Merge branch 'master' of https://github.com/kesshijordan/nipype into fix_dtitk_nonlinear_wf

Author: kesshijordan

nipype/algorithms/confounds.py

@@ -390,13 +390,12 @@ class CompCorInputSpec(BaseInterfaceInputSpec):
         desc='Detrend time series prior to component '
         'extraction')
     use_regress_poly = traits.Bool(
-        True,
         deprecated='0.15.0',
         new_name='pre_filter',
         desc=('use polynomial regression '
               'pre-component extraction'))
     regress_poly_degree = traits.Range(
-        low=1, default=1, usedefault=True, desc='the degree polynomial to use')
+        low=1, value=1, usedefault=True, desc='the degree polynomial to use')
     header_prefix = traits.Str(
         desc=('the desired header for the output tsv '
               'file (one column). If undefined, will '

nipype/algorithms/mesh.py

@@ -289,6 +289,7 @@ class MeshWarpMathsInputSpec(BaseInterfaceInputSpec):
         float_trait,
         File(exists=True),
         default=1.0,
+        usedefault=True,
         mandatory=True,
         desc='image, float or tuple of floats to act as operator')
 

nipype/algorithms/metrics.py

@@ -19,12 +19,12 @@
 from scipy.ndimage.measurements import center_of_mass, label
 
 from .. import config, logging
-from ..utils.misc import package_check
 
-from ..interfaces.base import (BaseInterface, traits, TraitedSpec, File,
-                               InputMultiPath, BaseInterfaceInputSpec,
-                               isdefined)
-from ..utils import NUMPY_MMAP
+from ..interfaces.base import (
+    SimpleInterface, BaseInterface, traits, TraitedSpec, File,
+    InputMultiPath, BaseInterfaceInputSpec,
+    isdefined)
+from ..interfaces.nipy.base import NipyBaseInterface
 
 iflogger = logging.getLogger('interface')
 
@@ -383,6 +383,7 @@ class FuzzyOverlapInputSpec(BaseInterfaceInputSpec):
         File(exists=True),
         mandatory=True,
         desc='Test image. Requires the same dimensions as in_ref.')
+    in_mask = File(exists=True, desc='calculate overlap only within mask')
     weighting = traits.Enum(
         'none',
         'volume',
@@ -403,10 +404,6 @@ class FuzzyOverlapInputSpec(BaseInterfaceInputSpec):
 class FuzzyOverlapOutputSpec(TraitedSpec):
     jaccard = traits.Float(desc='Fuzzy Jaccard Index (fJI), all the classes')
     dice = traits.Float(desc='Fuzzy Dice Index (fDI), all the classes')
-    diff_file = File(
-        exists=True,
-        desc=
-        'resulting difference-map of all classes, using the chosen weighting')
     class_fji = traits.List(
         traits.Float(),
         desc='Array containing the fJIs of each computed class')
@@ -415,7 +412,7 @@ class FuzzyOverlapOutputSpec(TraitedSpec):
         desc='Array containing the fDIs of each computed class')
 
 
-class FuzzyOverlap(BaseInterface):
+class FuzzyOverlap(SimpleInterface):
     """Calculates various overlap measures between two maps, using the fuzzy
     definition proposed in: Crum et al., Generalized Overlap Measures for
     Evaluation and Validation in Medical Image Analysis, IEEE Trans. Med.
@@ -439,77 +436,75 @@ class FuzzyOverlap(BaseInterface):
     output_spec = FuzzyOverlapOutputSpec
 
