Merge remote-tracking branch 'upstream/master' into fix/1795

Author: oesteban

.travis.yml

@@ -28,9 +28,10 @@ before_install:
   export PATH=/home/travis/anaconda/bin:$PATH &&
   conda config --append channels conda-forge &&
   conda update --all -y python=$TRAVIS_PYTHON_VERSION &&
-  conda install -y nipype icu==56.1 &&
-  rm -r /home/travis/miniconda/lib/python${TRAVIS_PYTHON_VERSION}/site-packages/nipype*}
-#  Add install of vtk and mayavi to test mesh (disabled): conda install -y vtk mayavi &&
+  conda install -y nipype icu &&
+  rm -r /home/travis/miniconda/lib/python${TRAVIS_PYTHON_VERSION}/site-packages/nipype* &&
+  pip install -r requirements.txt ; }
+#  Add install of vtk and mayavi to test mesh (disabled): conda install -y vtk mayavi
 - travis_retry bef_inst
 install:
 - travis_retry pip install -e .[$NIPYPE_EXTRAS]

nipype/algorithms/misc.py

@@ -1271,9 +1271,9 @@ def split_rois(in_file, mask=None, roishape=None):
         np.savez(iname, (nzels[0][first:last],))
 
         if fill > 0:
-            droi = np.vstack((droi, np.zeros((fill, nvols), dtype=np.float32)))
+            droi = np.vstack((droi, np.zeros((int(fill), int(nvols)), dtype=np.float32)))
             partialmsk = np.ones((roisize,), dtype=np.uint8)
-            partialmsk[-fill:] = 0
+            partialmsk[-int(fill):] = 0
             partname = op.abspath('partialmask.nii.gz')
             nb.Nifti1Image(partialmsk.reshape(roishape), None,
                            None).to_filename(partname)

nipype/algorithms/modelgen.py

@@ -655,12 +655,12 @@ def _gen_regress(self, i_onsets, i_durations, i_amplitudes, nscans):
             hrf = spm_hrf(dt * 1e-3)
         reg_scale = 1.0
         if self.inputs.scale_regressors:
-            boxcar = np.zeros((50.0 * 1e3 / dt))
+            boxcar = np.zeros(int(50.0 * 1e3 / dt))
             if self.inputs.stimuli_as_impulses:
-                boxcar[1.0 * 1e3 / dt] = 1.0
+                boxcar[int(1.0 * 1e3 / dt)] = 1.0
                 reg_scale = float(TA / dt)
             else:
-                boxcar[(1.0 * 1e3 / dt):(2.0 * 1e3 / dt)] = 1.0
+                boxcar[int(1.0 * 1e3 / dt):int(2.0 * 1e3 / dt)] = 1.0
             if isdefined(self.inputs.model_hrf) and self.inputs.model_hrf:
                 response = np.convolve(boxcar, hrf)
                 reg_scale = 1.0 / response.max()