Applying pre-commit changes

Author: ckrapu

examples/case_studies/factor_analysis.ipynb

@@ -53,13 +53,14 @@
    "outputs": [],
    "source": [
     "from itertools import product\n",
-    "from numpy.random import default_rng\n",
     "\n",
     "import arviz as az\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import pymc3 as pm"
+    "import pymc3 as pm\n",
+    "\n",
+    "from numpy.random import default_rng"
    ]
   },
   {
@@ -227,8 +228,8 @@
     "\n",
     "# Jitter the data slightly so that none of the points fall exactly\n",
     "# on cell boundaries\n",
-    "eps  = 1e-3\n",
-    "rng  = default_rng()\n",
+    "eps = 1e-3\n",
+    "rng = default_rng()\n",
     "xy = xy.astype(\"float\") + rng.standard_normal(xy.shape) * eps\n",
     "\n",
     "resolution = 20\n",
@@ -324,7 +325,7 @@
     "with pm.Model() as lgcp_model:\n",
     "    mu = pm.Normal(\"mu\", sigma=3)\n",
     "    rho = pm.Uniform(\"rho\", lower=25, upper=300)\n",
-    "    variance = pm.InverseGamma('variance', alpha=1, beta=1)\n",
+    "    variance = pm.InverseGamma(\"variance\", alpha=1, beta=1)\n",
     "    cov_func = variance * pm.gp.cov.Matern52(2, ls=rho)\n",
     "    mean_func = pm.gp.mean.Constant(mu)"
    ]
@@ -573,7 +574,7 @@
     "with lgcp_model:\n",
     "    intensity_new = gp.conditional(\"intensity_new\", Xnew=xy_new)\n",
     "    spp_trace = pm.sample_posterior_predictive(trace, var_names=[\"intensity_new\"])\n",
-    "    intensity_samples = spp_trace['intensity_new']"
+    "    intensity_samples = spp_trace[\"intensity_new\"]"
    ]
   },
   {

examples/case_studies/log-gaussian-cox-process.ipynb

@@ -2493,4 +2493,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

examples/gaussian_processes/GP-MeansAndCovs.ipynb

@@ -652,4 +652,4 @@
    ]
   }
  ]
-}
+}