *, copy: bool, dtype: Type[int]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, _0: Type[int], /, *, casting: Literal["unsafe"], copy: bool): + """ + usage.sklearn: 3 + """ + ... + + @overload + def astype(self, _0: Literal["int64"], /, *, copy: bool): + """ + usage.sklearn: 2 + """ + ... + + @overload + def astype(self, _0: Literal["int"], /, *, casting: Literal["unsafe"], copy: bool): + """ + usage.sklearn: 6 + """ + ... + + @overload + def astype(self, _0: Literal["S32"], /): + """ + usage.sklearn: 2 + """ + ... + @overload def astype( - self, - _0: Union[type, str, None, numpy.dtype] = ..., - /, - *, - copy: bool = ..., - dtype: Union[type, Literal[">u4"]] = ..., - casting: Literal["unsafe"] = ..., + self, _0: Type[numpy.int32], /, *, casting: Literal["unsafe"], copy: bool ): """ - usage.sklearn: 555 + usage.sklearn: 2 """ ... @@ -60287,6 +73499,7 @@ def copy(self, /): usage.matplotlib: 21 usage.pandas: 284 usage.skimage: 93 + usage.sklearn: 213 usage.xarray: 17 """ ... @@ -60299,9 +73512,30 @@ def copy(self, _0: Literal["F", "C"] = ..., /, *, order: Literal["F", "C"] = ... ... @overload - def copy(self, _0: Literal["F", "C"] = ..., /, *, order: Literal["F", "K"] = ...): + def copy(self, _0: Literal["C"], /): """ - usage.sklearn: 227 + usage.sklearn: 3 + """ + ... + + @overload + def copy(self, _0: Literal["F"], /): + """ + usage.sklearn: 7 + """ + ... + + @overload + def copy(self, /, *, order: Literal["K"]): + """ + usage.sklearn: 2 + """ + ... + + @overload + def copy(self, /, *, order: Literal["F"]): + """ + usage.sklearn: 2 """ ... @@ -60387,6 +73621,7 @@ def dot(self, _0: numpy.ndarray, /): """ usage.matplotlib: 1 usage.skimage: 1 + usage.sklearn: 82 """ ... @@ -60405,9 +73640,9 @@ def dot(self, _0: Union[numpy.ndarray, dask.array.core.Array], /): ... @overload - def dot(self, _0: Union[numpy.bool_, numpy.ndarray], /): + def dot(self, _0: numpy.bool_, /): """ - usage.sklearn: 83 + usage.sklearn: 1 """ ... @@ -60427,6 +73662,7 @@ def dot( def fill(self, _0: int, /): """ usage.skimage: 4 + usage.sklearn: 15 """ ... @@ -60434,6 +73670,7 @@ def fill(self, _0: int, /): def fill(self, _0: bool, /): """ usage.skimage: 1 + usage.sklearn: 2 """ ... @@ -60446,9 +73683,16 @@ def fill(self, _0: object, /): ... @overload - def fill(self, _0: Union[numpy.float64, float, bool, int], /): + def fill(self, _0: float, /): """ - usage.sklearn: 29 + usage.sklearn: 10 + """ + ... + + @overload + def fill(self, _0: numpy.float64, /): + """ + usage.sklearn: 2 """ ... @@ -60553,6 +73797,7 @@ def max(self, /): """ usage.matplotlib: 57 usage.skimage: 123 + usage.sklearn: 71 usage.xarray: 16 """ ... @@ -60579,9 +73824,9 @@ def max(self, /, *, axis: int = ..., keepdims: bool = ...): ... @overload - def max(self, /, *, axis: int = ...): + def max(self, /, *, axis: int): """ - usage.sklearn: 88 + usage.sklearn: 17 """ ... @@ -60605,6 +73850,7 @@ def mean(self, /): usage.matplotlib: 6 usage.sample-usage: 2 usage.skimage: 26 + usage.sklearn: 93 """ ... @@ -60613,6 +73859,7 @@ def mean(self, /, *, axis: int): """ usage.matplotlib: 8 usage.skimage: 16 + usage.sklearn: 114 usage.xarray: 2 """ ... @@ -60621,6 +73868,7 @@ def mean(self, /, *, axis: int): def mean(self, _0: int, /): """ usage.skimage: 2 + usage.sklearn: 43 usage.xarray: 1 """ ... @@ -60695,13 +73943,6 @@ def mean( """ ... - @overload - def mean(self, _0: int = ..., /, *, axis: int = ...): - """ - usage.sklearn: 250 - """ - ... - def mean( self, _0: Union[int, None] = ..., @@ -60728,6 +73969,7 @@ def min(self, /): """ usage.matplotlib: 50 usage.skimage: 89 + usage.sklearn: 71 usage.xarray: 18 """ ... @@ -60750,7 +73992,6 @@ def min(self, _0: Union[int, None] = ..., /, *, axis: int = ...): def min(self, /, *, axis: int = ...): """ usage.scipy: 84 - usage.sklearn: 82 """ ... @@ -60763,6 +74004,13 @@ def min( """ ... + @overload + def min(self, /, *, axis: int): + """ + usage.sklearn: 11 + """ + ... + def min( self, _0: Union[None, int] = ..., @@ -60984,9 +74232,16 @@ def repeat(self, _0: int, /, *, axis: int = ...): ... @overload - def repeat(self, _0: Union[Tuple[int], numpy.ndarray], /): + def repeat(self, _0: numpy.ndarray, /): """ - usage.sklearn: 3 + usage.sklearn: 2 + """ + ... + + @overload + def repeat(self, _0: Tuple[int], /): + """ + usage.sklearn: 1 """ ... @@ -61015,6 +74270,7 @@ def reshape(self, _0: Tuple[int, int], /): usage.matplotlib: 50 usage.sample-usage: 1 usage.skimage: 34 + usage.sklearn: 85 usage.xarray: 51 """ ... @@ -61025,6 +74281,7 @@ def reshape(self, _0: int, _1: int, /): usage.matplotlib: 35 usage.sample-usage: 1 usage.skimage: 52 + usage.sklearn: 230 usage.xarray: 86 """ ... @@ -61048,6 +74305,7 @@ def reshape(self, _0: Tuple[int, int, int], /): """ usage.matplotlib: 20 usage.skimage: 12 + usage.sklearn: 12 usage.xarray: 27 """ ... @@ -61056,6 +74314,7 @@ def reshape(self, _0: Tuple[int, int, int], /): def reshape(self, _0: Tuple[int, int, int, int], /): """ usage.skimage: 7 + usage.sklearn: 2 usage.xarray: 13 """ ... @@ -61072,6 +74331,7 @@ def reshape(self, _0: Tuple[int], /): """ usage.matplotlib: 4 usage.skimage: 3 + usage.sklearn: 7 usage.xarray: 38 """ ... @@ -61090,6 +74350,7 @@ def reshape(self, _0: int, /): """ usage.matplotlib: 3 usage.skimage: 8 + usage.sklearn: 22 usage.xarray: 14 """ ... @@ -61120,6 +74381,7 @@ def reshape(self, _0: Tuple[int, numpy.int64, numpy.int64], /): def reshape(self, _0: int, _1: int, _2: int, _3: int, /): """ usage.skimage: 2 + usage.sklearn: 5 usage.xarray: 2 """ ... @@ -61129,6 +74391,7 @@ def reshape(self, _0: int, _1: int, _2: int, /): """ usage.matplotlib: 1 usage.skimage: 7 + usage.sklearn: 10 usage.xarray: 36 """ ... @@ -61194,17 +74457,9 @@ def reshape( ... @overload - def reshape( - self, - _0: Union[Tuple[int, ...], int], - _1: int = ..., - _2: int = ..., - _3: int = ..., - _4: int = ..., - /, - ): + def reshape(self, _0: int, _1: int, _2: int, _3: int, _4: int, /): """ - usage.sklearn: 374 + usage.sklearn: 1 """ ... @@ -61243,6 +74498,7 @@ def resize(self, _0: Tuple[int, int, int], /): def round(self, /): """ usage.scipy: 2 + usage.sklearn: 6 usage.xarray: 2 """ ... @@ -61251,6 +74507,7 @@ def round(self, /): def round(self, _0: int, /): """ usage.dask: 2 + usage.sklearn: 4 usage.xarray: 2 """ ... @@ -61270,9 +74527,9 @@ def round(self, _0: Union[numpy.int64, int] = ..., /): ... @overload - def round(self, _0: int = ..., /, *, decimals: int = ...): + def round(self, /, *, decimals: int): """ - usage.sklearn: 12 + usage.sklearn: 2 """ ... @@ -61467,6 +74724,7 @@ def searchsorted(self, _0: float, /): def searchsorted(self, _0: numpy.float64, /): """ usage.matplotlib: 2 + usage.sklearn: 2 """ ... @@ -61485,15 +74743,37 @@ def searchsorted(self, _0: numpy.float64, _1: Literal["right"], /): ... @overload - def searchsorted( - self, - _0: Union[ - Literal["two", "three", "one"], numpy.int64, numpy.float64, numpy.str_ - ], - /, - ): + def searchsorted(self, _0: numpy.int64, /): """ - usage.sklearn: 10 + usage.sklearn: 1 + """ + ... + + @overload + def searchsorted(self, _0: numpy.str_, /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def searchsorted(self, _0: Literal["one"], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def searchsorted(self, _0: Literal["two"], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def searchsorted(self, _0: Literal["three"], /): + """ + usage.sklearn: 1 """ ... @@ -61533,6 +74813,7 @@ def sort(self, /, *, axis: int): """ usage.sample-usage: 1 usage.skimage: 1 + usage.sklearn: 2 """ ... @@ -61542,6 +74823,7 @@ def sort(self, /): usage.matplotlib: 4 usage.pandas: 3 usage.sample-usage: 1 + usage.sklearn: 5 """ ... @@ -61559,13 +74841,6 @@ def sort(self, /, *, kind: Literal["mergesort"] = ...): """ ... - @overload - def sort(self, /, *, axis: int = ...): - """ - usage.sklearn: 7 - """ - ... - def sort( self, /, @@ -61598,6 +74873,7 @@ def squeeze(self, /): usage.matplotlib: 6 usage.pandas: 12 usage.scipy: 40 + usage.sklearn: 17 usage.xarray: 3 """ ... @@ -61610,9 +74886,9 @@ def squeeze(self, /, *, axis: Union[Tuple[int, int], None, int]): ... @overload - def squeeze(self, /, *, axis: int = ...): + def squeeze(self, /, *, axis: int): """ - usage.sklearn: 21 + usage.sklearn: 4 """ ... @@ -61633,6 +74909,7 @@ def squeeze( def std(self, /): """ usage.skimage: 64 + usage.sklearn: 13 usage.xarray: 2 """ ... @@ -61640,6 +74917,7 @@ def std(self, /): @overload def std(self, /, *, axis: int): """ + usage.sklearn: 18 usage.xarray: 1 """ ... @@ -61655,7 +74933,6 @@ def std(self, /, *, axis: Tuple[int, int]): def std(self, /, *, axis: int = ..., ddof: int = ...): """ usage.pandas: 8 - usage.sklearn: 36 """ ... @@ -61681,6 +74958,13 @@ def std(self, /, *, keepdims: bool = ...): """ ... + @overload + def std(self, /, *, axis: int, ddof: int): + """ + usage.sklearn: 5 + """ + ... + def std( self, _0: int = ..., @@ -61705,6 +74989,7 @@ def sum(self, /): """ usage.matplotlib: 14 usage.skimage: 68 + usage.sklearn: 242 usage.xarray: 8 """ ... @@ -61713,6 +74998,7 @@ def sum(self, /): def sum(self, /, *, axis: int): """ usage.skimage: 15 + usage.sklearn: 164 usage.xarray: 3 """ ... @@ -61722,6 +75008,7 @@ def sum(self, _0: int, /): """ usage.matplotlib: 2 usage.skimage: 17 + usage.sklearn: 39 usage.xarray: 4 """ ... @@ -61770,17 +75057,23 @@ def sum( ... @overload - def sum( - self, - _0: int = ..., - /, - *, - axis: Union[None, int] = ..., - dtype: Type[numpy.float64] = ..., - keepdims: bool = ..., - ): + def sum(self, /, *, axis: int, dtype: Type[numpy.float64]): """ - usage.sklearn: 458 + usage.sklearn: 7 + """ + ... + + @overload + def sum(self, /, *, axis: int, keepdims: bool): + """ + usage.sklearn: 4 + """ + ... + + @overload + def sum(self, /, *, axis: None): + """ + usage.sklearn: 2 """ ... @@ -61819,6 +75112,7 @@ def swapaxes(self, _0: int, _1: int, /): @overload def take(self, _0: numpy.ndarray, /, *, axis: int): """ + usage.sklearn: 69 usage.xarray: 6 """ ... @@ -61858,20 +75152,42 @@ def take(self, _0: numpy.ndarray, /, *, axis: int, mode: Literal["clip"]): def take(self, _0: numpy.ndarray, /): """ usage.dask: 1 + usage.sklearn: 29 """ ... @overload - def take( - self, - _0: Union[numpy.ndarray, int, List[Union[numpy.int64, int]]], - /, - *, - axis: Union[int, None] = ..., - mode: Literal["clip"] = ..., - ): + def take(self, _0: int, /, *, axis: None): """ - usage.sklearn: 123 + usage.sklearn: 1 + """ + ... + + @overload + def take(self, _0: List[numpy.int64], /, *, axis: int): + """ + usage.sklearn: 6 + """ + ... + + @overload + def take(self, _0: numpy.ndarray, /, *, mode: Literal["clip"]): + """ + usage.sklearn: 13 + """ + ... + + @overload + def take(self, _0: int, /, *, axis: int): + """ + usage.sklearn: 1 + """ + ... + + @overload + def take(self, _0: List[int], /, *, axis: int): + """ + usage.sklearn: 4 """ ... @@ -61980,6 +75296,7 @@ def trace( def transpose(self, _0: int, _1: int, _2: int, /): """ usage.skimage: 1 + usage.sklearn: 2 """ ... @@ -61988,6 +75305,7 @@ def transpose(self, /): """ usage.matplotlib: 2 usage.skimage: 4 + usage.sklearn: 8 """ ... @@ -62008,6 +75326,7 @@ def transpose(self, _0: Tuple[int, int], /): @overload def transpose(self, _0: Tuple[int, int, int], /): """ + usage.sklearn: 2 usage.xarray: 3 """ ... @@ -62069,19 +75388,6 @@ def transpose( """ ... - @overload - def transpose( - self, - _0: Union[Tuple[int, int, int], int] = ..., - _1: int = ..., - _2: int = ..., - /, - ): - """ - usage.sklearn: 12 - """ - ... - def transpose( self, /, *_args: Union[int, numpy.ndarray, range, Tuple[int, ...], List[int]] ): @@ -62101,6 +75407,7 @@ def var(self, /, *, axis: int): """ usage.dask: 3 usage.skimage: 1 + usage.sklearn: 4 """ ... @@ -62108,6 +75415,7 @@ def var(self, /, *, axis: int): def var(self, /): """ usage.skimage: 6 + usage.sklearn: 3 """ ... @@ -62115,7 +75423,6 @@ def var(self, /): def var(self, /, *, axis: int = ...): """ usage.pandas: 2 - usage.sklearn: 7 """ ... @@ -62168,6 +75475,7 @@ def view(self, _0: Type[numpy.int64], /): def view(self, /): """ usage.skimage: 10 + usage.sklearn: 1 """ ... @@ -62182,6 +75490,7 @@ def view(self, _0: Type[numpy.int16], /): def view(self, _0: numpy.dtype, /): """ usage.skimage: 2 + usage.sklearn: 1 """ ... @@ -62314,17 +75623,37 @@ def view(self, _0: Union[Literal["i1", "i4", "i2"], numpy.dtype] = ..., /): ... @overload - def view( - self, - _0: Union[ - numpy.dtype, Literal[">u1"], List[Tuple[Literal[""], numpy.dtype]] - ] = ..., - /, - *, - dtype: Literal["|S512", "|S80", "|S16"] = ..., - ): + def view(self, _0: Literal[">u1"], /): """ - usage.sklearn: 7 + usage.sklearn: 1 + """ + ... + + @overload + def view(self, _0: List[Tuple[Literal[""], numpy.dtype]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def view(self, /, *, dtype: Literal["|S16"]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def view(self, /, *, dtype: Literal["|S80"]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def view(self, /, *, dtype: Literal["|S512"]): + """ + usage.sklearn: 1 """ ... @@ -62685,6 +76014,7 @@ def __eq__(self, _0: xarray.core.variable.Variable, /): @overload def __eq__(self, _0: numpy.ndarray, /): """ + usage.sklearn: 23 usage.xarray: 1 """ ... @@ -62697,9 +76027,16 @@ def __eq__(self, _0: Union[str, numpy.str_], /): ... @overload - def __eq__(self, _0: Union[numpy.int64, numpy.ndarray, numpy.str_], /): + def __eq__(self, _0: numpy.str_, /): """ - usage.sklearn: 26 + usage.sklearn: 2 + """ + ... + + @overload + def __eq__(self, _0: numpy.int64, /): + """ + usage.sklearn: 1 """ ... @@ -62919,9 +76256,30 @@ def __ne__(self, _0: Literal["b"], /): ... @overload - def __ne__(self, _0: Union[numpy.ndarray, Literal["foo", "baz", "bar"]], /): + def __ne__(self, _0: Literal["bar"], /): """ - usage.sklearn: 9 + usage.sklearn: 1 + """ + ... + + @overload + def __ne__(self, _0: Literal["baz"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __ne__(self, _0: Literal["foo"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __ne__(self, _0: numpy.ndarray, /): + """ + usage.sklearn: 6 """ ... @@ -63025,6 +76383,20 @@ def __radd__(self, _0: str, /): """ ... + @overload + def __rmod__(self, _0: Literal["%s"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __rmod__(self, _0: Literal["not %s"], /): + """ + usage.sklearn: 1 + """ + ... + def __rmod__(self, _0: Literal["not %s", "%s"], /): """ usage.sklearn: 2 @@ -63519,6 +76891,7 @@ def __call__(self, _0: numpy.ndarray, /): usage.matplotlib: 317 usage.sample-usage: 2 usage.skimage: 255 + usage.sklearn: 810 usage.xarray: 79 """ ... @@ -63528,6 +76901,7 @@ def __call__(self, _0: numpy.int64, /): """ usage.matplotlib: 6 usage.skimage: 3 + usage.sklearn: 7 """ ... @@ -63536,6 +76910,7 @@ def __call__(self, _0: numpy.float64, /): """ usage.matplotlib: 167 usage.skimage: 87 + usage.sklearn: 140 usage.xarray: 4 """ ... @@ -63552,6 +76927,7 @@ def __call__(self, _0: int, /): """ usage.matplotlib: 58 usage.skimage: 57 + usage.sklearn: 63 usage.xarray: 4 """ ... @@ -63567,6 +76943,7 @@ def __call__(self, _0: numpy.ndarray, _1: int, /, *, dtype: Type[numpy.float64]) def __call__(self, _0: numpy.ndarray, /, *, out: numpy.ndarray): """ usage.skimage: 7 + usage.sklearn: 19 """ ... @@ -63575,6 +76952,7 @@ def __call__(self, _0: numpy.ndarray, _1: float, /): """ usage.matplotlib: 1 usage.skimage: 9 + usage.sklearn: 28 usage.xarray: 3 """ ... @@ -63584,6 +76962,7 @@ def __call__(self, _0: int, _1: numpy.ndarray, /): """ usage.matplotlib: 3 usage.skimage: 2 + usage.sklearn: 12 usage.xarray: 4 """ ... @@ -63593,6 +76972,7 @@ def __call__(self, _0: List[float], /): """ usage.matplotlib: 6 usage.skimage: 2 + usage.sklearn: 4 """ ... @@ -63602,6 +76982,7 @@ def __call__(self, _0: numpy.ndarray, _1: numpy.ndarray, /): usage.matplotlib: 42 usage.sample-usage: 1 usage.skimage: 33 + usage.sklearn: 74 usage.xarray: 31 """ ... @@ -63610,6 +76991,7 @@ def __call__(self, _0: numpy.ndarray, _1: numpy.ndarray, /): def __call__(self, _0: numpy.ndarray, _1: int, /): """ usage.skimage: 9 + usage.sklearn: 43 usage.xarray: 6 """ ... @@ -63619,6 +77001,7 @@ def __call__(self, _0: numpy.float64, _1: numpy.float64, /): """ usage.matplotlib: 17 usage.skimage: 5 + usage.sklearn: 6 """ ... @@ -63633,6 +77016,7 @@ def __call__(self, _0: numpy.bool_, _1: numpy.bool_, /): def __call__(self, _0: numpy.float64, _1: int, /): """ usage.skimage: 2 + usage.sklearn: 1 """ ... @@ -63641,6 +77025,7 @@ def __call__(self, _0: float, /): """ usage.matplotlib: 102 usage.skimage: 58 + usage.sklearn: 83 usage.xarray: 7 """ ... @@ -63663,6 +77048,7 @@ def __call__(self, _0: Tuple[int, int], _1: numpy.ndarray, /): def __call__(self, _0: numpy.float64, _1: float, /): """ usage.skimage: 2 + usage.sklearn: 1 """ ... @@ -63707,6 +77093,7 @@ def __call__(self, _0: numpy.ndarray, _1: float, /, *, dtype: Type[numpy.float32 def __call__(self, _0: numpy.ndarray, _1: numpy.ndarray, /, *, out: numpy.ndarray): """ usage.skimage: 2 + usage.sklearn: 10 """ ... @@ -63721,6 +77108,7 @@ def __call__(self, _0: numpy.ndarray, _1: numpy.int64, /, *, out: numpy.ndarray) def __call__(self, _0: numpy.ndarray, _1: int, /, *, out: numpy.ndarray): """ usage.skimage: 1 + usage.sklearn: 2 """ ... @@ -63780,6 +77168,7 @@ def __call__(self, _0: numpy.int64, _1: numpy.int64, /): def __call__(self, _0: numpy.ndarray, _1: numpy.float64, /): """ usage.skimage: 1 + usage.sklearn: 14 usage.xarray: 1 """ ... @@ -63802,6 +77191,7 @@ def __call__(self, _0: numpy.ndarray, /, *, dtype: numpy.dtype): def __call__(self, _0: numpy.float32, /): """ usage.skimage: 1 + usage.sklearn: 11 """ ... @@ -63824,6 +77214,7 @@ def __call__(self, _0: numpy.ma.core.MaskedArray, /): """ usage.matplotlib: 18 usage.skimage: 2 + usage.sklearn: 1 """ ... @@ -63831,6 +77222,7 @@ def __call__(self, _0: numpy.ma.core.MaskedArray, /): def __call__(self, _0: List[numpy.ndarray], /): """ usage.skimage: 2 + usage.sklearn: 1 """ ... @@ -63839,6 +77231,7 @@ def __call__(self, _0: int, _1: int, /): """ usage.matplotlib: 2 usage.skimage: 1 + usage.sklearn: 3 usage.xarray: 4 """ ... @@ -64103,6 +77496,7 @@ def __call__( @overload def __call__(self, _0: numpy.float64, _1: numpy.ndarray, /): """ + usage.sklearn: 1 usage.xarray: 1 """ ... @@ -64195,6 +77589,7 @@ def __call__( def __call__(self, _0: float, _1: numpy.ndarray, /): """ usage.matplotlib: 3 + usage.sklearn: 4 usage.xarray: 1 """ ... @@ -64304,6 +77699,7 @@ def __call__(self, _0: object, _1: int, /): @overload def __call__(self, _0: bool, /): """ + usage.sklearn: 1 usage.xarray: 1 """ ... @@ -64377,6 +77773,7 @@ def __call__(self, _0: Tuple[int, int], /): def __call__(self, _0: range, /): """ usage.matplotlib: 2 + usage.sklearn: 1 """ ... @@ -64398,6 +77795,7 @@ def __call__(self, _0: Tuple[float, float], /): def __call__(self, _0: float, _1: float, /): """ usage.matplotlib: 2 + usage.sklearn: 9 """ ... @@ -64419,6 +77817,7 @@ def __call__(self, _0: List[Union[numpy.float64, int]], /): def __call__(self, _0: List[numpy.float64], /): """ usage.matplotlib: 3 + usage.sklearn: 1 """ ... @@ -64475,6 +77874,7 @@ def __call__(self, _0: List[Union[int, float]], /): def __call__(self, _0: numpy.ndarray, _1: int, _2: numpy.ndarray, /): """ usage.matplotlib: 1 + usage.sklearn: 1 """ ... @@ -64503,6 +77903,7 @@ def __call__(self, _0: Tuple[numpy.float64, numpy.float64], /): def __call__(self, _0: int, _1: numpy.float64, /): """ usage.matplotlib: 1 + usage.sklearn: 1 """ ... @@ -64548,20 +77949,130 @@ def __call__( """ ... + @overload + def __call__(self, _0: List[int], _1: float, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __call__(self, _0: List[int], _1: int, /): + """ + usage.sklearn: 4 + """ + ... + + @overload + def __call__(self, _0: float, _1: int, /): + """ + usage.sklearn: 8 + """ + ... + + @overload + def __call__(self, _0: numpy.ndarray, _1: numpy.ndarray, _2: numpy.ndarray, /): + """ + usage.sklearn: 4 + """ + ... + + @overload + def __call__(self, _0: numpy.matrix, /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __call__(self, _0: numpy.ndarray, _1: float, /, *, out: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __call__(self, _0: int, _1: numpy.int64, _2: numpy.ndarray, /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __call__(self, _0: int, _1: numpy.ndarray, /, *, out: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + @overload def __call__( self, - _0: object, - _1: object = ..., - _2: numpy.ndarray = ..., + _0: numpy.ndarray, + _1: numpy.ndarray, /, *, - out: numpy.ndarray = ..., - casting: Literal["no"] = ..., - dtype: Type[numpy.float64] = ..., + casting: Literal["no"], + out: numpy.ndarray, ): """ - usage.sklearn: 1396 + usage.sklearn: 2 + """ + ... + + @overload + def __call__(self, _0: numpy.memmap, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __call__(self, _0: List[List[numpy.float64]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __call__(self, _0: numpy.ndarray, _1: numpy.int64, /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __call__(self, _0: scipy.sparse.csr.csr_matrix, /): + """ + usage.sklearn: 4 + """ + ... + + @overload + def __call__(self, _0: scipy.sparse.csc.csc_matrix, /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __call__(self, _0: numpy.memmap, _1: numpy.ndarray, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __call__(self, _0: float, /, *, dtype: Type[numpy.float64]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __call__(self, _0: numpy.float32, _1: numpy.float32, /): + """ + usage.sklearn: 4 """ ... @@ -64910,17 +78421,12 @@ def __gt__(self, _0: Union[int, numpy.float64], /): """ ... - def __le__(self, _0: int, /): - """ - usage.sklearn: 1 - """ - ... - @overload def __lt__(self, _0: int, /): """ usage.matplotlib: 1 usage.skimage: 4 + usage.sklearn: 1 """ ... @@ -64931,10 +78437,11 @@ def __lt__(self, _0: numpy.float64, /): """ ... - def __lt__(self, _0: Union[numpy.float64, int], /): + def __lt__(self, _0: Union[int, numpy.float64], /): """ usage.matplotlib: 2 usage.skimage: 4 + usage.sklearn: 1 """ ... @@ -65709,6 +79216,7 @@ def __eq__(self, _0: int, /): """ usage.matplotlib: 1 usage.skimage: 8 + usage.sklearn: 1 """ ... @@ -65741,9 +79249,9 @@ def __eq__(self, _0: Union[numpy.ndarray, int], /): ... @overload - def __eq__(self, _0: Union[numpy.uint64, int], /): + def __eq__(self, _0: numpy.uint64, /): """ - usage.sklearn: 7 + usage.sklearn: 6 """ ... @@ -66445,6 +79953,12 @@ def __iadd__(self, _0: int, /): """ ... + def __le__(self, _0: int, /): + """ + usage.sklearn: 2 + """ + ... + @overload def __lt__(self, _0: int, /): """ @@ -66935,9 +80449,51 @@ def __getitem__(self, _0: Tuple[Union[None, ellipsis], ...], /): ... @overload - def __getitem__(self, _0: str, /): + def __getitem__(self, _0: Literal["value"], /): """ - usage.sklearn: 21 + usage.sklearn: 5 + """ + ... + + @overload + def __getitem__(self, _0: Literal["is_leaf"], /): + """ + usage.sklearn: 5 + """ + ... + + @overload + def __getitem__(self, _0: Literal["count"], /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __getitem__(self, _0: Literal["left"], /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __getitem__(self, _0: Literal["right"], /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __getitem__(self, _0: Literal["threshold"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["bin_threshold"], /): + """ + usage.sklearn: 1 """ ... @@ -66963,9 +80519,121 @@ def __setitem__( ... @overload - def __setitem__(self, _0: str, _1: Union[int, bool, numpy.float64, float], /): + def __setitem__(self, _0: Literal["count"], _1: int, /): """ - usage.sklearn: 25 + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["depth"], _1: int, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["gain"], _1: float, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["value"], _1: int, /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __setitem__(self, _0: Literal["feature_idx"], _1: int, /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __setitem__(self, _0: Literal["bin_threshold"], _1: int, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["missing_go_to_left"], _1: bool, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["threshold"], _1: numpy.float64, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["left"], _1: int, /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __setitem__(self, _0: Literal["value"], _1: float, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["gain"], _1: int, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["is_leaf"], _1: bool, /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __setitem__(self, _0: Literal["right"], _1: int, /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __setitem__(self, _0: Literal["value"], _1: numpy.float64, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["missing_go_to_left"], _1: int, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["threshold"], _1: float, /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __setitem__(self, _0: Literal["threshold"], _1: int, /): + """ + usage.sklearn: 1 """ ... diff --git a/data/typing/numpy.random.mtrand.py b/data/typing/numpy.random.mtrand.py index 72c2923..31a7916 100644 --- a/data/typing/numpy.random.mtrand.py +++ b/data/typing/numpy.random.mtrand.py @@ -71,21 +71,42 @@ def binomial( def binomial(self, _0: int, _1: float, /, *, size: Tuple[int]): """ usage.dask: 1 + usage.sklearn: 1 """ ... @overload - def binomial( - self, - _0: int, - _1: Union[float, numpy.float64], - _2: Tuple[int, int] = ..., - /, - *, - size: Union[Tuple[int, ...], int] = ..., - ): + def binomial(self, _0: int, _1: float, /, *, size: Tuple[int, int]): """ - usage.sklearn: 11 + usage.sklearn: 3 + """ + ... + + @overload + def binomial(self, _0: int, _1: numpy.float64, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def binomial(self, _0: int, _1: float, /, *, size: int): + """ + usage.sklearn: 2 + """ + ... + + @overload + def binomial(self, _0: int, _1: float, /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def binomial(self, _0: int, _1: float, _2: Tuple[int, int], /): + """ + usage.sklearn: 2 """ ... @@ -165,6 +186,7 @@ def choice(self, _0: List[Callable], /): def choice(self, _0: numpy.ndarray, /, *, replace: bool, size: int): """ usage.skimage: 1 + usage.sklearn: 5 """ ... @@ -172,6 +194,7 @@ def choice(self, _0: numpy.ndarray, /, *, replace: bool, size: int): def choice(self, _0: int, _1: int, /, *, replace: bool): """ usage.skimage: 2 + usage.sklearn: 2 usage.xarray: 2 """ ... @@ -180,6 +203,7 @@ def choice(self, _0: int, _1: int, /, *, replace: bool): def choice(self, _0: numpy.ndarray, _1: int, /, *, replace: bool): """ usage.skimage: 1 + usage.sklearn: 1 """ ... @@ -214,6 +238,7 @@ def choice(self, _0: List[Literal["d", "c", "b", "a"]], /, *, size: List[int]): @overload def choice(self, _0: int, _1: int, /): """ + usage.sklearn: 1 usage.xarray: 1 """ ... @@ -278,34 +303,144 @@ def choice( """ ... + @overload + def choice(self, _0: int, /, *, replace: bool, size: numpy.int64): + """ + usage.sklearn: 1 + """ + ... + + @overload + def choice(self, _0: numpy.ndarray, _1: numpy.int64, /, *, replace: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def choice( + self, _0: numpy.ndarray, /, *, p: numpy.ndarray, replace: bool, size: int + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def choice(self, _0: List[int], _1: int, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def choice(self, _0: numpy.ndarray, _1: int, /, *, p: numpy.ndarray, replace: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def choice(self, _0: numpy.ndarray, /, *, size: int): + """ + usage.sklearn: 1 + """ + ... + + @overload + def choice(self, _0: int, /, *, replace: bool, size: int): + """ + usage.sklearn: 2 + """ + ... + @overload def choice( self, - _0: Union[ - numpy.ndarray, - int, - List[ + _0: List[ + Dict[ + Literal["C", "kernel"], Union[ - int, - Dict[ - str, - Union[ - List[Union[str, int]], - scipy.stats._distn_infrastructure.rv_frozen, - ], - ], - ] - ], + List[Literal["linear", "rbf"]], + scipy.stats._distn_infrastructure.rv_frozen, + ], + ] ], - _1: Union[int, numpy.int64] = ..., /, - *, - size: Union[int, numpy.int64] = ..., - p: numpy.ndarray = ..., - replace: bool = ..., ): """ - usage.sklearn: 23 + usage.sklearn: 1 + """ + ... + + @overload + def choice( + self, + _0: List[Dict[Literal["C"], scipy.stats._distn_infrastructure.rv_frozen]], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def choice( + self, + _0: List[ + Dict[ + Literal["gamma", "C", "kernel", "degree"], + Union[ + List[Union[int, Literal["poly", "rbf"]]], + scipy.stats._distn_infrastructure.rv_frozen, + ], + ] + ], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def choice( + self, + _0: List[ + Dict[ + Literal["second", "first"], + Union[ + scipy.stats._distn_infrastructure.rv_frozen, + List[Literal["c", "b", "a"]], + ], + ] + ], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def choice( + self, + _0: List[Dict[Literal["a"], scipy.stats._distn_infrastructure.rv_frozen]], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def choice( + self, + _0: List[Dict[Literal["alpha"], scipy.stats._distn_infrastructure.rv_frozen]], + /, + ): + """ + usage.sklearn: 1 """ ... @@ -423,17 +558,16 @@ def gamma(self, _0: int, _1: int, /, *, size: Tuple[int]): ... @overload - def gamma( - self, - _0: Union[float, int], - _1: float = ..., - _2: Tuple[int, int] = ..., - /, - *, - size: int = ..., - ): + def gamma(self, _0: int, /, *, size: int): """ - usage.sklearn: 3 + usage.sklearn: 1 + """ + ... + + @overload + def gamma(self, _0: float, _1: float, _2: Tuple[int, int], /): + """ + usage.sklearn: 2 """ ... @@ -712,9 +846,16 @@ def multinomial( ... @overload - def multinomial(self, _0: int, _1: numpy.ndarray, /, *, size: int = ...): + def multinomial(self, _0: int, _1: numpy.ndarray, /): """ - usage.sklearn: 6 + usage.sklearn: 5 + """ + ... + + @overload + def multinomial(self, _0: int, _1: numpy.ndarray, /, *, size: int): + """ + usage.sklearn: 1 """ ... @@ -734,6 +875,31 @@ def multinomial( """ ... + @overload + def multivariate_normal( + self, _0: numpy.ndarray, _1: numpy.ndarray, _2: int = ..., /, *, size: int = ... + ): + """ + usage.scipy: 5 + """ + ... + + @overload + def multivariate_normal( + self, _0: numpy.ndarray, _1: numpy.ndarray, /, *, size: int + ): + """ + usage.sklearn: 5 + """ + ... + + @overload + def multivariate_normal(self, _0: numpy.ndarray, _1: numpy.ndarray, _2: int, /): + """ + usage.sklearn: 8 + """ + ... + def multivariate_normal( self, _0: numpy.ndarray, _1: numpy.ndarray, _2: int = ..., /, *, size: int = ... ): @@ -854,6 +1020,7 @@ def noncentral_f( def normal(self, /, *, size: Tuple[int, int]): """ usage.skimage: 16 + usage.sklearn: 66 usage.xarray: 2 """ ... @@ -937,6 +1104,7 @@ def normal( def normal(self, /, *, size: int): """ usage.matplotlib: 21 + usage.sklearn: 39 """ ... @@ -944,6 +1112,7 @@ def normal(self, /, *, size: int): def normal(self, _0: int, _1: int, _2: int, /): """ usage.matplotlib: 3 + usage.sklearn: 1 """ ... @@ -961,20 +1130,128 @@ def normal( """ ... + @overload + def normal( + self, /, *, loc: numpy.ndarray, scale: numpy.float64, size: Tuple[int, int] + ): + """ + usage.sklearn: 2 + """ + ... + + @overload + def normal(self, /, *, scale: float, size: Tuple[int, int]): + """ + usage.sklearn: 8 + """ + ... + @overload def normal( self, - _0: Union[int, numpy.ndarray] = ..., - _1: Union[float, int] = ..., - _2: Union[Tuple[int, int], int] = ..., /, *, - loc: Union[float, numpy.ndarray, int] = ..., - scale: Union[numpy.float64, numpy.int64, float, numpy.ndarray, int] = ..., - size: Union[Tuple[Union[numpy.int64, int], int], int] = ..., + loc: numpy.ndarray, + scale: numpy.float64, + size: Tuple[numpy.int64, int], ): """ - usage.sklearn: 141 + usage.sklearn: 1 + """ + ... + + @overload + def normal(self, /, *, loc: int, scale: int, size: Tuple[int, int]): + """ + usage.sklearn: 3 + """ + ... + + @overload + def normal(self, /, *, loc: numpy.ndarray, scale: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + + @overload + def normal(self, /, *, scale: float, size: int): + """ + usage.sklearn: 4 + """ + ... + + @overload + def normal(self, /, *, loc: float, scale: float, size: int): + """ + usage.sklearn: 1 + """ + ... + + @overload + def normal(self, /, *, loc: int, size: Tuple[int, int]): + """ + usage.sklearn: 3 + """ + ... + + @overload + def normal(self, /, *, scale: int, size: Tuple[int, int]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def normal(self, _0: int, _1: int, _2: Tuple[int, int], /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def normal(self, _0: int, _1: int, /, *, size: Tuple[int, int]): + """ + usage.sklearn: 2 + """ + ... + + @overload + def normal(self, _0: numpy.ndarray, _1: float, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def normal(self, _0: int, _1: float, _2: Tuple[int, int], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def normal( + self, /, *, loc: numpy.ndarray, scale: numpy.int64, size: Tuple[int, int] + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def normal(self, /, *, loc: float, scale: numpy.float64, size: Tuple[int, int]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def normal( + self, /, *, loc: float, scale: numpy.float64, size: Tuple[numpy.int64, int] + ): + """ + usage.sklearn: 1 """ ... @@ -1029,6 +1306,7 @@ def pareto( def permutation(self, _0: numpy.ndarray, /): """ usage.skimage: 1 + usage.sklearn: 3 """ ... @@ -1054,11 +1332,23 @@ def permutation(self, _0: Union[numpy.ndarray, int], /): ... @overload - def permutation( - self, _0: Union[int, numpy.int64, numpy.ndarray, List[numpy.int64]], / - ): + def permutation(self, _0: int, /): """ - usage.sklearn: 39 + usage.sklearn: 19 + """ + ... + + @overload + def permutation(self, _0: numpy.int64, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def permutation(self, _0: List[numpy.int64], /): + """ + usage.sklearn: 16 """ ... @@ -1099,22 +1389,36 @@ def poisson( ... @overload - def poisson( - self, - _0: Union[int, float, numpy.int64], - /, - *, - size: Union[int, Tuple[int, ...]], - ): + def poisson( + self, + _0: Union[int, float, numpy.int64], + /, + *, + size: Union[int, Tuple[int, ...]], + ): + """ + usage.dask: 3 + """ + ... + + @overload + def poisson(self, _0: int, /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def poisson(self, /, *, lam: numpy.ndarray): """ - usage.dask: 3 + usage.sklearn: 1 """ ... @overload - def poisson(self, _0: int = ..., /, *, lam: numpy.ndarray = ..., size: int = ...): + def poisson(self, /, *, size: int): """ - usage.sklearn: 6 + usage.sklearn: 3 """ ... @@ -1155,6 +1459,7 @@ def rand(self, _0: int, /): """ usage.matplotlib: 18 usage.skimage: 9 + usage.sklearn: 93 usage.xarray: 1 """ ... @@ -1164,6 +1469,7 @@ def rand(self, _0: int, _1: int, /): """ usage.matplotlib: 20 usage.skimage: 53 + usage.sklearn: 168 usage.xarray: 11 """ ... @@ -1187,6 +1493,7 @@ def rand(self, _0: int, _1: int, _2: int, _3: int, _4: int, /): def rand(self, /): """ usage.skimage: 1 + usage.sklearn: 7 """ ... @@ -1223,9 +1530,9 @@ def rand(self, _0: int, _1: int = ..., _2: int = ..., _3: int = ..., /): ... @overload - def rand(self, _0: Union[int, numpy.int64] = ..., _1: int = ..., /): + def rand(self, _0: numpy.int64, /): """ - usage.sklearn: 269 + usage.sklearn: 1 """ ... @@ -1254,6 +1561,7 @@ def randint( def randint(self, _0: int, _1: int, /, *, size: Tuple[int, int]): """ usage.skimage: 8 + usage.sklearn: 19 usage.xarray: 2 """ ... @@ -1262,6 +1570,7 @@ def randint(self, _0: int, _1: int, /, *, size: Tuple[int, int]): def randint(self, _0: int, _1: int, /): """ usage.skimage: 5 + usage.sklearn: 8 """ ... @@ -1270,6 +1579,7 @@ def randint(self, _0: int, _1: int, /, *, size: int): """ usage.matplotlib: 2 usage.skimage: 3 + usage.sklearn: 34 """ ... @@ -1277,6 +1587,7 @@ def randint(self, _0: int, _1: int, /, *, size: int): def randint(self, _0: int, /): """ usage.skimage: 2 + usage.sklearn: 11 usage.xarray: 1 """ ... @@ -1292,6 +1603,7 @@ def randint(self, _0: float, _1: int, /): def randint(self, _0: int, _1: int, _2: Tuple[int, int], /): """ usage.skimage: 1 + usage.sklearn: 6 """ ... @@ -1299,6 +1611,7 @@ def randint(self, _0: int, _1: int, _2: Tuple[int, int], /): def randint(self, _0: int, /, *, size: int): """ usage.skimage: 2 + usage.sklearn: 23 """ ... @@ -1334,6 +1647,7 @@ def randint(self, _0: int, _1: int, /, *, size: List[int]): @overload def randint(self, _0: int, _1: int, _2: int, /): """ + usage.sklearn: 30 usage.xarray: 4 """ ... @@ -1379,6 +1693,7 @@ def randint( def randint(self, _0: int, /, *, size: Tuple[int, int]): """ usage.matplotlib: 1 + usage.sklearn: 23 """ ... @@ -1399,21 +1714,110 @@ def randint( """ ... + @overload + def randint(self, _0: numpy.float64, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def randint(self, _0: int, /, *, dtype: Type[numpy.int32], size: int): + """ + usage.sklearn: 1 + """ + ... + + @overload + def randint(self, _0: int, /, *, size: numpy.int64): + """ + usage.sklearn: 2 + """ + ... + + @overload + def randint(self, /, *, high: int, low: int, size: int): + """ + usage.sklearn: 7 + """ + ... + + @overload + def randint(self, _0: int, /, *, dtype: Literal["u8"]): + """ + usage.sklearn: 1 + """ + ... + @overload def randint( - self, - _0: Union[int, numpy.float64] = ..., - _1: int = ..., - _2: Union[Tuple[int, ...], List[int], int, numpy.int64] = ..., - /, - *, - size: Union[Tuple[int, ...], int, numpy.int64] = ..., - dtype: Union[type, Literal["u8"]] = ..., - high: int = ..., - low: int = ..., + self, _0: int, _1: int, /, *, dtype: Type[numpy.uint8], size: Tuple[int, int] ): """ - usage.sklearn: 213 + usage.sklearn: 4 + """ + ... + + @overload + def randint(self, _0: int, _1: int, /, *, dtype: Type[numpy.uint8], size: int): + """ + usage.sklearn: 3 + """ + ... + + @overload + def randint(self, _0: int, /, *, size: Tuple[int]): + """ + usage.sklearn: 3 + """ + ... + + @overload + def randint(self, /, *, high: int, low: int, size: Tuple[int, int]): + """ + usage.sklearn: 2 + """ + ... + + @overload + def randint(self, _0: int, /, *, dtype: Type[bool], size: int): + """ + usage.sklearn: 1 + """ + ... + + @overload + def randint(self, _0: int, _1: int, _2: List[int], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def randint(self, _0: int, _1: int, _2: numpy.int64, /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def randint(self, _0: int, _1: int, /, *, size: Tuple[int]): + """ + usage.sklearn: 28 + """ + ... + + @overload + def randint(self, _0: int, _1: int, _2: Tuple[int], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def randint(self, _0: int, /, *, size: Tuple[int, int, int]): + """ + usage.sklearn: 1 """ ... @@ -1444,6 +1848,7 @@ def randn(self, _0: int, /): """ usage.matplotlib: 13 usage.skimage: 7 + usage.sklearn: 67 usage.xarray: 90 """ ... @@ -1452,6 +1857,7 @@ def randn(self, _0: int, /): def randn(self, _0: int, _1: int, _2: int, /): """ usage.skimage: 14 + usage.sklearn: 1 usage.xarray: 19 """ ... @@ -1461,6 +1867,7 @@ def randn(self, _0: int, _1: int, /): """ usage.matplotlib: 4 usage.skimage: 18 + usage.sklearn: 345 usage.xarray: 97 """ ... @@ -1510,15 +1917,23 @@ def randn(self, _0: int, _1: int = ..., _2: int = ..., /): ... @overload - def randn( - self, - _0: Union[numpy.int64, int] = ..., - _1: Union[int, numpy.int64] = ..., - _2: int = ..., - /, - ): + def randn(self, _0: int, _1: numpy.int64, /): """ - usage.sklearn: 419 + usage.sklearn: 1 + """ + ... + + @overload + def randn(self, _0: numpy.int64, _1: int, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def randn(self, /): + """ + usage.sklearn: 4 """ ... @@ -1539,6 +1954,7 @@ def random(self, _0: int, /): """ usage.matplotlib: 7 usage.skimage: 4 + usage.sklearn: 1 usage.xarray: 1 """ ... @@ -1548,6 +1964,7 @@ def random(self, _0: Tuple[int, int, int], /): """ usage.matplotlib: 1 usage.skimage: 7 + usage.sklearn: 8 usage.xarray: 2 """ ... @@ -1557,6 +1974,7 @@ def random(self, _0: Tuple[int, int], /): """ usage.matplotlib: 6 usage.skimage: 11 + usage.sklearn: 17 usage.xarray: 16 """ ... @@ -1652,13 +2070,6 @@ def random( """ ... - @overload - def random(self, _0: Union[Tuple[int, ...], int], /): - """ - usage.sklearn: 26 - """ - ... - def random( self, _0: Union[int, numpy.ndarray, Tuple[Union[None, int], ...], List[int]] = ..., @@ -1683,12 +2094,14 @@ def random_sample(self, _0: Tuple[int, int], /): """ usage.matplotlib: 1 usage.skimage: 1 + usage.sklearn: 119 """ ... @overload def random_sample(self, /, *, size: Tuple[int, int]): """ + usage.sklearn: 7 usage.xarray: 1 """ ... @@ -1715,11 +2128,44 @@ def random_sample(self, _0: int = ..., /, *, size: Tuple[int, ...] = ...): ... @overload - def random_sample( - self, _0: Union[Tuple[int, ...], int] = ..., /, *, size: Tuple[int, ...] = ... - ): + def random_sample(self, _0: int, /): """ - usage.sklearn: 156 + usage.sklearn: 21 + """ + ... + + @overload + def random_sample(self, /, *, size: Tuple[int]): + """ + usage.sklearn: 4 + """ + ... + + @overload + def random_sample(self, _0: Tuple[int, int, int], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def random_sample(self, _0: Tuple[int], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def random_sample(self, _0: Tuple[int, int, int, int, int], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def random_sample(self, _0: Tuple[int, int, int, int], /): + """ + usage.sklearn: 1 """ ... @@ -2072,6 +2518,7 @@ def triangular( def uniform(self, /, *, size: Tuple[int, int]): """ usage.skimage: 21 + usage.sklearn: 33 """ ... @@ -2093,6 +2540,7 @@ def uniform(self, _0: float, _1: float, /): def uniform(self, _0: int, _1: int, _2: Tuple[int, int], /): """ usage.skimage: 2 + usage.sklearn: 3 """ ... @@ -2100,6 +2548,7 @@ def uniform(self, _0: int, _1: int, _2: Tuple[int, int], /): def uniform(self, /, *, high: float, low: float, size: Tuple[int, int]): """ usage.skimage: 1 + usage.sklearn: 1 """ ... @@ -2107,6 +2556,7 @@ def uniform(self, /, *, high: float, low: float, size: Tuple[int, int]): def uniform(self, _0: int, _1: int, _2: int, /): """ usage.skimage: 2 + usage.sklearn: 4 """ ... @@ -2145,6 +2595,7 @@ def uniform( def uniform(self, /, *, high: int, low: int, size: Tuple[int, int]): """ usage.matplotlib: 1 + usage.sklearn: 4 """ ... @@ -2152,6 +2603,7 @@ def uniform(self, /, *, high: int, low: int, size: Tuple[int, int]): def uniform(self, /, *, size: int): """ usage.matplotlib: 2 + usage.sklearn: 10 """ ... @@ -2171,19 +2623,107 @@ def uniform( ... @overload - def uniform( - self, - _0: Union[int, numpy.ndarray, numpy.float64, float] = ..., - _1: Union[float, int, numpy.ndarray, numpy.float64] = ..., - _2: Union[List[int], Tuple[int, int], int] = ..., - /, - *, - size: Union[int, numpy.int64, Tuple[int, ...]] = ..., - high: Union[float, int] = ..., - low: Union[int, float] = ..., - ): + def uniform(self, _0: int, _1: int, /): """ - usage.sklearn: 84 + usage.sklearn: 1 + """ + ... + + @overload + def uniform(self, _0: float, _1: float, /, *, size: Tuple[int, int]): + """ + usage.sklearn: 4 + """ + ... + + @overload + def uniform(self, _0: int, _1: int, /, *, size: int): + """ + usage.sklearn: 2 + """ + ... + + @overload + def uniform(self, _0: int, _1: int, /, *, size: Tuple[int, int]): + """ + usage.sklearn: 3 + """ + ... + + @overload + def uniform(self, /, *, high: int, low: int, size: int): + """ + usage.sklearn: 1 + """ + ... + + @overload + def uniform(self, /, *, size: Tuple[int]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def uniform(self, _0: int, _1: int, /, *, size: numpy.int64): + """ + usage.sklearn: 4 + """ + ... + + @overload + def uniform(self, _0: numpy.ndarray, _1: numpy.ndarray, /): + """ + usage.sklearn: 4 + """ + ... + + @overload + def uniform(self, /, *, high: float, size: int): + """ + usage.sklearn: 1 + """ + ... + + @overload + def uniform(self, _0: float, _1: int, /, *, size: int): + """ + usage.sklearn: 1 + """ + ... + + @overload + def uniform(self, _0: numpy.float64, _1: numpy.float64, _2: Tuple[int, int], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def uniform(self, _0: numpy.float64, _1: numpy.float64, _2: int, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def uniform(self, _0: int, _1: int, _2: List[int], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def uniform(self, _0: float, _1: float, /, *, size: int): + """ + usage.sklearn: 2 + """ + ... + + @overload + def uniform(self, _0: int, _1: float, /, *, size: int): + """ + usage.sklearn: 2 """ ... diff --git a/data/typing/numpy.testing._private.utils.py b/data/typing/numpy.testing._private.utils.py index 8416462..f7ece77 100644 --- a/data/typing/numpy.testing._private.utils.py +++ b/data/typing/numpy.testing._private.utils.py @@ -46,6 +46,7 @@ def assert_allclose(actual: numpy.float64, desired: int, atol: float): """ usage.matplotlib: 4 usage.skimage: 1 + usage.sklearn: 2 """ ... @@ -54,6 +55,7 @@ def assert_allclose(actual: numpy.float64, desired: int, atol: float): def assert_allclose(actual: numpy.ndarray, desired: int, atol: float): """ usage.skimage: 15 + usage.sklearn: 2 """ ... @@ -63,6 +65,7 @@ def assert_allclose(actual: numpy.ndarray, desired: numpy.ndarray, rtol: float): """ usage.matplotlib: 3 usage.skimage: 11 + usage.sklearn: 70 """ ... @@ -72,6 +75,7 @@ def assert_allclose(actual: numpy.ndarray, desired: numpy.ndarray): """ usage.matplotlib: 22 usage.skimage: 57 + usage.sklearn: 264 usage.xarray: 