Fix logging in dipy interface.

Author: tsalo

nipype/interfaces/dipy/anisotropic_power.py

@@ -67,7 +67,7 @@ def _run_interface(self, runtime):
         apm = shm.anisotropic_power(peaks.shm_coeff)
         out_file = self._gen_filename('apm')
         nb.Nifti1Image(apm.astype("float32"), affine).to_filename(out_file)
-        IFLOGGER.info('APM qball image saved as {i}'.format(i=out_file))
+        IFLOGGER.info('APM qball image saved as %s', out_file)
 
         return runtime
 

nipype/interfaces/dipy/preprocess.py

@@ -68,7 +68,7 @@ def _run_interface(self, runtime):
         resample_proxy(self.inputs.in_file, order=order,
                        new_zooms=vox_size, out_file=out_file)
 
-        IFLOGGER.info('Resliced image saved as {i}'.format(i=out_file))
+        IFLOGGER.info('Resliced image saved as %s', out_file)
         return runtime
 
     def _list_outputs(self):
@@ -159,8 +159,8 @@ def _run_interface(self, runtime):
                              smask=signal_mask,
                              nmask=noise_mask,
                              out_file=out_file)
-        IFLOGGER.info(('Denoised image saved as {i}, estimated '
-                       'SNR={s}').format(i=out_file, s=str(s)))
+        IFLOGGER.info('Denoised image saved as %s, estimated SNR=%s',
+                      out_file, str(s))
         return runtime
 
     def _list_outputs(self):

nipype/interfaces/dipy/reconstruction.py

@@ -123,13 +123,12 @@ def _run_interface(self, runtime):
         sigma = mean_std * (1 + bias)
 
         if sigma == 0:
-            IFLOGGER.warn(
-                ('Noise std is 0.0, looks like data was masked and noise'
-                 ' cannot be estimated correctly. Using default tensor '
-                 'model instead of RESTORE.'))
+            IFLOGGER.warn('Noise std is 0.0, looks like data was masked and noise '
+                          'cannot be estimated correctly. Using default tensor '
+                          'model instead of RESTORE.')
             dti = TensorModel(gtab)
         else:
-            IFLOGGER.info(('Performing RESTORE with noise std=%.4f.') % sigma)
+            IFLOGGER.info('Performing RESTORE with noise std=%.4f.', sigma)
             dti = TensorModel(gtab, fit_method='RESTORE', sigma=sigma)
 
         try:
@@ -252,14 +251,13 @@ def _run_interface(self, runtime):
             ratio = abs(response[1] / response[0])
 
         if ratio > 0.25:
-            IFLOGGER.warn(('Estimated response is not prolate enough. '
-                           'Ratio=%0.3f.') % ratio)
+            IFLOGGER.warn('Estimated response is not prolate enough. '
+                          'Ratio=%0.3f.', ratio)
         elif ratio < 1.e-5 or np.any(np.isnan(response)):
             response = np.array([1.8e-3, 3.6e-4, 3.6e-4, S0])
-            IFLOGGER.warn(
-                ('Estimated response is not valid, using a default one'))
+            IFLOGGER.warn('Estimated response is not valid, using a default one')
         else:
-            IFLOGGER.info(('Estimated response: %s') % str(response[:3]))
+            IFLOGGER.info('Estimated response: %s', str(response[:3]))
 
         np.savetxt(op.abspath(self.inputs.response), response)
 
@@ -343,8 +341,8 @@ def _run_interface(self, runtime):
         ratio = response[0][1] / response[0][0]
 
         if abs(ratio - 0.2) > 0.1:
-            IFLOGGER.warn(('Estimated response is not prolate enough. '
-                           'Ratio=%0.3f.') % ratio)
+            IFLOGGER.warn('Estimated response is not prolate enough. '
+                          'Ratio=%0.3f.', ratio)
 
         csd_model = ConstrainedSphericalDeconvModel(
             gtab, response, sh_order=self.inputs.sh_order)

nipype/interfaces/dipy/simulate.py

@@ -10,6 +10,7 @@
 import os.path as op
 from builtins import range
 
+import numpy as np
 import nibabel as nb
 
 from ... import logging
@@ -227,8 +228,8 @@ def _run_interface(self, runtime):
             pool = Pool(processes=n_proc)
 
