Merge pull request #4 from satra/enh/py3k-fix

Enh/py3k fix

Author: satra

examples/fmri_ants_openfmri.py

@@ -13,7 +13,6 @@
 """
 from __future__ import division
 from builtins import range
-from past.utils import old_div
 
 from nipype import config
 config.enable_provenance()
@@ -79,7 +78,7 @@ def median(in_files):
             average = data
         else:
             average = average + data
-    median_img = nb.Nifti1Image(old_div(average,float(idx + 1)),
+    median_img = nb.Nifti1Image(average / float(idx + 1),
                                 img.get_affine(), img.get_header())
     filename = os.path.join(os.getcwd(), 'median.nii.gz')
     median_img.to_filename(filename)
@@ -574,7 +573,7 @@ def get_subjectinfo(subject_id, base_dir, task_id, model_id):
         import json
         with open(json_info, 'rt') as fp:
             data = json.load(fp)
-            TR = old_div(data['global']['const']['RepetitionTime'],1000.)
+            TR = data['global']['const']['RepetitionTime'] / 1000.
     else:
         task_scan_key = os.path.join(base_dir, subject_id, 'BOLD',
                                  'task%03d_run%03d' % (task_id, run_ids[task_id - 1][0]),
@@ -714,7 +713,7 @@ def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
                 ])
 
     def get_highpass(TR, hpcutoff):
-        return old_div(hpcutoff, (2 * TR))
+        return hpcutoff / (2. * TR)
     gethighpass = pe.Node(niu.Function(input_names=['TR', 'hpcutoff'],
                                        output_names=['highpass'],
                                        function=get_highpass),
@@ -773,7 +772,8 @@ def check_behav_list(behav, run_id, conds):
             behav = [behav]
         behav_array = np.array(behav).flatten()
         num_elements = behav_array.shape[0]
-        return behav_array.reshape(old_div(num_elements,num_conds), num_conds).tolist()
+        return behav_array.reshape(int(num_elements / num_conds),
+                                   num_conds).tolist()
 
     reshape_behav = pe.Node(niu.Function(input_names=['behav', 'run_id', 'conds'],
                                        output_names=['behav'],
@@ -836,8 +836,10 @@ def sort_copes(copes, varcopes, contrasts):
         n_runs = len(copes)
         all_copes = np.array(copes).flatten()
         all_varcopes = np.array(varcopes).flatten()
-        outcopes = all_copes.reshape(old_div(len(all_copes),num_copes), num_copes).T.tolist()
-        outvarcopes = all_varcopes.reshape(old_div(len(all_varcopes),num_copes), num_copes).T.tolist()
+        outcopes = all_copes.reshape(int(len(all_copes) / num_copes),
+                                     num_copes).T.tolist()
+        outvarcopes = all_varcopes.reshape(int(len(all_varcopes) / num_copes),
+                                           num_copes).T.tolist()
         return outcopes, outvarcopes, n_runs
 
     cope_sorter = pe.Node(niu.Function(input_names=['copes', 'varcopes',

examples/fmri_fsl.py

@@ -17,7 +17,6 @@
 from __future__ import print_function
 from __future__ import division
 from builtins import range
-from past.utils import old_div
 
 import os                                    # system functions
 
@@ -108,7 +107,7 @@ def getmiddlevolume(func):
     if isinstance(func, list):
         funcfile = func[0]
     _,_,_,timepoints = load(funcfile).get_shape()
-    return (old_div(timepoints,2))-1
+    return int(timepoints / 2) - 1
 
 preproc.connect(inputnode, ('func', getmiddlevolume), extract_ref, 't_min')
 
@@ -290,7 +289,7 @@ def getusans(x):
 """
 
 def getinormscale(medianvals):
-    return ['-mul %.10f'%(old_div(10000.,val)) for val in medianvals]
+    return ['-mul %.10f' % (10000. / val) for val in medianvals]
 preproc.connect(medianval, ('out_stat', getinormscale), intnorm, 'op_string')
 
 """
@@ -555,9 +554,9 @@ def num_copes(files):
 smoothnode.iterables = ('fwhm', [5.,10.])
 
 hpcutoff = 120
-TR = 3.
+TR = 3.  # ensure float
 firstlevel.inputs.preproc.highpass.suffix = '_hpf'
-firstlevel.inputs.preproc.highpass.op_string = '-bptf %d -1'%(old_div(hpcutoff,TR))
+firstlevel.inputs.preproc.highpass.op_string = '-bptf %d -1' % (hpcutoff / TR)
 