21 """ ... @@ -98,6 +102,7 @@ def assert_allclose(actual: numpy.ndarray, desired: List[float]): """ usage.matplotlib: 3 usage.skimage: 6 + usage.sklearn: 6 """ ... @@ -108,6 +113,7 @@ def assert_allclose( ): """ usage.skimage: 1 + usage.sklearn: 2 """ ... @@ -117,6 +123,7 @@ def assert_allclose(actual: numpy.ndarray, desired: int): """ usage.matplotlib: 2 usage.skimage: 22 + usage.sklearn: 1 """ ... @@ -125,6 +132,7 @@ def assert_allclose(actual: numpy.ndarray, desired: int): def assert_allclose(actual: numpy.float64, desired: numpy.float64): """ usage.skimage: 2 + usage.sklearn: 21 """ ... @@ -134,6 +142,7 @@ def assert_allclose(actual: numpy.ndarray, desired: numpy.ndarray, atol: float): """ usage.matplotlib: 100 usage.skimage: 31 + usage.sklearn: 27 """ ... @@ -150,6 +159,7 @@ def assert_allclose(actual: numpy.int64, desired: int, atol: float): def assert_allclose(actual: numpy.float64, desired: float, rtol: float): """ usage.skimage: 1 + usage.sklearn: 7 """ ... @@ -167,6 +177,7 @@ def assert_allclose(actual: numpy.float64, desired: int): """ usage.matplotlib: 1 usage.skimage: 1 + usage.sklearn: 1 """ ... @@ -190,6 +201,7 @@ def assert_allclose(actual: numpy.ndarray, desired: List[numpy.float64], atol: f @overload def assert_allclose(actual: numpy.ndarray, desired: numpy.float64): """ + usage.sklearn: 1 usage.xarray: 3 """ ... @@ -209,6 +221,7 @@ def assert_allclose( @overload def assert_allclose(actual: numpy.float64, desired: float): """ + usage.sklearn: 5 usage.xarray: 3 """ ... @@ -217,6 +230,7 @@ def assert_allclose(actual: numpy.float64, desired: float): @overload def assert_allclose(actual: numpy.ndarray, desired: float): """ + usage.sklearn: 2 usage.xarray: 1 """ ... @@ -233,6 +247,7 @@ def assert_allclose(actual: object, desired: object): @overload def assert_allclose(actual: numpy.int64, desired: numpy.int64): """ + usage.sklearn: 1 usage.xarray: 2 """ ... @@ -300,6 +315,7 @@ def assert_allclose( def assert_allclose(actual: numpy.ndarray, desired: List[float], rtol: float): """ usage.matplotlib: 3 + usage.sklearn: 6 """ ... @@ -332,6 +348,7 @@ def assert_allclose(actual: Tuple[int, int], desired: Tuple[int, int], rtol: flo def assert_allclose(actual: numpy.float64, desired: float, atol: float): """ usage.matplotlib: 1 + usage.sklearn: 3 """ ... @@ -340,6 +357,7 @@ def assert_allclose(actual: numpy.float64, desired: float, atol: float): def assert_allclose(actual: numpy.ndarray, desired: List[int]): """ usage.matplotlib: 4 + usage.sklearn: 9 """ ... @@ -348,6 +366,7 @@ def assert_allclose(actual: numpy.ndarray, desired: List[int]): def assert_allclose(actual: numpy.ndarray, desired: List[Union[float, int]]): """ usage.matplotlib: 2 + usage.sklearn: 1 """ ... @@ -444,480 +463,505 @@ def assert_allclose( @overload -def assert_allclose( - actual: object, - desired: object, - rtol: Union[float, int] = ..., - atol: Union[float, int] = ..., - err_msg: str = ..., -): +def assert_allclose(actual: numpy.ndarray, desired: List[List[Union[int, float]]]): """ - usage.sklearn: 760 + usage.sklearn: 1 """ ... -def assert_allclose( - actual: object, - desired: object, - rtol: Union[int, float, numpy.float64, bool] = ..., - atol: Union[int, float, numpy.float64, numpy.float32, numpy.float128] = ..., - err_msg: Union[ - str, Tuple[Union[complex, int, float, numpy.float64], ...], float, numpy.ndarray - ] = ..., - verbose: bool = ..., -): +@overload +def assert_allclose(actual: numpy.ndarray, desired: List[List[int]]): """ - usage.matplotlib: 184 - usage.scipy: 4704 - usage.skimage: 158 - usage.sklearn: 760 - usage.xarray: 38 + usage.sklearn: 5 """ ... @overload -def assert_almost_equal(actual: numpy.float64, desired: int): +def assert_allclose(actual: float, desired: float, rtol: float): """ - usage.skimage: 7 + usage.sklearn: 9 """ ... @overload -def assert_almost_equal(actual: numpy.float64, desired: float): +def assert_allclose(actual: float, desired: numpy.float32, rtol: float): """ - usage.matplotlib: 8 - usage.skimage: 47 + usage.sklearn: 7 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: numpy.ndarray, decimal: int): +def assert_allclose(actual: float, desired: numpy.float64, rtol: float): """ - usage.skimage: 15 + usage.sklearn: 7 """ ... @overload -def assert_almost_equal(actual: numpy.float64, desired: float, decimal: int): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + rtol: float, + atol: float, + err_msg: Literal[""], +): """ - usage.matplotlib: 3 - usage.skimage: 35 + usage.sklearn: 7 """ ... @overload -def assert_almost_equal(actual: numpy.float64, desired: numpy.float64, decimal: int): +def assert_allclose(actual: numpy.ndarray, desired: pandas.core.frame.DataFrame): """ - usage.skimage: 6 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: numpy.float16, desired: float, decimal: int): +def assert_allclose(actual: numpy.float64, desired: numpy.float64, rtol: float): """ - usage.skimage: 2 + usage.sklearn: 10 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: numpy.ndarray): +def assert_allclose( + actual: numpy.ndarray, desired: numpy.ndarray, rtol: float, atol: float +): """ - usage.matplotlib: 23 - usage.skimage: 116 + usage.sklearn: 15 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: int, decimal: int): +def assert_allclose(actual: numpy.ndarray, desired: List[numpy.float64], rtol: float): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: List[int], decimal: int): +def assert_allclose(actual: numpy.float64, desired: int, rtol: float): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: numpy.float64, decimal: int): +def assert_allclose(actual: numpy.ndarray, desired: List[numpy.float64]): """ - usage.skimage: 1 + usage.sklearn: 4 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: Tuple[int, int]): +def assert_allclose(actual: numpy.ndarray, desired: List[Union[numpy.float64, float]]): """ - usage.skimage: 12 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: numpy.float64, desired: int, decimal: int): +def assert_allclose(actual: numpy.ndarray, desired: numpy.ndarray, err_msg: str): """ - usage.skimage: 5 + usage.sklearn: 38 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: List[float]): +def assert_allclose( + actual: numpy.float64, + desired: numpy.float64, + rtol: float, + atol: float, + err_msg: Literal[""], +): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: List[int]): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["StackingClassifier"], +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: numpy.float64, desired: numpy.float64): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + rtol: float, + atol: float, + err_msg: str, +): """ - usage.matplotlib: 2 - usage.skimage: 21 + usage.sklearn: 5 """ ... @overload -def assert_almost_equal( - actual: Tuple[int, int, int], - desired: Tuple[numpy.float64, numpy.float64, numpy.float64], - decimal: int, +def assert_allclose( + actual: numpy.ndarray, desired: numpy.ndarray, atol: float, err_msg: str ): """ - usage.skimage: 1 + usage.sklearn: 7 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: int): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["StackingRegressor"], +): """ - usage.skimage: 5 + usage.sklearn: 2 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: numpy.float64): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["VotingRegressor"], +): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def assert_almost_equal(actual: Tuple[int, int], desired: List[float], decimal: int): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["VotingClassifier"], +): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal( - actual: Tuple[int, int, int], - desired: Tuple[numpy.float64, numpy.float64, numpy.float64], -): +def assert_allclose(actual: numpy.ndarray, desired: List[int], rtol: float): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: numpy.int64, desired: numpy.int64): +def assert_allclose(actual: List[numpy.int64], desired: List[numpy.int64]): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal( - actual: Tuple[int, int, int, int], desired: Tuple[int, int, int, int] +def assert_allclose( + actual: numpy.ndarray, desired: List[Union[int, float]], rtol: float ): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: int, desired: int): +def assert_allclose( + actual: numpy.float32, desired: numpy.float64, rtol: float, atol: float +): """ - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal( - actual: Tuple[numpy.float64, numpy.float64], - desired: Tuple[numpy.float64, numpy.float64], +def assert_allclose( + actual: numpy.float64, desired: numpy.float64, rtol: float, atol: float ): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def assert_almost_equal(actual: float, desired: float): +def assert_allclose( + actual: numpy.ndarray, desired: List[Union[float, int]], rtol: int, atol: float +): """ - usage.matplotlib: 1 - usage.skimage: 16 + usage.sklearn: 2 """ ... @overload -def assert_almost_equal(actual: List[numpy.float64], desired: List[numpy.float64]): +def assert_allclose(actual: numpy.float64, desired: float, rtol: int, atol: float): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal( - actual: Tuple[slice[int, int, int], slice[int, int, int]], - desired: Tuple[slice[int, int, int], slice[int, int, int]], -): +def assert_allclose(actual: float, desired: float): """ - usage.skimage: 1 + usage.sklearn: 3 """ ... @overload -def assert_almost_equal( - actual: Tuple[int, int, int, int, int, int], - desired: Tuple[int, int, int, int, int, int], -): +def assert_allclose(actual: numpy.float64, desired: numpy.float64, err_msg: str): """ - usage.skimage: 1 + usage.sklearn: 24 """ ... @overload -def assert_almost_equal( - actual: Tuple[numpy.float64, numpy.float64, numpy.float64], - desired: Tuple[numpy.float64, numpy.float64, numpy.float64], -): +def assert_allclose(actual: numpy.int64, desired: numpy.int64, err_msg: str): """ - usage.skimage: 1 + usage.sklearn: 16 """ ... @overload -def assert_almost_equal(actual: numpy.uint8, desired: numpy.uint8): +def assert_allclose(actual: float, desired: float, err_msg: str): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def assert_almost_equal( - actual: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], - desired: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], -): +def assert_allclose(actual: float, desired: int): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: numpy.int64, desired: int): +def assert_allclose(actual: int, desired: int): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: float, desired: numpy.float64, decimal: int): +def assert_allclose( + actual: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], + desired: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], + err_msg: str, +): """ - usage.skimage: 3 + usage.sklearn: 5 """ ... @overload -def assert_almost_equal(actual: float, desired: int): +def assert_allclose( + actual: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], + desired: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], +): """ - usage.skimage: 8 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: Tuple[float, float], desired: Tuple[float, float]): +def assert_allclose(actual: numpy.int64, desired: numpy.float64, err_msg: str): """ - usage.matplotlib: 1 - usage.skimage: 3 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: List[Union[int, float]]): +def assert_allclose(actual: float, desired: numpy.float64, err_msg: str): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: List[List[int]]): +def assert_allclose(actual: float, desired: numpy.float64): """ - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal( - actual: object, - desired: object, - decimal: int = ..., - verbose: bool = ..., - err_msg: str = ..., -): +def assert_allclose(actual: numpy.float64, desired: float, err_msg: str): """ - usage.scipy: 1344 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: List[numpy.float64], desired: numpy.ndarray): +def assert_allclose(actual: numpy.ndarray, desired: List[List[float]]): """ - usage.matplotlib: 3 + usage.sklearn: 2 """ ... @overload -def assert_almost_equal(actual: list, desired: numpy.ndarray): +def assert_allclose(actual: numpy.ndarray, desired: List[List[numpy.float64]]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: numpy.ndarray, desired: float): +def assert_allclose(actual: numpy.float64, desired: numpy.float64, atol: float): """ - usage.matplotlib: 4 + usage.sklearn: 3 """ ... @overload -def assert_almost_equal(actual: Tuple[float, float], desired: Tuple[int, int]): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LocalOutlierFactor"], +): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal(actual: List[numpy.float64], desired: List[float]): +def assert_allclose( + actual: numpy.float64, desired: numpy.float64, rtol: int, atol: float +): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def assert_almost_equal(actual: List[float], desired: numpy.ndarray): +def assert_allclose(actual: numpy.ndarray, desired: List[List[float]], rtol: float): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def assert_almost_equal( - actual: Union[numpy.ndarray, numpy.int64, numpy.float64, int, float], - desired: object, - decimal: int = ..., - err_msg: str = ..., +def assert_allclose( + actual: numpy.ndarray, desired: numpy.ndarray, rtol: int, atol: float ): """ - usage.sklearn: 965 + usage.sklearn: 3 """ ... -def assert_almost_equal( - actual: object, - desired: object, - decimal: int = ..., - verbose: bool = ..., - err_msg: str = ..., +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["ARDRegression"], ): """ - usage.matplotlib: 50 - usage.scipy: 1344 - usage.skimage: 333 - usage.sklearn: 965 + usage.sklearn: 2 """ ... @overload -def assert_approx_equal( - actual: Union[numpy.float64, float], - desired: Union[float, int, numpy.float64], - significant: int = ..., - err_msg: str = ..., -): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["AdaBoostClassifier"], +): """ - usage.scipy: 220 + usage.sklearn: 1 """ ... @overload -def assert_approx_equal(actual: numpy.float64, desired: float): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["AdaBoostRegressor"], +): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def assert_approx_equal( - actual: numpy.float64, desired: numpy.float64, significant: int = ... +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["BaggingClassifier"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["BaggingRegressor"], ): """ usage.sklearn: 2 @@ -925,1504 +969,4337 @@ def assert_approx_equal( ... -def assert_approx_equal( - actual: Union[numpy.float64, float], - desired: Union[numpy.float64, int, float], - significant: int = ..., - err_msg: str = ..., +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["BayesianRidge"], ): """ - usage.matplotlib: 1 - usage.scipy: 220 usage.sklearn: 2 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: List[List[Tuple[int, int, int]]]): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["BernoulliNB"], +): """ - usage.skimage: 4 + usage.sklearn: 1 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: numpy.ndarray): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["CategoricalNB"], +): """ - usage.matplotlib: 29 - usage.skimage: 39 + usage.sklearn: 1 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: Tuple[int, int, int]): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["ComplementNB"], +): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: List[int]): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["DummyClassifier"], +): """ - usage.matplotlib: 2 - usage.skimage: 6 + usage.sklearn: 1 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: List[Union[int, float]]): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["DummyRegressor"], +): """ - usage.matplotlib: 1 - usage.skimage: 3 + usage.sklearn: 2 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: List[float]): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["ElasticNet"], +): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: List[Tuple[int, int]]): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["ElasticNetCV"], +): """ - usage.skimage: 2 + usage.sklearn: 2 """ ... @overload -def assert_array_almost_equal( - x: Tuple[int, int, int, int], y: Tuple[int, int, int, int] +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["EllipticEnvelope"], ): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def assert_array_almost_equal( - x: Tuple[int, int, int, int, int, int], y: Tuple[int, int, int, int, int, int] +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["ExtraTreeClassifier"], ): """ - usage.skimage: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_almost_equal(x: int, y: int): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["ExtraTreeRegressor"], +): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_almost_equal( - x: Tuple[numpy.float64, numpy.float64], y: Tuple[float, float] +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["ExtraTreesClassifier"], ): """ - usage.skimage: 2 + usage.sklearn: 1 """ ... @overload -def assert_array_almost_equal( - x: Tuple[numpy.float64, numpy.float64, numpy.float64], y: Tuple[float, float, float] +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["ExtraTreesRegressor"], ): """ - usage.skimage: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: int): +def assert_allclose( + actual: numpy.memmap, desired: numpy.memmap, rtol: float, atol: float, err_msg: str +): """ - usage.skimage: 5 + usage.sklearn: 1 """ ... @overload -def assert_array_almost_equal( - x: object, y: object, decimal: int = ..., err_msg: str = ... +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["GammaRegressor"], ): """ - usage.scipy: 3904 + usage.sklearn: 2 """ ... @overload -def assert_array_almost_equal( - x: Tuple[numpy.float32, numpy.float32, numpy.float32, numpy.float32], - y: Tuple[float, float, float, float], - decimal: int, +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["GaussianNB"], ): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: numpy.ndarray, decimal: int): +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["HuberRegressor"], +): """ - usage.matplotlib: 4 + usage.sklearn: 2 """ ... @overload -def assert_array_almost_equal( - x: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], - y: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], - decimal: int, +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["IsolationForest"], ): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_almost_equal(x: numpy.float64, y: float): +def assert_allclose( + actual: float, desired: float, rtol: float, atol: float, err_msg: Literal[""] +): """ - usage.matplotlib: 4 + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["KNeighborsClassifier"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["KNeighborsRegressor"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["KernelRidge"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LabelPropagation"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LabelSpreading"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, desired: numpy.ndarray, atol: float, err_msg: Literal["Lars"] +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LarsCV"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["Lasso"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LassoCV"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LassoLars"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LassoLarsCV"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LassoLarsIC"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LinearRegression"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LinearSVC"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LinearSVR"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LogisticRegression"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["LogisticRegressionCV"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["MLPClassifier"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["MLPRegressor"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["MultiOutputRegressor"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["MultiTaskElasticNet"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["MultiTaskLasso"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["MultiTaskLassoCV"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["MultinomialNB"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["NearestCentroid"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["NuSVC"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["NuSVR"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["OneClassSVM"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["OneVsOneClassifier"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["OneVsRestClassifier"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["OutputCodeClassifier"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["PLSCanonical"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["PLSRegression"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["Perceptron"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["PoissonRegressor"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["RANSACRegressor"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, desired: numpy.ndarray, atol: float, err_msg: Literal["RFE"] +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["RFECV"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["RegressorChain"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["Ridge"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["RidgeCV"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["RidgeClassifier"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["RidgeClassifierCV"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["SGDClassifier"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["SGDRegressor"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, desired: numpy.ndarray, atol: float, err_msg: Literal["SVC"] +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, desired: numpy.ndarray, atol: float, err_msg: Literal["SVR"] +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["TheilSenRegressor"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["TweedieRegressor"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + rtol: float, + err_msg: Literal["solver svd"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + rtol: float, + err_msg: Literal["solver lsqr"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + rtol: float, + err_msg: Literal["solver eigen"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose(actual: numpy.ndarray, desired: numpy.ndarray, atol: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: