From 04174e026dc88246d3315243bfd43b9b0968079d Mon Sep 17 00:00:00 2001 From: mannypaeza Date: Mon, 26 Aug 2024 11:16:32 -0400 Subject: [PATCH 1/4] torch edits for 2p + ring cnn --- caiman/components_evaluation.py | 57 +- caiman/source_extraction/cnmf/online_cnmf.py | 74 +- caiman/tests/test_pytorch.py | 49 + caiman/train/__init__.py | 7 + caiman/train/ground_truth_cnmf_seeded.ipynb | 472 ++ caiman/train/match_seeded_gt.ipynb | 5469 ++++++++++++++++++ caiman/train/prepare_training_set.ipynb | 318 + caiman/train/train_cnn_model_keras.ipynb | 500 ++ caiman/train/train_cnn_model_keras.py | 97 + caiman/train/train_cnn_model_pytorch.ipynb | 352 ++ caiman/train/train_cnn_model_pytorch.py | 112 + caiman/train/training.md | 6 + caiman/utils/nn_models.py | 148 +- 13 files changed, 7542 insertions(+), 119 deletions(-) create mode 100644 caiman/tests/test_pytorch.py create mode 100644 caiman/train/__init__.py create mode 100644 caiman/train/ground_truth_cnmf_seeded.ipynb create mode 100644 caiman/train/match_seeded_gt.ipynb create mode 100644 caiman/train/prepare_training_set.ipynb create mode 100644 caiman/train/train_cnn_model_keras.ipynb create mode 100644 caiman/train/train_cnn_model_keras.py create mode 100644 caiman/train/train_cnn_model_pytorch.ipynb create mode 100644 caiman/train/train_cnn_model_pytorch.py create mode 100644 caiman/train/training.md diff --git a/caiman/components_evaluation.py b/caiman/components_evaluation.py index 5bf9a47c0..d0a74668d 100644 --- a/caiman/components_evaluation.py +++ b/caiman/components_evaluation.py @@ -6,7 +6,7 @@ import numpy as np import os import peakutils -import tensorflow as tf +import torch import scipy from scipy.sparse import csc_matrix from scipy.stats import norm @@ -273,42 +273,37 @@ def evaluate_components_CNN(A, if not isGPU and 'CAIMAN_ALLOW_GPU' not in os.environ: print("GPU run not requested, disabling use of GPUs") os.environ['CUDA_VISIBLE_DEVICES'] = '-1' - try: - os.environ["KERAS_BACKEND"] = "tensorflow" - from tensorflow.keras.models import model_from_json - use_keras = True - logger.info('Using Keras') + try: + os.environ["KERAS_BACKEND"] = "torch" + from keras.models import model_load + use_keras = True + logging.info('Using Keras') except (ModuleNotFoundError): - use_keras = False - logger.info('Using Tensorflow') + use_keras = False + logging.info('Using Torch') if loaded_model is None: if use_keras: - if os.path.isfile(os.path.join(caiman_datadir(), model_name + ".json")): - model_file = os.path.join(caiman_datadir(), model_name + ".json") - model_weights = os.path.join(caiman_datadir(), model_name + ".h5") - elif os.path.isfile(model_name + ".json"): - model_file = model_name + ".json" - model_weights = model_name + ".h5" + if os.path.isfile(os.path.join(caiman_datadir(), model_name + ".keras")): + model_file = os.path.join(caiman_datadir(), model_name + ".keras") + elif os.path.isfile(model_name + ".keras"): + model_file = model_name + ".keras" else: raise FileNotFoundError(f"File for requested model {model_name} not found") - with open(model_file, 'r') as json_file: - print(f"USING MODEL (keras API): {model_file}") - loaded_model_json = json_file.read() - - loaded_model = model_from_json(loaded_model_json) - loaded_model.load_weights(model_name + '.h5') + + print(f"USING MODEL (keras API): {model_file}") + loaded_model = model_load(model_file) 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") - elif os.path.isfile(model_name + ".h5.pb"): - model_file = model_name + ".h5.pb" + if os.path.isfile(os.path.join(caiman_datadir(), model_name + ".pt")): + model_file = os.path.join(caiman_datadir(), model_name + ".pt") + elif os.path.isfile(model_name + ".pt"): + model_file = model_name + ".pt" else: raise FileNotFoundError(f"File for requested model {model_name} not found") print(f"USING MODEL (tensorflow API): {model_file}") - loaded_model = caiman.utils.utils.load_graph(model_file) + loaded_model = torch.load(model_file) - logger.debug("Loaded model from disk") + logging.debug("Loaded model from disk") half_crop = np.minimum(gSig[0] * 4 + 1, patch_size), np.minimum(gSig[1] * 4 + 1, patch_size) dims = np.array(dims) @@ -323,11 +318,11 @@ def evaluate_components_CNN(A, if use_keras: predictions = loaded_model.predict(final_crops[:, :, :, np.newaxis], batch_size=32, verbose=1) else: - tf_in = loaded_model.get_tensor_by_name('prefix/conv2d_20_input:0') - tf_out = loaded_model.get_tensor_by_name('prefix/output_node0:0') - with tf.Session(graph=loaded_model) as sess: - predictions = sess.run(tf_out, feed_dict={tf_in: final_crops[:, :, :, np.newaxis]}) - sess.close() + final_crops = torch.tensor(final_crops, dtype=torch.float32) + final_crops = torch.reshape(final_crops, (-1, final_crops.shape[-1], + final_crops.shape[1], final_crops.shape[2])) + with torch.no_grad(): + prediction = loaded_model(final_crops[:, np.newaxis, :, :]) return predictions, final_crops diff --git a/caiman/source_extraction/cnmf/online_cnmf.py b/caiman/source_extraction/cnmf/online_cnmf.py index 55b2828ba..ce7a55ca7 100644 --- a/caiman/source_extraction/cnmf/online_cnmf.py +++ b/caiman/source_extraction/cnmf/online_cnmf.py @@ -13,6 +13,9 @@ imaging data in real time. In Advances in Neural Information Processing Systems (pp. 2381-2391). @url http://papers.nips.cc/paper/6832-onacid-online-analysis-of-calcium-imaging-data-in-real-time + +Implemented in PyTorch +Date: July 18, 2024 """ import cv2 @@ -26,7 +29,7 @@ from scipy.stats import norm from sklearn.decomposition import NMF from sklearn.preprocessing import normalize -import tensorflow as tf +import torch from time import time import caiman @@ -320,34 +323,30 @@ def _prepare_object(self, Yr, T, new_dims=None, idx_components=None): if self.params.get('online', 'path_to_model') is None or self.params.get('online', 'sniper_mode') is False: loaded_model = None self.params.set('online', {'sniper_mode': False}) - self.tf_in = None - self.tf_out = None + # self.tf_in = None + # self.tf_out = None + self.use_torch = None #fix else: - try: - from tensorflow.keras.models import model_from_json - logger.info('Using Keras') + try: + from keras.models import load_model + logging.info('Using Keras') use_keras = True except(ModuleNotFoundError): - use_keras = False - logger.info('Using Tensorflow') + use_keras = False + logging.info('Using Torch') + + path = self.params.get('online', 'path_to_model').split(".")[:-1] if use_keras: - path = self.params.get('online', 'path_to_model').split(".")[:-1] - json_path = ".".join(path + ["json"]) - model_path = ".".join(path + ["h5"]) - json_file = open(json_path, 'r') - loaded_model_json = json_file.read() - json_file.close() - loaded_model = model_from_json(loaded_model_json) - loaded_model.load_weights(model_path) - self.tf_in = None - self.tf_out = None - else: - path = self.params.get('online', 'path_to_model').split(".")[:-1] - model_path = '.'.join(path + ['h5', 'pb']) + # uses online model -> be careful + model_path = ".".join(path + ["keras"]) + loaded_model = model_load(model_path) + self.use_torch = False + else: + model_path = '.'.join(path + ['pt']) loaded_model = load_graph(model_path) - self.tf_in = loaded_model.get_tensor_by_name('prefix/conv2d_1_input:0') - self.tf_out = loaded_model.get_tensor_by_name('prefix/output_node0:0') - loaded_model = tf.Session(graph=loaded_model) + loaded_model = torch.load(model_file) + self.use_torch = True + self.loaded_model = loaded_model if self.is1p: @@ -548,7 +547,8 @@ def fit_next(self, t, frame_in, num_iters_hals=3): sniper_mode=self.params.get('online', 'sniper_mode'), use_peak_max=self.params.get('online', 'use_peak_max'), mean_buff=self.estimates.mean_buff, - tf_in=self.tf_in, tf_out=self.tf_out, + # tf_in=self.tf_in, tf_out=self.tf_out, + use_torch=self.use_torch, ssub_B=ssub_B, W=self.estimates.W if self.is1p else None, b0=self.estimates.b0 if self.is1p else None, corr_img=self.estimates.corr_img if use_corr else None, @@ -2003,7 +2003,8 @@ def get_candidate_components(sv, dims, Yres_buf, min_num_trial=3, gSig=(5, 5), patch_size=50, loaded_model=None, test_both=False, thresh_CNN_noisy=0.5, use_peak_max=False, thresh_std_peak_resid = 1, mean_buff=None, - tf_in=None, tf_out=None): + # tf_in=None, tf_out=None): + use_torch=None): """ Extract new candidate components from the residual buffer and test them using space correlation or the CNN classifier. The function runs the CNN @@ -2084,12 +2085,19 @@ def get_candidate_components(sv, dims, Yres_buf, min_num_trial=3, gSig=(5, 5), Ain2 /= np.std(Ain2,axis=1)[:,None] Ain2 = np.reshape(Ain2,(-1,) + tuple(np.diff(ijSig_cnn).squeeze()),order= 'F') Ain2 = np.stack([cv2.resize(ain,(patch_size ,patch_size)) for ain in Ain2]) - if tf_in is None: + if use_torch is None: predictions = loaded_model.predict(Ain2[:,:,:,np.newaxis], batch_size=min_num_trial, verbose=0) + keep_cnn = list(np.where(predictions[:, 0] > thresh_CNN_noisy)[0]) else: - predictions = loaded_model.run(tf_out, feed_dict={tf_in: Ain2[:, :, :, np.newaxis]}) - keep_cnn = list(np.where(predictions[:, 0] > thresh_CNN_noisy)[0]) - cnn_pos = Ain2[keep_cnn] + final_crops = torch.tensor(Ain2, dtype=torch.float32) + final_crops = torch.reshape(Ain2, (-1, Ain2.shape[-1], + Ain2.shape[1], Ain2.shape[2])) + with torch.no_grad(): + prediction = loaded_model(Ain2[:, np.newaxis, :, :]) + keep_cnn = list(torch.where(predictions[:, 0] > thresh_CNN_noisy)[0]) + + cnn_pos = Ain2[keep_cnn] #Make sure this works + # tensor.numpy() also works else: keep_cnn = [] # list(range(len(Ain_cnn))) @@ -2138,7 +2146,8 @@ def update_num_components(t, sv, Ab, Cf, Yres_buf, Y_buf, rho_buf, corr_img=None, first_moment=None, second_moment=None, crosscorr=None, col_ind=None, row_ind=None, corr_img_mode=None, max_img=None, downscale_matrix=None, upscale_matrix=None, - tf_in=None, tf_out=None): + # tf_in=None, tf_out=None): + torch_in=None, torch_out=None): """ Checks for new components in the residual buffer and incorporates them if they pass the acceptance tests """ @@ -2168,7 +2177,8 @@ def update_num_components(t, sv, Ab, Cf, Yres_buf, Y_buf, rho_buf, sniper_mode=sniper_mode, rval_thr=rval_thr, patch_size=50, loaded_model=loaded_model, thresh_CNN_noisy=thresh_CNN_noisy, use_peak_max=use_peak_max, test_both=test_both, mean_buff=mean_buff, - tf_in=tf_in, tf_out=tf_out) + # tf_in=tf_in, tf_out=tf_out) + torch_in=torch_in, torch_out=torch_out) ind_new_all = ijsig_all diff --git a/caiman/tests/test_pytorch.py b/caiman/tests/test_pytorch.py new file mode 100644 index 000000000..900aa702a --- /dev/null +++ b/caiman/tests/test_pytorch.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python + +import numpy as np +import os +import keras + +from caiman.paths import caiman_datadir +from caiman.utils.utils import load_graph + +try: + os.environ["KERAS_BACKEND"] = "torch" + from keras.models import load_model + use_keras = True +except(ModuleNotFoundError): + import torch + use_keras = False + +def test_torch(): + os.environ['CUDA_VISIBLE_DEVICES'] = '-1' + + try: + model_name = os.path.join(caiman_datadir(), 'model', 'cnn_model') + if use_keras: + model_file = model_name + ".keras" + print('USING MODEL:' + model_file) + + loaded_model = load_model(model_file) + loaded_model.compile('sgd', 'mse') + elif use_keras == True: + model_file = model_name + ".pth" + loaded_model = torch.load(model_file) + except: + raise Exception(f'NN model could not be loaded. use_keras = {use_keras}') + + A = np.random.randn(10, 50, 50, 1) + try: + if use_keras == False: + predictions = loaded_model.predict(A, batch_size=32) + elif use_keras == True: + A = torch.tensor(A, dtype=torch.float32) + A = torch.reshape(A, (-1, A.shape[-1], A.shape[1], A.shape[2])) + with torch.no_grad(): + predictions = loaded_model(A) + pass + except: + raise Exception('NN model could not be deployed. use_keras = ' + str(use_keras)) + +if __name__ == "__main__": + test_torch() \ No newline at end of file diff --git a/caiman/train/__init__.py b/caiman/train/__init__.py new file mode 100644 index 000000000..1540ec0dc --- /dev/null +++ b/caiman/train/__init__.py @@ -0,0 +1,7 @@ +#!/usr/bin/env python +import pkg_resources + +from caiman.train.train_cnn_model_keras import cnn_model_keras, save_model_keras, load_model_keras +from caiman.train.train_cnn_model_pytorch import cnn_model_pytorch, train_test_split, train, validate, get_batch_accuracy, save_model_pytorch, load_model_pytorch + +__version__ = pkg_resources.get_distribution('caiman').version \ No newline at end of file diff --git a/caiman/train/ground_truth_cnmf_seeded.ipynb b/caiman/train/ground_truth_cnmf_seeded.ipynb new file mode 100644 index 000000000..403208082 --- /dev/null +++ b/caiman/train/ground_truth_cnmf_seeded.ipynb @@ -0,0 +1,472 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Prepare ground truth built by matching with the results of CNMF" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "import numpy as np\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import caiman as cm\n", + "from caiman.utils.utils import download_demo\n", + "from caiman.base.rois import extract_binary_masks_blob\n", + "from caiman.utils.visualization import plot_contours, view_patches_bar\n", + "from caiman.source_extraction.cnmf import cnmf as cnmf\n", + "from caiman.motion_correction import MotionCorrect, tile_and_correct, motion_correction_piecewise \n", + "from caiman.components_evaluation import estimate_components_quality, evaluate_components\n", + "from caiman.tests.comparison import comparison" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading up the Ground Truth Files " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# neurofinder.03.00.test\n", + "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.03.00.test/images/final_map/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_.mmap'],\n", + " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.03.00.test/regions/joined_consensus_active_regions.npy'],\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 1, # merging threshold, max correlation allow\n", + " 'final_frate': 10,\n", + " # 'r_values_min_patch': .7, # threshold on space consistency\n", + " # 'fitness_min_patch': -20, # threshold on time variability\n", + " # # threshold on time variability (if nonsparse activity)\n", + " # 'fitness_delta_min_patch': -20,\n", + " # 'Npeaks': 10,\n", + " # 'r_values_min_full': .8,\n", + " # 'fitness_min_full': - 40,\n", + " # 'fitness_delta_min_full': - 40,\n", + " # 'only_init_patch': True,\n", + " 'gnb': 1,\n", + " # 'memory_fact': 1,\n", + " # 'n_chunks': 10,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " 'swap_dim': False # for some movies needed\n", + " }\n", + "\n", + "# neurofinder.04.00.test\n", + "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.04.00.test/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_3000_.mmap'],\n", + " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.04.00.test/regions/joined_consensus_active_regions.npy'],\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 1, # merging threshold, max correlation allow\n", + " 'final_frate': 10,\n", + " # 'r_values_min_patch': .7, # threshold on space consistency\n", + " # 'fitness_min_patch': -20, # threshold on time variability\n", + " # # threshold on time variability (if nonsparse activity)\n", + " # 'fitness_delta_min_patch': -20,\n", + " # 'Npeaks': 10,\n", + " # 'r_values_min_full': .8,\n", + " # 'fitness_min_full': - 40,\n", + " # 'fitness_delta_min_full': - 40,\n", + " # 'only_init_patch': True,\n", + " 'gnb': 1,\n", + " # 'memory_fact': 1,\n", + " # 'n_chunks': 10,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " 'swap_dim': False # for some movies needed\n", + "\n", + " }\n", + "\n", + "# Yi not clear neurons\n", + "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/Yi.data.001/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_7826_.mmap'],\n", + " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/Yi.data.001/regions/joined_consensus_active_regions.npy'],\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 1, # merging threshold, max correlation allow\n", + " 'final_frate': 30,\n", + " # 'r_values_min_patch': .7, # threshold on space consistency\n", + " # 'fitness_min_patch': -20, # threshold on time variability\n", + " # # threshold on time variability (if nonsparse activity)\n", + " # 'fitness_delta_min_patch': -20,\n", + " # 'Npeaks': 10,\n", + " # 'r_values_min_full': .8,\n", + " # 'fitness_min_full': - 40,\n", + " # 'fitness_delta_min_full': - 40,\n", + " # 'only_init_patch': True,\n", + " 'gnb': 1,\n", + " # 'memory_fact': 1,\n", + " # 'n_chunks': 10,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " }\n", + "\n", + "# neurofinder.02.00\n", + "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.02.00/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_8000_.mmap'],\n", + " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.02.00/regions/joined_consensus_active_regions.npy'],\n", + " 'merge_thresh': .8, # merging threshold, max correlation allow\n", + " 'final_frate': 10,\n", + " 'gnb': 1,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " 'swap_dim': False # for some movies needed\n", + " }\n", + "\n", + "# yuste: used kernel = np.ones((radius//4,radius//4),np.uint8)\n", + "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/yuste.Single_150u/images/final_map/Yr_d1_200_d2_256_d3_1_order_C_frames_3000_.mmap'],\n", + " 'gtname': ['/mnt/ceph/data/neuro/caiman//labeling/yuste.Single_150u/regions/joined_consensus_active_regions.npy'],\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 1, # merging threshold, max correlation allow\n", + " 'final_frate': 10,\n", + " 'gnb': 1,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " 'swap_dim': False # for some movies needed\n", + " }\n", + "\n", + "# neurofinder 00 00\n", + "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.00.00/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_2936_.mmap'],\n", + " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.00.00/regions/joined_consensus_active_regions.npy'],\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 1, # merging threshold, max correlation allow\n", + " 'final_frate': 10,\n", + " # 'r_values_min_patch': .7, # threshold on space consistency\n", + " # 'fitness_min_patch': -20, # threshold on time variability\n", + " # # threshold on time variability (if nonsparse activity)\n", + " # 'fitness_delta_min_patch': -20,\n", + " # 'Npeaks': 10,\n", + " # 'r_values_min_full': .8,\n", + " # 'fitness_min_full': - 40,\n", + " # 'fitness_delta_min_full': - 40,\n", + " # 'only_init_patch': True,\n", + " 'gnb': 1,\n", + " # 'memory_fact': 1,\n", + " # 'n_chunks': 10,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " 'swap_dim': False # for some movies needed\n", + " }\n", + "\n", + "# k53\n", + "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/k53_20160530/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_116043_.mmap'],\n", + " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/k53_20160530/regions/joined_consensus_active_regions.npy'],\n", + " 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/k53_20160530/regions/joined_consensus_active_regions.npy'],\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 1, # merging threshold, max correlation allow\n", + " 'final_frate': 30,\n", + " 'gnb': 1,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " 'swap_dim': False, # for some movies needed\n", + " 'kernel': None\n", + " }\n", + "\n", + "# neurofinder: 01.01\n", + "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.01.01/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_1825_.mmap'],\n", + " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.01.01/regions/joined_consensus_active_regions.npy'],\n", + " 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.01.01/regions/joined_consensus_active_regions.npy'],\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 1, # merging threshold, max correlation allow\n", + " 'final_frate': 10,\n", + " 'gnb': 1,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " 'swap_dim': False, # for some movies needed\n", + " 'kernel': None\n", + " }\n", + "\n", + "# J115: 01.01\n", + "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/J115_2015-12-09_L01_ELS/images/final_map/Yr_d1_463_d2_472_d3_1_order_C_frames_90000_.mmap'],\n", + " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/J115_2015-12-09_L01_ELS/regions/joined_consensus_active_regions.npy'],\n", + " 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/J115_2015-12-09_L01_ELS/regions/joined_consensus_active_regions.npy'],\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 1, # merging threshold, max correlation allow\n", + " 'final_frate': 10,\n", + " 'gnb': 1,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " 'swap_dim': False, # for some movies needed\n", + " 'kernel': None\n", + " }\n", + "\n", + "# J123\n", + "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/J123_2015-11-20_L01_0/images/final_map/Yr_d1_458_d2_477_d3_1_order_C_frames_41000_.mmap'],\n", + " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/J123_2015-11-20_L01_0/regions/joined_consensus_active_regions.npy'],\n", + " 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/J123_2015-11-20_L01_0/regions/joined_consensus_active_regions.npy'],\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 1, # merging threshold, max correlation allow\n", + " 'final_frate': 10,\n", + " 'gnb': 1,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " 'swap_dim': False, # for some movies needed\n", + " 'kernel': None\n", + " }\n", + "# Jan-AMG\n", + "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/Jan-AMG_exp3_001/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_115897_.mmap'],\n", + " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/Jan-AMG_exp3_001/regions/joined_consensus_active_regions.npy'],\n", + " 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/Jan-AMG_exp3_001/regions/joined_consensus_active_regions.npy'],\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 1, # merging threshold, max correlation allow\n", + " 'final_frate': 10,\n", + " 'gnb': 1,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " 'swap_dim': False, # for some movies needed\n", + " 'kernel': None,\n", + " 'crop_pix': 8,\n", + " }\n", + "\n", + "# sue k37, not nice because few events\n", + "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/k37_20160109_AM_150um_65mW_zoom2p2_00001_1-16/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_48000_.mmap'],\n", + " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/k37_20160109_AM_150um_65mW_zoom2p2_00001_1-16/regions/joined_consensus_active_regions.npy'],\n", + " 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/k37_20160109_AM_150um_65mW_zoom2p2_00001_1-16/regions/joined_consensus_active_regions.npy'],\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 1, # merging threshold, max correlation allow\n", + " 'final_frate': 30,\n", + " 'gnb': 2,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " 'swap_dim': False, # for some movies needed\n", + " 'kernel': None,\n", + " 'crop_pix': 7,\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameters for the Movie and Name of the Movie " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "params_display = {\n", + " 'downsample_ratio': .2,\n", + " 'thr_plot': 0.8\n", + "}\n", + "\n", + "# @params fname name of the movie\n", + "fname_new = params_movie['fname'][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analysis " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c, dview, n_processes = cm.cluster.setup_cluster(\n", + " backend='local', n_processes=None, single_thread=False)\n", + "\n", + "Yr, dims, T = cm.load_memmap(fname_new)\n", + "d1, d2 = dims\n", + "images = np.reshape(Yr.T, [T] + list(dims), order='F')\n", + "Y = np.reshape(Yr, dims + (T,), order='F')\n", + "m_images = cm.movie(images)\n", + "\n", + "# correlation image\n", + "if m_images.shape[0] < 10000:\n", + " Cn = m_images.local_correlations(\n", + " swap_dim=params_movie['swap_dim'], frames_per_chunk=1500)\n", + " Cn[np.isnan(Cn)] = 0\n", + "else:\n", + " Cn = np.array(cm.load(('/'.join(params_movie['gtname'][0].split('/')[:-2] + [\n", + " 'projections', 'correlation_image_better.tif'])))).squeeze()\n", + "\n", + "plt.imshow(Cn, cmap='gray', vmax=.95)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if not '.mat' in params_movie['seed_name'][0]:\n", + " roi_cons = np.load(params_movie['seed_name'][0])\n", + "else:\n", + " roi_cons = scipy.io.loadmat(params_movie['seed_name'][0])['comps'].reshape(\n", + " (dims[1], dims[0], -1), order='F').transpose([2, 1, 0]) * 1.\n", + "\n", + "radius = int(np.median(np.sqrt(np.sum(roi_cons, (1, 2)) / np.pi)))\n", + "\n", + "print(radius)\n", + "print(roi_cons.shape)\n", + "pl.imshow(roi_cons.sum(0))\n", + "\n", + "if params_movie['kernel'] is not None: # kernel usually two\n", + " kernel = np.ones(\n", + " (radius // params_movie['kernel'], radius // params_movie['kernel']), np.uint8)\n", + " roi_cons = np.vstack([cv2.dilate(rr, kernel, iterations=1)[\n", + " np.newaxis, :, :] > 0 for rr in roi_cons]) * 1.\n", + " pl.imshow(roi_cons.sum(0), alpha=0.5)\n", + "\n", + "A_in = np.reshape(roi_cons.transpose(\n", + " [2, 1, 0]), (-1, roi_cons.shape[0]), order='C')\n", + "pl.figure()\n", + "crd = plot_contours(A_in, Cn, thr=.99999)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# %% some parameter settings\n", + "# order of the autoregressive fit to calcium imaging in general one (slow gcamps) or two (fast gcamps fast scanning)\n", + "p = params_movie['p']\n", + "# merging threshold, max correlation allowed\n", + "merge_thresh = params_movie['merge_thresh']\n", + "\n", + "# %% Extract spatial and temporal components on patches\n", + "# TODO: todocument\n", + "if images.shape[0] > 10000:\n", + " check_nan = False\n", + "else:\n", + " check_nan = True\n", + "\n", + "cnm = cnmf.CNMF(check_nan=check_nan, n_processes=1, k=A_in.shape[-1], gSig=[radius, radius], merge_thresh=params_movie['merge_thresh'], p=params_movie['p'], Ain=A_in.astype(bool),\n", + " dview=dview, rf=None, stride=None, gnb=params_movie['gnb'], method_deconvolution='oasis', border_pix=0, low_rank_background=params_movie['low_rank_background'], n_pixels_per_process=1000)\n", + "cnm = cnm.fit(images)\n", + "\n", + "A = cnm.A\n", + "C = cnm.C\n", + "YrA = cnm.YrA\n", + "b = cnm.b\n", + "f = cnm.f\n", + "snt = cnm.sn\n", + "print(('Number of components:' + str(A.shape[-1])))\n", + "# %%\n", + "pl.figure()\n", + "# TODO: show screenshot 12`\n", + "# TODO : change the way it is used\n", + "crd = plot_contours(A, Cn, thr=params_display['thr_plot'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: needinfo\n", + "view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, :]), C[:, :], b, f, dims[0], dims[1],\n", + " YrA=YrA[:, :], img=Cn)\n", + "\n", + "#%%\n", + "c, dview, n_processes = cm.cluster.setup_cluster(\n", + " backend='local', n_processes=None, single_thread=False)\n", + "#%% thredshold components\n", + "min_size_neuro = 3 * 2 * np.pi\n", + "max_size_neuro = (2 * radius)**2 * np.pi\n", + "A_thr = cm.source_extraction.cnmf.spatial.threshold_components(A.tocsc()[:, :].toarray(), dims, medw=None, thr_method='max', maxthr=0.2, nrgthr=0.99, extract_cc=True,\n", + " se=None, ss=None, dview=dview)\n", + "\n", + "A_thr = A_thr > 0\n", + "size_neurons = A_thr.sum(0)\n", + "idx_size_neuro = np.where((size_neurons > min_size_neuro)\n", + " & (size_neurons < max_size_neuro))[0]\n", + "A_thr = A_thr[:, idx_size_neuro]\n", + "print(A_thr.shape)\n", + "#%%\n", + "crd = plot_contours(scipy.sparse.coo_matrix(\n", + " A_thr * 1.), Cn, thr=.99, vmax=0.35)\n", + "#%%\n", + "roi_cons = np.load(params_movie['gtname'][0])\n", + "print(roi_cons.shape)\n", + "pl.imshow(roi_cons.sum(0))\n", + "#%% compare CNMF sedded with ground truth\n", + "pl.figure(figsize=(30, 20))\n", + "tp_gt, tp_comp, fn_gt, fp_comp, performance_cons_off = cm.base.rois.nf_match_neurons_in_binary_masks(roi_cons, A_thr[:, :].reshape([dims[0], dims[1], -1], order='F').transpose([2, 0, 1]) * 1., thresh_cost=.7, min_dist=10,\n", + " print_assignment=False, plot_results=False, Cn=Cn, labels=['GT', 'Offline'])\n", + "pl.rcParams['pdf.fonttype'] = 42\n", + "font = {'family': 'Myriad Pro',\n", + " 'weight': 'regular',\n", + " 'size': 20}\n", + "pl.rc('font', **font)\n", + "\n", + "#%%\n", + "np.savez(os.path.join(os.path.split(fname_new)[0], os.path.split(fname_new)[1][:-4] + 'match_masks.npz'), Cn=Cn,\n", + " tp_gt=tp_gt, tp_comp=tp_comp, fn_gt=fn_gt, fp_comp=fp_comp, performance_cons_off=performance_cons_off, idx_size_neuro_gt=idx_size_neuro, A_thr=A_thr,\n", + " A_gt=A, C_gt=C, b_gt=b, f_gt=f, YrA_gt=YrA, d1=d1, d2=d2, idx_components_gt=idx_size_neuro[\n", + " tp_comp],\n", + " idx_components_bad_gt=idx_size_neuro[fp_comp], fname_new=fname_new)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "caiman_pytorch", + "language": "python", + "name": "caiman_pytorch" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/caiman/train/match_seeded_gt.ipynb b/caiman/train/match_seeded_gt.ipynb new file mode 100644 index 000000000..3f8685877 --- /dev/null +++ b/caiman/train/match_seeded_gt.ipynb @@ -0,0 +1,5469 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-04 22:39:01.243425: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-08-04 22:39:01.262158: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "#!/usr/bin/env python\n", + "\n", + "# @package demos\n", + "#\\brief for the user/programmer to understand and try the code\n", + "#\\details all of other usefull functions (demos available on jupyter notebook) -*- coding: utf-8 -*-\n", + "#\\version 1.0\n", + "#\\pre EXample.First initialize the system.\n", + "#\\bug\n", + "#\\warning\n", + "#\\copyright GNU General Public License v2.0\n", + "#\\date Created on Mon Nov 21 15:53:15 2016\n", + "#\\author agiovann, recreated by mpaez\n", + "\n", + "import cv2\n", + "import numpy as np\n", + "import os\n", + "import time\n", + "import pylab as pl\n", + "\n", + "import caiman as cm\n", + "from caiman.utils.utils import download_demo\n", + "from caiman.base.rois import extract_binary_masks_blob\n", + "from caiman.utils.visualization import plot_contours, view_patches_bar\n", + "from caiman.source_extraction.cnmf import cnmf as cnmf\n", + "from caiman.motion_correction import MotionCorrect, tile_and_correct, motion_correction_piecewise \n", + "from caiman.components_evaluation import estimate_components_quality, evaluate_components\n", + "from caiman.tests.comparison import comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "params_movie = {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.03.00.test/images/final_map/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_.mmap',\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 0.8, # merging threshold, max correlation allow\n", + " 'rf': 25, # half-size of the patches in pixels. rf=25, patches are 50x50 20\n", + " 'stride_cnmf': 10, # amounpl.it of overlap between the patches in pixels\n", + " 'K': 4, # number of components per patch\n", + " # if dendritic. In this case you need to set init_method to sparse_nmf\n", + " 'is_dendrites': False,\n", + " 'init_method': 'greedy_roi',\n", + " 'gSig': [8, 8], # expected half size of neurons\n", + " 'alpha_snmf': None, # this controls sparsity\n", + " 'final_frate': 10,\n", + " 'r_values_min_patch': .5, # threshold on space consistency\n", + " 'fitness_min_patch': -10, # threshold on time variability\n", + " # threshold on time variability (if nonsparse activity)\n", + " 'fitness_delta_min_patch': -5,\n", + " 'Npeaks': 5,\n", + " 'r_values_min_full': .8,\n", + " 'fitness_min_full': - 40,\n", + " 'fitness_delta_min_full': - 40,\n", + " 'only_init_patch': True,\n", + " 'gnb': 2,\n", + " 'memory_fact': 1,\n", + " 'n_chunks': 10,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " }\n", + "\n", + "params_movie = {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.04.00.test/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_3000_.mmap',\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 0.8, # merging threshold, max correlation allow\n", + " 'rf': 20, # half-size of the patches in pixels. rf=25, patches are 50x50 20\n", + " 'stride_cnmf': 10, # amounpl.it of overlap between the patches in pixels\n", + " 'K': 5, # number of components per patch\n", + " # if dendritic. In this case you need to set init_method to sparse_nmf\n", + " 'is_dendrites': False,\n", + " 'init_method': 'greedy_roi',\n", + " 'gSig': [5, 5], # expected half size of neurons\n", + " 'alpha_snmf': None, # this controls sparsity\n", + " 'final_frate': 10,\n", + " 'r_values_min_patch': .5, # threshold on space consistency\n", + " 'fitness_min_patch': -10, # threshold on time variability\n", + " # threshold on time variability (if nonsparse activity)\n", + " 'fitness_delta_min_patch': -10,\n", + " 'Npeaks': 5,\n", + " 'r_values_min_full': .8,\n", + " 'fitness_min_full': - 40,\n", + " 'fitness_delta_min_full': - 40,\n", + " 'only_init_patch': True,\n", + " 'gnb': 2,\n", + " 'memory_fact': 1,\n", + " 'n_chunks': 10,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " }\n", + "\n", + "params_movie = {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.04.00.test/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_3000_.mmap',\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 0.8, # merging threshold, max correlation allow\n", + " 'rf': 20, # half-size of the patches in pixels. rf=25, patches are 50x50 20\n", + " 'stride_cnmf': 10, # amounpl.it of overlap between the patches in pixels\n", + " 'K': 5, # number of components per patch\n", + " # if dendritic. In this case you need to set init_method to sparse_nmf\n", + " 'is_dendrites': False,\n", + " 'init_method': 'greedy_roi',\n", + " 'gSig': [5, 5], # expected half size of neurons\n", + " 'alpha_snmf': None, # this controls sparsity\n", + " 'final_frate': 10,\n", + " 'r_values_min_patch': .5, # threshold on space consistency\n", + " 'fitness_min_patch': -10, # threshold on time variability\n", + " # threshold on time variability (if nonsparse activity)\n", + " 'fitness_delta_min_patch': -10,\n", + " 'Npeaks': 5,\n", + " 'r_values_min_full': .8,\n", + " 'fitness_min_full': - 40,\n", + " 'fitness_delta_min_full': - 40,\n", + " 'only_init_patch': True,\n", + " 'gnb': 2,\n", + " 'memory_fact': 1,\n", + " 'n_chunks': 10,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " }\n", + "\n", + "# neurofinder 02.00\n", + "params_movie = {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.02.00/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_8000_.mmap',\n", + " 'p': 1, # order of the autoregressive system\n", + " 'merge_thresh': 0.8, # merging threshold, max correlation allow\n", + " 'rf': 20, # half-size of the patches in pixels. rf=25, patches are 50x50 20\n", + " 'stride_cnmf': 10, # amounpl.it of overlap between the patches in pixels\n", + " 'K': 6, # number of components per patch\n", + " # if dendritic. In this case you need to set init_method to sparse_nmf\n", + " 'is_dendrites': False,\n", + " 'init_method': 'greedy_roi',\n", + " 'gSig': [5, 5], # expected half size of neurons\n", + " 'alpha_snmf': None, # this controls sparsity\n", + " 'final_frate': 10,\n", + " 'r_values_min_patch': .5, # threshold on space consistency\n", + " 'fitness_min_patch': -10, # threshold on time variability\n", + " # threshold on time variability (if nonsparse activity)\n", + " 'fitness_delta_min_patch': -10,\n", + " 'Npeaks': 5,\n", + " 'r_values_min_full': .8,\n", + " 'fitness_min_full': - 40,\n", + " 'fitness_delta_min_full': - 40,\n", + " 'only_init_patch': True,\n", + " 'gnb': 2,\n", + " 'memory_fact': 1,\n", + " 'n_chunks': 10,\n", + " # whether to update the background components in the spatial phase\n", + " 'update_background_components': True,\n", + " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + " #(to be used with one background per patch)\n", + " 'swap_dim': False,\n", + " 'crop_pix': 10\n", + " }\n", + "\n", + "params_display = {\n", + " 'downsample_ratio': .2,\n", + " 'thr_plot': 0.8\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 45266 [cluster.py: setup_cluster():225] [1655335] The local backend is an alias for the multiprocessing backend, and the alias may be removed in some future version of Caiman\n", + " 46267 [movies.py: local_correlations():826] [1655335] number of chunks:0 frames: [0, 1500]\n", + " 48404 [movies.py: local_correlations():826] [1655335] number of chunks:1 frames: [1500, 3000]\n", + " 50038 [movies.py: local_correlations():826] [1655335] number of chunks:2 frames: [3000, 4500]\n", + " 51669 [movies.py: local_correlations():826] [1655335] number of chunks:3 frames: [4500, 6000]\n", + " 53304 [movies.py: local_correlations():837] [1655335] number of chunks:4 frames: [6000, 8000]\n", + " 56916 [font_manager.py: _findfont_cached():1416] [1655335] findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n", + " 56918 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", + " 56919 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56920 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56920 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56921 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", + " 56922 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56922 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56923 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56924 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56926 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56927 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56927 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56928 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56929 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56929 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56930 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56930 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56931 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56932 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56932 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56933 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56934 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56934 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56935 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56936 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56936 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56937 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56937 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56938 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56939 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56939 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", + " 56940 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56941 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56941 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", + " 56942 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56943 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56943 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56944 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56944 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Italic.ttf', name='Lato', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56944 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56944 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWGothic-DemiOblique.otf', name='URW Gothic', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", + " 56945 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSansNarrow-Bold.otf', name='Nimbus Sans Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + " 56945 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWGothic-Book.otf', name='URW Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56947 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", + " 56947 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56947 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Black.ttf', name='Lato', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", + " 56948 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusRoman-Italic.otf', name='Nimbus Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56948 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-carlito/Carlito-Bold.ttf', name='Carlito', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56948 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-mono/LiberationMono-BoldItalic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56948 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-HairlineItalic.ttf', name='Lato', style='italic', variant='normal', weight=250, stretch='normal', size='scalable')) = 11.1925\n", + " 56948 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansArabic.ttf', name='Droid Sans Arabic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56949 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerifCondensed-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", + " 56949 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-mono/LiberationMono-Bold.ttf', name='Liberation Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56949 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansGeorgian.ttf', name='Droid Sans Georgian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56949 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56949 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansCondensed-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 1.535\n", + " 56949 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansDevanagari-Regular.ttf', name='Droid Sans Devanagari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56950 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56950 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/fontawesome/fontawesome-webfont.ttf', name='FontAwesome', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56950 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56950 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-mono/LiberationMono-Regular.ttf', name='Liberation Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56950 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWGothic-Demi.otf', name='URW Gothic', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + " 56951 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusMonoPS-Bold.otf', name='Nimbus Mono PS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56951 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", + " 56951 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWBookman-LightItalic.otf', name='URW Bookman', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", + " 56951 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Semibold.ttf', name='Lato', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + " 56951 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWBookman-Light.otf', name='URW Bookman', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + " 56951 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSans-Italic.otf', name='Nimbus Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56952 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/D050000L.otf', name='D050000L', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56952 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/fontawesome/FontAwesome.otf', name='FontAwesome', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56952 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/opensymbol/opens___.ttf', name='OpenSymbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56952 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-serif/LiberationSerif-Regular.ttf', name='Liberation Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56952 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-BlackItalic.ttf', name='Lato', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525\n", + " 56952 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansHebrew-Bold.ttf', name='Droid Sans Hebrew', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + " 56953 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansEthiopic-Regular.ttf', name='Droid Sans Ethiopic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56953 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansHebrew-Regular.ttf', name='Droid Sans Hebrew', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56953 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Bold.ttf', name='Lato', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56953 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56953 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Thin.ttf', name='Lato', style='normal', variant='normal', weight=275, stretch='normal', size='scalable')) = 10.16875\n", + " 56953 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/P052-Roman.otf', name='P052', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56954 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-caladea/Caladea-Regular.ttf', name='Caladea', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56954 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusMonoPS-Regular.otf', name='Nimbus Mono PS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56954 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-sans/LiberationSans-Bold.ttf', name='Liberation Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56954 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56954 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Heavy.ttf', name='Lato', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43\n", + " 56955 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/C059-BdIta.otf', name='C059', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56955 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/abattis-cantarell/Cantarell-Bold.otf', name='Cantarell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56955 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSans-BoldItalic.otf', name='Nimbus Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56955 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-roboto-slab/RobotoSlab-Light.ttf', name='Roboto Slab', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + " 56955 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusRoman-BoldItalic.otf', name='Nimbus Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56966 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-caladea/Caladea-Italic.ttf', name='Caladea', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56966 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-roboto-slab/RobotoSlab-Bold.ttf', name='Roboto Slab', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56967 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-HeavyItalic.ttf', name='Lato', style='italic', variant='normal', weight=800, stretch='normal', size='scalable')) = 11.43\n", + " 56967 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSansNarrow-BoldOblique.otf', name='Nimbus Sans Narrow', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", + " 56967 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", + " 56967 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-serif/LiberationSerif-BoldItalic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56967 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansCondensed-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 1.25\n", + " 56968 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerifCondensed-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", + " 56968 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansTamil-Regular.ttf', name='Droid Sans Tamil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56968 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSans-ExtraLight.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=200, stretch='normal', size='scalable')) = 0.24\n", + " 56968 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/C059-Italic.otf', name='C059', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56968 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSansNarrow-Regular.otf', name='Nimbus Sans Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + " 56969 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/abattis-cantarell/Cantarell-BoldOblique.otf', name='Cantarell', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56969 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", + " 56969 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSans-Bold.otf', name='Nimbus Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56969 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-carlito/Carlito-Italic.ttf', name='Carlito', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56969 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWGothic-BookOblique.otf', name='URW Gothic', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56969 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSans.ttf', name='Droid Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusMonoPS-BoldItalic.otf', name='Nimbus Mono PS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSans-Regular.otf', name='Nimbus Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/C059-Bold.otf', name='C059', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerifCondensed-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", + " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansCondensed-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 0.5349999999999999\n", + " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansThai.ttf', name='Droid Sans Thai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-sans/LiberationSans-Italic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-carlito/Carlito-BoldItalic.ttf', name='Carlito', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/P052-Italic.otf', name='P052', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSansNarrow-Oblique.otf', name='Nimbus Sans Narrow', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", + " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/P052-BoldItalic.otf', name='P052', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-sans/LiberationSans-BoldItalic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-SemiboldItalic.ttf', name='Lato', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", + " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Hairline.ttf', name='Lato', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Light.ttf', name='Lato', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", + " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/abattis-cantarell/Cantarell-Oblique.otf', name='Cantarell', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusRoman-Bold.otf', name='Nimbus Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusMonoPS-Italic.otf', name='Nimbus Mono PS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansFallback.ttf', name='Droid Sans Fallback', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-ThinItalic.ttf', name='Lato', style='italic', variant='normal', weight=275, stretch='normal', size='scalable')) = 11.16875\n", + " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-roboto-slab/RobotoSlab-Regular.ttf', name='Roboto Slab', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerifCondensed.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", + " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-mono/LiberationMono-Italic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/abattis-cantarell/Cantarell-Regular.otf', name='Cantarell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/P052-Bold.otf', name='P052', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansCondensed.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 0.25\n", + " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/C059-Roman.otf', name='C059', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-caladea/Caladea-Bold.ttf', name='Caladea', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-LightItalic.ttf', name='Lato', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", + " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusRoman-Regular.otf', name='Nimbus Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Medium.ttf', name='Lato', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", + " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansTamil-Bold.ttf', name='Droid Sans Tamil', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-caladea/Caladea-BoldItalic.ttf', name='Caladea', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansArmenian.ttf', name='Droid Sans Armenian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Regular.ttf', name='Lato', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansEthiopic-Bold.ttf', name='Droid Sans Ethiopic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWBookman-DemiItalic.otf', name='URW Bookman', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", + " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansJapanese.ttf', name='Droid Sans Japanese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-MediumItalic.ttf', name='Lato', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", + " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSans-Bold.ttf', name='Droid Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-sans/LiberationSans-Regular.ttf', name='Liberation Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-serif/LiberationSerif-Italic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", + " 56976 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-BoldItalic.ttf', name='Lato', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", + " 56976 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-roboto-slab/RobotoSlab-Thin.ttf', name='Roboto Slab', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", + " 56976 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-carlito/Carlito-Regular.ttf', name='Carlito', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", + " 56976 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/Z003-MediumItalic.otf', name='Z003', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", + " 56976 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-serif/LiberationSerif-Bold.ttf', name='Liberation Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", + " 56976 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWBookman-Demi.otf', name='URW Bookman', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", + " 56976 [font_manager.py: _findfont_cached():1460] [1655335] findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ " 63429 [map_reduce.py: cnmf_patches():100] [1655521] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5220_25740file loaded\n", + " 63433 [map_reduce.py: cnmf_patches():100] [1655470] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20490_41010file loaded\n", + " 63418 [map_reduce.py: cnmf_patches():100] [1655539] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20610_41130file loaded\n", + " 63423 [map_reduce.py: cnmf_patches():100] [1655490] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51240_71760file loaded\n", + " 63437 [map_reduce.py: cnmf_patches():100] [1655505] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20550_41070file loaded\n", + " 63451 [map_reduce.py: cnmf_patches():100] [1655469] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5130_25650file loaded\n", + " 63453 [map_reduce.py: cnmf_patches():100] [1655556] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20640_41160file loaded\n", + " 63458 [map_reduce.py: cnmf_patches():100] [1655488] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20520_41040file loaded\n", + " 66055 [map_reduce.py: cnmf_patches():100] [1655563] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128160_148680file loaded\n", + " 67830 [params.py: set():1006] [1655515] Changing key rf in group patch from 20 to None\n", + " 68162 [params.py: set():1006] [1655515] Changing key stride in group patch from 10 to None\n", + " 68202 [params.py: set():1006] [1655531] Changing key rf in group patch from 20 to None\n", + " 68176 [params.py: set():1006] [1655535] Changing key rf in group patch from 20 to None\n", + " 68206 [params.py: set():1006] [1655549] Changing key rf in group patch from 20 to None\n", + " 68337 [params.py: set():1006] [1655553] Changing key rf in group patch from 20 to None\n", + " 68509 [params.py: set():1006] [1655515] Changing key nb in group init from 2 to 1\n", + " 68527 [params.py: set():1006] [1655531] Changing key stride in group patch from 10 to None\n", + " 68517 [params.py: set():1006] [1655552] Changing key rf in group patch from 20 to None\n", + " 68539 [params.py: set():1006] [1655535] Changing key stride in group patch from 10 to None\n", + " 68597 [params.py: set():1006] [1655549] Changing key stride in group patch from 10 to None\n", + " 68601 [params.py: set():1006] [1655530] Changing key rf in group patch from 20 to None\n", + " 68602 [params.py: set():1006] [1655550] Changing key rf in group patch from 20 to None\n", + " 68683 [params.py: set():1006] [1655553] Changing key stride in group patch from 10 to None\n", + " 68693 [params.py: set():1006] [1655546] Changing key rf in group patch from 20 to None\n", + " 68843 [params.py: set():1006] [1655515] Changing key nb in group temporal from 2 to 1\n", + " 68807 [params.py: set():1006] [1655551] Changing key rf in group patch from 20 to None\n", + " 68880 [params.py: set():1006] [1655531] Changing key nb in group init from 2 to 1\n", + " 68888 [params.py: set():1006] [1655535] Changing key nb in group init from 2 to 1\n", + " 68888 [params.py: set():1006] [1655552] Changing key stride in group patch from 10 to None\n", + " 68916 [params.py: set():1006] [1655549] Changing key nb in group init from 2 to 1\n", + " 68925 [params.py: set():1006] [1655550] Changing key stride in group patch from 10 to None\n", + " 68926 [params.py: set():1006] [1655530] Changing key stride in group patch from 10 to None\n", + " 68947 [params.py: set():1006] [1655546] Changing key stride in group patch from 10 to None\n", + " 68948 [params.py: set():1006] [1655553] Changing key nb in group init from 2 to 1\n", + " 68714 [params.py: set():1006] [1655536] Changing key rf in group patch from 20 to None\n", + " 68956 [params.py: set():1006] [1655470] Changing key rf in group patch from 20 to None\n", + " 68981 [params.py: set():1006] [1655473] Changing key rf in group patch from 20 to None\n", + " 68983 [params.py: set():1006] [1655537] Changing key rf in group patch from 20 to None\n", + " 68988 [params.py: set():1006] [1655494] Changing key rf in group patch from 20 to None\n", + " 68989 [params.py: set():1006] [1655490] Changing key rf in group patch from 20 to None\n", + " 68997 [params.py: set():1006] [1655543] Changing key rf in group patch from 20 to None\n", + " 69000 [params.py: set():1006] [1655525] Changing key rf in group patch from 20 to None\n", + " 69017 [params.py: set():1006] [1655522] Changing key rf in group patch from 20 to None\n", + " 69028 [params.py: set():1006] [1655519] Changing key rf in group patch from 20 to None\n", + " 69053 [params.py: set():1006] [1655557] Changing key rf in group patch from 20 to None\n", + " 69063 [params.py: set():1006] [1655540] Changing key rf in group patch from 20 to None\n", + " 69084 [params.py: set():1006] [1655500] Changing key rf in group patch from 20 to None\n", + " 69085 [params.py: set():1006] [1655515] Changing key nb in group spatial from 2 to 1\n", + " 69104 [params.py: set():1006] [1655529] Changing key rf in group patch from 20 to None\n", + " 69175 [params.py: set():1006] [1655551] Changing key stride in group patch from 10 to None\n", + " 69178 [params.py: set():1006] [1655534] Changing key rf in group patch from 20 to None\n", + " 69179 [params.py: set():1006] [1655531] Changing key nb in group temporal from 2 to 1\n", + " 68953 [params.py: set():1006] [1655489] Changing key rf in group patch from 20 to None\n", + " 69089 [params.py: set():1006] [1655480] Changing key rf in group patch from 20 to None\n", + " 69191 [params.py: set():1006] [1655535] Changing key nb in group temporal from 2 to 1\n", + " 69146 [params.py: set():1006] [1655482] Changing key rf in group patch from 20 to None\n", + " 69192 [params.py: set():1006] [1655552] Changing key nb in group init from 2 to 1\n", + " 69005 [params.py: set():1006] [1655526] Changing key rf in group patch from 20 to None\n", + " 69186 [params.py: set():1006] [1655533] Changing key rf in group patch from 20 to None\n", + " 69213 [params.py: set():1006] [1655512] Changing key rf in group patch from 20 to None\n", + " 69214 [params.py: set():1006] [1655511] Changing key rf in group patch from 20 to None\n", + " 69224 [params.py: set():1006] [1655508] Changing key rf in group patch from 20 to None\n", + " 69225 [params.py: set():1006] [1655549] Changing key nb in group temporal from 2 to 1\n", + " 69228 [params.py: set():1006] [1655507] Changing key rf in group patch from 20 to None\n", + " 69228 [params.py: set():1006] [1655530] Changing key nb in group init from 2 to 1\n", + " 69229 [params.py: set():1006] [1655550] Changing key nb in group init from 2 to 1\n", + " 69190 [params.py: set():1006] [1655497] Changing key rf in group patch from 20 to None\n", + " 68995 [params.py: set():1006] [1655558] Changing key rf in group patch from 20 to None\n", + " 69202 [params.py: set():1006] [1655524] Changing key rf in group patch from 20 to None\n", + " 69232 [params.py: set():1006] [1655513] Changing key rf in group patch from 20 to None\n", + " 69239 [params.py: set():1006] [1655546] Changing key nb in group init from 2 to 1\n", + " 69244 [params.py: set():1006] [1655560] Changing key rf in group patch from 20 to None\n", + " 69245 [params.py: set():1006] [1655553] Changing key nb in group temporal from 2 to 1\n", + " 69094 [params.py: set():1006] [1655509] Changing key rf in group patch from 20 to None\n", + " 69252 [params.py: set():1006] [1655470] Changing key stride in group patch from 10 to None\n", + " 69252 [params.py: set():1006] [1655536] Changing key stride in group patch from 10 to None\n", + " 69254 [params.py: set():1006] [1655520] Changing key rf in group patch from 20 to None\n", + " 69259 [params.py: set():1006] [1655547] Changing key rf in group patch from 20 to None\n", + " 69259 [params.py: set():1006] [1655537] Changing key stride in group patch from 10 to None\n", + " 69260 [params.py: set():1006] [1655494] Changing key stride in group patch from 10 to None\n", + " 69257 [params.py: set():1006] [1655517] Changing key rf in group patch from 20 to None\n", + " 69261 [params.py: set():1006] [1655490] Changing key stride in group patch from 10 to None\n", + " 69261 [params.py: set():1006] [1655473] Changing key stride in group patch from 10 to None\n", + " 69265 [params.py: set():1006] [1655543] Changing key stride in group patch from 10 to None\n", + " 69271 [params.py: set():1006] [1655525] Changing key stride in group patch from 10 to None\n", + " 69264 [params.py: set():1006] [1655523] Changing key rf in group patch from 20 to None\n", + " 69272 [params.py: set():1006] [1655522] Changing key stride in group patch from 10 to None\n", + " 69274 [params.py: set():1006] [1655518] Changing key rf in group patch from 20 to None\n", + " 69274 [params.py: set():1006] [1655519] Changing key stride in group patch from 10 to None\n", + " 69275 [params.py: set():1006] [1655557] Changing key stride in group patch from 10 to None\n", + " 69279 [params.py: set():1006] [1655540] Changing key stride in group patch from 10 to None\n", + " 69281 [params.py: set():1006] [1655500] Changing key stride in group patch from 10 to None\n", + " 69281 [params.py: set():1006] [1655515] Changing key p in group preprocess from 1 to 0\n", + " 69282 [params.py: set():1006] [1655486] Changing key rf in group patch from 20 to None\n", + " 69283 [params.py: set():1006] [1655514] Changing key rf in group patch from 20 to None\n", + " 69284 [params.py: set():1006] [1655529] Changing key stride in group patch from 10 to None\n", + " 69285 [params.py: set():1006] [1655541] Changing key rf in group patch from 20 to None\n", + " 69302 [params.py: set():1006] [1655554] Changing key rf in group patch from 20 to None\n", + " 69303 [params.py: set():1006] [1655488] Changing key rf in group patch from 20 to None\n", + " 69305 [params.py: set():1006] [1655506] Changing key rf in group patch from 20 to None\n", + " 69306 [params.py: set():1006] [1655531] Changing key nb in group spatial from 2 to 1\n", + " 69306 [params.py: set():1006] [1655551] Changing key nb in group init from 2 to 1\n", + " 69306 [params.py: set():1006] [1655534] Changing key stride in group patch from 10 to None\n", + " 69308 [params.py: set():1006] [1655489] Changing key stride in group patch from 10 to None\n", + " 69308 [params.py: set():1006] [1655535] Changing key nb in group spatial from 2 to 1\n", + " 69311 [params.py: set():1006] [1655479] Changing key rf in group patch from 20 to None\n", + " 69312 [params.py: set():1006] [1655527] Changing key rf in group patch from 20 to None\n", + " 69312 [params.py: set():1006] [1655480] Changing key stride in group patch from 10 to None\n", + " 69315 [params.py: set():1006] [1655492] Changing key rf in group patch from 20 to None\n", + " 69325 [params.py: set():1006] [1655499] Changing key rf in group patch from 20 to None\n", + " 69326 [params.py: set():1006] [1655552] Changing key nb in group temporal from 2 to 1\n", + " 69332 [params.py: set():1006] [1655521] Changing key rf in group patch from 20 to None\n", + " 69339 [params.py: set():1006] [1655482] Changing key stride in group patch from 10 to None\n", + " 69341 [params.py: set():1006] [1655562] Changing key rf in group patch from 20 to None\n", + " 69198 [params.py: set():1006] [1655532] Changing key rf in group patch from 20 to None\n", + " 69343 [params.py: set():1006] [1655481] Changing key rf in group patch from 20 to None\n", + " 69343 [params.py: set():1006] [1655526] Changing key stride in group patch from 10 to None\n", + " 69348 [params.py: set():1006] [1655564] Changing key rf in group patch from 20 to None\n", + " 69351 [params.py: set():1006] [1655487] Changing key rf in group patch from 20 to None\n", + " 69356 [params.py: set():1006] [1655533] Changing key stride in group patch from 10 to None\n", + " 69361 [params.py: set():1006] [1655511] Changing key stride in group patch from 10 to None\n", + " 69364 [params.py: set():1006] [1655483] Changing key rf in group patch from 20 to None\n", + " 69364 [params.py: set():1006] [1655512] Changing key stride in group patch from 10 to None\n", + " 69347 [params.py: set():1006] [1655485] Changing key rf in group patch from 20 to None\n", + " 69375 [params.py: set():1006] [1655549] Changing key nb in group spatial from 2 to 1\n", + " 69376 [params.py: set():1006] [1655508] Changing key stride in group patch from 10 to None\n", + " 69383 [params.py: set():1006] [1655530] Changing key nb in group temporal from 2 to 1\n", + " 69329 [params.py: set():1006] [1655516] Changing key rf in group patch from 20 to None\n", + " 69387 [params.py: set():1006] [1655507] Changing key stride in group patch from 10 to None\n", + " 69388 [params.py: set():1006] [1655550] Changing key nb in group temporal from 2 to 1\n", + " 69398 [params.py: set():1006] [1655469] Changing key rf in group patch from 20 to None\n", + " 69399 [params.py: set():1006] [1655497] Changing key stride in group patch from 10 to None\n", + " 69400 [params.py: set():1006] [1655498] Changing key rf in group patch from 20 to None\n", + " 69400 [params.py: set():1006] [1655513] Changing key stride in group patch from 10 to None\n", + " 69401 [params.py: set():1006] [1655558] Changing key stride in group patch from 10 to None\n", + " 69401 [params.py: set():1006] [1655524] Changing key stride in group patch from 10 to None\n", + " 69238 [params.py: set():1006] [1655495] Changing key rf in group patch from 20 to None\n", + " 69402 [params.py: set():1006] [1655546] Changing key nb in group temporal from 2 to 1\n", + " 69403 [params.py: set():1006] [1655560] Changing key stride in group patch from 10 to None\n", + " 69403 [params.py: set():1006] [1655553] Changing key nb in group spatial from 2 to 1\n", + " 68923 [params.py: set():1006] [1655476] Changing key rf in group patch from 20 to None\n", + " 69404 [params.py: set():1006] [1655509] Changing key stride in group patch from 10 to None\n", + " 69405 [params.py: set():1006] [1655470] Changing key nb in group init from 2 to 1\n", + " 69405 [params.py: set():1006] [1655536] Changing key nb in group init from 2 to 1\n", + " 69059 [params.py: set():1006] [1655542] Changing key rf in group patch from 20 to None\n", + " 69360 [params.py: set():1006] [1655555] Changing key rf in group patch from 20 to None\n", + " 69415 [params.py: set():1006] [1655561] Changing key rf in group patch from 20 to None\n", + " 69415 [params.py: set():1006] [1655504] Changing key rf in group patch from 20 to None\n", + " 69290 [params.py: set():1006] [1655474] Changing key rf in group patch from 20 to None\n", + " 69417 [params.py: set():1006] [1655502] Changing key rf in group patch from 20 to None\n", + " 69417 [params.py: set():1006] [1655520] Changing key stride in group patch from 10 to None\n", + " 69011 [params.py: set():1006] [1655544] Changing key rf in group patch from 20 to None\n", + " 69419 [params.py: set():1006] [1655537] Changing key nb in group init from 2 to 1\n", + " 69420 [params.py: set():1006] [1655494] Changing key nb in group init from 2 to 1\n", + " 69420 [params.py: set():1006] [1655547] Changing key stride in group patch from 10 to None\n", + " 69421 [params.py: set():1006] [1655490] Changing key nb in group init from 2 to 1\n", + " 69421 [params.py: set():1006] [1655473] Changing key nb in group init from 2 to 1\n", + " 69422 [params.py: set():1006] [1655517] Changing key stride in group patch from 10 to None\n", + " 69422 [params.py: set():1006] [1655543] Changing key nb in group init from 2 to 1\n", + " 69423 [params.py: set():1006] [1655525] Changing key nb in group init from 2 to 1\n", + " 69424 [params.py: set():1006] [1655522] Changing key nb in group init from 2 to 1\n", + " 69431 [params.py: set():1006] [1655519] Changing key nb in group init from 2 to 1\n", + " 69432 [params.py: set():1006] [1655518] Changing key stride in group patch from 10 to None\n", + " 69435 [params.py: set():1006] [1655472] Changing key rf in group patch from 20 to None\n", + " 69431 [params.py: set():1006] [1655523] Changing key stride in group patch from 10 to None\n", + " 69439 [params.py: set():1006] [1655557] Changing key nb in group init from 2 to 1\n", + " 69443 [params.py: set():1006] [1655559] Changing key rf in group patch from 20 to None\n", + " 69443 [params.py: set():1006] [1655540] Changing key nb in group init from 2 to 1\n", + " 69444 [params.py: set():1006] [1655500] Changing key nb in group init from 2 to 1\n", + " 69445 [params.py: set():1006] [1655515] Changing key p in group temporal from 1 to 0\n", + " 69446 [params.py: set():1006] [1655486] Changing key stride in group patch from 10 to None\n", + " 69446 [params.py: set():1006] [1655514] Changing key stride in group patch from 10 to None\n", + " 69447 [params.py: set():1006] [1655529] Changing key nb in group init from 2 to 1\n", + " 69438 [params.py: set():1006] [1655545] Changing key rf in group patch from 20 to None\n", + " 69447 [params.py: set():1006] [1655541] Changing key stride in group patch from 10 to None\n", + " 69449 [params.py: set():1006] [1655484] Changing key rf in group patch from 20 to None\n", + " 69451 [params.py: set():1006] [1655554] Changing key stride in group patch from 10 to None\n", + " 69111 [params.py: set():1006] [1655538] Changing key rf in group patch from 20 to None\n", + " 69451 [params.py: set():1006] [1655488] Changing key stride in group patch from 10 to None\n", + " 69452 [params.py: set():1006] [1655506] Changing key stride in group patch from 10 to None\n", + " 69453 [params.py: set():1006] [1655531] Changing key p in group preprocess from 1 to 0\n", + " 69453 [params.py: set():1006] [1655551] Changing key nb in group temporal from 2 to 1\n", + " 69370 [params.py: set():1006] [1655477] Changing key rf in group patch from 20 to None\n", + " 69453 [params.py: set():1006] [1655534] Changing key nb in group init from 2 to 1\n", + " 69457 [params.py: set():1006] [1655480] Changing key nb in group init from 2 to 1\n", + " 69456 [params.py: set():1006] [1655527] Changing key stride in group patch from 10 to None\n", + " 69455 [params.py: set():1006] [1655489] Changing key nb in group init from 2 to 1\n", + " 69455 [params.py: set():1006] [1655535] Changing key p in group preprocess from 1 to 0\n", + " 69456 [params.py: set():1006] [1655479] Changing key stride in group patch from 10 to None\n", + " 69458 [params.py: set():1006] [1655492] Changing key stride in group patch from 10 to None\n", + " 69467 [params.py: set():1006] [1655533] Changing key nb in group init from 2 to 1\n", + " 69458 [params.py: set():1006] [1655499] Changing key stride in group patch from 10 to None\n", + " 69461 [params.py: set():1006] [1655496] Changing key rf in group patch from 20 to None\n", + " 69461 [params.py: set():1006] [1655552] Changing key nb in group spatial from 2 to 1\n", + " 69462 [params.py: set():1006] [1655521] Changing key stride in group patch from 10 to None\n", + " 69462 [params.py: set():1006] [1655482] Changing key nb in group init from 2 to 1\n", + " 69463 [params.py: set():1006] [1655562] Changing key stride in group patch from 10 to None\n", + " 69464 [params.py: set():1006] [1655481] Changing key stride in group patch from 10 to None\n", + " 69465 [params.py: set():1006] [1655526] Changing key nb in group init from 2 to 1\n", + " 69466 [params.py: set():1006] [1655564] Changing key stride in group patch from 10 to None\n", + " 69466 [params.py: set():1006] [1655532] Changing key stride in group patch from 10 to None\n", + " 69466 [params.py: set():1006] [1655487] Changing key stride in group patch from 10 to None\n", + " 69468 [params.py: set():1006] [1655511] Changing key nb in group init from 2 to 1\n", + " 69469 [params.py: set():1006] [1655483] Changing key stride in group patch from 10 to None\n", + " 69469 [params.py: set():1006] [1655512] Changing key nb in group init from 2 to 1\n", + " 69471 [params.py: set():1006] [1655549] Changing key p in group preprocess from 1 to 0\n", + " 69471 [params.py: set():1006] [1655508] Changing key nb in group init from 2 to 1\n", + " 69471 [params.py: set():1006] [1655485] Changing key stride in group patch from 10 to None\n", + " 69472 [params.py: set():1006] [1655530] Changing key nb in group spatial from 2 to 1\n", + " 69473 [params.py: set():1006] [1655507] Changing key nb in group init from 2 to 1\n", + " 69474 [params.py: set():1006] [1655505] Changing key rf in group patch from 20 to None\n", + " 69474 [params.py: set():1006] [1655550] Changing key nb in group spatial from 2 to 1\n", + " 69475 [params.py: set():1006] [1655516] Changing key stride in group patch from 10 to None\n", + " 69476 [params.py: set():1006] [1655469] Changing key stride in group patch from 10 to None\n", + " 69476 [params.py: set():1006] [1655497] Changing key nb in group init from 2 to 1\n", + " 69477 [params.py: set():1006] [1655498] Changing key stride in group patch from 10 to None\n", + " 69479 [params.py: set():1006] [1655510] Changing key rf in group patch from 20 to None\n", + " 69479 [params.py: set():1006] [1655558] Changing key nb in group init from 2 to 1\n", + " 69480 [params.py: set():1006] [1655513] Changing key nb in group init from 2 to 1\n", + " 69480 [params.py: set():1006] [1655524] Changing key nb in group init from 2 to 1\n", + " 69481 [params.py: set():1006] [1655546] Changing key nb in group spatial from 2 to 1\n", + " 69482 [params.py: set():1006] [1655560] Changing key nb in group init from 2 to 1\n", + " 69487 [params.py: set():1006] [1655495] Changing key stride in group patch from 10 to None\n", + " 69488 [params.py: set():1006] [1655553] Changing key p in group preprocess from 1 to 0\n", + " 69491 [params.py: set():1006] [1655478] Changing key rf in group patch from 20 to None\n", + " 69491 [params.py: set():1006] [1655476] Changing key stride in group patch from 10 to None\n", + " 69492 [params.py: set():1006] [1655509] Changing key nb in group init from 2 to 1\n", + " 69493 [params.py: set():1006] [1655470] Changing key nb in group temporal from 2 to 1\n", + " 69493 [params.py: set():1006] [1655536] Changing key nb in group temporal from 2 to 1\n", + " 69496 [params.py: set():1006] [1655539] Changing key rf in group patch from 20 to None\n", + " 69497 [params.py: set():1006] [1655542] Changing key stride in group patch from 10 to None\n", + " 69497 [params.py: set():1006] [1655504] Changing key stride in group patch from 10 to None\n", + " 69498 [params.py: set():1006] [1655561] Changing key stride in group patch from 10 to None\n", + " 69499 [params.py: set():1006] [1655493] Changing key rf in group patch from 20 to None\n", + " 69500 [params.py: set():1006] [1655555] Changing key stride in group patch from 10 to None\n", + " 69430 [params.py: set():1006] [1655548] Changing key rf in group patch from 20 to None\n", + " 69501 [params.py: set():1006] [1655502] Changing key stride in group patch from 10 to None\n", + " 69501 [params.py: set():1006] [1655520] Changing key nb in group init from 2 to 1\n", + " 69502 [params.py: set():1006] [1655503] Changing key rf in group patch from 20 to None\n", + " 69503 [params.py: set():1006] [1655474] Changing key stride in group patch from 10 to None\n", + " 69504 [params.py: set():1006] [1655544] Changing key stride in group patch from 10 to None\n", + " 69505 [params.py: set():1006] [1655537] Changing key nb in group temporal from 2 to 1\n", + " 69505 [params.py: set():1006] [1655547] Changing key nb in group init from 2 to 1\n", + " 69506 [params.py: set():1006] [1655494] Changing key nb in group temporal from 2 to 1\n", + " 69506 [params.py: set():1006] [1655490] Changing key nb in group temporal from 2 to 1\n", + " 69507 [params.py: set():1006] [1655491] Changing key rf in group patch from 20 to None\n", + " 69507 [params.py: set():1006] [1655473] Changing key nb in group temporal from 2 to 1\n", + " 69508 [params.py: set():1006] [1655517] Changing key nb in group init from 2 to 1\n", + " 69509 [params.py: set():1006] [1655543] Changing key nb in group temporal from 2 to 1\n", + " 69509 [params.py: set():1006] [1655525] Changing key nb in group temporal from 2 to 1\n", + " 69510 [params.py: set():1006] [1655522] Changing key nb in group temporal from 2 to 1\n", + " 69510 [params.py: set():1006] [1655519] Changing key nb in group temporal from 2 to 1\n", + " 69511 [params.py: set():1006] [1655518] Changing key nb in group init from 2 to 1\n", + " 69512 [params.py: set():1006] [1655475] Changing key rf in group patch from 20 to None\n", + " 69412 [params.py: set():1006] [1655501] Changing key rf in group patch from 20 to None\n", + " 69512 [params.py: set():1006] [1655472] Changing key stride in group patch from 10 to None\n", + " 69513 [params.py: set():1006] [1655523] Changing key nb in group init from 2 to 1\n", + " 69514 [params.py: set():1006] [1655557] Changing key nb in group temporal from 2 to 1\n", + " 69514 [params.py: set():1006] [1655559] Changing key stride in group patch from 10 to None\n", + " 69515 [params.py: