caiman-logger: Further cleanups, fix problems CI revealed

caiman/base/rois.py

@@ -780,7 +780,7 @@ def find_matches(D_s, print_assignment: bool = False) -> tuple[list, list]:
             if print_assignment:
                 logger.debug(f'({row}, {column}) -> {value}')
             total.append(value)
-        logger.debug((f'FOV: {ii}, shape: {DD.shape[0]},{DD.shape[1]} total cost: {np.sum(total)}')
+        logger.debug(f'FOV: {ii}, shape: {DD.shape[0]},{DD.shape[1]} total cost: {np.sum(total)}')
         logger.debug(time.time() - t_start)
         costs.append(total)
         # send back the results in the format we want

caiman/components_evaluation.py

@@ -143,7 +143,7 @@ def find_activity_intervals(C, Npeaks: int = 5, tB=-3, tA=10, thres: float = 0.3
     for i in range(K):
         if np.sum(np.abs(np.diff(C[i, :]))) == 0:
             L.append([])
-            logger.debug('empty component at:' + str(i))
+            logger.debug(f'empty component at: {i}')
             continue
         indexes = peakutils.indexes(C[i, :], thres=thres)
         srt_ind = indexes[np.argsort(C[i, indexes])][::-1]

caiman/motion_correction.py

@@ -2823,7 +2823,7 @@ def motion_correct_batch_rigid(fname, max_shifts, dview=None, splits=56, num_spl
         logger.error('The movie contains NaNs. NaNs are not allowed!')
         raise Exception('The movie contains NaNs. NaNs are not allowed!')
     else:
-        logger.debug('Adding to movie ' + str(add_to_movie))
+        logger.debug(f'Adding to movie {add_to_movie}')
 
     save_movie = False
     fname_tot_rig = None

caiman/source_extraction/cnmf/cnmf.py

@@ -435,9 +435,7 @@ def fit(self, images, indices=(slice(None), slice(None))):
         if self.params.get('patch', 'rf') is None and (is_sliced or 'ndarray' in str(type(images))):
             images = images[tuple(indices)]
             self.dview = None
-            logger.info("Parallel processing in a single patch "
-                            "is not available for loaded in memory or sliced" +
-                            " data.")
+            logger.info("Parallel processing in a single patch is not available for loaded in memory or sliced data.")
 
         T = images.shape[0]
         self.params.set('online', {'init_batch': T})
@@ -465,7 +463,8 @@ def fit(self, images, indices=(slice(None), slice(None))):
         #                             'ss': np.ones((3,) * len(self.dims), dtype=np.uint8)
         #                             })
 
-        logger.info(('Using ' + str(self.params.get('patch', 'n_processes')) + ' processes'))
+        logger.info(f"Using {self.params.get('patch', 'n_processes')} processes")
+        # FIXME The code below is really ugly and it's hard to tell if it's doing the right thing
         if self.params.get('preprocess', 'n_pixels_per_process') is None:
             avail_memory_per_process = psutil.virtual_memory()[
                 1] / 2.**30 / self.params.get('patch', 'n_processes')

caiman/source_extraction/cnmf/map_reduce.py

@@ -14,6 +14,7 @@
 
 from caiman.cluster import extract_patch_coordinates
 from caiman.mmapping import load_memmap
+from caiman.source_extraction.cnmf import CNMF
 
 def cnmf_patches(args_in):
     """Function that is run for each patches
@@ -67,16 +68,16 @@ def cnmf_patches(args_in):
         """
 
     #FIXME Fix in-function imports
-    from . import cnmf
+    from caiman.source_extraction.cnmf import CNMF
     logger = logging.getLogger("caiman")
     file_name, idx_, shapes, params = args_in
 
     name_log = os.path.basename(
         file_name[:-5]) + '_LOG_ ' + str(idx_[0]) + '_' + str(idx_[-1])
 
-    logger.debug(name_log + 'START')
+    logger.debug(name_log + ' START')
 
-    logger.debug(name_log + 'Read file')
+    logger.debug(name_log + ' Read file')
     Yr, dims, timesteps = load_memmap(file_name)
 
     # slicing array (takes the min and max index in n-dimensional space and
@@ -107,7 +108,7 @@ def cnmf_patches(args_in):
         for group in ('preprocess', 'temporal'):
             opts.set(group, {'p': params.get('patch', 'p_patch')})
 
-        cnm = cnmf.CNMF(n_processes=1, params=opts)
+        cnm = CNMF(n_processes=1, params=opts)
 
         cnm = cnm.fit(images)
         return [idx_, shapes, scipy.sparse.coo_matrix(cnm.estimates.A),

caiman/source_extraction/cnmf/params.py

@@ -956,15 +956,13 @@ def check_consistency(self):
                 if len(self.motion[a]) != 3:
                     if self.motion[a][0] == self.motion[a][1]:
                         self.motion[a] = (self.motion[a][0],) * 3
-                        logger.warning("is3D=True, hence setting key " + a +
-                            " automatically to " + str(self.motion[a]))
+                        logger.warning(f"is3D=True, hence setting key {a} to {self.motion[a]}")
                     else:
-                        raise ValueError(a + ' has to be a tuple of length 3 for volumetric 3D data')
+                        raise ValueError(f'{a} must be a tuple of length 3 for volumetric 3D data')
         for key in ('max_num_added', 'min_num_trial'):
             if (self.online[key] == 0 and self.online['update_num_comps']):
                 self.set('online', {'update_num_comps': False})
-                logger.warning(key + "=0, hence setting key update_num_comps " +
-                                "in group online automatically to False.")
+                logger.warning(f"{key}=0, hence setting key online.update_num_comps to False.")
         # FIXME The authoritative value is stored in the init field. This should later be refactored out
         #     into a general section, once we're passing around the CNMFParams object rather than splatting it out
         #     from **get_group

caiman/utils/utils.py

@@ -477,7 +477,7 @@ def recursively_save_dict_contents_to_group(h5file:h5py.File, path:str, dic:dict
         if key == 'g':
             if item is None:
                 item = 0
-            logger.info(key + ' is an object type')
+            logger.info(f'{key} is an object type')
             try:
                 item = np.array(list(item))
             except:
@@ -522,7 +522,7 @@ def recursively_save_dict_contents_to_group(h5file:h5py.File, path:str, dic:dict
         elif isinstance(item, dict):
             recursively_save_dict_contents_to_group(h5file, path + key + '/', item)
         elif 'sparse' in str(type(item)):
-            logger.info(key + ' is sparse ****')
+            logger.info(f'{key} is sparse ****')
             h5file[path + key + '/data'] = item.tocsc().data
             h5file[path + key + '/indptr'] = item.tocsc().indptr
             h5file[path + key + '/indices'] = item.tocsc().indices
@@ -576,10 +576,10 @@ def recursively_load_dict_contents_from_group(h5file:h5py.File, path:str) -> dic
 
         elif isinstance(item, h5py._hl.group.Group):
             if key in ('A', 'W', 'Ab', 'downscale_matrix', 'upscale_matrix'):
-                data =  item[path + key + '/data']
+                data    = item[path + key + '/data']
                 indices = item[path + key + '/indices']
-                indptr = item[path + key + '/indptr']
-                shape = item[path + key + '/shape']
+                indptr  = item[path + key + '/indptr']
+                shape   = item[path + key + '/shape']
                 ans[key] = scipy.sparse.csc_matrix((data[:], indices[:],
                     indptr[:]), shape[:])
                 if key in ('W', 'upscale_matrix'):