 
 """
@@ -572,7 +571,7 @@ def num_copes(files):
 def subjectinfo(subject_id):
     from nipype.interfaces.base import Bunch
     from copy import deepcopy
-    print("Subject ID: %s\n"%str(subject_id))
+    print("Subject ID: %s\n" % str(subject_id))
     output = []
     names = ['Task-Odd','Task-Even']
     for r in range(4):

examples/fmri_fsl_feeds.py

@@ -14,7 +14,6 @@
 """
 from __future__ import division
 from builtins import range
-from past.utils import old_div
 
 import os                                    # system functions
 
@@ -72,7 +71,7 @@
 preproc = create_featreg_preproc(whichvol='first')
 TR = 3.
 preproc.inputs.inputspec.fwhm = 5
-preproc.inputs.inputspec.highpass = old_div(100,TR)
+preproc.inputs.inputspec.highpass = 100. / TR
 
 modelspec = pe.Node(interface=model.SpecifyModel(),
                     name="modelspec")

examples/fmri_fsl_reuse.py

@@ -17,7 +17,6 @@
 from __future__ import print_function
 from __future__ import division
 from builtins import range
-from past.utils import old_div
 
 import os                                    # system functions
 
@@ -29,8 +28,8 @@
 import nipype.algorithms.rapidart as ra      # artifact detection
 
 from nipype.workflows.fmri.fsl import (create_featreg_preproc,
-                                  create_modelfit_workflow,
-                                  create_fixed_effects_flow)
+                                       create_modelfit_workflow,
+                                       create_fixed_effects_flow)
 
 
 """
@@ -185,7 +184,7 @@ def num_copes(files):
 
 hpcutoff = 120.
 TR = 3.
-inputnode.inputs.highpass = old_div(hpcutoff,(2*TR))
+inputnode.inputs.highpass = hpcutoff / (2. * TR)
 
 """
 Setup a function that returns subject-specific information about the

examples/fmri_openfmri.py

@@ -13,7 +13,6 @@
 """
 from __future__ import division
 from builtins import range
-from past.utils import old_div
 
 
 from glob import glob
@@ -187,7 +186,7 @@ def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
                 ])
 
     def get_highpass(TR, hpcutoff):
-        return old_div(hpcutoff, (2 * TR))
+        return hpcutoff / (2. * TR)
     gethighpass = pe.Node(niu.Function(input_names=['TR', 'hpcutoff'],
                                        output_names=['highpass'],
                                        function=get_highpass),

examples/fmri_spm_face.py

@@ -18,7 +18,6 @@
 Import necessary modules from nipype."""
 from __future__ import division
 from builtins import range
-from past.utils import old_div
 
 import nipype.interfaces.io as nio           # Data i/o
 import nipype.interfaces.spm as spm          # spm
@@ -327,9 +326,9 @@ def makelist(item):
 slice_timingref = l1pipeline.inputs.preproc.slice_timing
 slice_timingref.num_slices = num_slices
 slice_timingref.time_repetition = TR
-slice_timingref.time_acquisition = TR - old_div(TR,float(num_slices))
+slice_timingref.time_acquisition = TR - TR / float(num_slices)
 slice_timingref.slice_order = list(range(num_slices,0,-1))
-slice_timingref.ref_slice = int(old_div(num_slices,2))
+slice_timingref.ref_slice = int(num_slices / 2)
 
 l1pipeline.inputs.preproc.smooth.fwhm = [8, 8, 8]
 

examples/rsfmri_vol_surface_preprocessing.py

@@ -44,8 +44,6 @@
 """
 from __future__ import division
 from builtins import range
-from past.utils import old_div
-
 
 import os
 
@@ -99,7 +97,7 @@ def get_info(dicom_files):
     meta = default_extractor(read_file(filename_to_list(dicom_files)[0],
                                        stop_before_pixels=True,
                                        force=True))
-    return (old_div(meta['RepetitionTime'],1000.), meta['CsaImage.MosaicRefAcqTimes'],
+    return (meta['RepetitionTime'] / 1000., meta['CsaImage.MosaicRefAcqTimes'],
             meta['SpacingBetweenSlices'])
 
