Remove ipython breakpoints, modernise some string interpolation, othe…

…r janitorial work

caiman/base/movies.py

@@ -343,7 +343,7 @@ def extract_shifts(self, max_shift_w: int = 5, max_shift_h: int = 5, template=No
 
         template = template[ms_h:h_i - ms_h, ms_w:w_i - ms_w].astype(np.float32)
 
-        #% run algorithm, press q to stop it
+        # run algorithm, press q to stop it
         shifts = []    # store the amount of shift in each frame
         xcorrs = []
 
@@ -604,7 +604,7 @@ def computeDFF(self, secsWindow: int = 5, quantilMin: int = 8, method: str = 'on
                         **self.__dict__)
         numFramesNew, linePerFrame, pixPerLine = np.shape(mov_out)
 
-        #% compute baseline quickly
+        # compute baseline quickly
         logging.debug("binning data ...")
         sys.stdout.flush()
 
@@ -627,7 +627,7 @@ def computeDFF(self, secsWindow: int = 5, quantilMin: int = 8, method: str = 'on
                                    cval=0.0,
                                    prefilter=False)
 
-        #% compute DF/F
+        # compute DF/F
         if not in_place:
             if method == 'delta_f_over_sqrt_f':
                 mov_out = (mov_out - movBL) / np.sqrt(movBL)

caiman/base/rois.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 import cv2
 import json
@@ -242,7 +241,7 @@ def nf_match_neurons_in_binary_masks(masks_gt,
     cm_cnmf = [scipy.ndimage.center_of_mass(mm) for mm in masks_comp]
 
     if D is None:
-        #% find distances and matches
+        # find distances and matches
         # find the distance between each masks
         D = distance_masks([A_ben, A_cnmf], [cm_ben, cm_cnmf], min_dist, enclosed_thr=enclosed_thr)
 
@@ -252,7 +251,7 @@ def nf_match_neurons_in_binary_masks(masks_gt,
     matches = matches[0]
     costs = costs[0]
 
-    #%% compute precision and recall
+    # compute precision and recall
     TP = np.sum(np.array(costs) < thresh_cost) * 1.
     FN = np.shape(masks_gt)[0] - TP
     FP = np.shape(masks_comp)[0] - TP
@@ -264,7 +263,7 @@ def nf_match_neurons_in_binary_masks(masks_gt,
     performance['accuracy'] = (TP + TN) / (TP + FP + FN + TN)
     performance['f1_score'] = 2 * TP / (2 * TP + FP + FN)
     logging.debug(performance)
-    #%%
+
     idx_tp = np.where(np.array(costs) < thresh_cost)[0]
     idx_tp_ben = matches[0][idx_tp]    # ground truth
     idx_tp_cnmf = matches[1][idx_tp]   # algorithm - comp
@@ -471,7 +470,7 @@ def register_ROIs(A1,
     matches = matches[0]
     costs = costs[0]
 
-    #%% store indices
+    # store indices
 
     idx_tp = np.where(np.array(costs) < thresh_cost)[0]
     if len(idx_tp) > 0:
@@ -488,7 +487,7 @@ def register_ROIs(A1,
         non_matched1 = list(range(D[0].shape[0]))
         non_matched2 = list(range(D[0].shape[1]))
 
-    #%% compute precision and recall
+    # compute precision and recall
 
     FN = D[0].shape[0] - TP
     FP = D[0].shape[1] - TP
@@ -1001,7 +1000,7 @@ def getfloat():
     fill_color = get32()
     subtype = get16()
     if subtype != 0:
-        raise ValueError('roireader: ROI subtype %s not supported (!= 0)' % subtype)
+        raise ValueError(f'roireader: ROI subtype {subtype} not supported (!= 0)')
     options = get16()
     arrow_style = get8()
     arrow_head_size = get8()

caiman/base/traces.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 import cv2
 import logging