     def _run_interface(self, runtime):
-        ncomp = len(self.inputs.in_ref)
-        assert (ncomp == len(self.inputs.in_tst))
-        weights = np.ones(shape=ncomp)
-
-        img_ref = np.array([
-            nb.load(fname, mmap=NUMPY_MMAP).get_data()
-            for fname in self.inputs.in_ref
-        ])
-        img_tst = np.array([
-            nb.load(fname, mmap=NUMPY_MMAP).get_data()
-            for fname in self.inputs.in_tst
-        ])
-
-        msk = np.sum(img_ref, axis=0)
-        msk[msk > 0] = 1.0
-        tst_msk = np.sum(img_tst, axis=0)
-        tst_msk[tst_msk > 0] = 1.0
-
-        # check that volumes are normalized
-        # img_ref[:][msk>0] = img_ref[:][msk>0] / (np.sum( img_ref, axis=0 ))[msk>0]
-        # img_tst[tst_msk>0] = img_tst[tst_msk>0] / np.sum( img_tst, axis=0 )[tst_msk>0]
-
-        self._jaccards = []
-        volumes = []
-
-        diff_im = np.zeros(img_ref.shape)
-
-        for ref_comp, tst_comp, diff_comp in zip(img_ref, img_tst, diff_im):
-            num = np.minimum(ref_comp, tst_comp)
-            ddr = np.maximum(ref_comp, tst_comp)
-            diff_comp[ddr > 0] += 1.0 - (num[ddr > 0] / ddr[ddr > 0])
-            self._jaccards.append(np.sum(num) / np.sum(ddr))
-            volumes.append(np.sum(ref_comp))
-
-        self._dices = 2.0 * (np.array(self._jaccards) /
-                             (np.array(self._jaccards) + 1.0))
+        # Load data
+        refdata = nb.concat_images(self.inputs.in_ref).get_data()
+        tstdata = nb.concat_images(self.inputs.in_tst).get_data()
+
+        # Data must have same shape
+        if not refdata.shape == tstdata.shape:
+            raise RuntimeError(
+                'Size of "in_tst" %s must match that of "in_ref" %s.' %
+                (tstdata.shape, refdata.shape))
 
+        ncomp = refdata.shape[-1]
+
+        # Load mask
+        mask = np.ones_like(refdata, dtype=bool)
+        if isdefined(self.inputs.in_mask):
+            mask = nb.load(self.inputs.in_mask).get_data()
+            mask = mask > 0
+            mask = np.repeat(mask[..., np.newaxis], ncomp, -1)
+            assert mask.shape == refdata.shape
+
+        # Drop data outside mask
+        refdata = refdata[mask]
+        tstdata = tstdata[mask]
+
+        if np.any(refdata < 0.0):
+            iflogger.warning('Negative values encountered in "in_ref" input, '
+                             'taking absolute values.')
+            refdata = np.abs(refdata)
+
+        if np.any(tstdata < 0.0):
+            iflogger.warning('Negative values encountered in "in_tst" input, '
+                             'taking absolute values.')
+            tstdata = np.abs(tstdata)
+
+        if np.any(refdata > 1.0):
+            iflogger.warning('Values greater than 1.0 found in "in_ref" input, '
+                             'scaling values.')
+            refdata /= refdata.max()
+
+        if np.any(tstdata > 1.0):
+            iflogger.warning('Values greater than 1.0 found in "in_tst" input, '
+                             'scaling values.')
+            tstdata /= tstdata.max()
+
+        numerators = np.atleast_2d(
+            np.minimum(refdata, tstdata).reshape((-1, ncomp)))
+        denominators = np.atleast_2d(
+            np.maximum(refdata, tstdata).reshape((-1, ncomp)))
+
+        jaccards = numerators.sum(axis=0) / denominators.sum(axis=0)
+
+        # Calculate weights
+        weights = np.ones_like(jaccards, dtype=float)
         if self.inputs.weighting != "none":
-            weights = 1.0 / np.array(volumes)
+            volumes = np.sum((refdata + tstdata) > 0, axis=1).reshape((-1, ncomp))
+            weights = 1.0 / volumes
             if self.inputs.weighting == "squared_vol":
                 weights = weights**2
 
         weights = weights / np.sum(weights)
+        dices = 2.0 * jaccards / (jaccards + 1.0)
 
-        setattr(self, '_jaccard', np.sum(weights * self._jaccards))
-        setattr(self, '_dice', np.sum(weights * self._dices))
-
-        diff = np.zeros(diff_im[0].shape)
-
-        for w, ch in zip(weights, diff_im):
-            ch[msk == 0] = 0
-            diff += w * ch
-
-        nb.save(
-            nb.Nifti1Image(diff,
-                           nb.load(self.inputs.in_ref[0]).affine,
-                           nb.load(self.inputs.in_ref[0]).header),
-            self.inputs.out_file)
-
+        # Fill-in the results object
+        self._results['jaccard'] = float(weights.dot(jaccards))
+        self._results['dice'] = float(weights.dot(dices))
+        self._results['class_fji'] = [float(v) for v in jaccards]
+        self._results['class_fdi'] = [float(v) for v in dices]
         return runtime
 