set():1006] [1655540] Changing key nb in group temporal from 2 to 1\n", + " 69516 [cnmf.py: fit():436] [1655515] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69516 [params.py: set():1006] [1655500] Changing key nb in group temporal from 2 to 1\n", + " 69517 [params.py: set():1006] [1655486] Changing key nb in group init from 2 to 1\n", + " 69518 [params.py: set():1006] [1655514] Changing key nb in group init from 2 to 1\n", + " 69518 [params.py: set():1006] [1655529] Changing key nb in group temporal from 2 to 1\n", + " 69519 [params.py: set():1006] [1655541] Changing key nb in group init from 2 to 1\n", + " 69520 [params.py: set():1006] [1655484] Changing key stride in group patch from 10 to None\n", + " 69519 [params.py: set():1006] [1655545] Changing key stride in group patch from 10 to None\n", + " 69522 [params.py: set():1006] [1655554] Changing key nb in group init from 2 to 1\n", + " 69522 [params.py: set():1006] [1655538] Changing key stride in group patch from 10 to None\n", + " 69523 [params.py: set():1006] [1655506] Changing key nb in group init from 2 to 1\n", + " 69523 [params.py: set():1006] [1655488] Changing key nb in group init from 2 to 1\n", + " 69524 [params.py: set():1006] [1655531] Changing key p in group temporal from 1 to 0\n", + " 69524 [params.py: set():1006] [1655551] Changing key nb in group spatial from 2 to 1\n", + " 69525 [params.py: set():1006] [1655534] Changing key nb in group temporal from 2 to 1\n", + " 69525 [params.py: set():1006] [1655477] Changing key stride in group patch from 10 to None\n", + " 69526 [params.py: set():1006] [1655489] Changing key nb in group temporal from 2 to 1\n", + " 69526 [params.py: set():1006] [1655535] Changing key p in group temporal from 1 to 0\n", + " 69526 [params.py: set():1006] [1655479] Changing key nb in group init from 2 to 1\n", + " 69528 [params.py: set():1006] [1655527] Changing key nb in group init from 2 to 1\n", + " 69528 [params.py: set():1006] [1655480] Changing key nb in group temporal from 2 to 1\n", + " 69529 [params.py: set():1006] [1655492] Changing key nb in group init from 2 to 1\n", + " 69529 [params.py: set():1006] [1655499] Changing key nb in group init from 2 to 1\n", + " 69530 [params.py: set():1006] [1655552] Changing key p in group preprocess from 1 to 0\n", + " 69530 [params.py: set():1006] [1655496] Changing key stride in group patch from 10 to None\n", + " 69531 [params.py: set():1006] [1655521] Changing key nb in group init from 2 to 1\n", + " 69531 [params.py: set():1006] [1655482] Changing key nb in group temporal from 2 to 1\n", + " 69531 [params.py: set():1006] [1655562] Changing key nb in group init from 2 to 1\n", + " 69532 [params.py: set():1006] [1655481] Changing key nb in group init from 2 to 1\n", + " 69533 [params.py: set():1006] [1655526] Changing key nb in group temporal from 2 to 1\n", + " 69533 [params.py: set():1006] [1655532] Changing key nb in group init from 2 to 1\n", + " 69534 [params.py: set():1006] [1655487] Changing key nb in group init from 2 to 1\n", + " 69534 [params.py: set():1006] [1655564] Changing key nb in group init from 2 to 1\n", + " 69535 [params.py: set():1006] [1655533] Changing key nb in group temporal from 2 to 1\n", + " 69535 [params.py: set():1006] [1655483] Changing key nb in group init from 2 to 1\n", + " 69536 [params.py: set():1006] [1655511] Changing key nb in group temporal from 2 to 1\n", + " 69536 [params.py: set():1006] [1655512] Changing key nb in group temporal from 2 to 1\n", + " 69537 [params.py: set():1006] [1655549] Changing key p in group temporal from 1 to 0\n", + " 69538 [params.py: set():1006] [1655508] Changing key nb in group temporal from 2 to 1\n", + " 69538 [params.py: set():1006] [1655485] Changing key nb in group init from 2 to 1\n", + " 69538 [params.py: set():1006] [1655530] Changing key p in group preprocess from 1 to 0\n", + " 69539 [params.py: set():1006] [1655505] Changing key stride in group patch from 10 to None\n", + " 69539 [params.py: set():1006] [1655507] Changing key nb in group temporal from 2 to 1\n", + " 69540 [params.py: set():1006] [1655550] Changing key p in group preprocess from 1 to 0\n", + " 69540 [params.py: set():1006] [1655516] Changing key nb in group init from 2 to 1\n", + " 69541 [params.py: set():1006] [1655469] Changing key nb in group init from 2 to 1\n", + " 69542 [params.py: set():1006] [1655497] Changing key nb in group temporal from 2 to 1\n", + " 69542 [params.py: set():1006] [1655498] Changing key nb in group init from 2 to 1\n", + " 69543 [params.py: set():1006] [1655510] Changing key stride in group patch from 10 to None\n", + " 69543 [params.py: set():1006] [1655513] Changing key nb in group temporal from 2 to 1\n", + " 69544 [params.py: set():1006] [1655558] Changing key nb in group temporal from 2 to 1\n", + " 69547 [params.py: set():1006] [1655556] Changing key rf in group patch from 20 to None\n", + " 69548 [params.py: set():1006] [1655524] Changing key nb in group temporal from 2 to 1\n", + " 69548 [params.py: set():1006] [1655546] Changing key p in group preprocess from 1 to 0\n", + " 69549 [params.py: set():1006] [1655560] Changing key nb in group temporal from 2 to 1\n", + " 69549 [params.py: set():1006] [1655495] Changing key nb in group init from 2 to 1\n", + " 69550 [params.py: set():1006] [1655553] Changing key p in group temporal from 1 to 0\n", + " 69551 [params.py: set():1006] [1655478] Changing key stride in group patch from 10 to None\n", + " 69551 [params.py: set():1006] [1655476] Changing key nb in group init from 2 to 1\n", + " 69552 [params.py: set():1006] [1655509] Changing key nb in group temporal from 2 to 1\n", + " 69552 [params.py: set():1006] [1655470] Changing key nb in group spatial from 2 to 1\n", + " 69553 [params.py: set():1006] [1655528] Changing key rf in group patch from 20 to None\n", + " 69554 [params.py: set():1006] [1655536] Changing key nb in group spatial from 2 to 1\n", + " 69554 [params.py: set():1006] [1655542] Changing key nb in group init from 2 to 1\n", + " 69555 [params.py: set():1006] [1655539] Changing key stride in group patch from 10 to None\n", + " 69555 [params.py: set():1006] [1655561] Changing key nb in group init from 2 to 1\n", + " 69556 [params.py: set():1006] [1655504] Changing key nb in group init from 2 to 1\n", + " 69556 [params.py: set():1006] [1655493] Changing key stride in group patch from 10 to None\n", + " 69557 [params.py: set():1006] [1655555] Changing key nb in group init from 2 to 1\n", + " 69557 [params.py: set():1006] [1655502] Changing key nb in group init from 2 to 1\n", + " 69558 [params.py: set():1006] [1655520] Changing key nb in group temporal from 2 to 1\n", + " 69559 [params.py: set():1006] [1655503] Changing key stride in group patch from 10 to None\n", + " 69559 [params.py: set():1006] [1655474] Changing key nb in group init from 2 to 1\n", + " 69560 [params.py: set():1006] [1655544] Changing key nb in group init from 2 to 1\n", + " 69561 [params.py: set():1006] [1655547] Changing key nb in group temporal from 2 to 1\n", + " 69561 [params.py: set():1006] [1655494] Changing key nb in group spatial from 2 to 1\n", + " 69561 [params.py: set():1006] [1655537] Changing key nb in group spatial from 2 to 1\n", + " 69562 [params.py: set():1006] [1655490] Changing key nb in group spatial from 2 to 1\n", + " 69562 [params.py: set():1006] [1655491] Changing key stride in group patch from 10 to None\n", + " 69563 [params.py: set():1006] [1655473] Changing key nb in group spatial from 2 to 1\n", + " 69563 [params.py: set():1006] [1655517] Changing key nb in group temporal from 2 to 1\n", + " 69564 [params.py: set():1006] [1655543] Changing key nb in group spatial from 2 to 1\n", + " 69559 [params.py: set():1006] [1655548] Changing key stride in group patch from 10 to None\n", + " 69564 [params.py: set():1006] [1655525] Changing key nb in group spatial from 2 to 1\n", + " 69565 [params.py: set():1006] [1655522] Changing key nb in group spatial from 2 to 1\n", + " 69566 [params.py: set():1006] [1655519] Changing key nb in group spatial from 2 to 1\n", + " 69566 [params.py: set():1006] [1655518] Changing key nb in group temporal from 2 to 1\n", + " 69567 [params.py: set():1006] [1655475] Changing key stride in group patch from 10 to None\n", + " 69568 [params.py: set():1006] [1655472] Changing key nb in group init from 2 to 1\n", + " 69568 [params.py: set():1006] [1655501] Changing key stride in group patch from 10 to None\n", + " 69568 [params.py: set():1006] [1655523] Changing key nb in group temporal from 2 to 1\n", + " 69569 [params.py: set():1006] [1655557] Changing key nb in group spatial from 2 to 1\n", + " 69570 [params.py: set():1006] [1655559] Changing key nb in group init from 2 to 1\n", + " 69570 [params.py: set():1006] [1655540] Changing key nb in group spatial from 2 to 1\n", + " 69571 [cnmf.py: fit():449] [1655515] (8000, 41, 41)\n", + " 69571 [params.py: set():1006] [1655500] Changing key nb in group spatial from 2 to 1\n", + " 69572 [params.py: set():1006] [1655486] Changing key nb in group temporal from 2 to 1\n", + " 69572 [params.py: set():1006] [1655514] Changing key nb in group temporal from 2 to 1\n", + " 69573 [params.py: set():1006] [1655529] Changing key nb in group spatial from 2 to 1\n", + " 69574 [params.py: set():1006] [1655541] Changing key nb in group temporal from 2 to 1\n", + " 69574 [params.py: set():1006] [1655563] Changing key rf in group patch from 20 to None\n", + " 69575 [params.py: set():1006] [1655545] Changing key nb in group init from 2 to 1\n", + " 69575 [params.py: set():1006] [1655484] Changing key nb in group init from 2 to 1\n", + " 69577 [params.py: set():1006] [1655538] Changing key nb in group init from 2 to 1\n", + " 69577 [params.py: set():1006] [1655554] Changing key nb in group temporal from 2 to 1\n", + " 69577 [params.py: set():1006] [1655506] Changing key nb in group temporal from 2 to 1\n", + " 69578 [params.py: set():1006] [1655488] Changing key nb in group temporal from 2 to 1\n", + " 69578 [cnmf.py: fit():436] [1655531] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69579 [params.py: set():1006] [1655551] Changing key p in group preprocess from 1 to 0\n", + " 69579 [params.py: set():1006] [1655534] Changing key nb in group spatial from 2 to 1\n", + " 69580 [params.py: set():1006] [1655477] Changing key nb in group init from 2 to 1\n", + " 69581 [params.py: set():1006] [1655479] Changing key nb in group temporal from 2 to 1\n", + " 69581 [cnmf.py: fit():436] [1655535] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69582 [params.py: set():1006] [1655489] Changing key nb in group spatial from 2 to 1\n", + " 69583 [params.py: set():1006] [1655480] Changing key nb in group spatial from 2 to 1\n", + " 69582 [params.py: set():1006] [1655527] Changing key nb in group temporal from 2 to 1\n", + " 69583 [params.py: set():1006] [1655492] Changing key nb in group temporal from 2 to 1\n", + " 69584 [params.py: set():1006] [1655499] Changing key nb in group temporal from 2 to 1\n", + " 69584 [params.py: set():1006] [1655496] Changing key nb in group init from 2 to 1\n", + " 69584 [params.py: set():1006] [1655552] Changing key p in group temporal from 1 to 0\n", + " 69585 [params.py: set():1006] [1655521] Changing key nb in group temporal from 2 to 1\n", + " 69586 [params.py: set():1006] [1655482] Changing key nb in group spatial from 2 to 1\n", + " 69586 [params.py: set():1006] [1655562] Changing key nb in group temporal from 2 to 1\n", + " 69587 [params.py: set():1006] [1655481] Changing key nb in group temporal from 2 to 1\n", + " 69587 [params.py: set():1006] [1655526] Changing key nb in group spatial from 2 to 1\n", + " 69588 [params.py: set():1006] [1655487] Changing key nb in group temporal from 2 to 1\n", + " 69589 [params.py: set():1006] [1655564] Changing key nb in group temporal from 2 to 1\n", + " 69589 [params.py: set():1006] [1655532] Changing key nb in group temporal from 2 to 1\n", + " 69589 [params.py: set():1006] [1655533] Changing key nb in group spatial from 2 to 1\n", + " 69590 [params.py: set():1006] [1655511] Changing key nb in group spatial from 2 to 1\n", + " 69590 [params.py: set():1006] [1655483] Changing key nb in group temporal from 2 to 1\n", + " 69591 [params.py: set():1006] [1655512] Changing key nb in group spatial from 2 to 1\n", + " 69593 [cnmf.py: fit():436] [1655549] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69592 [params.py: set():1006] [1655508] Changing key nb in group spatial from 2 to 1\n", + " 69593 [params.py: set():1006] [1655485] Changing key nb in group temporal from 2 to 1\n", + " 69593 [params.py: set():1006] [1655530] Changing key p in group temporal from 1 to 0\n", + " 69594 [params.py: set():1006] [1655507] Changing key nb in group spatial from 2 to 1\n", + " 69594 [params.py: set():1006] [1655505] Changing key nb in group init from 2 to 1\n", + " 69595 [params.py: set():1006] [1655550] Changing key p in group temporal from 1 to 0\n", + " 69595 [params.py: set():1006] [1655516] Changing key nb in group temporal from 2 to 1\n", + " 69596 [params.py: set():1006] [1655469] Changing key nb in group temporal from 2 to 1\n", + " 69597 [params.py: set():1006] [1655497] Changing key nb in group spatial from 2 to 1\n", + " 69597 [params.py: set():1006] [1655498] Changing key nb in group temporal from 2 to 1\n", + " 69603 [params.py: set():1006] [1655510] Changing key nb in group init from 2 to 1\n", + " 69604 [params.py: set():1006] [1655558] Changing key nb in group spatial from 2 to 1\n", + " 69604 [params.py: set():1006] [1655513] Changing key nb in group spatial from 2 to 1\n", + " 69605 [params.py: set():1006] [1655556] Changing key stride in group patch from 10 to None\n", + " 69605 [params.py: set():1006] [1655524] Changing key nb in group spatial from 2 to 1\n", + " 69606 [params.py: set():1006] [1655546] Changing key p in group temporal from 1 to 0\n", + " 69606 [params.py: set():1006] [1655560] Changing key nb in group spatial from 2 to 1\n", + " 69607 [params.py: set():1006] [1655495] Changing key nb in group temporal from 2 to 1\n", + " 69608 [cnmf.py: fit():436] [1655553] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69609 [params.py: set():1006] [1655476] Changing key nb in group temporal from 2 to 1\n", + " 69608 [params.py: set():1006] [1655478] Changing key nb in group init from 2 to 1\n", + " 69609 [params.py: set():1006] [1655470] Changing key p in group preprocess from 1 to 0\n", + " 69610 [params.py: set():1006] [1655509] Changing key nb in group spatial from 2 to 1\n", + " 69611 [params.py: set():1006] [1655528] Changing key stride in group patch from 10 to None\n", + " 69611 [params.py: set():1006] [1655536] Changing key p in group preprocess from 1 to 0\n", + " 69612 [params.py: set():1006] [1655542] Changing key nb in group temporal from 2 to 1\n", + " 69612 [params.py: set():1006] [1655539] Changing key nb in group init from 2 to 1\n", + " 69613 [params.py: set():1006] [1655561] Changing key nb in group temporal from 2 to 1\n", + " 69613 [params.py: set():1006] [1655504] Changing key nb in group temporal from 2 to 1\n", + " 69614 [params.py: set():1006] [1655555] Changing key nb in group temporal from 2 to 1\n", + " 69613 [params.py: set():1006] [1655493] Changing key nb in group init from 2 to 1\n", + " 69615 [params.py: set():1006] [1655502] Changing key nb in group temporal from 2 to 1\n", + " 69615 [params.py: set():1006] [1655520] Changing key nb in group spatial from 2 to 1\n", + " 69616 [params.py: set():1006] [1655503] Changing key nb in group init from 2 to 1\n", + " 69616 [params.py: set():1006] [1655474] Changing key nb in group temporal from 2 to 1\n", + " 69617 [params.py: set():1006] [1655544] Changing key nb in group temporal from 2 to 1\n", + " 69617 [params.py: set():1006] [1655494] Changing key p in group preprocess from 1 to 0\n", + " 69618 [params.py: set():1006] [1655547] Changing key nb in group spatial from 2 to 1\n", + " 69619 [params.py: set():1006] [1655491] Changing key nb in group init from 2 to 1\n", + " 69619 [params.py: set():1006] [1655473] Changing key p in group preprocess from 1 to 0\n", + " 69618 [params.py: set():1006] [1655537] Changing key p in group preprocess from 1 to 0\n", + " 69619 [params.py: set():1006] [1655490] Changing key p in group preprocess from 1 to 0\n", + " 69620 [params.py: set():1006] [1655517] Changing key nb in group spatial from 2 to 1\n", + " 69621 [params.py: set():1006] [1655543] Changing key p in group preprocess from 1 to 0\n", + " 69621 [params.py: set():1006] [1655548] Changing key nb in group init from 2 to 1\n", + " 69622 [params.py: set():1006] [1655525] Changing key p in group preprocess from 1 to 0\n", + " 69622 [params.py: set():1006] [1655522] Changing key p in group preprocess from 1 to 0\n", + " 69623 [params.py: set():1006] [1655519] Changing key p in group preprocess from 1 to 0\n", + " 69623 [params.py: set():1006] [1655518] Changing key nb in group spatial from 2 to 1\n", + " 69624 [params.py: set():1006] [1655475] Changing key nb in group init from 2 to 1\n", + " 69625 [params.py: set():1006] [1655472] Changing key nb in group temporal from 2 to 1\n", + " 69626 [params.py: set():1006] [1655557] Changing key p in group preprocess from 1 to 0\n", + " 69625 [params.py: set():1006] [1655523] Changing key nb in group spatial from 2 to 1\n", + " 69626 [params.py: set():1006] [1655501] Changing key nb in group init from 2 to 1\n", + " 69627 [params.py: set():1006] [1655559] Changing key nb in group temporal from 2 to 1\n", + " 69627 [params.py: set():1006] [1655540] Changing key p in group preprocess from 1 to 0\n", + " 69628 [cnmf.py: fit():466] [1655515] Using 1 processes\n", + " 69628 [params.py: set():1006] [1655500] Changing key p in group preprocess from 1 to 0\n", + " 69629 [params.py: set():1006] [1655486] Changing key nb in group spatial from 2 to 1\n", + " 69630 [params.py: set():1006] [1655514] Changing key nb in group spatial from 2 to 1\n", + " 69630 [params.py: set():1006] [1655529] Changing key p in group preprocess from 1 to 0\n", + " 69631 [params.py: set():1006] [1655541] Changing key nb in group spatial from 2 to 1\n", + " 69632 [params.py: set():1006] [1655484] Changing key nb in group temporal from 2 to 1\n", + " 69631 [params.py: set():1006] [1655563] Changing key stride in group patch from 10 to None\n", + " 69632 [params.py: set():1006] [1655545] Changing key nb in group temporal from 2 to 1\n", + " 69634 [params.py: set():1006] [1655506] Changing key nb in group spatial from 2 to 1\n", + " 69634 [params.py: set():1006] [1655538] Changing key nb in group temporal from 2 to 1\n", + " 69633 [params.py: set():1006] [1655554] Changing key nb in group spatial from 2 to 1\n", + " 69634 [params.py: set():1006] [1655488] Changing key nb in group spatial from 2 to 1\n", + " 69635 [cnmf.py: fit():449] [1655531] (8000, 41, 41)\n", + " 69635 [params.py: set():1006] [1655551] Changing key p in group temporal from 1 to 0\n", + " 69636 [params.py: set():1006] [1655534] Changing key p in group preprocess from 1 to 0\n", + " 69640 [cnmf.py: fit():449] [1655535] (8000, 41, 41)\n", + " 69640 [params.py: set():1006] [1655477] Changing key nb in group temporal from 2 to 1\n", + " 69640 [params.py: set():1006] [1655479] Changing key nb in group spatial from 2 to 1\n", + " 69641 [params.py: set():1006] [1655489] Changing key p in group preprocess from 1 to 0\n", + " 69641 [params.py: set():1006] [1655480] Changing key p in group preprocess from 1 to 0\n", + " 69642 [params.py: set():1006] [1655527] Changing key nb in group spatial from 2 to 1\n", + " 69642 [params.py: set():1006] [1655492] Changing key nb in group spatial from 2 to 1\n", + " 69643 [cnmf.py: fit():436] [1655552] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69643 [params.py: set():1006] [1655499] Changing key nb in group spatial from 2 to 1\n", + " 69644 [params.py: set():1006] [1655496] Changing key nb in group temporal from 2 to 1\n", + " 69644 [params.py: set():1006] [1655521] Changing key nb in group spatial from 2 to 1\n", + " 69645 [params.py: set():1006] [1655482] Changing key p in group preprocess from 1 to 0\n", + " 69645 [params.py: set():1006] [1655562] Changing key nb in group spatial from 2 to 1\n", + " 69646 [params.py: set():1006] [1655481] Changing key nb in group spatial from 2 to 1\n", + " 69646 [params.py: set():1006] [1655526] Changing key p in group preprocess from 1 to 0\n", + " 69647 [params.py: set():1006] [1655487] Changing key nb in group spatial from 2 to 1\n", + " 69647 [params.py: set():1006] [1655564] Changing key nb in group spatial from 2 to 1\n", + " 69648 [params.py: set():1006] [1655532] Changing key nb in group spatial from 2 to 1\n", + " 69648 [params.py: set():1006] [1655533] Changing key p in group preprocess from 1 to 0\n", + " 69649 [params.py: set():1006] [1655511] Changing key p in group preprocess from 1 to 0\n", + " 69649 [params.py: set():1006] [1655483] Changing key nb in group spatial from 2 to 1\n", + " 69650 [params.py: set():1006] [1655512] Changing key p in group preprocess from 1 to 0\n", + " 69651 [params.py: set():1006] [1655508] Changing key p in group preprocess from 1 to 0\n", + " 69650 [cnmf.py: fit():449] [1655549] (8000, 41, 41)\n", + " 69652 [cnmf.py: fit():436] [1655530] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69651 [params.py: set():1006] [1655485] Changing key nb in group spatial from 2 to 1\n", + " 69652 [params.py: set():1006] [1655507] Changing key p in group preprocess from 1 to 0\n", + " 69653 [params.py: set():1006] [1655505] Changing key nb in group temporal from 2 to 1\n", + " 69653 [cnmf.py: fit():436] [1655550] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69653 [params.py: set():1006] [1655516] Changing key nb in group spatial from 2 to 1\n", + " 69654 [params.py: set():1006] [1655469] Changing key nb in group spatial from 2 to 1\n", + " 69655 [params.py: set():1006] [1655498] Changing key nb in group spatial from 2 to 1\n", + " 69655 [params.py: set():1006] [1655497] Changing key p in group preprocess from 1 to 0\n", + " 69656 [params.py: set():1006] [1655510] Changing key nb in group temporal from 2 to 1\n", + " 69656 [params.py: set():1006] [1655558] Changing key p in group preprocess from 1 to 0\n", + " 69657 [params.py: set():1006] [1655513] Changing key p in group preprocess from 1 to 0\n", + " 69657 [params.py: set():1006] [1655556] Changing key nb in group init from 2 to 1\n", + " 69658 [params.py: set():1006] [1655524] Changing key p in group preprocess from 1 to 0\n", + " 69658 [cnmf.py: fit():436] [1655546] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69659 [params.py: set():1006] [1655560] Changing key p in group preprocess from 1 to 0\n", + " 69659 [params.py: set():1006] [1655495] Changing key nb in group spatial from 2 to 1\n", + " 69660 [cnmf.py: fit():449] [1655553] (8000, 41, 41)\n", + " 69661 [params.py: set():1006] [1655476] Changing key nb in group spatial from 2 to 1\n", + " 69660 [params.py: set():1006] [1655478] Changing key nb in group temporal from 2 to 1\n", + " 69661 [params.py: set():1006] [1655509] Changing key p in group preprocess from 1 to 0\n", + " 69661 [params.py: set():1006] [1655470] Changing key p in group temporal from 1 to 0\n", + " 69662 [params.py: set():1006] [1655536] Changing key p in group temporal from 1 to 0\n", + " 69662 [params.py: set():1006] [1655528] Changing key nb in group init from 2 to 1\n", + " 69663 [params.py: set():1006] [1655539] Changing key nb in group temporal from 2 to 1\n", + " 69663 [params.py: set():1006] [1655542] Changing key nb in group spatial from 2 to 1\n", + " 69664 [params.py: set():1006] [1655504] Changing key nb in group spatial from 2 to 1\n", + " 69664 [params.py: set():1006] [1655561] Changing key nb in group spatial from 2 to 1\n", + " 69665 [params.py: set():1006] [1655493] Changing key nb in group temporal from 2 to 1\n", + " 69665 [params.py: set():1006] [1655555] Changing key nb in group spatial from 2 to 1\n", + " 69666 [params.py: set():1006] [1655502] Changing key nb in group spatial from 2 to 1\n", + " 69667 [params.py: set():1006] [1655503] Changing key nb in group temporal from 2 to 1\n", + " 69666 [params.py: set():1006] [1655520] Changing key p in group preprocess from 1 to 0\n", + " 69667 [params.py: set():1006] [1655474] Changing key nb in group spatial from 2 to 1\n", + " 69668 [params.py: set():1006] [1655544] Changing key nb in group spatial from 2 to 1\n", + " 69669 [params.py: set():1006] [1655547] Changing key p in group preprocess from 1 to 0\n", + " 69668 [params.py: set():1006] [1655494] Changing key p in group temporal from 1 to 0\n", + " 69670 [params.py: set():1006] [1655491] Changing key nb in group temporal from 2 to 1\n", + " 69669 [params.py: set():1006] [1655473] Changing key p in group temporal from 1 to 0\n", + " 69670 [params.py: set():1006] [1655537] Changing key p in group temporal from 1 to 0\n", + " 69671 [params.py: set():1006] [1655490] Changing key p in group temporal from 1 to 0\n", + " 69671 [params.py: set():1006] [1655517] Changing key p in group preprocess from 1 to 0\n", + " 69672 [params.py: set():1006] [1655543] Changing key p in group temporal from 1 to 0\n", + " 69672 [params.py: set():1006] [1655548] Changing key nb in group temporal from 2 to 1\n", + " 69672 [params.py: set():1006] [1655525] Changing key p in group temporal from 1 to 0\n", + " 69673 [params.py: set():1006] [1655522] Changing key p in group temporal from 1 to 0\n", + " 69673 [params.py: set():1006] [1655519] Changing key p in group temporal from 1 to 0\n", + " 69674 [params.py: set():1006] [1655518] Changing key p in group preprocess from 1 to 0\n", + " 69674 [params.py: set():1006] [1655475] Changing key nb in group temporal from 2 to 1\n", + " 69675 [params.py: set():1006] [1655472] Changing key nb in group spatial from 2 to 1\n", + " 69676 [params.py: set():1006] [1655523] Changing key p in group preprocess from 1 to 0\n", + " 69675 [params.py: set():1006] [1655557] Changing key p in group temporal from 1 to 0\n", + " 69676 [params.py: set():1006] [1655501] Changing key nb in group temporal from 2 to 1\n", + " 69677 [params.py: set():1006] [1655559] Changing key nb in group spatial from 2 to 1\n", + " 69677 [params.py: set():1006] [1655540] Changing key p in group temporal from 1 to 0\n", + " 69678 [cnmf.py: fit():477] [1655515] using 400 pixels per process\n", + " 69678 [params.py: set():1006] [1655500] Changing key p in group temporal from 1 to 0\n", + " 69678 [params.py: set():1006] [1655486] Changing key p in group preprocess from 1 to 0\n", + " 69679 [params.py: set():1006] [1655529] Changing key p in group temporal from 1 to 0\n", + " 69679 [params.py: set():1006] [1655514] Changing key p in group preprocess from 1 to 0\n", + " 69684 [params.py: set():1006] [1655541] Changing key p in group preprocess from 1 to 0\n", + " 69685 [params.py: set():1006] [1655563] Changing key nb in group init from 2 to 1\n", + " 69685 [params.py: set():1006] [1655545] Changing key nb in group spatial from 2 to 1\n", + " 69684 [params.py: set():1006] [1655484] Changing key nb in group spatial from 2 to 1\n", + " 69686 [params.py: set():1006] [1655506] Changing key p in group preprocess from 1 to 0\n", + " 69686 [params.py: set():1006] [1655538] Changing key nb in group spatial from 2 to 1\n", + " 69687 [params.py: set():1006] [1655554] Changing key p in group preprocess from 1 to 0\n", + " 69687 [params.py: set():1006] [1655488] Changing key p in group preprocess from 1 to 0\n", + " 69688 [cnmf.py: fit():466] [1655531] Using 1 processes\n", + " 69689 [params.py: set():1006] [1655534] Changing key p in group temporal from 1 to 0\n", + " 69688 [cnmf.py: fit():436] [1655551] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69690 [params.py: set():1006] [1655477] Changing key nb in group spatial from 2 to 1\n", + " 69689 [cnmf.py: fit():466] [1655535] Using 1 processes\n", + " 69690 [params.py: set():1006] [1655489] Changing key p in group temporal from 1 to 0\n", + " 69690 [params.py: set():1006] [1655479] Changing key p in group preprocess from 1 to 0\n", + " 69691 [params.py: set():1006] [1655480] Changing key p in group temporal from 1 to 0\n", + " 69691 [params.py: set():1006] [1655527] Changing key p in group preprocess from 1 to 0\n", + " 69692 [params.py: set():1006] [1655492] Changing key p in group preprocess from 1 to 0\n", + " 69692 [cnmf.py: fit():449] [1655552] (8000, 41, 41)\n", + " 69693 [params.py: set():1006] [1655499] Changing key p in group preprocess from 1 to 0\n", + " 69698 [params.py: set():1006] [1655496] Changing key nb in group spatial from 2 to 1\n", + " 69699 [params.py: set():1006] [1655521] Changing key p in group preprocess from 1 to 0\n", + " 69699 [params.py: set():1006] [1655482] Changing key p in group temporal from 1 to 0\n", + " 69700 [params.py: set():1006] [1655562] Changing key p in group preprocess from 1 to 0\n", + " 69700 [params.py: set():1006] [1655481] Changing key p in group preprocess from 1 to 0\n", + " 69701 [params.py: set():1006] [1655526] Changing key p in group temporal from 1 to 0\n", + " 69701 [params.py: set():1006] [1655487] Changing key p in group preprocess from 1 to 0\n", + " 69702 [params.py: set():1006] [1655564] Changing key p in group preprocess from 1 to 0\n", + " 69702 [params.py: set():1006] [1655532] Changing key p in group preprocess from 1 to 0\n", + " 69702 [params.py: set():1006] [1655533] Changing key p in group temporal from 1 to 0\n", + " 69703 [params.py: set():1006] [1655511] Changing key p in group temporal from 1 to 0\n", + " 69703 [params.py: set():1006] [1655483] Changing key p in group preprocess from 1 to 0\n", + " 69704 [params.py: set():1006] [1655512] Changing key p in group temporal from 1 to 0\n", + " 69704 [params.py: set():1006] [1655508] Changing key p in group temporal from 1 to 0\n", + " 69705 [cnmf.py: fit():466] [1655549] Using 1 processes\n", + " 69705 [cnmf.py: fit():449] [1655530] (8000, 41, 41)\n", + " 69706 [params.py: set():1006] [1655485] Changing key p in group preprocess from 1 to 0\n", + " 69706 [params.py: set():1006] [1655507] Changing key p in group temporal from 1 to 0\n", + " 69707 [params.py: set():1006] [1655505] Changing key nb in group spatial from 2 to 1\n", + " 69707 [cnmf.py: fit():449] [1655550] (8000, 41, 41)\n", + " 69708 [params.py: set():1006] [1655516] Changing key p in group preprocess from 1 to 0\n", + " 69709 [params.py: set():1006] [1655469] Changing key p in group preprocess from 1 to 0\n", + " 69709 [params.py: set():1006] [1655497] Changing key p in group temporal from 1 to 0\n", + " 69708 [params.py: set():1006] [1655498] Changing key p in group preprocess from 1 to 0\n", + " 69710 [params.py: set():1006] [1655510] Changing key nb in group spatial from 2 to 1\n", + " 69710 [params.py: set():1006] [1655558] Changing key p in group temporal from 1 to 0\n", + " 69711 [params.py: set():1006] [1655513] Changing key p in group temporal from 1 to 0\n", + " 69711 [params.py: set():1006] [1655556] Changing key nb in group temporal from 2 to 1\n", + " 69711 [params.py: set():1006] [1655524] Changing key p in group temporal from 1 to 0\n", + " 69712 [cnmf.py: fit():449] [1655546] (8000, 41, 41)\n", + " 69712 [params.py: set():1006] [1655560] Changing key p in group temporal from 1 to 0\n", + " 69713 [params.py: set():1006] [1655495] Changing key p in group preprocess from 1 to 0\n", + " 69713 [cnmf.py: fit():466] [1655553] Using 1 processes\n", + " 69714 [params.py: set():1006] [1655478] Changing key nb in group spatial from 2 to 1\n", + " 69714 [params.py: set():1006] [1655476] Changing key p in group preprocess from 1 to 0\n", + " 69715 [cnmf.py: fit():436] [1655470] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69715 [params.py: set():1006] [1655509] Changing key p in group temporal from 1 to 0\n", + " 69716 [cnmf.py: fit():436] [1655536] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69716 [params.py: set():1006] [1655528] Changing key nb in group temporal from 2 to 1\n", + " 69717 [params.py: set():1006] [1655539] Changing key nb in group spatial from 2 to 1\n", + " 69717 [params.py: set():1006] [1655542] Changing key p in group preprocess from 1 to 0\n", + " 69718 [params.py: set():1006] [1655504] Changing key p in group preprocess from 1 to 0\n", + " 69718 [params.py: set():1006] [1655561] Changing key p in group preprocess from 1 to 0\n", + " 69719 [params.py: set():1006] [1655555] Changing key p in group preprocess from 1 to 0\n", + " 69719 [params.py: set():1006] [1655493] Changing key nb in group spatial from 2 to 1\n", + " 69721 [params.py: set():1006] [1655503] Changing key nb in group spatial from 2 to 1\n", + " 69720 [params.py: set():1006] [1655502] Changing key p in group preprocess from 1 to 0\n", + " 69721 [params.py: set():1006] [1655520] Changing key p in group temporal from 1 to 0\n", + " 69722 [params.py: set():1006] [1655544] Changing key p in group preprocess from 1 to 0\n", + " 69722 [params.py: set():1006] [1655474] Changing key p in group preprocess from 1 to 0\n", + " 69723 [params.py: set():1006] [1655547] Changing key p in group temporal from 1 to 0\n", + " 69723 [cnmf.py: fit():436] [1655494] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69724 [cnmf.py: fit():436] [1655473] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69724 [params.py: set():1006] [1655491] Changing key nb in group spatial from 2 to 1\n", + " 69725 [cnmf.py: fit():436] [1655537] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69725 [cnmf.py: fit():436] [1655490] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69726 [params.py: set():1006] [1655517] Changing key p in group temporal from 1 to 0\n", + " 69726 [cnmf.py: fit():436] [1655543] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69727 [params.py: set():1006] [1655548] Changing key nb in group spatial from 2 to 1\n", + " 69727 [cnmf.py: fit():436] [1655525] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69728 [cnmf.py: fit():436] [1655522] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69729 [cnmf.py: fit():436] [1655519] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69729 [params.py: set():1006] [1655518] Changing key p in group temporal from 1 to 0\n", + " 69729 [params.py: set():1006] [1655475] Changing key nb in group spatial from 2 to 1\n", + " 69730 [params.py: set():1006] [1655472] Changing key p in group preprocess from 1 to 0\n", + " 69731 [cnmf.py: fit():436] [1655557] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69731 [params.py: set():1006] [1655523] Changing key p in group temporal from 1 to 0\n", + " 69731 [params.py: set():1006] [1655501] Changing key nb in group spatial from 2 to 1\n", + " 69732 [params.py: set():1006] [1655559] Changing key p in group preprocess from 1 to 0\n", + " 69732 [cnmf.py: fit():436] [1655540] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69734 [cnmf.py: fit():436] [1655500] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69733 [cnmf.py: fit():478] [1655515] using 5000 block_size_temp\n", + " 69734 [params.py: set():1006] [1655486] Changing key p in group temporal from 1 to 0\n", + " 69735 [params.py: set():1006] [1655514] Changing key p in group temporal from 1 to 0\n", + " 69735 [cnmf.py: fit():436] [1655529] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69736 [params.py: set():1006] [1655541] Changing key p in group temporal from 1 to 0\n", + " 69737 [params.py: set():1006] [1655563] Changing key nb in group temporal from 2 to 1\n", + " 69736 [params.py: set():1006] [1655545] Changing key p in group preprocess from 1 to 0\n", + " 69737 [params.py: set():1006] [1655484] Changing key p in group preprocess from 1 to 0\n", + " 69738 [params.py: set():1006] [1655538] Changing key p in group preprocess from 1 to 0\n", + " 69738 [params.py: set():1006] [1655506] Changing key p in group temporal from 1 to 0\n", + " 69738 [params.py: set():1006] [1655488] Changing key p in group temporal from 1 to 0\n", + " 69739 [params.py: set():1006] [1655554] Changing key p in group temporal from 1 to 0\n", + " 69739 [cnmf.py: fit():477] [1655531] using 400 pixels per process\n", + " 69740 [cnmf.py: fit():436] [1655534] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69740 [cnmf.py: fit():449] [1655551] (8000, 41, 41)\n", + " 69748 [params.py: set():1006] [1655477] Changing key p in group preprocess from 1 to 0\n", + " 69748 [cnmf.py: fit():477] [1655535] using 400 pixels per process\n", + " 69749 [params.py: set():1006] [1655479] Changing key p in group temporal from 1 to 0\n", + " 69749 [cnmf.py: fit():436] [1655489] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69750 [cnmf.py: fit():436] [1655480] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69750 [params.py: set():1006] [1655527] Changing key p in group temporal from 1 to 0\n", + " 69751 [params.py: set():1006] [1655492] Changing key p in group temporal from 1 to 0\n", + " 69752 [cnmf.py: fit():466] [1655552] Using 1 processes\n", + " 69752 [params.py: set():1006] [1655496] Changing key p in group preprocess from 1 to 0\n", + " 69752 [params.py: set():1006] [1655499] Changing key p in group temporal from 1 to 0\n", + " 69753 [params.py: set():1006] [1655521] Changing key p in group temporal from 1 to 0\n", + " 69753 [cnmf.py: fit():436] [1655482] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69754 [params.py: set():1006] [1655562] Changing key p in group temporal from 1 to 0\n", + " 69754 [params.py: set():1006] [1655481] Changing key p in group temporal from 1 to 0\n", + " 69755 [cnmf.py: fit():436] [1655526] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69756 [params.py: set():1006] [1655487] Changing key p in group temporal from 1 to 0\n", + " 69756 [params.py: set():1006] [1655564] Changing key p in group temporal from 1 to 0\n", + " 69757 [params.py: set():1006] [1655532] Changing key p in group temporal from 1 to 0\n", + " 69757 [cnmf.py: fit():436] [1655533] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69758 [cnmf.py: fit():436] [1655511] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69758 [params.py: set():1006] [1655483] Changing key p in group temporal from 1 to 0\n", + " 69759 [cnmf.py: fit():436] [1655512] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69760 [cnmf.py: fit():436] [1655508] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69759 [cnmf.py: fit():477] [1655549] using 400 pixels per process\n", + " 69760 [cnmf.py: fit():466] [1655530] Using 1 processes\n", + " 69762 [params.py: set():1006] [1655505] Changing key p in group preprocess from 1 to 0\n", + " 69761 [cnmf.py: fit():436] [1655507] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69761 [params.py: set():1006] [1655485] Changing key p in group temporal from 1 to 0\n", + " 69763 [params.py: set():1006] [1655516] Changing key p in group temporal from 1 to 0\n", + " 69762 [cnmf.py: fit():466] [1655550] Using 1 processes\n", + " 69763 [params.py: set():1006] [1655469] Changing key p in group temporal from 1 to 0\n", + " 69764 [cnmf.py: fit():436] [1655497] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69764 [params.py: set():1006] [1655498] Changing key p in group temporal from 1 to 0\n", + " 69765 [params.py: set():1006] [1655510] Changing key p in group preprocess from 1 to 0\n", + " 69766 [cnmf.py: fit():436] [1655558] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69765 [cnmf.py: fit():436] [1655513] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69766 [params.py: set():1006] [1655556] Changing key nb in group spatial from 2 to 1\n", + " 69767 [cnmf.py: fit():436] [1655524] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69767 [cnmf.py: fit():466] [1655546] Using 1 processes\n", + " 69768 [cnmf.py: fit():436] [1655560] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69769 [params.py: set():1006] [1655495] Changing key p in group temporal from 1 to 0\n", + " 69770 [params.py: set():1006] [1655476] Changing key p in group temporal from 1 to 0\n", + " 69769 [cnmf.py: fit():477] [1655553] using 400 pixels per process\n", + " 69770 [params.py: set():1006] [1655478] Changing key p in group preprocess from 1 to 0\n", + " 69771 [cnmf.py: fit():436] [1655509] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69771 [cnmf.py: fit():449] [1655470] (8000, 41, 41)\n", + " 69772 [params.py: set():1006] [1655528] Changing key nb in group spatial from 2 to 1\n", + " 69772 [cnmf.py: fit():449] [1655536] (8000, 41, 41)\n", + " 69773 [params.py: set():1006] [1655539] Changing key p in group preprocess from 1 to 0\n", + " 69773 [params.py: set():1006] [1655542] Changing key p in group temporal from 1 to 0\n", + " 69774 [params.py: set():1006] [1655504] Changing key p in group temporal from 1 to 0\n", + " 69774 [params.py: set():1006] [1655561] Changing key p in group temporal from 1 to 0\n", + " 69774 [params.py: set():1006] [1655555] Changing key p in group temporal from 1 to 0\n", + " 69775 [params.py: set():1006] [1655493] Changing key p in group preprocess from 1 to 0\n", + " 69776 [params.py: set():1006] [1655503] Changing key p in group preprocess from 1 to 0\n", + " 69776 [cnmf.py: fit():436] [1655520] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69775 [params.py: set():1006] [1655502] Changing key p in group temporal from 1 to 0\n", + " 69777 [params.py: set():1006] [1655544] Changing key p in group temporal from 1 to 0\n", + " 69777 [params.py: set():1006] [1655474] Changing key p in group temporal from 1 to 0\n", + " 69778 [cnmf.py: fit():436] [1655547] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69778 [cnmf.py: fit():449] [1655494] (8000, 41, 41)\n", + " 69779 [cnmf.py: fit():449] [1655473] (8000, 41, 41)\n", + " 69779 [params.py: set():1006] [1655491] Changing key p in group preprocess from 1 to 0\n", + " 69780 [cnmf.py: fit():449] [1655537] (8000, 41, 40)\n", + " 69780 [cnmf.py: fit():449] [1655490] (8000, 41, 41)\n", + " 69781 [cnmf.py: fit():436] [1655517] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69781 [cnmf.py: fit():449] [1655543] (8000, 41, 41)\n", + " 69782 [params.py: set():1006] [1655548] Changing key p in group preprocess from 1 to 0\n", + " 69782 [cnmf.py: fit():449] [1655525] (8000, 41, 41)\n", + " 69783 [cnmf.py: fit():449] [1655522] (8000, 41, 41)\n", + " 69783 [cnmf.py: fit():449] [1655519] (8000, 41, 41)\n", + " 69784 [cnmf.py: fit():436] [1655518] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69791 [params.py: set():1006] [1655475] Changing key p in group preprocess from 1 to 0\n", + " 69792 [params.py: set():1006] [1655472] Changing key p in group temporal from 1 to 0\n", + " 69793 [cnmf.py: fit():449] [1655557] (8000, 41, 41)\n", + " 69793 [cnmf.py: fit():436] [1655523] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69793 [params.py: set():1006] [1655501] Changing key p in group preprocess from 1 to 0\n", + " 69794 [params.py: set():1006] [1655559] Changing key p in group temporal from 1 to 0\n", + " 69794 [cnmf.py: fit():449] [1655540] (8000, 41, 41)\n", + " 69795 [cnmf.py: fit():481] [1655515] preprocessing ...\n", + " 69795 [cnmf.py: fit():449] [1655500] (8000, 41, 41)\n", + " 69796 [cnmf.py: fit():436] [1655486] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69797 [cnmf.py: fit():449] [1655529] (8000, 41, 41)\n", + " 69797 [cnmf.py: fit():436] [1655514] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69798 [cnmf.py: fit():436] [1655541] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69799 [params.py: set():1006] [1655545] Changing key p in group temporal from 1 to 0\n", + " 69798 [params.py: set():1006] [1655563] Changing key nb in group spatial from 2 to 1\n", + " 69799 [params.py: set():1006] [1655484] Changing key p in group temporal from 1 to 0\n", + " 69800 [cnmf.py: fit():436] [1655506] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69800 [params.py: set():1006] [1655538] Changing key p in group temporal from 1 to 0\n", + " 69801 [cnmf.py: fit():436] [1655488] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69801 [cnmf.py: fit():436] [1655554] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69802 [cnmf.py: fit():478] [1655531] using 5000 block_size_temp\n", + " 69802 [cnmf.py: fit():449] [1655534] (8000, 41, 41)\n", + " 69803 [cnmf.py: fit():466] [1655551] Using 1 processes\n", + " 69804 [cnmf.py: fit():478] [1655535] using 5000 block_size_temp\n", + " 69803 [params.py: set():1006] [1655477] Changing key p in group temporal from 1 to 0\n", + " 69804 [cnmf.py: fit():436] [1655479] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69805 [cnmf.py: fit():449] [1655489] (8000, 41, 41)\n", + " 69806 [cnmf.py: fit():436] [1655527] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69805 [cnmf.py: fit():449] [1655480] (8000, 41, 41)\n", + " 69807 [cnmf.py: fit():436] [1655492] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69807 [cnmf.py: fit():477] [1655552] using 400 pixels per process\n", + " 69807 [params.py: set():1006] [1655496] Changing key p in group temporal from 1 to 0\n", + " 69808 [cnmf.py: fit():436] [1655499] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69808 [cnmf.py: fit():436] [1655521] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69809 [cnmf.py: fit():449] [1655482] (8000, 41, 41)\n", + " 69810 [cnmf.py: fit():436] [1655562] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69810 [cnmf.py: fit():436] [1655481] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69811 [cnmf.py: fit():436] [1655487] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69811 [cnmf.py: fit():449] [1655526] (8000, 41, 41)\n", + " 69812 [cnmf.py: fit():436] [1655564] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69813 [cnmf.py: fit():436] [1655532] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69813 [cnmf.py: fit():449] [1655533] (8000, 41, 41)\n", + " 69814 [cnmf.py: fit():436] [1655483] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69814 [cnmf.py: fit():449] [1655511] (8000, 41, 41)\n", + " 69815 [cnmf.py: fit():449] [1655512] (8000, 41, 41)\n", + " 69815 [cnmf.py: fit():449] [1655508] (8000, 41, 41)\n", + " 69816 [cnmf.py: fit():478] [1655549] using 5000 block_size_temp\n", + " 69816 [cnmf.py: fit():477] [1655530] using 400 pixels per process\n", + " 69817 [cnmf.py: fit():449] [1655507] (8000, 41, 41)\n", + " 69817 [params.py: set():1006] [1655505] Changing key p in group temporal from 1 to 0\n", + " 69818 [cnmf.py: fit():436] [1655485] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69818 [cnmf.py: fit():436] [1655516] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69819 [cnmf.py: fit():477] [1655550] using 400 pixels per process\n", + " 69819 [cnmf.py: fit():436] [1655469] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69820 [cnmf.py: fit():436] [1655498] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69820 [cnmf.py: fit():449] [1655497] (8000, 41, 41)\n", + " 69821 [params.py: set():1006] [1655510] Changing key p in group temporal from 1 to 0\n", + " 69821 [cnmf.py: fit():449] [1655513] (8000, 41, 41)\n", + " 69822 [cnmf.py: fit():449] [1655558] (8000, 41, 41)\n", + " 69822 [params.py: set():1006] [1655556] Changing key p in group preprocess from 1 to 0\n", + " 69823 [cnmf.py: fit():449] [1655524] (8000, 41, 41)\n", + " 69823 [cnmf.py: fit():477] [1655546] using 400 pixels per process\n", + " 69824 [cnmf.py: fit():449] [1655560] (8000, 41, 41)\n", + " 69824 [cnmf.py: fit():436] [1655495] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69825 [params.py: set():1006] [1655478] Changing key p in group temporal from 1 to 0\n", + " 69826 [cnmf.py: fit():436] [1655476] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69825 [cnmf.py: fit():478] [1655553] using 5000 block_size_temp\n", + " 69826 [cnmf.py: fit():466] [1655470] Using 1 processes\n", + " 69827 [cnmf.py: fit():449] [1655509] (8000, 41, 41)\n", + " 69827 [params.py: set():1006] [1655528] Changing key p in group preprocess from 1 to 0\n", + " 69828 [cnmf.py: fit():466] [1655536] Using 1 processes\n", + " 69829 [cnmf.py: fit():436] [1655542] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69828 [params.py: set():1006] [1655539] Changing key p in group temporal from 1 to 0\n", + " 69830 [cnmf.py: fit():436] [1655561] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69830 [cnmf.py: fit():436] [1655504] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69830 [params.py: set():1006] [1655493] Changing key p in group temporal from 1 to 0\n", + " 69831 [cnmf.py: fit():436] [1655555] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69832 [cnmf.py: fit():449] [1655520] (8000, 41, 40)\n", + " 69832 [params.py: set():1006] [1655503] Changing key p in group temporal from 1 to 0\n", + " 69832 [cnmf.py: fit():436] [1655502] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69834 [cnmf.py: fit():449] [1655547] (8000, 41, 41)\n", + " 69834 [cnmf.py: fit():436] [1655474] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69833 [cnmf.py: fit():436] [1655544] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69835 [cnmf.py: fit():466] [1655494] Using 1 processes\n", + " 69835 [cnmf.py: fit():466] [1655473] Using 1 processes\n", + " 69836 [params.py: set():1006] [1655491] Changing key p in group temporal from 1 to 0\n", + " 69836 [cnmf.py: fit():466] [1655537] Using 1 processes\n", + " 69837 [cnmf.py: fit():466] [1655490] Using 1 processes\n", + " 69837 [cnmf.py: fit():449] [1655517] (8000, 41, 41)\n", + " 69837 [cnmf.py: fit():466] [1655543] Using 1 processes\n", + " 69838 [cnmf.py: fit():466] [1655525] Using 1 processes\n", + " 69838 [params.py: set():1006] [1655548] Changing key p in group temporal from 1 to 0\n", + " 69839 [cnmf.py: fit():466] [1655522] Using 1 processes\n", + " 69839 [cnmf.py: fit():466] [1655519] Using 1 processes\n", + " 69840 [cnmf.py: fit():449] [1655518] (8000, 41, 41)\n", + " 69840 [params.py: set():1006] [1655475] Changing key p in group temporal from 1 to 0\n", + " 69841 [cnmf.py: fit():436] [1655472] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69841 [cnmf.py: fit():449] [1655523] (8000, 41, 41)\n", + " 69842 [cnmf.py: fit():466] [1655557] Using 1 processes\n", + " 69842 [params.py: set():1006] [1655501] Changing key p in group temporal from 1 to 0\n", + " 69843 [cnmf.py: fit():436] [1655559] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69843 [cnmf.py: fit():466] [1655540] Using 1 processes\n", + " 69844 [cnmf.py: fit():466] [1655500] Using 1 processes\n", + " 69844 [pre_processing.py:interpolate_missing_data():41] [1655515] Checking for missing data entries (NaN)\n", + " 69844 [cnmf.py: fit():449] [1655486] (8000, 41, 40)\n", + " 69845 [cnmf.py: fit():449] [1655514] (8000, 41, 41)\n", + " 69845 [cnmf.py: fit():466] [1655529] Using 1 processes\n", + " 69846 [cnmf.py: fit():449] [1655541] (8000, 41, 41)\n", + " 69847 [cnmf.py: fit():436] [1655545] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69847 [params.py: set():1006] [1655563] Changing key p in group preprocess from 1 to 0\n", + " 69848 [cnmf.py: fit():436] [1655484] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69848 [cnmf.py: fit():449] [1655506] (8000, 41, 41)\n", + " 69849 [cnmf.py: fit():436] [1655538] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69850 [cnmf.py: fit():449] [1655488] (8000, 41, 41)\n", + " 69849 [cnmf.py: fit():449] [1655554] (8000, 41, 40)\n", + " 69850 [cnmf.py: fit():481] [1655531] preprocessing ...\n", + " 69851 [cnmf.py: fit():466] [1655534] Using 1 processes\n", + " 69851 [cnmf.py: fit():477] [1655551] using 400 pixels per process\n", + " 69852 [cnmf.py: fit():481] [1655535] preprocessing ...\n", + " 69852 [cnmf.py: fit():436] [1655477] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69853 [cnmf.py: fit():466] [1655489] Using 1 processes\n", + " 69853 [cnmf.py: fit():449] [1655479] (8000, 41, 41)\n", + " 69854 [cnmf.py: fit():449] [1655527] (8000, 41, 41)\n", + " 69854 [cnmf.py: fit():466] [1655480] Using 1 processes\n", + " 69855 [cnmf.py: fit():449] [1655492] (8000, 41, 41)\n", + " 69855 [cnmf.py: fit():478] [1655552] using 5000 block_size_temp\n", + " 69856 [cnmf.py: fit():436] [1655496] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69856 [cnmf.py: fit():449] [1655499] (8000, 41, 41)\n", + " 69864 [cnmf.py: fit():449] [1655521] (8000, 41, 41)\n", + " 69864 [cnmf.py: fit():466] [1655482] Using 1 processes\n", + " 69865 [cnmf.py: fit():449] [1655562] (8000, 41, 41)\n", + " 69865 [cnmf.py: fit():449] [1655481] (8000, 41, 41)\n", + " 69866 [cnmf.py: fit():466] [1655526] Using 1 processes\n", + " 69866 [cnmf.py: fit():449] [1655487] (8000, 41, 41)\n", + " 69867 [cnmf.py: fit():449] [1655564] (8000, 41, 41)\n", + " 69868 [cnmf.py: fit():466] [1655533] Using 1 processes\n", + " 69868 [cnmf.py: fit():449] [1655532] (8000, 41, 41)\n", + " 69868 [cnmf.py: fit():449] [1655483] (8000, 41, 41)\n", + " 69869 [cnmf.py: fit():466] [1655511] Using 1 processes\n", + " 69869 [cnmf.py: fit():466] [1655512] Using 1 processes\n", + " 69870 [cnmf.py: fit():466] [1655508] Using 1 processes\n", + " 69870 [cnmf.py: fit():481] [1655549] preprocessing ...\n", + " 69871 [cnmf.py: fit():478] [1655530] using 5000 block_size_temp\n", + " 69872 [cnmf.py: fit():436] [1655505] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69871 [cnmf.py: fit():449] [1655485] (8000, 41, 41)\n", + " 69872 [cnmf.py: fit():466] [1655507] Using 1 processes\n", + " 69872 [cnmf.py: fit():449] [1655516] (8000, 41, 41)\n", + " 69873 [cnmf.py: fit():478] [1655550] using 5000 block_size_temp\n", + " 69873 [cnmf.py: fit():449] [1655469] (8000, 41, 41)\n", + " 69874 [cnmf.py: fit():449] [1655498] (8000, 41, 41)\n", + " 69874 [cnmf.py: fit():466] [1655497] Using 1 processes\n", + " 69875 [cnmf.py: fit():436] [1655510] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69876 [cnmf.py: fit():466] [1655558] Using 1 processes\n", + " 69876 [cnmf.py: fit():466] [1655513] Using 1 processes\n", + " 69876 [params.py: set():1006] [1655556] Changing key p in group temporal from 1 to 0\n", + " 69877 [cnmf.py: fit():466] [1655524] Using 1 processes\n", + " 69877 [cnmf.py: fit():478] [1655546] using 5000 block_size_temp\n", + " 69878 [cnmf.py: fit():466] [1655560] Using 1 processes\n", + " 69878 [cnmf.py: fit():449] [1655495] (8000, 41, 41)\n", + " 69879 [cnmf.py: fit():481] [1655553] preprocessing ...\n", + " 69879 [cnmf.py: fit():449] [1655476] (8000, 41, 41)\n", + " 69880 [cnmf.py: fit():436] [1655478] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69880 [cnmf.py: fit():466] [1655509] Using 1 processes\n", + " 69881 [cnmf.py: fit():477] [1655470] using 400 pixels per process\n", + " 69882 [params.py: set():1006] [1655528] Changing key p in group temporal from 1 to 0\n", + " 69881 [cnmf.py: fit():477] [1655536] using 400 pixels per process\n", + " 69883 [cnmf.py: fit():449] [1655542] (8000, 41, 41)\n", + " 69882 [cnmf.py: fit():436] [1655539] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69883 [cnmf.py: fit():449] [1655561] (8000, 41, 41)\n", + " 69884 [cnmf.py: fit():449] [1655504] (8000, 41, 41)\n", + " 69884 [cnmf.py: fit():436] [1655493] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69885 [cnmf.py: fit():449] [1655555] (8000, 41, 41)\n", + " 69885 [cnmf.py: fit():466] [1655520] Using 1 processes\n", + " 69886 [cnmf.py: fit():436] [1655503] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69886 [cnmf.py: fit():449] [1655502] (8000, 41, 41)\n", + " 69887 [cnmf.py: fit():466] [1655547] Using 1 processes\n", + " 69888 [cnmf.py: fit():449] [1655544] (8000, 41, 41)\n", + " 69887 [cnmf.py: fit():449] [1655474] (8000, 41, 41)\n", + " 69889 [cnmf.py: fit():477] [1655473] using 400 pixels per process\n", + " 69888 [cnmf.py: fit():477] [1655494] using 400 pixels per process\n", + " 69889 [cnmf.py: fit():436] [1655491] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69890 [cnmf.py: fit():477] [1655537] using 400 pixels per process\n", + " 69891 [cnmf.py: fit():477] [1655543] using 400 pixels per process\n", + " 69891 [cnmf.py: fit():477] [1655490] using 400 pixels per process\n", + " 69890 [cnmf.py: fit():466] [1655517] Using 1 processes\n", + " 69892 [cnmf.py: fit():477] [1655525] using 400 pixels per process\n", + " 69892 [cnmf.py: fit():436] [1655548] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69893 [cnmf.py: fit():477] [1655522] using 400 pixels per process\n", + " 69893 [cnmf.py: fit():477] [1655519] using 400 pixels per process\n", + " 69893 [cnmf.py: fit():466] [1655518] Using 1 processes\n", + " 69894 [cnmf.py: fit():436] [1655475] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69895 [cnmf.py: fit():449] [1655472] (8000, 41, 41)\n", + " 69895 [cnmf.py: fit():466] [1655523] Using 1 processes\n", + " 69896 [cnmf.py: fit():477] [1655557] using 400 pixels per process\n", + " 69896 [cnmf.py: fit():436] [1655501] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69897 [cnmf.py: fit():449] [1655559] (8000, 41, 41)\n", + " 69897 [cnmf.py: fit():477] [1655540] using 400 pixels per process\n", + " 69898 [cnmf.py: fit():477] [1655500] using 400 pixels per process\n", + " 69907 [cnmf.py: fit():466] [1655486] Using 1 processes\n", + " 69907 [cnmf.py: fit():466] [1655514] Using 1 processes\n", + " 69908 [cnmf.py: fit():477] [1655529] using 400 pixels per process\n", + " 69908 [cnmf.py: fit():466] [1655541] Using 1 processes\n", + " 69912 [cnmf.py: fit():449] [1655545] (8000, 41, 41)\n", + " 69913 [cnmf.py: fit():449] [1655484] (8000, 41, 41)\n", + " 69912 [params.py: set():1006] [1655563] Changing key p in group temporal from 1 to 0\n", + " 69913 [cnmf.py: fit():449] [1655538] (8000, 41, 41)\n", + " 69914 [cnmf.py: fit():466] [1655506] Using 1 processes\n", + " 69919 [cnmf.py: fit():466] [1655488] Using 1 processes\n", + " 69920 [cnmf.py: fit():466] [1655554] Using 1 processes\n", + " 69920 [pre_processing.py:interpolate_missing_data():41] [1655531] Checking for missing data entries (NaN)\n", + " 69921 [cnmf.py: fit():477] [1655534] using 400 pixels per process\n", + " 69921 [cnmf.py: fit():478] [1655551] using 5000 block_size_temp\n", + " 69922 [cnmf.py: fit():449] [1655477] (8000, 41, 41)\n", + " 69922 [pre_processing.py:interpolate_missing_data():41] [1655535] Checking for missing data entries (NaN)\n", + " 69923 [cnmf.py: fit():466] [1655479] Using 1 processes\n", + " 69923 [cnmf.py: fit():477] [1655489] using 400 pixels per process\n", + " 69924 [cnmf.py: fit():477] [1655480] using 400 pixels per process\n", + " 69924 [cnmf.py: fit():466] [1655492] Using 1 processes\n", + " 69924 [cnmf.py: fit():466] [1655527] Using 1 processes\n", + " 69925 [cnmf.py: fit():481] [1655552] preprocessing ...\n", + " 69926 [cnmf.py: fit():449] [1655496] (8000, 41, 41)\n", + " 69925 [cnmf.py: fit():466] [1655499] Using 1 processes\n", + " 69926 [cnmf.py: fit():466] [1655521] Using 1 processes\n", + " 69927 [cnmf.py: fit():477] [1655482] using 400 pixels per process\n", + " 69927 [cnmf.py: fit():466] [1655562] Using 1 processes\n", + " 69928 [cnmf.py: fit():466] [1655481] Using 1 processes\n", + " 69928 [cnmf.py: fit():477] [1655526] using 400 pixels per process\n", + " 69929 [cnmf.py: fit():466] [1655487] Using 1 processes\n", + " 69929 [cnmf.py: fit():466] [1655564] Using 1 processes\n", + " 69930 [cnmf.py: fit():477] [1655533] using 400 pixels per process\n", + " 69930 [cnmf.py: fit():466] [1655532] Using 1 processes\n", + " 69931 [cnmf.py: fit():477] [1655511] using 400 pixels per process\n", + " 69931 [cnmf.py: fit():466] [1655483] Using 1 processes\n", + " 69935 [cnmf.py: fit():477] [1655512] using 400 pixels per process\n", + " 69936 [pre_processing.py:interpolate_missing_data():41] [1655549] Checking for missing data entries (NaN)\n", + " 69936 [cnmf.py: fit():477] [1655508] using 400 pixels per process\n", + " 69937 [cnmf.py: fit():481] [1655530] preprocessing ...\n", + " 69938 [cnmf.py: fit():449] [1655505] (8000, 41, 41)\n", + " 69938 [cnmf.py: fit():477] [1655507] using 400 pixels per process\n", + " 69938 [cnmf.py: fit():466] [1655485] Using 1 processes\n", + " 69939 [cnmf.py: fit():481] [1655550] preprocessing ...\n", + " 69939 [cnmf.py: fit():466] [1655516] Using 1 processes\n", + " 69940 [cnmf.py: fit():466] [1655469] Using 1 processes\n", + " 69941 [cnmf.py: fit():477] [1655497] using 400 pixels per process\n", + " 69940 [cnmf.py: fit():466] [1655498] Using 1 processes\n", + " 69941 [cnmf.py: fit():449] [1655510] (8000, 41, 41)\n", + " 69942 [cnmf.py: fit():477] [1655513] using 400 pixels per process\n", + " 69942 [cnmf.py: fit():477] [1655558] using 400 pixels per process\n", + " 69943 [cnmf.py: fit():436] [1655556] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69943 [cnmf.py: fit():477] [1655524] using 400 pixels per process\n", + " 69944 [cnmf.py: fit():481] [1655546] preprocessing ...\n", + " 69944 [cnmf.py: fit():477] [1655560] using 400 pixels per process\n", + " 69945 [cnmf.py: fit():466] [1655495] Using 1 processes\n", + " 69946 [pre_processing.py:interpolate_missing_data():41] [1655553] Checking for missing data entries (NaN)\n", + " 69945 [cnmf.py: fit():466] [1655476] Using 1 processes\n", + " 69946 [cnmf.py: fit():449] [1655478] (8000, 41, 41)\n", + " 69947 [cnmf.py: fit():477] [1655509] using 400 pixels per process\n", + " 69947 [cnmf.py: fit():478] [1655470] using 5000 block_size_temp\n", + " 69948 [cnmf.py: fit():478] [1655536] using 5000 block_size_temp\n", + " 69948 [cnmf.py: fit():436] [1655528] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69949 [cnmf.py: fit():466] [1655542] Using 1 processes\n", + " 69949 [cnmf.py: fit():449] [1655539] (8000, 41, 41)\n", + " 69950 [cnmf.py: fit():466] [1655561] Using 1 processes\n", + " 69950 [cnmf.py: fit():466] [1655504] Using 1 processes\n", + " 69951 [cnmf.py: fit():449] [1655493] (8000, 41, 41)\n", + " 69951 [cnmf.py: fit():466] [1655555] Using 1 processes\n", + " 69952 [cnmf.py: fit():477] [1655520] using 400 pixels per process\n", + " 69952 [cnmf.py: fit():449] [1655503] (8000, 41, 40)\n", + " 69953 [cnmf.py: fit():466] [1655502] Using 1 processes\n", + " 69953 [cnmf.py: fit():477] [1655547] using 400 pixels per process\n", + " 69954 [cnmf.py: fit():466] [1655474] Using 1 processes\n", + " 69953 [cnmf.py: fit():466] [1655544] Using 1 processes\n", + " 69955 [cnmf.py: fit():478] [1655494] using 5000 block_size_temp\n", + " 69954 [cnmf.py: fit():478] [1655473] using 5000 block_size_temp\n", + " 69955 [cnmf.py: fit():449] [1655491] (8000, 41, 41)\n", + " 69956 [cnmf.py: fit():478] [1655537] using 5000 block_size_temp\n", + " 69956 [cnmf.py: fit():477] [1655517] using 400 pixels per process\n", + " 69957 [cnmf.py: fit():478] [1655490] using 5000 block_size_temp\n", + " 69957 [cnmf.py: fit():478] [1655543] using 5000 block_size_temp\n", + " 69958 [cnmf.py: fit():449] [1655548] (8000, 41, 41)\n", + " 69958 [cnmf.py: fit():478] [1655525] using 5000 block_size_temp\n", + " 69958 [cnmf.py: fit():478] [1655522] using 5000 block_size_temp\n", + " 69959 [cnmf.py: fit():478] [1655519] using 5000 block_size_temp\n", + " 69959 [cnmf.py: fit():477] [1655518] using 400 pixels per process\n", + " 69960 [cnmf.py: fit():449] [1655475] (8000, 41, 41)\n", + " 69961 [cnmf.py: fit():466] [1655472] Using 1 processes\n", + " 69961 [cnmf.py: fit():478] [1655557] using 5000 block_size_temp\n", + " 69961 [cnmf.py: fit():477] [1655523] using 400 pixels per process\n", + " 69962 [cnmf.py: fit():449] [1655501] (8000, 41, 41)\n", + " 69962 [cnmf.py: fit():466] [1655559] Using 1 processes\n", + " 69963 [cnmf.py: fit():478] [1655540] using 5000 block_size_temp\n", + " 69963 [cnmf.py: fit():478] [1655500] using 5000 block_size_temp\n", + " 69966 [cnmf.py: fit():477] [1655486] using 400 pixels per process\n", + " 69967 [cnmf.py: fit():477] [1655514] using 400 pixels per process\n", + " 69967 [cnmf.py: fit():478] [1655529] using 5000 block_size_temp\n", + " 69968 [cnmf.py: fit():477] [1655541] using 400 pixels per process\n", + " 69968 [cnmf.py: fit():466] [1655545] Using 1 processes\n", + " 69969 [cnmf.py: fit():436] [1655563] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 69969 [cnmf.py: fit():466] [1655484] Using 1 processes\n", + " 69970 [cnmf.py: fit():466] [1655538] Using 1 processes\n", + " 69970 [cnmf.py: fit():477] [1655506] using 400 pixels per process\n", + " 69971 [cnmf.py: fit():477] [1655488] using 400 pixels per process\n", + " 69971 [cnmf.py: fit():477] [1655554] using 400 pixels per process\n", + " 69988 [cnmf.py: fit():466] [1655477] Using 1 processes\n", + " 69988 [cnmf.py: fit():481] [1655551] preprocessing ...\n", + " 69987 [cnmf.py: fit():478] [1655534] using 5000 block_size_temp\n", + " 70003 [cnmf.py: fit():478] [1655489] using 5000 block_size_temp\n", + " 70004 [cnmf.py: fit():477] [1655479] using 400 pixels per process\n", + " 70005 [cnmf.py: fit():478] [1655480] using 5000 block_size_temp\n", + " 70005 [cnmf.py: fit():477] [1655492] using 400 pixels per process\n", + " 70006 [cnmf.py: fit():477] [1655527] using 400 pixels per process\n", + " 70006 [pre_processing.py:interpolate_missing_data():41] [1655552] Checking for missing data entries (NaN)\n", + " 70006 [cnmf.py: fit():466] [1655496] Using 1 processes\n", + " 70007 [cnmf.py: fit():477] [1655499] using 400 pixels per process\n", + " 70007 [cnmf.py: fit():477] [1655521] using 400 pixels per process\n", + " 70008 [cnmf.py: fit():478] [1655482] using 5000 block_size_temp\n", + " 70008 [cnmf.py: fit():477] [1655562] using 400 pixels per process\n", + " 70009 [cnmf.py: fit():478] [1655526] using 5000 block_size_temp\n", + " 70009 [cnmf.py: fit():477] [1655481] using 400 pixels per process\n", + " 70010 [cnmf.py: fit():477] [1655487] using 400 pixels per process\n", + " 70010 [cnmf.py: fit():477] [1655564] using 400 pixels per process\n", + " 70011 [cnmf.py: fit():478] [1655533] using 5000 block_size_temp\n", + " 70011 [cnmf.py: fit():477] [1655532] using 400 pixels per process\n", + " 70012 [cnmf.py: fit():478] [1655511] using 5000 block_size_temp\n", + " 70012 [cnmf.py: fit():477] [1655483] using 400 pixels per process\n", + " 70013 [cnmf.py: fit():478] [1655512] using 5000 block_size_temp\n", + " 70027 [cnmf.py: fit():478] [1655508] using 5000 block_size_temp\n", + " 70028 [cnmf.py: fit():466] [1655505] Using 1 processes\n", + " 70028 [pre_processing.py:interpolate_missing_data():41] [1655530] Checking for missing data entries (NaN)\n", + " 70029 [cnmf.py: fit():478] [1655507] using 5000 block_size_temp\n", + " 70036 [pre_processing.py:interpolate_missing_data():41] [1655550] Checking for missing data entries (NaN)\n", + " 70036 [cnmf.py: fit():477] [1655516] using 400 pixels per process\n", + " 70036 [cnmf.py: fit():477] [1655485] using 400 pixels per process\n", + " 70038 [cnmf.py: fit():478] [1655497] using 5000 block_size_temp\n", + " 70038 [cnmf.py: fit():477] [1655498] using 400 pixels per process\n", + " 70037 [cnmf.py: fit():477] [1655469] using 400 pixels per process\n", + " 70038 [cnmf.py: fit():466] [1655510] Using 1 processes\n", + " 70039 [cnmf.py: fit():478] [1655513] using 5000 block_size_temp\n", + " 70039 [cnmf.py: fit():478] [1655558] using 5000 block_size_temp\n", + " 70040 [cnmf.py: fit():449] [1655556] (8000, 41, 41)\n", + " 70041 [pre_processing.py:interpolate_missing_data():41] [1655546] Checking for missing data entries (NaN)\n", + " 70040 [cnmf.py: fit():478] [1655524] using 5000 block_size_temp\n", + " 70042 [cnmf.py: fit():477] [1655495] using 400 pixels per process\n", + " 70041 [cnmf.py: fit():478] [1655560] using 5000 block_size_temp\n", + " 70063 [cnmf.py: fit():477] [1655476] using 400 pixels per process\n", + " 70064 [cnmf.py: fit():466] [1655478] Using 1 processes\n", + " 70065 [cnmf.py: fit():481] [1655470] preprocessing ...\n", + " 70065 [cnmf.py: fit():478] [1655509] using 5000 block_size_temp\n", + " 70066 [cnmf.py: fit():449] [1655528] (8000, 41, 41)\n", + " 70066 [cnmf.py: fit():477] [1655542] using 400 pixels per process\n", + " 70067 [cnmf.py: fit():466] [1655539] Using 1 processes\n", + " 70066 [cnmf.py: fit():481] [1655536] preprocessing ...\n", + " 70069 [cnmf.py: fit():478] [1655520] using 5000 block_size_temp\n", + " 70069 [cnmf.py: fit():466] [1655493] Using 1 processes\n", + " 70068 [cnmf.py: fit():477] [1655561] using 400 pixels per process\n", + " 70068 [cnmf.py: fit():477] [1655555] using 400 pixels per process\n", + " 70068 [cnmf.py: fit():477] [1655504] using 400 pixels per process\n", + " 70069 [cnmf.py: fit():466] [1655503] Using 1 processes\n", + " 70071 [cnmf.py: fit():478] [1655547] using 5000 block_size_temp\n", + " 70071 [cnmf.py: fit():477] [1655474] using 400 pixels per process\n", + " 70071 [cnmf.py: fit():477] [1655544] using 400 pixels per process\n", + " 70070 [cnmf.py: fit():477] [1655502] using 400 pixels per process\n", + " 70073 [cnmf.py: fit():481] [1655490] preprocessing ...\n", + " 70073 [cnmf.py: fit():481] [1655537] preprocessing ...\n", + " 70074 [cnmf.py: fit():478] [1655517] using 5000 block_size_temp\n", + " 70073 [cnmf.py: fit():466] [1655491] Using 1 processes\n", + " 70074 [cnmf.py: fit():481] [1655543] preprocessing ...\n", + " 70072 [cnmf.py: fit():481] [1655473] preprocessing ...\n", + " 70072 [cnmf.py: fit():481] [1655494] preprocessing ...\n", + " 70075 [cnmf.py: fit():481] [1655525] preprocessing ...\n", + " 70075 [cnmf.py: fit():481] [1655522] preprocessing ...\n", + " 70075 [cnmf.py: fit():466] [1655548] Using 1 processes\n", + " 70076 [cnmf.py: fit():478] [1655518] using 5000 block_size_temp\n", + " 70076 [cnmf.py: fit():481] [1655519] preprocessing ...\n", + " 70079 [cnmf.py: fit():466] [1655475] Using 1 processes\n", + " 70084 [cnmf.py: fit():478] [1655523] using 5000 block_size_temp\n", + " 70084 [cnmf.py: fit():481] [1655557] preprocessing ...\n", + " 70083 [cnmf.py: fit():477] [1655472] using 400 pixels per process\n", + " 70085 [cnmf.py: fit():466] [1655501] Using 1 processes\n", + " 70085 [cnmf.py: fit():477] [1655559] using 400 pixels per process\n", + " 70085 [cnmf.py: fit():481] [1655540] preprocessing ...\n", + " 70086 [cnmf.py: fit():481] [1655500] preprocessing ...\n", + " 70092 [cnmf.py: fit():478] [1655514] using 5000 block_size_temp\n", + " 70091 [cnmf.py: fit():478] [1655486] using 5000 block_size_temp\n", + " 70092 [cnmf.py: fit():481] [1655529] preprocessing ...\n", + " 70093 [cnmf.py: fit():478] [1655541] using 5000 block_size_temp\n", + " 70093 [cnmf.py: fit():477] [1655545] using 400 pixels per process\n", + " 70094 [cnmf.py: fit():449] [1655563] (8000, 41, 41)\n", + " 70094 [cnmf.py: fit():477] [1655484] using 400 pixels per process\n", + " 70095 [cnmf.py: fit():477] [1655538] using 400 pixels per process\n", + " 70095 [cnmf.py: fit():478] [1655506] using 5000 block_size_temp\n", + " 70096 [cnmf.py: fit():478] [1655554] using 5000 block_size_temp\n", + " 70096 [cnmf.py: fit():478] [1655488] using 5000 block_size_temp\n", + " 70097 [cnmf.py: fit():477] [1655477] using 400 pixels per process\n", + " 70097 [pre_processing.py:interpolate_missing_data():41] [1655551] Checking for missing data entries (NaN)\n", + " 70097 [cnmf.py: fit():481] [1655534] preprocessing ...\n", + " 70103 [cnmf.py: fit():481] [1655489] preprocessing ...\n", + " 70104 [cnmf.py: fit():478] [1655479] using 5000 block_size_temp\n", + " 70104 [cnmf.py: fit():481] [1655480] preprocessing ...\n", + " 70105 [cnmf.py: fit():478] [1655492] using 5000 block_size_temp\n", + " 70105 [cnmf.py: fit():478] [1655527] using 5000 block_size_temp\n", + " 70119 [cnmf.py: fit():477] [1655496] using 400 pixels per process\n", + " 70120 [cnmf.py: fit():478] [1655499] using 5000 block_size_temp\n", + " 70120 [cnmf.py: fit():478] [1655521] using 5000 block_size_temp\n", + " 70121 [cnmf.py: fit():481] [1655482] preprocessing ...\n", + " 70121 [cnmf.py: fit():478] [1655562] using 5000 block_size_temp\n", + " 70122 [cnmf.py: fit():478] [1655481] using 5000 block_size_temp\n", + " 70122 [cnmf.py: fit():481] [1655526] preprocessing ...\n", + " 70123 [cnmf.py: fit():478] [1655487] using 5000 block_size_temp\n", + " 70124 [cnmf.py: fit():478] [1655564] using 5000 block_size_temp\n", + " 70123 [cnmf.py: fit():481] [1655533] preprocessing ...\n", + " 70124 [cnmf.py: fit():481] [1655511] preprocessing ...\n", + " 70124 [cnmf.py: fit():478] [1655532] using 5000 block_size_temp\n", + " 70125 [cnmf.py: fit():481] [1655512] preprocessing ...\n", + " 70125 [cnmf.py: fit():478] [1655483] using 5000 block_size_temp\n", + " 70126 [cnmf.py: fit():481] [1655508] preprocessing ...\n", + " 70126 [cnmf.py: fit():477] [1655505] using 400 pixels per process\n", + " 70155 [cnmf.py: fit():478] [1655516] using 5000 block_size_temp\n", + " 70140 [cnmf.py: fit():481] [1655507] preprocessing ...\n", + " 70156 [cnmf.py: fit():478] [1655485] using 5000 block_size_temp\n", + " 70158 [cnmf.py: fit():481] [1655558] preprocessing ...\n", + " 70159 [cnmf.py: fit():481] [1655513] preprocessing ...\n", + " 70158 [cnmf.py: fit():477] [1655510] using 400 pixels per process\n", + " 70157 [cnmf.py: fit():478] [1655498] using 5000 block_size_temp\n", + " 70158 [cnmf.py: fit():478] [1655469] using 5000 block_size_temp\n", + " 70157 [cnmf.py: fit():481] [1655497] preprocessing ...\n", + " 70160 [cnmf.py: fit():481] [1655524] preprocessing ...\n", + " 70175 [cnmf.py: fit():481] [1655560] preprocessing ...\n", + " 70159 [cnmf.py: fit():466] [1655556] Using 1 processes\n", + " 70176 [cnmf.py: fit():478] [1655495] using 5000 block_size_temp\n", + " 70180 [cnmf.py: fit():477] [1655478] using 400 pixels per process\n", + " 70179 [cnmf.py: fit():478] [1655476] using 5000 block_size_temp\n", + " 70188 [cnmf.py: fit():481] [1655509] preprocessing ...\n", + " 70187 [pre_processing.py:interpolate_missing_data():41] [1655470] Checking for missing data entries (NaN)\n", + " 70199 [cnmf.py: fit():481] [1655520] preprocessing ...\n", + " 70198 [cnmf.py: fit():478] [1655504] using 5000 block_size_temp\n", + " 70197 [cnmf.py: fit():478] [1655555] using 5000 block_size_temp\n", + " 70198 [cnmf.py: fit():478] [1655561] using 5000 block_size_temp\n", + " 70198 [cnmf.py: fit():477] [1655503] using 400 pixels per process\n", + " 70197 [pre_processing.py:interpolate_missing_data():41] [1655536] Checking for missing data entries (NaN)\n", + " 70197 [cnmf.py: fit():477] [1655493] using 400 pixels per process\n", + " 70196 [cnmf.py: fit():478] [1655542] using 5000 block_size_temp\n", + " 70196 [cnmf.py: fit():466] [1655528] Using 1 processes\n", + " 70196 [cnmf.py: fit():477] [1655539] using 400 pixels per process\n", + " 70204 [cnmf.py: fit():478] [1655544] using 5000 block_size_temp\n", + " 70204 [cnmf.py: fit():478] [1655474] using 5000 block_size_temp\n", + " 70204 [cnmf.py: fit():478] [1655502] using 5000 block_size_temp\n", + " 70203 [cnmf.py: fit():481] [1655547] preprocessing ...\n", + " 70207 [pre_processing.py:interpolate_missing_data():41] [1655543] Checking for missing data entries (NaN)\n", + " 70207 [pre_processing.py:interpolate_missing_data():41] [1655473] Checking for missing data entries (NaN)\n", + " 70206 [cnmf.py: fit():477] [1655491] using 400 pixels per process\n", + " 70207 [pre_processing.py:interpolate_missing_data():41] [1655494] Checking for missing data entries (NaN)\n", + " 70206 [pre_processing.py:interpolate_missing_data():41] [1655537] Checking for missing data entries (NaN)\n", + " 70206 [pre_processing.py:interpolate_missing_data():41] [1655490] Checking for missing data entries (NaN)\n", + " 70205 [cnmf.py: fit():481] [1655517] preprocessing ...\n", + " 70210 [cnmf.py: fit():477] [1655475] using 400 pixels per process\n", + " 70209 [cnmf.py: fit():477] [1655548] using 400 pixels per process\n", + " 70209 [pre_processing.py:interpolate_missing_data():41] [1655519] Checking for missing data entries (NaN)\n", + " 70210 [cnmf.py: fit():481] [1655518] preprocessing ...\n", + " 70209 [pre_processing.py:interpolate_missing_data():41] [1655522] Checking for missing data entries (NaN)\n", + " 70208 [pre_processing.py:interpolate_missing_data():41] [1655525] Checking for missing data entries (NaN)\n", + " 70216 [cnmf.py: fit():478] [1655472] using 5000 block_size_temp\n", + " 70216 [cnmf.py: fit():477] [1655501] using 400 pixels per process\n", + " 70216 [pre_processing.py:interpolate_missing_data():41] [1655557] Checking for missing data entries (NaN)\n", + " 70215 [cnmf.py: fit():481] [1655523] preprocessing ...\n", + " 70217 [cnmf.py: fit():478] [1655559] using 5000 block_size_temp\n", + " 70218 [pre_processing.py:interpolate_missing_data():41] [1655500] Checking for missing data entries (NaN)\n", + " 70217 [pre_processing.py:interpolate_missing_data():41] [1655540] Checking for missing data entries (NaN)\n", + " 70219 [pre_processing.py:interpolate_missing_data():41] [1655529] Checking for missing data entries (NaN)\n", + " 70218 [cnmf.py: fit():481] [1655486] preprocessing ...\n", + " 70219 [cnmf.py: fit():481] [1655514] preprocessing ...\n", + " 70220 [cnmf.py: fit():481] [1655541] preprocessing ...\n", + " 70221 [cnmf.py: fit():478] [1655484] using 5000 block_size_temp\n", + " 70220 [cnmf.py: fit():466] [1655563] Using 1 processes\n", + " 70220 [cnmf.py: fit():478] [1655545] using 5000 block_size_temp\n", + " 70221 [cnmf.py: fit():478] [1655538] using 5000 block_size_temp\n", + " 70222 [cnmf.py: fit():481] [1655506] preprocessing ...\n", + " 70222 [cnmf.py: fit():481] [1655554] preprocessing ...\n", + " 70223 [cnmf.py: fit():481] [1655488] preprocessing ...\n", + " 70223 [cnmf.py: fit():478] [1655477] using 5000 block_size_temp\n", + " 70239 [pre_processing.py:interpolate_missing_data():41] [1655534] Checking for missing data entries (NaN)\n", + " 70240 [pre_processing.py:interpolate_missing_data():41] [1655489] Checking for missing data entries (NaN)\n", + " 70242 [pre_processing.py:interpolate_missing_data():41] [1655480] Checking for missing data entries (NaN)\n", + " 70242 [cnmf.py: fit():481] [1655527] preprocessing ...\n", + " 70241 [cnmf.py: fit():481] [1655492] preprocessing ...\n", + " 70241 [cnmf.py: fit():481] [1655479] preprocessing ...\n", + " 70248 [cnmf.py: fit():481] [1655499] preprocessing ...\n", + " 70248 [cnmf.py: fit():478] [1655496] using 5000 block_size_temp\n", + " 70249 [pre_processing.py:interpolate_missing_data():41] [1655482] Checking for missing data entries (NaN)\n", + " 70249 [cnmf.py: fit():481] [1655521] preprocessing ...\n", + " 70256 [cnmf.py: fit():481] [1655481] preprocessing ...\n", + " 70256 [pre_processing.py:interpolate_missing_data():41] [1655526] Checking for missing data entries (NaN)\n", + " 70256 [cnmf.py: fit():481] [1655562] preprocessing ...\n", + " 70258 [pre_processing.py:interpolate_missing_data():41] [1655533] Checking for missing data entries (NaN)\n", + " 70257 [cnmf.py: fit():481] [1655564] preprocessing ...\n", + " 70260 [cnmf.py: fit():478] [1655505] using 5000 block_size_temp\n", + " 70258 [pre_processing.py:interpolate_missing_data():41] [1655511] Checking for missing data entries (NaN)\n", + " 70259 [cnmf.py: fit():481] [1655532] preprocessing ...\n", + " 70259 [cnmf.py: fit():481] [1655483] preprocessing ...\n", + " 70259 [pre_processing.py:interpolate_missing_data():41] [1655512] Checking for missing data entries (NaN)\n", + " 70260 [pre_processing.py:interpolate_missing_data():41] [1655508] Checking for missing data entries (NaN)\n", + " 70257 [cnmf.py: fit():481] [1655487] preprocessing ...\n", + " 70264 [pre_processing.py:interpolate_missing_data():41] [1655507] Checking for missing data entries (NaN)\n", + " 70263 [cnmf.py: fit():481] [1655516] preprocessing ...\n", + " 70264 [cnmf.py: fit():481] [1655485] preprocessing ...