[REF] Fix use of logging module in utils, algorithms and interfaces.

nipype/algorithms/confounds.py

@@ -30,7 +30,7 @@
 from ..utils import NUMPY_MMAP
 from ..utils.misc import normalize_mc_params
 
-IFLOG = logging.getLogger('interface')
+IFLOGGER = logging.getLogger('interface')
 
 
 class ComputeDVARSInputSpec(BaseInterfaceInputSpec):
@@ -286,7 +286,7 @@ def _run_interface(self, runtime):
                 tr = self.inputs.series_tr
 
             if self.inputs.normalize and tr is None:
-                IFLOG.warn('FD plot cannot be normalized if TR is not set')
+                IFLOGGER.warn('FD plot cannot be normalized if TR is not set')
 
             self._results['out_figure'] = op.abspath(self.inputs.out_figure)
             fig = plot_confound(fd_res, self.inputs.figsize, 'FD', units='mm',
@@ -601,8 +601,8 @@ def _process_masks(self, mask_images, timeseries=None):
             # save mask
             mask_file = os.path.abspath('mask_{:03d}.nii.gz'.format(i))
             out_image.to_filename(mask_file)
-            IFLOG.debug('tCompcor computed and saved mask of shape {} to '
-                        'mask_file {}'.format(mask.shape, mask_file))
+            IFLOGGER.debug('tCompcor computed and saved mask of shape %s to '
+                           'mask_file %s', str(mask.shape), mask_file)
             self._mask_files.append(mask_file)
             out_images.append(out_image)
         return out_images
@@ -919,7 +919,8 @@ def regress_poly(degree, data, remove_mean=True, axis=-1):
     :param int axis: numpy array axes along which regression is performed
 
     """
-    IFLOG.debug('Performing polynomial regression on data of shape ' + str(data.shape))
+    IFLOGGER.debug('Performing polynomial regression on data of shape %s',
+                   str(data.shape))
 
     datashape = data.shape
     timepoints = datashape[axis]
@@ -1147,7 +1148,7 @@ def _full_rank(X, cmax=1e15):
     c = smax / smin
     if c < cmax:
         return X, c
-    IFLOG.warn('Matrix is singular at working precision, regularizing...')
+    IFLOGGER.warn('Matrix is singular at working precision, regularizing...')
     lda = (smax - cmax * smin) / (cmax - 1)
     s = s + lda
     X = np.dot(U, np.dot(np.diag(s), V))

nipype/algorithms/misc.py

@@ -362,26 +362,23 @@ def _run_interface(self, runtime):
                 if isinstance(in_dict[key][0], np.ndarray):
                     saved_variables.append(key)
                 else:
-                    iflogger.info('One of the keys in the input file, {k}, is not a Numpy array'.format(k=key))
+                    iflogger.info('One of the keys in the input file, %s, is '
+                                  'not a Numpy array', key)
 
         if len(saved_variables) > 1:
-            iflogger.info(
-                '{N} variables found:'.format(N=len(saved_variables)))
+            iflogger.info('%i variables found:', len(saved_variables))
             iflogger.info(saved_variables)
             for variable in saved_variables:
-                iflogger.info(
-                    '...Converting {var} - type {ty} - to\
-                    CSV'.format(var=variable, ty=type(in_dict[variable]))
-                )
-                matlab2csv(
-                    in_dict[variable], variable, self.inputs.reshape_matrix)
+                iflogger.info('...Converting %s - type %s - to CSV',
+                              variable, type(in_dict[variable]))
+                matlab2csv(in_dict[variable], variable, self.inputs.reshape_matrix)
         elif len(saved_variables) == 1:
             _, name, _ = split_filename(self.inputs.in_file)
             variable = saved_variables[0]
-            iflogger.info('Single variable found {var}, type {ty}:'.format(
-                var=variable, ty=type(in_dict[variable])))
-            iflogger.info('...Converting {var} to CSV from {f}'.format(
-                var=variable, f=self.inputs.in_file))
+            iflogger.info('Single variable found %s, type %s:', variable,
+                          type(in_dict[variable]))
+            iflogger.info('...Converting %s to CSV from %s', variable,
+                          self.inputs.in_file)
             matlab2csv(in_dict[variable], name, self.inputs.reshape_matrix)
         else:
             iflogger.error('No values in the MATLAB file?!')
@@ -396,8 +393,8 @@ def _list_outputs(self):
                 if isinstance(in_dict[key][0], np.ndarray):
                     saved_variables.append(key)
                 else:
-                    iflogger.error('One of the keys in the input file, {k}, is\
-                                   not a Numpy array'.format(k=key))
+                    iflogger.error('One of the keys in the input file, %s, is '
+                                   'not a Numpy array', key)
 