Tuple[float, float, float, float], + desired: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + rtol: float, +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: numpy.ndarray, + desired: numpy.ndarray, + atol: float, + err_msg: Literal["estimator_name"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose(actual: numpy.ndarray, desired: List[numpy.ndarray], atol: float): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: List[List[int]], + desired: List[List[int]], + rtol: float, + atol: float, + err_msg: Literal[""], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: pandas.core.frame.DataFrame, + desired: pandas.core.frame.DataFrame, + rtol: float, + atol: float, + err_msg: Literal[""], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_allclose( + actual: List[int], + desired: List[int], + rtol: float, + atol: float, + err_msg: Literal[""], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_allclose( + actual: pandas.core.series.Series, + desired: pandas.core.series.Series, + rtol: float, + atol: float, + err_msg: Literal[""], +): + """ + usage.sklearn: 1 + """ + ... + + +def assert_allclose( + actual: object, + desired: object, + rtol: Union[int, float, numpy.float64, bool] = ..., + atol: Union[int, float, numpy.float64, numpy.float32, numpy.float128] = ..., + err_msg: Union[ + str, Tuple[Union[complex, int, float, numpy.float64], ...], float, numpy.ndarray + ] = ..., + verbose: bool = ..., +): + """ + usage.matplotlib: 184 + usage.scipy: 4704 + usage.skimage: 158 + usage.sklearn: 760 + usage.xarray: 38 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.float64, desired: int): + """ + usage.skimage: 7 + usage.sklearn: 141 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.float64, desired: float): + """ + usage.matplotlib: 8 + usage.skimage: 47 + usage.sklearn: 189 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: numpy.ndarray, decimal: int): + """ + usage.skimage: 15 + usage.sklearn: 77 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.float64, desired: float, decimal: int): + """ + usage.matplotlib: 3 + usage.skimage: 35 + usage.sklearn: 109 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.float64, desired: numpy.float64, decimal: int): + """ + usage.skimage: 6 + usage.sklearn: 47 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.float16, desired: float, decimal: int): + """ + usage.skimage: 2 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: numpy.ndarray): + """ + usage.matplotlib: 23 + usage.skimage: 116 + usage.sklearn: 127 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: int, decimal: int): + """ + usage.skimage: 1 + usage.sklearn: 3 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: List[int], decimal: int): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: numpy.float64, decimal: int): + """ + usage.skimage: 1 + usage.sklearn: 8 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: Tuple[int, int]): + """ + usage.skimage: 12 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.float64, desired: int, decimal: int): + """ + usage.skimage: 5 + usage.sklearn: 10 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: List[float]): + """ + usage.skimage: 2 + usage.sklearn: 3 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: List[int]): + """ + usage.skimage: 1 + usage.sklearn: 6 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.float64, desired: numpy.float64): + """ + usage.matplotlib: 2 + usage.skimage: 21 + usage.sklearn: 131 + """ + ... + + +@overload +def assert_almost_equal( + actual: Tuple[int, int, int], + desired: Tuple[numpy.float64, numpy.float64, numpy.float64], + decimal: int, +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: int): + """ + usage.skimage: 5 + usage.sklearn: 2 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: numpy.float64): + """ + usage.skimage: 1 + usage.sklearn: 9 + """ + ... + + +@overload +def assert_almost_equal(actual: Tuple[int, int], desired: List[float], decimal: int): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_almost_equal( + actual: Tuple[int, int, int], + desired: Tuple[numpy.float64, numpy.float64, numpy.float64], +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.int64, desired: numpy.int64): + """ + usage.skimage: 2 + usage.sklearn: 2 + """ + ... + + +@overload +def assert_almost_equal( + actual: Tuple[int, int, int, int], desired: Tuple[int, int, int, int] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: int, desired: int): + """ + usage.skimage: 3 + usage.sklearn: 2 + """ + ... + + +@overload +def assert_almost_equal( + actual: Tuple[numpy.float64, numpy.float64], + desired: Tuple[numpy.float64, numpy.float64], +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: float, desired: float): + """ + usage.matplotlib: 1 + usage.skimage: 16 + usage.sklearn: 33 + """ + ... + + +@overload +def assert_almost_equal(actual: List[numpy.float64], desired: List[numpy.float64]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def assert_almost_equal( + actual: Tuple[slice[int, int, int], slice[int, int, int]], + desired: Tuple[slice[int, int, int], slice[int, int, int]], +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_almost_equal( + actual: Tuple[int, int, int, int, int, int], + desired: Tuple[int, int, int, int, int, int], +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_almost_equal( + actual: Tuple[numpy.float64, numpy.float64, numpy.float64], + desired: Tuple[numpy.float64, numpy.float64, numpy.float64], +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.uint8, desired: numpy.uint8): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_almost_equal( + actual: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], + desired: Tuple[slice[int, int, int], slice[int, int, int], slice[int, int, int]], +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.int64, desired: int): + """ + usage.skimage: 2 + """ + ... + + +@overload +def assert_almost_equal(actual: float, desired: numpy.float64, decimal: int): + """ + usage.skimage: 3 + usage.sklearn: 4 + """ + ... + + +@overload +def assert_almost_equal(actual: float, desired: int): + """ + usage.skimage: 8 + usage.sklearn: 10 + """ + ... + + +@overload +def assert_almost_equal(actual: Tuple[float, float], desired: Tuple[float, float]): + """ + usage.matplotlib: 1 + usage.skimage: 3 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: List[Union[int, float]]): + """ + usage.skimage: 2 + usage.sklearn: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: List[List[int]]): + """ + usage.skimage: 4 + usage.sklearn: 1 + """ + ... + + +@overload +def assert_almost_equal( + actual: object, + desired: object, + decimal: int = ..., + verbose: bool = ..., + err_msg: str = ..., +): + """ + usage.scipy: 1344 + """ + ... + + +@overload +def assert_almost_equal(actual: List[numpy.float64], desired: numpy.ndarray): + """ + usage.matplotlib: 3 + """ + ... + + +@overload +def assert_almost_equal(actual: list, desired: numpy.ndarray): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: float): + """ + usage.matplotlib: 4 + usage.sklearn: 5 + """ + ... + + +@overload +def assert_almost_equal(actual: Tuple[float, float], desired: Tuple[int, int]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: List[numpy.float64], desired: List[float]): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: List[float], desired: numpy.ndarray): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def assert_almost_equal(actual: float, desired: numpy.float64): + """ + usage.sklearn: 9 + """ + ... + + +@overload +def assert_almost_equal( + actual: numpy.float64, + desired: numpy.float64, + decimal: int, + err_msg: Literal["Unexpected std"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.float64, desired: float, err_msg: str): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_almost_equal(actual: float, desired: float, decimal: int): + """ + usage.sklearn: 5 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.float64, desired: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.int64, desired: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: int, desired: numpy.float64): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: int, desired: numpy.float64, decimal: int): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def assert_almost_equal( + actual: numpy.ndarray, desired: numpy.ndarray, decimal: int, err_msg: str +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.float64, desired: numpy.float64, err_msg: str): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.int64, desired: numpy.int64, err_msg: str): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: numpy.ndarray, err_msg: str): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def assert_almost_equal(actual: float, desired: float, err_msg: str): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.int64, desired: float): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: List[Union[float, int]]): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def assert_almost_equal(actual: numpy.ndarray, desired: List[int], err_msg: str): + """ + usage.sklearn: 3 + """ + ... + + +def assert_almost_equal( + actual: object, + desired: object, + decimal: int = ..., + verbose: bool = ..., + err_msg: str = ..., +): + """ + usage.matplotlib: 50 + usage.scipy: 1344 + usage.skimage: 333 + usage.sklearn: 965 + """ + ... + + +@overload +def assert_approx_equal( + actual: Union[numpy.float64, float], + desired: Union[float, int, numpy.float64], + significant: int = ..., + err_msg: str = ..., +): + """ + usage.scipy: 220 + """ + ... + + +@overload +def assert_approx_equal(actual: numpy.float64, desired: float): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def assert_approx_equal(actual: numpy.float64, desired: numpy.float64): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_approx_equal( + actual: numpy.float64, desired: numpy.float64, significant: int +): + """ + usage.sklearn: 1 + """ + ... + + +def assert_approx_equal( + actual: Union[numpy.float64, float], + desired: Union[numpy.float64, int, float], + significant: int = ..., + err_msg: str = ..., +): + """ + usage.matplotlib: 1 + usage.scipy: 220 + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[List[Tuple[int, int, int]]]): + """ + usage.skimage: 4 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: numpy.ndarray): + """ + usage.matplotlib: 29 + usage.skimage: 39 + usage.sklearn: 886 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: Tuple[int, int, int]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[int]): + """ + usage.matplotlib: 2 + usage.skimage: 6 + usage.sklearn: 61 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[Union[int, float]]): + """ + usage.matplotlib: 1 + usage.skimage: 3 + usage.sklearn: 4 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[float]): + """ + usage.skimage: 1 + usage.sklearn: 67 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[Tuple[int, int]]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def assert_array_almost_equal( + x: Tuple[int, int, int, int], y: Tuple[int, int, int, int] +): + """ + usage.skimage: 2 + """ + ... + + +@overload +def assert_array_almost_equal( + x: Tuple[int, int, int, int, int, int], y: Tuple[int, int, int, int, int, int] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: int, y: int): + """ + usage.skimage: 1 + usage.sklearn: 7 + """ + ... + + +@overload +def assert_array_almost_equal( + x: Tuple[numpy.float64, numpy.float64], y: Tuple[float, float] +): + """ + usage.skimage: 2 + """ + ... + + +@overload +def assert_array_almost_equal( + x: Tuple[numpy.float64, numpy.float64, numpy.float64], y: Tuple[float, float, float] +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: int): + """ + usage.skimage: 5 + usage.sklearn: 14 + """ + ... + + +@overload +def assert_array_almost_equal( + x: object, y: object, decimal: int = ..., err_msg: str = ... +): + """ + usage.scipy: 3904 + """ + ... + + +@overload +def assert_array_almost_equal( + x: Tuple[numpy.float32, numpy.float32, numpy.float32, numpy.float32], + y: Tuple[float, float, float, float], + decimal: int, +): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: numpy.ndarray, decimal: int): + """ + usage.matplotlib: 4 + usage.sklearn: 226 + """ + ... + + +@overload +def assert_array_almost_equal( + x: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + y: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + decimal: int, +): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.float64, y: float): + """ + usage.matplotlib: 4 + usage.sklearn: 7 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: list): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def assert_array_almost_equal( + x: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + y: Tuple[int, int, int, int], + decimal: int, +): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: List[float], y: numpy.ndarray): + """ + usage.matplotlib: 1 + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.float64, y: List[int]): + """ + usage.matplotlib: 1 + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ma.core.MaskedArray, y: numpy.ma.core.MaskedArray +): + """ + usage.matplotlib: 4 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ma.core.MaskedArray, y: List[Union[int, float]]): + """ + usage.matplotlib: 3 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: numpy.ma.core.MaskedArray): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ma.core.MaskedArray, y: List[int]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ma.core.MaskedArray, y: numpy.ndarray): + """ + usage.matplotlib: 7 + """ + ... + + +@overload +def assert_array_almost_equal(x: List[numpy.float64], y: numpy.ma.core.MaskedArray): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ma.core.MaskedArray, y: List[float]): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: numpy.float64): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def assert_array_almost_equal( + x: Tuple[numpy.float64, numpy.float64], y: List[float], decimal: int +): + """ + usage.matplotlib: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[float], decimal: int): + """ + usage.matplotlib: 4 + usage.sklearn: 61 + """ + ... + + +@overload +def assert_array_almost_equal(x: List[float], y: numpy.ndarray, decimal: int): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: List[List[float]], y: numpy.ndarray): + """ + usage.matplotlib: 5 + usage.sklearn: 6 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.float64, y: numpy.float64): + """ + usage.matplotlib: 3 + usage.sklearn: 25 + """ + ... + + +@overload +def assert_array_almost_equal(x: Tuple[numpy.float64, numpy.float64], y: numpy.ndarray): + """ + usage.matplotlib: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: dask.array.core.Array, y: numpy.ndarray): + """ + usage.dask: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[List[Union[float, int]]]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: float, y: numpy.float64): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: float, y: float, decimal: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.float64, y: numpy.float64, decimal: int): + """ + usage.sklearn: 13 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.float64, y: numpy.ndarray, decimal: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ndarray, y: numpy.ndarray, decimal: int, err_msg: str +): + """ + usage.sklearn: 5 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: numpy.ndarray, err_msg: str): + """ + usage.sklearn: 8 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ndarray, y: numpy.ndarray, err_msg: Literal["X != TP'"] +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ndarray, y: numpy.ndarray, err_msg: Literal["Y != UQ'"] +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ndarray, y: numpy.ndarray, err_msg: Literal["rotation on X failed"] +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ndarray, y: numpy.ndarray, err_msg: Literal["rotation on Y failed"] +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: int, decimal: int): + """ + usage.sklearn: 17 + """ + ... + + +@overload +def assert_array_almost_equal( + x: Tuple[numpy.ndarray, numpy.ndarray], y: Tuple[numpy.ndarray, numpy.ndarray] +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[int], err_msg: str): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def assert_array_almost_equal(x: List[List[Union[int, float]]], y: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: List[Union[float, int]], y: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: numpy.memmap, decimal: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[List[int]]): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def assert_array_almost_equal(x: List[numpy.ndarray], y: List[numpy.ndarray]): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def assert_array_almost_equal(x: List[numpy.int64], y: numpy.ndarray): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ndarray, y: List[Union[float, int]], err_msg: str +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.float64, y: int): + """ + usage.sklearn: 7 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.float32, y: numpy.float64): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[int], decimal: int): + """ + usage.sklearn: 12 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: float, decimal: int): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.float32, y: numpy.float32): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.float32, y: numpy.float64, decimal: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: float, y: float): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[List[int]], decimal: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: List[numpy.int64], y: List[numpy.int64]): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ndarray, + y: numpy.ndarray, + decimal: int, + err_msg: Literal["with solver = sag"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ndarray, + y: numpy.ndarray, + decimal: int, + err_msg: Literal["with solver = saga"], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ndarray, + y: numpy.ndarray, + decimal: int, + err_msg: Literal["with solver = lbfgs"], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal( + x: List[numpy.float64], y: List[numpy.float64], decimal: int +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.float64, y: float, decimal: int): + """ + usage.sklearn: 25 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[List[float]]): + """ + usage.sklearn: 16 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ndarray, y: List[Union[float, int]], decimal: int +): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ndarray, y: List[Union[int, float]], decimal: int +): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[numpy.float64]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.matrix, y: numpy.ndarray): + """ + usage.sklearn: 5 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[numpy.float64], decimal: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[Union[float, int]]): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def assert_array_almost_equal(x: List[numpy.float64], y: List[numpy.float64]): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: numpy.ndarray, decimal: bool): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: float): + """ + usage.sklearn: 15 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[numpy.int64]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: numpy.ndarray, y: List[List[float]], decimal: int): + """ + usage.sklearn: 4 + """ + ... + + +@overload +def assert_array_almost_equal( + x: numpy.ndarray, y: Tuple[numpy.float64, numpy.float64], decimal: int +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: List[numpy.float64], y: List[float]): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: int, y: numpy.float64, decimal: int): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: float, y: numpy.float64, decimal: int): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_almost_equal(x: int, y: numpy.float64): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_almost_equal(x: int, y: numpy.int64): + """ + usage.sklearn: 1 + """ + ... + + +def assert_array_almost_equal( + x: object, y: object, decimal: Union[bool, int] = ..., err_msg: str = ... +): + """ + usage.dask: 1 + usage.matplotlib: 86 + usage.scipy: 3904 + usage.skimage: 69 + usage.sklearn: 1569 + """ + ... + + +@overload +def assert_array_almost_equal_nulp(x: numpy.ndarray, y: numpy.ndarray): + """ + usage.matplotlib: 1 + usage.skimage: 1 + """ + ... + + +@overload +def assert_array_almost_equal_nulp( + x: numpy.ma.core.MaskedArray, y: numpy.ma.core.MaskedArray +): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_array_almost_equal_nulp(x: numpy.ma.core.MaskedArray, y: numpy.float64): + """ + usage.skimage: 2 + """ + ... + + +@overload +def assert_array_almost_equal_nulp( + x: Union[numpy.float64, numpy.matrix, int, float, numpy.ndarray], + y: Union[float, numpy.ndarray, int, numpy.float64], + nulp: Union[int, float] = ..., +): + """ + usage.scipy: 67 + """ + ... + + +def assert_array_almost_equal_nulp( + x: object, + y: Union[numpy.ndarray, numpy.ma.core.MaskedArray, float, int, numpy.float64], + nulp: Union[int, float] = ..., +): + """ + usage.matplotlib: 1 + usage.scipy: 67 + usage.skimage: 4 + """ + ... + + +@overload +def assert_array_equal(x: List[int], y: List[int]): + """ + usage.matplotlib: 5 + usage.skimage: 6 + usage.sklearn: 13 + usage.xarray: 2 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ndarray, y: numpy.ndarray): + """ + usage.matplotlib: 106 + usage.skimage: 321 + usage.sklearn: 919 + usage.xarray: 164 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ndarray, y: int): + """ + usage.skimage: 13 + usage.sklearn: 10 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[numpy.uint8, numpy.uint8], y: List[int]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[int, int], y: List[int]): + """ + usage.skimage: 2 + usage.sklearn: 1 + usage.xarray: 5 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[numpy.float64, numpy.float64], y: List[float]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[float, float], y: List[float]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[int, int], y: Tuple[int, int]): + """ + usage.skimage: 3 + usage.sklearn: 10 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ndarray, y: List[int]): + """ + usage.matplotlib: 10 + usage.skimage: 11 + usage.sklearn: 190 + usage.xarray: 7 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ndarray, y: Tuple[bool, bool, bool, bool]): + """ + usage.skimage: 1 