\n", + " 70276 [pre_processing.py:interpolate_missing_data():41] [1655513] Checking for missing data entries (NaN)\n", + " 70276 [pre_processing.py:interpolate_missing_data():41] [1655558] Checking for missing data entries (NaN)\n", + " 70276 [cnmf.py: fit():478] [1655510] using 5000 block_size_temp\n", + " 70277 [cnmf.py: fit():481] [1655469] preprocessing ...\n", + " 70277 [pre_processing.py:interpolate_missing_data():41] [1655497] Checking for missing data entries (NaN)\n", + " 70275 [cnmf.py: fit():481] [1655498] preprocessing ...\n", + " 70284 [pre_processing.py:interpolate_missing_data():41] [1655524] Checking for missing data entries (NaN)\n", + " 70283 [pre_processing.py:interpolate_missing_data():41] [1655560] Checking for missing data entries (NaN)\n", + " 70284 [cnmf.py: fit():477] [1655556] using 400 pixels per process\n", + " 70285 [cnmf.py: fit():481] [1655495] preprocessing ...\n", + " 70285 [cnmf.py: fit():481] [1655476] preprocessing ...\n", + " 70285 [cnmf.py: fit():478] [1655478] using 5000 block_size_temp\n", + " 70286 [pre_processing.py:interpolate_missing_data():41] [1655509] Checking for missing data entries (NaN)\n", + " 70301 [cnmf.py: fit():478] [1655503] using 5000 block_size_temp\n", + " 70301 [cnmf.py: fit():481] [1655542] preprocessing ...\n", + " 70315 [cnmf.py: fit():481] [1655555] preprocessing ...\n", + " 70316 [cnmf.py: fit():481] [1655561] preprocessing ...\n", + " 70300 [cnmf.py: fit():478] [1655493] using 5000 block_size_temp\n", + " 70316 [cnmf.py: fit():477] [1655528] using 400 pixels per process\n", + " 70316 [pre_processing.py:interpolate_missing_data():41] [1655520] Checking for missing data entries (NaN)\n", + " 70301 [cnmf.py: fit():481] [1655504] preprocessing ...\n", + " 70319 [pre_processing.py:interpolate_missing_data():41] [1655547] Checking for missing data entries (NaN)\n", + " 70318 [cnmf.py: fit():481] [1655502] preprocessing ...\n", + " 70318 [cnmf.py: fit():481] [1655474] preprocessing ...\n", + " 70318 [cnmf.py: fit():481] [1655544] preprocessing ...\n", + " 70317 [cnmf.py: fit():478] [1655539] using 5000 block_size_temp\n", + " 70383 [cnmf.py: fit():478] [1655491] using 5000 block_size_temp\n", + " 70403 [pre_processing.py:interpolate_missing_data():41] [1655517] Checking for missing data entries (NaN)\n", + " 70410 [cnmf.py: fit():478] [1655548] using 5000 block_size_temp\n", + " 70430 [cnmf.py: fit():478] [1655475] using 5000 block_size_temp\n", + " 70410 [pre_processing.py:interpolate_missing_data():41] [1655518] Checking for missing data entries (NaN)\n", + " 70468 [cnmf.py: fit():478] [1655501] using 5000 block_size_temp\n", + " 70485 [pre_processing.py:interpolate_missing_data():41] [1655523] Checking for missing data entries (NaN)\n", + " 70490 [cnmf.py: fit():481] [1655559] preprocessing ...\n", + " 70468 [cnmf.py: fit():481] [1655472] preprocessing ...\n", + " 70524 [pre_processing.py:interpolate_missing_data():41] [1655486] Checking for missing data entries (NaN)\n", + " 70523 [pre_processing.py:interpolate_missing_data():41] [1655514] Checking for missing data entries (NaN)\n", + " 70545 [pre_processing.py:interpolate_missing_data():41] [1655554] Checking for missing data entries (NaN)\n", + " 70544 [cnmf.py: fit():481] [1655545] preprocessing ...\n", + " 70544 [cnmf.py: fit():481] [1655484] preprocessing ...\n", + " 70545 [cnmf.py: fit():481] [1655538] preprocessing ...\n", + " 70543 [cnmf.py: fit():477] [1655563] using 400 pixels per process\n", + " 70546 [pre_processing.py:interpolate_missing_data():41] [1655488] Checking for missing data entries (NaN)\n", + " 70545 [pre_processing.py:interpolate_missing_data():41] [1655506] Checking for missing data entries (NaN)\n", + " 70543 [pre_processing.py:interpolate_missing_data():41] [1655541] Checking for missing data entries (NaN)\n", + " 70552 [cnmf.py: fit():481] [1655477] preprocessing ...\n", + " 70584 [pre_processing.py:interpolate_missing_data():41] [1655492] Checking for missing data entries (NaN)\n", + " 70602 [pre_processing.py:interpolate_missing_data():41] [1655479] Checking for missing data entries (NaN)\n", + " 70583 [pre_processing.py:interpolate_missing_data():41] [1655527] Checking for missing data entries (NaN)\n", + " 70607 [pre_processing.py:interpolate_missing_data():41] [1655499] Checking for missing data entries (NaN)\n", + " 70608 [cnmf.py: fit():481] [1655496] preprocessing ...\n", + " 70625 [pre_processing.py:interpolate_missing_data():41] [1655521] Checking for missing data entries (NaN)\n", + " 70651 [pre_processing.py:interpolate_missing_data():41] [1655562] Checking for missing data entries (NaN)\n", + " 70631 [pre_processing.py:interpolate_missing_data():41] [1655481] Checking for missing data entries (NaN)\n", + " 70735 [pre_processing.py:interpolate_missing_data():41] [1655487] Checking for missing data entries (NaN)\n", + " 70715 [pre_processing.py:interpolate_missing_data():41] [1655483] Checking for missing data entries (NaN)\n", + " 70736 [cnmf.py: fit():481] [1655505] preprocessing ...\n", + " 70695 [pre_processing.py:interpolate_missing_data():41] [1655532] Checking for missing data entries (NaN)\n", + " 70674 [pre_processing.py:interpolate_missing_data():41] [1655564] Checking for missing data entries (NaN)\n", + " 70753 [pre_processing.py:interpolate_missing_data():41] [1655516] Checking for missing data entries (NaN)\n", + " 70754 [pre_processing.py:interpolate_missing_data():41] [1655485] Checking for missing data entries (NaN)\n", + " 70795 [pre_processing.py:interpolate_missing_data():41] [1655498] Checking for missing data entries (NaN)\n", + " 70774 [cnmf.py: fit():481] [1655510] preprocessing ...\n", + " 70795 [pre_processing.py:interpolate_missing_data():41] [1655469] Checking for missing data entries (NaN)\n", + " 70856 [pre_processing.py:interpolate_missing_data():41] [1655476] Checking for missing data entries (NaN)\n", + " 70855 [cnmf.py: fit():481] [1655478] preprocessing ...\n", + " 70855 [pre_processing.py:interpolate_missing_data():41] [1655495] Checking for missing data entries (NaN)\n", + " 70854 [cnmf.py: fit():478] [1655556] using 5000 block_size_temp\n", + " 70897 [cnmf.py: fit():478] [1655528] using 5000 block_size_temp\n", + " 70897 [pre_processing.py:interpolate_missing_data():41] [1655561] Checking for missing data entries (NaN)\n", + " 70897 [cnmf.py: fit():481] [1655493] preprocessing ...\n", + " 70898 [pre_processing.py:interpolate_missing_data():41] [1655504] Checking for missing data entries (NaN)\n", + " 70897 [cnmf.py: fit():481] [1655503] preprocessing ...\n", + " 70896 [pre_processing.py:interpolate_missing_data():41] [1655542] Checking for missing data entries (NaN)\n", + " 70896 [pre_processing.py:interpolate_missing_data():41] [1655555] Checking for missing data entries (NaN)\n", + " 70941 [cnmf.py: fit():481] [1655539] preprocessing ...\n", + " 70925 [pre_processing.py:interpolate_missing_data():41] [1655544] Checking for missing data entries (NaN)\n", + " 70924 [pre_processing.py:interpolate_missing_data():41] [1655502] Checking for missing data entries (NaN)\n", + " 70924 [pre_processing.py:interpolate_missing_data():41] [1655474] Checking for missing data entries (NaN)\n", + " 70947 [cnmf.py: fit():481] [1655491] preprocessing ...\n", + " 70974 [cnmf.py: fit():481] [1655475] preprocessing ...\n", + " 70974 [cnmf.py: fit():481] [1655548] preprocessing ...\n", + " 71016 [pre_processing.py:interpolate_missing_data():41] [1655559] Checking for missing data entries (NaN)\n", + " 71015 [pre_processing.py:interpolate_missing_data():41] [1655472] Checking for missing data entries (NaN)\n", + " 71015 [cnmf.py: fit():481] [1655501] preprocessing ...\n", + " 71147 [pre_processing.py:interpolate_missing_data():41] [1655538] Checking for missing data entries (NaN)\n", + " 71107 [pre_processing.py:interpolate_missing_data():41] [1655484] Checking for missing data entries (NaN)\n", + " 71076 [cnmf.py: fit():478] [1655563] using 5000 block_size_temp\n", + " 71076 [pre_processing.py:interpolate_missing_data():41] [1655545] Checking for missing data entries (NaN)\n", + " 71155 [pre_processing.py:interpolate_missing_data():41] [1655477] Checking for missing data entries (NaN)\n", + " 71195 [pre_processing.py:interpolate_missing_data():41] [1655496] Checking for missing data entries (NaN)\n", + " 71319 [pre_processing.py:interpolate_missing_data():41] [1655505] Checking for missing data entries (NaN)\n", + " 71399 [pre_processing.py:interpolate_missing_data():41] [1655510] Checking for missing data entries (NaN)\n", + " 71423 [pre_processing.py:interpolate_missing_data():41] [1655478] Checking for missing data entries (NaN)\n", + " 71455 [cnmf.py: fit():481] [1655556] preprocessing ...\n", + " 71511 [cnmf.py: fit():481] [1655528] preprocessing ...\n", + " 71512 [pre_processing.py:interpolate_missing_data():41] [1655503] Checking for missing data entries (NaN)\n", + " 71464 [pre_processing.py:interpolate_missing_data():41] [1655493] Checking for missing data entries (NaN)\n", + " 71547 [pre_processing.py:interpolate_missing_data():41] [1655539] Checking for missing data entries (NaN)\n", + " 71592 [pre_processing.py:interpolate_missing_data():41] [1655491] Checking for missing data entries (NaN)\n", + " 71592 [pre_processing.py:interpolate_missing_data():41] [1655548] Checking for missing data entries (NaN)\n", + " 71592 [pre_processing.py:interpolate_missing_data():41] [1655475] Checking for missing data entries (NaN)\n", + " 71623 [pre_processing.py:interpolate_missing_data():41] [1655501] Checking for missing data entries (NaN)\n", + " 71640 [cnmf.py: fit():481] [1655563] preprocessing ...\n", + " 71895 [pre_processing.py:interpolate_missing_data():41] [1655556] Checking for missing data entries (NaN)\n", + " 71939 [pre_processing.py:interpolate_missing_data():41] [1655528] Checking for missing data entries (NaN)\n", + " 72162 [pre_processing.py:interpolate_missing_data():41] [1655563] Checking for missing data entries (NaN)\n", + " 74658 [cnmf.py: fit():484] [1655515] initializing ...\n", + " 75019 [initialization.py:initialize_components():292] [1655515] Variance Normalization\n", + " 75041 [cnmf.py: fit():484] [1655531] initializing ...\n", + " 75066 [cnmf.py: fit():484] [1655549] initializing ...\n", + " 75102 [cnmf.py: fit():484] [1655553] initializing ...\n", + " 75244 [cnmf.py: fit():484] [1655535] initializing ...\n", + " 75310 [cnmf.py: fit():484] [1655552] initializing ...\n", + " 75352 [initialization.py:initialize_components():292] [1655531] Variance Normalization\n", + " 75367 [initialization.py:initialize_components():292] [1655549] Variance Normalization\n", + " 75407 [initialization.py:initialize_components():292] [1655553] Variance Normalization\n", + " 75489 [cnmf.py: fit():484] [1655546] initializing ...\n", + " 75523 [initialization.py:initialize_components():292] [1655535] Variance Normalization\n", + " 75553 [cnmf.py: fit():484] [1655550] initializing ...\n", + " 75560 [cnmf.py: fit():484] [1655530] initializing ...\n", + " 75507 [cnmf.py: fit():484] [1655551] initializing ...\n", + " 75659 [initialization.py:initialize_components():292] [1655552] Variance Normalization\n", + " 75759 [initialization.py:initialize_components():292] [1655546] Variance Normalization\n", + " 75774 [cnmf.py: fit():484] [1655519] initializing ...\n", + " 75802 [cnmf.py: fit():484] [1655543] initializing ...\n", + " 75808 [initialization.py:initialize_components():292] [1655550] Variance Normalization\n", + " 75823 [initialization.py:initialize_components():292] [1655530] Variance Normalization\n", + " 75826 [cnmf.py: fit():484] [1655525] initializing ...\n", + " 75832 [cnmf.py: fit():484] [1655536] initializing ...\n", + " 75836 [cnmf.py: fit():484] [1655537] initializing ...\n", + " 75873 [cnmf.py: fit():484] [1655470] initializing ...\n", + " 75909 [cnmf.py: fit():484] [1655490] initializing ...\n", + " 75909 [initialization.py:initialize_components():292] [1655551] Variance Normalization\n", + " 75815 [cnmf.py: fit():484] [1655480] initializing ...\n", + " 75933 [cnmf.py: fit():484] [1655540] initializing ...\n", + " 75918 [cnmf.py: fit():484] [1655508] initializing ...\n", + " 75946 [cnmf.py: fit():484] [1655511] initializing ...\n", + " 75830 [cnmf.py: fit():484] [1655533] initializing ...\n", + " 75974 [cnmf.py: fit():484] [1655507] initializing ...\n", + " 76009 [cnmf.py: fit():484] [1655534] initializing ...\n", + " 75878 [cnmf.py: fit():484] [1655497] initializing ...\n", + " 76010 [cnmf.py: fit():484] [1655494] initializing ...\n", + " 76020 [cnmf.py: fit():484] [1655522] initializing ...\n", + " 76045 [cnmf.py: fit():484] [1655524] initializing ...\n", + " 75695 [cnmf.py: fit():484] [1655473] initializing ...\n", + " 76054 [cnmf.py: fit():484] [1655529] initializing ...\n", + " 76061 [cnmf.py: fit():484] [1655509] initializing ...\n", + " 76075 [cnmf.py: fit():484] [1655500] initializing ...\n", + " 76083 [initialization.py:initialize_components():292] [1655519] Variance Normalization\n", + " 76053 [cnmf.py: fit():484] [1655523] initializing ...\n", + " 75755 [cnmf.py: fit():484] [1655557] initializing ...\n", + " 76084 [initialization.py:initialize_components():292] [1655543] Variance Normalization\n", + " 76092 [cnmf.py: fit():484] [1655482] initializing ...\n", + " 76094 [initialization.py:initialize_components():292] [1655525] Variance Normalization\n", + " 76098 [initialization.py:initialize_components():292] [1655536] Variance Normalization\n", + " 76099 [initialization.py:initialize_components():292] [1655537] Variance Normalization\n", + " 76114 [cnmf.py: fit():484] [1655520] initializing ...\n", + " 76049 [cnmf.py: fit():484] [1655514] initializing ...\n", + " 76121 [cnmf.py: fit():484] [1655554] initializing ...\n", + " 76122 [initialization.py:initialize_components():292] [1655470] Variance Normalization\n", + " 76122 [cnmf.py: fit():484] [1655489] initializing ...\n", + " 76123 [cnmf.py: fit():484] [1655512] initializing ...\n", + " 76130 [cnmf.py: fit():484] [1655488] initializing ...\n", + " 76131 [cnmf.py: fit():484] [1655526] initializing ...\n", + " 76135 [initialization.py:initialize_components():292] [1655490] Variance Normalization\n", + " 76141 [cnmf.py: fit():484] [1655517] initializing ...\n", + " 76155 [initialization.py:initialize_components():292] [1655480] Variance Normalization\n", + " 76158 [cnmf.py: fit():484] [1655506] initializing ...\n", + " 76170 [initialization.py:initialize_components():292] [1655540] Variance Normalization\n", + " 76170 [cnmf.py: fit():484] [1655513] initializing ...\n", + " 76173 [cnmf.py: fit():484] [1655492] initializing ...\n", + " 76098 [cnmf.py: fit():484] [1655562] initializing ...\n", + " 76175 [cnmf.py: fit():484] [1655486] initializing ...\n", + " 76183 [initialization.py:initialize_components():292] [1655511] Variance Normalization\n", + " 76184 [initialization.py:initialize_components():292] [1655508] Variance Normalization\n", + " 76184 [initialization.py:initialize_components():292] [1655533] Variance Normalization\n", + " 76202 [cnmf.py: fit():484] [1655560] initializing ...\n", + " 76202 [cnmf.py: fit():484] [1655564] initializing ...\n", + " 75903 [cnmf.py: fit():484] [1655558] initializing ...\n", + " 76194 [cnmf.py: fit():484] [1655483] initializing ...\n", + " 76214 [cnmf.py: fit():484] [1655527] initializing ...\n", + " 76214 [initialization.py:initialize_components():292] [1655507] Variance Normalization\n", + " 76119 [cnmf.py: fit():484] [1655469] initializing ...\n", + " 76224 [initialization.py:initialize_components():292] [1655497] Variance Normalization\n", + " 76225 [initialization.py:initialize_components():292] [1655534] Variance Normalization\n", + " 76169 [cnmf.py: fit():484] [1655498] initializing ...\n", + " 76225 [initialization.py:initialize_components():292] [1655494] Variance Normalization\n", + " 76235 [initialization.py:initialize_components():292] [1655522] Variance Normalization\n", + " 75963 [cnmf.py: fit():484] [1655547] initializing ...\n", + " 76260 [cnmf.py: fit():484] [1655541] initializing ...\n", + " 76262 [map_reduce.py: cnmf_patches():78] [1655515] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 158880_179400START\n", + " 76242 [cnmf.py: fit():484] [1655555] initializing ...\n", + " 76268 [cnmf.py: fit():484] [1655516] initializing ...\n", + " 76278 [initialization.py:initialize_components():292] [1655473] Variance Normalization\n", + " 76279 [cnmf.py: fit():484] [1655479] initializing ...\n", + " 76165 [cnmf.py: fit():484] [1655476] initializing ...\n", + " 76284 [initialization.py:initialize_components():292] [1655524] Variance Normalization\n", + " 76265 [cnmf.py: fit():484] [1655542] initializing ...\n", + " 76284 [initialization.py:initialize_components():292] [1655529] Variance Normalization\n", + " 76235 [cnmf.py: fit():484] [1655502] initializing ...\n", + " 76199 [cnmf.py: fit():484] [1655544] initializing ...\n", + " 76287 [cnmf.py: fit():484] [1655495] initializing ...\n", + " 76287 [initialization.py:initialize_components():292] [1655509] Variance Normalization\n", + " 76293 [cnmf.py: fit():484] [1655532] initializing ...\n", + " 76035 [cnmf.py: fit():484] [1655518] initializing ...\n", + " 76294 [initialization.py:initialize_components():292] [1655500] Variance Normalization\n", + " 76300 [initialization.py:initialize_components():292] [1655557] Variance Normalization\n", + " 76300 [initialization.py:initialize_components():292] [1655523] Variance Normalization\n", + " 76308 [cnmf.py: fit():484] [1655499] initializing ...\n", + " 76321 [cnmf.py: fit():484] [1655504] initializing ...\n", + " 76321 [initialization.py:initialize_components():292] [1655482] Variance Normalization\n", + " 76277 [cnmf.py: fit():484] [1655474] initializing ...\n", + " 76349 [cnmf.py: fit():484] [1655487] initializing ...\n", + " 76350 [initialization.py:initialize_components():292] [1655514] Variance Normalization\n", + " 76350 [initialization.py:initialize_components():292] [1655520] Variance Normalization\n", + " 76351 [initialization.py:initialize_components():292] [1655554] Variance Normalization\n", + " 76359 [initialization.py:initialize_components():292] [1655512] Variance Normalization\n", + " 76360 [initialization.py:initialize_components():292] [1655489] Variance Normalization\n", + " 76369 [cnmf.py: fit():484] [1655485] initializing ...\n", + " 76247 [cnmf.py: fit():484] [1655472] initializing ...\n", + " 76369 [initialization.py:initialize_components():292] [1655488] Variance Normalization\n", + " 76370 [initialization.py:initialize_components():292] [1655526] Variance Normalization\n", + " 76015 [cnmf.py: fit():484] [1655521] initializing ...\n", + " 76391 [initialization.py:initialize_components():292] [1655517] Variance Normalization\n", + " 76400 [initialization.py:initialize_components():292] [1655506] Variance Normalization\n", + " 76404 [cnmf.py: fit():484] [1655481] initializing ...\n", + " 76412 [initialization.py:initialize_components():292] [1655492] Variance Normalization\n", + " 76413 [initialization.py:initialize_components():292] [1655562] Variance Normalization\n", + " 76413 [initialization.py:initialize_components():292] [1655513] Variance Normalization\n", + " 76414 [initialization.py:initialize_components():292] [1655486] Variance Normalization\n", + " 76428 [map_reduce.py: cnmf_patches():78] [1655531] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174240_194760START\n", + " 76440 [map_reduce.py: cnmf_patches():78] [1655535] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189600_210120START\n", + " 76441 [initialization.py:initialize_components():292] [1655560] Variance Normalization\n", + " 76442 [initialization.py:initialize_components():292] [1655558] Variance Normalization\n", + " 76442 [initialization.py:initialize_components():292] [1655564] Variance Normalization\n", + " 76447 [initialization.py:initialize_components():292] [1655483] Variance Normalization\n", + " 76448 [initialization.py:initialize_components():292] [1655527] Variance Normalization\n", + " 76459 [cnmf.py: fit():484] [1655484] initializing ...\n", + " 76479 [initialization.py:initialize_components():292] [1655469] Variance Normalization\n", + " 76485 [cnmf.py: fit():484] [1655559] initializing ...\n", + " 76485 [initialization.py:initialize_components():292] [1655498] Variance Normalization\n", + " 76386 [cnmf.py: fit():484] [1655505] initializing ...\n", + " 76487 [map_reduce.py: cnmf_patches():78] [1655549] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 204960_225480START\n", + " 76487 [cnmf.py: fit():484] [1655561] initializing ...\n", + " 76502 [cnmf.py: fit():484] [1655503] initializing ...\n", + " 76502 [initialization.py:initialize_components():292] [1655547] Variance Normalization\n", + " 76505 [cnmf.py: fit():484] [1655496] initializing ...\n", + " 76511 [initialization.py:initialize_components():292] [1655555] Variance Normalization\n", + " 76506 [initialization.py:initialize_components():292] [1655541] Variance Normalization\n", + " 76506 [map_reduce.py: cnmf_patches():80] [1655515] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 158880_179400Read file\n", + " 76507 [initialization.py:initialize_components():292] [1655516] Variance Normalization\n", + " 76513 [cnmf.py: fit():484] [1655545] initializing ...\n", + " 76575 [initialization.py:initialize_components():292] [1655532] Variance Normalization\n", + " 76567 [initialization.py:initialize_components():292] [1655518] Variance Normalization\n", + " 76517 [initialization.py:initialize_components():292] [1655479] Variance Normalization\n", + " 76520 [cnmf.py: fit():484] [1655493] initializing ...\n", + " 76521 [map_reduce.py: cnmf_patches():78] [1655553] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220320_240840START\n", + " 76531 [initialization.py:initialize_components():292] [1655476] Variance Normalization\n", + " 76540 [initialization.py:initialize_components():292] [1655502] Variance Normalization\n", + " 76540 [initialization.py:initialize_components():292] [1655542] Variance Normalization\n", + " 76547 [initialization.py:initialize_components():292] [1655544] Variance Normalization\n", + " 76556 [initialization.py:initialize_components():292] [1655495] Variance Normalization\n", + " 76651 [cnmf.py: fit():484] [1655548] initializing ...\n", + " 76666 [initialization.py:initialize_components():292] [1655472] Variance Normalization\n", + " 76591 [initialization.py:initialize_components():292] [1655499] Variance Normalization\n", + " 76592 [initialization.py:initialize_components():292] [1655504] Variance Normalization\n", + " 76609 [initialization.py:initialize_components():292] [1655474] Variance Normalization\n", + " 76611 [cnmf.py: fit():484] [1655538] initializing ...\n", + " 76620 [initialization.py:initialize_components():292] [1655487] Variance Normalization\n", + " 76663 [initialization.py:initialize_components():292] [1655485] Variance Normalization\n", + " 76756 [map_reduce.py: cnmf_patches():80] [1655531] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174240_194760Read file\n", + " 76690 [cnmf.py: fit():484] [1655475] initializing ...\n", + " 76690 [initialization.py:initialize_components():292] [1655521] Variance Normalization\n", + " 76711 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+ " 76832 [initialization.py:initialize_components():292] [1655505] Variance Normalization\n", + " 76832 [initialization.py:initialize_components():292] [1655561] Variance Normalization\n", + " 76833 [cnmf.py: fit():484] [1655539] initializing ...\n", + " 76833 [map_reduce.py: cnmf_patches():80] [1655549] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 204960_225480Read file\n", + " 76875 [initialization.py:initialize_components():292] [1655496] Variance Normalization\n", + " 76463 [cnmf.py: fit():484] [1655478] initializing ...\n", + " 76880 [initialization.py:initialize_components():292] [1655545] Variance Normalization\n", + " 76881 [map_reduce.py: cnmf_patches():78] [1655550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5310_25830START\n", + " 76888 [initialization.py:initialize_components():292] [1655493] Variance Normalization\n", + " 76889 [map_reduce.py: cnmf_patches():78] [1655546] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20670_41190START\n", + " 76890 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[1655514] Changing key stride in group patch from 10 to None\n", + " 83024 [params.py: set():1006] [1655514] Changing key nb in group init from 2 to 1\n", + " 83314 [params.py: set():1006] [1655514] Changing key nb in group temporal from 2 to 1\n", + " 83673 [params.py: set():1006] [1655514] Changing key nb in group spatial from 2 to 1\n", + " 83974 [params.py: set():1006] [1655514] Changing key p in group preprocess from 1 to 0\n", + " 84059 [params.py: set():1006] [1655530] Changing key rf in group patch from 20 to None\n", + " 87120 [params.py: set():1006] [1655535] Changing key rf in group patch from 20 to None\n", + " 87338 [params.py: set():1006] [1655513] Changing key rf in group patch from 20 to None\n", + " 87186 [params.py: set():1006] [1655537] Changing key rf in group patch from 20 to None\n", + " 87262 [params.py: set():1006] [1655511] Changing key rf in group patch from 20 to None\n", + " 87140 [params.py: set():1006] [1655554] Changing key rf in group patch from 20 to None\n", + " 87158 [params.py: set():1006] [1655520] Changing key rf in group patch from 20 to None\n", + " 87290 [params.py: set():1006] [1655519] Changing key rf in group patch from 20 to None\n", + " 87389 [params.py: set():1006] [1655472] Changing key rf in group patch from 20 to None\n", + " 87133 [params.py: set():1006] [1655530] Changing key stride in group patch from 10 to None\n", + " 87527 [params.py: set():1006] [1655553] Changing key rf in group patch from 20 to None\n", + " 84168 [params.py: set():1006] [1655514] Changing key p in group temporal from 1 to 0\n", + " 87682 [params.py: set():1006] [1655476] Changing key rf in group patch from 20 to None\n", + " 87765 [params.py: set():1006] [1655552] Changing key rf in group patch from 20 to None\n", + " 87662 [params.py: set():1006] [1655489] Changing key rf in group patch from 20 to None\n", + " 87688 [params.py: set():1006] [1655531] Changing key rf in group patch from 20 to None\n", + " 87762 [params.py: set():1006] [1655559] Changing key rf in group patch from 20 to None\n", + " 87797 [params.py: set():1006] [1655497] Changing key rf in group patch from 20 to None\n", + " 87754 [params.py: set():1006] [1655496] Changing key rf in group patch from 20 to None\n", + " 87537 [params.py: set():1006] [1655490] Changing key rf in group patch from 20 to None\n", + " 87804 [params.py: set():1006] [1655499] Changing key rf in group patch from 20 to None\n", + " 87611 [params.py: set():1006] [1655543] Changing key rf in group patch from 20 to None\n", + " 87813 [params.py: set():1006] [1655551] Changing key rf in group patch from 20 to None\n", + " 87719 [params.py: set():1006] [1655534] Changing key rf in group patch from 20 to None\n", + " 87769 [params.py: set():1006] [1655485] Changing key rf in group patch from 20 to None\n", + " 87681 [params.py: set():1006] [1655517] Changing key rf in group patch from 20 to None\n", + " 87701 [params.py: set():1006] [1655512] Changing key rf in group patch from 20 to None\n", + " 87656 [params.py: set():1006] [1655486] Changing key rf in group patch from 20 to None\n", + " 87673 [params.py: set():1006] [1655521] Changing key rf in group patch from 20 to None\n", + " 87654 [params.py: set():1006] [1655533] Changing key rf in group patch from 20 to None\n", + " 87738 [params.py: set():1006] [1655500] Changing key rf in group patch from 20 to None\n", + " 87531 [params.py: set():1006] [1655479] Changing key rf in group patch from 20 to None\n", + " 87853 [params.py: set():1006] [1655532] Changing key rf in group patch from 20 to None\n", + " 87515 [params.py: set():1006] [1655527] Changing key rf in group patch from 20 to None\n", + " 87523 [params.py: set():1006] [1655502] Changing key rf in group patch from 20 to None\n", + " 87561 [params.py: set():1006] [1655529] Changing key rf in group patch from 20 to None\n", + " 87527 [params.py: set():1006] [1655469] Changing key rf in group patch from 20 to None\n", + " 87858 [params.py: set():1006] [1655564] Changing key rf in group patch from 20 to None\n", + " 87860 [params.py: set():1006] [1655547] Changing key rf in group patch from 20 to None\n", + " 87873 [params.py: set():1006] [1655492] Changing key rf in group patch from 20 to None\n", + " 87874 [params.py: set():1006] [1655483] Changing key rf in group patch from 20 to None\n", + " 87884 [params.py: set():1006] [1655549] Changing key rf in group patch from 20 to None\n", + " 87850 [params.py: set():1006] [1655523] Changing key rf in group patch from 20 to None\n", + " 87886 [params.py: set():1006] [1655516] Changing key rf in group patch from 20 to None\n", + " 87888 [params.py: set():1006] [1655541] Changing key rf in group patch from 20 to None\n", + " 87890 [params.py: set():1006] [1655546] Changing key rf in group patch from 20 to None\n", + " 87893 [params.py: set():1006] [1655560] Changing key rf in group patch from 20 to None\n", + " 87898 [params.py: set():1006] [1655508] Changing key rf in group patch from 20 to None\n", + " 87903 [params.py: set():1006] [1655525] Changing key rf in group patch from 20 to None\n", + " 87905 [params.py: set():1006] [1655473] Changing key rf in group patch from 20 to None\n", + " 87907 [params.py: set():1006] [1655507] Changing key rf in group patch from 20 to None\n", + " 87863 [params.py: set():1006] [1655556] Changing key rf in group patch from 20 to None\n", + " 87907 [params.py: set():1006] [1655540] Changing key rf in group patch from 20 to None\n", + " 87901 [params.py: set():1006] [1655488] Changing key rf in group patch from 20 to None\n", + " 87751 [params.py: set():1006] [1655522] Changing key rf in group patch from 20 to None\n", + " 87921 [params.py: set():1006] [1655481] Changing key rf in group patch from 20 to None\n", + " 87926 [params.py: set():1006] [1655554] Changing key stride in group patch from 10 to None\n", + " 87927 [params.py: set():1006] [1655520] Changing key stride in group patch from 10 to None\n", + " 87927 [params.py: set():1006] [1655511] Changing key stride in group patch from 10 to None\n", + " 87928 [params.py: set():1006] [1655519] Changing key stride in group patch from 10 to None\n", + " 87928 [params.py: set():1006] [1655537] Changing key stride in group patch from 10 to None\n", + " 87929 [params.py: set():1006] [1655472] Changing key stride in group patch from 10 to None\n", + " 87929 [params.py: set():1006] [1655513] Changing key stride in group patch from 10 to None\n", + " 87930 [params.py: set():1006] [1655530] Changing key nb in group init from 2 to 1\n", + " 87930 [params.py: set():1006] [1655553] Changing key stride in group patch from 10 to None\n", + " 87930 [params.py: set():1006] [1655535] Changing key stride in group patch from 10 to None\n", + " 87931 [cnmf.py: fit():436] [1655514] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 87932 [params.py: set():1006] [1655476] Changing key stride in group patch from 10 to None\n", + " 87932 [params.py: set():1006] [1655552] Changing key stride in group patch from 10 to None\n", + " 87933 [params.py: set():1006] [1655531] Changing key stride in group patch from 10 to None\n", + " 87933 [params.py: set():1006] [1655489] Changing key stride in group patch from 10 to None\n", + " 87933 [params.py: set():1006] [1655559] Changing key stride in group patch from 10 to None\n", + " 87747 [params.py: set():1006] [1655509] Changing key rf in group patch from 20 to None\n", + " 87939 [params.py: set():1006] [1655496] Changing key stride in group patch from 10 to None\n", + " 87940 [params.py: set():1006] [1655497] Changing key stride in group patch from 10 to None\n", + " 87940 [params.py: set():1006] [1655490] Changing key stride in group patch from 10 to None\n", + " 87918 [params.py: set():1006] [1655542] Changing key rf in group patch from 20 to None\n", + " 87914 [params.py: set():1006] [1655475] Changing key rf in group patch from 20 to None\n", + " 87946 [params.py: set():1006] [1655536] Changing key rf in group patch from 20 to None\n", + " 87947 [params.py: set():1006] [1655561] Changing key rf in group patch from 20 to None\n", + " 87661 [params.py: set():1006] [1655474] Changing key rf in group patch from 20 to None\n", + " 87964 [params.py: set():1006] [1655543] Changing key stride in group patch from 10 to None\n", + " 87964 [params.py: set():1006] [1655499] Changing key stride in group patch from 10 to None\n", + " 88118 [params.py: set():1006] [1655493] Changing key rf in group patch from 20 to None\n", + " 88104 [params.py: set():1006] [1655477] Changing key rf in group patch from 20 to None\n", + " 88102 [params.py: set():1006] [1655510] Changing key rf in group patch from 20 to None\n", + " 87897 [params.py: set():1006] [1655539] Changing key rf in group patch from 20 to None\n", + " 87967 [params.py: set():1006] [1655551] Changing key stride in group patch from 10 to None\n", + " 87966 [params.py: set():1006] [1655534] Changing key stride in group patch from 10 to None\n", + " 87967 [params.py: set():1006] [1655485] Changing key stride in group patch from 10 to None\n", + " 87970 [params.py: set():1006] [1655526] Changing key rf in group patch from 20 to None\n", + " 87970 [params.py: set():1006] [1655517] Changing key stride in group patch from 10 to None\n", + " 87974 [params.py: set():1006] [1655562] Changing key rf in group patch from 20 to None\n", + " 87975 [params.py: set():1006] [1655486] Changing key stride in group patch from 10 to None\n", + " 87972 [params.py: set():1006] [1655512] Changing key stride in group patch from 10 to None\n", + " 87951 [params.py: set():1006] [1655557] Changing key rf in group patch from 20 to None\n", + " 87975 [params.py: set():1006] [1655521] Changing key stride in group patch from 10 to None\n", + " 87717 [params.py: set():1006] [1655498] Changing key rf in group patch from 20 to None\n", + " 87971 [params.py: set():1006] [1655524] Changing key rf in group patch from 20 to None\n", + " 87976 [params.py: set():1006] [1655533] Changing key stride in group patch from 10 to None\n", + " 87979 [params.py: set():1006] [1655491] Changing key rf in group patch from 20 to None\n", + " 87978 [params.py: set():1006] [1655544] Changing key rf in group patch from 20 to None\n", + " 87980 [params.py: set():1006] [1655506] Changing key rf in group patch from 20 to None\n", + " 87980 [params.py: set():1006] [1655500] Changing key stride in group patch from 10 to None\n", + " 87987 [params.py: set():1006] [1655479] Changing key stride in group patch from 10 to None\n", + " 87966 [params.py: set():1006] [1655482] Changing key rf in group patch from 20 to None\n", + " 87823 [params.py: set():1006] [1655470] Changing key rf in group patch from 20 to None\n", + " 87991 [params.py: set():1006] [1655527] Changing key stride in group patch from 10 to None\n", + " 87992 [params.py: set():1006] [1655532] Changing key stride in group patch from 10 to None\n", + " 87992 [params.py: set():1006] [1655502] Changing key stride in group patch from 10 to None\n", + " 87558 [params.py: set():1006] [1655480] Changing key rf in group patch from 20 to None\n", + " 87993 [params.py: set():1006] [1655529] Changing key stride in group patch from 10 to None\n", + " 87995 [params.py: set():1006] [1655478] Changing key rf in group patch from 20 to None\n", + " 87996 [params.py: set():1006] [1655469] Changing key stride in group patch from 10 to None\n", + " 87997 [params.py: set():1006] [1655564] Changing key stride in group patch from 10 to None\n", + " 87997 [params.py: set():1006] [1655547] Changing key stride in group patch from 10 to None\n", + " 87963 [params.py: set():1006] [1655548] Changing key rf in group patch from 20 to None\n", + " 88010 [params.py: set():1006] [1655492] Changing key stride in group patch from 10 to None\n", + " 88012 [params.py: set():1006] [1655483] Changing key stride in group patch from 10 to None\n", + " 88019 [params.py: set():1006] [1655546] Changing key stride in group patch from 10 to None\n", + " 88019 [params.py: set():1006] [1655525] Changing key stride in group patch from 10 to None\n", + " 88018 [params.py: set():1006] [1655549] Changing key stride in group patch from 10 to None\n", + " 88019 [params.py: set():1006] [1655508] Changing key stride in group patch from 10 to None\n", + " 88020 [params.py: set():1006] [1655541] Changing key stride in group patch from 10 to None\n", + " 88020 [params.py: set():1006] [1655516] Changing key stride in group patch from 10 to None\n", + " 88018 [params.py: set():1006] [1655560] Changing key stride in group patch from 10 to None\n", + " 88021 [params.py: set():1006] [1655473] Changing key stride in group patch from 10 to None\n", + " 88021 [params.py: set():1006] [1655507] Changing key stride in group patch from 10 to None\n", + " 88027 [params.py: set():1006] [1655523] Changing key stride in group patch from 10 to None\n", + " 88027 [params.py: set():1006] [1655556] Changing key stride in group patch from 10 to None\n", + " 88027 [params.py: set():1006] [1655550] Changing key rf in group patch from 20 to None\n", + " 88028 [params.py: set():1006] [1655540] Changing key stride in group patch from 10 to None\n", + " 88029 [params.py: set():1006] [1655488] Changing key stride in group patch from 10 to None\n", + " 88029 [params.py: set():1006] [1655522] Changing key stride in group patch from 10 to None\n", + " 88032 [params.py: set():1006] [1655487] Changing key rf in group patch from 20 to None\n", + " 88037 [params.py: set():1006] [1655481] Changing key stride in group patch from 10 to None\n", + " 88039 [params.py: set():1006] [1655537] Changing key nb in group init from 2 to 1\n", + " 88039 [params.py: set():1006] [1655513] Changing key nb in group init from 2 to 1\n", + " 88041 [params.py: set():1006] [1655472] Changing key nb in group init from 2 to 1\n", + " 88038 [params.py: set():1006] [1655535] Changing key nb in group init from 2 to 1\n", + " 88038 [params.py: set():1006] [1655476] Changing key nb in group init from 2 to 1\n", + " 88040 [params.py: set():1006] [1655519] Changing key nb in group init from 2 to 1\n", + " 88040 [params.py: set():1006] [1655559] Changing key nb in group init from 2 to 1\n", + " 88039 [params.py: set():1006] [1655552] Changing key nb in group init from 2 to 1\n", + " 88041 [params.py: set():1006] [1655489] Changing key nb in group init from 2 to 1\n", + " 88042 [params.py: set():1006] [1655520] Changing key nb in group init from 2 to 1\n", + " 88041 [cnmf.py: fit():449] [1655514] (8000, 41, 41)\n", + " 88042 [params.py: set():1006] [1655553] Changing key nb in group init from 2 to 1\n", + " 88040 [params.py: set():1006] [1655531] Changing key nb in group init from 2 to 1\n", + " 88042 [params.py: set():1006] [1655530] Changing key nb in group temporal from 2 to 1\n", + " 88043 [params.py: set():1006] [1655511] Changing key nb in group init from 2 to 1\n", + " 88044 [params.py: set():1006] [1655509] Changing key stride in group patch from 10 to None\n", + " 88043 [params.py: set():1006] [1655554] Changing key nb in group init from 2 to 1\n", + " 88045 [params.py: set():1006] [1655497] Changing key nb in group init from 2 to 1\n", + " 88044 [params.py: set():1006] [1655496] Changing key nb in group init from 2 to 1\n", + " 88045 [params.py: set():1006] [1655490] Changing key nb in group init from 2 to 1\n", + " 87871 [params.py: set():1006] [1655545] Changing key rf in group patch from 20 to None\n", + " 88046 [params.py: set():1006] [1655542] Changing key stride in group patch from 10 to None\n", + " 88049 [params.py: set():1006] [1655501] Changing key rf in group patch from 20 to None\n", + " 87879 [params.py: set():1006] [1655495] Changing key rf in group patch from 20 to None\n", + " 88050 [params.py: set():1006] [1655484] Changing key rf in group patch from 20 to None\n", + " 88051 [params.py: set():1006] [1655475] Changing key stride in group patch from 10 to None\n", + " 88051 [params.py: set():1006] [1655536] Changing key stride in group patch from 10 to None\n", + " 88051 [params.py: set():1006] [1655561] Changing key stride in group patch from 10 to None\n", + " 88067 [params.py: set():1006] [1655543] Changing key nb in group init from 2 to 1\n", + " 88068 [params.py: set():1006] [1655474] Changing key stride in group patch from 10 to None\n", + " 88068 [params.py: set():1006] [1655499] Changing key nb in group init from 2 to 1\n", + " 88055 [map_reduce.py: cnmf_patches():78] [1660129] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51540_72060START\n", + " 88017 [params.py: set():1006] [1655503] Changing key rf in group patch from 20 to None\n", + " 88085 [params.py: set():1006] [1655528] Changing key rf in group patch from 20 to None\n", + " 87945 [params.py: set():1006] [1655494] Changing key rf in group patch from 20 to None\n", + " 88097 [params.py: set():1006] [1655563] Changing key rf in group patch from 20 to None\n", + " 88009 [params.py: set():1006] [1655538] Changing key rf in group patch from 20 to None\n", + " 88101 [params.py: set():1006] [1655504] Changing key rf in group patch from 20 to None\n", + " 88079 [params.py: set():1006] [1655505] Changing key rf in group patch from 20 to None\n", + " 88165 [params.py: set():1006] [1655503] Changing key stride in group patch from 10 to None\n", + " 88156 [params.py: set():1006] [1655553] Changing key nb in group temporal from 2 to 1\n", + " 88163 [params.py: set():1006] [1655474] Changing key nb in group init from 2 to 1\n", + " 88166 [params.py: set():1006] [1655563] Changing key stride in group patch from 10 to None\n", + " 88152 [params.py: set():1006] [1655481] Changing key nb in group init from 2 to 1\n", + " 88155 [params.py: set():1006] [1655520] Changing key nb in group temporal from 2 to 1\n", + " 88151 [params.py: set():1006] [1655487] Changing key stride in group patch from 10 to None\n", + " 88163 [params.py: set():1006] [1655561] Changing key nb in group init from 2 to 1\n", + " 88146 [params.py: set():1006] [1655549] Changing key nb in group init from 2 to 1\n", + " 88169 [params.py: set():1006] [1655493] Changing key stride in group patch from 10 to None\n", + " 88164 [params.py: set():1006] [1655499] Changing key nb in group temporal from 2 to 1\n", + " 88164 [params.py: set():1006] [1655543] Changing key nb in group temporal from 2 to 1\n", + " 88139 [params.py: set():1006] [1655532] Changing key nb in group init from 2 to 1\n", + " 88159 [params.py: set():1006] [1655490] Changing key nb in group temporal from 2 to 1\n", + " 88144 [params.py: set():1006] [1655483] Changing key nb in group init from 2 to 1\n", + " 88167 [params.py: set():1006] [1655538] Changing key stride in group patch from 10 to None\n", + " 88162 [params.py: set():1006] [1655475] Changing key nb in group init from 2 to 1\n", + " 88150 [params.py: set():1006] [1655522] Changing key nb in group init from 2 to 1\n", + " 88166 [params.py: set():1006] [1655494] Changing key stride in group patch from 10 to None\n", + " 88150 [params.py: set():1006] [1655488] Changing key nb in group init from 2 to 1\n", + " 88159 [params.py: set():1006] [1655496] Changing key nb in group temporal from 2 to 1\n", + " 88145 [params.py: set():1006] [1655525] Changing key nb in group init from 2 to 1\n", + " 88153 [params.py: set():1006] [1655472] Changing key nb in group temporal from 2 to 1\n", + " 88160 [params.py: set():1006] [1655545] Changing key stride in group patch from 10 to None\n", + " 88147 [params.py: set():1006] [1655560] Changing key nb in group init from 2 to 1\n", + " 88155 [params.py: set():1006] [1655513] Changing key nb in group temporal from 2 to 1\n", + " 88166 [params.py: set():1006] [1655528] Changing key stride in group patch from 10 to None\n", + " 88162 [params.py: set():1006] [1655536] Changing key nb in group init from 2 to 1\n", + " 88146 [params.py: set():1006] [1655541] Changing key nb in group init from 2 to 1\n", + " 88162 [params.py: set():1006] [1655484] Changing key stride in group patch from 10 to None\n", + " 88161 [params.py: set():1006] [1655542] Changing key nb in group init from 2 to 1\n", + " 88161 [params.py: set():1006] [1655495] Changing key stride in group patch from 10 to None\n", + " 88140 [params.py: set():1006] [1655470] Changing key stride in group patch from 10 to None\n", + " 88148 [params.py: set():1006] [1655473] Changing key nb in group init from 2 to 1\n", + " 88157 [params.py: set():1006] [1655531] Changing key nb in group temporal from 2 to 1\n", + " 88138 [params.py: set():1006] [1655482] Changing key stride in group patch from 10 to None\n", + " 88153 [params.py: set():1006] [1655535] Changing key nb in group temporal from 2 to 1\n", + " 88157 [params.py: set():1006] [1655530] Changing key nb in group spatial from 2 to 1\n", + " 88150 [params.py: set():1006] [1655550] Changing key stride in group patch from 10 to None\n", + " 88159 [params.py: set():1006] [1655497] Changing key nb in group temporal from 2 to 1\n", + " 88169 [params.py: set():1006] [1655477] Changing key stride in group patch from 10 to None\n", + " 88153 [params.py: set():1006] [1655476] Changing key nb in group temporal from 2 to 1\n", + " 88154 [params.py: set():1006] [1655519] Changing key nb in group temporal from 2 to 1\n", + " 88137 [params.py: set():1006] [1655544] Changing key stride in group patch from 10 to None\n", + " 88138 [params.py: set():1006] [1655479] Changing key nb in group init from 2 to 1\n", + " 88154 [params.py: set():1006] [1655559] Changing key nb in group temporal from 2 to 1\n", + " 88155 [params.py: set():1006] [1655489] Changing key nb in group temporal from 2 to 1\n", + " 88136 [params.py: set():1006] [1655491] Changing key stride in group patch from 10 to None\n", + " 88148 [params.py: set():1006] [1655523] Changing key nb in group init from 2 to 1\n", + " 88137 [params.py: set():1006] [1655506] Changing key stride in group patch from 10 to None\n", + " 88140 [params.py: set():1006] [1655502] Changing key nb in group init from 2 to 1\n", + " 88142 [params.py: set():1006] [1655469] Changing key nb in group init from 2 to 1\n", + " 88168 [params.py: set():1006] [1655510] Changing key stride in group patch from 10 to None\n", + " 88149 [params.py: set():1006] [1655540] Changing key nb in group init from 2 to 1\n", + " 88135 [params.py: set():1006] [1655524] Changing key stride in group patch from 10 to None\n", + " 88137 [params.py: set():1006] [1655500] Changing key nb in group init from 2 to 1\n", + " 88139 [params.py: set():1006] [1655527] Changing key nb in group init from 2 to 1\n", + " 88154 [params.py: set():1006] [1655552] Changing key nb in group temporal from 2 to 1\n", + " 88135 [params.py: set():1006] [1655498] Changing key stride in group patch from 10 to None\n", + " 88143 [params.py: set():1006] [1655547] Changing key nb in group init from 2 to 1\n", + " 88156 [cnmf.py: fit():466] [1655514] Using 1 processes\n", + " 88136 [params.py: set():1006] [1655533] Changing key nb in group init from 2 to 1\n", + " 88146 [params.py: set():1006] [1655508] Changing key nb in group init from 2 to 1\n", + " 88141 [params.py: set():1006] [1655480] Changing key stride in group patch from 10 to None\n", + " 88161 [params.py: set():1006] [1655501] Changing key stride in group patch from 10 to None\n", + " 88158 [params.py: set():1006] [1655511] Changing key nb in group temporal from 2 to 1\n", + " 88146 [params.py: set():1006] [1655546] Changing key nb in group init from 2 to 1\n", + " 88143 [params.py: set():1006] [1655548] Changing key stride in group patch from 10 to None\n", + " 88168 [params.py: set():1006] [1655505] Changing key stride in group patch from 10 to None\n", + " 88142 [params.py: set():1006] [1655564] Changing key nb in group init from 2 to 1\n", + " 88141 [params.py: set():1006] [1655529] Changing key nb in group init from 2 to 1\n", + " 88142 [params.py: set():1006] [1655478] Changing key stride in group patch from 10 to None\n", + " 88134 [params.py: set():1006] [1655521] Changing key nb in group init from 2 to 1\n", + " 88148 [params.py: set():1006] [1655507] Changing key nb in group init from 2 to 1\n", + " 88149 [params.py: set():1006] [1655556] Changing key nb in group init from 2 to 1\n", + " 88157 [params.py: set():1006] [1655554] Changing key nb in group temporal from 2 to 1\n", + " 88158 [params.py: set():1006] [1655509] Changing key nb in group init from 2 to 1\n", + " 88147 [params.py: set():1006] [1655516] Changing key nb in group init from 2 to 1\n", + " 88167 [params.py: set():1006] [1655504] Changing key stride in group patch from 10 to None\n", + " 88144 [params.py: set():1006] [1655492] Changing key nb in group init from 2 to 1\n", + " 88132 [params.py: set():1006] [1655517] Changing key nb in group init from 2 to 1\n", + " 88130 [params.py: set():1006] [1655534] Changing key nb in group init from 2 to 1\n", + " 88156 [params.py: set():1006] [1655537] Changing key nb in group temporal from 2 to 1\n", + " 88132 [params.py: set():1006] [1655562] Changing key stride in group patch from 10 to None\n", + " 88134 [params.py: set():1006] [1655557] Changing key stride in group patch from 10 to None\n", + " 88133 [params.py: set():1006] [1655486] Changing key nb in group init from 2 to 1\n", + " 88131 [params.py: set():1006] [1655526] Changing key stride in group patch from 10 to None\n", + " 88131 [params.py: set():1006] [1655485] Changing key nb in group init from 2 to 1\n", + " 88133 [params.py: set():1006] [1655512] Changing key nb in group init from 2 to 1\n", + " 88130 [params.py: set():1006] [1655551] Changing key nb in group init from 2 to 1\n", + " 88165 [map_reduce.py: cnmf_patches():80] [1660129] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51540_72060Read file\n", + " 88130 [params.py: set():1006] [1655539] Changing key stride in group patch from 10 to None\n", + " 88173 [params.py: set():1006] [1655518] Changing key rf in group patch from 20 to None\n", + " 88204 [map_reduce.py: cnmf_patches():78] [1660361] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66900_87420START\n", + " 88216 [params.py: set():1006] [1655538] Changing key nb in group init from 2 to 1\n", + " 88216 [params.py: set():1006] [1655543] Changing key nb in group spatial from 2 to 1\n", + " 88217 [params.py: set():1006] [1655541] Changing key nb in group temporal from 2 to 1\n", + " 88217 [params.py: set():1006] [1655563] Changing key nb in group init from 2 to 1\n", + " 88217 [params.py: set():1006] [1655553] Changing key nb in group spatial from 2 to 1\n", + " 88218 [params.py: set():1006] [1655493] Changing key nb in group init from 2 to 1\n", + " 88218 [params.py: set():1006] [1655487] Changing key nb in group init from 2 to 1\n", + " 88218 [params.py: set():1006] [1655561] Changing key nb in group temporal from 2 to 1\n", + " 88219 [params.py: set():1006] [1655482] Changing key nb in group init from 2 to 1\n", + " 88219 [params.py: set():1006] [1655474] Changing key nb in group temporal from 2 to 1\n", + " 88219 [params.py: set():1006] [1655473] Changing key nb in group temporal from 2 to 1\n", + " 88220 [params.py: set():1006] [1655475] Changing key nb in group temporal from 2 to 1\n", + " 88220 [params.py: set():1006] [1655499] Changing key nb in group spatial from 2 to 1\n", + " 88220 [params.py: set():1006] [1655528] Changing key nb in group init from 2 to 1\n", + " 88221 [params.py: set():1006] [1655488] Changing key nb in group temporal from 2 to 1\n", + " 88221 [params.py: set():1006] [1655522] Changing key nb in group temporal from 2 to 1\n", + " 88221 [params.py: set():1006] [1655481] Changing key nb in group temporal from 2 to 1\n", + " 88221 [params.py: set():1006] [1655470] Changing key nb in group init from 2 to 1\n", + " 88222 [params.py: set():1006] [1655560] Changing key nb in group temporal from 2 to 1\n", + " 88222 [params.py: set():1006] [1655494] Changing key nb in group init from 2 to 1\n", + " 88222 [params.py: set():1006] [1655513] Changing key nb in group spatial from 2 to 1\n", + " 88223 [params.py: set():1006] [1655495] Changing key nb in group init from 2 to 1\n", + " 88223 [params.py: set():1006] [1655549] Changing key nb in group temporal from 2 to 1\n", + " 88223 [params.py: set():1006] [1655544] Changing key nb in group init from 2 to 1\n", + " 88224 [params.py: set():1006] [1655490] Changing key nb in group spatial from 2 to 1\n", + " 88224 [params.py: set():1006] [1655532] Changing key nb in group temporal from 2 to 1\n", + " 88224 [params.py: set():1006] [1655496] Changing key nb in group spatial from 2 to 1\n", + " 88225 [params.py: set():1006] [1655531] Changing key nb in group spatial from 2 to 1\n", + " 88225 [params.py: set():1006] [1655542] Changing key nb in group temporal from 2 to 1\n", + " 88225 [params.py: set():1006] [1655483] Changing key nb in group temporal from 2 to 1\n", + " 88226 [params.py: set():1006] [1655502] Changing key nb in group temporal from 2 to 1\n", + " 88226 [params.py: set():1006] [1655472] Changing key nb in group spatial from 2 to 1\n", + " 88226 [params.py: set():1006] [1655476] Changing key nb in group spatial from 2 to 1\n", + " 88226 [params.py: set():1006] [1655550] Changing key nb in group init from 2 to 1\n", + " 88227 [params.py: set():1006] [1655546] Changing key nb in group temporal from 2 to 1\n", + " 88227 [params.py: set():1006] [1655484] Changing key nb in group init from 2 to 1\n", + " 88227 [params.py: set():1006] [1655530] Changing key p in group preprocess from 1 to 0\n", + " 88228 [params.py: set():1006] [1655525] Changing key nb in group temporal from 2 to 1\n", + " 88228 [params.py: set():1006] [1655519] Changing key nb in group spatial from 2 to 1\n", + " 88228 [params.py: set():1006] [1655510] Changing key nb in group init from 2 to 1\n", + " 88229 [params.py: set():1006] [1655540] Changing key nb in group temporal from 2 to 1\n", + " 88229 [params.py: set():1006] [1655535] Changing key nb in group spatial from 2 to 1\n", + " 88229 [params.py: set():1006] [1655545] Changing key nb in group init from 2 to 1\n", + " 88230 [params.py: set():1006] [1655536] Changing key nb in group temporal from 2 to 1\n", + " 88230 [params.py: set():1006] [1655489] Changing key nb in group spatial from 2 to 1\n", + " 88230 [params.py: set():1006] [1655479] Changing key nb in group temporal from 2 to 1\n", + " 88230 [params.py: set():1006] [1655497] Changing key nb in group spatial from 2 to 1\n", + " 88231 [params.py: set():1006] [1655552] Changing key nb in group spatial from 2 to 1\n", + " 88231 [params.py: set():1006] [1655507] Changing key nb in group temporal from 2 to 1\n", + " 88231 [params.py: set():1006] [1655556] Changing key nb in group temporal from 2 to 1\n", + " 88232 [params.py: set():1006] [1655511] Changing key nb in group spatial from 2 to 1\n", + " 88232 [params.py: set():1006] [1655547] Changing key nb in group temporal from 2 to 1\n", + " 88232 [params.py: set():1006] [1655506] Changing key nb in group init from 2 to 1\n", + " 88233 [params.py: set():1006] [1655500] Changing key nb in group temporal from 2 to 1\n", + " 88233 [params.py: set():1006] [1655469] Changing key nb in group temporal from 2 to 1\n", + " 88233 [params.py: set():1006] [1655501] Changing key nb in group init from 2 to 1\n", + " 88234 [cnmf.py: fit():477] [1655514] using 400 pixels per process\n", + " 88234 [params.py: set():1006] [1655478] Changing key nb in group init from 2 to 1\n", + " 88234 [params.py: set():1006] [1655491] Changing key nb in group init from 2 to 1\n", + " 88235 [params.py: set():1006] [1655529] Changing key nb in group temporal from 2 to 1\n", + " 88235 [params.py: set():1006] [1655554] Changing key nb in group spatial from 2 to 1\n", + " 88235 [params.py: set():1006] [1655534] Changing key nb in group temporal from 2 to 1\n", + " 88236 [params.py: set():1006] [1655521] Changing key nb in group temporal from 2 to 1\n", + " 88236 [params.py: set():1006] [1655559] Changing key nb in group spatial from 2 to 1\n", + " 88236 [params.py: set():1006] [1655477] Changing key nb in group init from 2 to 1\n", + " 88236 [params.py: set():1006] [1655508] Changing key nb in group temporal from 2 to 1\n", + " 88237 [params.py: set():1006] [1655523] Changing key nb in group temporal from 2 to 1\n", + " 88237 [params.py: set():1006] [1655537] Changing key nb in group spatial from 2 to 1\n", + " 88237 [params.py: set():1006] [1655564] Changing key nb in group temporal from 2 to 1\n", + " 88238 [params.py: set():1006] [1655520] Changing key nb in group spatial from 2 to 1\n", + " 88238 [params.py: set():1006] [1655509] Changing key nb in group temporal from 2 to 1\n", + " 88238 [params.py: set():1006] [1655480] Changing key nb in group init from 2 to 1\n", + " 88239 [params.py: set():1006] [1655524] Changing key nb in group init from 2 to 1\n", + " 88239 [params.py: set():1006] [1655533] Changing key nb in group temporal from 2 to 1\n", + " 88239 [params.py: set():1006] [1655516] Changing key nb in group temporal from 2 to 1\n", + " 88240 [params.py: set():1006] [1655504] Changing key nb in group init from 2 to 1\n", + " 88240 [params.py: set():1006] [1655548] Changing key nb in group init from 2 to 1\n", + " 88240 [params.py: set():1006] [1655498] Changing key nb in group init from 2 to 1\n", + " 88241 [params.py: set():1006] [1655505] Changing key nb in group init from 2 to 1\n", + " 88241 [params.py: set():1006] [1655492] Changing key nb in group temporal from 2 to 1\n", + " 88241 [params.py: set():1006] [1655557] Changing key nb in group init from 2 to 1\n", + " 88242 [params.py: set():1006] [1655512] Changing key nb in group temporal from 2 to 1\n", + " 88242 [params.py: set():1006] [1655503] Changing key nb in group init from 2 to 1\n", + " 88242 [params.py: set():1006] [1655485] Changing key nb in group temporal from 2 to 1\n", + " 88243 [params.py: set():1006] [1655551] Changing key nb in group temporal from 2 to 1\n", + " 88243 [params.py: set():1006] [1655517] Changing key nb in group temporal from 2 to 1\n", + " 88243 [params.py: set():1006] [1655486] Changing key nb in group temporal from 2 to 1\n", + " 88244 [params.py: set():1006] [1655562] Changing key nb in group init from 2 to 1\n", + " 88244 [params.py: set():1006] [1655526] Changing key nb in group init from 2 to 1\n", + " 88245 [params.py: set():1006] [1655527] Changing key nb in group temporal from 2 to 1\n", + " 88245 [params.py: set():1006] [1655539] Changing key nb in group init from 2 to 1\n", + " 88245 [params.py: set():1006] [1655518] Changing key stride in group patch from 10 to None\n", + " 88285 [params.py: set():1006] [1655539] Changing key nb in group temporal from 2 to 1\n", + " 88287 [params.py: set():1006] [1655526] Changing key nb in group temporal from 2 to 1\n", + " 88288 [params.py: set():1006] [1655562] Changing key nb in group temporal from 2 to 1\n", + " 88288 [params.py: set():1006] [1655480] Changing key nb in group temporal from 2 to 1\n", + " 88289 [params.py: set():1006] [1655551] Changing key nb in group spatial from 2 to 1\n", + " 88289 [params.py: set():1006] [1655517] Changing key nb in group spatial from 2 to 1\n", + " 88290 [params.py: set():1006] [1655485] Changing key nb in group spatial from 2 to 1\n", + " 88290 [params.py: set():1006] [1655563] Changing key nb in group temporal from 2 to 1\n", + " 88291 [params.py: set():1006] [1655486] Changing key nb in group spatial from 2 to 1\n", + " 88291 [params.py: set():1006] [1655527] Changing key nb in group spatial from 2 to 1\n", + " 88292 [params.py: set():1006] [1655503] Changing key nb in group temporal from 2 to 1\n", + " 88292 [params.py: set():1006] [1655489] Changing key p in group preprocess from 1 to 0\n", + " 88293 [params.py: set():1006] [1655501] Changing key nb in group temporal from 2 to 1\n", + " 88293 [params.py: set():1006] [1655475] Changing key nb in group spatial from 2 to 1\n", + " 88294 [params.py: set():1006] [1655553] Changing key p in group preprocess from 1 to 0\n", + " 88294 [params.py: set():1006] [1655510] Changing key nb in group temporal from 2 to 1\n", + " 88294 [params.py: set():1006] [1655525] Changing key nb in group spatial from 2 to 1\n", + " 88295 [params.py: set():1006] [1655509] Changing key nb in group spatial from 2 to 1\n", + " 88295 [params.py: set():1006] [1655536] Changing key nb in group spatial from 2 to 1\n", + " 88295 [params.py: set():1006] [1655535] Changing key p in group preprocess from 1 to 0\n", + " 88296 [params.py: set():1006] [1655557] Changing key nb in group temporal from 2 to 1\n", + " 88296 [params.py: set():1006] [1655492] Changing key nb in group spatial from 2 to 1\n", + " 88296 [params.py: set():1006] [1655507] Changing key nb in group spatial from 2 to 1\n", + " 88297 [params.py: set():1006] [1655469] Changing key nb in group spatial from 2 to 1\n", + " 88297 [params.py: set():1006] [1655512] Changing key nb in group spatial from 2 to 1\n", + " 88297 [params.py: set():1006] [1655543] Changing key p in group preprocess from 1 to 0\n", + " 88298 [params.py: set():1006] [1655528] Changing key nb in group temporal from 2 to 1\n", + " 88298 [params.py: set():1006] [1655511] Changing key p in group preprocess from 1 to 0\n", + " 88298 [params.py: set():1006] [1655540] Changing key nb in group spatial from 2 to 1\n", + " 88299 [params.py: set():1006] [1655545] Changing key nb in group temporal from 2 to 1\n", + " 88299 [params.py: set():1006] [1655549] Changing key nb in group spatial from 2 to 1\n", + " 88299 [params.py: set():1006] [1655547] Changing key nb in group spatial from 2 to 1\n", + " 88300 [params.py: set():1006] [1655516] Changing key nb in group spatial from 2 to 1\n", + " 88300 [params.py: set():1006] [1655521] Changing key nb in group spatial from 2 to 1\n", + " 88300 [params.py: set():1006] [1655559] Changing key p in group preprocess from 1 to 0\n", + " 88301 [params.py: set():1006] [1655532] Changing key nb in group spatial from 2 to 1\n", + " 88301 [params.py: set():1006] [1655519] Changing key p in group preprocess from 1 to 0\n", + " 88301 [params.py: set():1006] [1655491] Changing key nb in group temporal from 2 to 1\n", + " 88302 [params.py: set():1006] [1655506] Changing key nb in group temporal from 2 to 1\n", + " 88302 [params.py: set():1006] [1655564] Changing key nb in group spatial from 2 to 1\n", + " 88302 [params.py: set():1006] [1655552] Changing key p in group preprocess from 1 to 0\n", + " 88303 [params.py: set():1006] [1655533] Changing key nb in group spatial from 2 to 1\n", + " 88303 [params.py: set():1006] [1655482] Changing key nb in group temporal from 2 to 1\n", + " 88303 [params.py: set():1006] [1655470] Changing key nb in group temporal from 2 to 1\n", + " 88304 [params.py: set():1006] [1655500] Changing key nb in group spatial from 2 to 1\n", + " 88304 [params.py: set():1006] [1655518] Changing key nb in group init from 2 to 1\n", + " 88304 [params.py: set():1006] [1655554] Changing key p in group preprocess from 1 to 0\n", + " 88304 [params.py: set():1006] [1655524] Changing key nb in group temporal from 2 to 1\n", + " 88305 [params.py: set():1006] [1655546] Changing key nb in group spatial from 2 to 1\n", + " 88305 [params.py: set():1006] [1655530] Changing key p in group temporal from 1 to 0\n", + " 88305 [params.py: set():1006] [1655529] Changing key nb in group spatial from 2 to 1\n", + " 88306 [params.py: set():1006] [1655520] Changing key p in group preprocess from 1 to 0\n", + " 88306 [params.py: set():1006] [1655541] Changing key nb in group spatial from 2 to 1\n", + " 88306 [params.py: set():1006] [1655534] Changing key nb in group spatial from 2 to 1\n", + " 88307 [params.py: set():1006] [1655531] Changing key p in group preprocess from 1 to 0\n", + " 88307 [params.py: set():1006] [1655472] Changing key p in group preprocess from 1 to 0\n", + " 88307 [params.py: set():1006] [1655495] Changing key nb in group temporal from 2 to 1\n", + " 88308 [params.py: set():1006] [1655493] Changing key nb in group temporal from 2 to 1\n", + " 88308 [params.py: set():1006] [1655487] Changing key nb in group temporal from 2 to 1\n", + " 88308 [params.py: set():1006] [1655550] Changing key nb in group temporal from 2 to 1\n", + " 88309 [params.py: set():1006] [1655538] Changing key nb in group temporal from 2 to 1\n", + " 88309 [params.py: set():1006] [1655560] Changing key nb in group spatial from 2 to 1\n", + " 88309 [params.py: set():1006] [1655488] Changing key nb in group spatial from 2 to 1\n", + " 88310 [params.py: set():1006] [1655494] Changing key nb in group temporal from 2 to 1\n", + " 88310 [params.py: set():1006] [1655556] Changing key nb in group spatial from 2 to 1\n", + " 88310 [params.py: set():1006] [1655537] Changing key p in group preprocess from 1 to 0\n", + " 88311 [params.py: set():1006] [1655478] Changing key nb in group temporal from 2 to 1\n", + " 88311 [params.py: set():1006] [1655513] Changing key p in group preprocess from 1 to 0\n", + " 88311 [params.py: set():1006] [1655499] Changing key p in group preprocess from 1 to 0\n", + " 88312 [params.py: set():1006] [1655523] Changing key nb in group spatial from 2 to 1\n", + " 88312 [params.py: set():1006] [1655502] Changing key nb in group spatial from 2 to 1\n", + " 88312 [params.py: set():1006] [1655504] Changing key nb in group temporal from 2 to 1\n", + " 88312 [params.py: set():1006] [1655561] Changing key nb in group spatial from 2 to 1\n", + " 88313 [params.py: set():1006] [1655481] Changing key nb in group spatial from 2 to 1\n", + " 88313 [params.py: set():1006] [1655474] Changing key nb in group spatial from 2 to 1\n", + " 88313 [params.py: set():1006] [1655496] Changing key p in group preprocess from 1 to 0\n", + " 88314 [params.py: set():1006] [1655522] Changing key nb in group spatial from 2 to 1\n", + " 88314 [params.py: set():1006] [1655497] Changing key p in group preprocess from 1 to 0\n", + " 88314 [params.py: set():1006] [1655505] Changing key nb in group temporal from 2 to 1\n", + " 88315 [params.py: set():1006] [1655479] Changing key nb in group spatial from 2 to 1\n", + " 88315 [params.py: set():1006] [1655473] Changing key nb in group spatial from 2 to 1\n", + " 88315 [params.py: set():1006] [1655542] Changing key nb in group spatial from 2 to 1\n", + " 88316 [params.py: set():1006] [1655544] Changing key nb in group temporal from 2 to 1\n", + " 88316 [params.py: set():1006] [1655476] Changing key p in group preprocess from 1 to 0\n", + " 88316 [params.py: set():1006] [1655490] Changing key p in group preprocess from 1 to 0\n", + " 88316 [params.py: set():1006] [1655484] Changing key nb in group temporal from 2 to 1\n", + " 88317 [params.py: set():1006] [1655477] Changing key nb in group temporal from 2 to 1\n", + " 88317 [params.py: set():1006] [1655548] Changing key nb in group temporal from 2 to 1\n", + " 88318 [params.py: set():1006] [1655483] Changing key nb in group spatial from 2 to 1\n", + " 88318 [params.py: set():1006] [1655508] Changing key nb in group spatial from 2 to 1\n", + " 88322 [map_reduce.py: cnmf_patches():78] [1660550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82260_102780START\n", + " 88319 [map_reduce.py: cnmf_patches():80] [1660361] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66900_87420Read file\n", + " 88319 [params.py: set():1006] [1655498] Changing key nb in group temporal from 2 to 1\n", + " 88318 [cnmf.py: fit():478] [1655514] using 5000 block_size_temp\n", + " 88350 [params.py: set():1006] [1655538] Changing key nb in group spatial from 2 to 1\n", + " 88350 [params.py: set():1006] [1655495] Changing key nb in group spatial from 2 to 1\n", + " 88350 [params.py: set():1006] [1655493] Changing key nb in group spatial from 2 to 1\n", + " 88351 [params.py: set():1006] [1655550] Changing key nb in group spatial from 2 to 1\n", + " 88351 [params.py: set():1006] [1655556] Changing key p in group preprocess from 1 to 0\n", + " 88351 [params.py: set():1006] [1655489] Changing key p in group temporal from 1 to 0\n", + " 88352 [params.py: set():1006] [1655488] Changing key p in group preprocess from 1 to 0\n", + " 88352 [params.py: set():1006] [1655537] Changing key p in group temporal from 1 to 0\n", + " 88352 [params.py: set():1006] [1655486] Changing key p in group preprocess from 1 to 0\n", + " 88353 [params.py: set():1006] [1655501] Changing key nb in group spatial from 2 to 1\n", + " 88353 [params.py: set():1006] [1655472] Changing key p in group temporal from 1 to 0\n", + " 88354 [params.py: set():1006] [1655527] Changing key p in group preprocess from 1 to 0\n", + " 88354 [params.py: set():1006] [1655487] Changing key nb in group spatial from 2 to 1\n", + " 88354 [params.py: set():1006] [1655559] Changing key p in group temporal from 1 to 0\n", + " 88354 [params.py: set():1006] [1655503] Changing key nb in group spatial from 2 to 1\n", + " 88355 [params.py: set():1006] [1655517] Changing key p in group preprocess from 1 to 0\n", + " 88355 [params.py: set():1006] [1655516] Changing key p in group preprocess from 1 to 0\n", + " 88356 [params.py: set():1006] [1655536] Changing key p in group preprocess from 1 to 0\n", + " 88356 [params.py: set():1006] [1655519] Changing key p in group temporal from 1 to 0\n", + " 88356 [params.py: set():1006] [1655560] Changing key p in group preprocess from 1 to 0\n", + " 88356 [params.py: set():1006] [1655532] Changing key p in group preprocess from 1 to 0\n", + " 88357 [params.py: set():1006] [1655529] Changing key p in group preprocess from 1 to 0\n", + " 88357 [params.py: set():1006] [1655513] Changing key p in group temporal from 1 to 0\n", + " 88357 [params.py: set():1006] [1655518] Changing key nb in group temporal from 2 to 1\n", + " 88358 [params.py: set():1006] [1655504] Changing key nb in group spatial from 2 to 1\n", + " 88358 [params.py: set():1006] [1655551] Changing key p in group preprocess from 1 to 0\n", + " 88358 [params.py: set():1006] [1655491] Changing key nb in group spatial from 2 to 1\n", + " 88359 [params.py: set():1006] [1655478] Changing key nb in group spatial from 2 to 1\n", + " 88359 [params.py: set():1006] [1655563] Changing key nb in group spatial from 2 to 1\n", + " 88359 [params.py: set():1006] [1655509] Changing key p in group preprocess from 1 to 0\n", + " 88360 [params.py: set():1006] [1655521] Changing key p in group preprocess from 1 to 0\n", + " 88360 [params.py: set():1006] [1655500] Changing key p in group preprocess from 1 to 0\n", + " 88360 [params.py: set():1006] [1655543] Changing key p in group temporal from 1 to 0\n", + " 88360 [params.py: set():1006] [1655561] Changing key p in group preprocess from 1 to 0\n", + " 88361 [params.py: set():1006] [1655553] Changing key p in group temporal from 1 to 0\n", + " 88361 [params.py: set():1006] [1655470] Changing key nb in group spatial from 2 to 1\n", + " 88361 [params.py: set():1006] [1655494] Changing key nb in group spatial from 2 to 1\n", + " 88362 [params.py: set():1006] [1655479] Changing key p in group preprocess from 1 to 0\n", + " 88362 [params.py: set():1006] [1655499] Changing key p in group temporal from 1 to 0\n", + " 88362 [params.py: set():1006] [1655525] Changing key p in group preprocess from 1 to 0\n", + " 88363 [params.py: set():1006] [1655502] Changing key p in group preprocess from 1 to 0\n", + " 88363 [params.py: set():1006] [1655485] Changing key p in group preprocess from 1 to 0\n", + " 88363 [params.py: set():1006] [1655562] Changing key nb in group spatial from 2 to 1\n", + " 88364 [params.py: set():1006] [1655541] Changing key p in group preprocess from 1 to 0\n", + " 88364 [params.py: set():1006] [1655524] Changing key nb in group spatial from 2 to 1\n", + " 88364 [params.py: set():1006] [1655528] Changing key nb in group spatial from 2 to 1\n", + " 88365 [params.py: set():1006] [1655522] Changing key p in group preprocess from 1 to 0\n", + " 88367 [params.py: set():1006] [1655564] Changing key p in group preprocess from 1 to 0\n", + " 88365 [params.py: set():1006] [1655520] Changing key p in group temporal from 1 to 0\n", + " 88366 [params.py: set():1006] [1655511] Changing key p in group temporal from 1 to 0\n", + " 88367 [params.py: set():1006] [1655546] Changing key p in group preprocess from 1 to 0\n", + " 88370 [params.py: set():1006] [1655547] Changing key p in group preprocess from 1 to 0\n", + " 88367 [params.py: set():1006] [1655497] Changing key p in group temporal from 1 to 0\n", + " 88366 [params.py: set():1006] [1655557] Changing key nb in group spatial from 2 to 1\n", + " 88368 [params.py: set():1006] [1655552] Changing key p in group temporal from 1 to 0\n", + " 88366 [params.py: set():1006] [1655533] Changing key p in group preprocess from 1 to 0\n", + " 88365 [params.py: set():1006] [1655531] Changing key p in group temporal from 1 to 0\n", + " 88368 [params.py: set():1006] [1655540] Changing key p in group preprocess from 1 to 0\n", + " 88366 [params.py: set():1006] [1655512] Changing key p in group preprocess from 1 to 0\n", + " 88369 [params.py: set():1006] [1655534] Changing key p in group preprocess from 1 to 0\n", + " 88370 [cnmf.py: fit():436] [1655530] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88371 [params.py: set():1006] [1655506] Changing key nb in group spatial from 2 to 1\n", + " 88369 [params.py: set():1006] [1655545] Changing key nb in group spatial from 2 to 1\n", + " 88369 [params.py: set():1006] [1655475] Changing key p in group preprocess from 1 to 0\n", + " 88371 [params.py: set():1006] [1655535] Changing key p in group temporal from 1 to 0\n", + " 88372 [params.py: set():1006] [1655492] Changing key p in group preprocess from 1 to 0\n", + " 88370 [params.py: set():1006] [1655473] Changing key p in group preprocess from 1 to 0\n", + " 88371 [params.py: set():1006] [1655482] Changing key nb in group spatial from 2 to 1\n", + " 88369 [params.py: set():1006] [1655469] Changing key p in group preprocess from 1 to 0\n", + " 88368 [params.py: set():1006] [1655507] Changing key p in group preprocess from 1 to 0\n", + " 88372 [params.py: set():1006] [1655474] Changing key p in group preprocess from 1 to 0\n", + " 88373 [params.py: set():1006] [1655505] Changing key nb in group spatial from 2 to 1\n", + " 88372 [params.py: set():1006] [1655510] Changing key nb in group spatial from 2 to 1\n", + " 88381 [map_reduce.py: cnmf_patches():80] [1660550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82260_102780Read file\n", + " 88379 [params.py: set():1006] [1655498] Changing key nb in group spatial from 2 to 1\n", + " 88379 [cnmf.py: fit():481] [1655514] preprocessing ...