Uniformize verbose level in modules

scilpy/image/volume_operations.py

@@ -241,7 +241,7 @@ def register_image(static, static_grid2world, moving, moving_grid2world,
 def compute_snr(dwi, bval, bvec, b0_thr, mask,
                 noise_mask=None, noise_map=None,
                 split_shells=False,
-                basename=None, verbose=False):
+                basename=None):
     """
     Compute snr
 
@@ -264,16 +264,10 @@ def compute_snr(dwi, bval, bvec, b0_thr, mask,
     basename: string
         Basename used for naming all output files.
 
-    verbose: boolean
-        Set to use logging
-
     Return
     ------
     Dictionary of values (bvec, bval, mean, std, snr) for all volumes.
     """
-    if verbose:
-        logging.getLogger().setLevel(logging.INFO)
-
     data = dwi.get_fdata(dtype=np.float32)
     affine = dwi.affine
     mask = get_data_as_mask(mask, dtype=bool)
@@ -416,17 +410,17 @@ def resample_volume(img, ref=None, res=None, iso_min=False, zoom=None,
     if interp not in interp_choices:
         raise ValueError("interp must be one of 'nn', 'lin', 'quad', 'cubic'.")
 
-    logging.debug('Data shape: %s', data.shape)
-    logging.debug('Data affine: %s', affine)
-    logging.debug('Data affine setup: %s', nib.aff2axcodes(affine))
-    logging.debug('Resampling data to %s with mode %s', new_zooms, interp)
+    logging.info('Data shape: %s', data.shape)
+    logging.info('Data affine: %s', affine)
+    logging.info('Data affine setup: %s', nib.aff2axcodes(affine))
+    logging.info('Resampling data to %s with mode %s', new_zooms, interp)
 
     data2, affine2 = reslice(data, affine, original_zooms, new_zooms,
                              _interp_code_to_order(interp))
 
-    logging.debug('Resampled data shape: %s', data2.shape)
-    logging.debug('Resampled data affine: %s', affine2)
-    logging.debug('Resampled data affine setup: %s', nib.aff2axcodes(affine2))
+    logging.info('Resampled data shape: %s', data2.shape)
+    logging.info('Resampled data affine: %s', affine2)
+    logging.info('Resampled data affine setup: %s', nib.aff2axcodes(affine2))
 
     if enforce_dimensions:
         if ref is None:

scilpy/reconst/fodf.py

@@ -91,13 +91,13 @@ def get_ventricles_max_fodf(data, fa, md, zoom, args):
                     count += 1
                     mask[i, j, k] = 1
 
-    logging.debug('Number of voxels detected: {}'.format(count))
+    logging.info('Number of voxels detected: {}'.format(count))
     if count == 0:
         logging.warning('No voxels found for evaluation! Change your fa '
                         'and/or md thresholds')
         return 0, mask
 
-    logging.debug('Average max fodf value: {}'.format(sum_of_max / count))
+    logging.info('Average max fodf value: {}'.format(sum_of_max / count))
     return sum_of_max / count, mask
 
 

scilpy/reconst/frf.py

@@ -107,7 +107,7 @@ def compute_ssst_frf(data, bvals, bvecs, mask=None, mask_wm=None,
         nvox = np.sum(mask)
         response, ratio = response_from_mask_ssst(gtab, data, mask)
 
-        logging.debug(
+        logging.info(
             "Number of indices is {:d} with threshold of {:.2f}".format(
                 nvox, fa_thresh))
         fa_thresh -= 0.05
@@ -117,14 +117,14 @@ def compute_ssst_frf(data, bvals, bvecs, mask=None, mask_wm=None,
             "Could not find at least {:d} voxels with sufficient FA "
             "to estimate the FRF!".format(min_nvox))
 
-    logging.debug(
+    logging.info(
         "Found {:d} voxels with FA threshold {:.2f} for "
         "FRF estimation".format(nvox, fa_thresh + 0.05))
-    logging.debug("FRF eigenvalues: {}".format(str(response[0])))
-    logging.debug("Ratio for smallest to largest eigen value "
-                  "is {:.3f}".format(ratio))
-    logging.debug("Mean of the b=0 signal for voxels used "
-                  "for FRF: {}".format(response[1]))
+    logging.info("FRF eigenvalues: {}".format(str(response[0])))
+    logging.info("Ratio for smallest to largest eigen value "
+                 "is {:.3f}".format(ratio))
+    logging.info("Mean of the b=0 signal for voxels used "
+                 "for FRF: {}".format(response[1]))
 
     full_response = np.array([response[0][0], response[0][1],
                               response[0][2], response[1]])

scilpy/stats/matrix_stats.py

@@ -67,19 +67,19 @@ def ttest_two_matrices(matrices_g1, matrices_g2, paired, tail, fdr,
     sum_both_groups = np.sum(matrices_g1, axis=2) + np.sum(matrices_g2, axis=2)
     nbr_non_zeros = np.count_nonzero(np.triu(sum_both_groups))
 