caiman/components_evaluation.py

@@ -437,7 +437,7 @@ def evaluate_components(Y: np.ndarray,
             padafter = int(np.ceil(elm_missing / 2.))
             tr_tmp = np.pad(traces.T, ((padbefore, padafter), (0, 0)), mode='reflect')
             numFramesNew, num_traces = np.shape(tr_tmp)
-                                                                                             #% compute baseline quickly
+                                                                                             # compute baseline quickly
             logging.debug("binning data ...")
             tr_BL = np.reshape(tr_tmp, (downsampfact, numFramesNew // downsampfact, num_traces), order='F')
             tr_BL = np.percentile(tr_BL, 8, axis=0)

caiman/mmapping.py

@@ -1,16 +1,15 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 import ipyparallel as parallel
 from itertools import chain
 import logging
 import numpy as np
 import os
+import pathlib
 import pickle
 import sys
 import tifffile
 from typing import Any, Optional, Union
-import pathlib
 
 import caiman
 import caiman.paths

caiman/motion_correction.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 """
 

caiman/source_extraction/cnmf/cnmf_optional_outputs.py

@@ -1,16 +1,9 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 """
 Constrained Nonnegative Matrix Factorization
 
 A similar CNMF class that will output data that are not usually present in the regular CNMF process
-
-Created on Fri Aug 26 15:44:32 2016
-
-@author: agiovann
-
-
 """
 
 import numpy as np

caiman/source_extraction/cnmf/deconvolution.py

@@ -1,10 +1,7 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
-"""Extract neural activity from a fluorescence trace using a constrained deconvolution approach
-
-Created on Tue Sep  1 16:11:25 2015
-@author: Eftychios A. Pnevmatikakis, based on an implementation by T. Machado,  Andrea Giovannucci & Ben Deverett
+"""
+Extract neural activity from a fluorescence trace using a constrained deconvolution approach
 """
 
 import numpy as np
@@ -15,8 +12,6 @@
 from math import log, sqrt, exp
 
 import sys
-#%%
-
 
 def constrained_foopsi(fluor, bl=None,  c1=None, g=None,  sn=None, p=None, method_deconvolution='oasis', bas_nonneg=True,
                        noise_range=[.25, .5], noise_method='logmexp', lags=5, fudge_factor=1.,
@@ -104,9 +99,6 @@ def constrained_foopsi(fluor, bl=None,  c1=None, g=None,  sn=None, p=None, metho
     References:
         * Pnevmatikakis et al. 2016. Neuron, in press, http://dx.doi.org/10.1016/j.neuron.2015.11.037
         * Machado et al. 2015. Cell 162(2):338-350
-
-    \image: docs/img/deconvolution.png
-    \image: docs/img/evaluationcomponent.png
     """
 
     if p is None:

caiman/source_extraction/cnmf/online_cnmf.py

@@ -1375,8 +1375,6 @@ def create_frame(self, frame_cor, show_residuals=True, resize_fact=3, transpose=
             self.dims = self.dims[::-1]
         return vid_frame
 
-
-#%%
 def bare_initialization(Y, init_batch=1000, k=1, method_init='greedy_roi', gnb=1,
                         gSig=[5, 5], motion_flag=False, p=1,
                         return_object=True, **kwargs):
@@ -1456,8 +1454,6 @@ def bare_initialization(Y, init_batch=1000, k=1, method_init='greedy_roi', gnb=1
         except:
             return Ain, np.array(b_in), Cin, f_in, YrA
 
-
-#%%
 def seeded_initialization(Y, Ain, dims=None, init_batch=1000, order_init=None, gnb=1, p=1,
                           return_object=True, **kwargs):
 
@@ -1513,7 +1509,7 @@ def seeded_initialization(Y, Ain, dims=None, init_batch=1000, order_init=None, g
 
         model_comp = NMF(n_components=1, init='nndsvdar', max_iter=50)
         for count, idx_in in enumerate(order_init):
-            if count%10 == 0:
+            if count % 10 == 0:
                 print(count)
             idx_domain = np.where(Ain[:,idx_in])[0]
             Ain[idx_domain,idx_in] = model_comp.fit_transform(\
@@ -1771,7 +1767,7 @@ def refit(o, c):
     return C, noisyC, OASISinstances
 