\n", + " 88378 [params.py: set():1006] [1655508] Changing key p in group preprocess from 1 to 0\n", + " 88375 [params.py: set():1006] [1655554] Changing key p in group temporal from 1 to 0\n", + " 88377 [params.py: set():1006] [1655483] Changing key p in group preprocess from 1 to 0\n", + " 88377 [params.py: set():1006] [1655484] Changing key nb in group spatial from 2 to 1\n", + " 88378 [params.py: set():1006] [1655539] Changing key nb in group spatial from 2 to 1\n", + " 88377 [params.py: set():1006] [1655476] Changing key p in group temporal from 1 to 0\n", + " 88376 [params.py: set():1006] [1655549] Changing key p in group preprocess from 1 to 0\n", + " 88376 [params.py: set():1006] [1655477] Changing key nb in group spatial from 2 to 1\n", + " 88375 [params.py: set():1006] [1655544] Changing key nb in group spatial from 2 to 1\n", + " 88377 [params.py: set():1006] [1655548] Changing key nb in group spatial from 2 to 1\n", + " 88376 [params.py: set():1006] [1655542] Changing key p in group preprocess from 1 to 0\n", + " 88374 [params.py: set():1006] [1655481] Changing key p in group preprocess from 1 to 0\n", + " 88374 [params.py: set():1006] [1655496] Changing key p in group temporal from 1 to 0\n", + " 88375 [params.py: set():1006] [1655490] Changing key p in group temporal from 1 to 0\n", + " 88373 [params.py: set():1006] [1655526] Changing key nb in group spatial from 2 to 1\n", + " 88374 [params.py: set():1006] [1655480] Changing key nb in group spatial from 2 to 1\n", + " 88374 [params.py: set():1006] [1655523] Changing key p in group preprocess from 1 to 0\n", + " 88394 [map_reduce.py: cnmf_patches():100] [1660129] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51540_72060file loaded\n", + " 88412 [params.py: set():1006] [1655494] Changing key p in group preprocess from 1 to 0\n", + " 88413 [params.py: set():1006] [1655485] Changing key p in group temporal from 1 to 0\n", + " 88413 [params.py: set():1006] [1655470] Changing key p in group preprocess from 1 to 0\n", + " 88414 [params.py: set():1006] [1655562] Changing key p in group preprocess from 1 to 0\n", + " 88414 [cnmf.py: fit():436] [1655559] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88420 [params.py: set():1006] [1655502] Changing key p in group temporal from 1 to 0\n", + " 88421 [params.py: set():1006] [1655478] Changing key p in group preprocess from 1 to 0\n", + " 88417 [params.py: set():1006] [1655509] Changing key p in group temporal from 1 to 0\n", + " 88417 [params.py: set():1006] [1655486] Changing key p in group temporal from 1 to 0\n", + " 88420 [params.py: set():1006] [1655563] Changing key p in group preprocess from 1 to 0\n", + " 88421 [params.py: set():1006] [1655516] Changing key p in group temporal from 1 to 0\n", + " 88423 [params.py: set():1006] [1655561] Changing key p in group temporal from 1 to 0\n", + " 88424 [params.py: set():1006] [1655503] Changing key p in group preprocess from 1 to 0\n", + " 88423 [params.py: set():1006] [1655524] Changing key p in group preprocess from 1 to 0\n", + " 88422 [params.py: set():1006] [1655527] Changing key p in group temporal from 1 to 0\n", + " 88418 [cnmf.py: fit():436] [1655553] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88422 [params.py: set():1006] [1655501] Changing key p in group preprocess from 1 to 0\n", + " 88429 [cnmf.py: fit():436] [1655535] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88421 [cnmf.py: fit():436] [1655543] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88419 [params.py: set():1006] [1655517] Changing key p in group temporal from 1 to 0\n", + " 88428 [params.py: set():1006] [1655540] Changing key p in group temporal from 1 to 0\n", + " 88417 [params.py: set():1006] [1655536] Changing key p in group temporal from 1 to 0\n", + " 88426 [params.py: set():1006] [1655532] Changing key p in group temporal from 1 to 0\n", + " 88427 [params.py: set():1006] [1655545] Changing key p in group preprocess from 1 to 0\n", + " 88418 [cnmf.py: fit():436] [1655499] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88428 [params.py: set():1006] [1655551] Changing key p in group temporal from 1 to 0\n", + " 88427 [params.py: set():1006] [1655504] Changing key p in group preprocess from 1 to 0\n", + " 88426 [params.py: set():1006] [1655500] Changing key p in group temporal from 1 to 0\n", + " 88430 [params.py: set():1006] [1655512] Changing key p in group temporal from 1 to 0\n", + " 88416 [params.py: set():1006] [1655491] Changing key p in group preprocess from 1 to 0\n", + " 88430 [cnmf.py: fit():436] [1655472] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88425 [cnmf.py: fit():436] [1655497] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88424 [cnmf.py: fit():436] [1655519] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88416 [params.py: set():1006] [1655528] Changing key p in group preprocess from 1 to 0\n", + " 88427 [cnmf.py: fit():449] [1655530] (8000, 41, 40)\n", + " 88426 [cnmf.py: fit():436] [1655552] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88431 [params.py: set():1006] [1655493] Changing key p in group preprocess from 1 to 0\n", + " 88430 [params.py: set():1006] [1655533] Changing key p in group temporal from 1 to 0\n", + " 88425 [params.py: set():1006] [1655557] Changing key p in group preprocess from 1 to 0\n", + " 88424 [params.py: set():1006] [1655547] Changing key p in group temporal from 1 to 0\n", + " 88428 [params.py: set():1006] [1655560] Changing key p in group temporal from 1 to 0\n", + " 88422 [params.py: set():1006] [1655518] Changing key nb in group spatial from 2 to 1\n", + " 88419 [cnmf.py: fit():436] [1655513] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88418 [params.py: set():1006] [1655525] Changing key p in group temporal from 1 to 0\n", + " 88429 [cnmf.py: fit():436] [1655489] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88431 [params.py: set():1006] [1655546] Changing key p in group temporal from 1 to 0\n", + " 88415 [params.py: set():1006] [1655522] Changing key p in group temporal from 1 to 0\n", + " 88420 [params.py: set():1006] [1655479] Changing key p in group temporal from 1 to 0\n", + " 88432 [params.py: set():1006] [1655492] Changing key p in group temporal from 1 to 0\n", + " 88416 [params.py: set():1006] [1655541] Changing key p in group temporal from 1 to 0\n", + " 88431 [params.py: set():1006] [1655506] Changing key p in group preprocess from 1 to 0\n", + " 88415 [params.py: set():1006] [1655487] Changing key p in group preprocess from 1 to 0\n", + " 88432 [params.py: set():1006] [1655521] Changing key p in group temporal from 1 to 0\n", + " 88423 [params.py: set():1006] [1655529] Changing key p in group temporal from 1 to 0\n", + " 88429 [cnmf.py: fit():436] [1655511] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88432 [cnmf.py: fit():436] [1655531] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88434 [params.py: set():1006] [1655534] Changing key p in group temporal from 1 to 0\n", + " 88434 [cnmf.py: fit():436] [1655520] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88434 [params.py: set():1006] [1655564] Changing key p in group temporal from 1 to 0\n", + " 88435 [params.py: set():1006] [1655482] Changing key p in group preprocess from 1 to 0\n", + " 88435 [params.py: set():1006] [1655475] Changing key p in group temporal from 1 to 0\n", + " 88436 [params.py: set():1006] [1655488] Changing key p in group temporal from 1 to 0\n", + " 88435 [params.py: set():1006] [1655474] Changing key p in group temporal from 1 to 0\n", + " 88433 [cnmf.py: fit():436] [1655537] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88436 [params.py: set():1006] [1655469] Changing key p in group temporal from 1 to 0\n", + " 88433 [params.py: set():1006] [1655556] Changing key p in group temporal from 1 to 0\n", + " 88438 [cnmf.py: fit():436] [1655490] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88437 [params.py: set():1006] [1655507] Changing key p in group temporal from 1 to 0\n", + " 88437 [params.py: set():1006] [1655473] Changing key p in group temporal from 1 to 0\n", + " 88438 [params.py: set():1006] [1655510] Changing key p in group preprocess from 1 to 0\n", + " 88439 [params.py: set():1006] [1655526] Changing key p in group preprocess from 1 to 0\n", + " 88439 [params.py: set():1006] [1655548] Changing key p in group preprocess from 1 to 0\n", + " 88437 [params.py: set():1006] [1655550] Changing key p in group preprocess from 1 to 0\n", + " 88440 [cnmf.py: fit():436] [1655496] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88440 [params.py: set():1006] [1655480] Changing key p in group preprocess from 1 to 0\n", + " 88441 [params.py: set():1006] [1655495] Changing key p in group preprocess from 1 to 0\n", + " 88442 [cnmf.py: fit():436] [1655476] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88442 [params.py: set():1006] [1655477] Changing key p in group preprocess from 1 to 0\n", + " 88442 [params.py: set():1006] [1655542] Changing key p in group temporal from 1 to 0\n", + " 88441 [params.py: set():1006] [1655523] Changing key p in group temporal from 1 to 0\n", + " 88440 [params.py: set():1006] [1655544] Changing key p in group preprocess from 1 to 0\n", + " 88443 [params.py: set():1006] [1655539] Changing key p in group preprocess from 1 to 0\n", + " 88438 [params.py: set():1006] [1655505] Changing key p in group preprocess from 1 to 0\n", + " 88443 [params.py: set():1006] [1655549] Changing key p in group temporal from 1 to 0\n", + " 88444 [params.py: set():1006] [1655484] Changing key p in group preprocess from 1 to 0\n", + " 88443 [params.py: set():1006] [1655481] Changing key p in group temporal from 1 to 0\n", + " 88445 [cnmf.py: fit():436] [1655554] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88444 [params.py: set():1006] [1655483] Changing key p in group temporal from 1 to 0\n", + " 88444 [params.py: set():1006] [1655538] Changing key p in group preprocess from 1 to 0\n", + " 88445 [params.py: set():1006] [1655508] Changing key p in group temporal from 1 to 0\n", + " 88445 [pre_processing.py:interpolate_missing_data():41] [1655514] Checking for missing data entries (NaN)\n", + " 88446 [params.py: set():1006] [1655498] Changing key p in group preprocess from 1 to 0\n", + " 88477 [params.py: set():1006] [1655563] Changing key p in group temporal from 1 to 0\n", + " 88479 [params.py: set():1006] [1655524] Changing key p in group temporal from 1 to 0\n", + " 88477 [cnmf.py: fit():436] [1655516] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88482 [params.py: set():1006] [1655494] Changing key p in group temporal from 1 to 0\n", + " 88477 [params.py: set():1006] [1655503] Changing key p in group temporal from 1 to 0\n", + " 88479 [cnmf.py: fit():436] [1655536] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88478 [cnmf.py: fit():449] [1655535] (8000, 41, 41)\n", + " 88484 [cnmf.py: fit():466] [1655530] Using 1 processes\n", + " 88480 [params.py: set():1006] [1655493] Changing key p in group temporal from 1 to 0\n", + " 88481 [cnmf.py: fit():436] [1655509] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88476 [cnmf.py: fit():449] [1655543] (8000, 41, 41)\n", + " 88479 [params.py: set():1006] [1655545] Changing key p in group temporal from 1 to 0\n", + " 88482 [cnmf.py: fit():436] [1655512] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88482 [cnmf.py: fit():436] [1655533] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88483 [cnmf.py: fit():449] [1655499] (8000, 41, 41)\n", + " 88480 [params.py: set():1006] [1655501] Changing key p in group temporal from 1 to 0\n", + " 88487 [cnmf.py: fit():436] [1655551] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88489 [cnmf.py: fit():449] [1655519] (8000, 41, 41)\n", + " 88485 [cnmf.py: fit():436] [1655502] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88486 [cnmf.py: fit():436] [1655540] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88487 [cnmf.py: fit():436] [1655561] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88484 [params.py: set():1006] [1655528] Changing key p in group temporal from 1 to 0\n", + " 88486 [cnmf.py: fit():436] [1655560] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88481 [cnmf.py: fit():436] [1655532] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88483 [cnmf.py: fit():449] [1655472] (8000, 41, 41)\n", + " 88489 [params.py: set():1006] [1655557] Changing key p in group temporal from 1 to 0\n", + " 88486 [cnmf.py: fit():449] [1655497] (8000, 41, 40)\n", + " 88485 [cnmf.py: fit():436] [1655547] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88484 [cnmf.py: fit():436] [1655546] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88487 [cnmf.py: fit():436] [1655525] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88478 [cnmf.py: fit():436] [1655527] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88488 [cnmf.py: fit():436] [1655517] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88488 [cnmf.py: fit():436] [1655541] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88480 [cnmf.py: fit():436] [1655500] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88485 [params.py: set():1006] [1655491] Changing key p in group temporal from 1 to 0\n", + " 88490 [cnmf.py: fit():436] [1655492] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88489 [params.py: set():1006] [1655504] Changing key p in group temporal from 1 to 0\n", + " 88491 [cnmf.py: fit():436] [1655522] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88490 [cnmf.py: fit():449] [1655552] (8000, 41, 41)\n", + " 88490 [cnmf.py: fit():449] [1655513] (8000, 41, 41)\n", + " 88475 [cnmf.py: fit():449] [1655553] (8000, 41, 41)\n", + " 88491 [cnmf.py: fit():436] [1655479] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88475 [params.py: set():1006] [1655478] Changing key p in group temporal from 1 to 0\n", + " 88474 [cnmf.py: fit():436] [1655485] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88492 [params.py: set():1006] [1655518] Changing key p in group preprocess from 1 to 0\n", + " 88474 [cnmf.py: fit():436] [1655486] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88492 [cnmf.py: fit():436] [1655564] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88476 [params.py: set():1006] [1655470] Changing key p in group temporal from 1 to 0\n", + " 88493 [params.py: set():1006] [1655506] Changing key p in group temporal from 1 to 0\n", + " 88493 [cnmf.py: fit():449] [1655489] (8000, 41, 41)\n", + " 88476 [cnmf.py: fit():449] [1655559] (8000, 41, 41)\n", + " 88494 [cnmf.py: fit():436] [1655474] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88496 [params.py: set():1006] [1655487] Changing key p in group temporal from 1 to 0\n", + " 88496 [cnmf.py: fit():449] [1655520] (8000, 41, 41)\n", + " 88494 [cnmf.py: fit():449] [1655531] (8000, 41, 41)\n", + " 88495 [cnmf.py: fit():436] [1655534] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88495 [cnmf.py: fit():436] [1655521] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88495 [cnmf.py: fit():449] [1655537] (8000, 41, 41)\n", + " 88496 [cnmf.py: fit():449] [1655511] (8000, 41, 41)\n", + " 88497 [cnmf.py: fit():436] [1655469] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88494 [cnmf.py: fit():436] [1655529] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88497 [cnmf.py: fit():436] [1655475] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88498 [cnmf.py: fit():436] [1655488] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88473 [params.py: set():1006] [1655562] Changing key p in group temporal from 1 to 0\n", + " 88499 [cnmf.py: fit():449] [1655490] (8000, 41, 41)\n", + " 88499 [cnmf.py: fit():436] [1655556] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88498 [params.py: set():1006] [1655482] Changing key p in group temporal from 1 to 0\n", + " 88500 [cnmf.py: fit():436] [1655507] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88501 [params.py: set():1006] [1655480] Changing key p in group temporal from 1 to 0\n", + " 88502 [cnmf.py: fit():449] [1655496] (8000, 41, 41)\n", + " 88502 [params.py: set():1006] [1655548] Changing key p in group temporal from 1 to 0\n", + " 88503 [cnmf.py: fit():449] [1655476] (8000, 41, 41)\n", + " 88500 [params.py: set():1006] [1655526] Changing key p in group temporal from 1 to 0\n", + " 88501 [params.py: set():1006] [1655495] Changing key p in group temporal from 1 to 0\n", + " 88504 [params.py: set():1006] [1655550] Changing key p in group temporal from 1 to 0\n", + " 88503 [params.py: set():1006] [1655477] Changing key p in group temporal from 1 to 0\n", + " 88502 [cnmf.py: fit():436] [1655473] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88501 [params.py: set():1006] [1655510] Changing key p in group temporal from 1 to 0\n", + " 88505 [params.py: set():1006] [1655544] Changing key p in group temporal from 1 to 0\n", + " 88505 [cnmf.py: fit():436] [1655523] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88506 [cnmf.py: fit():436] [1655549] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88504 [cnmf.py: fit():436] [1655542] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88506 [params.py: set():1006] [1655539] Changing key p in group temporal from 1 to 0\n", + " 88508 [params.py: set():1006] [1655538] Changing key p in group temporal from 1 to 0\n", + " 88507 [cnmf.py: fit():449] [1655554] (8000, 41, 41)\n", + " 88506 [params.py: set():1006] [1655505] Changing key p in group temporal from 1 to 0\n", + " 88508 [cnmf.py: fit():436] [1655481] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88507 [params.py: set():1006] [1655484] Changing key p in group temporal from 1 to 0\n", + " 88508 [cnmf.py: fit():436] [1655483] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88509 [cnmf.py: fit():436] [1655508] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88527 [params.py: set():1006] [1655498] Changing key p in group temporal from 1 to 0\n", + " 88554 [cnmf.py: fit():477] [1655530] using 400 pixels per process\n", + " 88557 [cnmf.py: fit():449] [1655527] (8000, 41, 41)\n", + " 88552 [cnmf.py: fit():466] [1655499] Using 1 processes\n", + " 88555 [cnmf.py: fit():449] [1655540] (8000, 41, 41)\n", + " 88552 [cnmf.py: fit():449] [1655533] (8000, 41, 41)\n", + " 88554 [cnmf.py: fit():466] [1655472] Using 1 processes\n", + " 88557 [cnmf.py: fit():449] [1655547] (8000, 41, 41)\n", + " 88556 [cnmf.py: fit():466] [1655519] Using 1 processes\n", + " 88556 [cnmf.py: fit():436] [1655524] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88553 [cnmf.py: fit():436] [1655545] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88555 [cnmf.py: fit():436] [1655528] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88556 [cnmf.py: fit():466] [1655497] Using 1 processes\n", + " 88553 [cnmf.py: fit():449] [1655512] (8000, 41, 41)\n", + " 88559 [cnmf.py: fit():449] [1655541] (8000, 41, 41)\n", + " 88560 [cnmf.py: fit():449] [1655492] (8000, 41, 41)\n", + " 88546 [cnmf.py: fit():449] [1655536] (8000, 41, 41)\n", + " 88558 [cnmf.py: fit():449] [1655561] (8000, 41, 41)\n", + " 88558 [cnmf.py: fit():449] [1655525] (8000, 41, 41)\n", + " 88552 [cnmf.py: fit():466] [1655535] Using 1 processes\n", + " 88559 [cnmf.py: fit():449] [1655502] (8000, 41, 41)\n", + " 88558 [cnmf.py: fit():449] [1655516] (8000, 41, 41)\n", + " 88561 [cnmf.py: fit():436] [1655557] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88557 [cnmf.py: fit():449] [1655517] (8000, 41, 41)\n", + " 88561 [cnmf.py: fit():436] [1655501] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88560 [cnmf.py: fit():436] [1655504] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88564 [cnmf.py: fit():436] [1655563] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88562 [cnmf.py: fit():449] [1655546] (8000, 41, 41)\n", + " 88551 [cnmf.py: fit():466] [1655543] Using 1 processes\n", + " 88555 [cnmf.py: fit():449] [1655560] (8000, 41, 41)\n", + " 88563 [cnmf.py: fit():449] [1655551] (8000, 41, 41)\n", + " 88561 [cnmf.py: fit():449] [1655532] (8000, 41, 40)\n", + " 88554 [cnmf.py: fit():436] [1655503] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88560 [cnmf.py: fit():449] [1655500] (8000, 41, 40)\n", + " 88563 [cnmf.py: fit():436] [1655494] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88563 [cnmf.py: fit():466] [1655552] Using 1 processes\n", + " 88562 [cnmf.py: fit():466] [1655513] Using 1 processes\n", + " 88566 [cnmf.py: fit():449] [1655509] (8000, 41, 41)\n", + " 88564 [cnmf.py: fit():436] [1655491] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88566 [cnmf.py: fit():436] [1655506] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88568 [cnmf.py: fit():436] [1655470] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88569 [params.py: set():1006] [1655518] Changing key p in group temporal from 1 to 0\n", + " 88567 [cnmf.py: fit():449] [1655479] (8000, 41, 41)\n", + " 88569 [cnmf.py: fit():449] [1655486] (8000, 41, 41)\n", + " 88565 [cnmf.py: fit():449] [1655522] (8000, 41, 41)\n", + " 88568 [cnmf.py: fit():449] [1655485] (8000, 41, 41)\n", + " 88567 [cnmf.py: fit():466] [1655553] Using 1 processes\n", + " 88567 [cnmf.py: fit():466] [1655489] Using 1 processes\n", + " 88568 [cnmf.py: fit():449] [1655564] (8000, 41, 41)\n", + " 88570 [cnmf.py: fit():436] [1655478] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88546 [cnmf.py: fit():436] [1655493] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88571 [cnmf.py: fit():466] [1655559] Using 1 processes\n", + " 88573 [cnmf.py: fit():466] [1655537] Using 1 processes\n", + " 88572 [cnmf.py: fit():449] [1655474] (8000, 41, 40)\n", + " 88574 [cnmf.py: fit():436] [1655487] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88573 [cnmf.py: fit():449] [1655534] (8000, 41, 41)\n", + " 88573 [cnmf.py: fit():449] [1655521] (8000, 41, 41)\n", + " 88572 [cnmf.py: fit():466] [1655531] Using 1 processes\n", + " 88572 [cnmf.py: fit():466] [1655511] Using 1 processes\n", + " 88571 [cnmf.py: fit():466] [1655520] Using 1 processes\n", + " 88575 [cnmf.py: fit():449] [1655475] (8000, 41, 41)\n", + " 88575 [cnmf.py: fit():449] [1655469] (8000, 41, 41)\n", + " 88577 [cnmf.py: fit():449] [1655529] (8000, 41, 41)\n", + " 88576 [cnmf.py: fit():449] [1655507] (8000, 41, 41)\n", + " 88577 [cnmf.py: fit():466] [1655490] Using 1 processes\n", + " 88576 [cnmf.py: fit():436] [1655562] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88577 [cnmf.py: fit():436] [1655482] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88578 [cnmf.py: fit():449] [1655556] (8000, 41, 41)\n", + " 88578 [cnmf.py: fit():449] [1655488] (8000, 41, 41)\n", + " 88580 [cnmf.py: fit():436] [1655548] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88581 [cnmf.py: fit():436] [1655495] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88579 [cnmf.py: fit():436] [1655480] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88581 [cnmf.py: fit():466] [1655496] Using 1 processes\n", + " 88582 [cnmf.py: fit():436] [1655526] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88580 [cnmf.py: fit():436] [1655477] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88580 [cnmf.py: fit():466] [1655476] Using 1 processes\n", + " 88582 [cnmf.py: fit():436] [1655550] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88584 [cnmf.py: fit():449] [1655549] (8000, 41, 41)\n", + " 88582 [cnmf.py: fit():449] [1655473] (8000, 41, 41)\n", + " 88583 [cnmf.py: fit():436] [1655544] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88584 [cnmf.py: fit():436] [1655510] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88585 [cnmf.py: fit():449] [1655523] (8000, 41, 41)\n", + " 88584 [cnmf.py: fit():436] [1655539] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88585 [cnmf.py: fit():449] [1655542] (8000, 41, 41)\n", + " 88585 [cnmf.py: fit():436] [1655538] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88586 [cnmf.py: fit():449] [1655481] (8000, 41, 41)\n", + " 88586 [cnmf.py: fit():436] [1655484] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88587 [cnmf.py: fit():466] [1655554] Using 1 processes\n", + " 88587 [cnmf.py: fit():436] [1655505] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88587 [cnmf.py: fit():449] [1655483] (8000, 41, 41)\n", + " 88588 [cnmf.py: fit():449] [1655508] (8000, 41, 41)\n", + " 88588 [cnmf.py: fit():436] [1655498] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88589 [map_reduce.py: cnmf_patches():100] [1660361] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66900_87420file loaded\n", + " 88623 [cnmf.py: fit():466] [1655536] Using 1 processes\n", + " 88621 [cnmf.py: fit():466] [1655547] Using 1 processes\n", + " 88620 [cnmf.py: fit():466] [1655541] Using 1 processes\n", + " 88621 [cnmf.py: fit():466] [1655533] Using 1 processes\n", + " 88625 [cnmf.py: fit():466] [1655517] Using 1 processes\n", + " 88619 [cnmf.py: fit():449] [1655524] (8000, 41, 41)\n", + " 88622 [cnmf.py: fit():477] [1655499] using 400 pixels per process\n", + " 88624 [cnmf.py: fit():466] [1655540] Using 1 processes\n", + " 88624 [cnmf.py: fit():477] [1655472] using 400 pixels per process\n", + " 88622 [cnmf.py: fit():466] [1655525] Using 1 processes\n", + " 88623 [cnmf.py: fit():477] [1655535] using 400 pixels per process\n", + " 88622 [cnmf.py: fit():449] [1655528] (8000, 41, 41)\n", + " 88620 [cnmf.py: fit():466] [1655492] Using 1 processes\n", + " 88619 [cnmf.py: fit():477] [1655519] using 400 pixels per process\n", + " 88620 [cnmf.py: fit():449] [1655545] (8000, 41, 41)\n", + " 88624 [cnmf.py: fit():466] [1655561] Using 1 processes\n", + " 88627 [cnmf.py: fit():477] [1655497] using 400 pixels per process\n", + " 88625 [cnmf.py: fit():449] [1655504] (8000, 41, 41)\n", + " 88627 [cnmf.py: fit():449] [1655563] (8000, 41, 41)\n", + " 88626 [cnmf.py: fit():466] [1655560] Using 1 processes\n", + " 88628 [cnmf.py: fit():466] [1655532] Using 1 processes\n", + " 88625 [cnmf.py: fit():478] [1655530] using 5000 block_size_temp\n", + " 88627 [cnmf.py: fit():477] [1655552] using 400 pixels per process\n", + " 88621 [cnmf.py: fit():466] [1655512] Using 1 processes\n", + " 88625 [cnmf.py: fit():466] [1655546] Using 1 processes\n", + " 88626 [cnmf.py: fit():466] [1655516] Using 1 processes\n", + " 88629 [cnmf.py: fit():466] [1655551] Using 1 processes\n", + " 88629 [cnmf.py: fit():449] [1655501] (8000, 41, 41)\n", + " 88630 [cnmf.py: fit():466] [1655500] Using 1 processes\n", + " 88628 [cnmf.py: fit():477] [1655513] using 400 pixels per process\n", + " 88628 [cnmf.py: fit():449] [1655557] (8000, 41, 41)\n", + " 88630 [cnmf.py: fit():477] [1655543] using 400 pixels per process\n", + " 88631 [cnmf.py: fit():449] [1655503] (8000, 41, 41)\n", + " 88631 [cnmf.py: fit():466] [1655502] Using 1 processes\n", + " 88632 [cnmf.py: fit():449] [1655494] (8000, 41, 41)\n", + " 88618 [cnmf.py: fit():466] [1655527] Using 1 processes\n", + " 88632 [cnmf.py: fit():466] [1655509] Using 1 processes\n", + " 88634 [cnmf.py: fit():466] [1655485] Using 1 processes\n", + " 88633 [cnmf.py: fit():436] [1655518] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 88633 [cnmf.py: fit():449] [1655506] (8000, 41, 41)\n", + " 88634 [cnmf.py: fit():477] [1655553] using 400 pixels per process\n", + " 88635 [cnmf.py: fit():449] [1655491] (8000, 41, 41)\n", + " 88636 [cnmf.py: fit():466] [1655486] Using 1 processes\n", + " 88635 [cnmf.py: fit():449] [1655470] (8000, 41, 41)\n", + " 88636 [cnmf.py: fit():466] [1655522] Using 1 processes\n", + " 88637 [cnmf.py: fit():449] [1655478] (8000, 41, 41)\n", + " 88638 [cnmf.py: fit():477] [1655489] using 400 pixels per process\n", + " 88635 [cnmf.py: fit():466] [1655479] Using 1 processes\n", + " 88636 [cnmf.py: fit():466] [1655564] Using 1 processes\n", + " 88639 [cnmf.py: fit():466] [1655474] Using 1 processes\n", + " 88641 [cnmf.py: fit():466] [1655534] Using 1 processes\n", + " 88638 [cnmf.py: fit():477] [1655511] using 400 pixels per process\n", + " 88640 [cnmf.py: fit():477] [1655559] using 400 pixels per process\n", + " 88640 [cnmf.py: fit():449] [1655493] (8000, 41, 41)\n", + " 88639 [cnmf.py: fit():477] [1655531] using 400 pixels per process\n", + " 88640 [cnmf.py: fit():477] [1655537] using 400 pixels per process\n", + " 88642 [cnmf.py: fit():449] [1655487] (8000, 41, 41)\n", + " 88641 [cnmf.py: fit():466] [1655521] Using 1 processes\n", + " 88644 [cnmf.py: fit():477] [1655490] using 400 pixels per process\n", + " 88642 [cnmf.py: fit():477] [1655520] using 400 pixels per process\n", + " 88645 [cnmf.py: fit():449] [1655562] (8000, 41, 41)\n", + " 88644 [cnmf.py: fit():466] [1655529] Using 1 processes\n", + " 88643 [cnmf.py: fit():466] [1655469] Using 1 processes\n", + " 88643 [cnmf.py: fit():466] [1655475] Using 1 processes\n", + " 88646 [cnmf.py: fit():449] [1655548] (8000, 41, 40)\n", + " 88645 [cnmf.py: fit():466] [1655507] Using 1 processes\n", + " 88646 [cnmf.py: fit():466] [1655488] Using 1 processes\n", + " 88647 [cnmf.py: fit():449] [1655482] (8000, 41, 41)\n", + " 88647 [cnmf.py: fit():449] [1655495] (8000, 41, 41)\n", + " 88648 [cnmf.py: fit():449] [1655480] (8000, 41, 41)\n", + " 88649 [cnmf.py: fit():477] [1655496] using 400 pixels per process\n", + " 88648 [cnmf.py: fit():449] [1655477] (8000, 41, 41)\n", + " 88647 [cnmf.py: fit():466] [1655556] Using 1 processes\n", + " 88649 [cnmf.py: fit():477] [1655476] using 400 pixels per process\n", + " 88649 [cnmf.py: fit():449] [1655526] (8000, 41, 41)\n", + " 88650 [cnmf.py: fit():466] [1655549] Using 1 processes\n", + " 88651 [cnmf.py: fit():449] [1655510] (8000, 41, 41)\n", + " 88650 [cnmf.py: fit():466] [1655473] Using 1 processes\n", + " 88651 [cnmf.py: fit():449] [1655544] (8000, 41, 41)\n", + " 88651 [cnmf.py: fit():449] [1655550] (8000, 41, 41)\n", + " 88652 [cnmf.py: fit():449] [1655538] (8000, 41, 41)\n", + " 88653 [cnmf.py: fit():466] [1655481] Using 1 processes\n", + " 88653 [cnmf.py: fit():466] [1655542] Using 1 processes\n", + " 88653 [cnmf.py: fit():449] [1655484] (8000, 41, 41)\n", + " 88652 [cnmf.py: fit():449] [1655539] (8000, 41, 41)\n", + " 88654 [cnmf.py: fit():466] [1655523] Using 1 processes\n", + " 88654 [cnmf.py: fit():477] [1655554] using 400 pixels per process\n", + " 88655 [cnmf.py: fit():449] [1655505] (8000, 41, 41)\n", + " 88655 [cnmf.py: fit():466] [1655483] Using 1 processes\n", + " 88655 [cnmf.py: fit():466] [1655508] Using 1 processes\n", + " 88656 [cnmf.py: fit():449] [1655498] (8000, 41, 41)\n", + " 88682 [cnmf.py: fit():477] [1655540] using 400 pixels per process\n", + " 88685 [cnmf.py: fit():477] [1655541] using 400 pixels per process\n", + " 88681 [cnmf.py: fit():466] [1655528] Using 1 processes\n", + " 88684 [cnmf.py: fit():477] [1655560] using 400 pixels per process\n", + " 88681 [cnmf.py: fit():477] [1655547] using 400 pixels per process\n", + " 88685 [cnmf.py: fit():466] [1655545] Using 1 processes\n", + " 88684 [cnmf.py: fit():477] [1655532] using 400 pixels per process\n", + " 88682 [cnmf.py: fit():478] [1655535] using 5000 block_size_temp\n", + " 88686 [cnmf.py: fit():477] [1655546] using 400 pixels per process\n", + " 88683 [cnmf.py: fit():466] [1655524] Using 1 processes\n", + " 88681 [cnmf.py: fit():478] [1655472] using 5000 block_size_temp\n", + " 88686 [cnmf.py: fit():477] [1655512] using 400 pixels per process\n", + " 88684 [cnmf.py: fit():477] [1655533] using 400 pixels per process\n", + " 88686 [cnmf.py: fit():466] [1655563] Using 1 processes\n", + " 88688 [cnmf.py: fit():466] [1655504] Using 1 processes\n", + " 88683 [cnmf.py: fit():481] [1655530] preprocessing ...\n", + " 88688 [cnmf.py: fit():478] [1655497] using 5000 block_size_temp\n", + " 88687 [cnmf.py: fit():477] [1655551] using 400 pixels per process\n", + " 88688 [cnmf.py: fit():478] [1655499] using 5000 block_size_temp\n", + " 88687 [cnmf.py: fit():477] [1655561] using 400 pixels per process\n", + " 88687 [cnmf.py: fit():477] [1655536] using 400 pixels per process\n", + " 88689 [cnmf.py: fit():478] [1655552] using 5000 block_size_temp\n", + " 88680 [cnmf.py: fit():477] [1655525] using 400 pixels per process\n", + " 88683 [cnmf.py: fit():477] [1655492] using 400 pixels per process\n", + " 88689 [cnmf.py: fit():466] [1655557] Using 1 processes\n", + " 88685 [cnmf.py: fit():478] [1655519] using 5000 block_size_temp\n", + " 88690 [cnmf.py: fit():477] [1655500] using 400 pixels per process\n", + " 88689 [cnmf.py: fit():466] [1655501] Using 1 processes\n", + " 88690 [cnmf.py: fit():477] [1655516] using 400 pixels per process\n", + " 88691 [cnmf.py: fit():478] [1655543] using 5000 block_size_temp\n", + " 88691 [cnmf.py: fit():478] [1655513] using 5000 block_size_temp\n", + " 88692 [cnmf.py: fit():466] [1655503] Using 1 processes\n", + " 88692 [cnmf.py: fit():466] [1655494] Using 1 processes\n", + " 88693 [cnmf.py: fit():477] [1655502] using 400 pixels per process\n", + " 88694 [cnmf.py: fit():477] [1655485] using 400 pixels per process\n", + " 88697 [cnmf.py: fit():466] [1655478] Using 1 processes\n", + " 88695 [cnmf.py: fit():466] [1655506] Using 1 processes\n", + " 88694 [cnmf.py: fit():449] [1655518] (8000, 41, 41)\n", + " 88695 [cnmf.py: fit():466] [1655470] Using 1 processes\n", + " 88694 [cnmf.py: fit():477] [1655509] using 400 pixels per process\n", + " 88696 [cnmf.py: fit():477] [1655527] using 400 pixels per process\n", + " 88696 [cnmf.py: fit():466] [1655491] Using 1 processes\n", + " 88697 [cnmf.py: fit():478] [1655489] using 5000 block_size_temp\n", + " 88699 [cnmf.py: fit():478] [1655559] using 5000 block_size_temp\n", + " 88698 [cnmf.py: fit():478] [1655553] using 5000 block_size_temp\n", + " 88699 [cnmf.py: fit():477] [1655522] using 400 pixels per process\n", + " 88699 [cnmf.py: fit():478] [1655531] using 5000 block_size_temp\n", + " 88698 [cnmf.py: fit():477] [1655564] using 400 pixels per process\n", + " 88700 [cnmf.py: fit():477] [1655534] using 400 pixels per process\n", + " 88699 [cnmf.py: fit():478] [1655511] using 5000 block_size_temp\n", + " 88680 [cnmf.py: fit():477] [1655517] using 400 pixels per process\n", + " 88702 [cnmf.py: fit():477] [1655474] using 400 pixels per process\n", + " 88700 [cnmf.py: fit():477] [1655486] using 400 pixels per process\n", + " 88701 [cnmf.py: fit():477] [1655479] using 400 pixels per process\n", + " 88703 [cnmf.py: fit():478] [1655490] using 5000 block_size_temp\n", + " 88701 [cnmf.py: fit():478] [1655537] using 5000 block_size_temp\n", + " 88701 [cnmf.py: fit():466] [1655493] Using 1 processes\n", + " 88704 [cnmf.py: fit():477] [1655529] using 400 pixels per process\n", + " 88703 [cnmf.py: fit():466] [1655487] Using 1 processes\n", + " 88703 [cnmf.py: fit():477] [1655521] using 400 pixels per process\n", + " 88706 [cnmf.py: fit():477] [1655488] using 400 pixels per process\n", + " 88705 [cnmf.py: fit():466] [1655482] Using 1 processes\n", + " 88705 [cnmf.py: fit():477] [1655507] using 400 pixels per process\n", + " 88706 [cnmf.py: fit():466] [1655548] Using 1 processes\n", + " 88705 [cnmf.py: fit():478] [1655520] using 5000 block_size_temp\n", + " 88706 [cnmf.py: fit():466] [1655562] Using 1 processes\n", + " 88707 [cnmf.py: fit():477] [1655475] using 400 pixels per process\n", + " 88707 [cnmf.py: fit():477] [1655469] using 400 pixels per process\n", + " 88711 [cnmf.py: fit():466] [1655510] Using 1 processes\n", + " 88710 [cnmf.py: fit():466] [1655526] Using 1 processes\n", + " 88709 [cnmf.py: fit():477] [1655556] using 400 pixels per process\n", + " 88708 [cnmf.py: fit():478] [1655496] using 5000 block_size_temp\n", + " 88709 [cnmf.py: fit():466] [1655477] Using 1 processes\n", + " 88707 [cnmf.py: fit():466] [1655495] Using 1 processes\n", + " 88710 [cnmf.py: fit():477] [1655549] using 400 pixels per process\n", + " 88710 [cnmf.py: fit():478] [1655476] using 5000 block_size_temp\n", + " 88708 [cnmf.py: fit():466] [1655480] Using 1 processes\n", + " 88711 [cnmf.py: fit():466] [1655544] Using 1 processes\n", + " 88711 [cnmf.py: fit():477] [1655473] using 400 pixels per process\n", + " 88714 [cnmf.py: fit():466] [1655539] Using 1 processes\n", + " 88715 [cnmf.py: fit():478] [1655554] using 5000 block_size_temp\n", + " 88714 [cnmf.py: fit():477] [1655542] using 400 pixels per process\n", + " 88713 [cnmf.py: fit():477] [1655481] using 400 pixels per process\n", + " 88714 [cnmf.py: fit():477] [1655523] using 400 pixels per process\n", + " 88713 [cnmf.py: fit():466] [1655484] Using 1 processes\n", + " 88713 [cnmf.py: fit():466] [1655538] Using 1 processes\n", + " 88712 [cnmf.py: fit():466] [1655550] Using 1 processes\n", + " 88716 [cnmf.py: fit():477] [1655508] using 400 pixels per process\n", + " 88717 [cnmf.py: fit():466] [1655498] Using 1 processes\n", + " 88716 [cnmf.py: fit():477] [1655483] using 400 pixels per process\n", + " 88715 [cnmf.py: fit():466] [1655505] Using 1 processes\n", + " 88740 [cnmf.py: fit():478] [1655532] using 5000 block_size_temp\n", + " 88741 [cnmf.py: fit():481] [1655535] preprocessing ...\n", + " 88741 [cnmf.py: fit():478] [1655560] using 5000 block_size_temp\n", + " 88744 [cnmf.py: fit():481] [1655472] preprocessing ...\n", + " 88742 [cnmf.py: fit():478] [1655533] using 5000 block_size_temp\n", + " 88743 [cnmf.py: fit():477] [1655528] using 400 pixels per process\n", + " 88741 [cnmf.py: fit():478] [1655540] using 5000 block_size_temp\n", + " 88743 [cnmf.py: fit():478] [1655547] using 5000 block_size_temp\n", + " 88742 [cnmf.py: fit():477] [1655504] using 400 pixels per process\n", + " 88740 [cnmf.py: fit():477] [1655545] using 400 pixels per process\n", + " 88744 [cnmf.py: fit():481] [1655497] preprocessing ...\n", + " 88745 [cnmf.py: fit():478] [1655512] using 5000 block_size_temp\n", + " 88744 [cnmf.py: fit():481] [1655552] preprocessing ...\n", + " 88743 [cnmf.py: fit():481] [1655499] preprocessing ...\n", + " 88742 [cnmf.py: fit():478] [1655546] using 5000 block_size_temp\n", + " 88746 [cnmf.py: fit():478] [1655551] using 5000 block_size_temp\n", + " 88740 [cnmf.py: fit():478] [1655541] using 5000 block_size_temp\n", + " 88745 [cnmf.py: fit():478] [1655525] using 5000 block_size_temp\n", + " 88745 [pre_processing.py:interpolate_missing_data():41] [1655530] Checking for missing data entries (NaN)\n", + " 88747 [cnmf.py: fit():478] [1655536] using 5000 block_size_temp\n", + " 88746 [cnmf.py: fit():478] [1655492] using 5000 block_size_temp\n", + " 88747 [cnmf.py: fit():478] [1655561] using 5000 block_size_temp\n", + " 88748 [cnmf.py: fit():477] [1655557] using 400 pixels per process\n", + " 88746 [cnmf.py: fit():477] [1655563] using 400 pixels per process\n", + " 88749 [cnmf.py: fit():478] [1655500] using 5000 block_size_temp\n", + " 88749 [cnmf.py: fit():481] [1655519] preprocessing ...\n", + " 88749 [cnmf.py: fit():478] [1655516] using 5000 block_size_temp\n", + " 88748 [cnmf.py: fit():477] [1655501] using 400 pixels per process\n", + " 88750 [cnmf.py: fit():481] [1655513] preprocessing ...\n", + " 88750 [cnmf.py: fit():481] [1655543] preprocessing ...\n", + " 88754 [cnmf.py: fit():477] [1655506] using 400 pixels per process\n", + " 88751 [cnmf.py: fit():477] [1655503] using 400 pixels per process\n", + " 88752 [cnmf.py: fit():478] [1655502] using 5000 block_size_temp\n", + " 88754 [cnmf.py: fit():466] [1655518] Using 1 processes\n", + " 88753 [cnmf.py: fit():477] [1655494] using 400 pixels per process\n", + " 88753 [cnmf.py: fit():478] [1655509] using 5000 block_size_temp\n", + " 88752 [cnmf.py: fit():478] [1655485] using 5000 block_size_temp\n", + " 88755 [cnmf.py: fit():477] [1655491] using 400 pixels per process\n", + " 88754 [cnmf.py: fit():478] [1655527] using 5000 block_size_temp\n", + " 88755 [cnmf.py: fit():481] [1655559] preprocessing ...\n", + " 88756 [cnmf.py: fit():478] [1655522] using 5000 block_size_temp\n", + " 88739 [cnmf.py: fit():477] [1655524] using 400 pixels per process\n", + " 88756 [cnmf.py: fit():481] [1655553] preprocessing ...\n", + " 88757 [cnmf.py: fit():481] [1655489] preprocessing ...\n", + " 88756 [cnmf.py: fit():477] [1655478] using 400 pixels per process\n", + " 88755 [cnmf.py: fit():477] [1655470] using 400 pixels per process\n", + " 88760 [cnmf.py: fit():478] [1655479] using 5000 block_size_temp\n", + " 88759 [cnmf.py: fit():478] [1655486] using 5000 block_size_temp\n", + " 88757 [cnmf.py: fit():481] [1655531] preprocessing ...\n", + " 88757 [cnmf.py: fit():481] [1655511] preprocessing ...\n", + " 88761 [cnmf.py: fit():478] [1655517] using 5000 block_size_temp\n", + " 88760 [cnmf.py: fit():481] [1655490] preprocessing ...\n", + " 88759 [cnmf.py: fit():478] [1655564] using 5000 block_size_temp\n", + " 88759 [cnmf.py: fit():478] [1655474] using 5000 block_size_temp\n", + " 88760 [cnmf.py: fit():481] [1655537] preprocessing ...\n", + " 88758 [cnmf.py: fit():478] [1655534] using 5000 block_size_temp\n", + " 88761 [cnmf.py: fit():477] [1655493] using 400 pixels per process\n", + " 88762 [cnmf.py: fit():478] [1655529] using 5000 block_size_temp\n", + " 88762 [cnmf.py: fit():477] [1655487] using 400 pixels per process\n", + " 88762 [cnmf.py: fit():478] [1655521] using 5000 block_size_temp\n", + " 88767 [cnmf.py: fit():478] [1655488] using 5000 block_size_temp\n", + " 88769 [cnmf.py: fit():477] [1655548] using 400 pixels per process\n", + " 88770 [cnmf.py: fit():478] [1655475] using 5000 block_size_temp\n", + " 88770 [cnmf.py: fit():477] [1655562] using 400 pixels per process\n", + " 88770 [cnmf.py: fit():478] [1655469] using 5000 block_size_temp\n", + " 88769 [cnmf.py: fit():477] [1655482] using 400 pixels per process\n", + " 88769 [cnmf.py: fit():481] [1655520] preprocessing ...\n", + " 88768 [cnmf.py: fit():478] [1655507] using 5000 block_size_temp\n", + " 88779 [cnmf.py: fit():477] [1655480] using 400 pixels per process\n", + " 88780 [cnmf.py: fit():478] [1655473] using 5000 block_size_temp\n", + " 88779 [cnmf.py: fit():481] [1655496] preprocessing ...\n", + " 88778 [cnmf.py: fit():477] [1655510] using 400 pixels per process\n", + " 88778 [cnmf.py: fit():481] [1655476] preprocessing ...\n", + " 88778 [cnmf.py: fit():478] [1655556] using 5000 block_size_temp\n", + " 88778 [cnmf.py: fit():478] [1655549] using 5000 block_size_temp\n", + " 88779 [cnmf.py: fit():477] [1655544] using 400 pixels per process\n", + " 88777 [cnmf.py: fit():477] [1655495] using 400 pixels per process\n", + " 88777 [cnmf.py: fit():477] [1655477] using 400 pixels per process\n", + " 88777 [cnmf.py: fit():477] [1655526] using 400 pixels per process\n", + " 88783 [cnmf.py: fit():477] [1655538] using 400 pixels per process\n", + " 88783 [cnmf.py: fit():477] [1655484] using 400 pixels per process\n", + " 88782 [cnmf.py: fit():478] [1655523] using 5000 block_size_temp\n", + " 88783 [cnmf.py: fit():477] [1655550] using 400 pixels per process\n", + " 88782 [cnmf.py: fit():481] [1655554] preprocessing ...\n", + " 88782 [cnmf.py: fit():478] [1655481] using 5000 block_size_temp\n", + " 88781 [cnmf.py: fit():477] [1655539] using 400 pixels per process\n", + " 88781 [cnmf.py: fit():478] [1655542] using 5000 block_size_temp\n", + " 88785 [cnmf.py: fit():477] [1655505] using 400 pixels per process\n", + " 88785 [cnmf.py: fit():478] [1655483] using 5000 block_size_temp\n", + " 88784 [cnmf.py: fit():477] [1655498] using 400 pixels per process\n", + " 88784 [cnmf.py: fit():478] [1655508] using 5000 block_size_temp\n", + " 88800 [cnmf.py: fit():481] [1655536] preprocessing ...\n", + " 88800 [cnmf.py: fit():481] [1655540] preprocessing ...\n", + " 88795 [map_reduce.py: cnmf_patches():100] [1660550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82260_102780file loaded\n", + " 88799 [cnmf.py: fit():481] [1655533] preprocessing ...\n", + " 88816 [cnmf.py: fit():481] [1655532] preprocessing ...\n", + " 88815 [cnmf.py: fit():481] [1655547] preprocessing ...\n", + " 88801 [cnmf.py: fit():478] [1655545] using 5000 block_size_temp\n", + " 88800 [pre_processing.py:interpolate_missing_data():41] [1655472] Checking for missing data entries (NaN)\n", + " 88815 [pre_processing.py:interpolate_missing_data():41] [1655552] Checking for missing data entries (NaN)\n", + " 88816 [pre_processing.py:interpolate_missing_data():41] [1655497] Checking for missing data entries (NaN)\n", + " 88798 [pre_processing.py:interpolate_missing_data():41] [1655535] Checking for missing data entries (NaN)\n", + " 88799 [cnmf.py: fit():481] [1655560] preprocessing ...\n", + " 88816 [pre_processing.py:interpolate_missing_data():41] [1655499] Checking for missing data entries (NaN)\n", + " 88802 [cnmf.py: fit():478] [1655528] using 5000 block_size_temp\n", + " 88818 [cnmf.py: fit():481] [1655492] preprocessing ...\n", + " 88817 [cnmf.py: fit():481] [1655512] preprocessing ...\n", + " 88801 [cnmf.py: fit():478] [1655504] using 5000 block_size_temp\n", + " 88817 [cnmf.py: fit():481] [1655546] preprocessing ...\n", + " 88801 [cnmf.py: fit():481] [1655551] preprocessing ...\n", + " 88818 [cnmf.py: fit():481] [1655541] preprocessing ...\n", + " 88817 [cnmf.py: fit():481] [1655525] preprocessing ...\n", + " 88819 [cnmf.py: fit():481] [1655561] preprocessing ...\n", + " 88819 [cnmf.py: fit():478] [1655557] using 5000 block_size_temp\n", + " 88820 [cnmf.py: fit():478] [1655563] using 5000 block_size_temp\n", + " 88821 [cnmf.py: fit():481] [1655516] preprocessing ...\n", + " 88821 [cnmf.py: fit():481] [1655500] preprocessing ...\n", + " 88822 [pre_processing.py:interpolate_missing_data():41] [1655543] Checking for missing data entries (NaN)\n", + " 88821 [cnmf.py: fit():478] [1655501] using 5000 block_size_temp\n", + " 88825 [cnmf.py: fit():478] [1655506] using 5000 block_size_temp\n", + " 88824 [cnmf.py: fit():477] [1655518] using 400 pixels per process\n", + " 88825 [pre_processing.py:interpolate_missing_data():41] [1655513] Checking for missing data entries (NaN)\n", + " 88824 [cnmf.py: fit():481] [1655485] preprocessing ...\n", + " 88824 [cnmf.py: fit():478] [1655491] using 5000 block_size_temp\n", + " 88827 [cnmf.py: fit():478] [1655524] using 5000 block_size_temp\n", + " 88825 [cnmf.py: fit():478] [1655494] using 5000 block_size_temp\n", + " 88822 [pre_processing.py:interpolate_missing_data():41] [1655519] Checking for missing data entries (NaN)\n", + " 88823 [cnmf.py: fit():481] [1655509] preprocessing ...\n", + " 88826 [cnmf.py: fit():478] [1655478] using 5000 block_size_temp\n", + " 88828 [pre_processing.py:interpolate_missing_data():41] [1655489] Checking for missing data entries (NaN)\n", + " 88828 [pre_processing.py:interpolate_missing_data():41] [1655559] Checking for missing data entries (NaN)\n", + " 88828 [cnmf.py: fit():481] [1655522] preprocessing ...\n", + " 88827 [cnmf.py: fit():481] [1655527] preprocessing ...\n", + " 88829 [cnmf.py: fit():481] [1655479] preprocessing ...\n", + " 88826 [cnmf.py: fit():481] [1655502] preprocessing ...\n", + " 88829 [cnmf.py: fit():478] [1655503] using 5000 block_size_temp\n", + " 88829 [cnmf.py: fit():478] [1655470] using 5000 block_size_temp\n", + " 88826 [pre_processing.py:interpolate_missing_data():41] [1655553] Checking for missing data entries (NaN)\n", + " 88830 [cnmf.py: fit():481] [1655486] preprocessing ...\n", + " 88831 [pre_processing.py:interpolate_missing_data():41] [1655511] Checking for missing data entries (NaN)\n", + " 88831 [cnmf.py: fit():481] [1655517] preprocessing ...\n", + " 88830 [pre_processing.py:interpolate_missing_data():41] [1655490] Checking for missing data entries (NaN)\n", + " 88830 [pre_processing.py:interpolate_missing_data():41] [1655531] Checking for missing data entries (NaN)\n", + " 88831 [cnmf.py: fit():481] [1655474] preprocessing ...\n", + " 88832 [pre_processing.py:interpolate_missing_data():41] [1655537] Checking for missing data entries (NaN)\n", + " 88832 [cnmf.py: fit():481] [1655564] preprocessing ...\n", + " 88832 [cnmf.py: fit():481] [1655534] preprocessing ...\n", + " 88833 [cnmf.py: fit():481] [1655521] preprocessing ...\n", + " 88834 [cnmf.py: fit():478] [1655487] using 5000 block_size_temp\n", + " 88833 [cnmf.py: fit():481] [1655529] preprocessing ...\n", + " 88833 [cnmf.py: fit():478] [1655493] using 5000 block_size_temp\n", + " 88835 [cnmf.py: fit():481] [1655488] preprocessing ...\n", + " 88841 [pre_processing.py:interpolate_missing_data():41] [1655520] Checking for missing data entries (NaN)\n", + " 88841 [cnmf.py: fit():481] [1655507] preprocessing ...\n", + " 88841 [cnmf.py: fit():478] [1655482] using 5000 block_size_temp\n", + " 88840 [cnmf.py: fit():478] [1655562] using 5000 block_size_temp\n", + " 88840 [cnmf.py: fit():481] [1655475] preprocessing ...\n", + " 88840 [cnmf.py: fit():478] [1655548] using 5000 block_size_temp\n", + " 88839 [cnmf.py: fit():481] [1655469] preprocessing ...\n", + " 88846 [cnmf.py: fit():478] [1655526] using 5000 block_size_temp\n", + " 88844 [cnmf.py: fit():478] [1655477] using 5000 block_size_temp\n", + " 88845 [cnmf.py: fit():481] [1655473] preprocessing ...\n", + " 88844 [cnmf.py: fit():478] [1655510] using 5000 block_size_temp\n", + " 88845 [cnmf.py: fit():478] [1655480] using 5000 block_size_temp\n", + " 88845 [cnmf.py: fit():478] [1655544] using 5000 block_size_temp\n", + " 88845 [cnmf.py: fit():478] [1655495] using 5000 block_size_temp\n", + " 88843 [cnmf.py: fit():481] [1655556] preprocessing ...\n", + " 88843 [pre_processing.py:interpolate_missing_data():41] [1655476] Checking for missing data entries (NaN)\n", + " 88844 [cnmf.py: fit():481] [1655549] preprocessing ...\n", + " 88843 [pre_processing.py:interpolate_missing_data():41] [1655496] Checking for missing data entries (NaN)\n", + " 88849 [cnmf.py: fit():478] [1655539] using 5000 block_size_temp\n", + " 88848 [cnmf.py: fit():478] [1655538] using 5000 block_size_temp\n", + " 88848 [cnmf.py: fit():481] [1655523] preprocessing ...\n", + " 88848 [pre_processing.py:interpolate_missing_data():41] [1655554] Checking for missing data entries (NaN)\n", + " 88848 [cnmf.py: fit():481] [1655481] preprocessing ...\n", + " 88847 [cnmf.py: fit():478] [1655550] using 5000 block_size_temp\n", + " 88849 [cnmf.py: fit():481] [1655542] preprocessing ...\n", + " 88847 [cnmf.py: fit():478] [1655484] using 5000 block_size_temp\n", + " 88853 [cnmf.py: fit():478] [1655498] using 5000 block_size_temp\n", + " 88853 [cnmf.py: fit():481] [1655483] preprocessing ...\n", + " 88854 [cnmf.py: fit():481] [1655508] preprocessing ...\n", + " 88853 [cnmf.py: fit():478] [1655505] using 5000 block_size_temp\n", + " 88916 [pre_processing.py:interpolate_missing_data():41] [1655512] Checking for missing data entries (NaN)\n", + " 88916 [pre_processing.py:interpolate_missing_data():41] [1655547] Checking for missing data entries (NaN)\n", + " 88936 [pre_processing.py:interpolate_missing_data():41] [1655536] Checking for missing data entries (NaN)\n", + " 88936 [pre_processing.py:interpolate_missing_data():41] [1655492] Checking for missing data entries (NaN)\n", + " 88937 [pre_processing.py:interpolate_missing_data():41] [1655560] Checking for missing data entries (NaN)\n", + " 88935 [pre_processing.py:interpolate_missing_data():41] [1655551] Checking for missing data entries (NaN)\n", + " 88936 [pre_processing.py:interpolate_missing_data():41] [1655540] Checking for missing data entries (NaN)\n", + " 88937 [cnmf.py: fit():481] [1655504] preprocessing ...\n", + " 88937 [cnmf.py: fit():481] [1655545] preprocessing ...\n", + " 88938 [pre_processing.py:interpolate_missing_data():41] [1655546] Checking for missing data entries (NaN)\n", + " 88938 [pre_processing.py:interpolate_missing_data():41] [1655525] Checking for missing data entries (NaN)\n", + " 88915 [pre_processing.py:interpolate_missing_data():41] [1655532] Checking for missing data entries (NaN)\n", + " 88951 [pre_processing.py:interpolate_missing_data():41] [1655541] Checking for missing data entries (NaN)\n", + " 88953 [cnmf.py: fit():481] [1655557] preprocessing ...\n", + " 88952 [pre_processing.py:interpolate_missing_data():41] [1655561] Checking for missing data entries (NaN)\n", + " 88955 [pre_processing.py:interpolate_missing_data():41] [1655500] Checking for missing data entries (NaN)\n", + " 88973 [cnmf.py: fit():481] [1655501] preprocessing ...\n", + " 88973 [cnmf.py: fit():481] [1655563] preprocessing ...\n", + " 88972 [pre_processing.py:interpolate_missing_data():41] [1655516] Checking for missing data entries (NaN)\n", + " 88988 [cnmf.py: fit():478] [1655518] using 5000 block_size_temp\n", + " 88899 [pre_processing.py:interpolate_missing_data():41] [1655533] Checking for missing data entries (NaN)\n", + " 88988 [cnmf.py: fit():481] [1655491] preprocessing ...\n", + " 88989 [pre_processing.py:interpolate_missing_data():41] [1655485] Checking for missing data entries (NaN)\n", + " 88988 [pre_processing.py:interpolate_missing_data():41] [1655509] Checking for missing data entries (NaN)\n", + " 89019 [cnmf.py: fit():481] [1655478] preprocessing ...\n", + " 89020 [pre_processing.py:interpolate_missing_data():41] [1655479] Checking for missing data entries (NaN)\n", + " 88989 [cnmf.py: fit():481] [1655494] preprocessing ...\n", + " 89035 [pre_processing.py:interpolate_missing_data():41] [1655527] Checking for missing data entries (NaN)\n", + " 88954 [cnmf.py: fit():481] [1655528] preprocessing ...\n", + " 89051 [cnmf.py: fit():481] [1655524] preprocessing ...\n", + " 88971 [cnmf.py: fit():481] [1655506] preprocessing ...\n", + " 89003 [pre_processing.py:interpolate_missing_data():41] [1655522] Checking for missing data entries (NaN)\n", + " 89052 [pre_processing.py:interpolate_missing_data():41] [1655502] Checking for missing data entries (NaN)\n", + " 89052 [cnmf.py: fit():481] [1655470] preprocessing ...\n", + " 89083 [cnmf.py: fit():481] [1655503] preprocessing ...\n", + " 89084 [pre_processing.py:interpolate_missing_data():41] [1655486] Checking for missing data entries (NaN)\n", + " 89099 [pre_processing.py:interpolate_missing_data():41] [1655517] Checking for missing data entries (NaN)\n", + " 89100 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Checking for missing data entries (NaN)\n", + " 89136 [pre_processing.py:interpolate_missing_data():41] [1655475] Checking for missing data entries (NaN)\n", + " 89138 [cnmf.py: fit():481] [1655548] preprocessing ...\n", + " 89136 [pre_processing.py:interpolate_missing_data():41] [1655488] Checking for missing data entries (NaN)\n", + " 89162 [cnmf.py: fit():481] [1655526] preprocessing ...\n", + " 89162 [pre_processing.py:interpolate_missing_data():41] [1655556] Checking for missing data entries (NaN)\n", + " 89180 [cnmf.py: fit():481] [1655480] preprocessing ...\n", + " 89162 [cnmf.py: fit():481] [1655495] preprocessing ...\n", + " 89179 [pre_processing.py:interpolate_missing_data():41] [1655549] Checking for missing data entries (NaN)\n", + " 89161 [cnmf.py: fit():481] [1655477] preprocessing ...\n", + " 89162 [pre_processing.py:interpolate_missing_data():41] [1655473] Checking for missing data entries (NaN)\n", + " 89161 [cnmf.py: fit():481] [1655544] preprocessing ...\n", + " 89161 [cnmf.py: fit():481] [1655510] preprocessing ...\n", + " 89201 [cnmf.py: fit():481] [1655550] preprocessing ...\n", + " 89201 [pre_processing.py:interpolate_missing_data():41] [1655481] Checking for missing data entries (NaN)\n", + " 89201 [cnmf.py: fit():481] [1655539] preprocessing ...\n", + " 89201 [cnmf.py: fit():481] [1655538] preprocessing ...\n", + " 89219 [pre_processing.py:interpolate_missing_data():41] [1655542] Checking for missing data entries (NaN)\n", + " 89220 [cnmf.py: fit():481] [1655484] preprocessing ...\n", + " 89200 [pre_processing.py:interpolate_missing_data():41] [1655523] Checking for missing data entries (NaN)\n", + " 89228 [pre_processing.py:interpolate_missing_data():41] [1655483] Checking for missing data entries (NaN)\n", + " 89228 [pre_processing.py:interpolate_missing_data():41] [1655508] Checking for missing data entries (NaN)\n", + " 89229 [cnmf.py: fit():481] [1655505] preprocessing ...\n", + " 89228 [cnmf.py: fit():481] [1655498] preprocessing 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entries (NaN)\n", + " 89760 [pre_processing.py:interpolate_missing_data():41] [1655562] Checking for missing data entries (NaN)\n", + " 89881 [pre_processing.py:interpolate_missing_data():41] [1655510] Checking for missing data entries (NaN)\n", + " 89880 [pre_processing.py:interpolate_missing_data():41] [1655526] Checking for missing data entries (NaN)\n", + " 89880 [pre_processing.py:interpolate_missing_data():41] [1655477] Checking for missing data entries (NaN)\n", + " 89880 [pre_processing.py:interpolate_missing_data():41] [1655495] Checking for missing data entries (NaN)\n", + " 89881 [pre_processing.py:interpolate_missing_data():41] [1655544] Checking for missing data entries (NaN)\n", + " 89879 [pre_processing.py:interpolate_missing_data():41] [1655480] Checking for missing data entries (NaN)\n", + " 89912 [pre_processing.py:interpolate_missing_data():41] [1655538] Checking for missing data entries (NaN)\n", + " 89911 [pre_processing.py:interpolate_missing_data():41] [1655550] Checking for missing data entries (NaN)\n", + " 89912 [pre_processing.py:interpolate_missing_data():41] [1655484] Checking for missing data entries (NaN)\n", + " 89896 [pre_processing.py:interpolate_missing_data():41] [1655539] Checking for missing data entries (NaN)\n", + " 89976 [pre_processing.py:interpolate_missing_data():41] [1655498] Checking for missing data entries (NaN)\n", + " 89975 [pre_processing.py:interpolate_missing_data():41] [1655505] Checking for missing data entries (NaN)\n", + " 90095 [pre_processing.py:interpolate_missing_data():41] [1655518] Checking for missing data entries (NaN)\n", + " 90698 [params.py: set():1006] [1660129] Changing key rf in group patch from 20 to None\n", + " 90983 [params.py: set():1006] [1660129] Changing key stride in group patch from 10 to None\n", + " 91245 [params.py: set():1006] [1660361] Changing key rf in group patch from 20 to None\n", + " 91291 [params.py: set():1006] [1660129] Changing key nb in group init from 2 to 1\n", + " 91519 [params.py: set():1006] [1660361] Changing key stride in group patch from 10 to None\n", + " 91539 [params.py: set():1006] [1660129] Changing key nb in group temporal from 2 to 1\n", + " 91867 [params.py: set():1006] [1660361] Changing key nb in group init from 2 to 1\n", + " 91871 [params.py: set():1006] [1660129] Changing key nb in group spatial from 2 to 1\n", + " 92135 [params.py: set():1006] [1660361] Changing key nb in group temporal from 2 to 1\n", + " 92136 [params.py: set():1006] [1660129] Changing key p in group preprocess from 1 to 0\n", + " 92234 [params.py: set():1006] [1660550] Changing key rf in group patch from 20 to None\n", + " 92465 [params.py: set():1006] [1660129] Changing key p in group temporal from 1 to 0\n", + " 92466 [params.py: set():1006] [1660361] Changing key nb in group spatial from 2 to 1\n", + " 92591 [params.py: set():1006] [1660550] Changing key stride in group patch from 10 to None\n", + " 92707 [cnmf.py: fit():484] [1655514] initializing ...\n", + " 92747 [params.py: set():1006] [1660361] Changing key p in group preprocess from 1 to 0\n", + " 92754 [cnmf.py: fit():436] [1660129] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 92851 [params.py: set():1006] [1660550] Changing key nb in group init from 2 to 1\n", + " 93066 [initialization.py:initialize_components():292] [1655514] Variance Normalization\n", + " 93079 [params.py: set():1006] [1660361] Changing key p in group temporal from 1 to 0\n", + " 93080 [cnmf.py: fit():449] [1660129] (8000, 41, 41)\n", + " 93155 [params.py: set():1006] [1660550] Changing key nb in group temporal from 2 to 1\n", + " 93375 [cnmf.py: fit():466] [1660129] Using 1 processes\n", + " 93376 [cnmf.py: fit():436] [1660361] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 93467 [params.py: set():1006] [1660550] Changing key nb in group spatial from 2 to 1\n", + " 93663 [cnmf.py: fit():449] [1660361] (8000, 41, 41)\n", + " 93664 [cnmf.py: fit():477] [1660129] using 400 pixels per process\n", + " 93771 [params.py: set():1006] [1660550] Changing key p in group preprocess from 1 to 0\n", + " 93900 [cnmf.py: fit():478] [1660129] using 5000 block_size_temp\n", + " 93907 [cnmf.py: fit():466] [1660361] Using 1 processes\n", + " 93987 [params.py: set():1006] [1660550] Changing key p in group temporal from 1 to 0\n", + " 94056 [map_reduce.py: cnmf_patches():78] [1655514] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97620_118140START\n", + " 93751 [cnmf.py: fit():484] [1655530] initializing ...\n", + " 94191 [cnmf.py: fit():477] [1660361] using 400 pixels per process\n", + " 94195 [cnmf.py: fit():481] [1660129] preprocessing ...\n", + " 94210 [cnmf.py: fit():484] [1655497] initializing ...\n", + " 94250 [cnmf.py: fit():484] [1655531] initializing ...\n", + " 94299 [cnmf.py: fit():436] [1660550] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 94214 [cnmf.py: fit():484] [1655489] initializing ...\n", + " 94346 [cnmf.py: fit():484] [1655535] initializing ...\n", + " 94348 [cnmf.py: fit():484] [1655559] initializing ...\n", + " 94355 [map_reduce.py: cnmf_patches():80] [1655514] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97620_118140Read file\n", + " 94314 [cnmf.py: fit():484] [1655532] initializing ...\n", + " 94360 [cnmf.py: fit():484] [1655537] initializing ...\n", + " 94397 [cnmf.py: fit():484] [1655519] initializing ...\n", + " 94399 [cnmf.py: fit():484] [1655511] initializing ...\n", + " 94405 [cnmf.py: fit():484] [1655499] initializing ...\n", + " 94405 [initialization.py:initialize_components():292] [1655530] Variance Normalization\n", + " 94420 [cnmf.py: fit():484] [1655554] initializing ...\n", + " 94435 [pre_processing.py:interpolate_missing_data():41] [1660129] Checking for missing data entries (NaN)\n", + " 94441 [cnmf.py: fit():484] [1655553] initializing ...\n", + " 94441 [cnmf.py: fit():478] [1660361] using 5000 block_size_temp\n", + " 94451 [cnmf.py: fit():484] [1655552] initializing ...\n", + " 94476 [cnmf.py: fit():484] [1655543] initializing ...\n", + " 94480 [initialization.py:initialize_components():292] [1655497] Variance Normalization\n", + " 94492 [cnmf.py: fit():484] [1655490] initializing ...\n", + " 94493 [cnmf.py: fit():484] [1655513] initializing ...\n", + " 94167 [cnmf.py: fit():484] [1655520] initializing ...\n", + " 94424 [cnmf.py: fit():484] [1655474] initializing ...\n", + " 94531 [cnmf.py: fit():484] [1655472] initializing ...\n", + " 94533 [cnmf.py: fit():484] [1655496] initializing ...\n", + " 94459 [cnmf.py: fit():484] [1655540] initializing ...\n", + " 94523 [cnmf.py: fit():484] [1655541] initializing ...\n", + " 94542 [cnmf.py: fit():484] [1655486] initializing ...\n", + " 94480 [cnmf.py: fit():484] [1655547] initializing ...\n", + " 94463 [cnmf.py: fit():484] [1655546] initializing ...\n", + " 94555 [initialization.py:initialize_components():292] [1655531] Variance Normalization\n", + " 94467 [cnmf.py: fit():484] [1655479] initializing ...\n", + " 94556 [cnmf.py: fit():449] [1660550] (8000, 41, 41)\n", + " 94439 [cnmf.py: fit():484] [1655485] initializing ...\n", + " 94445 [cnmf.py: fit():484] [1655512] initializing ...\n", + " 94564 [cnmf.py: fit():484] [1655476] initializing ...\n", + " 94450 [cnmf.py: fit():484] [1655561] initializing ...\n", + " 94365 [cnmf.py: fit():484] [1655522] initializing ...\n", + " 94574 [cnmf.py: fit():484] [1655492] initializing ...\n", + " 94574 [initialization.py:initialize_components():292] [1655489] Variance Normalization\n", + " 94608 [initialization.py:initialize_components():292] [1655559] Variance Normalization\n", + " 94608 [initialization.py:initialize_components():292] [1655535] Variance Normalization\n", + " 94611 [cnmf.py: fit():484] [1655516] initializing ...\n", + " 94614 [cnmf.py: fit():484] [1655560] initializing ...\n", + " 94629 [cnmf.py: fit():484] [1655536] initializing 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[initialization.py:initialize_components():292] [1655520] Variance Normalization\n", + " 94733 [cnmf.py: fit():484] [1655529] initializing ...\n", + " 94650 [cnmf.py: fit():484] [1655503] initializing ...\n", + " 94736 [cnmf.py: fit():484] [1655523] initializing ...\n", + " 94737 [initialization.py:initialize_components():292] [1655474] Variance Normalization\n", + " 94737 [initialization.py:initialize_components():292] [1655472] Variance Normalization\n", + " 94738 [initialization.py:initialize_components():292] [1655496] Variance Normalization\n", + " 94419 [cnmf.py: fit():484] [1655525] initializing ...\n", + " 94739 [initialization.py:initialize_components():292] [1655540] Variance Normalization\n", + " 94740 [initialization.py:initialize_components():292] [1655541] Variance Normalization\n", + " 94740 [initialization.py:initialize_components():292] [1655486] Variance Normalization\n", + " 94742 [cnmf.py: fit():484] [1655469] initializing ...\n", + " 94743 [initialization.py:initialize_components():292] [1655547] Variance Normalization\n", + " 94743 [initialization.py:initialize_components():292] [1655546] Variance Normalization\n", + " 94753 [cnmf.py: fit():484] [1655507] initializing ...\n", + " 94753 [initialization.py:initialize_components():292] [1655479] Variance Normalization\n", + " 94763 [cnmf.py: fit():466] [1660550] Using 1 processes\n", + " 94764 [initialization.py:initialize_components():292] [1655512] Variance Normalization\n", + " 94765 [initialization.py:initialize_components():292] [1655476] Variance Normalization\n", + " 94765 [initialization.py:initialize_components():292] [1655485] Variance Normalization\n", + " 94766 [initialization.py:initialize_components():292] [1655561] Variance Normalization\n", + " 94766 [initialization.py:initialize_components():292] [1655522] Variance Normalization\n", + " 94769 [cnmf.py: fit():484] [1655545] initializing ...\n", + " 94770 [cnmf.py: fit():484] [1655534] initializing ...\n", + " 94771 [initialization.py:initialize_components():292] [1655492] Variance Normalization\n", + " 94783 [cnmf.py: fit():484] [1655564] initializing ...\n", + " 94622 [cnmf.py: fit():484] [1655491] initializing ...\n", + " 94792 [cnmf.py: fit():484] [1655481] initializing ...\n", + " 94794 [cnmf.py: fit():484] [1655542] initializing ...\n", + " 94796 [cnmf.py: fit():484] [1655548] initializing ...\n", + " 94708 [cnmf.py: fit():484] [1655494] initializing ...\n", + " 94808 [initialization.py:initialize_components():292] [1655516] Variance Normalization\n", + " 94823 [initialization.py:initialize_components():292] [1655560] Variance Normalization\n", + " 94826 [cnmf.py: fit():484] [1655495] initializing ...\n", + " 94827 [initialization.py:initialize_components():292] [1655536] Variance Normalization\n", + " 94840 [initialization.py:initialize_components():292] [1655488] Variance Normalization\n", + " 94850 [cnmf.py: fit():484] [1655524] initializing ...\n", + " 94851 [initialization.py:initialize_components():292] [1655517] Variance Normalization\n", + " 94851 [initialization.py:initialize_components():292] [1655500] Variance Normalization\n", + " 94852 [initialization.py:initialize_components():292] [1655509] Variance Normalization\n", + " 94852 [initialization.py:initialize_components():292] [1655521] Variance Normalization\n", + " 94855 [cnmf.py: fit():484] [1655544] initializing ...\n", + " 94861 [cnmf.py: fit():484] [1655501] initializing ...\n", + " 94862 [cnmf.py: fit():484] [1655483] initializing ...\n", + " 94863 [initialization.py:initialize_components():292] [1655502] Variance Normalization\n", + " 94868 [initialization.py:initialize_components():292] [1655551] Variance Normalization\n", + " 94877 [cnmf.py: fit():484] [1655482] initializing ...\n", + " 94878 [initialization.py:initialize_components():292] [1655533] Variance Normalization\n", + " 94878 [initialization.py:initialize_components():292] [1655556] Variance Normalization\n", + " 94722 [cnmf.py: fit():484] [1655539] initializing ...\n", + " 94900 [cnmf.py: fit():484] [1655478] initializing ...\n", + " 94758 [cnmf.py: fit():484] [1655550] initializing ...\n", + " 94898 [cnmf.py: fit():484] [1655562] initializing ...\n", + " 94888 [initialization.py:initialize_components():292] [1655475] Variance Normalization\n", + " 94901 [cnmf.py: fit():484] [1655504] initializing ...\n", + " 94947 [initialization.py:initialize_components():292] [1655527] Variance Normalization\n", + " 94941 [cnmf.py: fit():484] [1655557] initializing ...\n", + " 94909 [cnmf.py: fit():484] [1655493] initializing ...\n", + " 94916 [pre_processing.py:interpolate_missing_data():41] [1660361] Checking for missing data entries (NaN)\n", + " 94813 [cnmf.py: fit():484] [1655498] initializing ...\n", + " 94928 [initialization.py:initialize_components():292] [1655473] Variance Normalization\n", + " 94937 [cnmf.py: fit():484] [1655563] initializing ...\n", + " 94939 [cnmf.py: fit():484] [1655487] initializing ...\n", + " 94953 [initialization.py:initialize_components():292] [1655508] Variance Normalization\n", + " 94961 [cnmf.py: fit():484] [1655477] initializing ...\n", + " 95029 [cnmf.py: fit():484] [1655528] initializing ...\n", + " 95028 [cnmf.py: fit():484] [1655470] initializing ...\n", + " 94963 [cnmf.py: fit():484] [1655506] initializing ...\n", + " 94984 [initialization.py:initialize_components():292] [1655549] Variance Normalization\n", + " 94985 [initialization.py:initialize_components():292] [1655523] Variance Normalization\n", + " 94991 [initialization.py:initialize_components():292] [1655503] Variance Normalization\n", + " 95007 [initialization.py:initialize_components():292] [1655529] Variance Normalization\n", + " 95008 [initialization.py:initialize_components():292] [1655525] Variance Normalization\n", + " 95017 [cnmf.py: fit():484] [1655480] initializing ...\n", + " 95035 [initialization.py:initialize_components():292] [1655469] Variance Normalization\n", + " 95114 [initialization.py:initialize_components():292] [1655542] Variance Normalization\n", + " 95114 [initialization.py:initialize_components():292] [1655494] Variance Normalization\n", + " 95051 [cnmf.py: fit():477] [1660550] using 400 pixels per process\n", + " 95052 [initialization.py:initialize_components():292] [1655507] Variance Normalization\n", + " 95079 [initialization.py:initialize_components():292] [1655534] Variance Normalization\n", + " 95080 [initialization.py:initialize_components():292] [1655545] Variance Normalization\n", + " 94952 [cnmf.py: fit():484] [1655518] initializing ...\n", + " 95095 [initialization.py:initialize_components():292] [1655564] Variance Normalization\n", + " 95104 [map_reduce.py: cnmf_patches():78] [1655530] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 112980_133500START\n", + " 95104 [initialization.py:initialize_components():292] [1655491] Variance Normalization\n", + " 95105 [initialization.py:initialize_components():292] [1655481] Variance Normalization\n", + " 95112 [initialization.py:initialize_components():292] [1655548] Variance Normalization\n", + " 95113 [cnmf.py: fit():484] [1655526] initializing ...\n", + " 95263 [initialization.py:initialize_components():292] [1655550] Variance Normalization\n", + " 94859 [cnmf.py: fit():484] [1655484] initializing ...\n", + " 95256 [initialization.py:initialize_components():292] [1655482] Variance Normalization\n", + " 95141 [cnmf.py: fit():484] [1655505] initializing ...\n", + " 95171 [initialization.py:initialize_components():292] [1655495] Variance Normalization\n", + " 95180 [cnmf.py: fit():484] [1655538] initializing ...\n", + " 94779 [cnmf.py: fit():484] [1655510] initializing ...\n", + " 95196 [initialization.py:initialize_components():292] [1655524] Variance Normalization\n", + " 95227 [initialization.py:initialize_components():292] [1655544] Variance Normalization\n", + " 95236 [initialization.py:initialize_components():292] [1655501] Variance Normalization\n", + " 95236 [initialization.py:initialize_components():292] [1655483] Variance Normalization\n", + " 95268 [initialization.py:initialize_components():292] [1655562] Variance Normalization\n", + " 95273 [initialization.py:initialize_components():292] [1655478] Variance Normalization\n", + " 95274 [initialization.py:initialize_components():292] [1655504] Variance Normalization\n", + " 95274 [initialization.py:initialize_components():292] [1655539] Variance Normalization\n", + " 95278 [initialization.py:initialize_components():292] [1655493] Variance Normalization\n", + " 95315 [initialization.py:initialize_components():292] [1655498] Variance Normalization\n", + " 95319 [initialization.py:initialize_components():292] [1655487] Variance Normalization\n", + " 95319 [initialization.py:initialize_components():292] [1655563] Variance Normalization\n", + " 95328 [initialization.py:initialize_components():292] [1655477] Variance Normalization\n", + " 95335 [initialization.py:initialize_components():292] [1655557] Variance Normalization\n", + " 95336 [initialization.py:initialize_components():292] [1655506] Variance Normalization\n", + " 95420 [cnmf.py: fit():478] [1660550] using 5000 block_size_temp\n", + " 95387 [initialization.py:initialize_components():292] [1655470] Variance Normalization\n", + " 95388 [initialization.py:initialize_components():292] [1655480] Variance Normalization\n", + " 95408 [initialization.py:initialize_components():292] [1655528] Variance Normalization\n", + " 95477 [initialization.py:initialize_components():292] [1655526] Variance Normalization\n", + " 95447 [initialization.py:initialize_components():292] [1655518] Variance Normalization\n", + " 95463 [map_reduce.py: cnmf_patches():80] [1655530] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 112980_133500Read file\n", + " 95506 [initialization.py:initialize_components():292] [1655505] Variance Normalization\n", + " 95516 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Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128340_148860Read file\n", + " 95904 [map_reduce.py: cnmf_patches():80] [1655472] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220500_241020Read file\n", + " 95904 [map_reduce.py: cnmf_patches():80] [1655499] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205140_225660Read file\n", + " 95904 [map_reduce.py: cnmf_patches():80] [1655553] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189780_210300Read file\n", + " 95905 [map_reduce.py: cnmf_patches():80] [1655554] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235860_256380Read file\n", + " 95935 [map_reduce.py: cnmf_patches():78] [1655475] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236884_256892START\n", + " 95936 [pre_processing.py:interpolate_missing_data():41] [1660550] Checking for missing data entries (NaN)\n", + " 96044 [map_reduce.py: cnmf_patches():78] [1655511] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5490_26010START\n", + " 96045 [map_reduce.py: 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cnmf_patches():78] [1655543] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143730_164250START\n", + " 96053 [map_reduce.py: cnmf_patches():78] [1655535] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 159090_179610START\n", + " 96054 [map_reduce.py: cnmf_patches():78] [1655527] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174450_194970START\n", + " 96054 [map_reduce.py: cnmf_patches():78] [1655534] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189810_210330START\n", + " 96056 [map_reduce.py: cnmf_patches():78] [1655486] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205170_225690START\n", + " 96056 [map_reduce.py: cnmf_patches():78] [1655489] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220530_241050START\n", + " 96058 [map_reduce.py: cnmf_patches():78] [1655519] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235890_256410START\n", + " 96059 [map_reduce.py: cnmf_patches():78] [1655492] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236914_256922START\n", + " 96059 [map_reduce.py: cnmf_patches():78] [1655541] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5520_26040START\n", + " 96060 [map_reduce.py: cnmf_patches():78] [1655536] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20880_41400START\n", + " 96061 [map_reduce.py: cnmf_patches():78] [1655516] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 36240_56760START\n", + " 96062 [map_reduce.py: cnmf_patches():78] [1655561] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51600_72120START\n", + " 96062 [map_reduce.py: cnmf_patches():78] [1655522] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66960_87480START\n", + " 96063 [map_reduce.py: cnmf_patches():78] [1655512] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82320_102840START\n", + " 96064 [map_reduce.py: cnmf_patches():78] [1655525] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97680_118200START\n", + " 96065 [map_reduce.py: cnmf_patches():78] [1655551] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 113040_133560START\n", + " 96065 [map_reduce.py: cnmf_patches():78] [1655488] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128400_148920START\n", + " 96066 [map_reduce.py: cnmf_patches():78] [1655507] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143760_164280START\n", + " 96067 [map_reduce.py: cnmf_patches():78] [1655540] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 159120_179640START\n", + " 96069 [map_reduce.py: cnmf_patches():78] [1655476] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174480_195000START\n", + " 96069 [map_reduce.py: cnmf_patches():78] [1655474] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189840_210360START\n", + " 96070 [map_reduce.py: cnmf_patches():78] [1655564] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205200_225720START\n", + " 96070 [map_reduce.py: cnmf_patches():78] [1655469] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220560_241080START\n", + " 96071 [map_reduce.py: cnmf_patches():78] [1655524] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235920_256440START\n", + " 96074 [map_reduce.py: cnmf_patches():78] [1655544] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236944_256952START\n", + " 96092 [map_reduce.py: cnmf_patches():80] [1655475] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236884_256892Read file\n", + " 96095 [map_reduce.py: cnmf_patches():78] [1655485] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5550_26070START\n", + " 96096 [map_reduce.py: cnmf_patches():78] [1655502] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20910_41430START\n", + " 96097 [map_reduce.py: cnmf_patches():78] [1655560] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 36270_56790START\n", + " 96098 [map_reduce.py: cnmf_patches():78] [1655547] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51630_72150START\n", + " 96098 [map_reduce.py: cnmf_patches():78] [1655529] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66990_87510START\n", + " 96099 [map_reduce.py: cnmf_patches():78] [1655556] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82350_102870START\n", + " 96100 [map_reduce.py: cnmf_patches():78] [1655521] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97710_118230START\n", + " 96101 [map_reduce.py: cnmf_patches():78] [1655479] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 113070_133590START\n", + " 96102 [map_reduce.py: cnmf_patches():78] [1655549] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128430_148950START\n", + " 96102 [map_reduce.py: cnmf_patches():78] [1655523] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143790_164310START\n", + " 96103 [map_reduce.py: cnmf_patches():78] [1655545] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 159150_179670START\n", + " 96104 [map_reduce.py: cnmf_patches():78] [1655503] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174510_195030START\n", + " 96105 [map_reduce.py: cnmf_patches():78] [1655546] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189870_210390START\n", + " 96106 [map_reduce.py: cnmf_patches():78] [1655509] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 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init from 2 to 1\n", + " 113937 [params.py: set():1006] [1655547] Changing key nb in group init from 2 to 1\n", + " 113929 [params.py: set():1006] [1655519] Changing key stride in group patch from 10 to None\n", + " 113928 [params.py: set():1006] [1655502] Changing key stride in group patch from 10 to None\n", + " 113927 [params.py: set():1006] [1655536] Changing key nb in group init from 2 to 1\n", + " 113926 [params.py: set():1006] [1655535] Changing key stride in group patch from 10 to None\n", + " 113964 [params.py: set():1006] [1655494] Changing key rf in group patch from 20 to None\n", + " 113967 [params.py: set():1006] [1655521] Changing key rf in group patch from 20 to None\n", + " 113927 [params.py: set():1006] [1655546] Changing key stride in group patch from 10 to None\n", + " 113627 [params.py: set():1006] [1655525] Changing key rf in group patch from 20 to None\n", + " 114005 [params.py: set():1006] [1655514] Changing key rf in group patch from 20 to None\n", + " 113834 [params.py: set():1006] [1655523] Changing key rf in group patch from 20 to None\n", + " 113924 [params.py: set():1006] [1655533] Changing key stride in group patch from 10 to None\n", + " 113551 [map_reduce.py: cnmf_patches():78] [1663745] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220622_241141START\n", + " 113647 [params.py: set():1006] [1655543] Changing key rf in group patch from 20 to None\n", + " 114016 [params.py: set():1006] [1655497] Changing key rf in group patch from 20 to None\n", + " 113982 [params.py: set():1006] [1655476] Changing key rf in group patch from 20 to None\n", + " 113871 [params.py: set():1006] [1655482] Changing key rf in group patch from 20 to None\n", + " 114013 [params.py: set():1006] [1655511] Changing key rf in group patch from 20 to None\n", + " 114042 [map_reduce.py: cnmf_patches():78] [1660361] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 237006_257013START\n", + " 114072 [params.py: set():1006] [1655520] Changing key rf in group patch from 20 to None\n", + " 114061 [params.py: set():1006] [1655506] Changing key rf in group patch from 20 to None\n", + " 114041 [params.py: set():1006] [1655559] Changing key rf in group patch from 20 to None\n", + " 113439 [params.py: set():1006] [1655474] Changing key rf in group patch from 20 to None\n", + " 114081 [params.py: set():1006] [1655531] Changing key rf in group patch from 20 to None\n", + " 114064 [params.py: set():1006] [1655552] Changing key rf in group patch from 20 to None\n", + " 114020 [params.py: set():1006] [1655509] Changing key rf in group patch from 20 to None\n", + " 114100 [map_reduce.py: cnmf_patches():80] [1660550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235982_256501Read file\n", + " 114109 [params.py: set():1006] [1655548] Changing key rf in group patch from 20 to None\n", + " 113920 [params.py: set():1006] [1655526] Changing key rf in group patch from 20 to None\n", + " 114112 [params.py: set():1006] [1655481] Changing key rf in group patch from 20 to None\n", + " 114085 [params.py: set():1006] [1655493] Changing key rf in group patch from 20 to None\n", + " 114122 [params.py: set():1006] [1655484] Changing key rf in group patch from 20 to None\n", + " 114113 [params.py: set():1006] [1655553] Changing key rf in group patch from 20 to None\n", + " 114154 [params.py: set():1006] [1655538] Changing key rf in group patch from 20 to None\n", + " 114157 [params.py: set():1006] [1655470] Changing key rf in group patch from 20 to None\n", + " 114151 [params.py: set():1006] [1655549] Changing key rf in group patch from 20 to None\n", + " 114162 [params.py: set():1006] [1655527] Changing key rf in group patch from 20 to None\n", + " 114198 [params.py: set():1006] [1655475] Changing key rf in group patch from 20 to None\n", + " 114200 [params.py: set():1006] [1655503] Changing key rf in group patch from 20 to None\n", + " 114193 [params.py: set():1006] [1655472] Changing key rf in group patch from 20 to None\n", + " 113809 [params.py: set():1006] [1655488] Changing key rf in group patch from 20 to None\n", + " 114207 [params.py: set():1006] [1655517] Changing key rf in group patch from 20 to None\n", + " 114161 [params.py: set():1006] [1655518] Changing key rf in group patch from 20 to None\n", + " 114213 [params.py: set():1006] [1655510] Changing key rf in group patch from 20 to None\n", + " 114136 [params.py: set():1006] [1655477] Changing key rf in group patch from 20 to None\n", + " 114070 [params.py: set():1006] [1655554] Changing key rf in group patch from 20 to None\n", + " 114196 [params.py: set():1006] [1655534] Changing key rf in group patch from 20 to None\n", + " 114222 [params.py: set():1006] [1655499] Changing key rf in group patch from 20 to None\n", + " 114187 [params.py: set():1006] [1655544] Changing key rf in group patch from 20 to None\n", + " 114211 [params.py: set():1006] [1655489] Changing key rf in group patch from 20 to None\n", + " 114191 [params.py: set():1006] [1655557] Changing key rf in group patch from 20 to None\n", + " 114251 [params.py: set():1006] [1655563] Changing key rf in group patch from 20 to None\n", + " 114234 [params.py: set():1006] [1655487] Changing key rf in group patch from 20 to None\n", + " 114253 [params.py: set():1006] [1655561] Changing key rf in group patch from 20 to None\n", + " 114249 [params.py: set():1006] [1655500] Changing key rf in group patch from 20 to None\n", + " 114267 [params.py: set():1006] [1655478] Changing key rf in group patch from 20 to None\n", + " 114284 [params.py: set():1006] [1655529] Changing key rf in group patch from 20 to None\n", + " 114290 [params.py: set():1006] [1655551] Changing key rf in group patch from 20 to None\n", + " 114132 [params.py: set():1006] [1655504] Changing key rf in group patch from 20 to None\n", + " 114169 [params.py: set():1006] [1655490] Changing key rf in group patch from 20 to None\n", + " 114245 [params.py: set():1006] [1655556] Changing key rf in group patch from 20 to None\n", + " 114183 [params.py: set():1006] [1655479] Changing key rf in group patch from 20 to None\n", + " 114289 [params.py: set():1006] [1655491] Changing key rf in group patch from 20 to None\n", + " 114295 [params.py: set():1006] [1655550] Changing key rf in group patch from 20 to None\n", + " 114296 [params.py: set():1006] [1655498] Changing key rf in group patch from 20 to None\n", + " 114303 [params.py: set():1006] [1655480] Changing key rf in group patch from 20 to None\n", + " 114273 [params.py: set():1006] [1655483] Changing key rf in group patch from 20 to None\n", + " 114308 [params.py: set():1006] [1655495] Changing key rf in group patch from 20 to None\n", + " 114314 [params.py: set():1006] [1655532] Changing key stride in group patch from 10 to None\n", + " 114315 [params.py: set():1006] [1655537] Changing key stride in group patch from 10 to None\n", + " 114315 [params.py: set():1006] [1655522] Changing key stride in group patch from 10 to None\n", + " 114316 [params.py: set():1006] [1655508] Changing key nb in group temporal from 2 to 1\n", + " 114317 [params.py: set():1006] [1655541] Changing key nb in group temporal from 2 to 1\n", + " 114317 [params.py: set():1006] [1655486] Changing key nb in group temporal from 2 to 1\n", + " 114317 [params.py: set():1006] [1655547] Changing key nb in group temporal from 2 to 1\n", + " 114318 [params.py: set():1006] [1655519] Changing key nb in group init from 2 to 1\n", + " 114318 [params.py: set():1006] [1655502] Changing key nb in group init from 2 to 1\n", + " 114318 [params.py: set():1006] [1655536] Changing key nb in group temporal from 2 to 1\n", + " 114319 [params.py: set():1006] [1655546] Changing key nb in group init from 2 to 1\n", + " 114319 [params.py: set():1006] [1655535] Changing key nb in group init from 2 to 1\n", + " 114320 [params.py: set():1006] [1655521] Changing key stride in group patch from 10 to None\n", + " 114320 [params.py: set():1006] [1655524] Changing key nb in group init from 2 to 1\n", + " 114320 [params.py: set():1006] [1655494] Changing key stride in group patch from 10 to None\n", + " 114321 [params.py: set():1006] [1655525] Changing key stride in group patch from 10 to None\n", + " 114321 [params.py: set():1006] [1655514] Changing key stride in group patch from 10 to None\n", + " 114322 [params.py: set():1006] [1655533] Changing key nb in group init from 2 to 1\n", + " 114322 [params.py: set():1006] [1655523] Changing key stride in group patch from 10 to None\n", + " 114322 [map_reduce.py: cnmf_patches():80] [1663745] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220622_241141Read file\n", + " 114323 [params.py: set():1006] [1655543] Changing key stride in group patch from 10 to None\n", + " 114323 [params.py: set():1006] [1655497] Changing key stride in group patch from 10 to None\n", + " 114324 [params.py: set():1006] [1655476] Changing key stride in group patch from 10 to None\n", + " 114324 [params.py: set():1006] [1655482] Changing key stride in group patch from 10 to None\n", + " 114324 [params.py: set():1006] [1655511] Changing key stride in group patch from 10 to None\n", + " 114325 [map_reduce.py: cnmf_patches():80] [1660361] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 237006_257013Read file\n", + " 114325 [params.py: set():1006] [1655520] Changing key stride in group patch from 10 to None\n", + " 114326 [params.py: set():1006] [1655506] Changing key stride in group patch from 10 to None\n", + " 114326 [params.py: set():1006] [1655559] Changing key stride in group patch from 10 to None\n", + " 114327 [params.py: set():1006] [1655474] Changing key stride in group patch from 10 to None\n", + " 114327 [params.py: set():1006] [1655531] Changing key stride in group patch from 10 to None\n", + " 114328 [params.py: set():1006] [1655552] Changing key stride in group patch from 10 to None\n", + " 114328 [params.py: set():1006] [1655509] Changing key stride in group patch from 10 to None\n", + " 114329 [params.py: set():1006] [1655548] Changing key stride in group patch from 10 to None\n", + " 114329 [params.py: set():1006] [1655481] Changing key stride in group patch from 10 to None\n", + " 114329 [params.py: set():1006] [1655526] Changing key stride in group patch from 10 to None\n", + " 114330 [params.py: set():1006] [1655484] Changing key stride in group patch from 10 to None\n", + " 114330 [params.py: set():1006] [1655553] Changing key stride in group patch from 10 to None\n", + " 114331 [params.py: set():1006] [1655493] Changing key stride in group patch from 10 to None\n", + " 114332 [params.py: set():1006] [1655538] Changing key stride in group patch from 10 to None\n", + " 114333 [params.py: set():1006] [1655470] Changing key stride in group patch from 10 to None\n", + " 114333 [params.py: set():1006] [1655549] Changing key stride in group patch from 10 to None\n", + " 114334 [params.py: set():1006] [1655527] Changing key stride in group patch from 10 to None\n", + " 114336 [params.py: set():1006] [1655503] Changing key stride in group patch from 10 to None\n", + " 114336 [params.py: set():1006] [1655472] Changing key stride in group patch from 10 to None\n", + " 114336 [params.py: set():1006] [1655475] Changing key stride in group patch from 10 to None\n", + " 114337 [params.py: set():1006] [1655517] Changing key stride in group patch from 10 to None\n", + " 114337 [params.py: set():1006] [1655488] Changing key stride in group patch from 10 to None\n", + " 114339 [params.py: set():1006] [1655518] Changing key stride in group patch from 10 to None\n", + " 114339 [params.py: set():1006] [1655510] Changing key stride in group patch from 10 to None\n", + " 114340 [params.py: set():1006] [1655477] Changing key stride in group patch from 10 to None\n", + " 114340 [params.py: set():1006] [1655554] Changing key stride in group patch from 10 to None\n", + " 114341 [params.py: set():1006] [1655534] Changing key stride in group patch from 10 to None\n", + " 114341 [params.py: set():1006] [1655499] Changing key stride in group patch from 10 to None\n", + " 114342 [params.py: set():1006] [1655544] Changing key stride in group patch from 10 to None\n", + " 114343 [map_reduce.py: cnmf_patches():100] [1660129] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205262_225781file loaded\n", + " 114344 [params.py: set():1006] [1655489] Changing key stride in group patch from 10 to None\n", + " 114344 [params.py: set():1006] [1655557] Changing key stride in group patch from 10 to None\n", + " 114345 [params.py: set():1006] [1655563] Changing key stride in group patch from 10 to None\n", + " 114346 [params.py: set():1006] [1655561] Changing key stride in group patch from 10 to None\n", + " 114346 [params.py: set():1006] [1655487] Changing key stride in group patch from 10 to None\n", + " 114347 [params.py: set():1006] [1655500] Changing key stride in group patch from 10 to None\n", + " 114347 [params.py: set():1006] [1655478] Changing key stride in group patch from 10 to None\n", + " 114349 [params.py: set():1006] [1655507] Changing key rf in group patch from 20 to None\n", + " 114143 [params.py: set():1006] [1655562] Changing key rf in group patch from 20 to None\n", + " 114219 [params.py: set():1006] [1655473] Changing key rf in group patch from 20 to None\n", + " 114350 [params.py: set():1006] [1655529] Changing key stride in group patch from 10 to None\n", + " 114301 [params.py: set():1006] [1655505] Changing key rf in group patch from 20 to None\n", + " 114149 [params.py: set():1006] [1655542] Changing key rf in group patch from 20 to None\n", + " 114351 [params.py: set():1006] [1655551] Changing key stride in group patch from 10 to None\n", + " 114352 [params.py: set():1006] [1655504] Changing key stride in group patch from 10 to None\n", + " 114363 [params.py: set():1006] [1655469] Changing key rf in group patch from 20 to None\n", + " 114364 [params.py: set():1006] [1655490] Changing key stride in group patch from 10 to None\n", + " 114364 [params.py: set():1006] [1655556] Changing key stride in group patch from 10 to None\n", + " 114365 [params.py: set():1006] [1655479] Changing key stride in group patch from 10 to None\n", + " 114365 [params.py: set():1006] [1655491] Changing key stride in group patch from 10 to None\n", + " 114366 [params.py: set():1006] [1655498] Changing key stride in group patch from 10 to None\n", + " 114366 [params.py: set():1006] [1655550] Changing key stride in group patch from 10 to None\n", + " 114367 [params.py: set():1006] [1655480] Changing key stride in group patch from 10 to None\n", + " 114367 [params.py: set():1006] [1655483] Changing key stride in group patch from 10 to None\n", + " 114369 [params.py: set():1006] [1655539] Changing key rf in group patch from 20 to None\n", + " 114382 [params.py: set():1006] [1655484] Changing key nb in group init from 2 to 1\n", + " 114383 [params.py: set():1006] [1655509] Changing key nb in group init from 2 to 1\n", + " 114380 [params.py: set():1006] [1655563] Changing key nb in group init from 2 to 1\n", + " 114381 [params.py: set():1006] [1655494] Changing key nb in group init from 2 to 1\n", + " 114382 [params.py: set():1006] [1655527] Changing key nb in group init from 2 to 1\n", + " 114387 [params.py: set():1006] [1655526] Changing key nb in group init from 2 to 1\n", + " 114382 [params.py: set():1006] [1655500] Changing key nb in group init from 2 to 1\n", + " 114381 [params.py: set():1006] [1655523] Changing key nb in group init from 2 to 1\n", + " 114386 [params.py: set():1006] [1655475] Changing key nb in group init from 2 to 1\n", + " 114380 [params.py: set():1006] [1655549] Changing key nb in group init from 2 to 1\n", + " 114385 [params.py: set():1006] [1655476] Changing key nb in group init from 2 to 1\n", + " 114391 [params.py: set():1006] [1655482] Changing key nb in group init from 2 to 1\n", + " 114383 [params.py: set():1006] [1655534] Changing key nb in group init from 2 to 1\n", + " 114391 [params.py: set():1006] [1655517] Changing key nb in group init from 2 to 1\n", + " 114387 [params.py: set():1006] [1655544] Changing key nb in group init from 2 to 1\n", + " 114389 [params.py: set():1006] [1655538] Changing key nb in group init from 2 to 1\n", + " 114383 [params.py: set():1006] [1655497] Changing key nb in group init from 2 to 1\n", + " 114385 [params.py: set():1006] [1655489] Changing key nb in group init from 2 to 1\n", + " 114389 [params.py: set():1006] [1655535] Changing key nb in group temporal from 2 to 1\n", + " 114384 [params.py: set():1006] [1655520] Changing key nb in group init from 2 to 1\n", + " 114385 [params.py: set():1006] [1655477] Changing key nb in group init from 2 to 1\n", + " 114390 [params.py: set():1006] [1655474] Changing key nb in group init from 2 to 1\n", + " 114388 [params.py: set():1006] [1655536] Changing key nb in group spatial from 2 to 1\n", + " 114387 [params.py: set():1006] [1655557] Changing key nb in group init from 2 to 1\n", + " 114384 [params.py: set():1006] [1655472] Changing key nb in group init from 2 to 1\n", + " 114386 [params.py: set():1006] [1655531] Changing key nb in group init from 2 to 1\n", + " 114388 [params.py: set():1006] [1655503] Changing key nb in group init from 2 to 1\n", + " 114388 [params.py: set():1006] [1655543] Changing key nb in group init from 2 to 1\n", + " 114386 [params.py: set():1006] [1655499] Changing key nb in group init from 2 to 1\n", + " 114389 [params.py: set():1006] [1655546] Changing key nb in group temporal from 2 to 1\n", + " 114391 [params.py: set():1006] [1655510] Changing key nb in group init from 2 to 1\n", + " 114390 [params.py: set():1006] [1655511] Changing key nb in group init from 2 to 1\n", + " 114392 [params.py: set():1006] [1655514] Changing key nb in group init from 2 to 1\n", + " 114390 [params.py: set():1006] [1655525] Changing key nb in group init from 2 to 1\n", + " 114393 [params.py: set():1006] [1655561] Changing key nb in group init from 2 to 1\n", + " 114388 [params.py: set():1006] [1655533] Changing key nb in group temporal from 2 to 1\n", + " 114394 [params.py: set():1006] [1655518] Changing key nb in group init from 2 to 1\n", + " 114392 [params.py: set():1006] [1655552] Changing key nb in group init from 2 to 1\n", + " 114384 [params.py: set():1006] [1655521] Changing key nb in group init from 2 to 1\n", + " 114391 [params.py: set():1006] [1655553] Changing key nb in group init from 2 to 1\n", + " 114393 [params.py: set():1006] [1655519] Changing key nb in group temporal from 2 to 1\n", + " 114394 [params.py: set():1006] [1655554] Changing key nb in group init from 2 to 1\n", + " 114392 [params.py: set():1006] [1655470] Changing key nb in group init from 2 to 1\n", + " 114377 [params.py: set():1006] [1655495] Changing key stride in group patch from 10 to None\n", + " 114393 [params.py: set():1006] [1655481] Changing key nb in group init from 2 to 1\n", + " 114380 [params.py: set():1006] [1655488] Changing key nb in group init from 2 to 1\n", + " 114394 [params.py: set():1006] [1655541] Changing key nb in group spatial from 2 to 1\n", + " 114394 [params.py: set():1006] [1655502] Changing key nb in group temporal from 2 to 1\n", + " 114385 [params.py: set():1006] [1655548] Changing key nb in group init from 2 to 1\n", + " 114399 [params.py: set():1006] [1655537] Changing key nb in group init from 2 to 1\n", + " 114381 [params.py: set():1006] [1655506] Changing key nb in group init from 2 to 1\n", + " 114399 [params.py: set():1006] [1655524] Changing key nb in group temporal from 2 to 1\n", + " 114400 [params.py: set():1006] [1655487] Changing key nb in group init from 2 to 1\n", + " 114401 [params.py: set():1006] [1655522] Changing key nb in group init from 2 to 1\n", + " 114400 [params.py: set():1006] [1655559] Changing key nb in group init from 2 to 1\n", + " 114402 [params.py: set():1006] [1655493] Changing key nb in group init from 2 to 1\n", + " 114401 [params.py: set():1006] [1655508] Changing key nb in group spatial from 2 to 1\n", + " 114401 [params.py: set():1006] [1655486] Changing key nb in group spatial from 2 to 1\n", + " 114402 [params.py: set():1006] [1655547] Changing key nb in group spatial from 2 to 1\n", + " 114403 [params.py: set():1006] [1655532] Changing key nb in group init from 2 to 1\n", + " 114404 [params.py: set():1006] [1655478] Changing key nb in group init from 2 to 1\n", + " 114420 [params.py: set():1006] [1655564] Changing key rf in group patch from 20 to None\n", + " 114423 [params.py: set():1006] [1655507] Changing key stride in group patch from 10 to None\n", + " 114428 [params.py: set():1006] [1655528] Changing key rf in group patch from 20 to None\n", + " 114430 [params.py: set():1006] [1655562] Changing key stride in group patch from 10 to None\n", + " 114432 [params.py: set():1006] [1655473] Changing key stride in group patch from 10 to None\n", + " 114432 [params.py: set():1006] [1655529] Changing key nb in group init from 2 to 1\n", + " 114433 [params.py: set():1006] [1655505] Changing key stride in group patch from 10 to None\n", + " 114434 [params.py: set():1006] [1655542] Changing key stride in group patch from 10 to None\n", + " 114444 [params.py: set():1006] [1655551] Changing key nb in group init from 2 to 1\n", + " 114443 [params.py: set():1006] [1655545] Changing key rf in group patch from 20 to None\n", + " 114447 [map_reduce.py: cnmf_patches():100] [1663745] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220622_241141file loaded\n", + " 114447 [params.py: set():1006] [1655504] Changing key nb in group init from 2 to 1\n", + " 114491 [map_reduce.py: cnmf_patches():100] [1660361] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 237006_257013file loaded\n", + " 114490 [map_reduce.py: cnmf_patches():100] [1660550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235982_256501file loaded\n", + " 114518 [params.py: set():1006] [1655520] Changing key nb in group temporal from 2 to 1\n", + " 114517 [params.py: set():1006] [1655563] Changing key nb in group temporal from 2 to 1\n", + " 114515 [params.py: set():1006] [1655554] Changing key nb in group temporal from 2 to 1\n", + " 114519 [params.py: set():1006] [1655495] Changing key nb in group init from 2 to 1\n", + " 114520 [params.py: set():1006] [1655477] Changing key nb in group temporal from 2 to 1\n", + " 114519 [params.py: set():1006] [1655488] Changing key nb in group temporal from 2 to 1\n", + " 114520 [params.py: set():1006] [1655484] Changing key nb in group temporal from 2 to 1\n", + " 114510 [params.py: set():1006] [1655476] Changing key nb in group temporal from 2 to 1\n", + " 114517 [params.py: set():1006] [1655518] Changing key nb in group temporal from 2 to 1\n", + " 114525 [params.py: set():1006] [1655478] Changing key nb in group temporal from 2 to 1\n", + " 114507 [params.py: set():1006] [1655497] Changing key nb in group temporal from 2 to 1\n", + " 114511 [params.py: set():1006] [1655510] Changing key nb in group temporal from 2 to 1\n", + " 114527 [params.py: set():1006] [1655473] Changing key nb in group init from 2 to 1\n", + " 114520 [params.py: set():1006] [1655537] Changing key nb in group temporal from 2 to 1\n", + " 114506 [params.py: set():1006] [1655509] Changing key nb in group temporal from 2 to 1\n", + " 114515 [params.py: set():1006] [1655481] Changing key nb in group temporal from 2 to 1\n", + " 114517 [params.py: set():1006] [1655519] Changing key nb in group spatial from 2 to 1\n", + " 114508 [params.py: set():1006] [1655534] Changing key nb in group temporal from 2 to 1\n", + " 114509 [params.py: set():1006] [1655523] Changing key nb in group temporal from 2 to 1\n", + " 114515 [params.py: set():1006] [1655499] Changing key nb in group temporal from 2 to 1\n", + " 114516 [params.py: set():1006] [1655521] Changing key nb in group temporal from 2 to 1\n", + " 114527 [params.py: set():1006] [1655562] Changing key nb in group init from 2 to 1\n", + " 114526 [params.py: set():1006] [1655528] Changing key stride in group patch from 10 to None\n", + " 114511 [params.py: set():1006] [1655543] Changing key nb in group temporal from 2 to 1\n", + " 114531 [params.py: set():1006] [1655504] Changing key nb in group temporal from 2 to 1\n", + " 114519 [params.py: set():1006] [1655502] Changing key nb in group spatial from 2 to 1\n", + " 114527 [params.py: set():1006] [1655529] Changing key nb in group temporal from 2 to 1\n", + " 114524 [params.py: set():1006] [1655493] Changing key nb in group temporal from 2 to 1\n", + " 114529 [params.py: set():1006] [1655545] Changing key stride in group patch from 10 to None\n", + " 114509 [params.py: set():1006] [1655517] Changing key nb in group temporal from 2 to 1\n", + " 114511 [params.py: set():1006] [1655475] Changing key nb in group temporal from 2 to 1\n", + " 114516 [params.py: set():1006] [1655552] Changing key nb in group temporal from 2 to 1\n", + " 114517 [params.py: set():1006] [1655525] Changing key nb in group temporal from 2 to 1\n", + " 114514 [params.py: set():1006] [1655511] Changing key nb in group temporal from 2 to 1\n", + " 114501 [params.py: set():1006] [1655539] Changing key stride in group patch from 10 to None\n", + " 114510 [params.py: set():1006] [1655538] Changing key nb in group temporal from 2 to 1\n", + " 114526 [params.py: set():1006] [1655507] Changing key nb in group init from 2 to 1\n", + " 114509 [params.py: set():1006] [1655546] Changing key nb in group spatial from 2 to 1\n", + " 114523 [params.py: set():1006] [1655506] Changing key nb in group temporal from 2 to 1\n", + " 114524 [params.py: set():1006] [1655532] Changing key nb in group temporal from 2 to 1\n", + " 114506 [params.py: set():1006] [1655549] Changing key nb in group temporal from 2 to 1\n", + " 114513 [params.py: set():1006] [1655489] Changing key nb in group temporal from 2 to 1\n", + " 114507 [params.py: set():1006] [1655535] Changing key nb in group spatial from 2 to 1\n", + " 114518 [params.py: set():1006] [1655541] Changing key p in group preprocess from 1 to 0\n", + " 114510 [params.py: set():1006] [1655482] Changing key nb in group temporal from 2 to 1\n", + " 114515 [params.py: set():1006] [1655561] Changing key nb in group temporal from 2 to 1\n", + " 114512 [params.py: set():1006] [1655536] Changing key p in group preprocess from 1 to 0\n", + " 114524 [params.py: set():1006] [1655547] Changing key p in group preprocess from 1 to 0\n", + " 114514 [params.py: set():1006] [1655503] Changing key nb in group temporal from 2 to 1\n", + " 114528 [params.py: set():1006] [1655505] Changing key nb in group init from 2 to 1\n", + " 114528 [params.py: set():1006] [1655542] Changing key nb in group init from 2 to 1\n", + " 114513 [params.py: set():1006] [1655544] Changing key nb in group temporal from 2 to 1\n", + " 114518 [params.py: set():1006] [1655514] Changing key nb in group temporal from 2 to 1\n", + " 114510 [params.py: set():1006] [1655474] Changing key nb in group temporal from 2 to 1\n", + " 114512 [params.py: set():1006] [1655557] Changing key nb in group temporal from 2 to 1\n", + " 114523 [params.py: set():1006] [1655524] Changing key nb in group spatial from 2 to 1\n", + " 114508 [params.py: set():1006] [1655494] Changing key nb in group temporal from 2 to 1\n", + " 114529 [params.py: set():1006] [1655551] Changing key nb in group temporal from 2 to 1\n", + " 114522 [params.py: set():1006] [1655522] Changing key nb in group temporal from 2 to 1\n", + " 114525 [params.py: set():1006] [1655564] Changing key stride in group patch from 10 to None\n", + " 114501 [params.py: set():1006] [1655483] Changing key nb in group init from 2 to 1\n", + " 114508 [params.py: set():1006] [1655500] Changing key nb in group temporal from 2 to 1\n", + " 114516 [params.py: set():1006] [1655470] Changing key nb in group temporal from 2 to 1\n", + " 114507 [params.py: set():1006] [1655527] Changing key nb in group temporal from 2 to 1\n", + " 114523 [params.py: set():1006] [1655559] Changing key nb in group temporal from 2 to 1\n", + " 114507 [params.py: set():1006] [1655526] Changing key nb in group temporal from 2 to 1\n", + " 114512 [params.py: set():1006] [1655472] Changing key nb in group temporal from 2 to 1\n", + " 114500 [params.py: set():1006] [1655491] Changing key nb in group init from 2 to 1\n", + " 114499 [params.py: set():1006] [1655498] Changing key nb in group init from 2 to 1\n", + " 114500 [params.py: set():1006] [1655480] Changing key nb in group init from 2 to 1\n", + " 114512 [params.py: set():1006] [1655531] Changing key nb in group temporal from 2 to 1\n", + " 114522 [params.py: set():1006] [1655486] Changing key p in group preprocess from 1 to 0\n", + " 114498 [params.py: set():1006] [1655469] Changing key stride in group patch from 10 to None\n", + " 114523 [params.py: set():1006] [1655508] Changing key p in group preprocess from 1 to 0\n", + " 114521 [params.py: set():1006] [1655487] Changing key nb in group temporal from 2 to 1\n", + " 114521 [params.py: set():1006] [1655548] Changing key nb in group temporal from 2 to 1\n", + " 114513 [params.py: set():1006] [1655533] Changing key nb in group spatial from 2 to 1\n", + " 114499 [params.py: set():1006] [1655479] Changing key nb in group init from 2 to 1\n", + " 114500 [params.py: set():1006] [1655550] Changing key nb in group init from 2 to 1\n", + " 114498 [params.py: set():1006] [1655556] Changing key nb in group init from 2 to 1\n", + " 114514 [params.py: set():1006] [1655553] Changing key nb in group temporal from 2 to 1\n", + " 114497 [params.py: set():1006] [1655490] Changing key nb in group init from 2 to 1\n", + " 114627 [params.py: set():1006] [1655499] Changing key nb in group spatial from 2 to 1\n", + " 114628 [params.py: set():1006] [1655557] Changing key nb in group spatial from 2 to 1\n", + " 114640 [params.py: set():1006] [1655553] Changing key nb in group spatial from 2 to 1\n", + " 114638 [params.py: set():1006] [1655562] Changing key nb in group temporal from 2 to 1\n", + " 114633 [params.py: set():1006] [1655535] Changing key p in group preprocess from 1 to 0\n", + " 114641 [params.py: set():1006] [1655490] Changing key nb in group temporal from 2 to 1\n", + " 114628 [params.py: set():1006] [1655510] Changing key nb in group spatial from 2 to 1\n", + " 114618 [params.py: set():1006] [1655517] Changing key nb in group spatial from 2 to 1\n", + " 114635 [params.py: set():1006] [1655483] Changing key nb in group temporal from 2 to 1\n", + " 114631 [params.py: set():1006] [1655549] Changing key nb in group spatial from 2 to 1\n", + " 114636 [params.py: set():1006] [1655486] Changing key p in group temporal from 1 to 0\n", + " 114640 [params.py: set():1006] [1655556] Changing key nb in group temporal from 2 to 1\n", + " 114629 [params.py: set():1006] [1655514] Changing key nb in group spatial from 2 to 1\n", + " 114635 [params.py: set():1006] [1655472] Changing key nb in group spatial from 2 to 1\n", + " 114630 [params.py: set():1006] [1655470] Changing key nb in group spatial from 2 to 1\n", + " 114630 [params.py: set():1006] [1655476] Changing key nb in group spatial from 2 to 1\n", + " 114624 [params.py: set():1006] [1655484] Changing key nb in group spatial from 2 to 1\n", + " 114620 [params.py: set():1006] [1655497] Changing key nb in group spatial from 2 to 1\n", + " 114637 [params.py: set():1006] [1655491] Changing key nb in group temporal from 2 to 1\n", + " 114630 [params.py: set():1006] [1655488] Changing key nb in group spatial from 2 to 1\n", + " 114625 [params.py: set():1006] [1655547] Changing key p in group temporal from 1 to 0\n", + " 114626 [params.py: set():1006] [1655545] Changing key nb in group init from 2 to 1\n", + " 114630 [params.py: set():1006] [1655522] Changing key nb in group spatial from 2 to 1\n", + " 114636 [params.py: set():1006] [1655498] Changing key nb in group temporal from 2 to 1\n", + " 114636 [params.py: set():1006] [1655480] Changing key nb in group temporal from 2 to 1\n", + " 114626 [params.py: set():1006] [1655544] Changing key nb in group spatial from 2 to 1\n", + " 114637 [params.py: set():1006] [1655533] Changing key p in group preprocess from 1 to 0\n", + " 114633 [params.py: set():1006] [1655526] Changing key nb in group spatial from 2 to 1\n", + " 114639 [params.py: set():1006] [1655536] Changing key p in group temporal from 1 to 0\n", + " 114639 [params.py: set():1006] [1655487] Changing key nb in group spatial from 2 to 1\n", + " 114619 [params.py: set():1006] [1655475] Changing key nb in group spatial from 2 to 1\n", + " 114632 [params.py: set():1006] [1655548] Changing key nb in group spatial from 2 to 1\n", + " 114625 [params.py: set():1006] [1655554] Changing key nb in group spatial from 2 to 1\n", + " 114620 [params.py: set():1006] [1655519] Changing key p in group preprocess from 1 to 0\n", + " 114621 [params.py: set():1006] [1655477] Changing key nb in group spatial from 2 to 1\n", + " 114623 [params.py: set():1006] [1655502] Changing key p in group preprocess from 1 to 0\n", + " 114620 [params.py: set():1006] [1655543] Changing key nb in group spatial from 2 to 1\n", + " 114637 [params.py: set():1006] [1655534] Changing key nb in group spatial from 2 to 1\n", + " 114623 [params.py: set():1006] [1655528] Changing key nb in group init from 2 to 1\n", + " 114638 [params.py: set():1006] [1655479] Changing key nb in group temporal from 2 to 1\n", + " 114634 [params.py: set():1006] [1655527] Changing key nb in group spatial from 2 to 1\n", + " 114624 [params.py: set():1006] [1655482] Changing key nb in group spatial from 2 to 1\n", + " 114631 [params.py: set():1006] [1655509] Changing key nb in group spatial from 2 to 1\n", + " 114626 [params.py: set():1006] [1655564] Changing key nb in group init from 2 to 1\n", + " 114626 [params.py: set():1006] [1655561] Changing key nb in group spatial from 2 to 1\n", + " 114634 [params.py: set():1006] [1655531] Changing key nb in group spatial from 2 to 1\n", + " 114619 [params.py: set():1006] [1655503] Changing key nb in group spatial from 2 to 1\n", + " 114634 [params.py: set():1006] [1655495] Changing key nb in group temporal from 2 to 1\n", + " 114633 [params.py: set():1006] [1655551] Changing key nb in group spatial from 2 to 1\n", + " 114625 [params.py: set():1006] [1655511] Changing key nb in group spatial from 2 to 1\n", + " 114619 [params.py: set():1006] [1655507] Changing key nb in group temporal from 2 to 1\n", + " 114623 [params.py: set():1006] [1655474] Changing key nb in group spatial from 2 to 1\n", + " 114637 [params.py: set():1006] [1655469] Changing key nb in group init from 2 to 1\n", + " 114622 [params.py: set():1006] [1655500] Changing key nb in group spatial from 2 to 1\n", + " 114618 [params.py: set():1006] [1655473] Changing key nb in group temporal from 2 to 1\n", + " 114617 [params.py: set():1006] [1655506] Changing key nb in group spatial from 2 to 1\n", + " 114629 [params.py: set():1006] [1655542] Changing key nb in group temporal from 2 to 1\n", + " 114640 [params.py: set():1006] [1655520] Changing key nb in group spatial from 2 to 1\n", + " 114617 [params.py: set():1006] [1655524] Changing key p in group preprocess from 1 to 0\n", + " 114628 [params.py: set():1006] [1655559] Changing key nb in group spatial from 2 to 1\n", + " 114629 [params.py: set():1006] [1655529] Changing key nb in group spatial from 2 to 1\n", + " 114627 [params.py: set():1006] [1655552] Changing key nb in group spatial from 2 to 1\n", + " 114624 [params.py: set():1006] [1655504] Changing key nb in group spatial from 2 to 1\n", + " 114621 [params.py: set():1006] [1655523] Changing key nb in group spatial from 2 to 1\n", + " 114633 [params.py: set():1006] [1655537] Changing key nb in group spatial from 2 to 1\n", + " 114638 [params.py: set():1006] [1655508] Changing key p in group temporal from 1 to 0\n", + " 114622 [params.py: set():1006] [1655478] Changing key nb in group spatial from 2 to 1\n", + " 114622 [params.py: set():1006] [1655525] Changing key nb in group spatial from 2 to 1\n", + " 114618 [params.py: set():1006] [1655493] Changing key nb in group spatial from 2 to 1\n", + " 114627 [params.py: set():1006] [1655538] Changing key nb in group spatial from 2 to 1\n", + " 114622 [params.py: set():1006] [1655505] Changing key nb in group temporal from 2 to 1\n", + " 114621 [params.py: set():1006] [1655546] Changing key p in group preprocess from 1 to 0\n", + " 114632 [params.py: set():1006] [1655539] Changing key nb in group init from 2 to 1\n", + " 114635 [params.py: set():1006] [1655550] Changing key nb in group temporal from 2 to 1\n", + " 114632 [params.py: set():1006] [1655494] Changing key nb in group spatial from 2 to 1\n", + " 114617 [params.py: set():1006] [1655532] Changing key nb in group spatial from 2 to 1\n", + " 114616 [params.py: set():1006] [1655489] Changing key nb in group spatial from 2 to 1\n", + " 114618 [params.py: set():1006] [1655518] Changing key nb in group spatial from 2 to 1\n", + " 114616 [params.py: set():1006] [1655563] Changing key nb in group spatial from 2 to 1\n", + " 114616 [params.py: set():1006] [1655541] Changing key p in group temporal from 1 to 0\n", + " 114631 [params.py: set():1006] [1655481] Changing key nb in group spatial from 2 to 1\n", + " 114615 [params.py: set():1006] [1655521] Changing key nb in group spatial from 2 to 1\n", + " 114719 [params.py: set():1006] [1660129] Changing key rf in group patch from 20 to None\n", + " 114778 [params.py: set():1006] [1655479] Changing key nb in group spatial from 2 to 1\n", + " 114774 [params.py: set():1006] [1655544] Changing key p in group preprocess from 1 to 0\n", + " 114777 [params.py: set():1006] [1655563] Changing key p in group preprocess from 1 to 0\n", + " 114782 [params.py: set():1006] [1655521] Changing key p in group preprocess from 1 to 0\n", + " 114778 [params.py: set():1006] [1655489] Changing key p in group preprocess from 1 to 0\n", + " 114776 [params.py: set():1006] [1655493] Changing key p in group preprocess from 1 to 0\n", + " 114779 [params.py: set():1006] [1655537] Changing key p in group preprocess from 1 to 0\n", + " 114774 [params.py: set():1006] [1655546] Changing key p in group temporal from 1 to 0\n", + " 114777 [params.py: set():1006] [1655538] Changing key p in group preprocess from 1 to 0\n", + " 114775 [params.py: set():1006] [1655502] Changing key p in group temporal from 1 to 0\n", + " 114778 [params.py: set():1006] [1655551] Changing key p in group preprocess from 1 to 0\n", + " 114780 [params.py: set():1006] [1655500] Changing key p in group preprocess from 1 to 0\n", + " 114776 [params.py: set():1006] [1655523] Changing key p in group preprocess from 1 to 0\n", + " 114770 [params.py: set():1006] [1655526] Changing key p in group preprocess from 1 to 0\n", + " 114768 [params.py: set():1006] [1655542] Changing key nb in group spatial from 2 to 1\n", + " 114776 [params.py: set():1006] [1655550] Changing key nb in group spatial from 2 to 1\n", + " 114768 [params.py: set():1006] [1655487] Changing key p in group preprocess from 1 to 0\n", + " 114770 [params.py: set():1006] [1655476] Changing key p in group preprocess from 1 to 0\n", + " 114778 [params.py: set():1006] [1655518] Changing key p in group preprocess from 1 to 0\n", + " 114766 [cnmf.py: fit():436] [1655547] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114779 [params.py: set():1006] [1655532] Changing key p in group preprocess from 1 to 0\n", + " 114773 [params.py: set():1006] [1655524] Changing key p in group temporal from 1 to 0\n", + " 114777 [cnmf.py: fit():436] [1655508] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114767 [params.py: set():1006] [1655533] Changing key p in group temporal from 1 to 0\n", + " 114775 [params.py: set():1006] [1655528] Changing key nb in group temporal from 2 to 1\n", + " 114781 [params.py: set():1006] [1655510] Changing key p in group preprocess from 1 to 0\n", + " 114765 [params.py: set():1006] [1655549] Changing key p in group preprocess from 1 to 0\n", + " 114782 [params.py: set():1006] [1655545] Changing key nb in group temporal from 2 to 1\n", + " 114779 [params.py: set():1006] [1655505] Changing key nb in group spatial from 2 to 1\n", + " 114771 [params.py: set():1006] [1655475] Changing key p in group preprocess from 1 to 0\n", + " 114772 [params.py: set():1006] [1655539] Changing key nb in group temporal from 2 to 1\n", + " 114772 [params.py: set():1006] [1655504] Changing key p in group preprocess from 1 to 0\n", + " 114774 [params.py: set():1006] [1655534] Changing key p in group preprocess from 1 to 0\n", + " 114769 [params.py: set():1006] [1655548] Changing key p in group preprocess from 1 to 0\n", + " 114764 [params.py: set():1006] [1655519] Changing key p in group temporal from 1 to 0\n", + " 114765 [params.py: set():1006] [1655553] Changing key p in group preprocess from 1 to 0\n", + " 114765 [params.py: set():1006] [1655557] Changing key p in group preprocess from 1 to 0\n", + " 114780 [cnmf.py: fit():436] [1655541] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114764 [params.py: set():1006] [1655561] Changing key p in group preprocess from 1 to 0\n", + " 114763 [params.py: set():1006] [1655554] Changing key p in group preprocess from 1 to 0\n", + " 114780 [params.py: set():1006] [1655509] Changing key p in group preprocess from 1 to 0\n", + " 114769 [params.py: set():1006] [1655473] Changing key nb in group spatial from 2 to 1\n", + " 114781 [params.py: set():1006] [1655481] Changing key p in group preprocess from 1 to 0\n", + " 114770 [params.py: set():1006] [1655527] Changing key p in group preprocess from 1 to 0\n", + " 114763 [params.py: set():1006] [1655507] Changing key nb in group spatial from 2 to 1\n", + " 114766 [params.py: set():1006] [1655556] Changing key nb in group spatial from 2 to 1\n", + " 114781 [params.py: set():1006] [1655499] Changing key p in group preprocess from 1 to 0\n", + " 114765 [params.py: set():1006] [1655484] Changing key p in group preprocess from 1 to 0\n", + " 114773 [params.py: set():1006] [1655470] Changing key p in group preprocess from 1 to 0\n", + " 114768 [params.py: set():1006] [1655529] Changing key p in group preprocess from 1 to 0\n", + " 114773 [params.py: set():1006] [1655525] Changing key p in group preprocess from 1 to 0\n", + " 114767 [params.py: set():1006] [1655543] Changing key p in group preprocess from 1 to 0\n", + " 114767 [params.py: set():1006] [1655506] Changing key p in group preprocess from 1 to 0\n", + " 114768 [params.py: set():1006] [1655469] Changing key nb in group temporal from 2 to 1\n", + " 114760 [params.py: set():1006] [1655522] Changing key p in group preprocess from 1 to 0\n", + " 114769 [params.py: set():1006] [1655482] Changing key p in group preprocess from 1 to 0\n", + " 114764 [params.py: set():1006] [1655488] Changing key p in group preprocess from 1 to 0\n", + " 114763 [params.py: set():1006] [1655559] Changing key p in group preprocess from 1 to 0\n", + " 114773 [params.py: set():1006] [1655494] Changing key p in group preprocess from 1 to 0\n", + " 114762 [params.py: set():1006] [1655483] Changing key nb in group spatial from 2 to 1\n", + " 114771 [params.py: set():1006] [1655503] Changing key p in group preprocess from 1 to 0\n", + " 114766 [params.py: set():1006] [1655562] Changing key nb in group spatial from 2 to 1\n", + " 114759 [params.py: set():1006] [1655490] Changing key nb in group spatial from 2 to 1\n", + " 114771 [params.py: set():1006] [1655474] Changing key p in group preprocess from 1 to 0\n", + " 114759 [cnmf.py: fit():436] [1655486] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114771 [params.py: set():1006] [1655491] Changing key nb in group spatial from 2 to 1\n", + " 114761 [params.py: set():1006] [1655514] Changing key p in group preprocess from 1 to 0\n", + " 114775 [params.py: set():1006] [1655478] Changing key p in group preprocess from 1 to 0\n", + " 114761 [params.py: set():1006] [1655535] Changing key p in group temporal from 1 to 0\n", + " 114760 [params.py: set():1006] [1655480] Changing key nb in group spatial from 2 to 1\n", + " 114761 [params.py: set():1006] [1655517] Changing key p in group preprocess from 1 to 0\n", + " 114759 [params.py: set():1006] [1655495] Changing key nb in group spatial from 2 to 1\n", + " 114762 [params.py: set():1006] [1655477] Changing key p in group preprocess from 1 to 0\n", + " 114762 [params.py: set():1006] [1655511] Changing key p in group preprocess from 1 to 0\n", + " 114760 [params.py: set():1006] [1655497] Changing key p in group preprocess from 1 to 0\n", + " 114758 [params.py: set():1006] [1655498] Changing key nb in group spatial from 2 to 1\n", + " 114761 [cnmf.py: fit():436] [1655536] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114772 [params.py: set():1006] [1655520] Changing key p in group preprocess from 1 to 0\n", + " 114758 [params.py: set():1006] [1655531] Changing key p in group preprocess from 1 to 0\n", + " 114757 [params.py: set():1006] [1655552] Changing key p in group preprocess from 1 to 0\n", + " 114758 [params.py: set():1006] [1655564] Changing key nb in group temporal from 2 to 1\n", + " 114757 [params.py: set():1006] [1655472] Changing key p in group preprocess from 1 to 0\n", + " 114750 [params.py: set():1006] [1663745] Changing key rf in group patch from 20 to None\n", + " 114812 [params.py: set():1006] [1660361] Changing key rf in group patch from 20 to None\n", + " 114808 [params.py: set():1006] [1660550] Changing key rf in group patch from 20 to None\n", + " 114860 [cnmf.py: fit():449] [1655536] (8000, 41, 41)\n", + " 114858 [params.py: set():1006] [1655564] Changing key nb in group spatial from 2 to 1\n", + " 114859 [params.py: set():1006] [1655477] Changing key p in group temporal from 1 to 0\n", + " 114858 [params.py: set():1006] [1655552] Changing key p in group temporal from 1 to 0\n", + " 114861 [params.py: set():1006] [1660361] Changing key stride in group patch from 10 to None\n", + " 114855 [cnmf.py: fit():436] [1655535] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114857 [params.py: set():1006] [1655559] Changing key p in group temporal from 1 to 0\n", + " 114857 [params.py: set():1006] [1655498] Changing key p in group preprocess from 1 to 0\n", + " 114853 [params.py: set():1006] [1655561] Changing key p in group temporal from 1 to 0\n", + " 114844 [params.py: set():1006] [1655549] Changing key p in group temporal from 1 to 0\n", + " 114856 [params.py: set():1006] [1655480] Changing key p in group preprocess from 1 to 0\n", + " 114856 [params.py: set():1006] [1655490] Changing key p in group preprocess from 1 to 0\n", + " 114858 [params.py: set():1006] [1655531] Changing key p in group temporal from 1 to 0\n", + " 114852 [params.py: set():1006] [1655474] Changing key p in group temporal from 1 to 0\n", + " 114855 [params.py: set():1006] [1655503] Changing key p in group temporal from 1 to 0\n", + " 114851 [params.py: set():1006] [1655473] Changing key p in group preprocess from 1 to 0\n", + " 114852 [params.py: set():1006] [1655482] Changing key p in group temporal from 1 to 0\n", + " 114852 [params.py: set():1006] [1655499] Changing key p in group temporal from 1 to 0\n", + " 114859 [params.py: set():1006] [1655514] Changing key p in group temporal from 1 to 0\n", + " 114847 [params.py: set():1006] [1655505] Changing key p in group preprocess from 1 to 0\n", + " 114859 [params.py: set():1006] [1655511] Changing key p in group temporal from 1 to 0\n", + " 114858 [params.py: set():1006] [1655483] Changing key p in group preprocess from 1 to 0\n", + " 114842 [cnmf.py: fit():449] [1655541] (8000, 41, 41)\n", + " 114860 [params.py: set():1006] [1655476] Changing key p in group temporal from 1 to 0\n", + " 114851 [params.py: set():1006] [1655484] Changing key p in group temporal from 1 to 0\n", + " 114854 [params.py: set():1006] [1655497] Changing key p in group temporal from 1 to 0\n", + " 114851 [params.py: set():1006] [1655537] Changing key p in group temporal from 1 to 0\n", + " 114853 [cnmf.py: fit():449] [1655486] (8000, 41, 41)\n", + " 114850 [cnmf.py: fit():436] [1655519] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114862 [params.py: set():1006] [1660550] Changing key stride in group patch from 10 to None\n", + " 114844 [params.py: set():1006] [1655475] Changing key p in group temporal from 1 to 0\n", + " 114849 [params.py: set():1006] [1655557] Changing key p in group temporal from 1 to 0\n", + " 114850 [params.py: set():1006] [1655478] Changing key p in group temporal from 1 to 0\n", + " 114842 [params.py: set():1006] [1655528] Changing key nb in group spatial from 2 to 1\n", + " 114848 [params.py: set():1006] [1655494] Changing key p in group temporal from 1 to 0\n", + " 114839 [params.py: set():1006] [1655487] Changing key p in group temporal from 1 to 0\n", + " 114854 [params.py: set():1006] [1655507] Changing key p in group preprocess from 1 to 0\n", + " 114843 [params.py: set():1006] [1655510] Changing key p in group temporal from 1 to 0\n", + " 114846 [params.py: set():1006] [1655481] Changing key p in group temporal from 1 to 0\n", + " 114849 [params.py: set():1006] [1655488] Changing key p in group temporal from 1 to 0\n", + " 114845 [params.py: set():1006] [1655556] Changing key p in group preprocess from 1 to 0\n", + " 114854 [params.py: set():1006] [1655491] Changing key p in group preprocess from 1 to 0\n", + " 114854 [params.py: set():1006] [1655520] Changing key p in group temporal from 1 to 0\n", + " 114860 [params.py: set():1006] [1655472] Changing key p in group temporal from 1 to 0\n", + " 114847 [params.py: set():1006] [1655532] Changing key p in group temporal from 1 to 0\n", + " 114839 [params.py: set():1006] [1655500] Changing key p in group temporal from 1 to 0\n", + " 114841 [params.py: set():1006] [1655538] Changing key p in group temporal from 1 to 0\n", + " 114849 [params.py: set():1006] [1655529] Changing key p in group temporal from 1 to 0\n", + " 114844 [params.py: set():1006] [1655548] Changing key p in group temporal from 1 to 0\n", + " 114856 [params.py: set():1006] [1655517] Changing key p in group temporal from 1 to 0\n", + " 114841 [params.py: set():1006] [1655542] Changing key p in group preprocess from 1 to 0\n", + " 114853 [params.py: set():1006] [1655527] Changing key p in group temporal from 1 to 0\n", + " 114840 [cnmf.py: fit():436] [1655533] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114845 [params.py: set():1006] [1655554] Changing key p in group temporal from 1 to 0\n", + " 114842 [params.py: set():1006] [1655543] Changing key p in group temporal from 1 to 0\n", + " 114849 [params.py: set():1006] [1655470] Changing key p in group temporal from 1 to 0\n", + " 114843 [params.py: set():1006] [1655539] Changing key nb in group spatial from 2 to 1\n", + " 114838 [params.py: set():1006] [1655489] Changing key p in group temporal from 1 to 0\n", + " 114845 [params.py: set():1006] [1655562] Changing key p in group preprocess from 1 to 0\n", + " 114840 [cnmf.py: fit():436] [1655502] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114846 [params.py: set():1006] [1655553] Changing key p in group temporal from 1 to 0\n", + " 114838 [params.py: set():1006] [1655523] Changing key p in group temporal from 1 to 0\n", + " 114839 [cnmf.py: fit():449] [1655547] (8000, 41, 41)\n", + " 114861 [params.py: set():1006] [1663745] Changing key stride in group patch from 10 to None\n", + " 114850 [params.py: set():1006] [1655506] Changing key p in group temporal from 1 to 0\n", + " 114840 [params.py: set():1006] [1655504] Changing key p in group temporal from 1 to 0\n", + " 114846 [params.py: set():1006] [1655534] Changing key p in group temporal from 1 to 0\n", + " 114836 [cnmf.py: fit():436] [1655546] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114847 [params.py: set():1006] [1655469] Changing key nb in group spatial from 2 to 1\n", + " 114845 [params.py: set():1006] [1655550] Changing key p in group preprocess from 1 to 0\n", + " 114836 [params.py: set():1006] [1655521] Changing key p in group temporal from 1 to 0\n", + " 114839 [params.py: set():1006] [1655551] Changing key p in group temporal from 1 to 0\n", + " 114837 [params.py: set():1006] [1655526] Changing key p in group temporal from 1 to 0\n", + " 114848 [params.py: set():1006] [1655509] Changing key p in group temporal from 1 to 0\n", + " 114843 [cnmf.py: fit():436] [1655524] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114835 [params.py: set():1006] [1655544] Changing key p in group temporal from 1 to 0\n", + " 114838 [params.py: set():1006] [1655493] Changing key p in group temporal from 1 to 0\n", + " 114848 [params.py: set():1006] [1655522] Changing key p in group temporal from 1 to 0\n", + " 114857 [params.py: set():1006] [1655495] Changing key p in group preprocess from 1 to 0\n", + " 114835 [params.py: set():1006] [1655545] Changing key nb in group spatial from 2 to 1\n", + " 114837 [params.py: set():1006] [1655518] Changing key p in group temporal from 1 to 0\n", + " 114836 [cnmf.py: fit():449] [1655508] (8000, 41, 40)\n", + " 114837 [params.py: set():1006] [1655479] Changing key p in group preprocess from 1 to 0\n", + " 114841 [params.py: set():1006] [1655525] Changing key p in group temporal from 1 to 0\n", + " 114835 [params.py: set():1006] [1655563] Changing key p in group temporal from 1 to 0\n", + " 114826 [params.py: set():1006] [1660129] Changing key stride in group patch from 10 to None\n", + " 114931 [cnmf.py: fit():436] [1655509] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114924 [cnmf.py: fit():436] [1655532] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114929 [cnmf.py: fit():436] [1655525] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114929 [cnmf.py: fit():449] [1655535] (8000, 41, 41)\n", + " 114930 [params.py: set():1006] [1655545] Changing key p in group preprocess from 1 to 0\n", + " 114933 [params.py: set():1006] [1655495] Changing key p in group temporal from 1 to 0\n", + " 114931 [cnmf.py: fit():436] [1655526] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114929 [cnmf.py: fit():449] [1655524] (8000, 41, 41)\n", + " 114930 [params.py: set():1006] [1655550] Changing key p in group temporal from 1 to 0\n", + " 114930 [params.py: set():1006] [1655469] Changing key p in group preprocess from 1 to 0\n", + " 114927 [cnmf.py: fit():436] [1655518] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114933 [params.py: set():1006] [1660129] Changing key nb in group init from 2 to 1\n", + " 114927 [cnmf.py: fit():436] [1655553] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114918 [cnmf.py: fit():436] [1655520] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114912 [cnmf.py: fit():436] [1655484] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114925 [cnmf.py: fit():436] [1655521] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114932 [cnmf.py: fit():436] [1655561] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114922 [cnmf.py: fit():466] [1655547] Using 1 processes\n", + " 114925 [cnmf.py: fit():436] [1655543] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114908 [params.py: set():1006] [1655490] Changing key p in group temporal from 1 to 0\n", + " 114920 [cnmf.py: fit():436] [1655529] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114920 [params.py: set():1006] [1655473] Changing key p in group temporal from 1 to 0\n", + " 114926 [cnmf.py: fit():436] [1655522] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114928 [cnmf.py: fit():436] [1655544] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114921 [cnmf.py: fit():436] [1655548] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114928 [cnmf.py: fit():466] [1655536] Using 1 processes\n", + " 114907 [cnmf.py: fit():436] [1655482] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114916 [cnmf.py: fit():436] [1655557] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114920 [cnmf.py: fit():436] [1655481] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114928 [cnmf.py: fit():436] [1655500] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114919 [params.py: set():1006] [1655491] Changing key p in group temporal from 1 to 0\n", + " 114918 [cnmf.py: fit():436] [1655510] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114932 [params.py: set():1006] [1655479] Changing key p in group temporal from 1 to 0\n", + " 114919 [params.py: set():1006] [1655556] Changing key p in group temporal from 1 to 0\n", + " 114919 [cnmf.py: fit():436] [1655504] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114922 [cnmf.py: fit():436] [1655534] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114927 [cnmf.py: fit():436] [1655493] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114925 [params.py: set():1006] [1655539] Changing key p in group preprocess from 1 to 0\n", + " 114913 [cnmf.py: fit():436] [1655476] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114921 [cnmf.py: fit():436] [1655488] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114909 [params.py: set():1006] [1655483] Changing key p in group temporal from 1 to 0\n", + " 114932 [cnmf.py: fit():436] [1655551] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114912 [params.py: set():1006] [1655507] Changing key p in group temporal from 1 to 0\n", + " 114933 [cnmf.py: fit():436] [1655563] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114931 [cnmf.py: fit():466] [1655508] Using 1 processes\n", + " 114910 [cnmf.py: fit():436] [1655497] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114918 [cnmf.py: fit():436] [1655554] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114926 [cnmf.py: fit():436] [1655489] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114909 [cnmf.py: fit():436] [1655499] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114910 [cnmf.py: fit():436] [1655511] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114911 [cnmf.py: fit():436] [1655527] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114916 [cnmf.py: fit():436] [1655517] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114923 [cnmf.py: fit():436] [1655472] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114912 [cnmf.py: fit():436] [1655487] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114923 [cnmf.py: fit():436] [1655523] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114921 [cnmf.py: fit():449] [1655533] (8000, 41, 41)\n", + " 114924 [params.py: set():1006] [1655542] Changing key p in group temporal from 1 to 0\n", + " 114923 [cnmf.py: fit():436] [1655538] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114913 [cnmf.py: fit():436] [1655478] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114917 [cnmf.py: fit():436] [1655494] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114911 [params.py: set():1006] [1655528] Changing key p in group preprocess from 1 to 0\n", + " 114922 [cnmf.py: fit():449] [1655502] (8000, 41, 41)\n", + " 114926 [cnmf.py: fit():449] [1655546] (8000, 41, 41)\n", + " 114911 [cnmf.py: fit():466] [1655541] Using 1 processes\n", + " 114915 [cnmf.py: fit():436] [1655503] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114911 [cnmf.py: fit():449] [1655519] (8000, 41, 41)\n", + " 114914 [cnmf.py: fit():436] [1655549] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114908 [cnmf.py: fit():436] [1655531] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114924 [params.py: set():1006] [1655562] Changing key p in group temporal from 1 to 0\n", + " 114910 [cnmf.py: fit():436] [1655537] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114913 [cnmf.py: fit():436] [1655470] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114907 [params.py: set():1006] [1655480] Changing key p in group temporal from 1 to 0\n", + " 114907 [cnmf.py: fit():466] [1655486] Using 1 processes\n", + " 114916 [cnmf.py: fit():436] [1655474] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114908 [cnmf.py: fit():436] [1655477] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114917 [cnmf.py: fit():436] [1655506] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114915 [params.py: set():1006] [1655505] Changing key p in group temporal from 1 to 0\n", + " 114906 [cnmf.py: fit():436] [1655552] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114917 [params.py: set():1006] [1663745] Changing key nb in group init from 2 to 1\n", + " 114909 [params.py: set():1006] [1655498] Changing key p in group temporal from 1 to 0\n", + " 114914 [cnmf.py: fit():436] [1655475] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114914 [params.py: set():1006] [1660550] Changing key nb in group init from 2 to 1\n", + " 114906 [params.py: set():1006] [1660361] Changing key nb in group init from 2 to 1\n", + " 114906 [cnmf.py: fit():436] [1655514] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114905 [params.py: set():1006] [1655564] Changing key p in group preprocess from 1 to 0\n", + " 114905 [cnmf.py: fit():436] [1655559] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 115003 [cnmf.py: fit():449] [1655559] (8000, 41, 41)\n", + " 115000 [cnmf.py: fit():449] [1655503] (8000, 41, 41)\n", + " 115002 [cnmf.py: fit():477] [1655541] using 400 pixels per process\n", + " 115001 [cnmf.py: fit():436] [1655505] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 115003 [params.py: set():1006] [1655564] Changing key p in group temporal from 1 to 0\n", + " 114996 [cnmf.py: fit():449] [1655477] (8000, 40, 41)\n", + " 115002 [cnmf.py: fit():466] [1655535] Using 1 processes\n", + " 114978 [cnmf.py: fit():449] [1655521] (8000, 41, 41)\n", + " 114984 [cnmf.py: fit():449] [1655517] (8000, 41, 41)\n", + " 114985 [cnmf.py: fit():436] [1655483] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 115000 [cnmf.py: fit():466] [1655502] Using 1 processes\n", + " 114999 [cnmf.py: fit():449] [1655475] (8000, 41, 40)\n", + " 114982 [cnmf.py: fit():436] [1655495] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114996 [cnmf.py: fit():449] [1655549] (8000, 41, 41)\n", + " 115001 [cnmf.py: fit():449] [1655514] (8000, 41, 41)\n", + " 114991 [cnmf.py: fit():449] [1655474] (8000, 41, 41)\n", + " 114986 [cnmf.py: fit():449] [1655482] (8000, 41, 41)\n", + " 114985 [cnmf.py: fit():449] [1655522] (8000, 41, 41)\n", + " 114989 [cnmf.py: fit():436] [1655507] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114996 [cnmf.py: fit():449] [1655523] (8000, 41, 41)\n", + " 114991 [cnmf.py: fit():477] [1655536] using 400 pixels per process\n", + " 114992 [cnmf.py: fit():466] [1655546] Using 1 processes\n", + " 114977 [cnmf.py: fit():449] [1655532] (8000, 41, 41)\n", + " 114997 [cnmf.py: fit():449] [1655487] (8000, 41, 41)\n", + " 114994 [cnmf.py: fit():466] [1655533] Using 1 processes\n", + " 114989 [cnmf.py: fit():449] [1655500] (8000, 41, 41)\n", + " 114998 [cnmf.py: fit():449] [1655531] (8000, 41, 41)\n", + " 114985 [cnmf.py: fit():436] [1655542] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114981 [cnmf.py: fit():477] [1655547] using 400 pixels per process\n", + " 114987 [cnmf.py: fit():436] [1655550] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114986 [cnmf.py: fit():449] [1655497] (8000, 41, 41)\n", + " 114992 [cnmf.py: fit():449] [1655476] (8000, 41, 41)\n", + " 114999 [cnmf.py: fit():436] [1655562] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114995 [cnmf.py: fit():449] [1655544] (8000, 41, 40)\n", + " 114979 [cnmf.py: fit():449] [1655548] (8000, 41, 41)\n", + " 114991 [cnmf.py: fit():449] [1655499] (8000, 41, 41)\n", + " 114986 [cnmf.py: fit():449] [1655511] (8000, 41, 41)\n", + " 114982 [cnmf.py: fit():449] [1655518] (8000, 40, 41)\n", + " 114988 [cnmf.py: fit():449] [1655525] (8000, 41, 41)\n", + " 114992 [cnmf.py: fit():466] [1655519] Using 1 processes\n", + " 114980 [cnmf.py: fit():449] [1655484] (8000, 41, 41)\n", + " 114998 [cnmf.py: fit():449] [1655506] (8000, 40, 41)\n", + " 114978 [cnmf.py: fit():449] [1655543] (8000, 41, 41)\n", + " 114983 [cnmf.py: fit():449] [1655534] (8000, 41, 41)\n", + " 114981 [params.py: set():1006] [1655539] Changing key p in group temporal from 1 to 0\n", + " 114989 [cnmf.py: fit():449] [1655552] (8000, 41, 41)\n", + " 114995 [cnmf.py: fit():449] [1655470] (8000, 41, 41)\n", + " 114995 [cnmf.py: fit():449] [1655537] (8000, 41, 41)\n", + " 114983 [cnmf.py: fit():449] [1655551] (8000, 41, 41)\n", + " 114983 [cnmf.py: fit():449] [1655529] (8000, 41, 41)\n", + " 115003 [params.py: set():1006] [1655528] Changing key p in group temporal from 1 to 0\n", + " 114987 [cnmf.py: fit():449] [1655538] (8000, 40, 41)\n", + " 114990 [cnmf.py: fit():449] [1655493] (8000, 40, 41)\n", + " 114981 [params.py: set():1006] [1655469] Changing key p in group temporal from 1 to 0\n", + " 115001 [params.py: set():1006] [1660129] Changing key nb in group temporal from 2 to 1\n", + " 114993 [cnmf.py: fit():449] [1655472] (8000, 41, 41)\n", + " 114988 [cnmf.py: fit():436] [1655556] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114979 [cnmf.py: fit():449] [1655557] (8000, 41, 40)\n", + " 114987 [cnmf.py: fit():449] [1655489] (8000, 41, 41)\n", + " 114994 [cnmf.py: fit():449] [1655478] (8000, 41, 41)\n", + " 114978 [cnmf.py: fit():449] [1655526] (8000, 40, 41)\n", + " 114977 [cnmf.py: fit():449] [1655504] (8000, 40, 41)\n", + " 114976 [cnmf.py: fit():436] [1655491] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114988 [params.py: set():1006] [1655545] Changing key p in group temporal from 1 to 0\n", + " 114997 [cnmf.py: fit():449] [1655494] (8000, 41, 41)\n", + " 114997 [cnmf.py: fit():449] [1655509] (8000, 41, 41)\n", + " 114982 [cnmf.py: fit():436] [1655473] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 115000 [params.py: set():1006] [1663745] Changing key nb in group temporal from 2 to 1\n", + " 114999 [params.py: set():1006] [1660550] Changing key nb in group temporal from 2 to 1\n", + " 114980 [cnmf.py: fit():436] [1655479] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114998 [cnmf.py: fit():436] [1655498] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114993 [cnmf.py: fit():449] [1655488] (8000, 41, 41)\n", + " 115002 [params.py: set():1006] [1660361] Changing key nb in group temporal from 2 to 1\n", + " 114977 [cnmf.py: fit():449] [1655563] (8000, 40, 41)\n", + " 114984 [cnmf.py: fit():449] [1655561] (8000, 41, 41)\n", + " 114994 [cnmf.py: fit():477] [1655486] using 400 pixels per process\n", + " 114980 [cnmf.py: fit():449] [1655527] (8000, 41, 41)\n", + " 114990 [cnmf.py: fit():436] [1655490] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114992 [cnmf.py: fit():436] [1655480] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 114993 [cnmf.py: fit():449] [1655553] (8000, 41, 41)\n", + " 114976 [cnmf.py: fit():449] [1655481] (8000, 41, 41)\n", + " 114984 [cnmf.py: fit():449] [1655554] (8000, 41, 41)\n", + " 114979 [cnmf.py: fit():477] [1655508] using 400 pixels per process\n", + " 114990 [cnmf.py: fit():449] [1655510] (8000, 41, 41)\n", + " 114975 [cnmf.py: fit():449] [1655520] (8000, 41, 41)\n", + " 114975 [cnmf.py: fit():466] [1655524] Using 1 processes\n", + " 115044 [cnmf.py: fit():466] [1655482] Using 1 processes\n", + " 115044 [cnmf.py: fit():477] [1655502] using 400 pixels per process\n", + " 115044 [cnmf.py: fit():436] [1655564] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 115045 [cnmf.py: fit():466] [1655549] Using 1 processes\n", + " 115045 [cnmf.py: fit():449] [1655505] (8000, 40, 41)\n", + " 115045 [cnmf.py: fit():466] [1655474] Using 1 processes\n", + " 115046 [cnmf.py: fit():466] [1655475] Using 1 processes\n", + " 115046 [cnmf.py: fit():466] [1655517] Using 1 processes\n", + " 115046 [cnmf.py: fit():466] [1655506] Using 1 processes\n", + " 115047 [cnmf.py: fit():436] [1655528] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", + " 115047 [cnmf.py: fit():466] [1655499] Using 1 processes\n", + " 115047 [cnmf.py: fit():466] [1655477] Using 1 processes\n", + " 115047 [cnmf.py: fit():449] [1655542] (8000, 41, 41)\n", + " 115048 [cnmf.py: fit():466] [1655522] Using 1 processes\n", + " 115048 [cnmf.py: fit():478] [1655541] using 5000 block_size_temp\n", + " 115048 [cnmf.py: fit():478] [1655536] using 5000 block_size_temp\n", + " 115049 [cnmf.py: fit():466] [1655511] Using 1 processes\n", + " 115049 [cnmf.py: fit():477] [1655519] using 400 pixels per process\n", + " 115049 [cnmf.py: fit():466] [1655476] Using 1 processes\n", + " 115049 [cnmf.py: fit():466] [1655544] Using 1 processes\n", + "IOPub message rate exceeded.\n", + "The Jupyter server will temporarily stop sending output\n", + "to the client in order to avoid crashing it.\n", + "To change this limit, set the config variable\n", + "`--ServerApp.iopub_msg_rate_limit`.\n", + "\n", + "Current values:\n", + "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", + "ServerApp.rate_limit_window=3.0 (secs)\n", + "\n" + ] + } + ], + "source": [ + "fname_new = params_movie['fname']\n", + "\n", + "\n", + "# %% RUN ANALYSIS\n", + "c, dview, n_processes = cm.cluster.setup_cluster(\n", + " backend='local', n_processes=None, single_thread=False)\n", + "\n", + "\n", + "# %% LOAD MEMMAP FILE\n", + "# fname_new='Yr_d1_501_d2_398_d3_1_order_F_frames_369_.mmap'\n", + "Yr, dims, T = cm.load_memmap(fname_new)\n", + "d1, d2 = dims\n", + "images = np.reshape(Yr.T, [T] + list(dims), order='F')\n", + "# TODO: needinfo\n", + "Y = np.reshape(Yr, dims + (T,), order='F')\n", + "m_images = cm.movie(images)\n", + "\n", + "# TODO: show screenshot 10\n", + "# %% correlation image\n", + "if m_images.shape[0] < 10000:\n", + " Cn = m_images.local_correlations(\n", + " swap_dim=params_movie['swap_dim'], frames_per_chunk=1500)\n", + " Cn[np.isnan(Cn)] = 0\n", + "else:\n", + " Cn = np.array(cm.load(('/'.join(fname_new.split('/') \n", + " [:-3] + ['projections', 'correlation_image_better.tif'])))).squeeze()\n", + "pl.imshow(Cn, cmap='gray', vmax=.95)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:24: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n", + "<>:24: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n", + "/tmp/ipykernel_1655335/2106226496.py:24: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n", + " if params_movie['init_method'] is not 'sparse_nmf':\n", + " 62256 [params.py: set():1006] [1655335] Changing key init_batch in group online from 200 to 8000\n", + " 62258 [cnmf.py: fit():449] [1655335] (8000, 512, 512)\n", + " 62258 [cnmf.py: fit():466] [1655335] Using 1 processes\n", + " 62258 [cnmf.py: fit():477] [1655335] using 4000 pixels per process\n", + " 62258 [cnmf.py: fit():478] [1655335] using 5000 block_size_temp\n", + " 62259 [params.py: set():1006] [1655335] Changing key n_pixels_per_process in group preprocess from 4000 to 400\n", + " 62259 [params.py: set():1006] [1655335] Changing key n_pixels_per_process in group spatial from 4000 to 400\n", + " 62277 [map_reduce.py: run_CNMF_patches():227] [1655335] Patch size: (40, 40)\n" + ] + } + ], + "source": [ + "\n", + "# %% some parameter settings\n", + "# order of the autoregressive fit to calcium imaging in general one (slow gcamps) or two (fast gcamps fast scanning)\n", + "p = params_movie['p']\n", + "# merging threshold, max correlation allowed\n", + "merge_thresh = params_movie['merge_thresh']\n", + "# half-size of the patches in pixels. rf=25, patches are 50x50\n", + "rf = params_movie['rf']\n", + "# amounpl.it of overlap between the patches in pixels\n", + "stride_cnmf = params_movie['stride_cnmf']\n", + "# number of components per patch\n", + "K = params_movie['K']\n", + "# if dendritic. In this case you need to set init_method to sparse_nmf\n", + "is_dendrites = params_movie['is_dendrites']\n", + "# iinit method can be greedy_roi for round shapes or sparse_nmf for denritic data\n", + "init_method = params_movie['init_method']\n", + "# expected half size of neurons\n", + "gSig = params_movie['gSig']\n", + "# this controls sparsity\n", + "alpha_snmf = params_movie['alpha_snmf']\n", + "# frame rate of movie (even considering eventual downsampling)\n", + "final_frate = params_movie['final_frate']\n", + "\n", + "if params_movie['is_dendrites'] == True:\n", + " if params_movie['init_method'] is not 'sparse_nmf':\n", + " raise Exception('dendritic requires sparse_nmf')\n", + " if params_movie['alpha_snmf'] is None:\n", + " raise Exception('need to set a value for alpha_snmf')\n", + "# %% Extract spatial and temporal components on patches\n", + "t1 = time.time()\n", + "# TODO: todocument\n", + "# TODO: warnings 3\n", + "cnm = cnmf.CNMF(n_processes=1, k=K, gSig=gSig, merge_thresh=params_movie['merge_thresh'], p=params_movie['p'],\n", + " dview=dview, rf=rf, stride=stride_cnmf, memory_fact=1,\n", + " method_init=init_method, alpha_snmf=alpha_snmf, only_init_patch=params_movie[\n", + " 'only_init_patch'],\n", + " gnb=params_movie['gnb'], method_deconvolution='oasis', border_pix=params_movie['crop_pix'], low_rank_background=params_movie['low_rank_background'])\n", + "cnm = cnm.fit(images)\n", + "\n", + "A_tot = cnm.A\n", + "C_tot = cnm.C\n", + "YrA_tot = cnm.YrA\n", + "b_tot = cnm.b\n", + "f_tot = cnm.f\n", + "sn_tot = cnm.sn\n", + "print(('Number of components:' + str(A_tot.shape[-1])))\n", + "# %%\n", + "pl.figure()\n", + "# TODO: show screenshot 12`\n", + "# TODO : change the way it is used\n", + "crd = plot_contours(A_tot, Cn, thr=params_display['thr_plot'])\n", + "# %% DISCARD LOW QUALITY COMPONENT\n", + "final_frate = params_movie['final_frate']\n", + "# threshold on space consistency\n", + "r_values_min = params_movie['r_values_min_patch']\n", + "# threshold on time variability\n", + "fitness_min = params_movie['fitness_delta_min_patch']\n", + "# threshold on time variability (if nonsparse activity)\n", + "fitness_delta_min = params_movie['fitness_delta_min_patch']\n", + "Npeaks = params_movie['Npeaks']\n", + "traces = C_tot + YrA_tot\n", + "# TODO: todocument\n", + "idx_components, idx_components_bad = estimate_components_quality(\n", + " traces, Y, A_tot, C_tot, b_tot, f_tot, final_frate=final_frate, Npeaks=Npeaks, r_values_min=r_values_min,\n", + " fitness_min=fitness_min, fitness_delta_min=fitness_delta_min)\n", + "print(('Keeping ' + str(len(idx_components)) +\n", + " ' and discarding ' + str(len(idx_components_bad))))\n", + "# %%\n", + "# TODO: show screenshot 13\n", + "pl.figure()\n", + "crd = plot_contours(\n", + " A_tot.tocsc()[:, idx_components], Cn, thr=params_display['thr_plot'])\n", + "# %%\n", + "A_tot = A_tot.tocsc()[:, idx_components]\n", + "C_tot = C_tot[idx_components]\n", + "# %% rerun updating the components to refine\n", + "t1 = time.time()\n", + "cnm = cnmf.CNMF(n_processes=1, k=A_tot.shape, gSig=gSig, merge_thresh=merge_thresh, p=p, dview=dview, Ain=A_tot,\n", + " Cin=C_tot, b_in=b_tot,\n", + " f_in=f_tot, rf=None, stride=None, method_deconvolution='oasis', gnb=params_movie['gnb'],\n", + " low_rank_background=params_movie['low_rank_background'], update_background_components=params_movie['update_background_components'])\n", + "\n", + "cnm = cnm.fit(images)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "A, C, b, f, YrA, sn = cnm.A, cnm.C, cnm.b, cnm.f, cnm.YrA, cnm.sn\n", + "# %% again recheck quality of components, stricter criteria\n", + "final_frate = params_movie['final_frate']\n", + "# threshold on space consistency\n", + "r_values_min = params_movie['r_values_min_full']\n", + "fitness_min = params_movie['fitness_min_full'] # threshold on time variability\n", + "# threshold on time variability (if nonsparse activity)\n", + "fitness_delta_min = params_movie['fitness_delta_min_full']\n", + "Npeaks = params_movie['Npeaks']\n", + "traces = C + YrA\n", + "idx_components, idx_components_bad, fitness_raw, fitness_delta, r_values = estimate_components_quality(\n", + " traces, Y, A, C, b, f, final_frate=final_frate, Npeaks=Npeaks, r_values_min=r_values_min, fitness_min=fitness_min,\n", + " fitness_delta_min=fitness_delta_min, return_all=True)\n", + "print(' ***** ')\n", + "print((len(traces)))\n", + "print((len(idx_components)))\n", + "# %% save results\n", + "np.savez(os.path.join(os.path.split(fname_new)[0], os.path.split(fname_new)[1][:-4] + 'results_analysis.npz'), Cn=Cn, fname_new=fname_new,\n", + " A=A,\n", + " C=C, b=b, f=f, YrA=YrA, sn=sn, d1=d1, d2=d2, idx_components=idx_components,\n", + " idx_components_bad=idx_components_bad,\n", + " fitness_raw=fitness_raw, fitness_delta=fitness_delta, r_values=r_values)\n", + "# we save it\n", + "# %%\n", + "# TODO: show screenshot 14\n", + "pl.subplot(1, 2, 1)\n", + "crd = plot_contours(A.tocsc()[:, idx_components],\n", + " Cn, thr=params_display['thr_plot'])\n", + "pl.subplot(1, 2, 2)\n", + "crd = plot_contours(A.tocsc()[:, idx_components_bad],\n", + " Cn, thr=params_display['thr_plot'])\n", + "# %%\n", + "# TODO: needinfo\n", + "view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, idx_components]), C[idx_components, :], b, f, dims[0], dims[1],\n", + " YrA=YrA[idx_components, :], img=Cn)\n", + "# %%\n", + "view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, idx_components_bad]), C[idx_components_bad, :], b, f, dims[0],\n", + " dims[1], YrA=YrA[idx_components_bad, :], img=Cn)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load Data\n", + "params_display = {\n", + " 'downsample_ratio': .2,\n", + " 'thr_plot': 0.8\n", + "}\n", + "try:\n", + " fname_new = fname_new[()]\n", + "except:\n", + " pass\n", + "#analysis_file = '/mnt/ceph/neuro/jeremie_analysis/neurofinder.03.00.test/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_._results_analysis.npz'\n", + "with np.load(os.path.join(os.path.split(fname_new)[0], os.path.split(fname_new)[1][:-4] + 'results_analysis.npz')) as ld:\n", + " print(ld.keys())\n", + " locals().update(ld)\n", + " dims_off = d1, d2\n", + " A = scipy.sparse.coo_matrix(A[()])\n", + " dims = (d1, d2)\n", + " gSig = params_movie['gSig']\n", + " fname_new = fname_new[()]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from caiman.components_evaluation import evaluate_components_CNN\n", + "predictions, final_crops = evaluate_components_CNN(\n", + " A, dims, gSig, model_name='model/cnn_model')\n", + "#%%\n", + "cm.movie(final_crops).play(gain=3, magnification=6, fr=5)\n", + "#%%\n", + "cm.movie(np.squeeze(final_crops[np.where(predictions[:, 1] >= 0.5)[0]])).play(\n", + " gain=2., magnification=5, fr=5)\n", + "#%%\n", + "cm.movie(np.squeeze(final_crops[np.where(predictions[:, 0] >= 0.5)[0]])).play(\n", + " gain=2., magnification=5, fr=5)\n", + "#%%\n", + "thresh = .5\n", + "idx_components_cnn = np.where(predictions[:, 1] >= thresh)[0]\n", + "idx_components_bad_cnn = np.where(predictions[:, 0] > (1 - thresh))[0]\n", + "\n", + "print(' ***** ')\n", + "print((len(final_crops)))\n", + "print((len(idx_components_cnn)))\n", + "# print((len(idx_blobs)))\n", + "#%%\n", + "idx_components_r = np.where((r_values >= .5))[0]\n", + "idx_components_raw = np.where(fitness_raw < -5)[0]\n", + "idx_components_delta = np.where(fitness_delta < -5)[0]\n", + "#idx_and_condition_1 = np.where((r_values >= .65) & ((fitness_raw < -20) | (fitness_delta < -20)) )[0]\n", + "\n", + "idx_components = np.union1d(idx_components_r, idx_components_raw)\n", + "idx_components = np.union1d(idx_components, idx_components_delta)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "idx_components_bad = np.setdiff1d(list(range(len(r_values))), idx_components)\n", + "\n", + "print(' ***** ')\n", + "print((len(r_values)))\n", + "print((len(idx_components)))\n", + "#%%\n", + "pl.subplot(1, 2, 1)\n", + "crd = plot_contours(A.tocsc()[:, idx_components],\n", + " Cn, thr=params_display['thr_plot'], vmax=0.35)\n", + "pl.subplot(1, 2, 2)\n", + "crd = plot_contours(A.tocsc()[:, idx_components_bad],\n", + " Cn, thr=params_display['thr_plot'], vmax=0.35)\n", + "#%%\n", + "c, dview, n_processes = cm.cluster.setup_cluster(\n", + " backend='local', n_processes=None, single_thread=False)\n", + "\n", + "#%%\n", + "gt_file = os.path.join(os.path.split(fname_new)[0], os.path.split(\n", + " fname_new)[1][:-4] + 'match_masks.npz')\n", + "with np.load(gt_file) as ld:\n", + " print(ld.keys())\n", + " locals().update(ld)\n", + " A_gt = scipy.sparse.coo_matrix(A_gt[()])\n", + " dims = (d1, d2)\n", + "#%%\n", + "view_patches_bar(Yr, scipy.sparse.coo_matrix(A_gt.toarray()[\n", + " :, idx_components_gt]), C_gt[idx_components_gt], b, f, dims[0], dims[1], YrA=YrA_gt[idx_components_gt], img=Cn)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.preprocessing import normalize\n", + "\n", + "dist_A = (normalize(A_gt.tocsc()[:, idx_components_gt], axis=0).T.dot(\n", + " normalize(A.tocsc()[:, :], axis=0))).toarray()\n", + "dist_C = normalize(C_gt[idx_components_gt], axis=1).dot(\n", + " normalize(C[:], axis=1).T)\n", + "dist_A = dist_A * (dist_A > 0)\n", + "\n", + "pl.figure(figsize=(30, 20))\n", + "tp_gt, tp_comp, fn_gt, fp_comp, performance_cons_off = cm.base.rois.nf_match_neurons_in_binary_masks(A_gt.toarray()[:, idx_components_gt].reshape([dims[0], dims[1], -1], order='F').transpose([2, 0, 1]),\n", + " A.toarray()[:, :].reshape([dims[0], dims[1], -1], order='F').transpose([2, 0, 1]), thresh_cost=.7, min_dist=10,\n", + " print_assignment=False, plot_results=True, Cn=Cn, labels=['GT', 'Offline'], D=[1 - dist_A * (dist_C > .8)])\n", + "pl.rcParams['pdf.fonttype'] = 42\n", + "font = {'family': 'Myriad Pro',\n", + " 'weight': 'regular',\n", + " 'size': 20}\n", + "pl.rc('font', **font)\n", + "#%%\n", + "# TODO: needinfo\n", + "idx_final = tp_comp[np.where(dist_A[tp_gt, tp_comp] > 0.7)[0]]\n", + "view_patches_bar(Yr, scipy.sparse.coo_matrix(A.toarray()[\n", + " :, idx_final]), C[idx_final], b, f, dims[0], dims[1], YrA=YrA[idx_final], img=Cn)\n", + "#%%\n", + "view_patches_bar(Yr, scipy.sparse.coo_matrix(A.toarray()[\n", + " :, fp_comp]), C[fp_comp], b, f, dims[0], dims[1], YrA=YrA[fp_comp], img=Cn)\n", + "#%%\n", + "view_patches_bar(Yr, scipy.sparse.coo_matrix(A_gt.toarray()[\n", + " :, fn_gt]), C_gt[fn_gt], b_gt, f_gt, dims[0], dims[1], YrA=YrA_gt[fn_gt], img=Cn)\n", + "#%%\n", + "pl.hist(r_values[tp_comp], 30)\n", + "#%%\n", + "np.savez(os.path.join(os.path.split(fname_new)[0], os.path.split(fname_new)[1][:-4] + '_training_set.npz'), fname_new=fname_new,\n", + " A_seeded=A_gt.tocsc()[\n", + " :, idx_components_gt], C_seeded=C_gt[idx_components_gt], YrA_seeded=YrA_gt[idx_components_gt],\n", + " A_matched=A.tocsc()[\n", + " :, idx_final], C_matched=C[idx_final], YrA_matched=YrA[idx_final],\n", + " A_unmatched=A_gt.tocsc()[\n", + " :, fn_gt], C_unmatched=C_gt[fn_gt], YrA_unmatched=YrA_gt[fn_gt],\n", + " A_negative=A.tocsc()[\n", + " :, fp_comp], C_negative=C[fp_comp], YrA_negative=YrA[fp_comp],\n", + " r_values=r_values, fitness_delta=fitness_delta, fitness_raw=fitness_raw, Cn=Cn, dims=dims\n", + ")\n", + "#%%\n", + "with np.load(os.path.join(os.path.split(fname_new)[0], os.path.split(fname_new)[1][:-4] + '_training_set.npz')) as ld:\n", + " print(ld.keys())\n", + " locals().update(ld)\n", + " fname_new = fname_new[()]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "thr = 0.98\n", + "# TODO: show screenshot 14\n", + "pl.subplot(1, 3, 1)\n", + "crd = plot_contours(A_matched[()], Cn, thr=thr)\n", + "pl.subplot(1, 3, 2)\n", + "crd = plot_contours(A_unmatched[()], Cn, thr=thr)\n", + "pl.subplot(1, 3, 3)\n", + "crd = plot_contours(A_negative[()], Cn, thr=thr)\n", + "#%%\n", + "pl.subplot(1, 3, 1)\n", + "crd = pl.imshow(A_matched[()].sum(1).reshape(\n", + " dims, order='F'), vmax=A_matched[()].max() * .2)\n", + "pl.subplot(1, 3, 2)\n", + "crd = pl.imshow(A_unmatched[()].sum(1).reshape(\n", + " dims, order='F'), vmax=A_unmatched[()].max() * .2)\n", + "pl.subplot(1, 3, 3)\n", + "crd = pl.imshow(A_negative[()].sum(1).reshape(\n", + " dims, order='F'), vmax=A_negative[()].max() * .2)\n", + "#%%\n", + "masks_sue = scipy.io.loadmat('/mnt/xfs1/home/agiovann/Downloads/yuste_sue_masks.mat')\n", + "\n", + "#%%\n", + "with h5py.File('/mnt/xfs1/home/agiovann/Downloads/yuste_1.protoroi.mat')as f:\n", + " print(f.keys())\n", + " print(list(f['repository']))\n", + " proto = f['prototypes']\n", + " print(list(proto['params']))\n", + " print(proto.keys())\n", + " spatial = proto['spatial']\n", + " print(spatial.keys())\n", + " locals().update((dict(spatial.attrs.iteritems())))\n", + " locals().update({k: np.array(l) for k, l in spatial.iteritems()})" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "caiman_pytorch", + "language": "python", + "name": "caiman_pytorch" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/caiman/train/prepare_training_set.ipynb