-    logging.debug('The provided matrices contain {} non zeros elements.'
-                  .format(nbr_non_zeros))
+    logging.info('The provided matrices contain {} non zeros elements.'
+                 .format(nbr_non_zeros))
 
     matrices_g1 = matrices_g1.reshape((np.prod(matrix_shape), nb_group_g1))
     matrices_g2 = matrices_g2.reshape((np.prod(matrix_shape), nb_group_g2))
     # Negative epsilon, to differentiate from null p-values
     matrix_pval = np.ones(np.prod(matrix_shape)) * -0.000001
 
     text = ' paired' if paired else ''
-    logging.debug('Performing{} t-test with "{}" hypothesis.'
-                  .format(text, tail))
-    logging.debug('Data has dimensions {}x{} with {} and {} observations.'
-                  .format(matrix_shape[0], matrix_shape[1],
+    logging.info('Performing{} t-test with "{}" hypothesis.'
+                 .format(text, tail))
+    logging.info('Data has dimensions {}x{} with {} and {} observations.'
+                 .format(matrix_shape[0], matrix_shape[1],
                           nb_group_g1, nb_group_g2))
 
     # For conversion to p-values
@@ -105,7 +105,7 @@ def ttest_two_matrices(matrices_g1, matrices_g2, paired, tail, fdr,
 
     corr_matrix_pval = matrix_pval.reshape(matrix_shape)
     if fdr:
-        logging.debug('Using FDR, the results will be q-values.')
+        logging.info('Using FDR, the results will be q-values.')
         corr_matrix_pval = np.triu(corr_matrix_pval)
         corr_matrix_pval[corr_matrix_pval > 0] = multipletests(
             corr_matrix_pval[corr_matrix_pval > 0], 0, method='fdr_bh')[1]
@@ -158,8 +158,8 @@ def omega_sigma(matrix):
     transitivity_latt_list = []
     path_length_rand_list = []
     for i in range(10):
-        logging.debug('Generating random and lattice matrices, '
-                      'iteration #{}.'.format(i))
+        logging.info('Generating random and lattice matrices, '
+                     'iteration #{}.'.format(i))
         random = bct.randmio_und(matrix, 10)[0]
         lattice = bct.latmio_und(matrix, 10)[1]
 

scilpy/stats/stats.py

@@ -32,10 +32,10 @@ def verify_normality(data, alpha=0.05):
     # First, we verify if sample pass Shapiro-Wilk test
     W, p_value = scipy.stats.shapiro(data)
     if p_value < alpha and len(data) < 30:
-        logging.debug('The data sample can not be considered normal')
+        logging.info('The data sample can not be considered normal')
         normality = False
     else:
-        logging.debug('The data sample pass the normality assumption.')
+        logging.info('The data sample pass the normality assumption.')
         normality = True
     return normality, p_value
 
@@ -76,12 +76,12 @@ def verify_homoscedasticity(data_by_group, normality=False, alpha=0.05):
     else:
         test = 'Levene'
         W, p_value = scipy.stats.levene(*data_by_group)
-    logging.debug('Test name: {}'.format(test))
+    logging.info('Test name: {}'.format(test))
     if p_value < alpha and mean_nb < 30:
-        logging.debug('The sample didnt pass the equal variance assumption')
+        logging.info('The sample didnt pass the equal variance assumption')
         homoscedasticity = False
     else:
-        logging.debug('The sample pass the equal variance assumption')
+        logging.info('The sample pass the equal variance assumption')
         homoscedasticity = True
 
     return test, homoscedasticity, p_value
@@ -145,12 +145,12 @@ def verify_group_difference(data_by_group, normality=False,
             test = 'Kruskalwallis'
             T, p_value = scipy.stats.kruskal(*data_by_group)
 
-    logging.debug('Test name: {}'.format(test))
+    logging.info('Test name: {}'.format(test))
     if p_value < alpha:
-        logging.debug('There is a difference between groups')
+        logging.info('There is a difference between groups')
         difference = True
     else:
-        logging.debug('We are not able to detect difference between the groups.')
+        logging.info('We are not able to detect difference between the groups.')
         difference = False
 
     return test, difference, p_value
@@ -191,9 +191,9 @@ def verify_post_hoc(data_by_group, groups_list, test,
     test : string
         Name of the test done to verify group difference
     """
-    logging.debug('We need to do a post-hoc analysis since '
-                  'there is a difference')
-    logging.debug('Post-hoc: {} pairwise'.format(test))
+    logging.info('We need to do a post-hoc analysis since '
+                 'there is a difference')
+    logging.info('Post-hoc: {} pairwise'.format(test))
     differences = []
     nb_group = len(groups_list)
 
