Merge pull request #939 from flatironinstitute/dev

Merge dev -> master for release

caiman/source_extraction/cnmf/initialization.py

@@ -30,9 +30,11 @@
 import scipy.sparse as spr
 from skimage.morphology import disk
 from sklearn.decomposition import NMF, FastICA
+from sklearn.exceptions import ConvergenceWarning
 from sklearn.utils.extmath import randomized_svd, squared_norm, randomized_range_finder
 import sys
 from typing import List
+import warnings
 
 import caiman
 from .deconvolution import constrained_foopsi
@@ -47,6 +49,8 @@
 except:
     pass
 
+warnings.filterwarnings(action='ignore', category=ConvergenceWarning)
+
 def resize(Y, size, interpolation=cv2.INTER_LINEAR):
     """faster and 3D compatible version of skimage.transform.resize"""
     if Y.ndim == 2:
@@ -1274,7 +1278,7 @@ def compute_B(b0, W, B):  # actually computes -B to efficiently compute Y-B in p
         for i in range(init_iter - 1):
             if max_number is not None:
                 max_number -= A.shape[-1]
-            if max_number is not 0:
+            if max_number != 0:
                 if i == init_iter-2 and seed_method.lower()[:4] == 'semi':
                     seed_method, min_corr, min_pnr = 'manual', 0, 0
                 logging.info('Searching for more neurons in the residual')

caiman/source_extraction/cnmf/spatial.py

@@ -29,6 +29,8 @@
 from scipy.ndimage.morphology import generate_binary_structure, iterate_structure
 import shutil
 from sklearn.decomposition import NMF
+from sklearn.preprocessing import StandardScaler
+from sklearn.pipeline import make_pipeline
 import tempfile
 import time
 import psutil
@@ -405,13 +407,12 @@ def regression_ipyparallel(pars):
             elif method_least_square == 'lasso_lars':  # lasso lars function from scikit learn
                 lambda_lasso = 0 if np.size(cct_) == 0 else \
                     .5 * noise_sn[px] * np.sqrt(np.max(cct_)) / T
-                clf = linear_model.LassoLars(alpha=lambda_lasso, positive=True,
-                                             fit_intercept=True)
-#                clf = linear_model.Lasso(alpha=lambda_lasso, positive=True,
-#                                         fit_intercept=True, normalize=True,
-#                                         selection='random')
-                a_lrs = clf.fit(np.array(c.T), np.ravel(y))
-                a = a_lrs.coef_
+                model = make_pipeline(
+                    StandardScaler(with_mean=False),
+                    linear_model.LassoLars(alpha=lambda_lasso, positive=True,
+                                                 fit_intercept=True, normalize=False)
+                    )
+                a = model.fit(np.array(c.T), np.ravel(y))['lassolars'].coef_
 
             else:
                 raise Exception(
@@ -1055,14 +1056,15 @@ def computing_indicator(Y, A_in, b, C, f, nb, method, dims, min_size, max_size,
             dist_indicator_av = old_div(dist_indicator.astype(
                 'float32'), np.sum(dist_indicator.astype('float32'), axis=0))
             px = (np.sum(dist_indicator, axis=1) > 0)
-            not_px = 1 - px
+            not_px = ~px
             if Y.shape[-1] < 30000:
                 f = Y[not_px, :].mean(0)
-            else:  # memory mapping fails here for some reasons
+            else:
                 print('estimating f')
                 f = 0
-                for xxx in not_px:
-                    f = (f + Y[xxx]) / 2
+                for xxx in np.where(not_px)[0]:
+                    f += Y[xxx]
+                f /= not_px.sum()
 
             f = np.atleast_2d(f)