b/caiman/train/prepare_training_set.ipynb new file mode 100644 index 000000000..622720957 --- /dev/null +++ b/caiman/train/prepare_training_set.ipynb @@ -0,0 +1,318 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import cv2\n", + "import numpy as np\n", + "import os\n", + "\n", + "import caiman as cm\n", + "from caiman.utils.utils import download_demo\n", + "from caiman.base.rois import extract_binary_masks_blob\n", + "from caiman.utils.visualization import plot_contours, view_patches_bar\n", + "from caiman.source_extraction.cnmf import cnmf as cnmf\n", + "from caiman.motion_correction import MotionCorrect, tile_and_correct, motion_correction_piecewise \n", + "from caiman.components_evaluation import estimate_components_quality, evaluate_components\n", + "from caiman.tests.comparison import comparison\n", + "\n", + "from caiman.base.rois import com\n", + "from sklearn.preprocessing import normalize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "inputs = [{'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.03.00.test/images/final_map/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_.mmap', 'gSig': [8, 8]},\n", + " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.04.00.test/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_3000_.mmap',\n", + " 'gSig': [5, 5]},\n", + " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.02.00/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_8000_.mmap',\n", + " 'gSig': [5, 5]},\n", + " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/yuste.Single_150u/images/final_map/Yr_d1_200_d2_256_d3_1_order_C_frames_3000_.mmap',\n", + " 'gSig': [5, 5]},\n", + " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.00.00/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_2936_.mmap',\n", + " 'gSig': [6, 6]},\n", + " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.01.01/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_1825_.mmap',\n", + " 'gSig': [6, 6]},\n", + " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/k53_20160530/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_116043_.mmap',\n", + " 'gSig': [6, 6]},\n", + " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/J115_2015-12-09_L01_ELS/images/final_map/Yr_d1_463_d2_472_d3_1_order_C_frames_90000_.mmap',\n", + " 'gSig': [7, 7]},\n", + " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/J123_2015-11-20_L01_0/images/final_map/Yr_d1_458_d2_477_d3_1_order_C_frames_41000_.mmap',\n", + " 'gSig': [12, 12]},\n", + " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/Jan-AMG_exp3_001/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_115897_.mmap',\n", + " 'gSig': [7, 7]} ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for dc in inputs[:]:\n", + " fname = dc['fname']\n", + " print(fname)\n", + " gSig = dc['gSig']\n", + " gt_file = os.path.join(os.path.split(fname)[0], os.path.split(fname)[\n", + " 1][:-4] + 'match_masks.npz')\n", + " # LOAD DATA\n", + " #analysis_file = '/mnt/ceph/neuro/jeremie_analysis/neurofinder.03.00.test/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_._results_analysis.npz'\n", + " with np.load(os.path.join(os.path.split(fname)[0], os.path.split(fname)[1][:-4] + 'results_analysis.npz'), encoding='latin1') as ld:\n", + " print(ld.keys())\n", + " locals().update(ld)\n", + " dims_off = d1, d2\n", + " A = scipy.sparse.coo_matrix(A[()])\n", + " dims = (d1, d2)\n", + "\n", + " gt_file = os.path.join(os.path.split(fname)[0], os.path.split(fname)[\n", + " 1][:-4] + 'match_masks.npz')\n", + " with np.load(gt_file, encoding='latin1') as ld:\n", + " print(ld.keys())\n", + " locals().update(ld)\n", + " A_gt = scipy.sparse.coo_matrix(A_gt[()])\n", + " dims = (d1, d2)\n", + "\n", + " pl.figure()\n", + " dist_A = (normalize(A_gt.tocsc()[:, idx_components_gt], axis=0).T.dot(\n", + " normalize(A.tocsc()[:, :], axis=0))).toarray()\n", + " dist_C = normalize(C_gt[idx_components_gt], axis=1).dot(\n", + " normalize(C[:], axis=1).T)\n", + " dist_A = dist_A * (dist_A > 0)\n", + "\n", + " pl.figure(figsize=(30, 20))\n", + " tp_gt, tp_comp, fn_gt, fp_comp, performance_cons_off = cm.base.rois.nf_match_neurons_in_binary_masks(A_gt.toarray()[:, idx_components_gt].reshape([dims[0], dims[1], -1], order='F').transpose([2, 0, 1]),\n", + " A.toarray()[:, :].reshape([dims[0], dims[1], -1], order='F').transpose([2, 0, 1]), thresh_cost=.7, min_dist=10,\n", + " print_assignment=False, plot_results=False, Cn=Cn, labels=['GT', 'Offline'], D=[1 - dist_A * (dist_C > .8)])\n", + " pl.rcParams['pdf.fonttype'] = 42\n", + " font = {'family': 'Myriad Pro',\n", + " 'weight': 'regular',\n", + " 'size': 20}\n", + " pl.rc('font', **font)\n", + " idx_final = tp_comp[np.where(dist_A[tp_gt, tp_comp] > 0.7)[0]]\n", + " np.savez(os.path.join(os.path.split(fname)[0], os.path.split(fname)[1][:-4] + '_training_set_minions.npz'), fname_new=fname,\n", + " A_seeded=A_gt.tocsc()[\n", + " :, idx_components_gt], C_seeded=C_gt[idx_components_gt], YrA_seeded=YrA_gt[idx_components_gt],\n", + " A_matched=A.tocsc()[\n", + " :, idx_final], C_matched=C[idx_final], YrA_matched=YrA[idx_final],\n", + " A_unmatched=A_gt.tocsc()[\n", + " :, fn_gt], C_unmatched=C_gt[fn_gt], YrA_unmatched=YrA_gt[fn_gt],\n", + " A_negative=A.tocsc()[\n", + " :, fp_comp], C_negative=C[fp_comp], YrA_negative=YrA[fp_comp],\n", + " r_values=r_values, fitness_delta=fitness_delta, fitness_raw=fitness_raw, Cn=Cn, dims=dims, gSig=gSig)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "training_files = [os.path.join(dp, f) for dp, dn, filenames in os.walk('/mnt/ceph/data/neuro/caiman/') for f in filenames if 'set_minions.npz' in f]\n", + "print(training_files)\n", + "crop_size = 50\n", + "half_crop = crop_size // 2\n", + "from sklearn.preprocessing import normalize\n", + "id_file = 0\n", + "reference_gSig_neuron = 5\n", + "#folder = '/mnt/xfs1/home/agiovann/SOFTWARE/CaImAn/images_examples'\n", + "all_masks_gt = []\n", + "labels_gt = []\n", + "traces_gt = []\n", + "for fl in training_files:\n", + "\n", + " with np.load(fl) as ld:\n", + " print(ld.keys())\n", + " locals().update(ld)\n", + " zoom = reference_gSig_neuron / gSig[0]\n", + " fname_new = fname_new[()]\n", + " name_base = os.path.split(fname_new)[-1][:-5]\n", + "# pl.figure()\n", + "# pl.subplot(1, 3, 1)\n", + "# pl.imshow(A_matched[()].sum(1).reshape(dims,order='F'), vmax = A_matched[()].max()*.2)\n", + "# pl.subplot(1, 3, 2)\n", + "# pl.imshow(A_unmatched[()].sum(1).reshape(dims,order='F'), vmax = A_unmatched[()].max()*.2)\n", + "# pl.subplot(1, 3, 3)\n", + "# pl.imshow(A_negative[()].sum(1).reshape(dims,order='F'), vmax = A_negative[()].max()*.2)\n", + "\n", + "# coms = com(scipy.sparse.coo_matrix(A_matched[()]), dims[0], dims[1])\n", + " if 'sparse' in str(type(A_matched[()])):\n", + " A_matched = A_matched[()].toarray()\n", + " A_unmatched = A_unmatched[()].toarray()\n", + " A_negative = A_negative[()].toarray()\n", + "\n", + " A_matched = normalize(A_matched, axis=0)\n", + " A_unmatched = normalize(A_unmatched, axis=0)\n", + " A_negative = normalize(A_negative, axis=0)\n", + "\n", + " masks_gt = np.concatenate([A_matched.reshape(tuple(dims) + (-1,), order='F').transpose([2, 0, 1]), A_unmatched.reshape(tuple(\n", + " dims) + (-1,), order='F').transpose([2, 0, 1]), A_negative.reshape(tuple(dims) + (-1,), order='F').transpose([2, 0, 1])], axis=0)\n", + " labels_gt = np.concatenate([labels_gt, np.ones(\n", + " A_matched.shape[-1]), np.ones(A_unmatched.shape[-1]), np.zeros(A_negative.shape[-1])])\n", + " traces_gt = traces_gt + list(YrA_matched + C_matched) + list(\n", + " C_unmatched + YrA_unmatched) + list(C_negative + YrA_negative)\n", + "# r_vals_gt = np.concatenate([r_vals_gt,])\n", + "# raw_fitness_gt = np.concatenate([raw_fitness_gt,])\n", + "# delta_fitness_gt = np.concatenate([delta_fitness_gt,])\n", + "\n", + " coms = [scipy.ndimage.center_of_mass(mm) for mm in masks_gt]\n", + " coms = np.maximum(coms, half_crop)\n", + " coms = np.array([np.minimum(cm, dims - half_crop) for cm in coms])\n", + "\n", + " count_neuro = 0\n", + " for com, img in zip(coms, masks_gt):\n", + " # if zoom and zoom[counter]==1:\n", + " # if zoom>1:\n", + " #\n", + " # elif zoom<1:\n", + " com = com.astype(int)\n", + " # Crop from x, y, w, h -> 100, 200, 300, 400\n", + " crop_img = img[com[0] - half_crop:com[0] + half_crop,\n", + " com[1] - half_crop:com[1] + half_crop].copy()\n", + "# crop_img = cv2.resize(crop_img,dsize=None,fx=zoom[id_file],fy=zoom[id_file])\n", + "# newshape = np.array(crop_img.shape)//2\n", + "# crop_img = crop_img[newshape[0]-half_crop:newshape[0]+half_crop,newshape[0]-half_crop:newshape[0]+half_crop]\n", + "\n", + " borders = np.array(crop_img.shape)\n", + " img_tmp = np.zeros_like(crop_img)\n", + " crop_img = cv2.resize(crop_img, dsize=None, fx=zoom, fy=zoom)\n", + "\n", + " deltaw = (half_crop * 2 - crop_img.shape[0]) // 2\n", + " deltah = (half_crop * 2 - crop_img.shape[1]) // 2\n", + " img_tmp[deltaw:deltaw + crop_img.shape[0],\n", + " deltah:deltah + crop_img.shape[1]] = crop_img\n", + " crop_img = img_tmp\n", + " crop_img = crop_img / np.linalg.norm(crop_img)\n", + " all_masks_gt.append(crop_img[np.newaxis, :, :, np.newaxis])\n", + " augment_test = False\n", + " cv2.imshow(\"cropped\", cv2.resize(crop_img, (480, 480)) * 10)\n", + " cv2.waitKey(1)\n", + " if augment_test:\n", + " datagen = ImageDataGenerator(\n", + " # featurewise_center=True,\n", + " # featurewise_std_normalization=True,\n", + " shear_range=0.3,\n", + " rotation_range=360,\n", + " width_shift_range=0.2,\n", + " height_shift_range=0.2,\n", + " zoom_range=[.5, 2],\n", + " horizontal_flip=True,\n", + " vertical_flip=True,\n", + " random_mult_range=[.25, 2]\n", + " )\n", + "\n", + " count_neuro += 1\n", + " for x_batch, y_batch in datagen.flow(np.repeat(crop_img[np.newaxis, :, :], 10, 0)[:, :, :, None], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0], batch_size=10):\n", + " print(y_batch)\n", + " for b_img in x_batch:\n", + " cv2.imshow(\"cropped\", cv2.resize(\n", + " b_img.squeeze(), (480, 480)) * 10)\n", + " cv2.waitKey(300)\n", + " count_neuro += 1\n", + " print(count_neuro)\n", + " break\n", + "\n", + "\n", + "# crop_img = cv2.resize(crop_img,dsize=None,fx=2,fy=2)\n", + "# newshape = np.array(crop_img.shape)//2\n", + "# crop_img = crop_img[newshape[0]-half_crop:newshape[0]+half_crop,newshape[0]-half_crop:newshape[0]+half_crop]\n", + " # NOTE: its img[y: y + h, x: x + w] and *not* img[x: x + w, y: y + h]\n", + "\n", + " id_file += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "all_masks_gt = np.vstack(all_masks_gt)\n", + "#%%\n", + "cm.movie(np.squeeze(all_masks_gt[labels_gt == 0])).play(\n", + " gain=3., magnification=10)\n", + "#%%\n", + "np.savez('ground_truth_components_minions.npz',\n", + " all_masks_gt=all_masks_gt, labels_gt=labels_gt, traces_gt=traces_gt)\n", + "#%%\n", + "import itertools\n", + "\n", + "\n", + "def grouper(n, iterable, fillvalue=None):\n", + " \"grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx\"\n", + " args = [iter(iterable)] * n\n", + " return itertools.zip_longest(*args, fillvalue=fillvalue)\n", + "\n", + "\n", + "#%% curate once more. Remove wrong negatives\n", + "negatives = np.where(labels_gt == 1)[0]\n", + "wrong = []\n", + "count = 0\n", + "for a in grouper(50, negatives):\n", + " print(np.max(a))\n", + " print(count)\n", + " a = np.array(a)[np.array(a) > 0].astype(int)\n", + " count += 1\n", + " img_mont_ = all_masks_gt[np.array(a)].squeeze()\n", + " shps_img = img_mont_.shape\n", + " img_mont = montage2d(img_mont_)\n", + " shps_img_mont = np.array(img_mont.shape) // 50\n", + " pl.figure(figsize=(20, 30))\n", + " pl.imshow(img_mont)\n", + " inp = pl.ginput(n=0, timeout=-100000)\n", + " imgs_to_exclude = []\n", + " inp = np.ceil(np.array(inp) / 50).astype(int) - 1\n", + " if len(inp) > 0:\n", + "\n", + " imgs_to_exclude = img_mont_[np.ravel_multi_index(\n", + " [inp[:, 1], inp[:, 0]], shps_img_mont)]\n", + "# pl.imshow(montage2d(imgs_to_exclude))\n", + " wrong.append(np.array(a)[np.ravel_multi_index(\n", + " [inp[:, 1], inp[:, 0]], shps_img_mont)])\n", + " np.save('temp_label_pos_minions.npy', wrong)\n", + " pl.close()\n", + "#%%\n", + "pl.imshow(montage2d(all_masks_gt[np.concatenate(wrong)].squeeze()))\n", + "#%%\n", + "lab_pos_wrong = np.load('temp_label_pos_minions.npy')\n", + "lab_neg_wrong = np.load('temp_label_neg_plus_minions.npy')\n", + "\n", + "labels_gt_cur = labels_gt.copy()\n", + "labels_gt_cur[np.concatenate(lab_pos_wrong)] = 0\n", + "labels_gt_cur[np.concatenate(lab_neg_wrong)] = 1\n", + "\n", + "np.savez('ground_truth_comoponents_curated_minions.npz',\n", + " all_masks_gt=all_masks_gt, labels_gt_cur=labels_gt_cur)\n", + "#%%\n", + "pl.imshow(montage2d(all_masks_gt[labels_gt_cur == 0].squeeze()))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "caiman_pytorch", + "language": "python", + "name": "caiman_pytorch" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/caiman/train/train_cnn_model_keras.ipynb b/caiman/train/train_cnn_model_keras.ipynb new file mode 100644 index 000000000..e908a26c2 --- /dev/null +++ b/caiman/train/train_cnn_model_keras.ipynb @@ -0,0 +1,500 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training the CNN Model for the 2D Spatial Components (Keras Version)\n", + "\n", + "This notebook will help to demonstrate how to train the CNN Model used in CaImAn to evaluate the shape of (2p) spatial components using the Keras API.\n", + "\n", + "The basic function for this is caiman.train.train_cnn_model_keras.keras_cnn_model(). It takes it the number of classes to build of a CNN model (based on a tutorial on the CIFAR dataset). The other functions, caiman.train.train_cnn_model.data_generation(), takes as input the model, the training and validation datasets, and the parameters for the model to train the model. caiman.train.train_cnn_model_keras.save_model() and caiman.train.train_cnn_model_keras.load_model() save and retrieve the model and weights of the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-06 20:34:40.739703: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-08-06 20:34:40.770178: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import os\n", + "import keras \n", + "from keras.layers import Input, Conv2D, Activation, MaxPooling2D, Dropout, Flatten, Dense \n", + "from keras.models import save_model, load_model \n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.utils import class_weight as cw\n", + "\n", + "import caiman as cm\n", + "from caiman.paths import caiman_datadir\n", + "from caiman.train.train_cnn_model_keras import cnn_model_keras, save_model_keras, load_model_keras\n", + "\n", + "os.environ[\"KERAS_BACKEND\"] = \"torch\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initalizing the Parameters for the Model " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 128\n", + "num_classes = 2\n", + "epochs = 1000 #Can be upgraded to 5000\n", + "test_fraction = 0.25\n", + "augmentation = False \n", + "img_rows, img_cols = 50, 50 #input image dimensions\n", + "\n", + "#Note: Augmentation is currently not working " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the Dataset of the Model " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "with np.load('/mnt/ceph/data/neuro/caiman/data_minions/ground_truth_components_curated_minions.npz') as ld:\n", + " all_masks_gt = ld['all_masks_gt']\n", + " labels_gt = ld['labels_gt_cur']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Training and Validation Set for the Model " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_train shape: (6771, 50, 50, 1)\n", + "6771 train samples\n", + "2257 test samples\n" + ] + } + ], + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(\n", + "all_masks_gt, labels_gt, test_size=test_fraction)\n", + "\n", + "# class_weight = cw.compute_class_weight(class_weight='balanced', classes=np.unique(y_train), y=y_train)\n", + "\n", + "if keras.config.image_data_format() == 'channels_first':\n", + " x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n", + " x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n", + " input_shape = (1, img_rows, img_cols)\n", + "else:\n", + " x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n", + " x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n", + " input_shape = (img_rows, img_cols, 1)\n", + " \n", + "x_train = x_train.astype('float32')\n", + "x_test = x_test.astype('float32')\n", + "print('x_train shape:', x_train.shape)\n", + "print(x_train.shape[0], 'train samples')\n", + "print(x_test.shape[0], 'test samples')\n", + "\n", + "# convert class vectors to binary class matrices\n", + "y_train = keras.utils.to_categorical(y_train, num_classes)\n", + "y_test = keras.utils.to_categorical(y_test, num_classes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build and Evaluate the Model " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 481ms/step - accuracy: 0.5579 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 2/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 460ms/step - accuracy: 0.5808 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 3/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 460ms/step - accuracy: 0.5753 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 4/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 467ms/step - accuracy: 0.5778 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 5/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m43s\u001b[0m 505ms/step - accuracy: 0.5852 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 6/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 468ms/step - accuracy: 0.5814 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 7/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 478ms/step - accuracy: 0.5762 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 8/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 470ms/step - accuracy: 0.5771 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 9/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 459ms/step - accuracy: 0.5719 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 10/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 463ms/step - accuracy: 0.5788 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 11/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 476ms/step - accuracy: 0.5736 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 12/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 466ms/step - accuracy: 0.5809 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 13/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 473ms/step - accuracy: 0.5676 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 14/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 465ms/step - accuracy: 0.5848 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 15/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 470ms/step - accuracy: 0.5714 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 16/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 467ms/step - accuracy: 0.5723 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 17/1000\n", + "\u001b[1m53/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 469ms/step - accuracy: 0.5959 - loss: nan - val_accuracy: 0.5950 - val_loss: nan\n", + "Epoch 18/1000\n", + "\u001b[1m42/53\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m4s\u001b[0m 436ms/step - accuracy: 0.5689 - loss: nan" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 9\u001b[0m\n\u001b[1;32m 3\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(loss\u001b[38;5;241m=\u001b[39mkeras\u001b[38;5;241m.\u001b[39mlosses\u001b[38;5;241m.\u001b[39mcategorical_crossentropy,\n\u001b[1;32m 4\u001b[0m optimizer\u001b[38;5;241m=\u001b[39mkeras\u001b[38;5;241m.\u001b[39moptimizers\u001b[38;5;241m.\u001b[39mAdam(learning_rate\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.01\u001b[39m), \n\u001b[1;32m 5\u001b[0m metrics\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 7\u001b[0m \u001b[38;5;66;03m# cnn_model_cifar = data_generation(cnn_model_cifar, augmentation, x_train, x_test, y_train, y_test, batch_size, epochs, class_weight) \u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m#Augmentation does not work!!!\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 12\u001b[0m \u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mx_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 15\u001b[0m score \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mevaluate(x_test, y_test, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTest loss:\u001b[39m\u001b[38;5;124m'\u001b[39m, score[\u001b[38;5;241m0\u001b[39m])\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:117\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 115\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 116\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 117\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 118\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 119\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/keras/src/backend/tensorflow/trainer.py:318\u001b[0m, in \u001b[0;36mTensorFlowTrainer.fit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq)\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m step, iterator \u001b[38;5;129;01min\u001b[39;00m epoch_iterator\u001b[38;5;241m.\u001b[39menumerate_epoch():\n\u001b[1;32m 317\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[0;32m--> 318\u001b[0m logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 319\u001b[0m logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pythonify_logs(logs)\n\u001b[1;32m 320\u001b[0m callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_end(step, logs)\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/tensorflow/python/util/traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback..error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 152\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:832\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 829\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 831\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[0;32m--> 832\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 834\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[1;32m 835\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py:877\u001b[0m, in \u001b[0;36mFunction._call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m 874\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[1;32m 875\u001b[0m \u001b[38;5;66;03m# In this case we have not created variables on the first call. So we can\u001b[39;00m\n\u001b[1;32m 876\u001b[0m \u001b[38;5;66;03m# run the first trace but we should fail if variables are created.\u001b[39;00m\n\u001b[0;32m--> 877\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 878\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_variable_creation_config\u001b[49m\n\u001b[1;32m 879\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 880\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_created_variables:\n\u001b[1;32m 881\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCreating variables on a non-first call to a function\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 882\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m decorated with tf.function.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[0;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[1;32m 137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[0;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m 140\u001b[0m \u001b[43m \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[1;32m 141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/concrete_function.py:1323\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[0;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[1;32m 1319\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[1;32m 1320\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[1;32m 1321\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[1;32m 1322\u001b[0m \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[0;32m-> 1323\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1324\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[1;32m 1325\u001b[0m args,\n\u001b[1;32m 1326\u001b[0m possible_gradient_type,\n\u001b[1;32m 1327\u001b[0m executing_eagerly)\n\u001b[1;32m 1328\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[0;34m(self, args)\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m 215\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 216\u001b[0m flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 217\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[1;32m 250\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[0;32m--> 251\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 252\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 253\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 254\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 255\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 257\u001b[0m outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 259\u001b[0m \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[1;32m 260\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[1;32m 261\u001b[0m )\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/tensorflow/python/eager/context.py:1486\u001b[0m, in \u001b[0;36mContext.call_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m 1484\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[1;32m 1485\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1486\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1487\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1488\u001b[0m \u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1489\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1490\u001b[0m \u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1491\u001b[0m \u001b[43m 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name)\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 52\u001b[0m ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 53\u001b[0m tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 54\u001b[0m \u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 56\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "model = cnn_model_keras(input_shape, num_classes)\n", + "\n", + "model.compile(loss=keras.losses.categorical_crossentropy,\n", + " optimizer=keras.optimizers.Adam(learning_rate=0.01), \n", + " metrics=['accuracy'])\n", + " \n", + "# cnn_model_cifar = data_generation(cnn_model_cifar, augmentation, x_train, x_test, y_train, y_test, batch_size, epochs, class_weight) \n", + "#Augmentation does not work!!!\n", + "model.fit(x_train, y_train,\n", + " batch_size=batch_size,\n", + " epochs=epochs,\n", + " verbose=1,\n", + " validation_data=(x_test, y_test))\n", + "\n", + "score = model.evaluate(x_test, y_test, verbose=0)\n", + "print('Test loss:', score[0])\n", + "print('Test accuracy:', score[1])\n", + "# Need to fix " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save the Model and its weights" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved trained model at /mnt/home/mpaez/caiman_data/model/cnn_model_test.keras \n" + ] + } + ], + "source": [ + "save_model_path = save_model_keras(model, name='cnn_model_test')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Results" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m283/283\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 25ms/step\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/numpy/lib/nanfunctions.py:1562: RuntimeWarning: Mean of empty slice\n", + " return np.nanmean(a, axis, out=out, keepdims=keepdims)\n" + ] + } + ], + "source": [ + "predictions = model.predict(all_masks_gt, batch_size=32, verbose=1)\n", + "cm.movie(np.squeeze(all_masks_gt[np.where(predictions[:, 0] >= 0.5)[0]])).play(\n", + " gain=3., magnification=5, fr=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieve the Model and its weights" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Load trained model at /mnt/home/mpaez/caiman_data/model/cnn_model_test.keras \n" + ] + }, + { + "data": { + "text/html": [ + "
Model: \"sequential_1\"\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1mModel: \"sequential_1\"\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
+       "┃ Layer (type)                    ┃ Output Shape           ┃       Param # ┃\n",
+       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
+       "│ conv2d_4 (Conv2D)               │ (None, 48, 48, 32)     │           320 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_5 (Conv2D)               │ (None, 46, 46, 32)     │         9,248 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_2 (MaxPooling2D)  │ (None, 23, 23, 32)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_3 (Dropout)             │ (None, 23, 23, 32)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_6 (Conv2D)               │ (None, 23, 23, 64)     │        18,496 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ conv2d_7 (Conv2D)               │ (None, 21, 21, 64)     │        36,928 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ max_pooling2d_3 (MaxPooling2D)  │ (None, 10, 10, 64)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_4 (Dropout)             │ (None, 10, 10, 64)     │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ flatten_1 (Flatten)             │ (None, 6400)           │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_2 (Dense)                 │ (None, 512)            │     3,277,312 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dropout_5 (Dropout)             │ (None, 512)            │             0 │\n",
+       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+       "│ dense_3 (Dense)                 │ (None, 2)              │         1,026 │\n",
+       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
+       "
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 Total params: 10,029,992 (38.26 MB)\n",
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 Trainable params: 3,343,330 (12.75 MB)\n",
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 Non-trainable params: 0 (0.00 B)\n",
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 Optimizer params: 6,686,662 (25.51 MB)\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m6,686,662\u001b[0m (25.51 MB)\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "loaded_model = load_model_keras(save_model_path)\n", + "loaded_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Results " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m283/283\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 25ms/step\n" + ] + } + ], + "source": [ + "predictions = loaded_model.predict(all_masks_gt, batch_size=32, verbose=1)\n", + "cm.movie(np.squeeze(all_masks_gt[np.where(predictions[:, 0] >= 0.5)[0]])).play(\n", + " gain=3., magnification=5, fr=10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "caiman_pytorch", + "language": "python", + "name": "caiman_pytorch" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/caiman/train/train_cnn_model_keras.py b/caiman/train/train_cnn_model_keras.py new file mode 100644 index 000000000..1d8167b32 --- /dev/null +++ b/caiman/train/train_cnn_model_keras.py @@ -0,0 +1,97 @@ +import numpy as np +import os +import keras +from keras.layers import Input, Conv2D, Activation, MaxPooling2D, Dropout, Flatten, Dense +from keras.models import save_model, load_model +from sklearn.model_selection import train_test_split +from sklearn.utils import class_weight as cw + +import caiman as cm +from caiman.paths import caiman_datadir +from caiman.utils.image_preprocessing_keras import ImageDataGenerator + +os.environ["KERAS_BACKEND"] = "torch" + +def cnn_model_keras(input_shape, num_classes): + sequential_model = keras.Sequential([ + Input(shape=input_shape, dtype="float32"), + Conv2D(filters=32, kernel_size=(3,3), strides=(1, 1), + activation="relu"), + Conv2D(filters=32, kernel_size=(3,3), strides=(1, 1), + activation="relu"), + MaxPooling2D(pool_size=(2, 2)), + Dropout(rate=0.25), + Conv2D(filters=64, kernel_size=(3,3), strides=(1, 1), + padding="same", activation="relu"), + Conv2D(filters=64, kernel_size=(3,3), strides=(1, 1), + activation="relu"), + MaxPooling2D(pool_size=(2, 2)), + Dropout(rate=0.25), + Flatten(), + Dense(units=512, activation="relu"), + Dropout(rate=0.5), + Dense(units=num_classes, activation="relu"), + ]) + return sequential_model + +def save_model_keras(model, name: str): + model_name = os.path.join(caiman_datadir(), 'model', name) + model_path = model_name + ".keras" + model.save(model_path) + print('Saved trained model at %s ' % model_path) + return model_path + +def load_model_keras(model_path: str): + loaded_model = load_model(model_path) + print('Load trained model at %s ' % model_path) + return loaded_model + +if __name__ == "__main__": + batch_size = 128 + num_classes = 2 + epochs = 5000 + test_fraction = 0.25 + augmentation = True + img_rows, img_cols = 50, 50 # input image dimensions + + with np.load('/mnt/ceph/data/neuro/caiman/data_minions/ground_truth_components_curated_minions.npz') as ld: + all_masks_gt = ld['all_masks_gt'] + labels_gt = ld['labels_gt_cur'] + + x_train, x_test, y_train, y_test = train_test_split( + all_masks_gt, labels_gt, test_size=test_fraction) + + class_weight = cw.compute_class_weight(class_weight='balanced', + classes=np.unique(y_train), y=y_train) + + if keras.config.image_data_format() == 'channels_first': + x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) + x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) + input_shape = (1, img_rows, img_cols) + else: + x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) + x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) + input_shape = (img_rows, img_cols, 1) + + x_train = x_train.astype('float32') + x_test = x_test.astype('float32') + print('x_train shape:', x_train.shape) + print(x_train.shape[0], 'train samples') + print(x_test.shape[0], 'test samples') + + # convert class vectors to binary class matrices + y_train = keras.utils.to_categorical(y_train, num_classes) + y_test = keras.utils.to_categorical(y_test, num_classes) + + cnn_model_cifar = keras_cnn_model_cifar(input_shape, num_classes) + cnn_model_cifar.summary() + + opt = keras.optimizers.RMSprop(learning_rate=0.0001, weight_decay=1e-6) + cnn_model_cifar.compile(loss=keras.losses.categorical_crossentropy, + optimizer=opt, metrics=['accuracy']) #don't need this + + #Augmentations + + score = cnn_model_cifar.evaluate(x_test, y_test, verbose=0) + print('Test loss:', score[0]) + print('Test accuracy:', score[1]) \ No newline at end of file diff --git a/caiman/train/train_cnn_model_pytorch.ipynb b/caiman/train/train_cnn_model_pytorch.ipynb new file mode 100644 index 000000000..00cf973e0 --- /dev/null +++ b/caiman/train/train_cnn_model_pytorch.ipynb @@ -0,0 +1,352 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Training the CNN Model for the 2D Spatial Components (Pytorch Version)\n", + "\n", + "This notebook will help to demonstrate how to train the CNN Model used in CaImAn to evaluate the shape of (2p) spatial components using the Torch API.\n", + "\n", + "The basic function for this is caiman.train.train_cnn_model_keras.cnn_model_pytorch(). It takes it the number of classes to build of a CNN model (based on a tutorial on the CIFAR dataset). The other functions, caiman.train.train_cnn_model.save_model_file() and caiman.train.train_cnn_model.load_model_file() save and retrieve the model and weights of the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-08-06 23:34:38.644002: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", + "2024-08-06 23:34:38.675416: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", + "/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML\n", + " warnings.warn(\"Can't initialize NVML\")\n" + ] + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "import os\n", + "import keras \n", + "\n", + "import torch\n", + "from torch.optim import Adam\n", + "from torch.utils.data import Dataset, TensorDataset, DataLoader\n", + "import torchvision.transforms.v2 as transforms\n", + "\n", + "import caiman as cm\n", + "from caiman.paths import caiman_datadir\n", + "from caiman.train.train_cnn_model_pytorch import cnn_model_pytorch, get_batch_accuracy, load_model_pytorch, save_model_pytorch\n", + "from caiman.train.train_cnn_model_pytorch import train_test_split, train, validate \n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Initalizing the Parameters for the Model (based on MNIST dataset) " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 128\n", + "num_classes = 2\n", + "epochs = 100\n", + "test_fraction = 0.25\n", + "augmentation = True #Fix this \n", + "img_rows, img_cols = 50, 50 # input image dimensions\n", + "in_channels = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading the Dataset of the Model " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "with np.load('/mnt/ceph/data/neuro/caiman/data_minions/ground_truth_components_curated_minions.npz') as ld:\n", + " all_masks_gt = torch.tensor(ld['all_masks_gt'], dtype=torch.float32)\n", + " labels_gt = torch.tensor(ld['labels_gt_cur'], dtype=torch.long)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Constructing the Training and Validation Set for the Model " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "all_masks_gt = torch.reshape(all_masks_gt, (-1, in_channels, img_rows, img_cols))\n", + "dataset = TensorDataset(all_masks_gt, labels_gt) \n", + "\n", + "train_dataset, test_dataset = train_test_split(dataset, test_fraction)\n", + "\n", + "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", + "train_N = len(train_loader.dataset)\n", + "valid_loader = DataLoader(test_dataset, batch_size=batch_size)\n", + "valid_N = len(valid_loader.dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build and Evaluate the Model " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0\n", + "Train - Loss: 36.1255 Accuracy: 0.5869\n", + "Valid - Loss: 36.0095 Accuracy: 1.7607\n", + "Epoch: 1\n", + "Train - Loss: 35.9488 Accuracy: 0.5869\n", + "Valid - Loss: 35.9438 Accuracy: 1.7607\n", + "Epoch: 2\n", + "Train - Loss: 36.0044 Accuracy: 0.5869\n", + "Valid - Loss: 35.9368 Accuracy: 1.7607\n", + "Epoch: 3\n", + "Train - Loss: 35.9931 Accuracy: 0.5869\n", + "Valid - Loss: 35.9785 Accuracy: 1.7607\n", + "Epoch: 4\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 8\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(epochs):\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEpoch: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(epoch))\n\u001b[0;32m----> 8\u001b[0m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mloss_function\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_N\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maugment\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m validate(model, train_loader, loss_function, optimizer, valid_N, augment\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/caiman/train/train_cnn_model_pytorch.py:70\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(model, train_loader, loss_function, optimizer, train_N, augment)\u001b[0m\n\u001b[1;32m 68\u001b[0m model\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m x, y \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[0;32m---> 70\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m \n\u001b[1;32m 71\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[1;32m 72\u001b[0m batch_loss \u001b[38;5;241m=\u001b[39m loss_function(output, y)\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1530\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1539\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1540\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 1544\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/caiman/train/train_cnn_model_pytorch.py:26\u001b[0m, in \u001b[0;36mcnn_model_pytorch.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 26\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrelu\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 27\u001b[0m x \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mrelu(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv2(x))\n\u001b[1;32m 28\u001b[0m x \u001b[38;5;241m=\u001b[39m F\u001b[38;5;241m.\u001b[39mdropout(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmaxpool2d1(x))\n", + "File \u001b[0;32m~/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/torch/nn/functional.py:1500\u001b[0m, in \u001b[0;36mrelu\u001b[0;34m(input, inplace)\u001b[0m\n\u001b[1;32m 1498\u001b[0m result \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrelu_(\u001b[38;5;28minput\u001b[39m)\n\u001b[1;32m 1499\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1500\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrelu\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1501\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "model = cnn_model_pytorch(in_channels, num_classes)\n", + "\n", + "loss_function = torch.nn.CrossEntropyLoss()\n", + "optimizer = Adam(model.parameters())\n", + "\n", + "for epoch in range(epochs):\n", + " print('Epoch: {}'.format(epoch))\n", + " train(model, train_loader, loss_function, optimizer, train_N, augment=None)\n", + " validate(model, train_loader, loss_function, optimizer, valid_N, augment=None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save the Model and its weights" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "save_model_pytorch() missing 1 required positional argument: 'name'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m save_model_path \u001b[38;5;241m=\u001b[39m \u001b[43msave_model_pytorch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: save_model_pytorch() missing 1 required positional argument: 'name'" + ] + } + ], + "source": [ + "save_model_path = save_model_pytorch(model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[0.7271, 0.2729],\n", + " [0.7409, 0.2591],\n", + " [0.7388, 0.2612],\n", + " ...,\n", + " [0.7291, 0.2709],\n", + " [0.7180, 0.2820],\n", + " [0.6821, 0.3179]])\n", + "torch tensor([[[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.]],\n", + "\n", + " [[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.]],\n", + "\n", + " [[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.]],\n", + "\n", + " ...,\n", + "\n", + " [[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.]],\n", + "\n", + " [[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.]],\n", + "\n", + " [[0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " ...,\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.]]])\n" + ] + } + ], + "source": [ + "# predictions = model.predict(all_masks_gt, batch_size=32, verbose=1) fix this \n", + "with torch.no_grad():\n", + " predictions = model(all_masks_gt) \n", + " \n", + "A = torch.squeeze(all_masks_gt[torch.where(predictions[:, 0] >= 0.5)[0]]).numpy()\n", + "cm.movie(A).play(gain=3., magnification=5, fr=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Retrieve the Model and its weights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "loaded_model = load_model_pytorch(save_model_path)\n", + "loaded_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualize Results " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "predictions = loaded_model.predict(all_masks_gt, batch_size=32, verbose=1)\n", + "cm.movie(np.squeeze(all_masks_gt[np.where(predictions[:, 0] >= 0.5)[0]])).play(\n", + " gain=3., magnification=5, fr=10)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "caiman_pytorch", + "language": "python", + "name": "caiman_pytorch" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/caiman/train/train_cnn_model_pytorch.py b/caiman/train/train_cnn_model_pytorch.py new file mode 100644 index 000000000..bb2a72b97 --- /dev/null +++ b/caiman/train/train_cnn_model_pytorch.py @@ -0,0 +1,112 @@ +import numpy as np +import os +import keras +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import Dataset, random_split + +import caiman as cm +from caiman.paths import caiman_datadir + +class cnn_model_pytorch(torch.nn.Module): + def __init__(self, in_channels, num_classes): + super(cnn_model_pytorch, self).__init__() + self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=32, kernel_size=(3,3), stride=(1, 1)) + self.conv2 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=(3,3), stride=(1, 1)) + self.maxpool2d1 = nn.MaxPool2d(kernel_size=(2, 2)) + self.conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3,3), stride=(1, 1), padding='same') + self.conv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3,3), stride=(1, 1)) + self.maxpool2d2 = nn.MaxPool2d(kernel_size=(2, 2)) + self.flatten = nn.Flatten() + self.dense1 = nn.Linear(in_features=6400, out_features=512) + self.dense2 = nn.Linear(in_features=512, out_features=num_classes) + + def forward(self, x): + x = F.relu(self.conv1(x)) + x = F.relu(self.conv2(x)) + x = F.dropout(self.maxpool2d1(x)) + x = F.relu(self.conv3(x)) + x = F.relu(self.conv4(x)) + x = F.dropout(self.maxpool2d2(x), p=0.25) + x = self.flatten(x) + x = F.relu(self.dense1(x)) + x = F.dropout(x, p=0.5) + x = F.softmax(self.dense2(x), dim=1) + return x + +def save_model_pytorch(model, name: str): + model_name = os.path.join(caiman_datadir(), 'model', name) + model_path = model_name + ".pth" + torch.save(model, model_path) + print('Saved trained model at %s ' % model_path) + return model_path + +def load_model_pytorch(model_path: str): + load_model = torch.load(model_path) + print('Load trained model at %s ' % model_path) + return load_model + +def train_test_split(dataset: Dataset, test_fraction: float): + train_ratio = 1 - test_fraction + train_size = int(train_ratio * len(dataset)) + test_size = len(dataset) - train_size + lengths = [train_size, test_size] + train_dataset, test_dataset = random_split(dataset, lengths) + return train_dataset, test_dataset + +def get_batch_accuracy(output, y, N): + pred = output.argmax(dim=1, keepdim=True) + correct = pred.eq(y.view_as(pred)).sum().item() + return correct / N + +def train(model, train_loader, loss_function, optimizer, train_N, augment): + + loss = 0 + accuracy = 0 + + model.train() + for x, y in train_loader: + output = model(x) + optimizer.zero_grad() + batch_loss = loss_function(output, y) + batch_loss.backward() + optimizer.step() + + loss += batch_loss.item() + accuracy += get_batch_accuracy(output, y, train_N) + print('Train - Loss: {:.4f} Accuracy: {:.4f}'.format(loss, accuracy)) + +def validate(model, valid_loader, loss_function, optimizer, valid_N, augment): + + loss = 0 + accuracy = 0 + + model.eval() + with torch.no_grad(): + for x, y in valid_loader: + output = model(x) + + loss += loss_function(output, y).item() + accuracy += get_batch_accuracy(output, y, valid_N) + print('Valid - Loss: {:.4f} Accuracy: {:.4f}'.format(loss, accuracy)) + +if __name__ == "__main__": + batch_size = 128 + num_classes = 2 + epochs = 5000 + test_fraction = 0.25 + augmentation = True + img_rows, img_cols = 50, 50 # input image dimensions + + with np.load('/mnt/ceph/data/neuro/caiman/data_minions/ground_truth_components_curated_minions.npz') as ld: + all_masks_gt = ld['all_masks_gt'] + labels_gt = ld['labels_gt_cur'] + + in_channels = 1 + cnn_model = pytorch_cnn_model(in_channels, num_classes) + + # model_path = save_model_pytorch(cnn_model) + # loaded_model = load_model_pytorch(model_path) + # print(loaded_model) + diff --git a/caiman/train/training.md b/caiman/train/training.md new file mode 100644 index 000000000..65f000773 --- /dev/null +++ b/caiman/train/training.md @@ -0,0 +1,6 @@ +HOW TO GENERATE GROUND TRUTH DATA + +Step 1 go to script ground_truth_cnmf_seeded.py and generate new ground truth.This will generate a file ending in *match_masks.npz +Step 2 If you want to match the cnmf-seeded components from GT with the results of a CNMF run you can use the script match_seeded_gt.py +Step 3 You might want to clean up the components, you can use the prepare_training_set.py +Step 4 In order to train the network use either the train_net_minst.py or train_net_cifar.py \ No newline at end of file diff --git a/caiman/utils/nn_models.py b/caiman/utils/nn_models.py index fd1a63fc5..262e97dc1 100644 --- a/caiman/utils/nn_models.py +++ b/caiman/utils/nn_models.py @@ -7,20 +7,25 @@ import numpy as np import os -import tensorflow as tf -from tensorflow.keras.layers import Input, Dense, Reshape, Layer, Activation -from tensorflow.keras.models import Model -from tensorflow.keras.optimizers import Adam -from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping, LearningRateScheduler -from tensorflow.keras import backend as K -from tensorflow.keras.initializers import Constant, RandomUniform -from tensorflow.keras.utils import Sequence +os.environ["KERAS_BACKEND"] = "torch" import time +import torch +import torch.nn.functional as F +import torch.nn as nn +import keras +import keras.ops as ops +from keras.constraints import Constraint +from keras.layers import Input, Dense, Reshape, Layer, Activation +from keras.models import Model +from keras.optimizers import Adam +from keras.callbacks import ModelCheckpoint, EarlyStopping, LearningRateScheduler +from keras.initializers import Constant, RandomUniform +from keras.utils import Sequence + import caiman.base.movies from caiman.paths import caiman_datadir - class CalciumDataset(Sequence): def __init__(self, files, random_state=42, batch_size=32, train=True, var_name_hdf5='mov', subindices=None): @@ -94,12 +99,12 @@ class Masked_Conv2D(Layer): add a bias term to each convolution kernel Returns: - Masked_Conv2D: tensorflow.keras.layer + Masked_Conv2D: keras.layer A trainable layer implementing the convolution with a ring """ def __init__(self, output_dim=1, kernel_size=(5,5), strides=(1,1), radius_min=2, radius_max=3, initializer='uniform', - use_bias=True): #, output_dim): + use_bias=True): self.output_dim = output_dim self.kernel_size = kernel_size self.radius_min = radius_min @@ -121,29 +126,23 @@ def __init__(self, output_dim=1, kernel_size=(5,5), strides=(1,1), super(Masked_Conv2D, self).__init__() def build(self, input_shape): - try: - n_filters = input_shape[-1].value # tensorflow < 2 - except: - n_filters = input_shape[-1] # tensorflow >= 2 + n_filters = input_shape[-1] self.h = self.add_weight(name='h', shape= self.kernel_size + (n_filters, self.output_dim,), initializer=self.initializer, - constraint=masked_constraint(self.mask), + constraint=MaskedConstraint(self.mask), trainable=True) self.b = self.add_weight(name='b', shape=(self.output_dim,), initializer=Constant(0), - trainable=self.use_bias) + trainable=self.use_bias) super(Masked_Conv2D, self).build(input_shape) def call(self, x): - #hm = tf.multiply(self.h, K.expand_dims(K.expand_dims(tf.cast(self.mask, float)))) - #hm = tf.multiply(hm, hm>0) - #hm = tf.where(hm>0, hm, 0) - y = K.conv2d(x, self.h, padding='same', strides=self.strides) + y = ops.conv(x, self.h, strides=self.strides, padding='same') if self.use_bias: - y = y + tf.expand_dims(self.b, axis=0) + y = y + torch.unsqueeze(self.b, dim=0) return y def compute_output_shape(self, input_shape): @@ -178,28 +177,18 @@ def get_mask(gSig=5, r_factor=1.5, width=5): R[R>0] = 1 return R -def masked_constraint(R): - """ Enforces constraint for kernel to be non-negative everywhere and zero outside the ring - - Args: - R: np.array - Binary mask that extracts - - Returns: - my_constraint: function - Function that enforces the constraint - """ - R = tf.cast(R, dtype=tf.float32) - R_exp = tf.expand_dims(tf.expand_dims(R, -1), -1) - def my_constraint(x): - Rt = tf.tile(R_exp, [1, 1, 1, x.shape[-1]]) - Z = tf.zeros_like(x) - return tf.where(Rt>0, x, Z) - return my_constraint +class MaskedConstraint(keras.constraints.Constraint): + def __init__(self, R): + R = torch.tensor(R).float() + self.R_exp = torch.unsqueeze(torch.unsqueeze(R, dim=-1), dim=-1) + def __call__(self, x): + Rt = torch.tile(self.R_exp, [1, 1, 1, x.shape[-1]]) + Z = torch.zeros_like(x) + return torch.where(Rt > 0, x, Z) class Hadamard(Layer): - """ Creates a tensorflow.keras multiplicative layer that performs + """ Creates a keras multiplicative layer that performs pointwise multiplication with a set of learnable weights. Args: @@ -217,8 +206,8 @@ def build(self, input_shape): super(Hadamard, self).build(input_shape) def call(self, x): - hm = tf.multiply(x, self.kernel) - sm = tf.reduce_sum(hm, axis=-1, keepdims=True) + hm = torch.multiply(x, self.kernel) + sm = torch.sum(hm, dim=-1, keepdim=True) return sm def compute_output_shape(self, input_shape): @@ -226,7 +215,7 @@ def compute_output_shape(self, input_shape): class Additive(Layer): - """ Creates a tensorflow.keras additive layer that performs + """ Creates a keras additive layer that performs pointwise addition with a set of learnable weights. Args: @@ -246,7 +235,7 @@ def build(self, input_shape): super(Additive, self).build(input_shape) def call(self, x): - hm = tf.add(x, self.kernel) + hm = torch.add(x, self.kernel) return hm def compute_output_shape(self, input_shape): @@ -265,9 +254,9 @@ def cropped_loss(gSig=0): """ def my_loss(y_true, y_pred): if gSig > 0: - error = tf.square(y_true[gSig:-gSig, gSig:-gSig] - y_pred[gSig:-gSig, gSig:-gSig]) + error = torch.square(y_true[gSig:-gSig, gSig:-gSig] - y_pred[gSig:-gSig, gSig:-gSig]) else: - error = tf.square(y_true - y_pred) + error = torch.square(y_true - y_pred) return error return my_loss @@ -284,7 +273,7 @@ def quantile_loss(qnt=.50): def my_qnt_loss(y_true, y_pred): error = y_true - y_pred pos_error = error > 0 - return tf.where(pos_error, error*qnt, error*(qnt-1)) + return torch.where(pos_error, error*qnt, error*(qnt-1)) return my_qnt_loss def rate_scheduler(factor=0.5, epoch_length=200, samples_length=1e4): @@ -300,12 +289,59 @@ def my_scheduler(epoch, lr): return rate return my_scheduler +def total_variation(image): + """ + Implements PyTorch version of the the anisotropic 2-D version of the formula described here: + https://en.wikipedia.org/wiki/Total_variation_denoising + + Args: + images: 4-D Tensor of shape `[batch, height, width, channels]` or 3-D Tensor + of shape `[height, width, channels]`. + name: A name for the operation (optional). + + Raises: + ValueError: if images.shape is not a 3-D or 4-D vector. + + Returns: + The total variation of `images`. + """ + ndim = image.ndim + if ndim == 3: + # The input is a single image with shape [height, width, channels]. + + # Calculate the difference of neighboring pixel-values. + # The images are shifted one pixel along the height and width by slicing. + pixel_dif1 = images[1:, :, :] - images[:-1, :, :] + pixel_dif2 = images[:, 1:, :] - images[:, :-1, :] + sum_axis = None + elif ndims == 4: + # The input is a batch of images with shape: + # [batch, height, width, channels]. + + # Calculate the difference of neighboring pixel-values. + # The images are shifted one pixel along the height and width by slicing. + pixel_dif1 = images[:, 1:, :, :] - images[:, :-1, :, :] + pixel_dif2 = images[:, :, 1:, :] - images[:, :, :-1, :] + + # Only sum for the last 3 axis. + # This results in a 1-D tensor with the total variation for each image. + sum_axis = [1, 2, 3] + else: + raise ValueError('\'images\' must be either 3 or 4-dimensional.') + + # Calculate the total variation by taking the absolute value of the + # pixel-differences and summing over the appropriate axis. + tot_var = (torch.sum(torch.abs(pixel_dif1), axis=sum_axis) + + torch.sum(torch.abs(pixel_dif2), axis=sum_axis)) + + return tot_var + def total_variation_loss(): """ Returns a total variation norm loss function that can be used for training. """ def my_total_variation_loss(y_true, y_pred): - error = tf.reduce_mean(tf.image.total_variation(y_true - y_pred)) - return error + error = torch.mean(total_variation(y_true - y_pred)) + return error return my_total_variation_loss def b0_initializer(Y, pct=10): @@ -320,12 +356,12 @@ def b0_initializer(Y, pct=10): Returns: b0_init: keras initializer """ - def b0_init(shape, dtype=tf.float32): + def b0_init(shape, dtype=torch.float32): mY = np.percentile(Y, pct, 0) - #mY = np.min(Y, axis=0) + mY = torch.from_numpy(mY) if mY.ndim == 2: - mY = tf.expand_dims(mY, -1) - mY = tf.cast(mY, dtype=tf.float32) + mY = torch.unsqueeze(mY, dim=-1) + mY = mY.float() return mY return b0_init @@ -391,7 +427,7 @@ def create_LN_model(Y=None, shape=(None, None, 1), n_channels=2, gSig=5, r_facto add a bias term to each convolution kernel Returns: - model_LIN: tf.keras model compiled and ready to be trained. + model_LIN: keras model compiled and ready to be trained. """ x_in = Input(shape=shape) radius_min = int(gSig*r_factor) @@ -463,7 +499,7 @@ def create_NL_model(Y=None, shape=(None, None, 1), n_channels=8, gSig=5, r_facto add a bias term to each convolution kernel Returns: - model_LIN: tf.keras model compiled and ready to be trained. + model_LIN: keras model compiled and ready to be trained. """ x_in = Input(shape=shape) radius_min = int(gSig*r_factor) From 0120624f46fad50f9038ff8d609593ad99ec79aa Mon Sep 17 00:00:00 2001 From: mannypaeza Date: Mon, 26 Aug 2024 13:21:09 -0400 Subject: [PATCH 2/4] torch dev for ring_cnn + 2p spatial --- caiman/source_extraction/cnmf/online_cnmf.py | 26 ++++++++++---------- caiman/utils/nn_models.py | 4 +-- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/caiman/source_extraction/cnmf/online_cnmf.py b/caiman/source_extraction/cnmf/online_cnmf.py index ce7a55ca7..1fe25d75d 100644 --- a/caiman/source_extraction/cnmf/online_cnmf.py +++ b/caiman/source_extraction/cnmf/online_cnmf.py @@ -323,9 +323,9 @@ def _prepare_object(self, Yr, T, new_dims=None, idx_components=None): if self.params.get('online', 'path_to_model') is None or self.params.get('online', 'sniper_mode') is False: loaded_model = None self.params.set('online', {'sniper_mode': False}) - # self.tf_in = None - # self.tf_out = None - self.use_torch = None #fix + self.tf_in = None + self.tf_out = None + # self.use_torch = None else: try: from keras.models import load_model @@ -340,12 +340,12 @@ def _prepare_object(self, Yr, T, new_dims=None, idx_components=None): # uses online model -> be careful model_path = ".".join(path + ["keras"]) loaded_model = model_load(model_path) - self.use_torch = False + # self.use_torch = False else: model_path = '.'.join(path + ['pt']) loaded_model = load_graph(model_path) loaded_model = torch.load(model_file) - self.use_torch = True + # self.use_torch = True self.loaded_model = loaded_model @@ -547,8 +547,8 @@ def fit_next(self, t, frame_in, num_iters_hals=3): sniper_mode=self.params.get('online', 'sniper_mode'), use_peak_max=self.params.get('online', 'use_peak_max'), mean_buff=self.estimates.mean_buff, - # tf_in=self.tf_in, tf_out=self.tf_out, - use_torch=self.use_torch, + tf_in=self.tf_in, tf_out=self.tf_out, + # use_torch=self.use_torch, ssub_B=ssub_B, W=self.estimates.W if self.is1p else None, b0=self.estimates.b0 if self.is1p else None, corr_img=self.estimates.corr_img if use_corr else None, @@ -2003,8 +2003,8 @@ def get_candidate_components(sv, dims, Yres_buf, min_num_trial=3, gSig=(5, 5), patch_size=50, loaded_model=None, test_both=False, thresh_CNN_noisy=0.5, use_peak_max=False, thresh_std_peak_resid = 1, mean_buff=None, - # tf_in=None, tf_out=None): - use_torch=None): + tf_in=None, tf_out=None): + # use_torch=None): """ Extract new candidate components from the residual buffer and test them using space correlation or the CNN classifier. The function runs the CNN @@ -2146,8 +2146,8 @@ def update_num_components(t, sv, Ab, Cf, Yres_buf, Y_buf, rho_buf, corr_img=None, first_moment=None, second_moment=None, crosscorr=None, col_ind=None, row_ind=None, corr_img_mode=None, max_img=None, downscale_matrix=None, upscale_matrix=None, - # tf_in=None, tf_out=None): - torch_in=None, torch_out=None): + tf_in=None, tf_out=None): + # torch_in=None, torch_out=None): """ Checks for new components in the residual buffer and incorporates them if they pass the acceptance tests """ @@ -2177,8 +2177,8 @@ def update_num_components(t, sv, Ab, Cf, Yres_buf, Y_buf, rho_buf, sniper_mode=sniper_mode, rval_thr=rval_thr, patch_size=50, loaded_model=loaded_model, thresh_CNN_noisy=thresh_CNN_noisy, use_peak_max=use_peak_max, test_both=test_both, mean_buff=mean_buff, - # tf_in=tf_in, tf_out=tf_out) - torch_in=torch_in, torch_out=torch_out) + tf_in=tf_in, tf_out=tf_out) + #torch_in=torch_in, torch_out=torch_out) ind_new_all = ijsig_all diff --git a/caiman/utils/nn_models.py b/caiman/utils/nn_models.py index 262e97dc1..adc3f02f0 100644 --- a/caiman/utils/nn_models.py +++ b/caiman/utils/nn_models.py @@ -555,7 +555,7 @@ def fit_NL_model(model_NL, Y, patience=5, val_split=0.2, batch_size=32, Y = np.expand_dims(Y, axis=-1) run_logdir = get_run_logdir() os.mkdir(run_logdir) - path_to_model = os.path.join(run_logdir, 'model.h5') + path_to_model = os.path.join(run_logdir, 'model.weights.h5') chk = ModelCheckpoint(filepath=path_to_model, verbose=0, save_best_only=True, save_weights_only=True) es = EarlyStopping(monitor='val_loss', patience=patience, @@ -566,7 +566,7 @@ def fit_NL_model(model_NL, Y, patience=5, val_split=0.2, batch_size=32, history_NL = model_NL.fit(Y, Y, epochs=epochs, batch_size=batch_size, shuffle=True, validation_split=val_split, callbacks=callbacks) - model_NL.load_weights(os.path.join(run_logdir, 'model.h5')) + model_NL.load_weights(os.path.join(run_logdir, 'model.weights.h5')) return model_NL, history_NL, path_to_model def get_MCNN_model(Y, gSig=5, n_channels=8, lr=1e-4, pct=10, r_factor=1.5, From 8469355605123deaade4dea16a1db2c2836a7d4c Mon Sep 17 00:00:00 2001 From: mannypaeza Date: Tue, 7 Jan 2025 12:11:48 -0500 Subject: [PATCH 3/4] changes --- caiman/train/__init__.py | 3 +- caiman/train/ground_truth_cnmf_seeded.ipynb | 132 +- caiman/train/match_seeded_gt.ipynb | 5165 +---------------- caiman/train/prepare_training_set.ipynb | 125 +- ...l_pytorch.py => train_cnn_model_helper.py} | 60 +- caiman/train/train_cnn_model_keras.ipynb | 2 +- caiman/train/train_cnn_model_keras.py | 97 - caiman/train/train_cnn_model_pytorch.ipynb | 6 +- caiman/train/training.md | 10 +- 9 files changed, 420 insertions(+), 5180 deletions(-) rename caiman/train/{train_cnn_model_pytorch.py => train_cnn_model_helper.py} (67%) delete mode 100644 caiman/train/train_cnn_model_keras.py diff --git a/caiman/train/__init__.py b/caiman/train/__init__.py index 1540ec0dc..4612a75d7 100644 --- a/caiman/train/__init__.py +++ b/caiman/train/__init__.py @@ -1,7 +1,6 @@ #!/usr/bin/env python import pkg_resources -from caiman.train.train_cnn_model_keras import cnn_model_keras, save_model_keras, load_model_keras -from caiman.train.train_cnn_model_pytorch import cnn_model_pytorch, train_test_split, train, validate, get_batch_accuracy, save_model_pytorch, load_model_pytorch +from caiman.train.train_cnn_model_helper import cnn_model_pytorch, train_test_split, train, validate, get_batch_accuracy, save_model_pytorch, load_model_pytorch, cnn_model_keras, save_model_keras, load_model_keras __version__ = pkg_resources.get_distribution('caiman').version \ No newline at end of file diff --git a/caiman/train/ground_truth_cnmf_seeded.ipynb b/caiman/train/ground_truth_cnmf_seeded.ipynb index 403208082..18db196a6 100644 --- a/caiman/train/ground_truth_cnmf_seeded.ipynb +++ b/caiman/train/ground_truth_cnmf_seeded.ipynb @@ -270,7 +270,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Parameters for the Movie and Name of the Movie " + "### Parameters for the Movie" ] }, { @@ -292,7 +292,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Analysis " + "## Run Analysis" ] }, { @@ -308,16 +308,30 @@ "d1, d2 = dims\n", "images = np.reshape(Yr.T, [T] + list(dims), order='F')\n", "Y = np.reshape(Yr, dims + (T,), order='F')\n", - "m_images = cm.movie(images)\n", - "\n", - "# correlation image\n", + "m_images = cm.movie(images)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Correlation Image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "if m_images.shape[0] < 10000:\n", " Cn = m_images.local_correlations(\n", " swap_dim=params_movie['swap_dim'], frames_per_chunk=1500)\n", " Cn[np.isnan(Cn)] = 0\n", "else:\n", + " #Saved as a tif file\n", " Cn = np.array(cm.load(('/'.join(params_movie['gtname'][0].split('/')[:-2] + [\n", - " 'projections', 'correlation_image_better.tif'])))).squeeze()\n", + " 'projections', 'correlation_image_better.tif'])))).squeeze() \n", "\n", "plt.imshow(Cn, cmap='gray', vmax=.95)" ] @@ -338,8 +352,15 @@ "\n", "print(radius)\n", "print(roi_cons.shape)\n", - "pl.imshow(roi_cons.sum(0))\n", - "\n", + "plt.imshow(roi_cons.sum(0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "if params_movie['kernel'] is not None: # kernel usually two\n", " kernel = np.ones(\n", " (radius // params_movie['kernel'], radius // params_movie['kernel']), np.uint8)\n", @@ -349,24 +370,42 @@ "\n", "A_in = np.reshape(roi_cons.transpose(\n", " [2, 1, 0]), (-1, roi_cons.shape[0]), order='C')\n", - "pl.figure()\n", + "plt.figure()\n", "crd = plot_contours(A_in, Cn, thr=.99999)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameter Setting" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# %% some parameter settings\n", "# order of the autoregressive fit to calcium imaging in general one (slow gcamps) or two (fast gcamps fast scanning)\n", "p = params_movie['p']\n", "# merging threshold, max correlation allowed\n", - "merge_thresh = params_movie['merge_thresh']\n", - "\n", - "# %% Extract spatial and temporal components on patches\n", - "# TODO: todocument\n", + "merge_thresh = params_movie['merge_thresh']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extract spatial and temporal components on patches" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "if images.shape[0] > 10000:\n", " check_nan = False\n", "else:\n", @@ -383,13 +422,17 @@ "f = cnm.f\n", "snt = cnm.sn\n", "print(('Number of components:' + str(A.shape[-1])))\n", - "# %%\n", - "pl.figure()\n", - "# TODO: show screenshot 12`\n", - "# TODO : change the way it is used\n", + "plt.figure()\n", "crd = plot_contours(A, Cn, thr=params_display['thr_plot'])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Threshold Components " + ] + }, { "cell_type": "code", "execution_count": null, @@ -400,10 +443,9 @@ "view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, :]), C[:, :], b, f, dims[0], dims[1],\n", " YrA=YrA[:, :], img=Cn)\n", "\n", - "#%%\n", "c, dview, n_processes = cm.cluster.setup_cluster(\n", " backend='local', n_processes=None, single_thread=False)\n", - "#%% thredshold components\n", + "\n", "min_size_neuro = 3 * 2 * np.pi\n", "max_size_neuro = (2 * radius)**2 * np.pi\n", "A_thr = cm.source_extraction.cnmf.spatial.threshold_components(A.tocsc()[:, :].toarray(), dims, medw=None, thr_method='max', maxthr=0.2, nrgthr=0.99, extract_cc=True,\n", @@ -415,29 +457,43 @@ " & (size_neurons < max_size_neuro))[0]\n", "A_thr = A_thr[:, idx_size_neuro]\n", "print(A_thr.shape)\n", - "#%%\n", + "\n", "crd = plot_contours(scipy.sparse.coo_matrix(\n", " A_thr * 1.), Cn, thr=.99, vmax=0.35)\n", - "#%%\n", + "\n", "roi_cons = np.load(params_movie['gtname'][0])\n", "print(roi_cons.shape)\n", - "pl.imshow(roi_cons.sum(0))\n", - "#%% compare CNMF sedded with ground truth\n", - "pl.figure(figsize=(30, 20))\n", + "plt.imshow(roi_cons.sum(0))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compare CNMF Seeded with ground truth" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(30, 20))\n", "tp_gt, tp_comp, fn_gt, fp_comp, performance_cons_off = cm.base.rois.nf_match_neurons_in_binary_masks(roi_cons, A_thr[:, :].reshape([dims[0], dims[1], -1], order='F').transpose([2, 0, 1]) * 1., thresh_cost=.7, min_dist=10,\n", " print_assignment=False, plot_results=False, Cn=Cn, labels=['GT', 'Offline'])\n", - "pl.rcParams['pdf.fonttype'] = 42\n", + "plt.rcParams['pdf.fonttype'] = 42\n", "font = {'family': 'Myriad Pro',\n", " 'weight': 'regular',\n", " 'size': 20}\n", - "pl.rc('font', **font)\n", - "\n", - "#%%\n", - "np.savez(os.path.join(os.path.split(fname_new)[0], os.path.split(fname_new)[1][:-4] + 'match_masks.npz'), Cn=Cn,\n", - " tp_gt=tp_gt, tp_comp=tp_comp, fn_gt=fn_gt, fp_comp=fp_comp, performance_cons_off=performance_cons_off, idx_size_neuro_gt=idx_size_neuro, A_thr=A_thr,\n", - " A_gt=A, C_gt=C, b_gt=b, f_gt=f, YrA_gt=YrA, d1=d1, d2=d2, idx_components_gt=idx_size_neuro[\n", - " tp_comp],\n", - " idx_components_bad_gt=idx_size_neuro[fp_comp], fname_new=fname_new)\n" + "plt.rc('font', **font)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generating match_masks.npz" ] }, { @@ -445,7 +501,13 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "np.savez(os.path.join(os.path.split(fname_new)[0], os.path.split(fname_new)[1][:-4] + 'match_masks.npz'), Cn=Cn,\n", + " tp_gt=tp_gt, tp_comp=tp_comp, fn_gt=fn_gt, fp_comp=fp_comp, performance_cons_off=performance_cons_off, idx_size_neuro_gt=idx_size_neuro, A_thr=A_thr,\n", + " A_gt=A, C_gt=C, b_gt=b, f_gt=f, YrA_gt=YrA, d1=d1, d2=d2, idx_components_gt=idx_size_neuro[\n", + " tp_comp],\n", + " idx_components_bad_gt=idx_size_neuro[fp_comp], fname_new=fname_new)" + ] } ], "metadata": { diff --git a/caiman/train/match_seeded_gt.ipynb b/caiman/train/match_seeded_gt.ipynb index 3f8685877..8bf16e0d4 100644 --- a/caiman/train/match_seeded_gt.ipynb +++ b/caiman/train/match_seeded_gt.ipynb @@ -1,39 +1,24 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Matching the Cnmf-Seeded Components from Ground Truths with the Results of a CNMF Run" + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-08-04 22:39:01.243425: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2024-08-04 22:39:01.262158: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" - ] - } - ], + "outputs": [], "source": [ - "#!/usr/bin/env python\n", - "\n", - "# @package demos\n", - "#\\brief for the user/programmer to understand and try the code\n", - "#\\details all of other usefull functions (demos available on jupyter notebook) -*- coding: utf-8 -*-\n", - "#\\version 1.0\n", - "#\\pre EXample.First initialize the system.\n", - "#\\bug\n", - "#\\warning\n", - "#\\copyright GNU General Public License v2.0\n", - "#\\date Created on Mon Nov 21 15:53:15 2016\n", - "#\\author agiovann, recreated by mpaez\n", - "\n", "import cv2\n", "import numpy as np\n", "import os\n", + "import pylab as plt\n", + "from sklearn.preprocessing import normalize\n", "import time\n", - "import pylab as pl\n", "\n", "import caiman as cm\n", "from caiman.utils.utils import download_demo\n", @@ -41,13 +26,20 @@ "from caiman.utils.visualization import plot_contours, view_patches_bar\n", "from caiman.source_extraction.cnmf import cnmf as cnmf\n", "from caiman.motion_correction import MotionCorrect, tile_and_correct, motion_correction_piecewise \n", - "from caiman.components_evaluation import estimate_components_quality, evaluate_components\n", + "from caiman.components_evaluation import estimate_components_quality, evaluate_components, evaluate_components_CNN\n", "from caiman.tests.comparison import comparison" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading Up the Ground Truth Files" + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -172,4920 +164,82 @@ " #(to be used with one background per patch)\n", " 'swap_dim': False,\n", " 'crop_pix': 10\n", - " }\n", - "\n", + " }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parameters for the Movie" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "params_display = {\n", " 'downsample_ratio': .2,\n", " 'thr_plot': 0.8\n", - "}" + "}\n", + "\n", + "# @params fname name of the movie\n", + "fname_new = params_movie['fname']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Analysis " ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 45266 [cluster.py: setup_cluster():225] [1655335] The local backend is an alias for the multiprocessing backend, and the alias may be removed in some future version of Caiman\n", - " 46267 [movies.py: local_correlations():826] [1655335] number of chunks:0 frames: [0, 1500]\n", - " 48404 [movies.py: local_correlations():826] [1655335] number of chunks:1 frames: [1500, 3000]\n", - " 50038 [movies.py: local_correlations():826] [1655335] number of chunks:2 frames: [3000, 4500]\n", - " 51669 [movies.py: local_correlations():826] [1655335] number of chunks:3 frames: [4500, 6000]\n", - " 53304 [movies.py: local_correlations():837] [1655335] number of chunks:4 frames: [6000, 8000]\n", - " 56916 [font_manager.py: _findfont_cached():1416] [1655335] findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n", - " 56918 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", - " 56919 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56920 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56920 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56921 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", - " 56922 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56922 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56923 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56924 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56926 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56927 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56927 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56928 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56929 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56929 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56930 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56930 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56931 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56932 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56932 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56933 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56934 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56934 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56935 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56936 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56936 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56937 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56937 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56938 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56939 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56939 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", - " 56940 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56941 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56941 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", - " 56942 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56943 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56943 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56944 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56944 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Italic.ttf', name='Lato', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56944 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56944 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWGothic-DemiOblique.otf', name='URW Gothic', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", - " 56945 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSansNarrow-Bold.otf', name='Nimbus Sans Narrow', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", - " 56945 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWGothic-Book.otf', name='URW Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56947 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n", - " 56947 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56947 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Black.ttf', name='Lato', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n", - " 56948 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusRoman-Italic.otf', name='Nimbus Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56948 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-carlito/Carlito-Bold.ttf', name='Carlito', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56948 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-mono/LiberationMono-BoldItalic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56948 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-HairlineItalic.ttf', name='Lato', style='italic', variant='normal', weight=250, stretch='normal', size='scalable')) = 11.1925\n", - " 56948 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansArabic.ttf', name='Droid Sans Arabic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56949 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerifCondensed-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", - " 56949 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-mono/LiberationMono-Bold.ttf', name='Liberation Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56949 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansGeorgian.ttf', name='Droid Sans Georgian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56949 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56949 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansCondensed-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 1.535\n", - " 56949 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansDevanagari-Regular.ttf', name='Droid Sans Devanagari', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56950 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56950 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/fontawesome/fontawesome-webfont.ttf', name='FontAwesome', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56950 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56950 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-mono/LiberationMono-Regular.ttf', name='Liberation Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56950 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWGothic-Demi.otf', name='URW Gothic', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - " 56951 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusMonoPS-Bold.otf', name='Nimbus Mono PS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56951 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n", - " 56951 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWBookman-LightItalic.otf', name='URW Bookman', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", - " 56951 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Semibold.ttf', name='Lato', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - " 56951 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWBookman-Light.otf', name='URW Bookman', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - " 56951 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSans-Italic.otf', name='Nimbus Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56952 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/D050000L.otf', name='D050000L', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56952 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/fontawesome/FontAwesome.otf', name='FontAwesome', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56952 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/opensymbol/opens___.ttf', name='OpenSymbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56952 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-serif/LiberationSerif-Regular.ttf', name='Liberation Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56952 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-BlackItalic.ttf', name='Lato', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525\n", - " 56952 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansHebrew-Bold.ttf', name='Droid Sans Hebrew', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - " 56953 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansEthiopic-Regular.ttf', name='Droid Sans Ethiopic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56953 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansHebrew-Regular.ttf', name='Droid Sans Hebrew', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56953 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Bold.ttf', name='Lato', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56953 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56953 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Thin.ttf', name='Lato', style='normal', variant='normal', weight=275, stretch='normal', size='scalable')) = 10.16875\n", - " 56953 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/P052-Roman.otf', name='P052', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56954 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-caladea/Caladea-Regular.ttf', name='Caladea', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56954 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusMonoPS-Regular.otf', name='Nimbus Mono PS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56954 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-sans/LiberationSans-Bold.ttf', name='Liberation Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56954 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56954 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Heavy.ttf', name='Lato', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43\n", - " 56955 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/C059-BdIta.otf', name='C059', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56955 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/abattis-cantarell/Cantarell-Bold.otf', name='Cantarell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56955 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSans-BoldItalic.otf', name='Nimbus Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56955 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-roboto-slab/RobotoSlab-Light.ttf', name='Roboto Slab', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - " 56955 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusRoman-BoldItalic.otf', name='Nimbus Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56966 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-caladea/Caladea-Italic.ttf', name='Caladea', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56966 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-roboto-slab/RobotoSlab-Bold.ttf', name='Roboto Slab', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56967 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-HeavyItalic.ttf', name='Lato', style='italic', variant='normal', weight=800, stretch='normal', size='scalable')) = 11.43\n", - " 56967 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSansNarrow-BoldOblique.otf', name='Nimbus Sans Narrow', style='oblique', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n", - " 56967 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n", - " 56967 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-serif/LiberationSerif-BoldItalic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56967 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansCondensed-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 1.25\n", - " 56968 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerifCondensed-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n", - " 56968 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansTamil-Regular.ttf', name='Droid Sans Tamil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56968 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSans-ExtraLight.