 
@@ -126,7 +124,7 @@ def median(in_files):
             average = data
         else:
             average = average + data
-    median_img = nb.Nifti1Image(old_div(average,float(idx + 1)),
+    median_img = nb.Nifti1Image(average / float(idx + 1),
                                 img.get_affine(), img.get_header())
     filename = os.path.join(os.getcwd(), 'median.nii.gz')
     median_img.to_filename(filename)
@@ -153,7 +151,7 @@ def bandpass_filter(files, lowpass_freq, highpass_freq, fs):
         img = nb.load(filename)
         timepoints = img.shape[-1]
         F = np.zeros((timepoints))
-        lowidx = old_div(timepoints,2) + 1
+        lowidx = int(timepoints / 2) + 1
         if lowpass_freq > 0:
             lowidx = np.round(lowpass_freq / fs * timepoints)
         highidx = 0
@@ -278,7 +276,7 @@ def extract_noise_components(realigned_file, mask_file, num_components=5,
         stdX[stdX == 0] = 1.
         stdX[np.isnan(stdX)] = 1.
         stdX[np.isinf(stdX)] = 1.
-        X = old_div((X - np.mean(X, axis=0)),stdX)
+        X = (X - np.mean(X, axis=0)) / stdX
         u, _, _ = svd(X, full_matrices=False)
         if components is None:
             components = u[:, :num_components]
@@ -610,9 +608,9 @@ def create_workflow(files,
     slice_timing = Node(interface=spm.SliceTiming(), name="slice_timing")
     slice_timing.inputs.num_slices = num_slices
     slice_timing.inputs.time_repetition = TR
-    slice_timing.inputs.time_acquisition = TR - old_div(TR,float(num_slices))
+    slice_timing.inputs.time_acquisition = TR - TR / float(num_slices)
     slice_timing.inputs.slice_order = (np.argsort(slice_times) + 1).tolist()
-    slice_timing.inputs.ref_slice = int(old_div(num_slices,2))
+    slice_timing.inputs.ref_slice = int(num_slices / 2)
 
     # Comute TSNR on realigned data regressing polynomials upto order 2
     tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
@@ -745,7 +743,7 @@ def merge_files(in1, in2):
                               function=bandpass_filter,
                               imports=imports),
                      name='bandpass_unsmooth')
-    bandpass.inputs.fs = old_div(1.,TR)
+    bandpass.inputs.fs = 1. / TR
     bandpass.inputs.highpass_freq = highpass_freq
     bandpass.inputs.lowpass_freq = lowpass_freq
     wf.connect(filter2, 'out_res', bandpass, 'files')
@@ -951,7 +949,7 @@ def create_resting_workflow(args, name=None):
     slice_times = args.slice_times
     if args.dicom_file:
         TR, slice_times, slice_thickness = get_info(args.dicom_file)
-        slice_times = (old_div(np.array(slice_times),1000.)).tolist()
+        slice_times = (np.array(slice_times) / 1000.).tolist()
     if name is None:
         name = 'resting_' + args.subject_id
     kwargs = dict(files=[os.path.abspath(filename) for filename in args.files],

examples/rsfmri_vol_surface_preprocessing_nipy.py

@@ -45,7 +45,6 @@
 """
 from __future__ import division
 from builtins import range
-from past.utils import old_div
 
 import os
 
@@ -94,7 +93,7 @@ def get_info(dicom_files):
     meta = default_extractor(read_file(filename_to_list(dicom_files)[0],
                                        stop_before_pixels=True,
                                        force=True))
-    return (old_div(meta['RepetitionTime'],1000.), meta['CsaImage.MosaicRefAcqTimes'],
+    return (meta['RepetitionTime'] / 1000., meta['CsaImage.MosaicRefAcqTimes'],
             meta['SpacingBetweenSlices'])
 
