Merge branch 'dev'

caiman/components_evaluation.py

@@ -284,7 +284,7 @@ def evaluate_components_CNN(A, dims, gSig, model_name:str=os.path.join(caiman_da
 
             loaded_model = model_from_json(loaded_model_json)
             loaded_model.load_weights(model_name + '.h5')
-            loaded_model.compile('sgd', 'mse')
+            #loaded_model.compile('sgd', 'mse')
         else:
             if os.path.isfile(os.path.join(caiman_datadir(), model_name + ".h5.pb")):
                 model_file    = os.path.join(caiman_datadir(), model_name + ".h5.pb")

caiman/source_extraction/cnmf/estimates.py

@@ -721,7 +721,7 @@ def normalize_components(self):
 
         nA = np.sqrt(np.ravel(self.A.power(2).sum(axis=0)))
         nA_mat = scipy.sparse.spdiags(nA, 0, nA.shape[0], nA.shape[0])
-        nA_inv_mat = scipy.sparse.spdiags(1. / nA, 0, nA.shape[0], nA.shape[0])
+        nA_inv_mat = scipy.sparse.spdiags(1. / (nA + np.finfo(np.float32).eps), 0, nA.shape[0], nA.shape[0])
         self.A = self.A * nA_inv_mat
         self.C = nA_mat * self.C
         if self.YrA is not None:
@@ -1310,9 +1310,12 @@ def remove_duplicates(self, predictions=None, r_values=None, dist_thr=0.1,
         else:
             components_to_keep = np.arange(self.A.shape[-1])
 
-
-
-        self.select_components(idx_components=components_to_keep)
+        if self.idx_components is None:
+            self.idx_components = np.arange(self.A.shape[-1])
+        self.idx_components = np.intersect1d(self.idx_components, components_to_keep)
+        self.idx_components_bad = np.setdiff1d(np.arange(self.A.shape[-1]), self.idx_components)
+        if select_comp:
+            self.select_components(use_object=True)
 
         return components_to_keep
 

caiman/source_extraction/cnmf/map_reduce.py

@@ -408,7 +408,7 @@ def run_CNMF_patches(file_name, shape, params, gnb=1, dview=None,
     logging.info("Constructing background")
 
     Im = scipy.sparse.csr_matrix(
-        (1. / mask, (np.arange(d), np.arange(d))), dtype=np.float32)
+        (1. / (mask + np.finfo(np.float32).eps), (np.arange(d), np.arange(d))), dtype=np.float32)
 
     if not del_duplicates:
         A_tot = Im.dot(A_tot)

caiman/source_extraction/cnmf/params.py

@@ -575,7 +575,7 @@ def __init__(self, fnames=None, dims=None, dxy=(1, 1),
             'decay_time': decay_time,
             'dxy': dxy,
             'var_name_hdf5': var_name_hdf5,
-            'caiman_version': '1.6',
+            'caiman_version': '1.6.1',
             'last_commit': None,
             'mmap_F': None,
             'mmap_C': None
@@ -812,7 +812,7 @@ def __init__(self, fnames=None, dims=None, dxy=(1, 1),
         if self.init['gSig'] is None:
             self.init['gSig'] = [-1, -1]
         if self.init['gSiz'] is None:
-            self.init['gSiz'] = [2*gs + 3 for gs in self.init['gSig']]
+            self.init['gSiz'] = [2*gs + 1 for gs in self.init['gSig']]
         self.init['gSiz'] = [gz if gz % 2 else gz + 1 for gz in self.init['gSiz']]
 
         if gnb <= 0:

caiman/source_extraction/cnmf/temporal.py

@@ -195,7 +195,7 @@ def update_temporal_components(Y, A, b, Cin, fin, bl=None, c1=None, g=None, sn=N
     A = scipy.sparse.hstack((A, b)).tocsc()
     S = np.zeros(np.shape(Cin))
     Cin = np.vstack((Cin, fin))
-    #C = Cin.copy()
+    C = Cin.copy()
     nA = np.ravel(A.power(2).sum(axis=0))
 
     logging.info('Generating residuals')
@@ -213,7 +213,7 @@ def update_temporal_components(Y, A, b, Cin, fin, bl=None, c1=None, g=None, sn=N
     # creating the patch of components to be computed in parrallel
     parrllcomp, len_parrllcomp = update_order_greedy(AA[:nr, :][:, :nr])
     logging.info("entering the deconvolution ")
-    C, S, bl, YrA, c1, sn, g, lam = update_iteration(parrllcomp, len_parrllcomp, nb, Cin, S, bl, nr,
+    C, S, bl, YrA, c1, sn, g, lam = update_iteration(parrllcomp, len_parrllcomp, nb, C, S, bl, nr,
                                                      ITER, YrA, c1, sn, g, Cin, T, nA, dview, debug, AA, kwargs)
     ff = np.where(np.sum(C, axis=1) == 0)  # remove empty components
     if np.size(ff) > 0:  # Eliminating empty temporal components

caiman/tests/comparison_humans_online.py

@@ -82,8 +82,8 @@
     'p': 1,                    # order of indicator dynamics
     'gnb': 2,                  # number of background components
     'epochs': 2,               # number of passes over the data
-    'rval_thr': 0.75,          # spatial correlation threshold
-    'max_thr': 0.15,           # parameter for thresholding components when cleaning up shapes
+    'rval_thr': 0.80,          # spatial correlation threshold
+    'max_thr': 0.25,           # parameter for thresholding components when cleaning up shapes
     'mot_corr': False,         # flag for motion correction (set to False to compare directly on the same FOV)
     'min_num_trial': 10,       # maximum number of candidate components per frame
     'use_peak_max': True,
@@ -129,7 +129,7 @@
 # % YST
 params_movie[3] = {
     'folder_name': 'YST/',
-    'epochs': 2,
+    'epochs': 3,
     'ds_factor': 1,
     'fr': 10,
     'decay_time': .75,
@@ -140,7 +140,7 @@
 # % neurofinder.04.00.test
 params_movie[4] = {
     'folder_name': 'N.04.00.t/',
-    'epochs': 2,
+    'epochs': 3,
     'ds_factor': 1,
     'fr': 8,
     'gSig': [7, 7],  # expected half size of neurons