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=200, stretch='normal', size='scalable')) = 0.24\n", - " 56968 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/C059-Italic.otf', name='C059', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56968 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSansNarrow-Regular.otf', name='Nimbus Sans Narrow', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", - " 56969 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/abattis-cantarell/Cantarell-BoldOblique.otf', name='Cantarell', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56969 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n", - " 56969 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSans-Bold.otf', name='Nimbus Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56969 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-carlito/Carlito-Italic.ttf', name='Carlito', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56969 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWGothic-BookOblique.otf', name='URW Gothic', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56969 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSans.ttf', name='Droid Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusMonoPS-BoldItalic.otf', name='Nimbus Mono PS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSans-Regular.otf', name='Nimbus Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/C059-Bold.otf', name='C059', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerifCondensed-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", - " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansCondensed-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 0.5349999999999999\n", - " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansThai.ttf', name='Droid Sans Thai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56970 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-sans/LiberationSans-Italic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-carlito/Carlito-BoldItalic.ttf', name='Carlito', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/P052-Italic.otf', name='P052', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusSansNarrow-Oblique.otf', name='Nimbus Sans Narrow', style='oblique', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n", - " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/P052-BoldItalic.otf', name='P052', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-sans/LiberationSans-BoldItalic.ttf', name='Liberation Sans', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-SemiboldItalic.ttf', name='Lato', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", - " 56971 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Hairline.ttf', name='Lato', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", - " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Light.ttf', name='Lato', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n", - " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/abattis-cantarell/Cantarell-Oblique.otf', name='Cantarell', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusRoman-Bold.otf', name='Nimbus Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusMonoPS-Italic.otf', name='Nimbus Mono PS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansFallback.ttf', name='Droid Sans Fallback', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-ThinItalic.ttf', name='Lato', style='italic', variant='normal', weight=275, stretch='normal', size='scalable')) = 11.16875\n", - " 56972 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-roboto-slab/RobotoSlab-Regular.ttf', name='Roboto Slab', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerifCondensed.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n", - " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-mono/LiberationMono-Italic.ttf', name='Liberation Mono', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/abattis-cantarell/Cantarell-Regular.otf', name='Cantarell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/P052-Bold.otf', name='P052', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/dejavu/DejaVuSansCondensed.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 0.25\n", - " 56973 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/C059-Roman.otf', name='C059', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-caladea/Caladea-Bold.ttf', name='Caladea', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-LightItalic.ttf', name='Lato', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n", - " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/NimbusRoman-Regular.otf', name='Nimbus Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Medium.ttf', name='Lato', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n", - " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansTamil-Bold.ttf', name='Droid Sans Tamil', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-caladea/Caladea-BoldItalic.ttf', name='Caladea', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56974 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansArmenian.ttf', name='Droid Sans Armenian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-Regular.ttf', name='Lato', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansEthiopic-Bold.ttf', name='Droid Sans Ethiopic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWBookman-DemiItalic.otf', name='URW Bookman', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n", - " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSansJapanese.ttf', name='Droid Sans Japanese', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-MediumItalic.ttf', name='Lato', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", - " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-droid/DroidSans-Bold.ttf', name='Droid Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-sans/LiberationSans-Regular.ttf', name='Liberation Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56975 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-serif/LiberationSerif-Italic.ttf', name='Liberation Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n", - " 56976 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/lato/Lato-BoldItalic.ttf', name='Lato', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n", - " 56976 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-roboto-slab/RobotoSlab-Thin.ttf', name='Roboto Slab', style='normal', variant='normal', weight=250, stretch='normal', size='scalable')) = 10.1925\n", - " 56976 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/google-crosextra-carlito/Carlito-Regular.ttf', name='Carlito', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n", - " 56976 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/Z003-MediumItalic.otf', name='Z003', style='italic', variant='normal', weight=500, stretch='normal', size='scalable')) = 11.145\n", - " 56976 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/liberation-serif/LiberationSerif-Bold.ttf', name='Liberation Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n", - " 56976 [font_manager.py: _findfont_cached():1428] [1655335] findfont: score(FontEntry(fname='/usr/share/fonts/urw-base35/URWBookman-Demi.otf', name='URW Bookman', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n", - " 56976 [font_manager.py: _findfont_cached():1460] [1655335] findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" 62323 [map_reduce.py: cnmf_patches():78] [1655505] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20550_41070START\n", - " 62324 [map_reduce.py: cnmf_patches():78] [1655506] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 35910_56430START\n", - " 62326 [map_reduce.py: cnmf_patches():78] [1655507] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51270_71790START\n", - " 62327 [map_reduce.py: cnmf_patches():78] [1655508] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66630_87150START\n", - " 62329 [map_reduce.py: cnmf_patches():78] [1655509] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 81990_102510START\n", - " 62330 [map_reduce.py: cnmf_patches():78] [1655510] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97350_117870START\n", - " 62332 [map_reduce.py: cnmf_patches():78] [1655511] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 112710_133230START\n", - " 62333 [map_reduce.py: cnmf_patches():78] [1655512] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128070_148590START\n", - " 62334 [map_reduce.py: cnmf_patches():78] [1655513] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143430_163950START\n", - " 62336 [map_reduce.py: cnmf_patches():78] [1655514] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 158790_179310START\n", - " 62337 [map_reduce.py: cnmf_patches():78] [1655515] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174150_194670START\n", - " 62338 [map_reduce.py: cnmf_patches():78] [1655516] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189510_210030START\n", - " 62339 [map_reduce.py: cnmf_patches():78] [1655517] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 204870_225390START\n", - " 62315 [map_reduce.py: cnmf_patches():78] [1655499] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189480_210000START\n", - " 62341 [map_reduce.py: cnmf_patches():78] [1655518] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220230_240750START\n", - " 62342 [map_reduce.py: cnmf_patches():78] [1655519] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235590_256110START\n", - " 62343 [map_reduce.py: cnmf_patches():78] [1655520] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236614_256622START\n", - " 62344 [map_reduce.py: cnmf_patches():78] [1655521] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5220_25740START\n", - " 62346 [map_reduce.py: cnmf_patches():78] [1655522] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20580_41100START\n", - " 62347 [map_reduce.py: cnmf_patches():78] [1655523] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 35940_56460START\n", - " 62348 [map_reduce.py: cnmf_patches():78] [1655524] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51300_71820START\n", - " 62349 [map_reduce.py: cnmf_patches():78] [1655525] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66660_87180START\n", - " 62350 [map_reduce.py: cnmf_patches():78] [1655526] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82020_102540START\n", - " 62352 [map_reduce.py: cnmf_patches():78] [1655527] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97380_117900START\n", - " 62353 [map_reduce.py: cnmf_patches():78] [1655528] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 112740_133260START\n", - " 62354 [map_reduce.py: cnmf_patches():78] [1655529] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128100_148620START\n", - " 62356 [map_reduce.py: cnmf_patches():78] [1655530] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143460_163980START\n", - " 62357 [map_reduce.py: cnmf_patches():78] [1655531] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 158820_179340START\n", - " 62358 [map_reduce.py: cnmf_patches():78] [1655532] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174180_194700START\n", - " 62359 [map_reduce.py: cnmf_patches():78] [1655533] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189540_210060START\n", - " 62360 [map_reduce.py: cnmf_patches():78] [1655534] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 204900_225420START\n", - " 62361 [map_reduce.py: cnmf_patches():78] [1655535] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220260_240780START\n", - " 62362 [map_reduce.py: cnmf_patches():78] [1655536] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235620_256140START\n", - " 62364 [map_reduce.py: cnmf_patches():78] [1655537] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236644_256652START\n", - " 62365 [map_reduce.py: cnmf_patches():78] [1655538] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5250_25770START\n", - " 62366 [map_reduce.py: cnmf_patches():78] [1655539] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20610_41130START\n", - " 62367 [map_reduce.py: cnmf_patches():78] [1655540] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 35970_56490START\n", - " 62368 [map_reduce.py: cnmf_patches():78] [1655541] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51330_71850START\n", - " 62370 [map_reduce.py: cnmf_patches():78] [1655542] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66690_87210START\n", - " 62371 [map_reduce.py: cnmf_patches():78] [1655543] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82050_102570START\n", - " 62372 [map_reduce.py: cnmf_patches():78] [1655544] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97410_117930START\n", - " 62373 [map_reduce.py: cnmf_patches():78] [1655545] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 112770_133290START\n", - " 62374 [map_reduce.py: cnmf_patches():78] [1655546] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128130_148650START\n", - " 62375 [map_reduce.py: cnmf_patches():78] [1655547] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143490_164010START\n", - " 62377 [map_reduce.py: cnmf_patches():78] [1655548] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 158850_179370START\n", - " 62378 [map_reduce.py: cnmf_patches():78] [1655549] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174210_194730START\n", - " 62379 [map_reduce.py: cnmf_patches():78] [1655550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189570_210090START\n", - " 62380 [map_reduce.py: cnmf_patches():78] [1655551] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 204930_225450START\n", - " 62381 [map_reduce.py: cnmf_patches():78] [1655552] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220290_240810START\n", - " 62383 [map_reduce.py: cnmf_patches():78] [1655553] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235650_256170START\n", - " 62384 [map_reduce.py: cnmf_patches():78] [1655554] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236674_256682START\n", - " 62385 [map_reduce.py: cnmf_patches():78] [1655555] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5280_25800START\n", - " 62386 [map_reduce.py: cnmf_patches():78] [1655556] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20640_41160START\n", - " 62388 [map_reduce.py: cnmf_patches():78] [1655557] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 36000_56520START\n", - " 62389 [map_reduce.py: cnmf_patches():78] [1655558] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51360_71880START\n", - " 62390 [map_reduce.py: cnmf_patches():78] [1655559] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66720_87240START\n", - " 62391 [map_reduce.py: cnmf_patches():78] [1655560] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82080_102600START\n", - " 62392 [map_reduce.py: cnmf_patches():78] [1655561] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97440_117960START\n", - " 62396 [map_reduce.py: cnmf_patches():78] [1655564] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143520_164040START\n", - " 62314 [map_reduce.py: cnmf_patches():78] [1655498] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174120_194640START\n", - " 62394 [map_reduce.py: cnmf_patches():78] [1655562] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 112800_133320START\n", - " 62278 [map_reduce.py: cnmf_patches():78] [1655469] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5130_25650START\n", - " 62395 [map_reduce.py: cnmf_patches():78] [1655563] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128160_148680START\n", - " 62440 [map_reduce.py: cnmf_patches():80] [1655560] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82080_102600Read file\n", - " 62440 [map_reduce.py: cnmf_patches():80] [1655549] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174210_194730Read file\n", - " 62440 [map_reduce.py: cnmf_patches():80] [1655561] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97440_117960Read file\n", - " 62441 [map_reduce.py: cnmf_patches():80] [1655548] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 158850_179370Read file\n", - " 62441 [map_reduce.py: cnmf_patches():80] [1655526] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82020_102540Read file\n", - " 62442 [map_reduce.py: cnmf_patches():80] [1655547] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143490_164010Read file\n", - " 62442 [map_reduce.py: cnmf_patches():80] [1655552] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220290_240810Read file\n", - " 62443 [map_reduce.py: cnmf_patches():80] [1655529] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128100_148620Read file\n", - " 62443 [map_reduce.py: cnmf_patches():80] [1655535] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220260_240780Read file\n", - " 62443 [map_reduce.py: cnmf_patches():80] [1655550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189570_210090Read file\n", - " 62444 [map_reduce.py: cnmf_patches():80] [1655516] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189510_210030Read file\n", - " 62445 [map_reduce.py: cnmf_patches():80] [1655559] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66720_87240Read file\n", - " 62445 [map_reduce.py: cnmf_patches():80] [1655553] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235650_256170Read file\n", - " 62445 [map_reduce.py: cnmf_patches():80] [1655514] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 158790_179310Read file\n", - " 62446 [map_reduce.py: cnmf_patches():80] [1655527] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97380_117900Read file\n", - " 62446 [map_reduce.py: cnmf_patches():80] [1655509] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 81990_102510Read file\n", - " 62446 [map_reduce.py: cnmf_patches():80] [1655551] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 204930_225450Read file\n", - " 62447 [map_reduce.py: cnmf_patches():80] [1655534] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 204900_225420Read file\n", - " 62447 [map_reduce.py: cnmf_patches():80] [1655513] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143430_163950Read file\n", - " 62447 [map_reduce.py: cnmf_patches():80] [1655556] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20640_41160Read file\n", - " 62448 [map_reduce.py: cnmf_patches():80] [1655536] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235620_256140Read file\n", - 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" 63336 [map_reduce.py: cnmf_patches():100] [1655501] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220200_240720file loaded\n", - " 63312 [map_reduce.py: cnmf_patches():100] [1655484] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220170_240690file loaded\n", - " 63308 [map_reduce.py: cnmf_patches():100] [1655495] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128040_148560file loaded\n", - " 63366 [map_reduce.py: cnmf_patches():80] [1655563] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128160_148680Read file\n", - " 63381 [map_reduce.py: cnmf_patches():100] [1655474] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66570_87090file loaded\n", - " 63403 [map_reduce.py: cnmf_patches():100] [1655562] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 112800_133320file loaded\n", - " 63406 [map_reduce.py: cnmf_patches():100] [1655504] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5190_25710file loaded\n", - " 63399 [map_reduce.py: cnmf_patches():100] [1655498] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174120_194640file loaded\n", - " 63429 [map_reduce.py: cnmf_patches():100] [1655521] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5220_25740file loaded\n", - " 63433 [map_reduce.py: cnmf_patches():100] [1655470] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20490_41010file loaded\n", - " 63418 [map_reduce.py: cnmf_patches():100] [1655539] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20610_41130file loaded\n", - " 63423 [map_reduce.py: cnmf_patches():100] [1655490] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51240_71760file loaded\n", - " 63437 [map_reduce.py: cnmf_patches():100] [1655505] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20550_41070file loaded\n", - " 63451 [map_reduce.py: cnmf_patches():100] [1655469] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5130_25650file loaded\n", - " 63453 [map_reduce.py: cnmf_patches():100] [1655556] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20640_41160file loaded\n", - " 63458 [map_reduce.py: cnmf_patches():100] [1655488] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20520_41040file loaded\n", - " 66055 [map_reduce.py: cnmf_patches():100] [1655563] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128160_148680file loaded\n", - " 67830 [params.py: set():1006] [1655515] Changing key rf in group patch from 20 to None\n", - " 68162 [params.py: set():1006] [1655515] Changing key stride in group patch from 10 to None\n", - " 68202 [params.py: set():1006] [1655531] Changing key rf in group patch from 20 to None\n", - " 68176 [params.py: set():1006] [1655535] Changing key rf in group patch from 20 to None\n", - " 68206 [params.py: set():1006] [1655549] Changing key rf in group patch from 20 to None\n", - " 68337 [params.py: set():1006] [1655553] Changing key rf in group patch from 20 to None\n", - " 68509 [params.py: set():1006] [1655515] Changing key nb in group init from 2 to 1\n", - " 68527 [params.py: set():1006] [1655531] Changing key stride in group patch from 10 to None\n", - " 68517 [params.py: set():1006] [1655552] Changing key rf in group patch from 20 to None\n", - " 68539 [params.py: set():1006] [1655535] Changing key stride in group patch from 10 to None\n", - " 68597 [params.py: set():1006] [1655549] Changing key stride in group patch from 10 to None\n", - " 68601 [params.py: set():1006] [1655530] Changing key rf in group patch from 20 to None\n", - " 68602 [params.py: set():1006] [1655550] Changing key rf in group patch from 20 to None\n", - " 68683 [params.py: set():1006] [1655553] Changing key stride in group patch from 10 to None\n", - " 68693 [params.py: set():1006] [1655546] Changing key rf in group patch from 20 to None\n", - " 68843 [params.py: set():1006] [1655515] Changing key nb in group temporal from 2 to 1\n", - " 68807 [params.py: set():1006] [1655551] Changing key rf in group patch from 20 to None\n", - " 68880 [params.py: set():1006] [1655531] Changing key nb in group init from 2 to 1\n", - " 68888 [params.py: set():1006] [1655535] Changing key nb in group init from 2 to 1\n", - " 68888 [params.py: set():1006] [1655552] Changing key stride in group patch from 10 to None\n", - " 68916 [params.py: set():1006] [1655549] Changing key nb in group init from 2 to 1\n", - " 68925 [params.py: set():1006] [1655550] Changing key stride in group patch from 10 to None\n", - " 68926 [params.py: set():1006] [1655530] Changing key stride in group patch from 10 to None\n", - " 68947 [params.py: set():1006] [1655546] Changing key stride in group patch from 10 to None\n", - " 68948 [params.py: set():1006] [1655553] Changing key nb in group init from 2 to 1\n", - " 68714 [params.py: set():1006] [1655536] Changing key rf in group patch from 20 to None\n", - " 68956 [params.py: set():1006] [1655470] Changing key rf in group patch from 20 to None\n", - " 68981 [params.py: set():1006] [1655473] Changing key rf in group patch from 20 to None\n", - " 68983 [params.py: set():1006] [1655537] Changing key rf in group patch from 20 to None\n", - " 68988 [params.py: set():1006] [1655494] Changing key rf in group patch from 20 to None\n", - " 68989 [params.py: set():1006] [1655490] Changing key rf in group patch from 20 to None\n", - " 68997 [params.py: set():1006] [1655543] Changing key rf in group patch from 20 to None\n", - " 69000 [params.py: set():1006] [1655525] Changing key rf in group patch from 20 to None\n", - " 69017 [params.py: set():1006] [1655522] Changing key rf in group patch from 20 to None\n", - " 69028 [params.py: set():1006] [1655519] Changing key rf in group patch from 20 to None\n", - " 69053 [params.py: set():1006] [1655557] Changing key rf in group patch from 20 to None\n", - " 69063 [params.py: set():1006] [1655540] Changing key rf in group patch from 20 to None\n", - " 69084 [params.py: set():1006] [1655500] Changing key rf in group patch from 20 to None\n", - " 69085 [params.py: set():1006] [1655515] Changing key nb in group spatial from 2 to 1\n", - " 69104 [params.py: set():1006] [1655529] Changing key rf in group patch from 20 to None\n", - " 69175 [params.py: set():1006] [1655551] Changing key stride in group patch from 10 to None\n", - " 69178 [params.py: set():1006] [1655534] Changing key rf in group patch from 20 to None\n", - " 69179 [params.py: set():1006] [1655531] Changing key nb in group temporal from 2 to 1\n", - " 68953 [params.py: set():1006] [1655489] Changing key rf in group patch from 20 to None\n", - " 69089 [params.py: set():1006] [1655480] Changing key rf in group patch from 20 to None\n", - " 69191 [params.py: set():1006] [1655535] Changing key nb in group temporal from 2 to 1\n", - " 69146 [params.py: set():1006] [1655482] Changing key rf in group patch from 20 to None\n", - " 69192 [params.py: set():1006] [1655552] Changing key nb in group init from 2 to 1\n", - " 69005 [params.py: set():1006] [1655526] Changing key rf in group patch from 20 to None\n", - " 69186 [params.py: set():1006] [1655533] Changing key rf in group patch from 20 to None\n", - " 69213 [params.py: set():1006] [1655512] Changing key rf in group patch from 20 to None\n", - " 69214 [params.py: set():1006] [1655511] Changing key rf in group patch from 20 to None\n", - " 69224 [params.py: set():1006] [1655508] Changing key rf in group patch from 20 to None\n", - " 69225 [params.py: set():1006] [1655549] Changing key nb in group temporal from 2 to 1\n", - " 69228 [params.py: set():1006] [1655507] Changing key rf in group patch from 20 to None\n", - " 69228 [params.py: set():1006] [1655530] Changing key nb in group init from 2 to 1\n", - " 69229 [params.py: set():1006] [1655550] Changing key nb in group init from 2 to 1\n", - " 69190 [params.py: set():1006] [1655497] Changing key rf in group patch from 20 to None\n", - " 68995 [params.py: set():1006] [1655558] Changing key rf in group patch from 20 to None\n", - " 69202 [params.py: set():1006] [1655524] Changing key rf in group patch from 20 to None\n", - " 69232 [params.py: set():1006] [1655513] Changing key rf in group patch from 20 to None\n", - " 69239 [params.py: set():1006] [1655546] Changing key nb in group init from 2 to 1\n", - " 69244 [params.py: set():1006] [1655560] Changing key rf in group patch from 20 to None\n", - " 69245 [params.py: set():1006] [1655553] Changing key nb in group temporal from 2 to 1\n", - " 69094 [params.py: set():1006] [1655509] Changing key rf in group patch from 20 to None\n", - " 69252 [params.py: set():1006] [1655470] Changing key stride in group patch from 10 to None\n", - " 69252 [params.py: set():1006] [1655536] Changing key stride in group patch from 10 to None\n", - " 69254 [params.py: set():1006] [1655520] Changing key rf in group patch from 20 to None\n", - " 69259 [params.py: set():1006] [1655547] Changing key rf in group patch from 20 to None\n", - " 69259 [params.py: set():1006] [1655537] Changing key stride in group patch from 10 to None\n", - " 69260 [params.py: set():1006] [1655494] Changing key stride in group patch from 10 to None\n", - " 69257 [params.py: set():1006] [1655517] Changing key rf in group patch from 20 to None\n", - " 69261 [params.py: set():1006] [1655490] Changing key stride in group patch from 10 to None\n", - " 69261 [params.py: set():1006] [1655473] Changing key stride in group patch from 10 to None\n", - " 69265 [params.py: set():1006] [1655543] Changing key stride in group patch from 10 to None\n", - " 69271 [params.py: set():1006] [1655525] Changing key stride in group patch from 10 to None\n", - " 69264 [params.py: set():1006] [1655523] Changing key rf in group patch from 20 to None\n", - " 69272 [params.py: set():1006] [1655522] Changing key stride in group patch from 10 to None\n", - " 69274 [params.py: set():1006] [1655518] Changing key rf in group patch from 20 to None\n", - " 69274 [params.py: set():1006] [1655519] Changing key stride in group patch from 10 to None\n", - " 69275 [params.py: set():1006] [1655557] Changing key stride in group patch from 10 to None\n", - " 69279 [params.py: set():1006] [1655540] Changing key stride in group patch from 10 to None\n", - " 69281 [params.py: set():1006] [1655500] Changing key stride in group patch from 10 to None\n", - " 69281 [params.py: set():1006] [1655515] Changing key p in group preprocess from 1 to 0\n", - " 69282 [params.py: set():1006] [1655486] Changing key rf in group patch from 20 to None\n", - " 69283 [params.py: set():1006] [1655514] Changing key rf in group patch from 20 to None\n", - " 69284 [params.py: set():1006] [1655529] Changing key stride in group patch from 10 to None\n", - " 69285 [params.py: set():1006] [1655541] Changing key rf in group patch from 20 to None\n", - " 69302 [params.py: set():1006] [1655554] Changing key rf in group patch from 20 to None\n", - " 69303 [params.py: set():1006] [1655488] Changing key rf in group patch from 20 to None\n", - " 69305 [params.py: set():1006] [1655506] Changing key rf in group patch from 20 to None\n", - " 69306 [params.py: set():1006] [1655531] Changing key nb in group spatial from 2 to 1\n", - " 69306 [params.py: set():1006] [1655551] Changing key nb in group init from 2 to 1\n", - " 69306 [params.py: set():1006] [1655534] Changing key stride in group patch from 10 to None\n", - " 69308 [params.py: set():1006] [1655489] Changing key stride in group patch from 10 to None\n", - " 69308 [params.py: set():1006] [1655535] Changing key nb in group spatial from 2 to 1\n", - " 69311 [params.py: set():1006] [1655479] Changing key rf in group patch from 20 to None\n", - " 69312 [params.py: set():1006] [1655527] Changing key rf in group patch from 20 to None\n", - " 69312 [params.py: set():1006] [1655480] Changing key stride in group patch from 10 to None\n", - " 69315 [params.py: set():1006] [1655492] Changing key rf in group patch from 20 to None\n", - " 69325 [params.py: set():1006] [1655499] Changing key rf in group patch from 20 to None\n", - " 69326 [params.py: set():1006] [1655552] Changing key nb in group temporal from 2 to 1\n", - " 69332 [params.py: set():1006] [1655521] Changing key rf in group patch from 20 to None\n", - " 69339 [params.py: set():1006] [1655482] Changing key stride in group patch from 10 to None\n", - " 69341 [params.py: set():1006] [1655562] Changing key rf in group patch from 20 to None\n", - " 69198 [params.py: set():1006] [1655532] Changing key rf in group patch from 20 to None\n", - " 69343 [params.py: set():1006] [1655481] Changing key rf in group patch from 20 to None\n", - " 69343 [params.py: set():1006] [1655526] Changing key stride in group patch from 10 to None\n", - " 69348 [params.py: set():1006] [1655564] Changing key rf in group patch from 20 to None\n", - " 69351 [params.py: set():1006] [1655487] Changing key rf in group patch from 20 to None\n", - " 69356 [params.py: set():1006] [1655533] Changing key stride in group patch from 10 to None\n", - " 69361 [params.py: set():1006] [1655511] Changing key stride in group patch from 10 to None\n", - " 69364 [params.py: set():1006] [1655483] Changing key rf in group patch from 20 to None\n", - " 69364 [params.py: set():1006] [1655512] Changing key stride in group patch from 10 to None\n", - " 69347 [params.py: set():1006] [1655485] Changing key rf in group patch from 20 to None\n", - " 69375 [params.py: set():1006] [1655549] Changing key nb in group spatial from 2 to 1\n", - " 69376 [params.py: set():1006] [1655508] Changing key stride in group patch from 10 to None\n", - " 69383 [params.py: set():1006] [1655530] Changing key nb in group temporal from 2 to 1\n", - " 69329 [params.py: set():1006] [1655516] Changing key rf in group patch from 20 to None\n", - " 69387 [params.py: set():1006] [1655507] Changing key stride in group patch from 10 to None\n", - " 69388 [params.py: set():1006] [1655550] Changing key nb in group temporal from 2 to 1\n", - " 69398 [params.py: set():1006] [1655469] Changing key rf in group patch from 20 to None\n", - " 69399 [params.py: set():1006] [1655497] Changing key stride in group patch from 10 to None\n", - " 69400 [params.py: set():1006] [1655498] Changing key rf in group patch from 20 to None\n", - " 69400 [params.py: set():1006] [1655513] Changing key stride in group patch from 10 to None\n", - " 69401 [params.py: set():1006] [1655558] Changing key stride in group patch from 10 to None\n", - " 69401 [params.py: set():1006] [1655524] Changing key stride in group patch from 10 to None\n", - " 69238 [params.py: set():1006] [1655495] Changing key rf in group patch from 20 to None\n", - " 69402 [params.py: set():1006] [1655546] Changing key nb in group temporal from 2 to 1\n", - " 69403 [params.py: set():1006] [1655560] Changing key stride in group patch from 10 to None\n", - " 69403 [params.py: set():1006] [1655553] Changing key nb in group spatial from 2 to 1\n", - " 68923 [params.py: set():1006] [1655476] Changing key rf in group patch from 20 to None\n", - " 69404 [params.py: set():1006] [1655509] Changing key stride in group patch from 10 to None\n", - " 69405 [params.py: set():1006] [1655470] Changing key nb in group init from 2 to 1\n", - " 69405 [params.py: set():1006] [1655536] Changing key nb in group init from 2 to 1\n", - " 69059 [params.py: set():1006] [1655542] Changing key rf in group patch from 20 to None\n", - " 69360 [params.py: set():1006] [1655555] Changing key rf in group patch from 20 to None\n", - " 69415 [params.py: set():1006] [1655561] Changing key rf in group patch from 20 to None\n", - " 69415 [params.py: set():1006] [1655504] Changing key rf in group patch from 20 to None\n", - " 69290 [params.py: set():1006] [1655474] Changing key rf in group patch from 20 to None\n", - " 69417 [params.py: set():1006] [1655502] Changing key rf in group patch from 20 to None\n", - " 69417 [params.py: set():1006] [1655520] Changing key stride in group patch from 10 to None\n", - " 69011 [params.py: set():1006] [1655544] Changing key rf in group patch from 20 to None\n", - " 69419 [params.py: set():1006] [1655537] Changing key nb in group init from 2 to 1\n", - " 69420 [params.py: set():1006] [1655494] Changing key nb in group init from 2 to 1\n", - " 69420 [params.py: set():1006] [1655547] Changing key stride in group patch from 10 to None\n", - " 69421 [params.py: set():1006] [1655490] Changing key nb in group init from 2 to 1\n", - " 69421 [params.py: set():1006] [1655473] Changing key nb in group init from 2 to 1\n", - " 69422 [params.py: set():1006] [1655517] Changing key stride in group patch from 10 to None\n", - " 69422 [params.py: set():1006] [1655543] Changing key nb in group init from 2 to 1\n", - " 69423 [params.py: set():1006] [1655525] Changing key nb in group init from 2 to 1\n", - " 69424 [params.py: set():1006] [1655522] Changing key nb in group init from 2 to 1\n", - " 69431 [params.py: set():1006] [1655519] Changing key nb in group init from 2 to 1\n", - " 69432 [params.py: set():1006] [1655518] Changing key stride in group patch from 10 to None\n", - " 69435 [params.py: set():1006] [1655472] Changing key rf in group patch from 20 to None\n", - " 69431 [params.py: set():1006] [1655523] Changing key stride in group patch from 10 to None\n", - " 69439 [params.py: set():1006] [1655557] Changing key nb in group init from 2 to 1\n", - " 69443 [params.py: set():1006] [1655559] Changing key rf in group patch from 20 to None\n", - " 69443 [params.py: set():1006] [1655540] Changing key nb in group init from 2 to 1\n", - " 69444 [params.py: set():1006] [1655500] Changing key nb in group init from 2 to 1\n", - " 69445 [params.py: set():1006] [1655515] Changing key p in group temporal from 1 to 0\n", - " 69446 [params.py: set():1006] [1655486] Changing key stride in group patch from 10 to None\n", - " 69446 [params.py: set():1006] [1655514] Changing key stride in group patch from 10 to None\n", - " 69447 [params.py: set():1006] [1655529] Changing key nb in group init from 2 to 1\n", - " 69438 [params.py: set():1006] [1655545] Changing key rf in group patch from 20 to None\n", - " 69447 [params.py: set():1006] [1655541] Changing key stride in group patch from 10 to None\n", - " 69449 [params.py: set():1006] [1655484] Changing key rf in group patch from 20 to None\n", - " 69451 [params.py: set():1006] [1655554] Changing key stride in group patch from 10 to None\n", - " 69111 [params.py: set():1006] [1655538] Changing key rf in group patch from 20 to None\n", - " 69451 [params.py: set():1006] [1655488] Changing key stride in group patch from 10 to None\n", - " 69452 [params.py: set():1006] [1655506] Changing key stride in group patch from 10 to None\n", - " 69453 [params.py: set():1006] [1655531] Changing key p in group preprocess from 1 to 0\n", - " 69453 [params.py: set():1006] [1655551] Changing key nb in group temporal from 2 to 1\n", - " 69370 [params.py: set():1006] [1655477] Changing key rf in group patch from 20 to None\n", - " 69453 [params.py: set():1006] [1655534] Changing key nb in group init from 2 to 1\n", - " 69457 [params.py: set():1006] [1655480] Changing key nb in group init from 2 to 1\n", - " 69456 [params.py: set():1006] [1655527] Changing key stride in group patch from 10 to None\n", - " 69455 [params.py: set():1006] [1655489] Changing key nb in group init from 2 to 1\n", - " 69455 [params.py: set():1006] [1655535] Changing key p in group preprocess from 1 to 0\n", - " 69456 [params.py: set():1006] [1655479] Changing key stride in group patch from 10 to None\n", - " 69458 [params.py: set():1006] [1655492] Changing key stride in group patch from 10 to None\n", - " 69467 [params.py: set():1006] [1655533] Changing key nb in group init from 2 to 1\n", - " 69458 [params.py: set():1006] [1655499] Changing key stride in group patch from 10 to None\n", - " 69461 [params.py: set():1006] [1655496] Changing key rf in group patch from 20 to None\n", - " 69461 [params.py: set():1006] [1655552] Changing key nb in group spatial from 2 to 1\n", - " 69462 [params.py: set():1006] [1655521] Changing key stride in group patch from 10 to None\n", - " 69462 [params.py: set():1006] [1655482] Changing key nb in group init from 2 to 1\n", - " 69463 [params.py: set():1006] [1655562] Changing key stride in group patch from 10 to None\n", - " 69464 [params.py: set():1006] [1655481] Changing key stride in group patch from 10 to None\n", - " 69465 [params.py: set():1006] [1655526] Changing key nb in group init from 2 to 1\n", - " 69466 [params.py: set():1006] [1655564] Changing key stride in group patch from 10 to None\n", - " 69466 [params.py: set():1006] [1655532] Changing key stride in group patch from 10 to None\n", - " 69466 [params.py: set():1006] [1655487] Changing key stride in group patch from 10 to None\n", - " 69468 [params.py: set():1006] [1655511] Changing key nb in group init from 2 to 1\n", - " 69469 [params.py: set():1006] [1655483] Changing key stride in group patch from 10 to None\n", - " 69469 [params.py: set():1006] [1655512] Changing key nb in group init from 2 to 1\n", - " 69471 [params.py: set():1006] [1655549] Changing key p in group preprocess from 1 to 0\n", - " 69471 [params.py: set():1006] [1655508] Changing key nb in group init from 2 to 1\n", - " 69471 [params.py: set():1006] [1655485] Changing key stride in group patch from 10 to None\n", - " 69472 [params.py: set():1006] [1655530] Changing key nb in group spatial from 2 to 1\n", - " 69473 [params.py: set():1006] [1655507] Changing key nb in group init from 2 to 1\n", - " 69474 [params.py: set():1006] [1655505] Changing key rf in group patch from 20 to None\n", - " 69474 [params.py: set():1006] [1655550] Changing key nb in group spatial from 2 to 1\n", - " 69475 [params.py: set():1006] [1655516] Changing key stride in group patch from 10 to None\n", - " 69476 [params.py: set():1006] [1655469] Changing key stride in group patch from 10 to None\n", - " 69476 [params.py: set():1006] [1655497] Changing key nb in group init from 2 to 1\n", - " 69477 [params.py: set():1006] [1655498] Changing key stride in group patch from 10 to None\n", - " 69479 [params.py: set():1006] [1655510] Changing key rf in group patch from 20 to None\n", - " 69479 [params.py: set():1006] [1655558] Changing key nb in group init from 2 to 1\n", - " 69480 [params.py: set():1006] [1655513] Changing key nb in group init from 2 to 1\n", - " 69480 [params.py: set():1006] [1655524] Changing key nb in group init from 2 to 1\n", - " 69481 [params.py: set():1006] [1655546] Changing key nb in group spatial from 2 to 1\n", - " 69482 [params.py: set():1006] [1655560] Changing key nb in group init from 2 to 1\n", - " 69487 [params.py: set():1006] [1655495] Changing key stride in group patch from 10 to None\n", - " 69488 [params.py: set():1006] [1655553] Changing key p in group preprocess from 1 to 0\n", - " 69491 [params.py: set():1006] [1655478] Changing key rf in group patch from 20 to None\n", - " 69491 [params.py: set():1006] [1655476] Changing key stride in group patch from 10 to None\n", - " 69492 [params.py: set():1006] [1655509] Changing key nb in group init from 2 to 1\n", - " 69493 [params.py: set():1006] [1655470] Changing key nb in group temporal from 2 to 1\n", - " 69493 [params.py: set():1006] [1655536] Changing key nb in group temporal from 2 to 1\n", - " 69496 [params.py: set():1006] [1655539] Changing key rf in group patch from 20 to None\n", - " 69497 [params.py: set():1006] [1655542] Changing key stride in group patch from 10 to None\n", - " 69497 [params.py: set():1006] [1655504] Changing key stride in group patch from 10 to None\n", - " 69498 [params.py: set():1006] [1655561] Changing key stride in group patch from 10 to None\n", - " 69499 [params.py: set():1006] [1655493] Changing key rf in group patch from 20 to None\n", - " 69500 [params.py: set():1006] [1655555] Changing key stride in group patch from 10 to None\n", - " 69430 [params.py: set():1006] [1655548] Changing key rf in group patch from 20 to None\n", - " 69501 [params.py: set():1006] [1655502] Changing key stride in group patch from 10 to None\n", - " 69501 [params.py: set():1006] [1655520] Changing key nb in group init from 2 to 1\n", - " 69502 [params.py: set():1006] [1655503] Changing key rf in group patch from 20 to None\n", - " 69503 [params.py: set():1006] [1655474] Changing key stride in group patch from 10 to None\n", - " 69504 [params.py: set():1006] [1655544] Changing key stride in group patch from 10 to None\n", - " 69505 [params.py: set():1006] [1655537] Changing key nb in group temporal from 2 to 1\n", - " 69505 [params.py: set():1006] [1655547] Changing key nb in group init from 2 to 1\n", - " 69506 [params.py: set():1006] [1655494] Changing key nb in group temporal from 2 to 1\n", - " 69506 [params.py: set():1006] [1655490] Changing key nb in group temporal from 2 to 1\n", - " 69507 [params.py: set():1006] [1655491] Changing key rf in group patch from 20 to None\n", - " 69507 [params.py: set():1006] [1655473] Changing key nb in group temporal from 2 to 1\n", - " 69508 [params.py: set():1006] [1655517] Changing key nb in group init from 2 to 1\n", - " 69509 [params.py: set():1006] [1655543] Changing key nb in group temporal from 2 to 1\n", - " 69509 [params.py: set():1006] [1655525] Changing key nb in group temporal from 2 to 1\n", - " 69510 [params.py: set():1006] [1655522] Changing key nb in group temporal from 2 to 1\n", - " 69510 [params.py: set():1006] [1655519] Changing key nb in group temporal from 2 to 1\n", - " 69511 [params.py: set():1006] [1655518] Changing key nb in group init from 2 to 1\n", - " 69512 [params.py: set():1006] [1655475] Changing key rf in group patch from 20 to None\n", - " 69412 [params.py: set():1006] [1655501] Changing key rf in group patch from 20 to None\n", - " 69512 [params.py: set():1006] [1655472] Changing key stride in group patch from 10 to None\n", - " 69513 [params.py: set():1006] [1655523] Changing key nb in group init from 2 to 1\n", - " 69514 [params.py: set():1006] [1655557] Changing key nb in group temporal from 2 to 1\n", - " 69514 [params.py: set():1006] [1655559] Changing key stride in group patch from 10 to None\n", - " 69515 [params.py: set():1006] [1655540] Changing key nb in group temporal from 2 to 1\n", - " 69516 [cnmf.py: fit():436] [1655515] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69516 [params.py: set():1006] [1655500] Changing key nb in group temporal from 2 to 1\n", - " 69517 [params.py: set():1006] [1655486] Changing key nb in group init from 2 to 1\n", - " 69518 [params.py: set():1006] [1655514] Changing key nb in group init from 2 to 1\n", - " 69518 [params.py: set():1006] [1655529] Changing key nb in group temporal from 2 to 1\n", - " 69519 [params.py: set():1006] [1655541] Changing key nb in group init from 2 to 1\n", - " 69520 [params.py: set():1006] [1655484] Changing key stride in group patch from 10 to None\n", - " 69519 [params.py: set():1006] [1655545] Changing key stride in group patch from 10 to None\n", - " 69522 [params.py: set():1006] [1655554] Changing key nb in group init from 2 to 1\n", - " 69522 [params.py: set():1006] [1655538] Changing key stride in group patch from 10 to None\n", - " 69523 [params.py: set():1006] [1655506] Changing key nb in group init from 2 to 1\n", - " 69523 [params.py: set():1006] [1655488] Changing key nb in group init from 2 to 1\n", - " 69524 [params.py: set():1006] [1655531] Changing key p in group temporal from 1 to 0\n", - " 69524 [params.py: set():1006] [1655551] Changing key nb in group spatial from 2 to 1\n", - " 69525 [params.py: set():1006] [1655534] Changing key nb in group temporal from 2 to 1\n", - " 69525 [params.py: set():1006] [1655477] Changing key stride in group patch from 10 to None\n", - " 69526 [params.py: set():1006] [1655489] Changing key nb in group temporal from 2 to 1\n", - " 69526 [params.py: set():1006] [1655535] Changing key p in group temporal from 1 to 0\n", - " 69526 [params.py: set():1006] [1655479] Changing key nb in group init from 2 to 1\n", - " 69528 [params.py: set():1006] [1655527] Changing key nb in group init from 2 to 1\n", - " 69528 [params.py: set():1006] [1655480] Changing key nb in group temporal from 2 to 1\n", - " 69529 [params.py: set():1006] [1655492] Changing key nb in group init from 2 to 1\n", - " 69529 [params.py: set():1006] [1655499] Changing key nb in group init from 2 to 1\n", - " 69530 [params.py: set():1006] [1655552] Changing key p in group preprocess from 1 to 0\n", - " 69530 [params.py: set():1006] [1655496] Changing key stride in group patch from 10 to None\n", - " 69531 [params.py: set():1006] [1655521] Changing key nb in group init from 2 to 1\n", - " 69531 [params.py: set():1006] [1655482] Changing key nb in group temporal from 2 to 1\n", - " 69531 [params.py: set():1006] [1655562] Changing key nb in group init from 2 to 1\n", - " 69532 [params.py: set():1006] [1655481] Changing key nb in group init from 2 to 1\n", - " 69533 [params.py: set():1006] [1655526] Changing key nb in group temporal from 2 to 1\n", - " 69533 [params.py: set():1006] [1655532] Changing key nb in group init from 2 to 1\n", - " 69534 [params.py: set():1006] [1655487] Changing key nb in group init from 2 to 1\n", - " 69534 [params.py: set():1006] [1655564] Changing key nb in group init from 2 to 1\n", - " 69535 [params.py: set():1006] [1655533] Changing key nb in group temporal from 2 to 1\n", - " 69535 [params.py: set():1006] [1655483] Changing key nb in group init from 2 to 1\n", - " 69536 [params.py: set():1006] [1655511] Changing key nb in group temporal from 2 to 1\n", - " 69536 [params.py: set():1006] [1655512] Changing key nb in group temporal from 2 to 1\n", - " 69537 [params.py: set():1006] [1655549] Changing key p in group temporal from 1 to 0\n", - " 69538 [params.py: set():1006] [1655508] Changing key nb in group temporal from 2 to 1\n", - " 69538 [params.py: set():1006] [1655485] Changing key nb in group init from 2 to 1\n", - " 69538 [params.py: set():1006] [1655530] Changing key p in group preprocess from 1 to 0\n", - " 69539 [params.py: set():1006] [1655505] Changing key stride in group patch from 10 to None\n", - " 69539 [params.py: set():1006] [1655507] Changing key nb in group temporal from 2 to 1\n", - " 69540 [params.py: set():1006] [1655550] Changing key p in group preprocess from 1 to 0\n", - " 69540 [params.py: set():1006] [1655516] Changing key nb in group init from 2 to 1\n", - " 69541 [params.py: set():1006] [1655469] Changing key nb in group init from 2 to 1\n", - " 69542 [params.py: set():1006] [1655497] Changing key nb in group temporal from 2 to 1\n", - " 69542 [params.py: set():1006] [1655498] Changing key nb in group init from 2 to 1\n", - " 69543 [params.py: set():1006] [1655510] Changing key stride in group patch from 10 to None\n", - " 69543 [params.py: set():1006] [1655513] Changing key nb in group temporal from 2 to 1\n", - " 69544 [params.py: set():1006] [1655558] Changing key nb in group temporal from 2 to 1\n", - " 69547 [params.py: set():1006] [1655556] Changing key rf in group patch from 20 to None\n", - " 69548 [params.py: set():1006] [1655524] Changing key nb in group temporal from 2 to 1\n", - " 69548 [params.py: set():1006] [1655546] Changing key p in group preprocess from 1 to 0\n", - " 69549 [params.py: set():1006] [1655560] Changing key nb in group temporal from 2 to 1\n", - " 69549 [params.py: set():1006] [1655495] Changing key nb in group init from 2 to 1\n", - " 69550 [params.py: set():1006] [1655553] Changing key p in group temporal from 1 to 0\n", - " 69551 [params.py: set():1006] [1655478] Changing key stride in group patch from 10 to None\n", - " 69551 [params.py: set():1006] [1655476] Changing key nb in group init from 2 to 1\n", - " 69552 [params.py: set():1006] [1655509] Changing key nb in group temporal from 2 to 1\n", - " 69552 [params.py: set():1006] [1655470] Changing key nb in group spatial from 2 to 1\n", - " 69553 [params.py: set():1006] [1655528] Changing key rf in group patch from 20 to None\n", - " 69554 [params.py: set():1006] [1655536] Changing key nb in group spatial from 2 to 1\n", - " 69554 [params.py: set():1006] [1655542] Changing key nb in group init from 2 to 1\n", - " 69555 [params.py: set():1006] [1655539] Changing key stride in group patch from 10 to None\n", - " 69555 [params.py: set():1006] [1655561] Changing key nb in group init from 2 to 1\n", - " 69556 [params.py: set():1006] [1655504] Changing key nb in group init from 2 to 1\n", - " 69556 [params.py: set():1006] [1655493] Changing key stride in group patch from 10 to None\n", - " 69557 [params.py: set():1006] [1655555] Changing key nb in group init from 2 to 1\n", - " 69557 [params.py: set():1006] [1655502] Changing key nb in group init from 2 to 1\n", - " 69558 [params.py: set():1006] [1655520] Changing key nb in group temporal from 2 to 1\n", - " 69559 [params.py: set():1006] [1655503] Changing key stride in group patch from 10 to None\n", - " 69559 [params.py: set():1006] [1655474] Changing key nb in group init from 2 to 1\n", - " 69560 [params.py: set():1006] [1655544] Changing key nb in group init from 2 to 1\n", - " 69561 [params.py: set():1006] [1655547] Changing key nb in group temporal from 2 to 1\n", - " 69561 [params.py: set():1006] [1655494] Changing key nb in group spatial from 2 to 1\n", - " 69561 [params.py: set():1006] [1655537] Changing key nb in group spatial from 2 to 1\n", - " 69562 [params.py: set():1006] [1655490] Changing key nb in group spatial from 2 to 1\n", - " 69562 [params.py: set():1006] [1655491] Changing key stride in group patch from 10 to None\n", - " 69563 [params.py: set():1006] [1655473] Changing key nb in group spatial from 2 to 1\n", - " 69563 [params.py: set():1006] [1655517] Changing key nb in group temporal from 2 to 1\n", - " 69564 [params.py: set():1006] [1655543] Changing key nb in group spatial from 2 to 1\n", - " 69559 [params.py: set():1006] [1655548] Changing key stride in group patch from 10 to None\n", - " 69564 [params.py: set():1006] [1655525] Changing key nb in group spatial from 2 to 1\n", - " 69565 [params.py: set():1006] [1655522] Changing key nb in group spatial from 2 to 1\n", - " 69566 [params.py: set():1006] [1655519] Changing key nb in group spatial from 2 to 1\n", - " 69566 [params.py: set():1006] [1655518] Changing key nb in group temporal from 2 to 1\n", - " 69567 [params.py: set():1006] [1655475] Changing key stride in group patch from 10 to None\n", - " 69568 [params.py: set():1006] [1655472] Changing key nb in group init from 2 to 1\n", - " 69568 [params.py: set():1006] [1655501] Changing key stride in group patch from 10 to None\n", - " 69568 [params.py: set():1006] [1655523] Changing key nb in group temporal from 2 to 1\n", - " 69569 [params.py: set():1006] [1655557] Changing key nb in group spatial from 2 to 1\n", - " 69570 [params.py: set():1006] [1655559] Changing key nb in group init from 2 to 1\n", - " 69570 [params.py: set():1006] [1655540] Changing key nb in group spatial from 2 to 1\n", - " 69571 [cnmf.py: fit():449] [1655515] (8000, 41, 41)\n", - " 69571 [params.py: set():1006] [1655500] Changing key nb in group spatial from 2 to 1\n", - " 69572 [params.py: set():1006] [1655486] Changing key nb in group temporal from 2 to 1\n", - " 69572 [params.py: set():1006] [1655514] Changing key nb in group temporal from 2 to 1\n", - " 69573 [params.py: set():1006] [1655529] Changing key nb in group spatial from 2 to 1\n", - " 69574 [params.py: set():1006] [1655541] Changing key nb in group temporal from 2 to 1\n", - " 69574 [params.py: set():1006] [1655563] Changing key rf in group patch from 20 to None\n", - " 69575 [params.py: set():1006] [1655545] Changing key nb in group init from 2 to 1\n", - " 69575 [params.py: set():1006] [1655484] Changing key nb in group init from 2 to 1\n", - " 69577 [params.py: set():1006] [1655538] Changing key nb in group init from 2 to 1\n", - " 69577 [params.py: set():1006] [1655554] Changing key nb in group temporal from 2 to 1\n", - " 69577 [params.py: set():1006] [1655506] Changing key nb in group temporal from 2 to 1\n", - " 69578 [params.py: set():1006] [1655488] Changing key nb in group temporal from 2 to 1\n", - " 69578 [cnmf.py: fit():436] [1655531] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69579 [params.py: set():1006] [1655551] Changing key p in group preprocess from 1 to 0\n", - " 69579 [params.py: set():1006] [1655534] Changing key nb in group spatial from 2 to 1\n", - " 69580 [params.py: set():1006] [1655477] Changing key nb in group init from 2 to 1\n", - " 69581 [params.py: set():1006] [1655479] Changing key nb in group temporal from 2 to 1\n", - " 69581 [cnmf.py: fit():436] [1655535] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69582 [params.py: set():1006] [1655489] Changing key nb in group spatial from 2 to 1\n", - " 69583 [params.py: set():1006] [1655480] Changing key nb in group spatial from 2 to 1\n", - " 69582 [params.py: set():1006] [1655527] Changing key nb in group temporal from 2 to 1\n", - " 69583 [params.py: set():1006] [1655492] Changing key nb in group temporal from 2 to 1\n", - " 69584 [params.py: set():1006] [1655499] Changing key nb in group temporal from 2 to 1\n", - " 69584 [params.py: set():1006] [1655496] Changing key nb in group init from 2 to 1\n", - " 69584 [params.py: set():1006] [1655552] Changing key p in group temporal from 1 to 0\n", - " 69585 [params.py: set():1006] [1655521] Changing key nb in group temporal from 2 to 1\n", - " 69586 [params.py: set():1006] [1655482] Changing key nb in group spatial from 2 to 1\n", - " 69586 [params.py: set():1006] [1655562] Changing key nb in group temporal from 2 to 1\n", - " 69587 [params.py: set():1006] [1655481] Changing key nb in group temporal from 2 to 1\n", - " 69587 [params.py: set():1006] [1655526] Changing key nb in group spatial from 2 to 1\n", - " 69588 [params.py: set():1006] [1655487] Changing key nb in group temporal from 2 to 1\n", - " 69589 [params.py: set():1006] [1655564] Changing key nb in group temporal from 2 to 1\n", - " 69589 [params.py: set():1006] [1655532] Changing key nb in group temporal from 2 to 1\n", - " 69589 [params.py: set():1006] [1655533] Changing key nb in group spatial from 2 to 1\n", - " 69590 [params.py: set():1006] [1655511] Changing key nb in group spatial from 2 to 1\n", - " 69590 [params.py: set():1006] [1655483] Changing key nb in group temporal from 2 to 1\n", - " 69591 [params.py: set():1006] [1655512] Changing key nb in group spatial from 2 to 1\n", - " 69593 [cnmf.py: fit():436] [1655549] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69592 [params.py: set():1006] [1655508] Changing key nb in group spatial from 2 to 1\n", - " 69593 [params.py: set():1006] [1655485] Changing key nb in group temporal from 2 to 1\n", - " 69593 [params.py: set():1006] [1655530] Changing key p in group temporal from 1 to 0\n", - " 69594 [params.py: set():1006] [1655507] Changing key nb in group spatial from 2 to 1\n", - " 69594 [params.py: set():1006] [1655505] Changing key nb in group init from 2 to 1\n", - " 69595 [params.py: set():1006] [1655550] Changing key p in group temporal from 1 to 0\n", - " 69595 [params.py: set():1006] [1655516] Changing key nb in group temporal from 2 to 1\n", - " 69596 [params.py: set():1006] [1655469] Changing key nb in group temporal from 2 to 1\n", - " 69597 [params.py: set():1006] [1655497] Changing key nb in group spatial from 2 to 1\n", - " 69597 [params.py: set():1006] [1655498] Changing key nb in group temporal from 2 to 1\n", - " 69603 [params.py: set():1006] [1655510] Changing key nb in group init from 2 to 1\n", - " 69604 [params.py: set():1006] [1655558] Changing key nb in group spatial from 2 to 1\n", - " 69604 [params.py: set():1006] [1655513] Changing key nb in group spatial from 2 to 1\n", - " 69605 [params.py: set():1006] [1655556] Changing key stride in group patch from 10 to None\n", - " 69605 [params.py: set():1006] [1655524] Changing key nb in group spatial from 2 to 1\n", - " 69606 [params.py: set():1006] [1655546] Changing key p in group temporal from 1 to 0\n", - " 69606 [params.py: set():1006] [1655560] Changing key nb in group spatial from 2 to 1\n", - " 69607 [params.py: set():1006] [1655495] Changing key nb in group temporal from 2 to 1\n", - " 69608 [cnmf.py: fit():436] [1655553] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69609 [params.py: set():1006] [1655476] Changing key nb in group temporal from 2 to 1\n", - " 69608 [params.py: set():1006] [1655478] Changing key nb in group init from 2 to 1\n", - " 69609 [params.py: set():1006] [1655470] Changing key p in group preprocess from 1 to 0\n", - " 69610 [params.py: set():1006] [1655509] Changing key nb in group spatial from 2 to 1\n", - " 69611 [params.py: set():1006] [1655528] Changing key stride in group patch from 10 to None\n", - " 69611 [params.py: set():1006] [1655536] Changing key p in group preprocess from 1 to 0\n", - " 69612 [params.py: set():1006] [1655542] Changing key nb in group temporal from 2 to 1\n", - " 69612 [params.py: set():1006] [1655539] Changing key nb in group init from 2 to 1\n", - " 69613 [params.py: set():1006] [1655561] Changing key nb in group temporal from 2 to 1\n", - " 69613 [params.py: set():1006] [1655504] Changing key nb in group temporal from 2 to 1\n", - " 69614 [params.py: set():1006] [1655555] Changing key nb in group temporal from 2 to 1\n", - " 69613 [params.py: set():1006] [1655493] Changing key nb in group init from 2 to 1\n", - " 69615 [params.py: set():1006] [1655502] Changing key nb in group temporal from 2 to 1\n", - " 69615 [params.py: set():1006] [1655520] Changing key nb in group spatial from 2 to 1\n", - " 69616 [params.py: set():1006] [1655503] Changing key nb in group init from 2 to 1\n", - " 69616 [params.py: set():1006] [1655474] Changing key nb in group temporal from 2 to 1\n", - " 69617 [params.py: set():1006] [1655544] Changing key nb in group temporal from 2 to 1\n", - " 69617 [params.py: set():1006] [1655494] Changing key p in group preprocess from 1 to 0\n", - " 69618 [params.py: set():1006] [1655547] Changing key nb in group spatial from 2 to 1\n", - " 69619 [params.py: set():1006] [1655491] Changing key nb in group init from 2 to 1\n", - " 69619 [params.py: set():1006] [1655473] Changing key p in group preprocess from 1 to 0\n", - " 69618 [params.py: set():1006] [1655537] Changing key p in group preprocess from 1 to 0\n", - " 69619 [params.py: set():1006] [1655490] Changing key p in group preprocess from 1 to 0\n", - " 69620 [params.py: set():1006] [1655517] Changing key nb in group spatial from 2 to 1\n", - " 69621 [params.py: set():1006] [1655543] Changing key p in group preprocess from 1 to 0\n", - " 69621 [params.py: set():1006] [1655548] Changing key nb in group init from 2 to 1\n", - " 69622 [params.py: set():1006] [1655525] Changing key p in group preprocess from 1 to 0\n", - " 69622 [params.py: set():1006] [1655522] Changing key p in group preprocess from 1 to 0\n", - " 69623 [params.py: set():1006] [1655519] Changing key p in group preprocess from 1 to 0\n", - " 69623 [params.py: set():1006] [1655518] Changing key nb in group spatial from 2 to 1\n", - " 69624 [params.py: set():1006] [1655475] Changing key nb in group init from 2 to 1\n", - " 69625 [params.py: set():1006] [1655472] Changing key nb in group temporal from 2 to 1\n", - " 69626 [params.py: set():1006] [1655557] Changing key p in group preprocess from 1 to 0\n", - " 69625 [params.py: set():1006] [1655523] Changing key nb in group spatial from 2 to 1\n", - " 69626 [params.py: set():1006] [1655501] Changing key nb in group init from 2 to 1\n", - " 69627 [params.py: set():1006] [1655559] Changing key nb in group temporal from 2 to 1\n", - " 69627 [params.py: set():1006] [1655540] Changing key p in group preprocess from 1 to 0\n", - " 69628 [cnmf.py: fit():466] [1655515] Using 1 processes\n", - " 69628 [params.py: set():1006] [1655500] Changing key p in group preprocess from 1 to 0\n", - " 69629 [params.py: set():1006] [1655486] Changing key nb in group spatial from 2 to 1\n", - " 69630 [params.py: set():1006] [1655514] Changing key nb in group spatial from 2 to 1\n", - " 69630 [params.py: set():1006] [1655529] Changing key p in group preprocess from 1 to 0\n", - " 69631 [params.py: set():1006] [1655541] Changing key nb in group spatial from 2 to 1\n", - " 69632 [params.py: set():1006] [1655484] Changing key nb in group temporal from 2 to 1\n", - " 69631 [params.py: set():1006] [1655563] Changing key stride in group patch from 10 to None\n", - " 69632 [params.py: set():1006] [1655545] Changing key nb in group temporal from 2 to 1\n", - " 69634 [params.py: set():1006] [1655506] Changing key nb in group spatial from 2 to 1\n", - " 69634 [params.py: set():1006] [1655538] Changing key nb in group temporal from 2 to 1\n", - " 69633 [params.py: set():1006] [1655554] Changing key nb in group spatial from 2 to 1\n", - " 69634 [params.py: set():1006] [1655488] Changing key nb in group spatial from 2 to 1\n", - " 69635 [cnmf.py: fit():449] [1655531] (8000, 41, 41)\n", - " 69635 [params.py: set():1006] [1655551] Changing key p in group temporal from 1 to 0\n", - " 69636 [params.py: set():1006] [1655534] Changing key p in group preprocess from 1 to 0\n", - " 69640 [cnmf.py: fit():449] [1655535] (8000, 41, 41)\n", - " 69640 [params.py: set():1006] [1655477] Changing key nb in group temporal from 2 to 1\n", - " 69640 [params.py: set():1006] [1655479] Changing key nb in group spatial from 2 to 1\n", - " 69641 [params.py: set():1006] [1655489] Changing key p in group preprocess from 1 to 0\n", - " 69641 [params.py: set():1006] [1655480] Changing key p in group preprocess from 1 to 0\n", - " 69642 [params.py: set():1006] [1655527] Changing key nb in group spatial from 2 to 1\n", - " 69642 [params.py: set():1006] [1655492] Changing key nb in group spatial from 2 to 1\n", - " 69643 [cnmf.py: fit():436] [1655552] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69643 [params.py: set():1006] [1655499] Changing key nb in group spatial from 2 to 1\n", - " 69644 [params.py: set():1006] [1655496] Changing key nb in group temporal from 2 to 1\n", - " 69644 [params.py: set():1006] [1655521] Changing key nb in group spatial from 2 to 1\n", - " 69645 [params.py: set():1006] [1655482] Changing key p in group preprocess from 1 to 0\n", - " 69645 [params.py: set():1006] [1655562] Changing key nb in group spatial from 2 to 1\n", - " 69646 [params.py: set():1006] [1655481] Changing key nb in group spatial from 2 to 1\n", - " 69646 [params.py: set():1006] [1655526] Changing key p in group preprocess from 1 to 0\n", - " 69647 [params.py: set():1006] [1655487] Changing key nb in group spatial from 2 to 1\n", - " 69647 [params.py: set():1006] [1655564] Changing key nb in group spatial from 2 to 1\n", - " 69648 [params.py: set():1006] [1655532] Changing key nb in group spatial from 2 to 1\n", - " 69648 [params.py: set():1006] [1655533] Changing key p in group preprocess from 1 to 0\n", - " 69649 [params.py: set():1006] [1655511] Changing key p in group preprocess from 1 to 0\n", - " 69649 [params.py: set():1006] [1655483] Changing key nb in group spatial from 2 to 1\n", - " 69650 [params.py: set():1006] [1655512] Changing key p in group preprocess from 1 to 0\n", - " 69651 [params.py: set():1006] [1655508] Changing key p in group preprocess from 1 to 0\n", - " 69650 [cnmf.py: fit():449] [1655549] (8000, 41, 41)\n", - " 69652 [cnmf.py: fit():436] [1655530] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69651 [params.py: set():1006] [1655485] Changing key nb in group spatial from 2 to 1\n", - " 69652 [params.py: set():1006] [1655507] Changing key p in group preprocess from 1 to 0\n", - " 69653 [params.py: set():1006] [1655505] Changing key nb in group temporal from 2 to 1\n", - " 69653 [cnmf.py: fit():436] [1655550] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69653 [params.py: set():1006] [1655516] Changing key nb in group spatial from 2 to 1\n", - " 69654 [params.py: set():1006] [1655469] Changing key nb in group spatial from 2 to 1\n", - " 69655 [params.py: set():1006] [1655498] Changing key nb in group spatial from 2 to 1\n", - " 69655 [params.py: set():1006] [1655497] Changing key p in group preprocess from 1 to 0\n", - " 69656 [params.py: set():1006] [1655510] Changing key nb in group temporal from 2 to 1\n", - " 69656 [params.py: set():1006] [1655558] Changing key p in group preprocess from 1 to 0\n", - " 69657 [params.py: set():1006] [1655513] Changing key p in group preprocess from 1 to 0\n", - " 69657 [params.py: set():1006] [1655556] Changing key nb in group init from 2 to 1\n", - " 69658 [params.py: set():1006] [1655524] Changing key p in group preprocess from 1 to 0\n", - " 69658 [cnmf.py: fit():436] [1655546] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69659 [params.py: set():1006] [1655560] Changing key p in group preprocess from 1 to 0\n", - " 69659 [params.py: set():1006] [1655495] Changing key nb in group spatial from 2 to 1\n", - " 69660 [cnmf.py: fit():449] [1655553] (8000, 41, 41)\n", - " 69661 [params.py: set():1006] [1655476] Changing key nb in group spatial from 2 to 1\n", - " 69660 [params.py: set():1006] [1655478] Changing key nb in group temporal from 2 to 1\n", - " 69661 [params.py: set():1006] [1655509] Changing key p in group preprocess from 1 to 0\n", - " 69661 [params.py: set():1006] [1655470] Changing key p in group temporal from 1 to 0\n", - " 69662 [params.py: set():1006] [1655536] Changing key p in group temporal from 1 to 0\n", - " 69662 [params.py: set():1006] [1655528] Changing key nb in group init from 2 to 1\n", - " 69663 [params.py: set():1006] [1655539] Changing key nb in group temporal from 2 to 1\n", - " 69663 [params.py: set():1006] [1655542] Changing key nb in group spatial from 2 to 1\n", - " 69664 [params.py: set():1006] [1655504] Changing key nb in group spatial from 2 to 1\n", - " 69664 [params.py: set():1006] [1655561] Changing key nb in group spatial from 2 to 1\n", - " 69665 [params.py: set():1006] [1655493] Changing key nb in group temporal from 2 to 1\n", - " 69665 [params.py: set():1006] [1655555] Changing key nb in group spatial from 2 to 1\n", - " 69666 [params.py: set():1006] [1655502] Changing key nb in group spatial from 2 to 1\n", - " 69667 [params.py: set():1006] [1655503] Changing key nb in group temporal from 2 to 1\n", - " 69666 [params.py: set():1006] [1655520] Changing key p in group preprocess from 1 to 0\n", - " 69667 [params.py: set():1006] [1655474] Changing key nb in group spatial from 2 to 1\n", - " 69668 [params.py: set():1006] [1655544] Changing key nb in group spatial from 2 to 1\n", - " 69669 [params.py: set():1006] [1655547] Changing key p in group preprocess from 1 to 0\n", - " 69668 [params.py: set():1006] [1655494] Changing key p in group temporal from 1 to 0\n", - " 69670 [params.py: set():1006] [1655491] Changing key nb in group temporal from 2 to 1\n", - " 69669 [params.py: set():1006] [1655473] Changing key p in group temporal from 1 to 0\n", - " 69670 [params.py: set():1006] [1655537] Changing key p in group temporal from 1 to 0\n", - " 69671 [params.py: set():1006] [1655490] Changing key p in group temporal from 1 to 0\n", - " 69671 [params.py: set():1006] [1655517] Changing key p in group preprocess from 1 to 0\n", - " 69672 [params.py: set():1006] [1655543] Changing key p in group temporal from 1 to 0\n", - " 69672 [params.py: set():1006] [1655548] Changing key nb in group temporal from 2 to 1\n", - " 69672 [params.py: set():1006] [1655525] Changing key p in group temporal from 1 to 0\n", - " 69673 [params.py: set():1006] [1655522] Changing key p in group temporal from 1 to 0\n", - " 69673 [params.py: set():1006] [1655519] Changing key p in group temporal from 1 to 0\n", - " 69674 [params.py: set():1006] [1655518] Changing key p in group preprocess from 1 to 0\n", - " 69674 [params.py: set():1006] [1655475] Changing key nb in group temporal from 2 to 1\n", - " 69675 [params.py: set():1006] [1655472] Changing key nb in group spatial from 2 to 1\n", - " 69676 [params.py: set():1006] [1655523] Changing key p in group preprocess from 1 to 0\n", - " 69675 [params.py: set():1006] [1655557] Changing key p in group temporal from 1 to 0\n", - " 69676 [params.py: set():1006] [1655501] Changing key nb in group temporal from 2 to 1\n", - " 69677 [params.py: set():1006] [1655559] Changing key nb in group spatial from 2 to 1\n", - " 69677 [params.py: set():1006] [1655540] Changing key p in group temporal from 1 to 0\n", - " 69678 [cnmf.py: fit():477] [1655515] using 400 pixels per process\n", - " 69678 [params.py: set():1006] [1655500] Changing key p in group temporal from 1 to 0\n", - " 69678 [params.py: set():1006] [1655486] Changing key p in group preprocess from 1 to 0\n", - " 69679 [params.py: set():1006] [1655529] Changing key p in group temporal from 1 to 0\n", - " 69679 [params.py: set():1006] [1655514] Changing key p in group preprocess from 1 to 0\n", - " 69684 [params.py: set():1006] [1655541] Changing key p in group preprocess from 1 to 0\n", - " 69685 [params.py: set():1006] [1655563] Changing key nb in group init from 2 to 1\n", - " 69685 [params.py: set():1006] [1655545] Changing key nb in group spatial from 2 to 1\n", - " 69684 [params.py: set():1006] [1655484] Changing key nb in group spatial from 2 to 1\n", - " 69686 [params.py: set():1006] [1655506] Changing key p in group preprocess from 1 to 0\n", - " 69686 [params.py: set():1006] [1655538] Changing key nb in group spatial from 2 to 1\n", - " 69687 [params.py: set():1006] [1655554] Changing key p in group preprocess from 1 to 0\n", - " 69687 [params.py: set():1006] [1655488] Changing key p in group preprocess from 1 to 0\n", - " 69688 [cnmf.py: fit():466] [1655531] Using 1 processes\n", - " 69689 [params.py: set():1006] [1655534] Changing key p in group temporal from 1 to 0\n", - " 69688 [cnmf.py: fit():436] [1655551] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69690 [params.py: set():1006] [1655477] Changing key nb in group spatial from 2 to 1\n", - " 69689 [cnmf.py: fit():466] [1655535] Using 1 processes\n", - " 69690 [params.py: set():1006] [1655489] Changing key p in group temporal from 1 to 0\n", - " 69690 [params.py: set():1006] [1655479] Changing key p in group preprocess from 1 to 0\n", - " 69691 [params.py: set():1006] [1655480] Changing key p in group temporal from 1 to 0\n", - " 69691 [params.py: set():1006] [1655527] Changing key p in group preprocess from 1 to 0\n", - " 69692 [params.py: set():1006] [1655492] Changing key p in group preprocess from 1 to 0\n", - " 69692 [cnmf.py: fit():449] [1655552] (8000, 41, 41)\n", - " 69693 [params.py: set():1006] [1655499] Changing key p in group preprocess from 1 to 0\n", - " 69698 [params.py: set():1006] [1655496] Changing key nb in group spatial from 2 to 1\n", - " 69699 [params.py: set():1006] [1655521] Changing key p in group preprocess from 1 to 0\n", - " 69699 [params.py: set():1006] [1655482] Changing key p in group temporal from 1 to 0\n", - " 69700 [params.py: set():1006] [1655562] Changing key p in group preprocess from 1 to 0\n", - " 69700 [params.py: set():1006] [1655481] Changing key p in group preprocess from 1 to 0\n", - " 69701 [params.py: set():1006] [1655526] Changing key p in group temporal from 1 to 0\n", - " 69701 [params.py: set():1006] [1655487] Changing key p in group preprocess from 1 to 0\n", - " 69702 [params.py: set():1006] [1655564] Changing key p in group preprocess from 1 to 0\n", - " 69702 [params.py: set():1006] [1655532] Changing key p in group preprocess from 1 to 0\n", - " 69702 [params.py: set():1006] [1655533] Changing key p in group temporal from 1 to 0\n", - " 69703 [params.py: set():1006] [1655511] Changing key p in group temporal from 1 to 0\n", - " 69703 [params.py: set():1006] [1655483] Changing key p in group preprocess from 1 to 0\n", - " 69704 [params.py: set():1006] [1655512] Changing key p in group temporal from 1 to 0\n", - " 69704 [params.py: set():1006] [1655508] Changing key p in group temporal from 1 to 0\n", - " 69705 [cnmf.py: fit():466] [1655549] Using 1 processes\n", - " 69705 [cnmf.py: fit():449] [1655530] (8000, 41, 41)\n", - " 69706 [params.py: set():1006] [1655485] Changing key p in group preprocess from 1 to 0\n", - " 69706 [params.py: set():1006] [1655507] Changing key p in group temporal from 1 to 0\n", - " 69707 [params.py: set():1006] [1655505] Changing key nb in group spatial from 2 to 1\n", - " 69707 [cnmf.py: fit():449] [1655550] (8000, 41, 41)\n", - " 69708 [params.py: set():1006] [1655516] Changing key p in group preprocess from 1 to 0\n", - " 69709 [params.py: set():1006] [1655469] Changing key p in group preprocess from 1 to 0\n", - " 69709 [params.py: set():1006] [1655497] Changing key p in group temporal from 1 to 0\n", - " 69708 [params.py: set():1006] [1655498] Changing key p in group preprocess from 1 to 0\n", - " 69710 [params.py: set():1006] [1655510] Changing key nb in group spatial from 2 to 1\n", - " 69710 [params.py: set():1006] [1655558] Changing key p in group temporal from 1 to 0\n", - " 69711 [params.py: set():1006] [1655513] Changing key p in group temporal from 1 to 0\n", - " 69711 [params.py: set():1006] [1655556] Changing key nb in group temporal from 2 to 1\n", - " 69711 [params.py: set():1006] [1655524] Changing key p in group temporal from 1 to 0\n", - " 69712 [cnmf.py: fit():449] [1655546] (8000, 41, 41)\n", - " 69712 [params.py: set():1006] [1655560] Changing key p in group temporal from 1 to 0\n", - " 69713 [params.py: set():1006] [1655495] Changing key p in group preprocess from 1 to 0\n", - " 69713 [cnmf.py: fit():466] [1655553] Using 1 processes\n", - " 69714 [params.py: set():1006] [1655478] Changing key nb in group spatial from 2 to 1\n", - " 69714 [params.py: set():1006] [1655476] Changing key p in group preprocess from 1 to 0\n", - " 69715 [cnmf.py: fit():436] [1655470] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69715 [params.py: set():1006] [1655509] Changing key p in group temporal from 1 to 0\n", - " 69716 [cnmf.py: fit():436] [1655536] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69716 [params.py: set():1006] [1655528] Changing key nb in group temporal from 2 to 1\n", - " 69717 [params.py: set():1006] [1655539] Changing key nb in group spatial from 2 to 1\n", - " 69717 [params.py: set():1006] [1655542] Changing key p in group preprocess from 1 to 0\n", - " 69718 [params.py: set():1006] [1655504] Changing key p in group preprocess from 1 to 0\n", - " 69718 [params.py: set():1006] [1655561] Changing key p in group preprocess from 1 to 0\n", - " 69719 [params.py: set():1006] [1655555] Changing key p in group preprocess from 1 to 0\n", - " 69719 [params.py: set():1006] [1655493] Changing key nb in group spatial from 2 to 1\n", - " 69721 [params.py: set():1006] [1655503] Changing key nb in group spatial from 2 to 1\n", - " 69720 [params.py: set():1006] [1655502] Changing key p in group preprocess from 1 to 0\n", - " 69721 [params.py: set():1006] [1655520] Changing key p in group temporal from 1 to 0\n", - " 69722 [params.py: set():1006] [1655544] Changing key p in group preprocess from 1 to 0\n", - " 69722 [params.py: set():1006] [1655474] Changing key p in group preprocess from 1 to 0\n", - " 69723 [params.py: set():1006] [1655547] Changing key p in group temporal from 1 to 0\n", - " 69723 [cnmf.py: fit():436] [1655494] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69724 [cnmf.py: fit():436] [1655473] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69724 [params.py: set():1006] [1655491] Changing key nb in group spatial from 2 to 1\n", - " 69725 [cnmf.py: fit():436] [1655537] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69725 [cnmf.py: fit():436] [1655490] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69726 [params.py: set():1006] [1655517] Changing key p in group temporal from 1 to 0\n", - " 69726 [cnmf.py: fit():436] [1655543] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69727 [params.py: set():1006] [1655548] Changing key nb in group spatial from 2 to 1\n", - " 69727 [cnmf.py: fit():436] [1655525] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69728 [cnmf.py: fit():436] [1655522] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69729 [cnmf.py: fit():436] [1655519] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69729 [params.py: set():1006] [1655518] Changing key p in group temporal from 1 to 0\n", - " 69729 [params.py: set():1006] [1655475] Changing key nb in group spatial from 2 to 1\n", - " 69730 [params.py: set():1006] [1655472] Changing key p in group preprocess from 1 to 0\n", - " 69731 [cnmf.py: fit():436] [1655557] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69731 [params.py: set():1006] [1655523] Changing key p in group temporal from 1 to 0\n", - " 69731 [params.py: set():1006] [1655501] Changing key nb in group spatial from 2 to 1\n", - " 69732 [params.py: set():1006] [1655559] Changing key p in group preprocess from 1 to 0\n", - " 69732 [cnmf.py: fit():436] [1655540] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69734 [cnmf.py: fit():436] [1655500] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69733 [cnmf.py: fit():478] [1655515] using 5000 block_size_temp\n", - " 69734 [params.py: set():1006] [1655486] Changing key p in group temporal from 1 to 0\n", - " 69735 [params.py: set():1006] [1655514] Changing key p in group temporal from 1 to 0\n", - " 69735 [cnmf.py: fit():436] [1655529] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69736 [params.py: set():1006] [1655541] Changing key p in group temporal from 1 to 0\n", - " 69737 [params.py: set():1006] [1655563] Changing key nb in group temporal from 2 to 1\n", - " 69736 [params.py: set():1006] [1655545] Changing key p in group preprocess from 1 to 0\n", - " 69737 [params.py: set():1006] [1655484] Changing key p in group preprocess from 1 to 0\n", - " 69738 [params.py: set():1006] [1655538] Changing key p in group preprocess from 1 to 0\n", - " 69738 [params.py: set():1006] [1655506] Changing key p in group temporal from 1 to 0\n", - " 69738 [params.py: set():1006] [1655488] Changing key p in group temporal from 1 to 0\n", - " 69739 [params.py: set():1006] [1655554] Changing key p in group temporal from 1 to 0\n", - " 69739 [cnmf.py: fit():477] [1655531] using 400 pixels per process\n", - " 69740 [cnmf.py: fit():436] [1655534] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69740 [cnmf.py: fit():449] [1655551] (8000, 41, 41)\n", - " 69748 [params.py: set():1006] [1655477] Changing key p in group preprocess from 1 to 0\n", - " 69748 [cnmf.py: fit():477] [1655535] using 400 pixels per process\n", - " 69749 [params.py: set():1006] [1655479] Changing key p in group temporal from 1 to 0\n", - " 69749 [cnmf.py: fit():436] [1655489] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69750 [cnmf.py: fit():436] [1655480] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69750 [params.py: set():1006] [1655527] Changing key p in group temporal from 1 to 0\n", - " 69751 [params.py: set():1006] [1655492] Changing key p in group temporal from 1 to 0\n", - " 69752 [cnmf.py: fit():466] [1655552] Using 1 processes\n", - " 69752 [params.py: set():1006] [1655496] Changing key p in group preprocess from 1 to 0\n", - " 69752 [params.py: set():1006] [1655499] Changing key p in group temporal from 1 to 0\n", - " 69753 [params.py: set():1006] [1655521] Changing key p in group temporal from 1 to 0\n", - " 69753 [cnmf.py: fit():436] [1655482] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69754 [params.py: set():1006] [1655562] Changing key p in group temporal from 1 to 0\n", - " 69754 [params.py: set():1006] [1655481] Changing key p in group temporal from 1 to 0\n", - " 69755 [cnmf.py: fit():436] [1655526] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69756 [params.py: set():1006] [1655487] Changing key p in group temporal from 1 to 0\n", - " 69756 [params.py: set():1006] [1655564] Changing key p in group temporal from 1 to 0\n", - " 69757 [params.py: set():1006] [1655532] Changing key p in group temporal from 1 to 0\n", - " 69757 [cnmf.py: fit():436] [1655533] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69758 [cnmf.py: fit():436] [1655511] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69758 [params.py: set():1006] [1655483] Changing key p in group temporal from 1 to 0\n", - " 69759 [cnmf.py: fit():436] [1655512] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69760 [cnmf.py: fit():436] [1655508] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69759 [cnmf.py: fit():477] [1655549] using 400 pixels per process\n", - " 69760 [cnmf.py: fit():466] [1655530] Using 1 processes\n", - " 69762 [params.py: set():1006] [1655505] Changing key p in group preprocess from 1 to 0\n", - " 69761 [cnmf.py: fit():436] [1655507] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69761 [params.py: set():1006] [1655485] Changing key p in group temporal from 1 to 0\n", - " 69763 [params.py: set():1006] [1655516] Changing key p in group temporal from 1 to 0\n", - " 69762 [cnmf.py: fit():466] [1655550] Using 1 processes\n", - " 69763 [params.py: set():1006] [1655469] Changing key p in group temporal from 1 to 0\n", - " 69764 [cnmf.py: fit():436] [1655497] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69764 [params.py: set():1006] [1655498] Changing key p in group temporal from 1 to 0\n", - " 69765 [params.py: set():1006] [1655510] Changing key p in group preprocess from 1 to 0\n", - " 69766 [cnmf.py: fit():436] [1655558] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69765 [cnmf.py: fit():436] [1655513] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69766 [params.py: set():1006] [1655556] Changing key nb in group spatial from 2 to 1\n", - " 69767 [cnmf.py: fit():436] [1655524] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69767 [cnmf.py: fit():466] [1655546] Using 1 processes\n", - " 69768 [cnmf.py: fit():436] [1655560] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69769 [params.py: set():1006] [1655495] Changing key p in group temporal from 1 to 0\n", - " 69770 [params.py: set():1006] [1655476] Changing key p in group temporal from 1 to 0\n", - " 69769 [cnmf.py: fit():477] [1655553] using 400 pixels per process\n", - " 69770 [params.py: set():1006] [1655478] Changing key p in group preprocess from 1 to 0\n", - " 69771 [cnmf.py: fit():436] [1655509] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69771 [cnmf.py: fit():449] [1655470] (8000, 41, 41)\n", - " 69772 [params.py: set():1006] [1655528] Changing key nb in group spatial from 2 to 1\n", - " 69772 [cnmf.py: fit():449] [1655536] (8000, 41, 41)\n", - " 69773 [params.py: set():1006] [1655539] Changing key p in group preprocess from 1 to 0\n", - " 69773 [params.py: set():1006] [1655542] Changing key p in group temporal from 1 to 0\n", - " 69774 [params.py: set():1006] [1655504] Changing key p in group temporal from 1 to 0\n", - " 69774 [params.py: set():1006] [1655561] Changing key p in group temporal from 1 to 0\n", - " 69774 [params.py: set():1006] [1655555] Changing key p in group temporal from 1 to 0\n", - " 69775 [params.py: set():1006] [1655493] Changing key p in group preprocess from 1 to 0\n", - " 69776 [params.py: set():1006] [1655503] Changing key p in group preprocess from 1 to 0\n", - " 69776 [cnmf.py: fit():436] [1655520] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69775 [params.py: set():1006] [1655502] Changing key p in group temporal from 1 to 0\n", - " 69777 [params.py: set():1006] [1655544] Changing key p in group temporal from 1 to 0\n", - " 69777 [params.py: set():1006] [1655474] Changing key p in group temporal from 1 to 0\n", - " 69778 [cnmf.py: fit():436] [1655547] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69778 [cnmf.py: fit():449] [1655494] (8000, 41, 41)\n", - " 69779 [cnmf.py: fit():449] [1655473] (8000, 41, 41)\n", - " 69779 [params.py: set():1006] [1655491] Changing key p in group preprocess from 1 to 0\n", - " 69780 [cnmf.py: fit():449] [1655537] (8000, 41, 40)\n", - " 69780 [cnmf.py: fit():449] [1655490] (8000, 41, 41)\n", - " 69781 [cnmf.py: fit():436] [1655517] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69781 [cnmf.py: fit():449] [1655543] (8000, 41, 41)\n", - " 69782 [params.py: set():1006] [1655548] Changing key p in group preprocess from 1 to 0\n", - " 69782 [cnmf.py: fit():449] [1655525] (8000, 41, 41)\n", - " 69783 [cnmf.py: fit():449] [1655522] (8000, 41, 41)\n", - " 69783 [cnmf.py: fit():449] [1655519] (8000, 41, 41)\n", - " 69784 [cnmf.py: fit():436] [1655518] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69791 [params.py: set():1006] [1655475] Changing key p in group preprocess from 1 to 0\n", - " 69792 [params.py: set():1006] [1655472] Changing key p in group temporal from 1 to 0\n", - " 69793 [cnmf.py: fit():449] [1655557] (8000, 41, 41)\n", - " 69793 [cnmf.py: fit():436] [1655523] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69793 [params.py: set():1006] [1655501] Changing key p in group preprocess from 1 to 0\n", - " 69794 [params.py: set():1006] [1655559] Changing key p in group temporal from 1 to 0\n", - " 69794 [cnmf.py: fit():449] [1655540] (8000, 41, 41)\n", - " 69795 [cnmf.py: fit():481] [1655515] preprocessing ...