-    def _list_outputs(self):
-        outputs = self._outputs().get()
-        for method in ("dice", "jaccard"):
-            outputs[method] = getattr(self, '_' + method)
-        # outputs['volume_difference'] = self._volume
-        outputs['diff_file'] = os.path.abspath(self.inputs.out_file)
-        outputs['class_fji'] = np.array(self._jaccards).astype(float).tolist()
-        outputs['class_fdi'] = self._dices.astype(float).tolist()
-        return outputs
-
 
 class ErrorMapInputSpec(BaseInterfaceInputSpec):
     in_ref = File(
@@ -651,7 +646,7 @@ class SimilarityOutputSpec(TraitedSpec):
         traits.Float(desc="Similarity between volume 1 and 2, frame by frame"))
 
 
-class Similarity(BaseInterface):
+class Similarity(NipyBaseInterface):
     """Calculates similarity between two 3D or 4D volumes. Both volumes have to be in
     the same coordinate system, same space within that coordinate system and
     with the same voxel dimensions.
@@ -674,19 +669,8 @@ class Similarity(BaseInterface):
 
     input_spec = SimilarityInputSpec
     output_spec = SimilarityOutputSpec
-    _have_nipy = True
-
-    def __init__(self, **inputs):
-        try:
-            package_check('nipy')
-        except Exception:
-            self._have_nipy = False
-        super(Similarity, self).__init__(**inputs)
 
     def _run_interface(self, runtime):
-        if not self._have_nipy:
-            raise RuntimeError('nipy is not installed')
-
         from nipy.algorithms.registration.histogram_registration import HistogramRegistration
         from nipy.algorithms.registration.affine import Affine
 

nipype/algorithms/misc.py

@@ -26,7 +26,7 @@
 from ..interfaces.base import (
     BaseInterface, traits, TraitedSpec, File, InputMultiPath, OutputMultiPath,
     BaseInterfaceInputSpec, isdefined, DynamicTraitedSpec, Undefined)
-from ..utils.filemanip import fname_presuffix, split_filename, filename_to_list
+from ..utils.filemanip import fname_presuffix, split_filename, ensure_list
 from ..utils import NUMPY_MMAP
 
 from . import confounds
@@ -1479,7 +1479,7 @@ def _gen_fname(self, suffix, idx=None, ext=None):
     def _run_interface(self, runtime):
         total = None
         self._median_files = []
-        for idx, fname in enumerate(filename_to_list(self.inputs.in_files)):
+        for idx, fname in enumerate(ensure_list(self.inputs.in_files)):
             img = nb.load(fname, mmap=NUMPY_MMAP)
             data = np.median(img.get_data(), axis=3)
             if self.inputs.median_per_file:

nipype/algorithms/modelgen.py

@@ -26,7 +26,7 @@
 from ..interfaces.base import (BaseInterface, TraitedSpec, InputMultiPath,
                                traits, File, Bunch, BaseInterfaceInputSpec,
                                isdefined)
-from ..utils.filemanip import filename_to_list
+from ..utils.filemanip import ensure_list
 from ..utils.misc import normalize_mc_params
 from .. import config, logging
 iflogger = logging.getLogger('interface')
@@ -383,7 +383,7 @@ def _generate_standard_design(self,
         if outliers is not None:
             for i, out in enumerate(outliers):
                 numscans = 0
-                for f in filename_to_list(sessinfo[i]['scans']):
+                for f in ensure_list(sessinfo[i]['scans']):
                     shape = load(f, mmap=NUMPY_MMAP).shape
                     if len(shape) == 3 or shape[3] == 1:
                         iflogger.warning('You are using 3D instead of 4D '
@@ -580,7 +580,7 @@ def _generate_design(self, infolist=None):
         else:
             infolist = gen_info(self.inputs.event_files)
         concatlist, nscans = self._concatenate_info(infolist)
-        functional_runs = [filename_to_list(self.inputs.functional_runs)]
+        functional_runs = [ensure_list(self.inputs.functional_runs)]
         realignment_parameters = []
         if isdefined(self.inputs.realignment_parameters):
             realignment_parameters = []