         # Simulate sticks using dipy
-        IFLOGGER.info(('Starting simulation of %d voxels, %d diffusion'
-                       ' directions.') % (len(args), ndirs))
+        IFLOGGER.info('Starting simulation of %d voxels, %d diffusion directions.',
+                      len(args), ndirs)
         result = np.array(pool.map(_compute_voxel, args))
         if np.shape(result)[1] != ndirs:
             raise RuntimeError(('Computed directions do not match number'
@@ -288,7 +289,6 @@ def _compute_voxel(args):
                 angles=args['sticks'], fractions=ffs, snr=snr)
         except Exception as e:
             pass
-            # IFLOGGER.warn('Exception simulating dwi signal: %s' % e)
 
     return signal.tolist()
 

nipype/interfaces/dipy/tensors.py

@@ -65,14 +65,14 @@ def _run_interface(self, runtime):
         img = nifti1_symmat(lower_triangular, affine)
         out_file = self._gen_filename('dti')
         nb.save(img, out_file)
-        IFLOGGER.info('DTI parameters image saved as {i}'.format(i=out_file))
+        IFLOGGER.info('DTI parameters image saved as %s', out_file)
 
         #FA MD RD and AD
         for metric in ["fa", "md", "rd", "ad"]:
             data = getattr(ten_fit,metric).astype("float32")
             out_name = self._gen_filename(metric)
             nb.Nifti1Image(data, affine).to_filename(out_name)
-            IFLOGGER.info('DTI {metric} image saved as {i}'.format(i=out_name, metric=metric))
+            IFLOGGER.info('DTI %s image saved as %s', metric, out_name)
 
         return runtime
 
@@ -147,7 +147,7 @@ def _run_interface(self, runtime):
         img = nb.Nifti1Image(mode_data, affine)
         out_file = self._gen_filename('mode')
         nb.save(img, out_file)
-        IFLOGGER.info('Tensor mode image saved as {i}'.format(i=out_file))
+        IFLOGGER.info('Tensor mode image saved as %s', out_file)
         return runtime
 
     def _list_outputs(self):

nipype/interfaces/dipy/tracks.py

@@ -71,9 +71,8 @@ def _run_interface(self, runtime):
             data_dims = refnii.shape[:3]
             kwargs = dict(affine=affine)
         else:
-            IFLOGGER.warn(
-                'voxel_dims and data_dims are deprecated as of dipy 0.7.1. Please use reference '
-                'input instead')
+            IFLOGGER.warn('voxel_dims and data_dims are deprecated as of dipy '
+                          '0.7.1. Please use reference input instead')
 
             if not isdefined(self.inputs.data_dims):
                 data_dims = header['dim']
@@ -93,9 +92,8 @@ def _run_interface(self, runtime):
         out_file = op.abspath(self.inputs.out_filename)
         nb.save(img, out_file)
 
-        IFLOGGER.info(
-            'Track density map saved as %s, size=%s, dimensions=%s',
-            out_file, img.shape, img.header.get_zooms())
+        IFLOGGER.info('Track density map saved as %s, size=%s, dimensions=%s',
+                      out_file, img.shape, img.header.get_zooms())
 
         return runtime
 
@@ -238,12 +236,12 @@ def _run_interface(self, runtime):
             seedps = np.array(np.where(seedmsk == 1), dtype=np.float32).T
             vseeds = seedps.shape[0]
             nsperv = (seeds // vseeds) + 1
-            IFLOGGER.info(('Seed mask is provided (%d voxels inside '
-                           'mask), computing seeds (%d seeds/voxel).') %
-                          (vseeds, nsperv))
+            IFLOGGER.info('Seed mask is provided (%d voxels inside '
+                          'mask), computing seeds (%d seeds/voxel).',
+                          vseeds, nsperv)
             if nsperv > 1:
-                IFLOGGER.info(('Needed %d seeds per selected voxel '
-                               '(total %d).') % (nsperv, vseeds))
+                IFLOGGER.info('Needed %d seeds per selected voxel (total %d).',
+                              nsperv, vseeds)
                 seedps = np.vstack(np.array([seedps] * nsperv))
                 voxcoord = seedps + np.random.uniform(-1, 1, size=seedps.shape)
                 nseeds = voxcoord.shape[0]

nipype/interfaces/utility/wrappers.py

@@ -21,7 +21,7 @@
 from ...utils.filemanip import filename_to_list
 from ...utils.functions import getsource, create_function_from_source
 
-logger = logging.getLogger('interface')
+iflogger = logging.getLogger('interface')
 
 class FunctionInputSpec(DynamicTraitedSpec, BaseInterfaceInputSpec):
     function_str = traits.Str(mandatory=True, desc='code for function')