 
-#%% Estimate shapes on small initial batch
+# Estimate shapes on small initial batch
 def init_shapes_and_sufficient_stats(Y, A, C, b, f, W=None, b0=None, ssub_B=1, bSiz=3,
                                      downscale_matrix=None, upscale_matrix=None):
     # smooth the components
@@ -1943,8 +1939,6 @@ def get_last_frames(self, num_frames):
         else:
             return np.concatenate([self[(self.cur - num_frames):], self[:self.cur]], axis=0)
 
-
-#%%
 def csc_append(a, b):
     """ Takes in 2 csc_matrices and appends the second one to the right of the first one.
     Much faster than scipy.sparse.hstack but assumes the type to be csc and overwrites
@@ -2000,7 +1994,6 @@ def rank1nmf(Ypx, ain, iters=10):
     cin = np.maximum(cin_res, 0)
     return ain, cin, cin_res
 
-#%%
 @profile
 def get_candidate_components(sv, dims, Yres_buf, min_num_trial=3, gSig=(5, 5),
                              gHalf=(5, 5), sniper_mode=True, rval_thr=0.85,
@@ -2127,8 +2120,6 @@ def get_candidate_components(sv, dims, Yres_buf, min_num_trial=3, gSig=(5, 5),
 
     return Ain, Cin, Cin_res, idx, ijsig_all, cnn_pos, local_maxima
 
-
-#%%
 @profile
 def update_num_components(t, sv, Ab, Cf, Yres_buf, Y_buf, rho_buf,
                           dims, gSig, gSiz, ind_A, CY, CC, groups, oases, gnb=1,
@@ -2371,7 +2362,7 @@ def update_num_components(t, sv, Ab, Cf, Yres_buf, Y_buf, rho_buf,
     return Ab, Cf, Yres_buf, rho_buf, CC, CY, ind_A, sv, groups, ind_new, ind_new_all, sv, cnn_pos
 
 
-#%% remove components online
+# remove components online
 def remove_components_online(ind_rem, gnb, Ab, use_dense, Ab_dense, AtA, CY,
                              CC, M, N, noisyC, OASISinstances, C_on, exp_comps):
 
@@ -2438,10 +2429,7 @@ def initialize_movie_online(Y, K, gSig, rf, stride, base_name,
     images = np.reshape(Yr.T, [T] + list(dims), order='F')
     Y = np.reshape(Yr, dims + (T,), order='F')
     Cn2 = caiman.local_correlations(Y)
-#    pl.imshow(Cn2)
-    #%
-    #% RUN ALGORITHM ON PATCHES
-#    pl.close('all')
+    # RUN ALGORITHM ON PATCHES
     cnm_init = caiman.source_extraction.cnmf.CNMF(n_processes, method_init='greedy_roi', k=K, gSig=gSig, merge_thresh=merge_thresh,
                                               p=0, dview=dview, Ain=None, rf=rf, stride=stride, method_deconvolution='oasis', skip_refinement=False,
                                               normalize_init=False, options_local_NMF=None,
@@ -2456,9 +2444,7 @@ def initialize_movie_online(Y, K, gSig, rf, stride, base_name,
     b_tot = cnm_init.b
     f_tot = cnm_init.f
 
-    print(('Number of components:' + str(A_tot.shape[-1])))
-
-    #%
+    print(f"Number of components: {A_tot.shape[-1]}")
 
     traces = C_tot + YrA_tot
     fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = caiman.components_evaluation.evaluate_components(
@@ -2478,7 +2464,7 @@ def initialize_movie_online(Y, K, gSig, rf, stride, base_name,
 
