Merge branch 'master' into eddy

Author: niklasfoe

nipype/algorithms/confounds.py

@@ -14,7 +14,6 @@
 import nibabel as nb
 import numpy as np
 from numpy.polynomial import Legendre
-from scipy import linalg
 
 from .. import config, logging
 from ..external.due import BibTeX
@@ -1186,7 +1185,7 @@ def compute_noise_components(imgseries, mask_images, num_components,
 
         # "The covariance matrix C = MMT was constructed and decomposed into its
         # principal components using a singular value decomposition."
-        u, _, _ = linalg.svd(M, full_matrices=False)
+        u, _, _ = np.linalg.svd(M, full_matrices=False)
         if components is None:
             components = u[:, :num_components]
         else:

nipype/algorithms/icc.py

@@ -5,8 +5,8 @@
 import os
 import numpy as np
 from numpy import ones, kron, mean, eye, hstack, dot, tile
+from numpy.linalg import pinv
 import nibabel as nb
-from scipy.linalg import pinv
 from ..interfaces.base import BaseInterfaceInputSpec, TraitedSpec, \
     BaseInterface, traits, File
 from ..utils import NUMPY_MMAP

nipype/algorithms/metrics.py

@@ -14,9 +14,6 @@
 
 import nibabel as nb
 import numpy as np
-from scipy.ndimage.morphology import binary_erosion
-from scipy.spatial.distance import cdist, euclidean, dice, jaccard
-from scipy.ndimage.measurements import center_of_mass, label
 
 from .. import config, logging
 
@@ -74,6 +71,7 @@ class Distance(BaseInterface):
     _hist_filename = "hist.pdf"
 
     def _find_border(self, data):
+        from scipy.ndimage.morphology import binary_erosion
         eroded = binary_erosion(data)
         border = np.logical_and(data, np.logical_not(eroded))
         return border
@@ -87,6 +85,7 @@ def _get_coordinates(self, data, affine):
         return coordinates[:3, :]
 
     def _eucl_min(self, nii1, nii2):
+        from scipy.spatial.distance import cdist, euclidean
         origdata1 = nii1.get_data().astype(np.bool)
         border1 = self._find_border(origdata1)
 
@@ -105,6 +104,8 @@ def _eucl_min(self, nii1, nii2):
                 set1_coordinates.T[point1, :], set2_coordinates.T[point2, :])
 
     def _eucl_cog(self, nii1, nii2):
+        from scipy.spatial.distance import cdist
+        from scipy.ndimage.measurements import center_of_mass, label
         origdata1 = np.logical_and(nii1.get_data() != 0,
                                    np.logical_not(np.isnan(nii1.get_data())))
         cog_t = np.array(center_of_mass(origdata1.copy())).reshape(-1, 1)
@@ -128,6 +129,7 @@ def _eucl_cog(self, nii1, nii2):
         return np.mean(dist_matrix)
 
     def _eucl_mean(self, nii1, nii2, weighted=False):
+        from scipy.spatial.distance import cdist
         origdata1 = nii1.get_data().astype(np.bool)
         border1 = self._find_border(origdata1)
 
@@ -154,6 +156,7 @@ def _eucl_mean(self, nii1, nii2, weighted=False):
             return np.mean(min_dist_matrix)
 
     def _eucl_max(self, nii1, nii2):
+        from scipy.spatial.distance import cdist
         origdata1 = nii1.get_data()
         origdata1 = np.logical_not(
             np.logical_or(origdata1 == 0, np.isnan(origdata1)))
@@ -287,6 +290,7 @@ class Overlap(BaseInterface):
     output_spec = OverlapOutputSpec
 
     def _bool_vec_dissimilarity(self, booldata1, booldata2, method):
+        from scipy.spatial.distance import dice, jaccard
         methods = {'dice': dice, 'jaccard': jaccard}
         if not (np.any(booldata1) or np.any(booldata2)):
             return 0

nipype/algorithms/misc.py

@@ -15,10 +15,7 @@
 import nibabel as nb
 import numpy as np
 from math import floor, ceil
-from scipy.ndimage.morphology import grey_dilation
-import scipy.io as sio
 import itertools
-import scipy.stats as stats
 import warnings
 
 from .. import logging
@@ -103,6 +100,7 @@ def _get_brodmann_area(self):
             newdata[:int(ceil(float(origdata.shape[0]) / 2)), :, :] = 0
 
         if self.inputs.dilation_size != 0:
+            from scipy.ndimage.morphology import grey_dilation
             newdata = grey_dilation(newdata,
                                     (2 * self.inputs.dilation_size + 1,
                                      2 * self.inputs.dilation_size + 1,
@@ -356,6 +354,7 @@ class Matlab2CSV(BaseInterface):
     output_spec = Matlab2CSVOutputSpec
 
     def _run_interface(self, runtime):
+        import scipy.io as sio
         in_dict = sio.loadmat(op.abspath(self.inputs.in_file))
 
         # Check if the file has multiple variables in it. If it does, loop
@@ -393,6 +392,7 @@ def _run_interface(self, runtime):
         return runtime
 
     def _list_outputs(self):
+        import scipy.io as sio
         outputs = self.output_spec().get()
         in_dict = sio.loadmat(op.abspath(self.inputs.in_file))
         saved_variables = list()
@@ -909,6 +909,7 @@ def calc_moments(timeseries_file, moment):
     timeseries_file -- text file with white space separated timepoints in rows
 