 
@@ -119,7 +118,7 @@ def median(in_files):
             average = data
         else:
             average = average + data
-    median_img = nb.Nifti1Image(old_div(average,float(idx + 1)),
+    median_img = nb.Nifti1Image(average / float(idx + 1),
                                 img.get_affine(), img.get_header())
     filename = os.path.join(os.getcwd(), 'median.nii.gz')
     median_img.to_filename(filename)
@@ -143,7 +142,7 @@ def bandpass_filter(files, lowpass_freq, highpass_freq, fs):
         img = nb.load(filename)
         timepoints = img.shape[-1]
         F = np.zeros((timepoints))
-        lowidx = old_div(timepoints,2) + 1
+        lowidx = int(timepoints / 2) + 1
         if lowpass_freq > 0:
             lowidx = np.round(float(lowpass_freq) / fs * timepoints)
         highidx = 0
@@ -260,7 +259,7 @@ def extract_noise_components(realigned_file, mask_file, num_components=5,
         stdX[stdX == 0] = 1.
         stdX[np.isnan(stdX)] = 1.
         stdX[np.isinf(stdX)] = 1.
-        X = old_div((X - np.mean(X, axis=0)),stdX)
+        X = (X - np.mean(X, axis=0)) / stdX
         u, _, _ = sp.linalg.svd(X, full_matrices=False)
         if components is None:
             components = u[:, :num_components]
@@ -735,7 +734,7 @@ def merge_files(in1, in2):
                               function=bandpass_filter,
                               imports=imports),
                      name='bandpass_unsmooth')
-    bandpass.inputs.fs = old_div(1.,TR)
+    bandpass.inputs.fs = 1. / TR
     bandpass.inputs.highpass_freq = highpass_freq
     bandpass.inputs.lowpass_freq = lowpass_freq
     wf.connect(filter2, 'out_res', bandpass, 'files')
@@ -961,7 +960,7 @@ def create_resting_workflow(args, name=None):
     slice_times = args.slice_times
     if args.dicom_file:
         TR, slice_times, slice_thickness = get_info(args.dicom_file)
-        slice_times = (old_div(np.array(slice_times),1000.)).tolist()
+        slice_times = (np.array(slice_times) / 1000.).tolist()
 
     if name is None:
         name = 'resting_' + args.subject_id

examples/test_spm.py

@@ -1,6 +1,5 @@
 from __future__ import division
 from builtins import range
-from past.utils import old_div
 import nipype.pipeline.engine as pe
 from nipype.interfaces import spm
 from nipype.interfaces import fsl
@@ -15,7 +14,7 @@
 stc = pe.Node(interface=spm.SliceTiming(), name='stc')
 stc.inputs.num_slices = 21
 stc.inputs.time_repetition = 1.0
-stc.inputs.time_acquisition = 2. - old_div(2.,32)
+stc.inputs.time_acquisition = 2. - 2. / 32
 stc.inputs.slice_order = list(range(21,0,-1)) 
 stc.inputs.ref_slice = 10 
 
@@ -50,7 +49,7 @@
 stc = pe.Node(interface=spm.SliceTiming(), name='stc')
 stc.inputs.num_slices = 21
 stc.inputs.time_repetition = 1.0
-stc.inputs.time_acquisition = 2. - old_div(2.,32)
+stc.inputs.time_acquisition = 2. - 2. / 32
 stc.inputs.slice_order = list(range(21,0,-1)) 
 stc.inputs.ref_slice = 10 
 

nipype/algorithms/icc.py

@@ -1,6 +1,5 @@
 from __future__ import division
 from builtins import range
-from past.utils import old_div
 from numpy import ones, kron, mean, eye, hstack, dot, tile
 from scipy.linalg import pinv
 from ..interfaces.base import BaseInterfaceInputSpec, TraitedSpec, \
@@ -110,22 +109,22 @@ def ICC_rep_anova(Y):
 
     residuals.shape = Y.shape
 
-    MSE = old_div(SSE, dfe)
+    MSE = SSE / dfe
 
     # Sum square session effect - between colums/sessions
     SSC = ((mean(Y, 0) - mean_Y) ** 2).sum() * nb_subjects
     MSC = SSC / dfc / nb_subjects
 
-    session_effect_F = old_div(MSC, MSE)
+    session_effect_F = MSC / MSE
 
     # Sum Square subject effect - between rows/subjects
     SSR = SST - SSC - SSE
-    MSR = old_div(SSR, dfr)
+    MSR = SSR / dfr
 
     # ICC(3,1) = (mean square subjeT - mean square error) / (mean square subjeT + (k-1)*-mean square error)
-    ICC = old_div((MSR - MSE), (MSR + dfc * MSE))
+    ICC = (MSR - MSE) / (MSR + dfc * MSE)
 
-    e_var = MSE #variance of error
-    r_var = old_div((MSR - MSE),nb_conditions) #variance between subjects
+    e_var = MSE  # variance of error
+    r_var = (MSR - MSE) / nb_conditions  # variance between subjects
 
     return ICC, r_var, e_var, session_effect_F, dfc, dfe