     A_tot = A_tot.tocsc()[:, idx_components]
     C_tot = C_tot[idx_components]
-    #%
+
     cnm_refine = caiman.source_extraction.cnmf.CNMF(n_processes, method_init='greedy_roi', k=A_tot.shape, gSig=gSig, merge_thresh=merge_thresh, rf=None, stride=None,
                                                 p=p, dview=dview, Ain=A_tot, Cin=C_tot, f_in=f_tot, method_deconvolution='oasis', skip_refinement=True,
                                                 normalize_init=False, options_local_NMF=None,
@@ -2487,9 +2473,9 @@ def initialize_movie_online(Y, K, gSig, rf, stride, base_name,
                                                 batch_update_suff_stat=True, max_comp_update_shape=5)
 
     cnm_refine = cnm_refine.fit(images)
-    #%
+
     A, C, b, f, YrA = cnm_refine.A, cnm_refine.C, cnm_refine.b, cnm_refine.f, cnm_refine.YrA
-    #%
+
     final_frate = 10
     Npeaks = 10
     traces = C + YrA
@@ -2510,7 +2496,7 @@ def initialize_movie_online(Y, K, gSig, rf, stride, base_name,
     print(' ***** ')
     print((len(traces)))
     print((len(idx_components)))
-    #%
+
     cnm_refine.sn = sn # FIXME: There is no sn in scope here
     cnm_refine.idx_components = idx_components
     cnm_refine.idx_components_bad = idx_components_bad
@@ -2519,11 +2505,6 @@ def initialize_movie_online(Y, K, gSig, rf, stride, base_name,
     cnm_refine.fitness_delta = fitness_delta
     cnm_refine.Cn2 = Cn2
 
-    #%
-
-#    cnm_init.dview = None
-#    save_object(cnm_init,fls[0][:-4]+ '_DS_' + str(ds)+ '_init.pkl')
-
     return cnm_refine, Cn2, fname_new
 
 def load_OnlineCNMF(filename, dview = None):

caiman/source_extraction/cnmf/utilities.py

@@ -580,7 +580,7 @@ def fast_prct_filt(input_data, level=8, frames_window=1000):
     padafter = int(np.ceil(elm_missing / 2.))
     tr_tmp = np.pad(data.T, ((padbefore, padafter), (0, 0)), mode='reflect')
     numFramesNew, num_traces = np.shape(tr_tmp)
-    #% compute baseline quickly
+    # compute baseline quickly
 
     tr_BL = np.reshape(tr_tmp, (downsampfact, int(numFramesNew / downsampfact),
                                 num_traces), order='F')
@@ -596,7 +596,7 @@ def fast_prct_filt(input_data, level=8, frames_window=1000):
         data -= tr_BL[padbefore:-padafter].T
 
     return data.squeeze()
-#%%
+
 def detrend_df_f_auto(A, b, C, f, dims=None, YrA=None, use_annulus = True, 
                       dist1 = 7, dist2 = 5, frames_window=1000, 
                       use_fast = False):
@@ -699,9 +699,6 @@ def detrend_df_f_auto(A, b, C, f, dims=None, YrA=None, use_annulus = True,
 
     return F_df
 
-#%%
-
-
 def manually_refine_components(Y, xxx_todo_changeme, A, C, Cn, thr=0.9, display_numbers=True,
                                max_number=None, cmap=None, **kwargs):
     """Plots contour of spatial components
@@ -1014,8 +1011,6 @@ def update_order_greedy(A, flag_AA=True):
             parllcomp.append([i])
     len_parrllcomp = [len(ls) for ls in parllcomp]
     return parllcomp, len_parrllcomp
-#%%
-
 
 def compute_residuals(Yr_mmap_file, A_, b_, C_, f_, dview=None, block_size=1000, num_blocks_per_run=5):
     '''compute residuals from memory mapped file and output of CNMF

caiman/utils/labelling.py

@@ -1,8 +1,8 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*-
 
 import logging
 from scipy.ndimage import filters as ft
+
 import caiman
 
 def pre_preprocess_movie_labeling(dview, file_names, median_filter_size=(2, 1, 1),