     """
+    import scipy.stats as stats
     timeseries = np.genfromtxt(timeseries_file)
 
     m2 = stats.moment(timeseries, 2, axis=0)

nipype/algorithms/modelgen.py

@@ -20,7 +20,6 @@
 
 from nibabel import load
 import numpy as np
-from scipy.special import gammaln
 
 from ..utils import NUMPY_MMAP
 from ..interfaces.base import (BaseInterface, TraitedSpec, InputMultiPath,
@@ -84,6 +83,7 @@ def spm_hrf(RT, P=None, fMRI_T=16):
       -1.46257507e-04]
 
     """
+    from scipy.special import gammaln
     p = np.array([6, 16, 1, 1, 6, 0, 32], dtype=float)
     if P is not None:
         p[0:len(P)] = P

nipype/algorithms/rapidart.py

@@ -21,8 +21,6 @@
 
 from nibabel import load, funcs, Nifti1Image
 import numpy as np
-from scipy import signal
-import scipy.io as sio
 
 from ..utils import NUMPY_MMAP
 from ..interfaces.base import (BaseInterface, traits, InputMultiPath,
@@ -151,7 +149,8 @@ def _calc_norm_affine(affines, use_differences, brain_pts=None):
                                (3, all_pts.shape[1])),
                     axis=0)))
     else:
-        newpos = np.abs(signal.detrend(newpos, axis=0, type='constant'))
+        from scipy.signal import detrend
+        newpos = np.abs(detrend(newpos, axis=0, type='constant'))
         normdata = np.sqrt(np.mean(np.power(newpos, 2), axis=1))
     return normdata, displacement
 
@@ -411,6 +410,7 @@ def _detect_outliers_core(self, imgfile, motionfile, runidx, cwd=None):
         """
         Core routine for detecting outliers
         """
+        from scipy import signal
         if not cwd:
             cwd = os.getcwd()
 
@@ -750,6 +750,7 @@ def _get_spm_submatrix(self, spmmat, sessidx, rows=None):
     def _run_interface(self, runtime):
         """Execute this module.
         """
+        import scipy.io as sio
         motparamlist = self.inputs.realignment_parameters
         intensityfiles = self.inputs.intensity_values
         spmmat = sio.loadmat(self.inputs.spm_mat_file, struct_as_record=False)

nipype/interfaces/base/core.py

@@ -1164,12 +1164,10 @@ class LibraryBaseInterface(BaseInterface):
     def __init__(self, check_import=True, *args, **kwargs):
         super(LibraryBaseInterface, self).__init__(*args, **kwargs)
         if check_import:
-            import importlib
+            import pkgutil
             failed_imports = []
             for pkg in (self._pkg,) + tuple(self.imports):
-                try:
-                    importlib.import_module(pkg)
-                except ImportError:
+                if pkgutil.find_loader(pkg) is None:
                     failed_imports.append(pkg)
             if failed_imports:
                 iflogger.warning('Unable to import %s; %s interface may fail to '

nipype/interfaces/cmtk/cmtk.py

@@ -11,7 +11,6 @@
 import numpy as np
 import nibabel as nb
 import networkx as nx
-import scipy.io as sio
 
 from ... import logging
 from ...utils.filemanip import split_filename
@@ -178,6 +177,7 @@ def cmat(track_file,
          endpoint_name,
          intersections=False):
     """ Create the connection matrix for each resolution using fibers and ROIs. """
+    import scipy.io as sio
 
     stats = {}
     iflogger.info('Running cmat function')

nipype/interfaces/cmtk/nx.py

@@ -10,7 +10,6 @@
 
 import numpy as np
 import networkx as nx
-import scipy.io as sio
 
 from ... import logging
 from ...utils.filemanip import split_filename
@@ -94,6 +93,7 @@ def average_networks(in_files, ntwk_res_file, group_id):
     """
     import networkx as nx
     import os.path as op
+    import scipy.io as sio
     iflogger.info('Creating average network for group: %s', group_id)
     matlab_network_list = []
     if len(in_files) == 1:
@@ -442,6 +442,7 @@ class NetworkXMetrics(BaseInterface):
     output_spec = NetworkXMetricsOutputSpec
 
     def _run_interface(self, runtime):
+        import scipy.io as sio
         global gpickled, nodentwks, edgentwks, kntwks, matlab
         gpickled = list()
         nodentwks = list()

nipype/interfaces/image.py

@@ -2,9 +2,6 @@
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 
-import numpy as np
-import nibabel as nb
-
 from ..utils.filemanip import fname_presuffix
 from .base import (SimpleInterface, TraitedSpec, BaseInterfaceInputSpec,
                    traits, File)
@@ -63,6 +60,9 @@ class Rescale(SimpleInterface):
     output_spec = RescaleOutputSpec
 
     def _run_interface(self, runtime):
+        import numpy as np
+        import nibabel as nb
+
         img = nb.load(self.inputs.in_file)
         data = img.get_data()
         ref_data = nb.load(self.inputs.ref_file).get_data()
@@ -171,6 +171,8 @@ class Reorient(SimpleInterface):
     output_spec = ReorientOutputSpec
 
     def _run_interface(self, runtime):
+        import numpy as np
+        import nibabel as nb
         from nibabel.orientations import (
             axcodes2ornt, ornt_transform, inv_ornt_aff)
 
@@ -211,6 +213,8 @@ def _run_interface(self, runtime):
 
 def _as_reoriented_backport(img, ornt):
     """Backport of img.as_reoriented as of nibabel 2.2.0"""
+    import numpy as np
+    import nibabel as nb
     from nibabel.orientations import inv_ornt_aff
     if np.array_equal(ornt, [[0, 1], [1, 1], [2, 1]]):
         return img