nipype/algorithms/rapidart.py

@@ -28,7 +28,7 @@
 from ..interfaces.base import (BaseInterface, traits, InputMultiPath,
                                OutputMultiPath, TraitedSpec, File,
                                BaseInterfaceInputSpec, isdefined)
-from ..utils.filemanip import filename_to_list, save_json, split_filename
+from ..utils.filemanip import ensure_list, save_json, split_filename
 from ..utils.misc import find_indices, normalize_mc_params
 from .. import logging, config
 iflogger = logging.getLogger('interface')
@@ -234,8 +234,9 @@ class ArtifactDetectInputSpec(BaseInterfaceInputSpec):
         desc=("Mask threshold to be used if mask_type"
               " is 'thresh'."))
     intersect_mask = traits.Bool(
-        True, desc=("Intersect the masks when computed from "
-                    "spm_global."))
+        True, usedefault=True,
+        desc=("Intersect the masks when computed from "
+              "spm_global."))
     save_plot = traits.Bool(
         True, desc="save plots containing outliers", usedefault=True)
     plot_type = traits.Enum(
@@ -376,7 +377,7 @@ def _list_outputs(self):
                 outputs['displacement_files'] = []
         if isdefined(self.inputs.save_plot) and self.inputs.save_plot:
             outputs['plot_files'] = []
-        for i, f in enumerate(filename_to_list(self.inputs.realigned_files)):
+        for i, f in enumerate(ensure_list(self.inputs.realigned_files)):
             (outlierfile, intensityfile, statsfile, normfile, plotfile,
              displacementfile, maskfile) = \
                 self._get_output_filenames(f, os.getcwd())
@@ -616,8 +617,8 @@ def _detect_outliers_core(self, imgfile, motionfile, runidx, cwd=None):
     def _run_interface(self, runtime):
         """Execute this module.
         """
-        funcfilelist = filename_to_list(self.inputs.realigned_files)
-        motparamlist = filename_to_list(self.inputs.realignment_parameters)
+        funcfilelist = ensure_list(self.inputs.realigned_files)
+        motparamlist = ensure_list(self.inputs.realignment_parameters)
         for i, imgf in enumerate(funcfilelist):
             self._detect_outliers_core(
                 imgf, motparamlist[i], i, cwd=os.getcwd())

nipype/algorithms/stats.py

@@ -0,0 +1,68 @@
+# -*- coding: utf-8 -*-
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+"""
+Managing statistical maps
+"""
+from __future__ import (print_function, division, unicode_literals,
+                        absolute_import)
+import os
+import nibabel as nb
+import numpy as np
+
+from ..interfaces.base import (
+    BaseInterfaceInputSpec, TraitedSpec, SimpleInterface,
+    traits, InputMultiPath, File
+)
+from ..utils.filemanip import split_filename
+
+
+class ActivationCountInputSpec(BaseInterfaceInputSpec):
+    in_files = InputMultiPath(File(exists=True), mandatory=True,
+                              desc='input file, generally a list of z-stat maps')
+    threshold = traits.Float(
+        mandatory=True, desc='binarization threshold. E.g. a threshold of 1.65 '
+                             'corresponds to a two-sided Z-test of p<.10')
+
+
+class ActivationCountOutputSpec(TraitedSpec):
+    out_file = File(exists=True, desc='output activation count map')
+    acm_pos = File(exists=True, desc='positive activation count map')
+    acm_neg = File(exists=True, desc='negative activation count map')
+
+
+class ActivationCount(SimpleInterface):
+    """
+    Calculate a simple Activation Count Maps
+
+    Adapted from: https://github.com/poldracklab/CNP_task_analysis/\
+    blob/61c27f5992db9d8800884f8ffceb73e6957db8af/CNP_2nd_level_ACM.py
+    """
+    input_spec = ActivationCountInputSpec
+    output_spec = ActivationCountOutputSpec
+
+    def _run_interface(self, runtime):
+        allmaps = nb.concat_images(self.inputs.in_files).get_data()
+        acm_pos = np.mean(allmaps > self.inputs.threshold,
+                          axis=3, dtype=np.float32)
+        acm_neg = np.mean(allmaps < -1.0 * self.inputs.threshold,
+                          axis=3, dtype=np.float32)
+        acm_diff = acm_pos - acm_neg
+
+        template_fname = self.inputs.in_files[0]
+        ext = split_filename(template_fname)[2]
+        fname_fmt = os.path.join(runtime.cwd, 'acm_{}' + ext).format
+
+        self._results['out_file'] = fname_fmt('diff')
+        self._results['acm_pos'] = fname_fmt('pos')
+        self._results['acm_neg'] = fname_fmt('neg')
+
+        img = nb.load(template_fname)
+        img.__class__(acm_diff, img.affine, img.header).to_filename(
+            self._results['out_file'])
+        img.__class__(acm_pos, img.affine, img.header).to_filename(
+            self._results['acm_pos'])
+        img.__class__(acm_neg, img.affine, img.header).to_filename(
+            self._results['acm_neg'])
+
+        return runtime