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ndarray, y: Tuple[float, float]): + """ + usage.skimage: 3 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ndarray, y: Tuple[int, int]): + """ + usage.skimage: 1 + usage.xarray: 2 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ndarray, y: List[List[List[Tuple[int, int]]]]): + """ + usage.skimage: 3 + """ + ... + + +@overload +def assert_array_equal(x: List[Tuple[int, int]], y: List[Tuple[int, int]]): + """ + usage.skimage: 8 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ndarray, y: List[List[int]]): + """ + usage.matplotlib: 12 + usage.skimage: 12 + usage.sklearn: 33 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ndarray, y: List[List[float]]): + """ + usage.matplotlib: 1 + usage.skimage: 6 + usage.sklearn: 14 + """ + ... + + +@overload +def assert_array_equal(x: int, y: int): + """ + usage.matplotlib: 3 + usage.skimage: 7 + usage.sklearn: 2 + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal(x: skimage.util._map_array.ArrayMap, y: numpy.ndarray): + """ + usage.skimage: 8 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[int, int, int], y: Tuple[float, float, int]): + """ + usage.skimage: 3 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[int, int], y: Tuple[float, float]): + """ + usage.skimage: 2 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[int], y: numpy.ndarray): + """ + usage.skimage: 4 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[int, int], y: numpy.ndarray): + """ + usage.skimage: 4 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[int, int, int], y: numpy.ndarray): + """ + usage.skimage: 4 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[int, int, int, int], y: numpy.ndarray): + """ + usage.skimage: 4 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[int, int, int], y: Tuple[int, int, int]): + """ + usage.skimage: 1 + usage.sklearn: 2 + """ + ... + + +@overload +def assert_array_equal(x: netCDF4._netCDF4.Variable, y: numpy.ndarray): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[int], y: Tuple[int]): + """ + usage.sklearn: 3 + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ma.core.MaskedArray, y: numpy.ma.core.MaskedArray): + """ + usage.matplotlib: 3 + usage.xarray: 2 + """ + ... + + +@overload +def assert_array_equal( + x: List[Literal["strings", "of", "list"]], y: List[Literal["strings", "of", "list"]] +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal(x: List[Literal["one element"]], y: Literal["one element"]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[float, float, float], y: List[float]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal(x: Tuple[float], y: List[float]): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ndarray, y: pandas.core.indexes.numeric.Int64Index): + """ + usage.xarray: 3 + """ + ... + + +@overload +def assert_array_equal( + x: pandas.core.indexes.datetimes.DatetimeIndex, + y: pandas.core.indexes.datetimes.DatetimeIndex, +): + """ + usage.xarray: 2 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ndarray, y: xarray.coding.strings.StackedBytesArray): + """ + usage.xarray: 2 + """ + ... + + +@overload +def assert_array_equal(x: xarray.coding.strings.StackedBytesArray, y: numpy.ndarray): + """ + usage.xarray: 2 + """ + ... + + +@overload +def assert_array_equal(x: int, y: numpy.int64): + """ + usage.xarray: 2 + """ + ... + + +@overload +def assert_array_equal(x: List[int], y: numpy.ndarray): + """ + usage.matplotlib: 1 + usage.sklearn: 26 + usage.xarray: 4 + """ + ... + + +@overload +def assert_array_equal(x: List[List[int]], y: numpy.ndarray): + """ + usage.sklearn: 22 + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal(x: numpy.ndarray, y: numpy.int64): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal(x: List[float], y: numpy.ndarray): + """ + usage.matplotlib: 1 + usage.xarray: 3 + """ + ... + + +@overload +def assert_array_equal(x: numpy.int32, y: numpy.int64): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal( + x: numpy.ndarray, y: pandas.core.indexes.datetimes.DatetimeIndex +): + """ + usage.xarray: 3 + """ + ... + + +@overload +def assert_array_equal(x: numpy.timedelta64, y: numpy.ndarray): + """ + usage.xarray: 2 + """ + ... + + +@overload +def assert_array_equal( + x: Dict[Literal["attr"], Literal["da"]], y: Dict[Literal["attr"], Literal["da"]] +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal( + x: Dict[Literal["attr"], Literal["da_coord"]], + y: Dict[Literal["attr"], Literal["da_coord"]], +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal( + x: Dict[Literal["attr"], Literal["ds"]], y: Dict[Literal["attr"], Literal["ds"]] +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal(x: xarray.conventions.BoolTypeArray, y: numpy.ndarray): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal(x: xarray.conventions.NativeEndiannessArray, y: numpy.ndarray): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal( + x: numpy.ndarray, y: List[Literal["2265-10-28T00:00:00", "2000-01-01T00:00:00"]] +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal( + x: xarray.core.variable.Variable, y: xarray.core.dataarray.DataArray +): + """ + usage.xarray: 1 + """ + ... + + +@overload +def assert_array_equal(x: xarray.core.dataarray.DataArray, y: numpy.ndarray): + """ + usage.xarray: 6 + """ + ... + + +@overload +def assert_array_equal( + x: xarray.core.dataarray.DataArray, y: xarray.core.variable.Variable +): + """ + usage.xarray: 7 + """ + ... + + +@overload +def assert_array_equal( + x: xarray.core.dataarray.DataArray, y: xarray.core.dataarray.DataArray +): + """ + usage.xarray: 9 + """ + ... + + +@overload +def assert_array_equal(x: range, y: List[int]): + """ + usage.sklearn: 1 + usage.xarray: 2 + """ + ... + + +@overload +def assert_array_equal( + x: pandas.core.indexes.base.Index, y: pandas.core.indexes.base.Index +): + """ + usage.sklearn: 1 + usage.xarray: 3 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: list): +def assert_array_equal(x: Literal["x"], y: Literal["x"]): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def assert_array_almost_equal( - x: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], - y: Tuple[int, int, int, int], - decimal: int, +def assert_array_equal( + x: pandas.core.indexes.base.Index, y: List[Literal["c", "b", "a"]] ): """ - usage.matplotlib: 2 + usage.xarray: 1 """ ... @overload -def assert_array_almost_equal(x: List[float], y: numpy.ndarray): +def assert_array_equal(x: pandas.core.indexes.numeric.Int64Index, y: List[int]): """ - usage.matplotlib: 1 + usage.xarray: 5 """ ... @overload -def assert_array_almost_equal(x: numpy.float64, y: List[int]): +def assert_array_equal(x: Literal["foo"], y: Literal["foo"]): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def assert_array_almost_equal( - x: numpy.ma.core.MaskedArray, y: numpy.ma.core.MaskedArray -): +def assert_array_equal(x: numpy.ma.core.MaskedArray, y: numpy.ndarray): """ - usage.matplotlib: 4 + usage.matplotlib: 2 + usage.xarray: 1 """ ... @overload -def assert_array_almost_equal(x: numpy.ma.core.MaskedArray, y: List[Union[int, float]]): +def assert_array_equal(x: xarray.core.dataarray.DataArray, y: numpy.float64): """ - usage.matplotlib: 3 + usage.xarray: 3 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: numpy.ma.core.MaskedArray): +def assert_array_equal( + x: xarray.core.dataarray.DataArray, y: pandas.core.series.Series +): """ - usage.matplotlib: 2 + usage.xarray: 1 """ ... @overload -def assert_array_almost_equal(x: numpy.ma.core.MaskedArray, y: List[int]): +def assert_array_equal(x: xarray.core.dataarray.DataArray, y: List[Literal["b", "a"]]): """ - usage.matplotlib: 2 + usage.xarray: 2 """ ... @overload -def assert_array_almost_equal(x: numpy.ma.core.MaskedArray, y: numpy.ndarray): +def assert_array_equal(x: xarray.core.dataarray.DataArray, y: List[str]): """ - usage.matplotlib: 7 + usage.xarray: 2 """ ... @overload -def assert_array_almost_equal(x: List[numpy.float64], y: numpy.ma.core.MaskedArray): +def assert_array_equal(x: xarray.core.dataarray.DataArray, y: List[Literal["b"]]): """ - usage.matplotlib: 2 + usage.xarray: 3 """ ... @overload -def assert_array_almost_equal(x: numpy.ma.core.MaskedArray, y: List[float]): +def assert_array_equal(x: xarray.core.dataarray.DataArray, y: List[int]): """ - usage.matplotlib: 2 + usage.xarray: 2 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: numpy.float64): +def assert_array_equal(x: numpy.ndarray, y: Literal["DJF"]): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def assert_array_almost_equal( - x: Tuple[numpy.float64, numpy.float64], y: List[float], decimal: int +def assert_array_equal( + x: List[Literal["SON", "JJA", "MAM", "DJF"]], y: xarray.core.dataarray.DataArray ): """ - usage.matplotlib: 2 + usage.xarray: 1 """ ... @overload -def assert_array_almost_equal(x: numpy.ndarray, y: List[float], decimal: int): +def assert_array_equal( + x: xarray.core.dataarray.DataArray, y: xarray.coding.cftimeindex.CFTimeIndex +): """ - usage.matplotlib: 4 + usage.xarray: 1 """ ... @overload -def assert_array_almost_equal(x: List[float], y: numpy.ndarray, decimal: int): +def assert_array_equal(x: numpy.ndarray, y: xarray.core.dataarray.DataArray): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def assert_array_almost_equal(x: List[List[float]], y: numpy.ndarray): +def assert_array_equal(x: dask.array.core.Array, y: numpy.ndarray): """ - usage.matplotlib: 5 + usage.xarray: 4 """ ... @overload -def assert_array_almost_equal(x: numpy.float64, y: numpy.float64): +def assert_array_equal(x: numpy.ndarray, y: numpy.float64): """ - usage.matplotlib: 3 + usage.sklearn: 5 + usage.xarray: 2 """ ... @overload -def assert_array_almost_equal(x: Tuple[numpy.float64, numpy.float64], y: numpy.ndarray): +def assert_array_equal(x: None, y: None): """ - usage.matplotlib: 1 + usage.xarray: 1 """ ... @overload -def assert_array_almost_equal(x: dask.array.core.Array, y: numpy.ndarray): +def assert_array_equal(x: numpy.ndarray, y: List[bool]): """ - usage.dask: 1 + usage.sklearn: 1 + usage.xarray: 1 """ ... @overload -def assert_array_almost_equal( - x: object, y: object, decimal: Union[int, bool] = ..., err_msg: str = ... +def assert_array_equal( + x: slice[numpy.int64, numpy.int64, numpy.int64], y: slice[int, int, int] ): """ - usage.sklearn: 1569 + usage.xarray: 1 """ ... -def assert_array_almost_equal( - x: object, y: object, decimal: Union[bool, int] = ..., err_msg: str = ... +@overload +def assert_array_equal( + x: pandas.core.indexes.multi.MultiIndex, y: pandas.core.indexes.multi.MultiIndex ): """ - usage.dask: 1 - usage.matplotlib: 86 - usage.scipy: 3904 - usage.skimage: 69 - usage.sklearn: 1569 + usage.xarray: 2 """ ... @overload -def assert_array_almost_equal_nulp(x: numpy.ndarray, y: numpy.ndarray): +def assert_array_equal(x: numpy.ndarray, y: xarray.core.variable.Variable): """ - usage.matplotlib: 1 - usage.skimage: 1 + usage.xarray: 2 """ ... @overload -def assert_array_almost_equal_nulp( - x: numpy.ma.core.MaskedArray, y: numpy.ma.core.MaskedArray +def assert_array_equal( + x: xarray.core.variable.Variable, y: xarray.core.variable.Variable ): """ - usage.skimage: 1 + usage.xarray: 9 """ ... @overload -def assert_array_almost_equal_nulp(x: numpy.ma.core.MaskedArray, y: numpy.float64): +def assert_array_equal(x: xarray.core.indexing.CopyOnWriteArray, y: numpy.ndarray): """ - usage.skimage: 2 + usage.xarray: 3 """ ... @overload -def assert_array_almost_equal_nulp( - x: Union[numpy.float64, numpy.matrix, int, float, numpy.ndarray], - y: Union[float, numpy.ndarray, int, numpy.float64], - nulp: Union[int, float] = ..., -): +def assert_array_equal(x: xarray.core.indexing.MemoryCachedArray, y: numpy.ndarray): """ - usage.scipy: 67 + usage.xarray: 2 """ ... -def assert_array_almost_equal_nulp( - x: object, - y: Union[numpy.ndarray, numpy.ma.core.MaskedArray, float, int, numpy.float64], - nulp: Union[int, float] = ..., -): +@overload +def assert_array_equal(x: Tuple[numpy.ndarray], y: List[numpy.ndarray]): """ - usage.matplotlib: 1 - usage.scipy: 67 - usage.skimage: 4 + usage.xarray: 1 """ ... @overload -def assert_array_equal(x: List[int], y: List[int]): +def assert_array_equal(x: numpy.float64, y: numpy.float64): """ - usage.matplotlib: 5 - usage.skimage: 6 + usage.sklearn: 8 usage.xarray: 2 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: numpy.ndarray): +def assert_array_equal(x: bool, y: bool): """ - usage.matplotlib: 106 - usage.skimage: 321 - usage.xarray: 164 + usage.xarray: 2 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: int): +def assert_array_equal(x: numpy.ndarray, y: dask.array.core.Array): """ - usage.skimage: 13 + usage.xarray: 2 """ ... @overload -def assert_array_equal(x: Tuple[numpy.uint8, numpy.uint8], y: List[int]): +def assert_array_equal( + x: pandas.core.indexes.datetimes.DatetimeIndex, y: numpy.ndarray +): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def assert_array_equal(x: Tuple[int, int], y: List[int]): +def assert_array_equal(x: int, y: numpy.float64): """ - usage.skimage: 2 - usage.xarray: 5 + usage.xarray: 1 """ ... @overload -def assert_array_equal(x: Tuple[numpy.float64, numpy.float64], y: List[float]): +def assert_array_equal(x: numpy.float64, y: float): """ - usage.skimage: 1 + usage.sklearn: 1 + usage.xarray: 1 """ ... @overload -def assert_array_equal(x: Tuple[float, float], y: List[float]): +def assert_array_equal(x: numpy.int32, y: int): """ - usage.skimage: 1 + usage.xarray: 1 """ ... @overload -def assert_array_equal(x: Tuple[int, int], y: Tuple[int, int]): +def assert_array_equal(x: xarray.core.variable.Variable, y: numpy.ndarray): """ - usage.skimage: 3 + usage.xarray: 38 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: List[int]): +def assert_array_equal(x: xarray.core.variable.Variable, y: numpy.int64): """ - usage.matplotlib: 10 - usage.skimage: 11 - usage.xarray: 7 + usage.xarray: 9 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: Tuple[bool, bool, bool, bool]): +def assert_array_equal(x: object, y: numpy.ndarray): """ - usage.skimage: 1 + usage.xarray: 3 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: Tuple[float, float]): +def assert_array_equal(x: xarray.core.variable.Variable, y: object): """ - usage.skimage: 3 + usage.xarray: 23 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: Tuple[int, int]): +def assert_array_equal( + x: pandas.core.indexes.timedeltas.TimedeltaIndex, + y: pandas.core.indexes.timedeltas.TimedeltaIndex, +): """ - usage.skimage: 1 - usage.xarray: 2 + usage.xarray: 1 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: List[List[List[Tuple[int, int]]]]): +def assert_array_equal( + x: xarray.coding.cftimeindex.CFTimeIndex, y: xarray.coding.cftimeindex.CFTimeIndex +): """ - usage.skimage: 3 + usage.xarray: 1 """ ... @overload -def assert_array_equal(x: List[Tuple[int, int]], y: List[Tuple[int, int]]): +def assert_array_equal(x: pandas.core.indexes.frozen.FrozenList, y: List[List[int]]): """ - usage.skimage: 8 + usage.xarray: 2 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: List[List[int]]): +def assert_array_equal(x: pandas.core.indexes.base.Index, y: List[Literal["a", "b"]]): """ - usage.matplotlib: 12 - usage.skimage: 12 + usage.xarray: 2 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: List[List[float]]): +def assert_array_equal(x: xarray.core.variable.IndexVariable, y: numpy.ndarray): """ - usage.matplotlib: 1 - usage.skimage: 6 + usage.xarray: 4 """ ... @overload -def assert_array_equal(x: int, y: int): +def assert_array_equal( + x: xarray.core.variable.IndexVariable, y: xarray.core.variable.Variable +): """ - usage.matplotlib: 3 - usage.skimage: 7 usage.xarray: 1 """ ... @overload -def assert_array_equal(x: skimage.util._map_array.ArrayMap, y: numpy.ndarray): +def assert_array_equal(x: xarray.core.variable.IndexVariable, y: numpy.int64): """ - usage.skimage: 8 + usage.xarray: 2 """ ... @overload -def assert_array_equal(x: Tuple[int, int, int], y: Tuple[float, float, int]): +def assert_array_equal(x: numpy.ndarray, y: xarray.core.variable.IndexVariable): """ - usage.skimage: 3 + usage.xarray: 1 """ ... @overload -def assert_array_equal(x: Tuple[int, int], y: Tuple[float, float]): +def assert_array_equal(x: object, y: object, err_msg: str = ...): """ - usage.skimage: 2 + usage.scipy: 1623 """ ... @overload -def assert_array_equal(x: Tuple[int], y: numpy.ndarray): +def assert_array_equal( + x: Tuple[numpy.float64, numpy.float64], y: Tuple[numpy.float64, numpy.float64] +): """ - usage.skimage: 4 + usage.matplotlib: 2 """ ... @overload -def assert_array_equal(x: Tuple[int, int], y: numpy.ndarray): +def assert_array_equal(x: Tuple[numpy.float64, numpy.float64], y: Tuple[float, float]): """ - usage.skimage: 4 + usage.matplotlib: 2 """ ... @overload -def assert_array_equal(x: Tuple[int, int, int], y: numpy.ndarray): +def assert_array_equal(x: List[int], y: range): """ - usage.skimage: 4 + usage.matplotlib: 1 """ ... @overload -def assert_array_equal(x: Tuple[int, int, int, int], y: numpy.ndarray): +def assert_array_equal(x: numpy.ndarray, y: List[numpy.float64]): """ - usage.skimage: 4 + usage.matplotlib: 6 + usage.sklearn: 8 """ ... @overload -def assert_array_equal(x: Tuple[int, int, int], y: Tuple[int, int, int]): +def assert_array_equal(x: numpy.ndarray, y: float): """ - usage.skimage: 1 + usage.matplotlib: 4 """ ... @overload -def assert_array_equal(x: netCDF4._netCDF4.Variable, y: numpy.ndarray): +def assert_array_equal(x: numpy.ndarray, y: Literal["red"]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def assert_array_equal(x: Tuple[int], y: Tuple[int]): +def assert_array_equal(x: numpy.ndarray, y: List[Union[int, float]]): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: numpy.ma.core.MaskedArray, y: numpy.ma.core.MaskedArray): +def assert_array_equal( + x: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + y: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], +): """ usage.matplotlib: 3 - usage.xarray: 2 """ ... @overload def assert_array_equal( - x: List[Literal["strings", "of", "list"]], y: List[Literal["strings", "of", "list"]] + x: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + y: numpy.ndarray, ): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def assert_array_equal(x: List[Literal["one element"]], y: Literal["one element"]): +def assert_array_equal(x: numpy.ma.core.MaskedArray, y: List[int]): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload -def assert_array_equal(x: Tuple[float, float, float], y: List[float]): +def assert_array_equal(x: numpy.ma.core.MaskedArray, y: int): """ - usage.xarray: 1 + usage.matplotlib: 3 """ ... @overload -def assert_array_equal(x: Tuple[float], y: List[float]): +def assert_array_equal(x: numpy.ma.core.MaskedArray, y: List[Union[float, int]]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: pandas.core.indexes.numeric.Int64Index): +def assert_array_equal(x: numpy.ndarray, y: List[Tuple[int, int, int, int]]): """ - usage.xarray: 3 + usage.matplotlib: 1 """ ... @overload def assert_array_equal( - x: pandas.core.indexes.datetimes.DatetimeIndex, - y: pandas.core.indexes.datetimes.DatetimeIndex, + x: numpy.ndarray, y: List[Tuple[int, Union[float, int], int, int]] ): """ - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: xarray.coding.strings.StackedBytesArray): +def assert_array_equal( + x: List[ + Literal[ + "2000-10-31T11:50:23", + "2054-06-20T14:31:45", + "1983-07-09T17:17:34", + "1976-03-05T00:00:01", + "2014-01-11T00:00:00", + ] + ], + y: List[ + Literal[ + "2000-10-31T11:50:23", + "2054-06-20T14:31:45", + "1983-07-09T17:17:34", + "1976-03-05T00:00:01", + "2014-01-11T00:00:00", + ] + ], +): """ - usage.xarray: 2 + usage.matplotlib: 1 """ ... @overload -def assert_array_equal(x: xarray.coding.strings.StackedBytesArray, y: numpy.ndarray): +def assert_array_equal(x: list, y: list): """ - usage.xarray: 2 + usage.matplotlib: 2 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: int, y: numpy.int64): +def assert_array_equal(x: List[numpy.ndarray], y: List[List[List[int]]]): """ - usage.xarray: 2 + usage.matplotlib: 5 """ ... @overload -def assert_array_equal(x: List[int], y: numpy.ndarray): +def assert_array_equal(x: List[List[numpy.ndarray]], y: List[List[List[int]]]): """ usage.matplotlib: 1 - usage.xarray: 4 """ ... @overload -def assert_array_equal(x: List[List[int]], y: numpy.ndarray): +def assert_array_equal(x: numpy.ndarray, y: List[float]): """ - usage.xarray: 1 + usage.matplotlib: 1 + usage.sklearn: 11 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: numpy.int64): +def assert_array_equal(x: numpy.ndarray, y: List[List[bool]]): """ - usage.xarray: 1 + usage.matplotlib: 1 """ ... @overload -def assert_array_equal(x: List[float], y: numpy.ndarray): +def assert_array_equal(x: numpy.ndarray, y: bool): """ usage.matplotlib: 1 - usage.xarray: 3 """ ... @overload -def assert_array_equal(x: numpy.int32, y: numpy.int64): +def assert_array_equal( + x: Union[numpy.ndarray, dask.array.core.Array, dask.dataframe.core.Index], + y: Union[numpy.ndarray, List[int]], +): """ - usage.xarray: 1 + usage.dask: 36 """ ... @overload -def assert_array_equal( - x: numpy.ndarray, y: pandas.core.indexes.datetimes.DatetimeIndex -): +def assert_array_equal(x: list, y: numpy.ndarray): """ - usage.xarray: 3 + usage.sklearn: 4 """ ... @overload -def assert_array_equal(x: numpy.timedelta64, y: numpy.ndarray): +def assert_array_equal(x: numpy.ndarray, y: list): """ - usage.xarray: 2 + usage.sklearn: 5 """ ... @overload -def assert_array_equal( - x: Dict[Literal["attr"], Literal["da"]], y: Dict[Literal["attr"], Literal["da"]] -): +def assert_array_equal(x: numpy.ndarray, y: numpy.ndarray, err_msg: str): """ - usage.xarray: 1 + usage.sklearn: 35 """ ... @overload -def assert_array_equal( - x: Dict[Literal["attr"], Literal["da_coord"]], - y: Dict[Literal["attr"], Literal["da_coord"]], -): +def assert_array_equal(x: numpy.ndarray, y: List[Union[float, int]]): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_equal( - x: Dict[Literal["attr"], Literal["ds"]], y: Dict[Literal["attr"], Literal["ds"]] -): +def assert_array_equal(x: numpy.ndarray, y: numpy.ndarray, err_msg: Literal[""]): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: xarray.conventions.BoolTypeArray, y: numpy.ndarray): +def assert_array_equal( + x: List[Literal["col_float", "col_int"]], y: List[Literal["col_float", "col_int"]] +): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: xarray.conventions.NativeEndiannessArray, y: numpy.ndarray): +def assert_array_equal(x: List[Literal["col_str"]], y: List[Literal["col_str"]]): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_equal( - x: numpy.ndarray, y: List[Literal["2265-10-28T00:00:00", "2000-01-01T00:00:00"]] -): +def assert_array_equal(x: List[Literal["col_float"]], y: List[Literal["col_float"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal( - x: xarray.core.variable.Variable, y: xarray.core.dataarray.DataArray -): +def assert_array_equal(x: List[Literal["col_int"]], y: List[Literal["col_int"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: xarray.core.dataarray.DataArray, y: numpy.ndarray): +def assert_array_equal( + x: List[Literal["col_str", "col_float"]], y: List[Literal["col_str", "col_float"]] +): """ - usage.xarray: 6 + usage.sklearn: 1 """ ... @overload def assert_array_equal( - x: xarray.core.dataarray.DataArray, y: xarray.core.variable.Variable + x: List[Literal["col_str", "col_float", "col_int"]], + y: List[Literal["col_str", "col_float", "col_int"]], ): """ - usage.xarray: 7 + usage.sklearn: 1 """ ... @overload -def assert_array_equal( - x: xarray.core.dataarray.DataArray, y: xarray.core.dataarray.DataArray -): +def assert_array_equal(x: numpy.ndarray, y: pandas.core.series.Series): """ - usage.xarray: 9 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: range, y: List[int]): +def assert_array_equal(x: numpy.ndarray, y: int, err_msg: str): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload def assert_array_equal( - x: pandas.core.indexes.base.Index, y: pandas.core.indexes.base.Index + x: numpy.ndarray, y: List[List[Literal["blue", "red", "green", "purple", "yellow"]]] ): """ - usage.xarray: 3 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: Literal["x"], y: Literal["x"]): +def assert_array_equal(x: List[numpy.ndarray], y: List[List[int]]): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_equal( - x: pandas.core.indexes.base.Index, y: List[Literal["c", "b", "a"]] -): +def assert_array_equal(x: numpy.matrix, y: int): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: pandas.core.indexes.numeric.Int64Index, y: List[int]): +def assert_array_equal(x: numpy.ndarray, y: List[str]): """ - usage.xarray: 5 + usage.sklearn: 3 """ ... @overload -def assert_array_equal(x: Literal["foo"], y: Literal["foo"]): +def assert_array_equal(x: numpy.ndarray, y: List[Literal["1", "-1"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: numpy.ma.core.MaskedArray, y: numpy.ndarray): +def assert_array_equal(x: numpy.ndarray, y: List[Union[int, Literal["foo"]]]): """ - usage.matplotlib: 2 - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: xarray.core.dataarray.DataArray, y: numpy.float64): +def assert_array_equal(x: List[str], y: List[str]): """ - usage.xarray: 3 + usage.sklearn: 5 """ ... @overload def assert_array_equal( - x: xarray.core.dataarray.DataArray, y: pandas.core.series.Series + x: List[Literal["e", "d", "c", "b", "a"]], y: List[Literal["e", "d", "c", "b", "a"]] ): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: xarray.core.dataarray.DataArray, y: List[Literal["b", "a"]]): +def assert_array_equal(x: List[Literal["I", "G", "E", "C", "A"]], y: numpy.ndarray): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: xarray.core.dataarray.DataArray, y: List[str]): +def assert_array_equal(x: List[bool], y: numpy.ndarray): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: xarray.core.dataarray.DataArray, y: List[Literal["b"]]): +def assert_array_equal(x: List[numpy.int64], y: List[int]): """ - usage.xarray: 3 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: xarray.core.dataarray.DataArray, y: List[int]): +def assert_array_equal(x: numpy.ndarray, y: List[Union[int, float]], err_msg: str): """ - usage.xarray: 2 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: Literal["DJF"]): +def assert_array_equal(x: List[numpy.ndarray], y: numpy.ndarray): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal( - x: List[Literal["SON", "JJA", "MAM", "DJF"]], y: xarray.core.dataarray.DataArray -): +def assert_array_equal(x: float, y: float): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_equal( - x: xarray.core.dataarray.DataArray, y: xarray.coding.cftimeindex.CFTimeIndex -): +def assert_array_equal(x: Tuple[int, int, int, int], y: Tuple[int, int, int, int]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: xarray.core.dataarray.DataArray): +def assert_array_equal(x: numpy.ndarray, y: List[Literal["three", "two", "one"]]): """ - usage.xarray: 1 + usage.sklearn: 3 """ ... @overload -def assert_array_equal(x: dask.array.core.Array, y: numpy.ndarray): +def assert_array_equal(x: numpy.ndarray, y: List[List[List[int]]]): """ - usage.xarray: 4 + usage.sklearn: 9 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: numpy.float64): +def assert_array_equal(x: numpy.float64, y: int): """ - usage.xarray: 2 + usage.sklearn: 3 """ ... @overload -def assert_array_equal(x: None, y: None): +def assert_array_equal( + x: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], + y: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray], +): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: List[bool]): +def assert_array_equal(x: numpy.float64, y: numpy.float64, err_msg: str): """ - usage.xarray: 1 + usage.sklearn: 7 + """ + ... + + +@overload +def assert_array_equal(x: numpy.int64, y: numpy.int64, err_msg: str): + """ + usage.sklearn: 3 """ ... @overload def assert_array_equal( - x: slice[numpy.int64, numpy.int64, numpy.int64], y: slice[int, int, int] + x: Tuple[ + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], + ], + y: numpy.ndarray, ): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload def assert_array_equal( - x: pandas.core.indexes.multi.MultiIndex, y: pandas.core.indexes.multi.MultiIndex + x: List[Literal["recall", "accuracy"]], + y: Tuple[Literal["accuracy"], Literal["recall"]], ): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: xarray.core.variable.Variable): +def assert_array_equal(x: numpy.ma.core.MaskedArray, y: List[Union[None, int]]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload def assert_array_equal( - x: xarray.core.variable.Variable, y: xarray.core.variable.Variable + x: numpy.ma.core.MaskedArray, y: numpy.ma.core.MaskedArray, err_msg: str ): """ - usage.xarray: 9 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: xarray.core.indexing.CopyOnWriteArray, y: numpy.ndarray): +def assert_array_equal( + x: List[Dict[Literal["max_depth"], int]], + y: numpy.ndarray, + err_msg: Literal["Checking params"], +): """ - usage.xarray: 3 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: xarray.core.indexing.MemoryCachedArray, y: numpy.ndarray): +def assert_array_equal( + x: List[Dict[Literal["max_depth", "min_samples_split"], int]], + y: numpy.ndarray, + err_msg: Literal["Checking params"], +): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: Tuple[numpy.ndarray], y: List[numpy.ndarray]): +def assert_array_equal( + x: Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray], + y: List[numpy.ndarray], +): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: numpy.float64, y: numpy.float64): +def assert_array_equal(x: List[numpy.ndarray], y: List[numpy.ndarray]): """ - usage.xarray: 2 + usage.sklearn: 3 """ ... @overload -def assert_array_equal(x: bool, y: bool): +def assert_array_equal( + x: List[Literal["א", "☮", "\x01F40D", "1"]], + y: List[Literal["א", "☮", "\x01F40D", "1"]], +): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: dask.array.core.Array): +def assert_array_equal(x: List[str], y: numpy.ndarray): """ - usage.xarray: 2 + usage.sklearn: 2 """ ... @overload -def assert_array_equal( - x: pandas.core.indexes.datetimes.DatetimeIndex, y: numpy.ndarray -): +def assert_array_equal(x: List[Literal["x0_dat2", "x0_c❤t1"]], y: numpy.ndarray): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: int, y: numpy.float64): +def assert_array_equal(x: List[Literal["n👍me_dat2", "n👍me_c❤t1"]], y: numpy.ndarray): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: numpy.float64, y: float): +def assert_array_equal(x: List[Literal["x2_b", "x0_c"]], y: numpy.ndarray): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: numpy.int32, y: int): +def assert_array_equal(x: List[Literal["x2_b", "x1_2", "x0_c"]], y: numpy.ndarray): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: xarray.core.variable.Variable, y: numpy.ndarray): +def assert_array_equal(x: List[Literal["x2_a", "x0_b"]], y: numpy.ndarray): """ - usage.xarray: 38 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: xarray.core.variable.Variable, y: numpy.int64): +def assert_array_equal( + x: List[Union[int, Literal["def"]]], y: List[Union[int, Literal["def"]]] +): """ - usage.xarray: 9 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: object, y: numpy.ndarray): +def assert_array_equal(x: numpy.ndarray, y: List[Literal["pos"]]): """ - usage.xarray: 3 + usage.sklearn: 4 """ ... @overload -def assert_array_equal(x: xarray.core.variable.Variable, y: object): +def assert_array_equal(x: numpy.ndarray, y: List[Literal["pos", "neg"]]): """ - usage.xarray: 23 + usage.sklearn: 1 """ ... @overload -def assert_array_equal( - x: pandas.core.indexes.timedeltas.TimedeltaIndex, - y: pandas.core.indexes.timedeltas.TimedeltaIndex, -): +def assert_array_equal(x: numpy.ndarray, y: List[Literal["neg", "pos"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal( - x: xarray.coding.cftimeindex.CFTimeIndex, y: xarray.coding.cftimeindex.CFTimeIndex -): +def assert_array_equal(x: numpy.ndarray, y: List[Literal["spam", "ham", "eggs", "0"]]): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: pandas.core.indexes.frozen.FrozenList, y: List[List[int]]): +def assert_array_equal(x: numpy.ndarray, y: List[Literal["0", "ham", "eggs", "spam"]]): """ - usage.xarray: 2 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: pandas.core.indexes.base.Index, y: List[Literal["a", "b"]]): +def assert_array_equal(x: List[Tuple[numpy.int64]], y: List[List[int]]): """ - usage.xarray: 2 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: xarray.core.variable.IndexVariable, y: numpy.ndarray): +def assert_array_equal(x: numpy.ndarray, y: List[Literal["3", "2", "1"]]): """ - usage.xarray: 4 + usage.sklearn: 2 """ ... @overload def assert_array_equal( - x: xarray.core.variable.IndexVariable, y: xarray.core.variable.Variable + x: List[Tuple[Literal["3", "2", "1"], ...]], + y: List[Tuple[Literal["3", "2", "1"], ...]], ): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: xarray.core.variable.IndexVariable, y: numpy.int64): +def assert_array_equal(x: numpy.ndarray, y: List[Literal["c", "b", "a"]]): """ - usage.xarray: 2 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: xarray.core.variable.IndexVariable): +def assert_array_equal( + x: List[Tuple[Literal["c", "b", "a"], ...]], + y: List[Tuple[Literal["c", "b", "a"], ...]], +): """ - usage.xarray: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: object, y: object, err_msg: str = ...): +def assert_array_equal( + x: List[Tuple[Tuple[int], ...]], y: List[Tuple[Tuple[int], ...]] +): """ - usage.scipy: 1623 - usage.sklearn: 1475 + usage.sklearn: 2 """ ... @overload -def assert_array_equal( - x: Tuple[numpy.float64, numpy.float64], y: Tuple[numpy.float64, numpy.float64] -): +def assert_array_equal(x: numpy.ndarray, y: Tuple[List[int], List[int]]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: Tuple[numpy.float64, numpy.float64], y: Tuple[float, float]): +def assert_array_equal(x: numpy.ndarray, y: Tuple[float, float, float]): """ - usage.matplotlib: 2 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: List[int], y: range): +def assert_array_equal( + x: numpy.ndarray, y: numpy.ndarray, err_msg: Literal["solver svd"] +): """ - usage.matplotlib: 1 + usage.sklearn: 3 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: List[numpy.float64]): +def assert_array_equal( + x: numpy.ndarray, y: numpy.ndarray, err_msg: Literal["solver lsqr"] +): """ - usage.matplotlib: 6 + usage.sklearn: 3 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: float): +def assert_array_equal( + x: numpy.ndarray, y: numpy.ndarray, err_msg: Literal["solver eigen"] +): """ - usage.matplotlib: 4 + usage.sklearn: 3 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: Literal["red"]): +def assert_array_equal(x: numpy.ndarray, y: List[Literal["paris"]]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: List[Union[int, float]]): +def assert_array_equal(x: List[Union[int, float]], y: numpy.ndarray): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_equal( - x: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], - y: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], -): +def assert_array_equal(x: List[Union[float, int]], y: numpy.ndarray): """ - usage.matplotlib: 3 + usage.sklearn: 1 """ ... @overload -def assert_array_equal( - x: Tuple[numpy.float64, numpy.float64, numpy.float64, numpy.float64], - y: numpy.ndarray, -): +def assert_array_equal(x: numpy.str_, y: List[Literal["eggs"]]): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: numpy.ma.core.MaskedArray, y: List[int]): +def assert_array_equal(x: List[list], y: numpy.ndarray): """ - usage.matplotlib: 3 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: numpy.ma.core.MaskedArray, y: int): +def assert_array_equal(x: numpy.ndarray, y: List[int], err_msg: str): """ - usage.matplotlib: 3 + usage.sklearn: 5 """ ... @overload -def assert_array_equal(x: numpy.ma.core.MaskedArray, y: List[Union[float, int]]): +def assert_array_equal(x: numpy.flatiter, y: List[float]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: List[Tuple[int, int, int, int]]): +def assert_array_equal(x: numpy.flatiter, y: List[Union[float, int]]): """ - usage.matplotlib: 1 + usage.sklearn: 2 """ ... @overload -def assert_array_equal( - x: numpy.ndarray, y: List[Tuple[int, Union[float, int], int, int]] -): +def assert_array_equal(x: numpy.matrix, y: numpy.matrix): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal( - x: List[ - Literal[ - "2000-10-31T11:50:23", - "2054-06-20T14:31:45", - "1983-07-09T17:17:34", - "1976-03-05T00:00:01", - "2014-01-11T00:00:00", - ] - ], - y: List[ - Literal[ - "2000-10-31T11:50:23", - "2054-06-20T14:31:45", - "1983-07-09T17:17:34", - "1976-03-05T00:00:01", - "2014-01-11T00:00:00", - ] - ], -): +def assert_array_equal(x: int, y: numpy.ndarray): """ - usage.matplotlib: 1 + usage.sklearn: 4 """ ... @overload -def assert_array_equal(x: list, y: list): +def assert_array_equal(x: numpy.float32, y: numpy.float32): """ - usage.matplotlib: 2 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: List[numpy.ndarray], y: List[List[List[int]]]): +def assert_array_equal(x: numpy.ndarray, y: numpy.memmap): """ - usage.matplotlib: 5 + usage.sklearn: 2 """ ... @overload -def assert_array_equal(x: List[List[numpy.ndarray]], y: List[List[List[int]]]): +def assert_array_equal(x: List[slice[int, int, int]], y: List[slice[int, int, int]]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: List[float]): +def assert_array_equal(x: numpy.ndarray, y: List[Literal["a", "b", "c"]]): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: List[List[bool]]): +def assert_array_equal(x: sklearn.utils._mocking.MockDataFrame, y: numpy.ndarray): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload -def assert_array_equal(x: numpy.ndarray, y: bool): +def assert_array_equal(x: numpy.memmap, y: numpy.memmap): """ - usage.matplotlib: 1 + usage.sklearn: 1 """ ... @overload def assert_array_equal( - x: Union[numpy.ndarray, dask.array.core.Array, dask.dataframe.core.Index], - y: Union[numpy.ndarray, List[int]], + x: pandas.core.series.Series, + y: Tuple[Type[numpy.float16], Type[numpy.float32], Type[numpy.float32]], ): """ - usage.dask: 36 + usage.sklearn: 1 """ ... @@ -2443,6 +5320,7 @@ def assert_array_equal(x: object, y: object, err_msg: str = ...): def assert_array_less(x: numpy.float64, y: numpy.float64): """ usage.skimage: 16 + usage.sklearn: 1 """ ... @@ -2451,6 +5329,7 @@ def assert_array_less(x: numpy.float64, y: numpy.float64): def assert_array_less(x: numpy.ndarray, y: numpy.ndarray): """ usage.skimage: 2 + usage.sklearn: 4 """ ... @@ -2459,6 +5338,7 @@ def assert_array_less(x: numpy.ndarray, y: numpy.ndarray): def assert_array_less(x: numpy.float64, y: float): """ usage.skimage: 2 + usage.sklearn: 1 """ ... @@ -2480,13 +5360,49 @@ def assert_array_less(x: numpy.ma.core.MaskedArray, y: float): @overload -def assert_array_less( - x: Union[numpy.float64, int, float, numpy.ndarray], - y: Union[int, numpy.ndarray, float, numpy.float64], - err_msg: str = ..., -): +def assert_array_less(x: numpy.ndarray, y: int): """ - usage.sklearn: 18 + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_less(x: float, y: numpy.ndarray): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def assert_array_less(x: int, y: numpy.ndarray): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_less(x: numpy.ndarray, y: float): + """ + usage.sklearn: 3 + """ + ... + + +@overload +def assert_array_less(x: int, y: numpy.float64): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_array_less(x: numpy.float64, y: int, err_msg: str): + """ + usage.sklearn: 3 """ ... @@ -2548,6 +5464,7 @@ def assert_equal(actual: numpy.ndarray, desired: numpy.ndarray): """ usage.matplotlib: 3 usage.skimage: 241 + usage.sklearn: 2 usage.xarray: 3 """ ... @@ -3207,6 +6124,7 @@ def assert_equal(actual: float, desired: numpy.float64): def assert_equal(actual: numpy.ndarray, desired: List[float]): """ usage.matplotlib: 1 + usage.sklearn: 1 """ ... @@ -3231,6 +6149,7 @@ def assert_equal(actual: List[numpy.ndarray], desired: numpy.ndarray): def assert_equal(actual: List[float], desired: numpy.ndarray): """ usage.matplotlib: 1 + usage.sklearn: 1 """ ... @@ -3311,43 +6230,92 @@ def assert_equal( @overload def assert_equal( - actual: Union[ - List[Union[float, Tuple[numpy.ndarray, numpy.ndarray]]], - numpy.ndarray, - Dict[ - str, - Union[ - List[ - Dict[ - Literal["kernel", "gamma", "C", "degree"], - Union[int, float, numpy.float64, Literal["rbf", "poly"]], - ] - ], - numpy.ma.core.MaskedArray, - numpy.ndarray, + actual: Dict[ + str, + Union[ + numpy.ndarray, + numpy.ma.core.MaskedArray, + List[ + Dict[ + Literal["kernel", "gamma", "C", "degree"], + Union[Literal["rbf", "poly"], float, int], + ] ], ], ], - desired: Union[ - List[Union[Tuple[numpy.ndarray, numpy.ndarray], float]], - Dict[ - str, - Union[ - List[ - Dict[ - Literal["kernel", "gamma", "C", "degree"], - Union[int, float, numpy.float64, Literal["rbf", "poly"]], - ] - ], - numpy.ma.core.MaskedArray, - numpy.ndarray, + desired: Dict[ + str, + Union[ + numpy.ndarray, + numpy.ma.core.MaskedArray, + List[ + Dict[ + Literal["kernel", "gamma", "C", "degree"], + Union[Literal["rbf", "poly"], float, int], + ] ], ], - numpy.ndarray, ], ): """ - usage.sklearn: 27 + usage.sklearn: 1 + """ + ... + + +@overload +def assert_equal( + actual: Dict[ + str, + Union[ + numpy.ndarray, + numpy.ma.core.MaskedArray, + List[Dict[Literal["C", "gamma"], numpy.float64]], + ], + ], + desired: Dict[ + str, + Union[ + numpy.ndarray, + numpy.ma.core.MaskedArray, + List[Dict[Literal["C", "gamma"], numpy.float64]], + ], + ], +): + """ + usage.sklearn: 1 + """ + ... + + +@overload +def assert_equal( + actual: Dict[ + str, + Union[ + numpy.ndarray, numpy.ma.core.MaskedArray, List[Dict[Literal["C"], float]] + ], + ], + desired: Dict[ + str, + Union[ + numpy.ndarray, numpy.ma.core.MaskedArray, List[Dict[Literal["C"], float]] + ], + ], +): + """ + usage.sklearn: 2 + """ + ... + + +@overload +def assert_equal( + actual: List[Tuple[numpy.ndarray, numpy.ndarray]], + desired: List[Tuple[numpy.ndarray, numpy.ndarray]], +): + """ + usage.sklearn: 19 """ ... diff --git a/data/typing/pandas.core.arrays.sparse.accessor.py b/data/typing/pandas.core.arrays.sparse.accessor.py index 235b4c5..f36c383 100644 --- a/data/typing/pandas.core.arrays.sparse.accessor.py +++ b/data/typing/pandas.core.arrays.sparse.accessor.py @@ -8,9 +8,3 @@ def from_spmatrix(cls, /, data: scipy.sparse.csr.csr_matrix): usage.sklearn: 1 """ ... - - def to_coo(self, /): - """ - usage.sklearn: 1 - """ - ... diff --git a/data/typing/pandas.core.base.py b/data/typing/pandas.core.base.py index d9dea70..a042375 100644 --- a/data/typing/pandas.core.base.py +++ b/data/typing/pandas.core.base.py @@ -29,6 +29,5 @@ def searchsorted(self, /, value: numpy.ndarray): def tolist(self, /): """ usage.dask: 14 - usage.sklearn: 1 """ ... diff --git a/data/typing/pandas.core.frame.py b/data/typing/pandas.core.frame.py index 583f3b7..b0bbef4 100644 --- a/data/typing/pandas.core.frame.py +++ b/data/typing/pandas.core.frame.py @@ -305,6 +305,7 @@ def __getitem__(self, _0: List[Literal["foo", "C"]], /): @overload def __getitem__(self, _0: int, /): """ + usage.sklearn: 1 usage.xarray: 1 """ ... @@ -324,295 +325,2900 @@ def __getitem__(self, _0: object, /): ... @overload - def __getitem__(self, _0: Union[str, List[str], numpy.ndarray, int], /): + def __getitem__(self, _0: Literal["first"], /): """ - usage.sklearn: 213 + usage.sklearn: 1 """ ... - def __getitem__(self, _0: object, /): + @overload + def __getitem__(self, _0: Literal["second"], /): """ - usage.dask: 487 - usage.sklearn: 213 - usage.xarray: 8 + usage.sklearn: 1 """ ... @overload - def __gt__(self, _0: Union[numpy.ndarray, numpy.timedelta64], /): + def __getitem__(self, _0: List[Literal["col1"]], /): """ - usage.pandas: 4 + usage.sklearn: 1 """ ... @overload - def __gt__(self, _0: pandas.core.series.Series, /): + def __getitem__(self, _0: Literal["col_str"], /): """ - usage.dask: 1 + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: numpy.ndarray, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: List[Literal["target"]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: List[str], /): + """ + usage.sklearn: 22 + """ + ... + + @overload + def __getitem__( + self, + _0: List[ + Literal[ + "petal width (cm)", + "petal length (cm)", + "sepal width (cm)", + "sepal length (cm)", + ] + ], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: List[Literal["Jumps", "Situps", "Chins"]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: List[Literal["Pulse", "Waist", "Weight"]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, + _0: List[ + Literal["class", "petalwidth", "petallength", "sepalwidth", "sepallength"] + ], + /, + ): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Literal["sepallength"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["sepalwidth"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["petallength"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["petalwidth"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["class"], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__( + self, + _0: List[Literal["petalwidth", "petallength", "sepalwidth", "sepallength"]], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: List[Literal["class", "sepalwidth", "sepallength"]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: List[Literal["petallength", "petalwidth"]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["family"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["product-type"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["steel"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["carbon"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["hardness"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["temper_rolling"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["condition"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["formability"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["strength"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["non-ageing"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["surface-finish"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["surface-quality"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["enamelability"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["bc"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["bf"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["bt"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["bw%2Fme"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["bl"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["m"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["chrom"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["phos"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["cbond"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["marvi"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["exptl"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["ferro"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["corr"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: str, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["lustre"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["jurofm"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["s"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["p"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["shape"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["thick"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["width"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["len"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["oil"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["bore"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["packing"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["vendor"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["MYCT"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["MMIN"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["MMAX"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["CACH"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["CHMIN"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["CHMAX"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["age"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["workclass"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["fnlwgt:"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["education:"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["education-num:"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["marital-status:"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["occupation:"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["relationship:"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["race:"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["sex:"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["capital-gain:"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["capital-loss:"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["hours-per-week:"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["native-country:"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["DYRK1A_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["ITSN1_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BDNF_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["NR1_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["NR2A_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pAKT_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pBRAF_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pCAMKII_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pCREB_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pELK_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pERK_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pJNK_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["PKCA_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pMEK_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pNR1_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pNR2A_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pNR2B_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pPKCAB_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pRSK_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["AKT_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BRAF_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["CAMKII_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["CREB_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["ELK_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["ERK_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["GSK3B_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["JNK_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["MEK_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["TRKA_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["RSK_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["APP_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["Bcatenin_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["SOD1_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["MTOR_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["P38_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pMTOR_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["DSCR1_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["AMPKA_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["NR2B_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pNUMB_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["RAPTOR_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["TIAM1_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pP70S6_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["NUMB_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["P70S6_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pGSK3B_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pPKCG_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["CDK5_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["S6_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["ADARB1_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["AcetylH3K9_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["RRP1_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BAX_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["ARC_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["ERBB4_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["nNOS_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["Tau_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["GFAP_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["GluR3_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["GluR4_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["IL1B_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["P3525_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pCASP9_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["PSD95_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["SNCA_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["Ubiquitin_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pGSK3B_Tyr216_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["SHH_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BAD_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BCL2_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pS6_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pCFOS_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["SYP_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["H3AcK18_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["EGR1_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["H3MeK4_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["CaNA_N"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BH_LowPeakAmp"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BH_LowPeakBPM"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BH_HighPeakAmp"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BH_HighPeakBPM"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BH_HighLowRatio"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BHSUM1"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BHSUM2"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["BHSUM3"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["amazed.suprised"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["happy.pleased"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["relaxing.calm"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["quiet.still"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["sad.lonely"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["angry.aggresive"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["pclass"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["survived"], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Literal["name"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["sex"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["sibsp"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["parch"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["ticket"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["fare"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["cabin"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["embarked"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["boat"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["body"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["home.dest"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["0"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["c"], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Literal["A"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["B"], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Literal["C"], /): + """ + usage.sklearn: 1 + """ + ... + + def __getitem__(self, _0: object, /): + """ + usage.dask: 487 + usage.sklearn: 213 + usage.xarray: 8 + """ + ... + + @overload + def __gt__(self, _0: Union[numpy.ndarray, numpy.timedelta64], /): + """ + usage.pandas: 4 + """ + ... + + @overload + def __gt__(self, _0: pandas.core.series.Series, /): + """ + usage.dask: 1 + """ + ... + + def __gt__( + self, _0: Union[pandas.core.series.Series, numpy.timedelta64, numpy.ndarray], / + ): + """ + usage.dask: 1 + usage.pandas: 4 + """ + ... + + def __iadd__(self, _0: Union[numpy.ndarray, numpy.timedelta64], /): + """ + usage.pandas: 2 + """ + ... + + def __isub__(self, _0: Union[numpy.ndarray, numpy.timedelta64], /): + """ + usage.pandas: 2 + """ + ... + + def __iter__(self, /): + """ + usage.dask: 1 + """ + ... + + def __itruediv__(self, _0: pandas.core.frame.DataFrame, /): + """ + usage.dask: 1 + """ + ... + + def __le__( + self, + _0: Union[numpy.float64, float, pandas.core.series.Series, int, numpy.int64], + /, + ): + """ + usage.dask: 7 + """ + ... + + def __mod__(self, _0: numpy.timedelta64, /): + """ + usage.pandas: 1 + """ + ... + + @overload + def __mul__(self, _0: Union[numpy.ndarray, numpy.timedelta64, numpy.int64], /): + """ + usage.pandas: 20 + """ + ... + + @overload + def __mul__(self, _0: float, /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __mul__(self, _0: int, /): + """ + usage.sklearn: 2 + """ + ... + + def __mul__( + self, _0: Union[float, int, numpy.int64, numpy.timedelta64, numpy.ndarray], / + ): + """ + usage.pandas: 20 + usage.sklearn: 4 + """ + ... + + def __neg__(self, /): + """ + usage.dask: 4 + """ + ... + + def __or__(self, _0: pandas.core.frame.DataFrame, /): + """ + usage.dask: 2 + """ + ... + + @overload + def __pow__(self, _0: numpy.timedelta64, /): + """ + usage.pandas: 1 + """ + ... + + @overload + def __pow__(self, _0: int, /): + """ + usage.dask: 2 + """ + ... + + def __pow__(self, _0: Union[int, numpy.timedelta64], /): + """ + usage.dask: 2 + usage.pandas: 1 + """ + ... + + @overload + def __radd__( + self, _0: Union[numpy.ndarray, numpy.timedelta64, numpy.datetime64], / + ): + """ + usage.pandas: 30 + """ + ... + + @overload + def __radd__( + self, _0: Union[pandas.core.frame.DataFrame, dask.dataframe.core.Scalar], / + ): + """ + usage.dask: 2 + """ + ... + + def __radd__( + self, + _0: Union[ + dask.dataframe.core.Scalar, + pandas.core.frame.DataFrame, + numpy.datetime64, + numpy.timedelta64, + numpy.ndarray, + ], + /, + ): + """ + usage.dask: 2 + usage.pandas: 30 + """ + ... + + def __rfloordiv__(self, _0: numpy.timedelta64, /): + """ + usage.pandas: 1 + """ + ... + + def __rmul__(self, _0: Union[numpy.ndarray, numpy.timedelta64, numpy.int64], /): + """ + usage.pandas: 16 + """ + ... + + def __ror__(self, _0: pandas.core.frame.DataFrame, /): + """ + usage.dask: 2 + """ + ... + + def __rpow__(self, _0: numpy.timedelta64, /): + """ + usage.pandas: 1 + """ + ... + + @overload + def __rsub__( + self, _0: Union[numpy.ndarray, numpy.datetime64, numpy.timedelta64], / + ): + """ + usage.pandas: 26 + """ + ... + + @overload + def __rsub__(self, _0: pandas.core.frame.DataFrame, /): + """ + usage.dask: 1 + """ + ... + + def __rsub__( + self, + _0: Union[ + pandas.core.frame.DataFrame, + numpy.timedelta64, + numpy.datetime64, + numpy.ndarray, + ], + /, + ): + """ + usage.dask: 1 + usage.pandas: 26 + """ + ... + + @overload + def __rtruediv__(self, _0: Union[numpy.ndarray, numpy.timedelta64], /): + """ + usage.pandas: 20 + """ + ... + + @overload + def __rtruediv__(self, _0: pandas.core.frame.DataFrame, /): + """ + usage.dask: 1 + """ + ... + + def __rtruediv__( + self, + _0: Union[pandas.core.frame.DataFrame, numpy.timedelta64, numpy.ndarray], + /, + ): + """ + usage.dask: 1 + usage.pandas: 20 + """ + ... + + @overload + def __setitem__(self, _0: Literal["C"], _1: List[int], /): + """ + usage.xarray: 1 + """ + ... + + @overload + def __setitem__( + self, + _0: Union[ + str, + List[Literal["b", "a"]], + pandas.core.indexes.base.Index, + pandas.core.frame.DataFrame, + pandas.core.indexes.numeric.Int64Index, + ], + _1: object, + /, + ): + """ + usage.dask: 129 + """ + ... + + @overload + def __setitem__(self, _0: Literal["third"], _1: List[int], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["col_str"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["sepallength"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["sepalwidth"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["petallength"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["petalwidth"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["class"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["family"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["product-type"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["steel"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["carbon"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["hardness"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["temper_rolling"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["condition"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["formability"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["strength"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["non-ageing"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["surface-finish"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["surface-quality"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["enamelability"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["bc"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["bf"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["bt"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["bw%2Fme"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["bl"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["m"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["chrom"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["phos"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["cbond"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["marvi"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["exptl"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["ferro"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["corr"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: str, _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["lustre"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["jurofm"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["s"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["p"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["shape"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["thick"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["width"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["len"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["oil"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["bore"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["packing"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["vendor"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["MYCT"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["MMIN"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["MMAX"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["CACH"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["CHMIN"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["CHMAX"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["age"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["workclass"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["fnlwgt:"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["education:"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["education-num:"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["marital-status:"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["occupation:"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["relationship:"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["race:"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["sex:"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["capital-gain:"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["capital-loss:"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["hours-per-week:"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["native-country:"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["DYRK1A_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["ITSN1_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["BDNF_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["NR1_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["NR2A_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pAKT_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pBRAF_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pCAMKII_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pCREB_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pELK_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pERK_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pJNK_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["PKCA_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pMEK_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pNR1_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pNR2A_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pNR2B_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pPKCAB_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pRSK_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["AKT_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["BRAF_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["CAMKII_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["CREB_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["ELK_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["ERK_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["GSK3B_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["JNK_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["MEK_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["TRKA_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["RSK_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["APP_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["Bcatenin_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["SOD1_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["MTOR_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["P38_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pMTOR_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["DSCR1_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["AMPKA_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["NR2B_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pNUMB_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["RAPTOR_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["TIAM1_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pP70S6_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["NUMB_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["P70S6_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pGSK3B_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pPKCG_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["CDK5_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["S6_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["ADARB1_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["AcetylH3K9_N"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["RRP1_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["BAX_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["ARC_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["ERBB4_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["nNOS_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["Tau_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["GFAP_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["GluR3_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["GluR4_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["IL1B_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["P3525_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pCASP9_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["PSD95_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["SNCA_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["Ubiquitin_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["pGSK3B_Tyr216_N"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["SHH_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["BAD_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["BCL2_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pS6_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["pCFOS_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["SYP_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["H3AcK18_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["EGR1_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["H3MeK4_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__(self, _0: Literal["CaNA_N"], _1: pandas.core.series.Series, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["BH_LowPeakAmp"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["BH_LowPeakBPM"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Literal["BH_HighPeakAmp"], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 1 """ ... - def __gt__( - self, _0: Union[pandas.core.series.Series, numpy.timedelta64, numpy.ndarray], / + @overload + def __setitem__( + self, _0: Literal["BH_HighPeakBPM"], _1: pandas.core.series.Series, / ): """ - usage.dask: 1 - usage.pandas: 4 + usage.sklearn: 1 """ ... - def __iadd__(self, _0: Union[numpy.ndarray, numpy.timedelta64], /): + @overload + def __setitem__( + self, _0: Literal["BH_HighLowRatio"], _1: pandas.core.series.Series, / + ): """ - usage.pandas: 2 + usage.sklearn: 1 """ ... - def __isub__(self, _0: Union[numpy.ndarray, numpy.timedelta64], /): + @overload + def __setitem__(self, _0: Literal["BHSUM1"], _1: pandas.core.series.Series, /): """ - usage.pandas: 2 + usage.sklearn: 1 """ ... - def __iter__(self, /): + @overload + def __setitem__(self, _0: Literal["BHSUM2"], _1: pandas.core.series.Series, /): """ - usage.dask: 1 + usage.sklearn: 1 """ ... - def __itruediv__(self, _0: pandas.core.frame.DataFrame, /): + @overload + def __setitem__(self, _0: Literal["BHSUM3"], _1: pandas.core.series.Series, /): """ - usage.dask: 1 + usage.sklearn: 1 """ ... - def __le__( - self, - _0: Union[numpy.float64, float, pandas.core.series.Series, int, numpy.int64], - /, + @overload + def __setitem__( + self, _0: Literal["amazed.suprised"], _1: pandas.core.series.Series, / ): """ - usage.dask: 7 + usage.sklearn: 1 """ ... - def __mod__(self, _0: numpy.timedelta64, /): + @overload + def __setitem__( + self, _0: Literal["happy.pleased"], _1: pandas.core.series.Series, / + ): """ - usage.pandas: 1 + usage.sklearn: 1 """ ... @overload - def __mul__(self, _0: Union[numpy.ndarray, numpy.timedelta64, numpy.int64], /): + def __setitem__( + self, _0: Literal["relaxing.calm"], _1: pandas.core.series.Series, / + ): """ - usage.pandas: 20 + usage.sklearn: 1 """ ... @overload - def __mul__(self, _0: Union[int, float], /): + def __setitem__(self, _0: Literal["quiet.still"], _1: pandas.core.series.Series, /): """ - usage.sklearn: 4 + usage.sklearn: 1 """ ... - def __mul__( - self, _0: Union[float, int, numpy.int64, numpy.timedelta64, numpy.ndarray], / - ): + @overload + def __setitem__(self, _0: Literal["sad.lonely"], _1: pandas.core.series.Series, /): """ - usage.pandas: 20 - usage.sklearn: 4 + usage.sklearn: 1 """ ... - def __neg__(self, /): + @overload + def __setitem__( + self, _0: Literal["angry.aggresive"], _1: pandas.core.series.Series, / + ): """ - usage.dask: 4 + usage.sklearn: 1 """ ... - def __or__(self, _0: pandas.core.frame.DataFrame, /): + @overload + def __setitem__(self, _0: Literal["pclass"], _1: pandas.core.series.Series, /): """ - usage.dask: 2 + usage.sklearn: 1 """ ... @overload - def __pow__(self, _0: numpy.timedelta64, /): + def __setitem__(self, _0: Literal["survived"], _1: pandas.core.series.Series, /): """ - usage.pandas: 1 + usage.sklearn: 1 """ ... @overload - def __pow__(self, _0: int, /): + def __setitem__(self, _0: Literal["name"], _1: pandas.core.series.Series, /): """ - usage.dask: 2 + usage.sklearn: 1 """ ... - def __pow__(self, _0: Union[int, numpy.timedelta64], /): + @overload + def __setitem__(self, _0: Literal["sex"], _1: pandas.core.series.Series, /): """ - usage.dask: 2 - usage.pandas: 1 + usage.sklearn: 1 """ ... @overload - def __radd__( - self, _0: Union[numpy.ndarray, numpy.timedelta64, numpy.datetime64], / - ): + def __setitem__(self, _0: Literal["sibsp"], _1: pandas.core.series.Series, /): """ - usage.pandas: 30 + usage.sklearn: 1 """ ... @overload - def __radd__( - self, _0: Union[pandas.core.frame.DataFrame, dask.dataframe.core.Scalar], / - ): + def __setitem__(self, _0: Literal["parch"], _1: pandas.core.series.Series, /): """ - usage.dask: 2 + usage.sklearn: 1 """ ... - def __radd__( - self, - _0: Union[ - dask.dataframe.core.Scalar, - pandas.core.frame.DataFrame, - numpy.datetime64, - numpy.timedelta64, - numpy.ndarray, - ], - /, - ): + @overload + def __setitem__(self, _0: Literal["ticket"], _1: pandas.core.series.Series, /): """ - usage.dask: 2 - usage.pandas: 30 + usage.sklearn: 1 """ ... - def __rfloordiv__(self, _0: numpy.timedelta64, /): + @overload + def __setitem__(self, _0: Literal["fare"], _1: pandas.core.series.Series, /): """ - usage.pandas: 1 + usage.sklearn: 1 """ ... - def __rmul__(self, _0: Union[numpy.ndarray, numpy.timedelta64, numpy.int64], /): + @overload + def __setitem__(self, _0: Literal["cabin"], _1: pandas.core.series.Series, /): """ - usage.pandas: 16 + usage.sklearn: 1 """ ... - def __ror__(self, _0: pandas.core.frame.DataFrame, /): + @overload + def __setitem__(self, _0: Literal["embarked"], _1: pandas.core.series.Series, /): """ - usage.dask: 2 + usage.sklearn: 1 """ ... - def __rpow__(self, _0: numpy.timedelta64, /): + @overload + def __setitem__(self, _0: Literal["boat"], _1: pandas.core.series.Series, /): """ - usage.pandas: 1 + usage.sklearn: 1 """ ... @overload - def __rsub__( - self, _0: Union[numpy.ndarray, numpy.datetime64, numpy.timedelta64], / - ): + def __setitem__(self, _0: Literal["body"], _1: pandas.core.series.Series, /): """ - usage.pandas: 26 + usage.sklearn: 1 """ ... @overload - def __rsub__(self, _0: pandas.core.frame.DataFrame, /): + def __setitem__(self, _0: Literal["home.dest"], _1: pandas.core.series.Series, /): """ - usage.dask: 1 + usage.sklearn: 1 """ ... - def __rsub__( - self, - _0: Union[ - pandas.core.frame.DataFrame, - numpy.timedelta64, - numpy.datetime64, - numpy.ndarray, - ], - /, - ): + @overload + def __setitem__(self, _0: Literal["correlated_feature"], _1: numpy.ndarray, /): """ - usage.dask: 1 - usage.pandas: 26 + usage.sklearn: 1 """ ... @overload - def __rtruediv__(self, _0: Union[numpy.ndarray, numpy.timedelta64], /): + def __setitem__(self, _0: int, _1: pandas.core.arrays.categorical.Categorical, /): """ - usage.pandas: 20 + usage.sklearn: 1 """ ... @overload - def __rtruediv__(self, _0: pandas.core.frame.DataFrame, /): + def __setitem__( + self, _0: Literal["1"], _1: pandas.core.arrays.sparse.array.SparseArray, / + ): """ - usage.dask: 1 + usage.sklearn: 1 """ ... - def __rtruediv__( - self, - _0: Union[pandas.core.frame.DataFrame, numpy.timedelta64, numpy.ndarray], - /, + @overload + def __setitem__( + self, _0: Literal["2"], _1: pandas.core.arrays.sparse.array.SparseArray, / ): """ - usage.dask: 1 - usage.pandas: 20 + usage.sklearn: 1 """ ... @overload - def __setitem__(self, _0: Literal["C"], _1: List[int], /): + def __setitem__( + self, _0: Literal["3"], _1: pandas.core.arrays.sparse.array.SparseArray, / + ): """ - usage.xarray: 1 + usage.sklearn: 1 """ ... @overload def __setitem__( - self, - _0: Union[ - str, - List[Literal["b", "a"]], - pandas.core.indexes.base.Index, - pandas.core.frame.DataFrame, - pandas.core.indexes.numeric.Int64Index, - ], - _1: object, - /, + self, _0: Literal["0"], _1: pandas.core.arrays.sparse.array.SparseArray, / ): """ - usage.dask: 129 + usage.sklearn: 1 """ ... @overload - def __setitem__( - self, - _0: Union[str, int], - _1: Union[ - pandas.core.series.Series, - numpy.ndarray, - pandas.core.arrays.categorical.Categorical, - pandas.core.arrays.sparse.array.SparseArray, - List[int], - ], - /, - ): + def __setitem__(self, _0: Literal["c"], _1: pandas.core.series.Series, /): """ - usage.sklearn: 178 + usage.sklearn: 2 """ ... @@ -806,9 +3412,30 @@ def astype( ... @overload - def astype(self, /, dtype: Union[type, None]): + def astype(self, /, dtype: None): """ - usage.sklearn: 4 + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, /, dtype: Type[numpy.float64]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, /, dtype: Type[numpy.float32]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, /, dtype: Type[numpy.int16]): + """ + usage.sklearn: 1 """ ... @@ -1730,15 +4357,75 @@ def select_dtypes( """ ... + @overload + def select_dtypes(self, /, include: Type[numpy.number], exclude: None): + """ + usage.sklearn: 1 + """ + ... + + @overload + def select_dtypes(self, /, include: None, exclude: Type[object]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def select_dtypes(self, /, include: List[type], exclude: None): + """ + usage.sklearn: 2 + """ + ... + + @overload + def select_dtypes(self, /, include: List[Type[object]], exclude: None): + """ + usage.sklearn: 1 + """ + ... + + @overload + def select_dtypes(self, /, include: Type[object], exclude: None): + """ + usage.sklearn: 1 + """ + ... + + @overload + def select_dtypes(self, /, include: Type[float], exclude: None): + """ + usage.sklearn: 1 + """ + ... + + @overload + def select_dtypes(self, /, include: List[Type[numpy.number]], exclude: None): + """ + usage.sklearn: 1 + """ + ... + + @overload + def select_dtypes(self, /, include: List[Type[int]], exclude: None): + """ + usage.sklearn: 1 + """ + ... + + @overload + def select_dtypes(self, /, include: None, exclude: List[Type[int]]): + """ + usage.sklearn: 1 + """ + ... + @overload def select_dtypes( - self, - /, - include: Union[List[Union[type, Literal["category"]]], None, type], - exclude: Union[None, List[Type[int]], Type[object]], + self, /, include: List[Union[Type[object], Literal["category"]]], exclude: None ): """ - usage.sklearn: 11 + usage.sklearn: 1 """ ... diff --git a/data/typing/pandas.core.indexes.base.py b/data/typing/pandas.core.indexes.base.py index 4025e30..84b0942 100644 --- a/data/typing/pandas.core.indexes.base.py +++ b/data/typing/pandas.core.indexes.base.py @@ -73,10 +73,35 @@ def __eq__( """ ... + @overload + def __eq__( + self, + _0: List[Literal["petalwidth", "petallength", "sepalwidth", "sepallength"]], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __eq__(self, _0: List[Literal["class", "sepalwidth", "sepallength"]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __eq__(self, _0: List[Literal["petallength", "petalwidth"]], /): + """ + usage.sklearn: 1 + """ + ... + @overload def __eq__(self, _0: List[str], /): """ - usage.sklearn: 6 + usage.sklearn: 3 """ ... @@ -100,6 +125,7 @@ def __getitem__(self, _0: int, /): @overload def __getitem__(self, _0: numpy.ndarray, /): """ + usage.sklearn: 2 usage.xarray: 2 """ ... @@ -107,6 +133,7 @@ def __getitem__(self, _0: numpy.ndarray, /): @overload def __getitem__(self, _0: slice[None, int, None], /): """ + usage.sklearn: 1 usage.xarray: 2 """ ... @@ -135,6 +162,7 @@ def __getitem__(self, _0: slice[None, None, None], /): @overload def __getitem__(self, _0: slice[int, int, int], /): """ + usage.sklearn: 1 usage.xarray: 2 """ ... @@ -163,15 +191,9 @@ def __getitem__( ... @overload - def __getitem__( - self, - _0: Union[ - slice[Union[None, int], int, Union[None, int]], List[int], numpy.ndarray - ], - /, - ): + def __getitem__(self, _0: List[int], /): """ - usage.sklearn: 6 + usage.sklearn: 2 """ ... @@ -536,22 +558,8 @@ def get_loc(self, /, key: Literal["C++"], method: None, tolerance: None): """ ... - @overload - def get_loc(self, /, key: Union[str, numpy.str_]): - """ - usage.sklearn: 20 - """ - ... - - def get_loc( - self, - /, - key: Union[numpy.str_, bool, str], - method: None = ..., - tolerance: None = ..., - ): + def get_loc(self, /, key: Union[str, bool], method: None, tolerance: None): """ - usage.sklearn: 20 usage.xarray: 22 """ ... diff --git a/data/typing/pandas.core.indexes.range.py b/data/typing/pandas.core.indexes.range.py index 28d79bb..8b66fd7 100644 --- a/data/typing/pandas.core.indexes.range.py +++ b/data/typing/pandas.core.indexes.range.py @@ -280,33 +280,12 @@ def get_indexer(self, /, target: numpy.ndarray, method: None, tolerance: None): """ ... - @overload def get_loc(self, /, key: int, method: None, tolerance: None): """ usage.xarray: 1 """ ... - @overload - def get_loc(self, /, key: Literal["random"]): - """ - usage.sklearn: 1 - """ - ... - - def get_loc( - self, - /, - key: Union[Literal["random"], int], - method: None = ..., - tolerance: None = ..., - ): - """ - usage.sklearn: 1 - usage.xarray: 1 - """ - ... - def map(self, /, mapper: Callable, na_action: None): """ usage.dask: 1 diff --git a/data/typing/pandas.core.indexing.py b/data/typing/pandas.core.indexing.py index 5bf4732..11ecd4f 100644 --- a/data/typing/pandas.core.indexing.py +++ b/data/typing/pandas.core.indexing.py @@ -219,6 +219,7 @@ def __getitem__( @overload def __getitem__(self, _0: numpy.ndarray, /): """ + usage.sklearn: 1 usage.xarray: 2 """ ... @@ -230,30 +231,246 @@ def __getitem__(self, _0: object, /): """ ... + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], Literal["first"]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[slice[None, None, None], List[Literal["first"]]], / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[slice[None, None, None], List[Literal["second", "first"]]], / + ): + """ + usage.sklearn: 1 + """ + ... + @overload def __getitem__( self, - _0: Union[ - Tuple[ - slice[None, None, None], - Union[ - numpy.ndarray, - Literal["first", "col1", "second", "col_2"], - List[Union[str, bool]], - slice[ - Union[None, Literal["col_1", "first"]], - Literal["col_2", "first", "second"], - Union[None, Literal["col_1", "first"]], - ], - ], - ], - List[bool], - numpy.ndarray, + _0: Tuple[ + slice[None, None, None], + slice[Literal["first"], Literal["second"], Literal["first"]], ], /, ): """ - usage.sklearn: 30 + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], numpy.ndarray], /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[slice[None, None, None], List[Literal["second"]]], / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], Literal["col1"]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[slice[None, None, None], List[Literal["col1", "col0"]]], / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], List[Literal["col1"]]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[slice[None, None, None], slice[None, Literal["first"], None]], / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[Literal["first"], Literal["first"], Literal["first"]], + ], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], Literal["second"]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[slice[None, None, None], List[Literal["b", "a"]]], / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], List[Literal["a"]]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[slice[None, None, None], List[Literal["col_str", "col_cat"]]], / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, + _0: Tuple[slice[None, None, None], List[Literal["col_float", "col_int"]]], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, + _0: Tuple[slice[None, None, None], List[Literal["col_int", "col_float"]]], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[slice[None, None, None], List[Literal["col2", "col1"]]], / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], + List[Literal["petal length (cm)", "sepal length (cm)"]], + ], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], + List[Literal["petal width (cm)", "sepal width (cm)"]], + ], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], List[Literal["col2"]]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, _0: Tuple[slice[None, None, None], List[Literal["col_2", "col_1"]]], / + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__( + self, + _0: Tuple[ + slice[None, None, None], + slice[Literal["col_1"], Literal["col_2"], Literal["col_1"]], + ], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: List[bool], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], List[bool]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], Literal["col_2"]], /): + """ + usage.sklearn: 1 """ ... @@ -270,6 +487,7 @@ class _iLocIndexer: @overload def __getitem__(self, _0: int, /): """ + usage.sklearn: 1 usage.xarray: 2 """ ... @@ -277,6 +495,7 @@ def __getitem__(self, _0: int, /): @overload def __getitem__(self, _0: slice[None, int, None], /): """ + usage.sklearn: 1 usage.xarray: 3 """ ... @@ -304,28 +523,82 @@ def __getitem__( """ ... + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], int], /): + """ + usage.sklearn: 8 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], List[int]], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], numpy.ndarray], /): + """ + usage.sklearn: 2 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], slice[int, int, int]], /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[slice[None, None, None], slice[int, int, int]], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: numpy.ndarray, /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[numpy.ndarray, int], /): + """ + usage.sklearn: 1 + """ + ... + @overload def __getitem__( - self, - _0: Union[ - int, - numpy.ndarray, - List[int], - Tuple[ - Union[int, numpy.ndarray, slice[None, Union[int, None], None]], - Union[ - List[int], - numpy.ndarray, - int, - slice[Union[None, int], Union[None, int], Union[None, int]], - ], - ], - slice[Union[int, None], int, Union[int, None]], - ], - /, + self, _0: Tuple[slice[None, int, None], slice[None, None, None]], / ): """ - usage.sklearn: 24 + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: Tuple[int, int], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: List[int], /): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __getitem__(self, _0: slice[int, int, int], /): + """ + usage.sklearn: 1 """ ... @@ -377,13 +650,19 @@ def __setitem__( @overload def __setitem__( - self, - _0: Tuple[slice[None, None, None], Union[numpy.int32, int]], - _1: Union[pandas.core.series.Series, numpy.float64], - /, + self, _0: Tuple[slice[None, None, None], numpy.int32], _1: numpy.float64, / ): """ - usage.sklearn: 5 + usage.sklearn: 1 + """ + ... + + @overload + def __setitem__( + self, _0: Tuple[slice[None, None, None], int], _1: pandas.core.series.Series, / + ): + """ + usage.sklearn: 4 """ ... diff --git a/data/typing/pandas.core.reshape.concat.py b/data/typing/pandas.core.reshape.concat.py index 9e156a3..0965adc 100644 --- a/data/typing/pandas.core.reshape.concat.py +++ b/data/typing/pandas.core.reshape.concat.py @@ -18,11 +18,17 @@ def concat( @overload -def concat( - objs: List[pandas.core.frame.DataFrame], ignore_index: bool = ..., axis: int = ... -): +def concat(objs: List[pandas.core.frame.DataFrame], axis: int): """ - usage.sklearn: 3 + usage.sklearn: 1 + """ + ... + + +@overload +def concat(objs: List[pandas.core.frame.DataFrame], ignore_index: bool): + """ + usage.sklearn: 2 """ ... diff --git a/data/typing/pandas.core.series.py b/data/typing/pandas.core.series.py index b063f20..4c0555c 100644 --- a/data/typing/pandas.core.series.py +++ b/data/typing/pandas.core.series.py @@ -184,17 +184,52 @@ def __eq__(self, _0: object, /): """ ... + @overload + def __eq__(self, _0: Type[numpy.float64], /): + """ + usage.sklearn: 3 + """ + ... + + @overload + def __eq__(self, _0: List[Type[numpy.float64]], /): + """ + usage.sklearn: 2 + """ + ... + @overload def __eq__( self, - _0: Union[ - List[Union[type, pandas.core.dtypes.dtypes.CategoricalDtype]], - Type[numpy.float64], + _0: List[ + Union[pandas.core.dtypes.dtypes.CategoricalDtype, Type[numpy.float64]] ], /, ): """ - usage.sklearn: 10 + usage.sklearn: 3 + """ + ... + + @overload + def __eq__( + self, + _0: List[ + Union[Type[numpy.float64], pandas.core.dtypes.dtypes.CategoricalDtype] + ], + /, + ): + """ + usage.sklearn: 1 + """ + ... + + @overload + def __eq__( + self, _0: List[Union[type, pandas.core.dtypes.dtypes.CategoricalDtype]], / + ): + """ + usage.sklearn: 1 """ ... @@ -738,9 +773,16 @@ def __rmul__(self, _0: pandas.core.series.Series, /): ... @overload - def __rmul__(self, _0: Union[int, float], /): + def __rmul__(self, _0: float, /): """ - usage.sklearn: 2 + usage.sklearn: 1 + """ + ... + + @overload + def __rmul__(self, _0: int, /): + """ + usage.sklearn: 1 """ ... @@ -1028,18 +1070,58 @@ def astype( ... @overload - def astype( - self, - /, - dtype: Union[ - type, - Literal["float", "int", "category"], - pandas.core.dtypes.dtypes.CategoricalDtype, - ], - copy: bool = ..., - ): + def astype(self, /, dtype: Literal["category"]): """ - usage.sklearn: 10 + usage.sklearn: 2 + """ + ... + + @overload + def astype(self, /, dtype: Type[numpy.float64], copy: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, /, dtype: pandas.core.dtypes.dtypes.CategoricalDtype, copy: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, /, dtype: Type[object], copy: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, /, dtype: Literal["int"]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, /, dtype: Literal["float"]): + """ + usage.sklearn: 1 + """ + ... + + @overload + def astype(self, /, dtype: Type[numpy.float16]): + """ + usage.sklearn: 2 + """ + ... + + @overload + def astype(self, /, dtype: Type[numpy.int16]): + """ + usage.sklearn: 1 """ ... diff --git a/data/typing/pandas.core.strings.py b/data/typing/pandas.core.strings.py index a5b34dd..90034ac 100644 --- a/data/typing/pandas.core.strings.py +++ b/data/typing/pandas.core.strings.py @@ -39,14 +39,37 @@ def contains( ... @overload - def contains( - self, - /, - pat: Literal["str$", "^col_s", "float|str", "^col_int", "at$"], - regex: bool, - ): + def contains(self, /, pat: Literal["at$"], regex: bool): """ - usage.sklearn: 5 + usage.sklearn: 1 + """ + ... + + @overload + def contains(self, /, pat: Literal["^col_int"], regex: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def contains(self, /, pat: Literal["float|str"], regex: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def contains(self, /, pat: Literal["^col_s"], regex: bool): + """ + usage.sklearn: 1 + """ + ... + + @overload + def contains(self, /, pat: Literal["str$"], regex: bool): + """ + usage.sklearn: 1 """ ...