@@ -214,7 +214,7 @@ def verify_post_hoc(data_by_group, groups_list, test,
                 data_by_group[x], data_by_group[y])
         differences.append((groups_list[x], groups_list[y],
                             p_value < alpha, p_value))
-    logging.debug('Result:')
-    logging.debug(differences)
+    logging.info('Result:')
+    logging.info(differences)
 
     return test, differences

scilpy/stats/utils.py

@@ -51,8 +51,8 @@ def __init__(self, json_file, participants):
                     self.data_dictionnary[participant['participant_id']]\
                         [variable] = participant[variable]
 
-        logging.debug('Data_dictionnary')
-        logging.debug(self.data_dictionnary[self.get_first_participant()])
+        logging.info('Data_dictionnary')
+        logging.info(self.data_dictionnary[self.get_first_participant()])
 
         with open('data.json', 'w') as fp:
             json.dump(self.data_dictionnary, fp, indent=4)
@@ -64,31 +64,33 @@ def validation_participant_id(self, json_info, participants_info):
         # Create the list of participants id from the json dictionnary
 
         participants_from_json = list(json_info.keys())
-        logging.debug('participant list from json dictionnary:')
-        logging.debug(participants_from_json)
+        logging.info('participant list from json dictionnary:')
+        logging.info(participants_from_json)
 
         # Create the list of participants id from the tsv list of dictionnary
         participants_from_tsv = []
         for participant in participants_info:
             participants_from_tsv.append(participant['participant_id'])
-        logging.debug('participant list from tsv file:')
-        logging.debug(participants_from_tsv)
+        logging.info('participant list from tsv file:')
+        logging.info(participants_from_tsv)
 
         # Compare the two list
         participants_from_json.sort()
         participants_from_tsv.sort()
 
         if not participants_from_json == participants_from_tsv:
             if not len(participants_from_json) == len(participants_from_tsv):
-                logging.debug('The number of participants from json file is not the same '
-                              'as the one in the tsv file.')
+                logging.info('The number of participants from json file is '
+                             'not the same as the one in the tsv file.')
             is_in_tsv = np.in1d(participants_from_json, participants_from_tsv)
             is_in_json = np.in1d(participants_from_tsv, participants_from_json)
 
-            logging.debug('participants list from json file missing in tsv file :')
-            logging.debug(np.asarray(participants_from_json)[~is_in_tsv])
-            logging.debug('participants list from tsv file missing in json file :')
-            logging.debug(np.asarray(participants_from_tsv)[~is_in_json])
+            logging.info('participants list from json file missing in tsv '
+                         'file :')
+            logging.info(np.asarray(participants_from_json)[~is_in_tsv])
+            logging.info('participants list from tsv file missing in json '
+                         'file :')
+            logging.info(np.asarray(participants_from_tsv)[~is_in_json])
 
             logging.error('The subjects from the json file does not fit '
                           'with the subjects of the tsv file. '
@@ -97,7 +99,7 @@ def validation_participant_id(self, json_info, participants_info):
                                 'with the subjects of the tsv file. '
                                 'Impossible to build the data_for_stat object')
         else:
-            logging.debug('The json and the tsv are compatible')
+            logging.info('The json and the tsv are compatible')
 
     def get_participants_list(self):
         # Construct the list of participant_id from the data_dictionnary
@@ -492,6 +494,6 @@ def visualise_distribution(data_by_group, participants_id, bundle, metric,
 
     fig.savefig(os.path.join(oFolder, 'Graph', bundle, metric))
 
-    logging.debug('outliers:[(id, group)]')
-    logging.debug(outliers)
+    logging.info('outliers:[(id, group)]')
+    logging.info(outliers)
     return outliers

scilpy/tracking/tracker.py

@@ -179,8 +179,8 @@ def _set_nbr_processes(self, nbr_processes):
 
         if nbr_processes > self.nbr_seeds:
             nbr_processes = self.nbr_seeds
-            logging.debug("Setting number of processes to {} since there were "
-                          "less seeds than processes.".format(nbr_processes))
+            logging.info("Setting number of processes to {} since there were "
+                         "less seeds than processes.".format(nbr_processes))
         return nbr_processes
 
     def _prepare_multiprocessing_pool(self, tmpdir):

scilpy/tractanalysis/features.py

@@ -115,9 +115,9 @@ def remove_loops_and_sharp_turns(streamlines,
                 if tm.mean_curvature(clusters.centroids[i]) <= mean_curvature:
                     ids.extend(clusters[i].indices)
         else:
-            logging.debug("Impossible to use the use_qb option because " +
-                          "not more than one streamline left from the\n" +
-                          "input file.")
+            logging.info("Impossible to use the use_qb option because " +
+                         "not more than one streamline left from the\n" +
+                         "input file.")
     return ids
 