         if len(saved_variables) > 1:
             outputs['csv_files'] = replaceext(saved_variables, '.csv')
@@ -555,28 +552,25 @@ def _run_interface(self, runtime):
             iflogger.info('Column headings have been provided:')
             headings = self.inputs.column_headings
         else:
-            iflogger.info(
-                'Column headings not provided! Pulled from input filenames:')
+            iflogger.info('Column headings not provided! Pulled from input filenames:')
             headings = remove_identical_paths(self.inputs.in_files)
 
         if isdefined(self.inputs.extra_field):
             if isdefined(self.inputs.extra_column_heading):
                 extraheading = self.inputs.extra_column_heading
-                iflogger.info('Extra column heading provided: {col}'.format(
-                    col=extraheading))
+                iflogger.info('Extra column heading provided: %s', extraheading)
             else:
                 extraheading = 'type'
-                iflogger.info(
-                    'Extra column heading was not defined. Using "type"')
+                iflogger.info('Extra column heading was not defined. Using "type"')
             headings.append(extraheading)
             extraheadingBool = True
 
         if len(self.inputs.in_files) == 1:
             iflogger.warn('Only one file input!')
 
         if isdefined(self.inputs.row_headings):
-            iflogger.info('Row headings have been provided. Adding "labels"\
-                          column header.')
+            iflogger.info('Row headings have been provided. Adding "labels"'
+                          'column header.')
             prefix = '"{p}","'.format(p=self.inputs.row_heading_title)
             csv_headings = prefix + '","'.join(itertools.chain(
                 headings)) + '"\n'
@@ -1310,7 +1304,7 @@ def merge_rois(in_files, in_idxs, in_ref,
     # to avoid memory errors
     if op.splitext(in_ref)[1] == '.gz':
         try:
-            iflogger.info('uncompress %i' % in_ref)
+            iflogger.info('uncompress %i', in_ref)
             sp.check_call(['gunzip', in_ref], stdout=sp.PIPE, shell=True)
             in_ref = op.splitext(in_ref)[0]
         except:

nipype/algorithms/modelgen.py

@@ -374,9 +374,9 @@ def _generate_standard_design(self, infolist, functional_runs=None,
                 for f in filename_to_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 '
-                                          'files. Are you sure this was '
-                                          'intended?'))
+                        iflogger.warning('You are using 3D instead of 4D '
+                                         'files. Are you sure this was '
+                                         'intended?')
                         numscans += 1
                     else:
                         numscans += shape[3]
@@ -686,7 +686,7 @@ def _gen_regress(self, i_onsets, i_durations, i_amplitudes, nscans):
 
         if dt < 1:
             raise Exception('Time multiple less than 1 ms')
-        iflogger.info('Setting dt = %d ms\n' % dt)
+        iflogger.info('Setting dt = %d ms\n', dt)
         npts = int(np.ceil(total_time / dt))
         times = np.arange(0, total_time, dt) * 1e-3
         timeline = np.zeros((npts))
@@ -705,9 +705,9 @@ def _gen_regress(self, i_onsets, i_durations, i_amplitudes, nscans):
             if isdefined(self.inputs.model_hrf) and self.inputs.model_hrf:
                 response = np.convolve(boxcar, hrf)
                 reg_scale = 1.0 / response.max()
-                iflogger.info('response sum: %.4f max: %.4f' % (response.sum(),
-                                                                response.max()))
-            iflogger.info('reg_scale: %.4f' % reg_scale)
+                iflogger.info('response sum: %.4f max: %.4f', response.sum(),
+                              response.max())
+            iflogger.info('reg_scale: %.4f', reg_scale)
 
         for i, t in enumerate(onsets):
             idx = int(np.round(t / dt))

nipype/interfaces/ants/base.py

@@ -11,7 +11,7 @@
 from ... import logging, LooseVersion
 from ..base import (CommandLine, CommandLineInputSpec, traits, isdefined,
                     PackageInfo)
-logger = logging.getLogger('interface')
+iflogger = logging.getLogger('interface')
 
 # -Using -1 gives primary responsibilty to ITKv4 to do the correct
 #  thread limitings.

nipype/interfaces/ants/registration.py

@@ -672,7 +672,7 @@ class Registration(ANTSCommand):
 
     One can use multiple similarity metrics in a single registration stage.The Node below first
     performs a linear registation using only the Mutual Information ('Mattes')-metric.
-    In a second stage, it performs a non-linear registration ('Syn') using both a 
+    In a second stage, it performs a non-linear registration ('Syn') using both a
     Mutual Information and a local cross-correlation ('CC')-metric. Both metrics are weighted
     equally ('metric_weight' is .5 for both). The Mutual Information- metric uses 32 bins.
     The local cross-correlations (correlations between every voxel's neighborhoods) is computed