 

scilpy/tractograms/streamline_operations.py

@@ -176,19 +176,19 @@ def filter_streamlines_by_total_length_per_dim(
         total_per_orientation = np.abs(np.asarray(
             [np.sum(d, axis=0) for d in all_dirs]))
 
-    logging.debug("Total length per orientation is:\n"
-                  "Average: x: {:.2f}, y: {:.2f}, z: {:.2f} \n"
-                  "Min: x: {:.2f}, y: {:.2f}, z: {:.2f} \n"
-                  "Max: x: {:.2f}, y: {:.2f}, z: {:.2f} \n"
-                  .format(np.mean(total_per_orientation[:, 0]),
-                          np.mean(total_per_orientation[:, 1]),
-                          np.mean(total_per_orientation[:, 2]),
-                          np.min(total_per_orientation[:, 0]),
-                          np.min(total_per_orientation[:, 1]),
-                          np.min(total_per_orientation[:, 2]),
-                          np.max(total_per_orientation[:, 0]),
-                          np.max(total_per_orientation[:, 1]),
-                          np.max(total_per_orientation[:, 2])))
+    logging.info("Total length per orientation is:\n"
+                 "Average: x: {:.2f}, y: {:.2f}, z: {:.2f} \n"
+                 "Min: x: {:.2f}, y: {:.2f}, z: {:.2f} \n"
+                 "Max: x: {:.2f}, y: {:.2f}, z: {:.2f} \n"
+                 .format(np.mean(total_per_orientation[:, 0]),
+                         np.mean(total_per_orientation[:, 1]),
+                         np.mean(total_per_orientation[:, 2]),
+                         np.min(total_per_orientation[:, 0]),
+                         np.min(total_per_orientation[:, 1]),
+                         np.min(total_per_orientation[:, 2]),
+                         np.max(total_per_orientation[:, 0]),
+                         np.max(total_per_orientation[:, 1]),
+                         np.max(total_per_orientation[:, 2])))
 
     # Find good ids
     mask_good_x = np.logical_and(limits_x[0] < total_per_orientation[:, 0],
@@ -264,11 +264,11 @@ def resample_streamlines_step_size(sft, step_size):
     if step_size == 0:
         raise ValueError("Step size can't be 0!")
     elif step_size < 0.1:
-        logging.debug("The value of your step size seems suspiciously low. "
-                      "Please check.")
+        logging.info("The value of your step size seems suspiciously low. "
+                     "Please check.")
     elif step_size > np.max(sft.voxel_sizes):
-        logging.debug("The value of your step size seems suspiciously high. "
-                      "Please check.")
+        logging.info("The value of your step size seems suspiciously high. "
+                     "Please check.")
 
     # Make sure we are in world space
     orig_space = sft.space
@@ -299,9 +299,9 @@ def _warn_and_save(new_streamlines, sft):
     Warn that we loose data_per_point, then create resampled SFT."""
 
     if sft.data_per_point is not None and sft.data_per_point.keys():
-        logging.debug("Initial StatefulTractogram contained data_per_point. "
-                      "This information will not be carried in the final "
-                      "tractogram.")
+        logging.info("Initial StatefulTractogram contained data_per_point. "
+                     "This information will not be carried in the final "
+                     "tractogram.")
     new_sft = StatefulTractogram.from_sft(
         new_streamlines, sft, data_per_streamline=sft.data_per_streamline)
 
@@ -328,7 +328,7 @@ def smooth_line_gaussian(streamline, sigma):
         raise ValueError('Cant have a 0 sigma with gaussian.')
 
     if length(streamline) < 1:
-        logging.debug('Streamline shorter than 1mm, corner cases possible.')
+        logging.info('Streamline shorter than 1mm, corner cases possible.')
 
     # Smooth each dimension separately
     x, y, z = streamline.T
@@ -367,7 +367,7 @@ def smooth_line_spline(streamline, smoothing_parameter, nb_ctrl_points):
         raise ValueError('Cant have a 0 sigma with spline.')
 
     if length(streamline) < 1:
-        logging.debug('Streamline shorter than 1mm, corner cases possible.')
+        logging.info('Streamline shorter than 1mm, corner cases possible.')
 
     if nb_ctrl_points < 3:
         nb_ctrl_points = 3