\n", - " 69795 [cnmf.py: fit():449] [1655500] (8000, 41, 41)\n", - " 69796 [cnmf.py: fit():436] [1655486] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69797 [cnmf.py: fit():449] [1655529] (8000, 41, 41)\n", - " 69797 [cnmf.py: fit():436] [1655514] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69798 [cnmf.py: fit():436] [1655541] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69799 [params.py: set():1006] [1655545] Changing key p in group temporal from 1 to 0\n", - " 69798 [params.py: set():1006] [1655563] Changing key nb in group spatial from 2 to 1\n", - " 69799 [params.py: set():1006] [1655484] Changing key p in group temporal from 1 to 0\n", - " 69800 [cnmf.py: fit():436] [1655506] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69800 [params.py: set():1006] [1655538] Changing key p in group temporal from 1 to 0\n", - " 69801 [cnmf.py: fit():436] [1655488] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69801 [cnmf.py: fit():436] [1655554] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69802 [cnmf.py: fit():478] [1655531] using 5000 block_size_temp\n", - " 69802 [cnmf.py: fit():449] [1655534] (8000, 41, 41)\n", - " 69803 [cnmf.py: fit():466] [1655551] Using 1 processes\n", - " 69804 [cnmf.py: fit():478] [1655535] using 5000 block_size_temp\n", - " 69803 [params.py: set():1006] [1655477] Changing key p in group temporal from 1 to 0\n", - " 69804 [cnmf.py: fit():436] [1655479] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69805 [cnmf.py: fit():449] [1655489] (8000, 41, 41)\n", - " 69806 [cnmf.py: fit():436] [1655527] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69805 [cnmf.py: fit():449] [1655480] (8000, 41, 41)\n", - " 69807 [cnmf.py: fit():436] [1655492] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69807 [cnmf.py: fit():477] [1655552] using 400 pixels per process\n", - " 69807 [params.py: set():1006] [1655496] Changing key p in group temporal from 1 to 0\n", - " 69808 [cnmf.py: fit():436] [1655499] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69808 [cnmf.py: fit():436] [1655521] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69809 [cnmf.py: fit():449] [1655482] (8000, 41, 41)\n", - " 69810 [cnmf.py: fit():436] [1655562] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69810 [cnmf.py: fit():436] [1655481] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69811 [cnmf.py: fit():436] [1655487] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69811 [cnmf.py: fit():449] [1655526] (8000, 41, 41)\n", - " 69812 [cnmf.py: fit():436] [1655564] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69813 [cnmf.py: fit():436] [1655532] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69813 [cnmf.py: fit():449] [1655533] (8000, 41, 41)\n", - " 69814 [cnmf.py: fit():436] [1655483] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69814 [cnmf.py: fit():449] [1655511] (8000, 41, 41)\n", - " 69815 [cnmf.py: fit():449] [1655512] (8000, 41, 41)\n", - " 69815 [cnmf.py: fit():449] [1655508] (8000, 41, 41)\n", - " 69816 [cnmf.py: fit():478] [1655549] using 5000 block_size_temp\n", - " 69816 [cnmf.py: fit():477] [1655530] using 400 pixels per process\n", - " 69817 [cnmf.py: fit():449] [1655507] (8000, 41, 41)\n", - " 69817 [params.py: set():1006] [1655505] Changing key p in group temporal from 1 to 0\n", - " 69818 [cnmf.py: fit():436] [1655485] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69818 [cnmf.py: fit():436] [1655516] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69819 [cnmf.py: fit():477] [1655550] using 400 pixels per process\n", - " 69819 [cnmf.py: fit():436] [1655469] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69820 [cnmf.py: fit():436] [1655498] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69820 [cnmf.py: fit():449] [1655497] (8000, 41, 41)\n", - " 69821 [params.py: set():1006] [1655510] Changing key p in group temporal from 1 to 0\n", - " 69821 [cnmf.py: fit():449] [1655513] (8000, 41, 41)\n", - " 69822 [cnmf.py: fit():449] [1655558] (8000, 41, 41)\n", - " 69822 [params.py: set():1006] [1655556] Changing key p in group preprocess from 1 to 0\n", - " 69823 [cnmf.py: fit():449] [1655524] (8000, 41, 41)\n", - " 69823 [cnmf.py: fit():477] [1655546] using 400 pixels per process\n", - " 69824 [cnmf.py: fit():449] [1655560] (8000, 41, 41)\n", - " 69824 [cnmf.py: fit():436] [1655495] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69825 [params.py: set():1006] [1655478] Changing key p in group temporal from 1 to 0\n", - " 69826 [cnmf.py: fit():436] [1655476] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69825 [cnmf.py: fit():478] [1655553] using 5000 block_size_temp\n", - " 69826 [cnmf.py: fit():466] [1655470] Using 1 processes\n", - " 69827 [cnmf.py: fit():449] [1655509] (8000, 41, 41)\n", - " 69827 [params.py: set():1006] [1655528] Changing key p in group preprocess from 1 to 0\n", - " 69828 [cnmf.py: fit():466] [1655536] Using 1 processes\n", - " 69829 [cnmf.py: fit():436] [1655542] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69828 [params.py: set():1006] [1655539] Changing key p in group temporal from 1 to 0\n", - " 69830 [cnmf.py: fit():436] [1655561] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69830 [cnmf.py: fit():436] [1655504] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69830 [params.py: set():1006] [1655493] Changing key p in group temporal from 1 to 0\n", - " 69831 [cnmf.py: fit():436] [1655555] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69832 [cnmf.py: fit():449] [1655520] (8000, 41, 40)\n", - " 69832 [params.py: set():1006] [1655503] Changing key p in group temporal from 1 to 0\n", - " 69832 [cnmf.py: fit():436] [1655502] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69834 [cnmf.py: fit():449] [1655547] (8000, 41, 41)\n", - " 69834 [cnmf.py: fit():436] [1655474] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69833 [cnmf.py: fit():436] [1655544] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69835 [cnmf.py: fit():466] [1655494] Using 1 processes\n", - " 69835 [cnmf.py: fit():466] [1655473] Using 1 processes\n", - " 69836 [params.py: set():1006] [1655491] Changing key p in group temporal from 1 to 0\n", - " 69836 [cnmf.py: fit():466] [1655537] Using 1 processes\n", - " 69837 [cnmf.py: fit():466] [1655490] Using 1 processes\n", - " 69837 [cnmf.py: fit():449] [1655517] (8000, 41, 41)\n", - " 69837 [cnmf.py: fit():466] [1655543] Using 1 processes\n", - " 69838 [cnmf.py: fit():466] [1655525] Using 1 processes\n", - " 69838 [params.py: set():1006] [1655548] Changing key p in group temporal from 1 to 0\n", - " 69839 [cnmf.py: fit():466] [1655522] Using 1 processes\n", - " 69839 [cnmf.py: fit():466] [1655519] Using 1 processes\n", - " 69840 [cnmf.py: fit():449] [1655518] (8000, 41, 41)\n", - " 69840 [params.py: set():1006] [1655475] Changing key p in group temporal from 1 to 0\n", - " 69841 [cnmf.py: fit():436] [1655472] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69841 [cnmf.py: fit():449] [1655523] (8000, 41, 41)\n", - " 69842 [cnmf.py: fit():466] [1655557] Using 1 processes\n", - " 69842 [params.py: set():1006] [1655501] Changing key p in group temporal from 1 to 0\n", - " 69843 [cnmf.py: fit():436] [1655559] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69843 [cnmf.py: fit():466] [1655540] Using 1 processes\n", - " 69844 [cnmf.py: fit():466] [1655500] Using 1 processes\n", - " 69844 [pre_processing.py:interpolate_missing_data():41] [1655515] Checking for missing data entries (NaN)\n", - " 69844 [cnmf.py: fit():449] [1655486] (8000, 41, 40)\n", - " 69845 [cnmf.py: fit():449] [1655514] (8000, 41, 41)\n", - " 69845 [cnmf.py: fit():466] [1655529] Using 1 processes\n", - " 69846 [cnmf.py: fit():449] [1655541] (8000, 41, 41)\n", - " 69847 [cnmf.py: fit():436] [1655545] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69847 [params.py: set():1006] [1655563] Changing key p in group preprocess from 1 to 0\n", - " 69848 [cnmf.py: fit():436] [1655484] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69848 [cnmf.py: fit():449] [1655506] (8000, 41, 41)\n", - " 69849 [cnmf.py: fit():436] [1655538] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69850 [cnmf.py: fit():449] [1655488] (8000, 41, 41)\n", - " 69849 [cnmf.py: fit():449] [1655554] (8000, 41, 40)\n", - " 69850 [cnmf.py: fit():481] [1655531] preprocessing ...\n", - " 69851 [cnmf.py: fit():466] [1655534] Using 1 processes\n", - " 69851 [cnmf.py: fit():477] [1655551] using 400 pixels per process\n", - " 69852 [cnmf.py: fit():481] [1655535] preprocessing ...\n", - " 69852 [cnmf.py: fit():436] [1655477] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69853 [cnmf.py: fit():466] [1655489] Using 1 processes\n", - " 69853 [cnmf.py: fit():449] [1655479] (8000, 41, 41)\n", - " 69854 [cnmf.py: fit():449] [1655527] (8000, 41, 41)\n", - " 69854 [cnmf.py: fit():466] [1655480] Using 1 processes\n", - " 69855 [cnmf.py: fit():449] [1655492] (8000, 41, 41)\n", - " 69855 [cnmf.py: fit():478] [1655552] using 5000 block_size_temp\n", - " 69856 [cnmf.py: fit():436] [1655496] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69856 [cnmf.py: fit():449] [1655499] (8000, 41, 41)\n", - " 69864 [cnmf.py: fit():449] [1655521] (8000, 41, 41)\n", - " 69864 [cnmf.py: fit():466] [1655482] Using 1 processes\n", - " 69865 [cnmf.py: fit():449] [1655562] (8000, 41, 41)\n", - " 69865 [cnmf.py: fit():449] [1655481] (8000, 41, 41)\n", - " 69866 [cnmf.py: fit():466] [1655526] Using 1 processes\n", - " 69866 [cnmf.py: fit():449] [1655487] (8000, 41, 41)\n", - " 69867 [cnmf.py: fit():449] [1655564] (8000, 41, 41)\n", - " 69868 [cnmf.py: fit():466] [1655533] Using 1 processes\n", - " 69868 [cnmf.py: fit():449] [1655532] (8000, 41, 41)\n", - " 69868 [cnmf.py: fit():449] [1655483] (8000, 41, 41)\n", - " 69869 [cnmf.py: fit():466] [1655511] Using 1 processes\n", - " 69869 [cnmf.py: fit():466] [1655512] Using 1 processes\n", - " 69870 [cnmf.py: fit():466] [1655508] Using 1 processes\n", - " 69870 [cnmf.py: fit():481] [1655549] preprocessing ...\n", - " 69871 [cnmf.py: fit():478] [1655530] using 5000 block_size_temp\n", - " 69872 [cnmf.py: fit():436] [1655505] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69871 [cnmf.py: fit():449] [1655485] (8000, 41, 41)\n", - " 69872 [cnmf.py: fit():466] [1655507] Using 1 processes\n", - " 69872 [cnmf.py: fit():449] [1655516] (8000, 41, 41)\n", - " 69873 [cnmf.py: fit():478] [1655550] using 5000 block_size_temp\n", - " 69873 [cnmf.py: fit():449] [1655469] (8000, 41, 41)\n", - " 69874 [cnmf.py: fit():449] [1655498] (8000, 41, 41)\n", - " 69874 [cnmf.py: fit():466] [1655497] Using 1 processes\n", - " 69875 [cnmf.py: fit():436] [1655510] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69876 [cnmf.py: fit():466] [1655558] Using 1 processes\n", - " 69876 [cnmf.py: fit():466] [1655513] Using 1 processes\n", - " 69876 [params.py: set():1006] [1655556] Changing key p in group temporal from 1 to 0\n", - " 69877 [cnmf.py: fit():466] [1655524] Using 1 processes\n", - " 69877 [cnmf.py: fit():478] [1655546] using 5000 block_size_temp\n", - " 69878 [cnmf.py: fit():466] [1655560] Using 1 processes\n", - " 69878 [cnmf.py: fit():449] [1655495] (8000, 41, 41)\n", - " 69879 [cnmf.py: fit():481] [1655553] preprocessing ...\n", - " 69879 [cnmf.py: fit():449] [1655476] (8000, 41, 41)\n", - " 69880 [cnmf.py: fit():436] [1655478] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69880 [cnmf.py: fit():466] [1655509] Using 1 processes\n", - " 69881 [cnmf.py: fit():477] [1655470] using 400 pixels per process\n", - " 69882 [params.py: set():1006] [1655528] Changing key p in group temporal from 1 to 0\n", - " 69881 [cnmf.py: fit():477] [1655536] using 400 pixels per process\n", - " 69883 [cnmf.py: fit():449] [1655542] (8000, 41, 41)\n", - " 69882 [cnmf.py: fit():436] [1655539] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69883 [cnmf.py: fit():449] [1655561] (8000, 41, 41)\n", - " 69884 [cnmf.py: fit():449] [1655504] (8000, 41, 41)\n", - " 69884 [cnmf.py: fit():436] [1655493] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69885 [cnmf.py: fit():449] [1655555] (8000, 41, 41)\n", - " 69885 [cnmf.py: fit():466] [1655520] Using 1 processes\n", - " 69886 [cnmf.py: fit():436] [1655503] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69886 [cnmf.py: fit():449] [1655502] (8000, 41, 41)\n", - " 69887 [cnmf.py: fit():466] [1655547] Using 1 processes\n", - " 69888 [cnmf.py: fit():449] [1655544] (8000, 41, 41)\n", - " 69887 [cnmf.py: fit():449] [1655474] (8000, 41, 41)\n", - " 69889 [cnmf.py: fit():477] [1655473] using 400 pixels per process\n", - " 69888 [cnmf.py: fit():477] [1655494] using 400 pixels per process\n", - " 69889 [cnmf.py: fit():436] [1655491] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69890 [cnmf.py: fit():477] [1655537] using 400 pixels per process\n", - " 69891 [cnmf.py: fit():477] [1655543] using 400 pixels per process\n", - " 69891 [cnmf.py: fit():477] [1655490] using 400 pixels per process\n", - " 69890 [cnmf.py: fit():466] [1655517] Using 1 processes\n", - " 69892 [cnmf.py: fit():477] [1655525] using 400 pixels per process\n", - " 69892 [cnmf.py: fit():436] [1655548] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69893 [cnmf.py: fit():477] [1655522] using 400 pixels per process\n", - " 69893 [cnmf.py: fit():477] [1655519] using 400 pixels per process\n", - " 69893 [cnmf.py: fit():466] [1655518] Using 1 processes\n", - " 69894 [cnmf.py: fit():436] [1655475] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69895 [cnmf.py: fit():449] [1655472] (8000, 41, 41)\n", - " 69895 [cnmf.py: fit():466] [1655523] Using 1 processes\n", - " 69896 [cnmf.py: fit():477] [1655557] using 400 pixels per process\n", - " 69896 [cnmf.py: fit():436] [1655501] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69897 [cnmf.py: fit():449] [1655559] (8000, 41, 41)\n", - " 69897 [cnmf.py: fit():477] [1655540] using 400 pixels per process\n", - " 69898 [cnmf.py: fit():477] [1655500] using 400 pixels per process\n", - " 69907 [cnmf.py: fit():466] [1655486] Using 1 processes\n", - " 69907 [cnmf.py: fit():466] [1655514] Using 1 processes\n", - " 69908 [cnmf.py: fit():477] [1655529] using 400 pixels per process\n", - " 69908 [cnmf.py: fit():466] [1655541] Using 1 processes\n", - " 69912 [cnmf.py: fit():449] [1655545] (8000, 41, 41)\n", - " 69913 [cnmf.py: fit():449] [1655484] (8000, 41, 41)\n", - " 69912 [params.py: set():1006] [1655563] Changing key p in group temporal from 1 to 0\n", - " 69913 [cnmf.py: fit():449] [1655538] (8000, 41, 41)\n", - " 69914 [cnmf.py: fit():466] [1655506] Using 1 processes\n", - " 69919 [cnmf.py: fit():466] [1655488] Using 1 processes\n", - " 69920 [cnmf.py: fit():466] [1655554] Using 1 processes\n", - " 69920 [pre_processing.py:interpolate_missing_data():41] [1655531] Checking for missing data entries (NaN)\n", - " 69921 [cnmf.py: fit():477] [1655534] using 400 pixels per process\n", - " 69921 [cnmf.py: fit():478] [1655551] using 5000 block_size_temp\n", - " 69922 [cnmf.py: fit():449] [1655477] (8000, 41, 41)\n", - " 69922 [pre_processing.py:interpolate_missing_data():41] [1655535] Checking for missing data entries (NaN)\n", - " 69923 [cnmf.py: fit():466] [1655479] Using 1 processes\n", - " 69923 [cnmf.py: fit():477] [1655489] using 400 pixels per process\n", - " 69924 [cnmf.py: fit():477] [1655480] using 400 pixels per process\n", - " 69924 [cnmf.py: fit():466] [1655492] Using 1 processes\n", - " 69924 [cnmf.py: fit():466] [1655527] Using 1 processes\n", - " 69925 [cnmf.py: fit():481] [1655552] preprocessing ...\n", - " 69926 [cnmf.py: fit():449] [1655496] (8000, 41, 41)\n", - " 69925 [cnmf.py: fit():466] [1655499] Using 1 processes\n", - " 69926 [cnmf.py: fit():466] [1655521] Using 1 processes\n", - " 69927 [cnmf.py: fit():477] [1655482] using 400 pixels per process\n", - " 69927 [cnmf.py: fit():466] [1655562] Using 1 processes\n", - " 69928 [cnmf.py: fit():466] [1655481] Using 1 processes\n", - " 69928 [cnmf.py: fit():477] [1655526] using 400 pixels per process\n", - " 69929 [cnmf.py: fit():466] [1655487] Using 1 processes\n", - " 69929 [cnmf.py: fit():466] [1655564] Using 1 processes\n", - " 69930 [cnmf.py: fit():477] [1655533] using 400 pixels per process\n", - " 69930 [cnmf.py: fit():466] [1655532] Using 1 processes\n", - " 69931 [cnmf.py: fit():477] [1655511] using 400 pixels per process\n", - " 69931 [cnmf.py: fit():466] [1655483] Using 1 processes\n", - " 69935 [cnmf.py: fit():477] [1655512] using 400 pixels per process\n", - " 69936 [pre_processing.py:interpolate_missing_data():41] [1655549] Checking for missing data entries (NaN)\n", - " 69936 [cnmf.py: fit():477] [1655508] using 400 pixels per process\n", - " 69937 [cnmf.py: fit():481] [1655530] preprocessing ...\n", - " 69938 [cnmf.py: fit():449] [1655505] (8000, 41, 41)\n", - " 69938 [cnmf.py: fit():477] [1655507] using 400 pixels per process\n", - " 69938 [cnmf.py: fit():466] [1655485] Using 1 processes\n", - " 69939 [cnmf.py: fit():481] [1655550] preprocessing ...\n", - " 69939 [cnmf.py: fit():466] [1655516] Using 1 processes\n", - " 69940 [cnmf.py: fit():466] [1655469] Using 1 processes\n", - " 69941 [cnmf.py: fit():477] [1655497] using 400 pixels per process\n", - " 69940 [cnmf.py: fit():466] [1655498] Using 1 processes\n", - " 69941 [cnmf.py: fit():449] [1655510] (8000, 41, 41)\n", - " 69942 [cnmf.py: fit():477] [1655513] using 400 pixels per process\n", - " 69942 [cnmf.py: fit():477] [1655558] using 400 pixels per process\n", - " 69943 [cnmf.py: fit():436] [1655556] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69943 [cnmf.py: fit():477] [1655524] using 400 pixels per process\n", - " 69944 [cnmf.py: fit():481] [1655546] preprocessing ...\n", - " 69944 [cnmf.py: fit():477] [1655560] using 400 pixels per process\n", - " 69945 [cnmf.py: fit():466] [1655495] Using 1 processes\n", - " 69946 [pre_processing.py:interpolate_missing_data():41] [1655553] Checking for missing data entries (NaN)\n", - " 69945 [cnmf.py: fit():466] [1655476] Using 1 processes\n", - " 69946 [cnmf.py: fit():449] [1655478] (8000, 41, 41)\n", - " 69947 [cnmf.py: fit():477] [1655509] using 400 pixels per process\n", - " 69947 [cnmf.py: fit():478] [1655470] using 5000 block_size_temp\n", - " 69948 [cnmf.py: fit():478] [1655536] using 5000 block_size_temp\n", - " 69948 [cnmf.py: fit():436] [1655528] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69949 [cnmf.py: fit():466] [1655542] Using 1 processes\n", - " 69949 [cnmf.py: fit():449] [1655539] (8000, 41, 41)\n", - " 69950 [cnmf.py: fit():466] [1655561] Using 1 processes\n", - " 69950 [cnmf.py: fit():466] [1655504] Using 1 processes\n", - " 69951 [cnmf.py: fit():449] [1655493] (8000, 41, 41)\n", - " 69951 [cnmf.py: fit():466] [1655555] Using 1 processes\n", - " 69952 [cnmf.py: fit():477] [1655520] using 400 pixels per process\n", - " 69952 [cnmf.py: fit():449] [1655503] (8000, 41, 40)\n", - " 69953 [cnmf.py: fit():466] [1655502] Using 1 processes\n", - " 69953 [cnmf.py: fit():477] [1655547] using 400 pixels per process\n", - " 69954 [cnmf.py: fit():466] [1655474] Using 1 processes\n", - " 69953 [cnmf.py: fit():466] [1655544] Using 1 processes\n", - " 69955 [cnmf.py: fit():478] [1655494] using 5000 block_size_temp\n", - " 69954 [cnmf.py: fit():478] [1655473] using 5000 block_size_temp\n", - " 69955 [cnmf.py: fit():449] [1655491] (8000, 41, 41)\n", - " 69956 [cnmf.py: fit():478] [1655537] using 5000 block_size_temp\n", - " 69956 [cnmf.py: fit():477] [1655517] using 400 pixels per process\n", - " 69957 [cnmf.py: fit():478] [1655490] using 5000 block_size_temp\n", - " 69957 [cnmf.py: fit():478] [1655543] using 5000 block_size_temp\n", - " 69958 [cnmf.py: fit():449] [1655548] (8000, 41, 41)\n", - " 69958 [cnmf.py: fit():478] [1655525] using 5000 block_size_temp\n", - " 69958 [cnmf.py: fit():478] [1655522] using 5000 block_size_temp\n", - " 69959 [cnmf.py: fit():478] [1655519] using 5000 block_size_temp\n", - " 69959 [cnmf.py: fit():477] [1655518] using 400 pixels per process\n", - " 69960 [cnmf.py: fit():449] [1655475] (8000, 41, 41)\n", - " 69961 [cnmf.py: fit():466] [1655472] Using 1 processes\n", - " 69961 [cnmf.py: fit():478] [1655557] using 5000 block_size_temp\n", - " 69961 [cnmf.py: fit():477] [1655523] using 400 pixels per process\n", - " 69962 [cnmf.py: fit():449] [1655501] (8000, 41, 41)\n", - " 69962 [cnmf.py: fit():466] [1655559] Using 1 processes\n", - " 69963 [cnmf.py: fit():478] [1655540] using 5000 block_size_temp\n", - " 69963 [cnmf.py: fit():478] [1655500] using 5000 block_size_temp\n", - " 69966 [cnmf.py: fit():477] [1655486] using 400 pixels per process\n", - " 69967 [cnmf.py: fit():477] [1655514] using 400 pixels per process\n", - " 69967 [cnmf.py: fit():478] [1655529] using 5000 block_size_temp\n", - " 69968 [cnmf.py: fit():477] [1655541] using 400 pixels per process\n", - " 69968 [cnmf.py: fit():466] [1655545] Using 1 processes\n", - " 69969 [cnmf.py: fit():436] [1655563] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 69969 [cnmf.py: fit():466] [1655484] Using 1 processes\n", - " 69970 [cnmf.py: fit():466] [1655538] Using 1 processes\n", - " 69970 [cnmf.py: fit():477] [1655506] using 400 pixels per process\n", - " 69971 [cnmf.py: fit():477] [1655488] using 400 pixels per process\n", - " 69971 [cnmf.py: fit():477] [1655554] using 400 pixels per process\n", - " 69988 [cnmf.py: fit():466] [1655477] Using 1 processes\n", - " 69988 [cnmf.py: fit():481] [1655551] preprocessing ...\n", - " 69987 [cnmf.py: fit():478] [1655534] using 5000 block_size_temp\n", - " 70003 [cnmf.py: fit():478] [1655489] using 5000 block_size_temp\n", - " 70004 [cnmf.py: fit():477] [1655479] using 400 pixels per process\n", - " 70005 [cnmf.py: fit():478] [1655480] using 5000 block_size_temp\n", - " 70005 [cnmf.py: fit():477] [1655492] using 400 pixels per process\n", - " 70006 [cnmf.py: fit():477] [1655527] using 400 pixels per process\n", - " 70006 [pre_processing.py:interpolate_missing_data():41] [1655552] Checking for missing data entries (NaN)\n", - " 70006 [cnmf.py: fit():466] [1655496] Using 1 processes\n", - " 70007 [cnmf.py: fit():477] [1655499] using 400 pixels per process\n", - " 70007 [cnmf.py: fit():477] [1655521] using 400 pixels per process\n", - " 70008 [cnmf.py: fit():478] [1655482] using 5000 block_size_temp\n", - " 70008 [cnmf.py: fit():477] [1655562] using 400 pixels per process\n", - " 70009 [cnmf.py: fit():478] [1655526] using 5000 block_size_temp\n", - " 70009 [cnmf.py: fit():477] [1655481] using 400 pixels per process\n", - " 70010 [cnmf.py: fit():477] [1655487] using 400 pixels per process\n", - " 70010 [cnmf.py: fit():477] [1655564] using 400 pixels per process\n", - " 70011 [cnmf.py: fit():478] [1655533] using 5000 block_size_temp\n", - " 70011 [cnmf.py: fit():477] [1655532] using 400 pixels per process\n", - " 70012 [cnmf.py: fit():478] [1655511] using 5000 block_size_temp\n", - " 70012 [cnmf.py: fit():477] [1655483] using 400 pixels per process\n", - " 70013 [cnmf.py: fit():478] [1655512] using 5000 block_size_temp\n", - " 70027 [cnmf.py: fit():478] [1655508] using 5000 block_size_temp\n", - " 70028 [cnmf.py: fit():466] [1655505] Using 1 processes\n", - " 70028 [pre_processing.py:interpolate_missing_data():41] [1655530] Checking for missing data entries (NaN)\n", - " 70029 [cnmf.py: fit():478] [1655507] using 5000 block_size_temp\n", - " 70036 [pre_processing.py:interpolate_missing_data():41] [1655550] Checking for missing data entries (NaN)\n", - " 70036 [cnmf.py: fit():477] [1655516] using 400 pixels per process\n", - " 70036 [cnmf.py: fit():477] [1655485] using 400 pixels per process\n", - " 70038 [cnmf.py: fit():478] [1655497] using 5000 block_size_temp\n", - " 70038 [cnmf.py: fit():477] [1655498] using 400 pixels per process\n", - " 70037 [cnmf.py: fit():477] [1655469] using 400 pixels per process\n", - " 70038 [cnmf.py: fit():466] [1655510] Using 1 processes\n", - " 70039 [cnmf.py: fit():478] [1655513] using 5000 block_size_temp\n", - " 70039 [cnmf.py: fit():478] [1655558] using 5000 block_size_temp\n", - " 70040 [cnmf.py: fit():449] [1655556] (8000, 41, 41)\n", - " 70041 [pre_processing.py:interpolate_missing_data():41] [1655546] Checking for missing data entries (NaN)\n", - " 70040 [cnmf.py: fit():478] [1655524] using 5000 block_size_temp\n", - " 70042 [cnmf.py: fit():477] [1655495] using 400 pixels per process\n", - " 70041 [cnmf.py: fit():478] [1655560] using 5000 block_size_temp\n", - " 70063 [cnmf.py: fit():477] [1655476] using 400 pixels per process\n", - " 70064 [cnmf.py: fit():466] [1655478] Using 1 processes\n", - " 70065 [cnmf.py: fit():481] [1655470] preprocessing ...\n", - " 70065 [cnmf.py: fit():478] [1655509] using 5000 block_size_temp\n", - " 70066 [cnmf.py: fit():449] [1655528] (8000, 41, 41)\n", - " 70066 [cnmf.py: fit():477] [1655542] using 400 pixels per process\n", - " 70067 [cnmf.py: fit():466] [1655539] Using 1 processes\n", - " 70066 [cnmf.py: fit():481] [1655536] preprocessing ...\n", - " 70069 [cnmf.py: fit():478] [1655520] using 5000 block_size_temp\n", - " 70069 [cnmf.py: fit():466] [1655493] Using 1 processes\n", - " 70068 [cnmf.py: fit():477] [1655561] using 400 pixels per process\n", - " 70068 [cnmf.py: fit():477] [1655555] using 400 pixels per process\n", - " 70068 [cnmf.py: fit():477] [1655504] using 400 pixels per process\n", - " 70069 [cnmf.py: fit():466] [1655503] Using 1 processes\n", - " 70071 [cnmf.py: fit():478] [1655547] using 5000 block_size_temp\n", - " 70071 [cnmf.py: fit():477] [1655474] using 400 pixels per process\n", - " 70071 [cnmf.py: fit():477] [1655544] using 400 pixels per process\n", - " 70070 [cnmf.py: fit():477] [1655502] using 400 pixels per process\n", - " 70073 [cnmf.py: fit():481] [1655490] preprocessing ...\n", - " 70073 [cnmf.py: fit():481] [1655537] preprocessing ...\n", - " 70074 [cnmf.py: fit():478] [1655517] using 5000 block_size_temp\n", - " 70073 [cnmf.py: fit():466] [1655491] Using 1 processes\n", - " 70074 [cnmf.py: fit():481] [1655543] preprocessing ...\n", - " 70072 [cnmf.py: fit():481] [1655473] preprocessing ...\n", - " 70072 [cnmf.py: fit():481] [1655494] preprocessing ...\n", - " 70075 [cnmf.py: fit():481] [1655525] preprocessing ...\n", - " 70075 [cnmf.py: fit():481] [1655522] preprocessing ...\n", - " 70075 [cnmf.py: fit():466] [1655548] Using 1 processes\n", - " 70076 [cnmf.py: fit():478] [1655518] using 5000 block_size_temp\n", - " 70076 [cnmf.py: fit():481] [1655519] preprocessing ...\n", - " 70079 [cnmf.py: fit():466] [1655475] Using 1 processes\n", - " 70084 [cnmf.py: fit():478] [1655523] using 5000 block_size_temp\n", - " 70084 [cnmf.py: fit():481] [1655557] preprocessing ...\n", - " 70083 [cnmf.py: fit():477] [1655472] using 400 pixels per process\n", - " 70085 [cnmf.py: fit():466] [1655501] Using 1 processes\n", - " 70085 [cnmf.py: fit():477] [1655559] using 400 pixels per process\n", - " 70085 [cnmf.py: fit():481] [1655540] preprocessing ...\n", - " 70086 [cnmf.py: fit():481] [1655500] preprocessing ...\n", - " 70092 [cnmf.py: fit():478] [1655514] using 5000 block_size_temp\n", - " 70091 [cnmf.py: fit():478] [1655486] using 5000 block_size_temp\n", - " 70092 [cnmf.py: fit():481] [1655529] preprocessing ...\n", - " 70093 [cnmf.py: fit():478] [1655541] using 5000 block_size_temp\n", - " 70093 [cnmf.py: fit():477] [1655545] using 400 pixels per process\n", - " 70094 [cnmf.py: fit():449] [1655563] (8000, 41, 41)\n", - " 70094 [cnmf.py: fit():477] [1655484] using 400 pixels per process\n", - " 70095 [cnmf.py: fit():477] [1655538] using 400 pixels per process\n", - " 70095 [cnmf.py: fit():478] [1655506] using 5000 block_size_temp\n", - " 70096 [cnmf.py: fit():478] [1655554] using 5000 block_size_temp\n", - " 70096 [cnmf.py: fit():478] [1655488] using 5000 block_size_temp\n", - " 70097 [cnmf.py: fit():477] [1655477] using 400 pixels per process\n", - " 70097 [pre_processing.py:interpolate_missing_data():41] [1655551] Checking for missing data entries (NaN)\n", - " 70097 [cnmf.py: fit():481] [1655534] preprocessing ...\n", - " 70103 [cnmf.py: fit():481] [1655489] preprocessing ...\n", - " 70104 [cnmf.py: fit():478] [1655479] using 5000 block_size_temp\n", - " 70104 [cnmf.py: fit():481] [1655480] preprocessing ...\n", - " 70105 [cnmf.py: fit():478] [1655492] using 5000 block_size_temp\n", - " 70105 [cnmf.py: fit():478] [1655527] using 5000 block_size_temp\n", - " 70119 [cnmf.py: fit():477] [1655496] using 400 pixels per process\n", - " 70120 [cnmf.py: fit():478] [1655499] using 5000 block_size_temp\n", - " 70120 [cnmf.py: fit():478] [1655521] using 5000 block_size_temp\n", - " 70121 [cnmf.py: fit():481] [1655482] preprocessing ...\n", - " 70121 [cnmf.py: fit():478] [1655562] using 5000 block_size_temp\n", - " 70122 [cnmf.py: fit():478] [1655481] using 5000 block_size_temp\n", - " 70122 [cnmf.py: fit():481] [1655526] preprocessing ...\n", - " 70123 [cnmf.py: fit():478] [1655487] using 5000 block_size_temp\n", - " 70124 [cnmf.py: fit():478] [1655564] using 5000 block_size_temp\n", - " 70123 [cnmf.py: fit():481] [1655533] preprocessing ...\n", - " 70124 [cnmf.py: fit():481] [1655511] preprocessing ...\n", - " 70124 [cnmf.py: fit():478] [1655532] using 5000 block_size_temp\n", - " 70125 [cnmf.py: fit():481] [1655512] preprocessing ...\n", - " 70125 [cnmf.py: fit():478] [1655483] using 5000 block_size_temp\n", - " 70126 [cnmf.py: fit():481] [1655508] preprocessing ...\n", - " 70126 [cnmf.py: fit():477] [1655505] using 400 pixels per process\n", - " 70155 [cnmf.py: fit():478] [1655516] using 5000 block_size_temp\n", - " 70140 [cnmf.py: fit():481] [1655507] preprocessing ...\n", - " 70156 [cnmf.py: fit():478] [1655485] using 5000 block_size_temp\n", - " 70158 [cnmf.py: fit():481] [1655558] preprocessing ...\n", - " 70159 [cnmf.py: fit():481] [1655513] preprocessing ...\n", - " 70158 [cnmf.py: fit():477] [1655510] using 400 pixels per process\n", - " 70157 [cnmf.py: fit():478] [1655498] using 5000 block_size_temp\n", - " 70158 [cnmf.py: fit():478] [1655469] using 5000 block_size_temp\n", - " 70157 [cnmf.py: fit():481] [1655497] preprocessing ...\n", - " 70160 [cnmf.py: fit():481] [1655524] preprocessing ...\n", - " 70175 [cnmf.py: fit():481] [1655560] preprocessing ...\n", - " 70159 [cnmf.py: fit():466] [1655556] Using 1 processes\n", - " 70176 [cnmf.py: fit():478] [1655495] using 5000 block_size_temp\n", - " 70180 [cnmf.py: fit():477] [1655478] using 400 pixels per process\n", - " 70179 [cnmf.py: fit():478] [1655476] using 5000 block_size_temp\n", - " 70188 [cnmf.py: fit():481] [1655509] preprocessing ...\n", - " 70187 [pre_processing.py:interpolate_missing_data():41] [1655470] Checking for missing data entries (NaN)\n", - " 70199 [cnmf.py: fit():481] [1655520] preprocessing ...\n", - " 70198 [cnmf.py: fit():478] [1655504] using 5000 block_size_temp\n", - " 70197 [cnmf.py: fit():478] [1655555] using 5000 block_size_temp\n", - " 70198 [cnmf.py: fit():478] [1655561] using 5000 block_size_temp\n", - " 70198 [cnmf.py: fit():477] [1655503] using 400 pixels per process\n", - " 70197 [pre_processing.py:interpolate_missing_data():41] [1655536] Checking for missing data entries (NaN)\n", - " 70197 [cnmf.py: fit():477] [1655493] using 400 pixels per process\n", - " 70196 [cnmf.py: fit():478] [1655542] using 5000 block_size_temp\n", - " 70196 [cnmf.py: fit():466] [1655528] Using 1 processes\n", - " 70196 [cnmf.py: fit():477] [1655539] using 400 pixels per process\n", - " 70204 [cnmf.py: fit():478] [1655544] using 5000 block_size_temp\n", - " 70204 [cnmf.py: fit():478] [1655474] using 5000 block_size_temp\n", - " 70204 [cnmf.py: fit():478] [1655502] using 5000 block_size_temp\n", - " 70203 [cnmf.py: fit():481] [1655547] preprocessing ...\n", - " 70207 [pre_processing.py:interpolate_missing_data():41] [1655543] Checking for missing data entries (NaN)\n", - " 70207 [pre_processing.py:interpolate_missing_data():41] [1655473] Checking for missing data entries (NaN)\n", - " 70206 [cnmf.py: fit():477] [1655491] using 400 pixels per process\n", - " 70207 [pre_processing.py:interpolate_missing_data():41] [1655494] Checking for missing data entries (NaN)\n", - " 70206 [pre_processing.py:interpolate_missing_data():41] [1655537] Checking for missing data entries (NaN)\n", - " 70206 [pre_processing.py:interpolate_missing_data():41] [1655490] Checking for missing data entries (NaN)\n", - " 70205 [cnmf.py: fit():481] [1655517] preprocessing ...\n", - " 70210 [cnmf.py: fit():477] [1655475] using 400 pixels per process\n", - " 70209 [cnmf.py: fit():477] [1655548] using 400 pixels per process\n", - " 70209 [pre_processing.py:interpolate_missing_data():41] [1655519] Checking for missing data entries (NaN)\n", - " 70210 [cnmf.py: fit():481] [1655518] preprocessing ...\n", - " 70209 [pre_processing.py:interpolate_missing_data():41] [1655522] Checking for missing data entries (NaN)\n", - " 70208 [pre_processing.py:interpolate_missing_data():41] [1655525] Checking for missing data entries (NaN)\n", - " 70216 [cnmf.py: fit():478] [1655472] using 5000 block_size_temp\n", - " 70216 [cnmf.py: fit():477] [1655501] using 400 pixels per process\n", - " 70216 [pre_processing.py:interpolate_missing_data():41] [1655557] Checking for missing data entries (NaN)\n", - " 70215 [cnmf.py: fit():481] [1655523] preprocessing ...\n", - " 70217 [cnmf.py: fit():478] [1655559] using 5000 block_size_temp\n", - " 70218 [pre_processing.py:interpolate_missing_data():41] [1655500] Checking for missing data entries (NaN)\n", - " 70217 [pre_processing.py:interpolate_missing_data():41] [1655540] Checking for missing data entries (NaN)\n", - " 70219 [pre_processing.py:interpolate_missing_data():41] [1655529] Checking for missing data entries (NaN)\n", - " 70218 [cnmf.py: fit():481] [1655486] preprocessing ...\n", - " 70219 [cnmf.py: fit():481] [1655514] preprocessing ...\n", - " 70220 [cnmf.py: fit():481] [1655541] preprocessing ...\n", - " 70221 [cnmf.py: fit():478] [1655484] using 5000 block_size_temp\n", - " 70220 [cnmf.py: fit():466] [1655563] Using 1 processes\n", - " 70220 [cnmf.py: fit():478] [1655545] using 5000 block_size_temp\n", - " 70221 [cnmf.py: fit():478] [1655538] using 5000 block_size_temp\n", - " 70222 [cnmf.py: fit():481] [1655506] preprocessing ...\n", - " 70222 [cnmf.py: fit():481] [1655554] preprocessing ...\n", - " 70223 [cnmf.py: fit():481] [1655488] preprocessing ...\n", - " 70223 [cnmf.py: fit():478] [1655477] using 5000 block_size_temp\n", - " 70239 [pre_processing.py:interpolate_missing_data():41] [1655534] Checking for missing data entries (NaN)\n", - " 70240 [pre_processing.py:interpolate_missing_data():41] [1655489] Checking for missing data entries (NaN)\n", - " 70242 [pre_processing.py:interpolate_missing_data():41] [1655480] Checking for missing data entries (NaN)\n", - " 70242 [cnmf.py: fit():481] [1655527] preprocessing ...\n", - " 70241 [cnmf.py: fit():481] [1655492] preprocessing ...\n", - " 70241 [cnmf.py: fit():481] [1655479] preprocessing ...\n", - " 70248 [cnmf.py: fit():481] [1655499] preprocessing ...\n", - " 70248 [cnmf.py: fit():478] [1655496] using 5000 block_size_temp\n", - " 70249 [pre_processing.py:interpolate_missing_data():41] [1655482] Checking for missing data entries (NaN)\n", - " 70249 [cnmf.py: fit():481] [1655521] preprocessing ...\n", - " 70256 [cnmf.py: fit():481] [1655481] preprocessing ...\n", - " 70256 [pre_processing.py:interpolate_missing_data():41] [1655526] Checking for missing data entries (NaN)\n", - " 70256 [cnmf.py: fit():481] [1655562] preprocessing ...\n", - " 70258 [pre_processing.py:interpolate_missing_data():41] [1655533] Checking for missing data entries (NaN)\n", - " 70257 [cnmf.py: fit():481] [1655564] preprocessing ...\n", - " 70260 [cnmf.py: fit():478] [1655505] using 5000 block_size_temp\n", - " 70258 [pre_processing.py:interpolate_missing_data():41] [1655511] Checking for missing data entries (NaN)\n", - " 70259 [cnmf.py: fit():481] [1655532] preprocessing ...\n", - " 70259 [cnmf.py: fit():481] [1655483] preprocessing ...\n", - " 70259 [pre_processing.py:interpolate_missing_data():41] [1655512] Checking for missing data entries (NaN)\n", - " 70260 [pre_processing.py:interpolate_missing_data():41] [1655508] Checking for missing data entries (NaN)\n", - " 70257 [cnmf.py: fit():481] [1655487] preprocessing ...\n", - " 70264 [pre_processing.py:interpolate_missing_data():41] [1655507] Checking for missing data entries (NaN)\n", - " 70263 [cnmf.py: fit():481] [1655516] preprocessing ...\n", - " 70264 [cnmf.py: fit():481] [1655485] preprocessing ...\n", - " 70276 [pre_processing.py:interpolate_missing_data():41] [1655513] Checking for missing data entries (NaN)\n", - " 70276 [pre_processing.py:interpolate_missing_data():41] [1655558] Checking for missing data entries (NaN)\n", - " 70276 [cnmf.py: fit():478] [1655510] using 5000 block_size_temp\n", - " 70277 [cnmf.py: fit():481] [1655469] preprocessing ...\n", - " 70277 [pre_processing.py:interpolate_missing_data():41] [1655497] Checking for missing data entries (NaN)\n", - " 70275 [cnmf.py: fit():481] [1655498] preprocessing ...\n", - " 70284 [pre_processing.py:interpolate_missing_data():41] [1655524] Checking for missing data entries (NaN)\n", - " 70283 [pre_processing.py:interpolate_missing_data():41] [1655560] Checking for missing data entries (NaN)\n", - " 70284 [cnmf.py: fit():477] [1655556] using 400 pixels per process\n", - " 70285 [cnmf.py: fit():481] [1655495] preprocessing ...\n", - " 70285 [cnmf.py: fit():481] [1655476] preprocessing ...\n", - " 70285 [cnmf.py: fit():478] [1655478] using 5000 block_size_temp\n", - " 70286 [pre_processing.py:interpolate_missing_data():41] [1655509] Checking for missing data entries (NaN)\n", - " 70301 [cnmf.py: fit():478] [1655503] using 5000 block_size_temp\n", - " 70301 [cnmf.py: fit():481] [1655542] preprocessing ...\n", - " 70315 [cnmf.py: fit():481] [1655555] preprocessing ...\n", - " 70316 [cnmf.py: fit():481] [1655561] preprocessing ...\n", - " 70300 [cnmf.py: fit():478] [1655493] using 5000 block_size_temp\n", - " 70316 [cnmf.py: fit():477] [1655528] using 400 pixels per process\n", - " 70316 [pre_processing.py:interpolate_missing_data():41] [1655520] Checking for missing data entries (NaN)\n", - " 70301 [cnmf.py: fit():481] [1655504] preprocessing ...\n", - " 70319 [pre_processing.py:interpolate_missing_data():41] [1655547] Checking for missing data entries (NaN)\n", - " 70318 [cnmf.py: fit():481] [1655502] preprocessing ...\n", - " 70318 [cnmf.py: fit():481] [1655474] preprocessing ...\n", - " 70318 [cnmf.py: fit():481] [1655544] preprocessing ...\n", - " 70317 [cnmf.py: fit():478] [1655539] using 5000 block_size_temp\n", - " 70383 [cnmf.py: fit():478] [1655491] using 5000 block_size_temp\n", - " 70403 [pre_processing.py:interpolate_missing_data():41] [1655517] Checking for missing data entries (NaN)\n", - " 70410 [cnmf.py: fit():478] [1655548] using 5000 block_size_temp\n", - " 70430 [cnmf.py: fit():478] [1655475] using 5000 block_size_temp\n", - " 70410 [pre_processing.py:interpolate_missing_data():41] [1655518] Checking for missing data entries (NaN)\n", - " 70468 [cnmf.py: fit():478] [1655501] using 5000 block_size_temp\n", - " 70485 [pre_processing.py:interpolate_missing_data():41] [1655523] Checking for missing data entries (NaN)\n", - " 70490 [cnmf.py: fit():481] [1655559] preprocessing ...\n", - " 70468 [cnmf.py: fit():481] [1655472] preprocessing ...\n", - " 70524 [pre_processing.py:interpolate_missing_data():41] [1655486] Checking for missing data entries (NaN)\n", - " 70523 [pre_processing.py:interpolate_missing_data():41] [1655514] Checking for missing data entries (NaN)\n", - " 70545 [pre_processing.py:interpolate_missing_data():41] [1655554] Checking for missing data entries (NaN)\n", - " 70544 [cnmf.py: fit():481] [1655545] preprocessing ...\n", - " 70544 [cnmf.py: fit():481] [1655484] preprocessing ...\n", - " 70545 [cnmf.py: fit():481] [1655538] preprocessing ...\n", - " 70543 [cnmf.py: fit():477] [1655563] using 400 pixels per process\n", - " 70546 [pre_processing.py:interpolate_missing_data():41] [1655488] Checking for missing data entries (NaN)\n", - " 70545 [pre_processing.py:interpolate_missing_data():41] [1655506] Checking for missing data entries (NaN)\n", - " 70543 [pre_processing.py:interpolate_missing_data():41] [1655541] Checking for missing data entries (NaN)\n", - " 70552 [cnmf.py: fit():481] [1655477] preprocessing ...\n", - " 70584 [pre_processing.py:interpolate_missing_data():41] [1655492] Checking for missing data entries (NaN)\n", - " 70602 [pre_processing.py:interpolate_missing_data():41] [1655479] Checking for missing data entries (NaN)\n", - " 70583 [pre_processing.py:interpolate_missing_data():41] [1655527] Checking for missing data entries (NaN)\n", - " 70607 [pre_processing.py:interpolate_missing_data():41] [1655499] Checking for missing data entries (NaN)\n", - " 70608 [cnmf.py: fit():481] [1655496] preprocessing ...\n", - " 70625 [pre_processing.py:interpolate_missing_data():41] [1655521] Checking for missing data entries (NaN)\n", - " 70651 [pre_processing.py:interpolate_missing_data():41] [1655562] Checking for missing data entries (NaN)\n", - " 70631 [pre_processing.py:interpolate_missing_data():41] [1655481] Checking for missing data entries (NaN)\n", - " 70735 [pre_processing.py:interpolate_missing_data():41] [1655487] Checking for missing data entries (NaN)\n", - " 70715 [pre_processing.py:interpolate_missing_data():41] [1655483] Checking for missing data entries (NaN)\n", - " 70736 [cnmf.py: fit():481] [1655505] preprocessing ...\n", - " 70695 [pre_processing.py:interpolate_missing_data():41] [1655532] Checking for missing data entries (NaN)\n", - " 70674 [pre_processing.py:interpolate_missing_data():41] [1655564] Checking for missing data entries (NaN)\n", - " 70753 [pre_processing.py:interpolate_missing_data():41] [1655516] Checking for missing data entries (NaN)\n", - " 70754 [pre_processing.py:interpolate_missing_data():41] [1655485] Checking for missing data entries (NaN)\n", - " 70795 [pre_processing.py:interpolate_missing_data():41] [1655498] Checking for missing data entries (NaN)\n", - " 70774 [cnmf.py: fit():481] [1655510] preprocessing ...\n", - " 70795 [pre_processing.py:interpolate_missing_data():41] [1655469] Checking for missing data entries (NaN)\n", - " 70856 [pre_processing.py:interpolate_missing_data():41] [1655476] Checking for missing data entries (NaN)\n", - " 70855 [cnmf.py: fit():481] [1655478] preprocessing ...\n", - " 70855 [pre_processing.py:interpolate_missing_data():41] [1655495] Checking for missing data entries (NaN)\n", - " 70854 [cnmf.py: fit():478] [1655556] using 5000 block_size_temp\n", - " 70897 [cnmf.py: fit():478] [1655528] using 5000 block_size_temp\n", - " 70897 [pre_processing.py:interpolate_missing_data():41] [1655561] Checking for missing data entries (NaN)\n", - " 70897 [cnmf.py: fit():481] [1655493] preprocessing ...\n", - " 70898 [pre_processing.py:interpolate_missing_data():41] [1655504] Checking for missing data entries (NaN)\n", - " 70897 [cnmf.py: fit():481] [1655503] preprocessing ...\n", - " 70896 [pre_processing.py:interpolate_missing_data():41] [1655542] Checking for missing data entries (NaN)\n", - " 70896 [pre_processing.py:interpolate_missing_data():41] [1655555] Checking for missing data entries (NaN)\n", - " 70941 [cnmf.py: fit():481] [1655539] preprocessing ...\n", - " 70925 [pre_processing.py:interpolate_missing_data():41] [1655544] Checking for missing data entries (NaN)\n", - " 70924 [pre_processing.py:interpolate_missing_data():41] [1655502] Checking for missing data entries (NaN)\n", - " 70924 [pre_processing.py:interpolate_missing_data():41] [1655474] Checking for missing data entries (NaN)\n", - " 70947 [cnmf.py: fit():481] [1655491] preprocessing ...\n", - " 70974 [cnmf.py: fit():481] [1655475] preprocessing ...\n", - " 70974 [cnmf.py: fit():481] [1655548] preprocessing ...\n", - " 71016 [pre_processing.py:interpolate_missing_data():41] [1655559] Checking for missing data entries (NaN)\n", - " 71015 [pre_processing.py:interpolate_missing_data():41] [1655472] Checking for missing data entries (NaN)\n", - " 71015 [cnmf.py: fit():481] [1655501] preprocessing ...\n", - " 71147 [pre_processing.py:interpolate_missing_data():41] [1655538] Checking for missing data entries (NaN)\n", - " 71107 [pre_processing.py:interpolate_missing_data():41] [1655484] Checking for missing data entries (NaN)\n", - " 71076 [cnmf.py: fit():478] [1655563] using 5000 block_size_temp\n", - " 71076 [pre_processing.py:interpolate_missing_data():41] [1655545] Checking for missing data entries (NaN)\n", - " 71155 [pre_processing.py:interpolate_missing_data():41] [1655477] Checking for missing data entries (NaN)\n", - " 71195 [pre_processing.py:interpolate_missing_data():41] [1655496] Checking for missing data entries (NaN)\n", - " 71319 [pre_processing.py:interpolate_missing_data():41] [1655505] Checking for missing data entries (NaN)\n", - " 71399 [pre_processing.py:interpolate_missing_data():41] [1655510] Checking for missing data entries (NaN)\n", - " 71423 [pre_processing.py:interpolate_missing_data():41] [1655478] Checking for missing data entries (NaN)\n", - " 71455 [cnmf.py: fit():481] [1655556] preprocessing ...\n", - " 71511 [cnmf.py: fit():481] [1655528] preprocessing ...\n", - " 71512 [pre_processing.py:interpolate_missing_data():41] [1655503] Checking for missing data entries (NaN)\n", - " 71464 [pre_processing.py:interpolate_missing_data():41] [1655493] Checking for missing data entries (NaN)\n", - " 71547 [pre_processing.py:interpolate_missing_data():41] [1655539] Checking for missing data entries (NaN)\n", - " 71592 [pre_processing.py:interpolate_missing_data():41] [1655491] Checking for missing data entries (NaN)\n", - " 71592 [pre_processing.py:interpolate_missing_data():41] [1655548] Checking for missing data entries (NaN)\n", - " 71592 [pre_processing.py:interpolate_missing_data():41] [1655475] Checking for missing data entries (NaN)\n", - " 71623 [pre_processing.py:interpolate_missing_data():41] [1655501] Checking for missing data entries (NaN)\n", - " 71640 [cnmf.py: fit():481] [1655563] preprocessing ...\n", - " 71895 [pre_processing.py:interpolate_missing_data():41] [1655556] Checking for missing data entries (NaN)\n", - " 71939 [pre_processing.py:interpolate_missing_data():41] [1655528] Checking for missing data entries (NaN)\n", - " 72162 [pre_processing.py:interpolate_missing_data():41] [1655563] Checking for missing data entries (NaN)\n", - " 74658 [cnmf.py: fit():484] [1655515] initializing ...\n", - " 75019 [initialization.py:initialize_components():292] [1655515] Variance Normalization\n", - " 75041 [cnmf.py: fit():484] [1655531] initializing ...\n", - " 75066 [cnmf.py: fit():484] [1655549] initializing ...\n", - " 75102 [cnmf.py: fit():484] [1655553] initializing ...\n", - " 75244 [cnmf.py: fit():484] [1655535] initializing ...\n", - " 75310 [cnmf.py: fit():484] [1655552] initializing ...\n", - " 75352 [initialization.py:initialize_components():292] [1655531] Variance Normalization\n", - " 75367 [initialization.py:initialize_components():292] [1655549] Variance Normalization\n", - " 75407 [initialization.py:initialize_components():292] [1655553] Variance Normalization\n", - " 75489 [cnmf.py: fit():484] [1655546] initializing ...\n", - " 75523 [initialization.py:initialize_components():292] [1655535] Variance Normalization\n", - " 75553 [cnmf.py: fit():484] [1655550] initializing ...\n", - " 75560 [cnmf.py: fit():484] [1655530] initializing ...\n", - " 75507 [cnmf.py: fit():484] [1655551] initializing ...\n", - " 75659 [initialization.py:initialize_components():292] [1655552] Variance Normalization\n", - " 75759 [initialization.py:initialize_components():292] [1655546] Variance Normalization\n", - " 75774 [cnmf.py: fit():484] [1655519] initializing ...\n", - " 75802 [cnmf.py: fit():484] [1655543] initializing ...\n", - " 75808 [initialization.py:initialize_components():292] [1655550] Variance Normalization\n", - " 75823 [initialization.py:initialize_components():292] [1655530] Variance Normalization\n", - " 75826 [cnmf.py: fit():484] [1655525] initializing ...\n", - " 75832 [cnmf.py: fit():484] [1655536] initializing ...\n", - " 75836 [cnmf.py: fit():484] [1655537] initializing ...\n", - " 75873 [cnmf.py: fit():484] [1655470] initializing ...\n", - " 75909 [cnmf.py: fit():484] [1655490] initializing ...\n", - " 75909 [initialization.py:initialize_components():292] [1655551] Variance Normalization\n", - " 75815 [cnmf.py: fit():484] [1655480] initializing ...\n", - " 75933 [cnmf.py: fit():484] [1655540] initializing ...\n", - " 75918 [cnmf.py: fit():484] [1655508] initializing ...\n", - " 75946 [cnmf.py: fit():484] [1655511] initializing ...\n", - " 75830 [cnmf.py: fit():484] [1655533] initializing ...\n", - " 75974 [cnmf.py: fit():484] [1655507] initializing ...\n", - " 76009 [cnmf.py: fit():484] [1655534] initializing ...\n", - " 75878 [cnmf.py: fit():484] [1655497] initializing ...\n", - " 76010 [cnmf.py: fit():484] [1655494] initializing ...\n", - " 76020 [cnmf.py: fit():484] [1655522] initializing ...\n", - " 76045 [cnmf.py: fit():484] [1655524] initializing ...\n", - " 75695 [cnmf.py: fit():484] [1655473] initializing ...\n", - " 76054 [cnmf.py: fit():484] [1655529] initializing ...\n", - " 76061 [cnmf.py: fit():484] [1655509] initializing ...\n", - " 76075 [cnmf.py: fit():484] [1655500] initializing ...\n", - " 76083 [initialization.py:initialize_components():292] [1655519] Variance Normalization\n", - " 76053 [cnmf.py: fit():484] [1655523] initializing ...\n", - " 75755 [cnmf.py: fit():484] [1655557] initializing ...\n", - " 76084 [initialization.py:initialize_components():292] [1655543] Variance Normalization\n", - " 76092 [cnmf.py: fit():484] [1655482] initializing ...\n", - " 76094 [initialization.py:initialize_components():292] [1655525] Variance Normalization\n", - " 76098 [initialization.py:initialize_components():292] [1655536] Variance Normalization\n", - " 76099 [initialization.py:initialize_components():292] [1655537] Variance Normalization\n", - " 76114 [cnmf.py: fit():484] [1655520] initializing ...\n", - " 76049 [cnmf.py: fit():484] [1655514] initializing ...\n", - " 76121 [cnmf.py: fit():484] [1655554] initializing ...\n", - " 76122 [initialization.py:initialize_components():292] [1655470] Variance Normalization\n", - " 76122 [cnmf.py: fit():484] [1655489] initializing ...\n", - " 76123 [cnmf.py: fit():484] [1655512] initializing ...\n", - " 76130 [cnmf.py: fit():484] [1655488] initializing ...\n", - " 76131 [cnmf.py: fit():484] [1655526] initializing ...\n", - " 76135 [initialization.py:initialize_components():292] [1655490] Variance Normalization\n", - " 76141 [cnmf.py: fit():484] [1655517] initializing ...\n", - " 76155 [initialization.py:initialize_components():292] [1655480] Variance Normalization\n", - " 76158 [cnmf.py: fit():484] [1655506] initializing ...\n", - " 76170 [initialization.py:initialize_components():292] [1655540] Variance Normalization\n", - " 76170 [cnmf.py: fit():484] [1655513] initializing ...\n", - " 76173 [cnmf.py: fit():484] [1655492] initializing ...\n", - " 76098 [cnmf.py: fit():484] [1655562] initializing ...\n", - " 76175 [cnmf.py: fit():484] [1655486] initializing ...\n", - " 76183 [initialization.py:initialize_components():292] [1655511] Variance Normalization\n", - " 76184 [initialization.py:initialize_components():292] [1655508] Variance Normalization\n", - " 76184 [initialization.py:initialize_components():292] [1655533] Variance Normalization\n", - " 76202 [cnmf.py: fit():484] [1655560] initializing ...\n", - " 76202 [cnmf.py: fit():484] [1655564] initializing ...\n", - " 75903 [cnmf.py: fit():484] [1655558] initializing ...\n", - " 76194 [cnmf.py: fit():484] [1655483] initializing ...\n", - " 76214 [cnmf.py: fit():484] [1655527] initializing ...\n", - " 76214 [initialization.py:initialize_components():292] [1655507] Variance Normalization\n", - " 76119 [cnmf.py: fit():484] [1655469] initializing ...\n", - " 76224 [initialization.py:initialize_components():292] [1655497] Variance Normalization\n", - " 76225 [initialization.py:initialize_components():292] [1655534] Variance Normalization\n", - " 76169 [cnmf.py: fit():484] [1655498] initializing ...\n", - " 76225 [initialization.py:initialize_components():292] [1655494] Variance Normalization\n", - " 76235 [initialization.py:initialize_components():292] [1655522] Variance Normalization\n", - " 75963 [cnmf.py: fit():484] [1655547] initializing ...\n", - " 76260 [cnmf.py: fit():484] [1655541] initializing ...\n", - " 76262 [map_reduce.py: cnmf_patches():78] [1655515] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 158880_179400START\n", - " 76242 [cnmf.py: fit():484] [1655555] initializing ...\n", - " 76268 [cnmf.py: fit():484] [1655516] initializing ...\n", - " 76278 [initialization.py:initialize_components():292] [1655473] Variance Normalization\n", - " 76279 [cnmf.py: fit():484] [1655479] initializing ...\n", - " 76165 [cnmf.py: fit():484] [1655476] initializing ...\n", - " 76284 [initialization.py:initialize_components():292] [1655524] Variance Normalization\n", - " 76265 [cnmf.py: fit():484] [1655542] initializing ...\n", - " 76284 [initialization.py:initialize_components():292] [1655529] Variance Normalization\n", - " 76235 [cnmf.py: fit():484] [1655502] initializing ...\n", - " 76199 [cnmf.py: fit():484] [1655544] initializing ...\n", - " 76287 [cnmf.py: fit():484] [1655495] initializing ...\n", - " 76287 [initialization.py:initialize_components():292] [1655509] Variance Normalization\n", - " 76293 [cnmf.py: fit():484] [1655532] initializing ...\n", - " 76035 [cnmf.py: fit():484] [1655518] initializing ...\n", - " 76294 [initialization.py:initialize_components():292] [1655500] Variance Normalization\n", - " 76300 [initialization.py:initialize_components():292] [1655557] Variance Normalization\n", - " 76300 [initialization.py:initialize_components():292] [1655523] Variance Normalization\n", - " 76308 [cnmf.py: fit():484] [1655499] initializing ...\n", - " 76321 [cnmf.py: fit():484] [1655504] initializing ...\n", - " 76321 [initialization.py:initialize_components():292] [1655482] Variance Normalization\n", - " 76277 [cnmf.py: fit():484] [1655474] initializing ...\n", - " 76349 [cnmf.py: fit():484] [1655487] initializing ...\n", - " 76350 [initialization.py:initialize_components():292] [1655514] Variance Normalization\n", - " 76350 [initialization.py:initialize_components():292] [1655520] Variance Normalization\n", - " 76351 [initialization.py:initialize_components():292] [1655554] Variance Normalization\n", - " 76359 [initialization.py:initialize_components():292] [1655512] Variance Normalization\n", - " 76360 [initialization.py:initialize_components():292] [1655489] Variance Normalization\n", - " 76369 [cnmf.py: fit():484] [1655485] initializing ...\n", - " 76247 [cnmf.py: fit():484] [1655472] initializing ...\n", - " 76369 [initialization.py:initialize_components():292] [1655488] Variance Normalization\n", - " 76370 [initialization.py:initialize_components():292] [1655526] Variance Normalization\n", - " 76015 [cnmf.py: fit():484] [1655521] initializing ...\n", - " 76391 [initialization.py:initialize_components():292] [1655517] Variance Normalization\n", - " 76400 [initialization.py:initialize_components():292] [1655506] Variance Normalization\n", - " 76404 [cnmf.py: fit():484] [1655481] initializing ...\n", - " 76412 [initialization.py:initialize_components():292] [1655492] Variance Normalization\n", - " 76413 [initialization.py:initialize_components():292] [1655562] Variance Normalization\n", - " 76413 [initialization.py:initialize_components():292] [1655513] Variance Normalization\n", - " 76414 [initialization.py:initialize_components():292] [1655486] Variance Normalization\n", - " 76428 [map_reduce.py: cnmf_patches():78] [1655531] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174240_194760START\n", - " 76440 [map_reduce.py: cnmf_patches():78] [1655535] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189600_210120START\n", - " 76441 [initialization.py:initialize_components():292] [1655560] Variance Normalization\n", - " 76442 [initialization.py:initialize_components():292] [1655558] Variance Normalization\n", - " 76442 [initialization.py:initialize_components():292] [1655564] Variance Normalization\n", - " 76447 [initialization.py:initialize_components():292] [1655483] Variance Normalization\n", - " 76448 [initialization.py:initialize_components():292] [1655527] Variance Normalization\n", - " 76459 [cnmf.py: fit():484] [1655484] initializing ...\n", - " 76479 [initialization.py:initialize_components():292] [1655469] Variance Normalization\n", - " 76485 [cnmf.py: fit():484] [1655559] initializing ...\n", - " 76485 [initialization.py:initialize_components():292] [1655498] Variance Normalization\n", - " 76386 [cnmf.py: fit():484] [1655505] initializing ...\n", - " 76487 [map_reduce.py: cnmf_patches():78] [1655549] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 204960_225480START\n", - " 76487 [cnmf.py: fit():484] [1655561] initializing ...\n", - " 76502 [cnmf.py: fit():484] [1655503] initializing ...\n", - " 76502 [initialization.py:initialize_components():292] [1655547] Variance Normalization\n", - " 76505 [cnmf.py: fit():484] [1655496] initializing ...\n", - " 76511 [initialization.py:initialize_components():292] [1655555] Variance Normalization\n", - " 76506 [initialization.py:initialize_components():292] [1655541] Variance Normalization\n", - " 76506 [map_reduce.py: cnmf_patches():80] [1655515] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 158880_179400Read file\n", - " 76507 [initialization.py:initialize_components():292] [1655516] Variance Normalization\n", - " 76513 [cnmf.py: fit():484] [1655545] initializing ...\n", - " 76575 [initialization.py:initialize_components():292] [1655532] Variance Normalization\n", - " 76567 [initialization.py:initialize_components():292] [1655518] Variance Normalization\n", - " 76517 [initialization.py:initialize_components():292] [1655479] Variance Normalization\n", - " 76520 [cnmf.py: fit():484] [1655493] initializing ...\n", - " 76521 [map_reduce.py: cnmf_patches():78] [1655553] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220320_240840START\n", - " 76531 [initialization.py:initialize_components():292] [1655476] Variance Normalization\n", - " 76540 [initialization.py:initialize_components():292] [1655502] Variance Normalization\n", - " 76540 [initialization.py:initialize_components():292] [1655542] Variance Normalization\n", - " 76547 [initialization.py:initialize_components():292] [1655544] Variance Normalization\n", - " 76556 [initialization.py:initialize_components():292] [1655495] Variance Normalization\n", - " 76651 [cnmf.py: fit():484] [1655548] initializing ...\n", - " 76666 [initialization.py:initialize_components():292] [1655472] Variance Normalization\n", - " 76591 [initialization.py:initialize_components():292] [1655499] Variance Normalization\n", - " 76592 [initialization.py:initialize_components():292] [1655504] Variance Normalization\n", - 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" 87561 [params.py: set():1006] [1655529] Changing key rf in group patch from 20 to None\n", - " 87527 [params.py: set():1006] [1655469] Changing key rf in group patch from 20 to None\n", - " 87858 [params.py: set():1006] [1655564] Changing key rf in group patch from 20 to None\n", - " 87860 [params.py: set():1006] [1655547] Changing key rf in group patch from 20 to None\n", - " 87873 [params.py: set():1006] [1655492] Changing key rf in group patch from 20 to None\n", - " 87874 [params.py: set():1006] [1655483] Changing key rf in group patch from 20 to None\n", - " 87884 [params.py: set():1006] [1655549] Changing key rf in group patch from 20 to None\n", - " 87850 [params.py: set():1006] [1655523] Changing key rf in group patch from 20 to None\n", - " 87886 [params.py: set():1006] [1655516] Changing key rf in group patch from 20 to None\n", - " 87888 [params.py: set():1006] [1655541] Changing key rf in group patch from 20 to None\n", - " 87890 [params.py: set():1006] [1655546] Changing key rf in group patch from 20 to None\n", - " 87893 [params.py: set():1006] [1655560] Changing key rf in group patch from 20 to None\n", - " 87898 [params.py: set():1006] [1655508] Changing key rf in group patch from 20 to None\n", - " 87903 [params.py: set():1006] [1655525] Changing key rf in group patch from 20 to None\n", - " 87905 [params.py: set():1006] [1655473] Changing key rf in group patch from 20 to None\n", - " 87907 [params.py: set():1006] [1655507] Changing key rf in group patch from 20 to None\n", - " 87863 [params.py: set():1006] [1655556] Changing key rf in group patch from 20 to None\n", - " 87907 [params.py: set():1006] [1655540] Changing key rf in group patch from 20 to None\n", - " 87901 [params.py: set():1006] [1655488] Changing key rf in group patch from 20 to None\n", - " 87751 [params.py: set():1006] [1655522] Changing key rf in group patch from 20 to None\n", - " 87921 [params.py: set():1006] [1655481] Changing key rf in group patch from 20 to None\n", - " 87926 [params.py: set():1006] [1655554] Changing key stride in group patch from 10 to None\n", - " 87927 [params.py: set():1006] [1655520] Changing key stride in group patch from 10 to None\n", - " 87927 [params.py: set():1006] [1655511] Changing key stride in group patch from 10 to None\n", - " 87928 [params.py: set():1006] [1655519] Changing key stride in group patch from 10 to None\n", - " 87928 [params.py: set():1006] [1655537] Changing key stride in group patch from 10 to None\n", - " 87929 [params.py: set():1006] [1655472] Changing key stride in group patch from 10 to None\n", - " 87929 [params.py: set():1006] [1655513] Changing key stride in group patch from 10 to None\n", - " 87930 [params.py: set():1006] [1655530] Changing key nb in group init from 2 to 1\n", - " 87930 [params.py: set():1006] [1655553] Changing key stride in group patch from 10 to None\n", - " 87930 [params.py: set():1006] [1655535] Changing key stride in group patch from 10 to None\n", - " 87931 [cnmf.py: fit():436] [1655514] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 87932 [params.py: set():1006] [1655476] Changing key stride in group patch from 10 to None\n", - " 87932 [params.py: set():1006] [1655552] Changing key stride in group patch from 10 to None\n", - " 87933 [params.py: set():1006] [1655531] Changing key stride in group patch from 10 to None\n", - " 87933 [params.py: set():1006] [1655489] Changing key stride in group patch from 10 to None\n", - " 87933 [params.py: set():1006] [1655559] Changing key stride in group patch from 10 to None\n", - " 87747 [params.py: set():1006] [1655509] Changing key rf in group patch from 20 to None\n", - " 87939 [params.py: set():1006] [1655496] Changing key stride in group patch from 10 to None\n", - " 87940 [params.py: set():1006] [1655497] Changing key stride in group patch from 10 to None\n", - " 87940 [params.py: set():1006] [1655490] Changing key stride in group patch from 10 to None\n", - " 87918 [params.py: set():1006] [1655542] Changing key rf in group patch from 20 to None\n", - " 87914 [params.py: set():1006] [1655475] Changing key rf in group patch from 20 to None\n", - " 87946 [params.py: set():1006] [1655536] Changing key rf in group patch from 20 to None\n", - " 87947 [params.py: set():1006] [1655561] Changing key rf in group patch from 20 to None\n", - " 87661 [params.py: set():1006] [1655474] Changing key rf in group patch from 20 to None\n", - " 87964 [params.py: set():1006] [1655543] Changing key stride in group patch from 10 to None\n", - " 87964 [params.py: set():1006] [1655499] Changing key stride in group patch from 10 to None\n", - " 88118 [params.py: set():1006] [1655493] Changing key rf in group patch from 20 to None\n", - " 88104 [params.py: set():1006] [1655477] Changing key rf in group patch from 20 to None\n", - " 88102 [params.py: set():1006] [1655510] Changing key rf in group patch from 20 to None\n", - " 87897 [params.py: set():1006] [1655539] Changing key rf in group patch from 20 to None\n", - " 87967 [params.py: set():1006] [1655551] Changing key stride in group patch from 10 to None\n", - " 87966 [params.py: set():1006] [1655534] Changing key stride in group patch from 10 to None\n", - " 87967 [params.py: set():1006] [1655485] Changing key stride in group patch from 10 to None\n", - " 87970 [params.py: set():1006] [1655526] Changing key rf in group patch from 20 to None\n", - " 87970 [params.py: set():1006] [1655517] Changing key stride in group patch from 10 to None\n", - " 87974 [params.py: set():1006] [1655562] Changing key rf in group patch from 20 to None\n", - " 87975 [params.py: set():1006] [1655486] Changing key stride in group patch from 10 to None\n", - " 87972 [params.py: set():1006] [1655512] Changing key stride in group patch from 10 to None\n", - " 87951 [params.py: set():1006] [1655557] Changing key rf in group patch from 20 to None\n", - " 87975 [params.py: set():1006] [1655521] Changing key stride in group patch from 10 to None\n", - " 87717 [params.py: set():1006] [1655498] Changing key rf in group patch from 20 to None\n", - " 87971 [params.py: set():1006] [1655524] Changing key rf in group patch from 20 to None\n", - " 87976 [params.py: set():1006] [1655533] Changing key stride in group patch from 10 to None\n", - " 87979 [params.py: set():1006] [1655491] Changing key rf in group patch from 20 to None\n", - " 87978 [params.py: set():1006] [1655544] Changing key rf in group patch from 20 to None\n", - " 87980 [params.py: set():1006] [1655506] Changing key rf in group patch from 20 to None\n", - " 87980 [params.py: set():1006] [1655500] Changing key stride in group patch from 10 to None\n", - " 87987 [params.py: set():1006] [1655479] Changing key stride in group patch from 10 to None\n", - " 87966 [params.py: set():1006] [1655482] Changing key rf in group patch from 20 to None\n", - " 87823 [params.py: set():1006] [1655470] Changing key rf in group patch from 20 to None\n", - " 87991 [params.py: set():1006] [1655527] Changing key stride in group patch from 10 to None\n", - " 87992 [params.py: set():1006] [1655532] Changing key stride in group patch from 10 to None\n", - " 87992 [params.py: set():1006] [1655502] Changing key stride in group patch from 10 to None\n", - " 87558 [params.py: set():1006] [1655480] Changing key rf in group patch from 20 to None\n", - " 87993 [params.py: set():1006] [1655529] Changing key stride in group patch from 10 to None\n", - " 87995 [params.py: set():1006] [1655478] Changing key rf in group patch from 20 to None\n", - " 87996 [params.py: set():1006] [1655469] Changing key stride in group patch from 10 to None\n", - " 87997 [params.py: set():1006] [1655564] Changing key stride in group patch from 10 to None\n", - " 87997 [params.py: set():1006] [1655547] Changing key stride in group patch from 10 to None\n", - " 87963 [params.py: set():1006] [1655548] Changing key rf in group patch from 20 to None\n", - " 88010 [params.py: set():1006] [1655492] Changing key stride in group patch from 10 to None\n", - " 88012 [params.py: set():1006] [1655483] Changing key stride in group patch from 10 to None\n", - " 88019 [params.py: set():1006] [1655546] Changing key stride in group patch from 10 to None\n", - " 88019 [params.py: set():1006] [1655525] Changing key stride in group patch from 10 to None\n", - " 88018 [params.py: set():1006] [1655549] Changing key stride in group patch from 10 to None\n", - " 88019 [params.py: set():1006] [1655508] Changing key stride in group patch from 10 to None\n", - " 88020 [params.py: set():1006] [1655541] Changing key stride in group patch from 10 to None\n", - " 88020 [params.py: set():1006] [1655516] Changing key stride in group patch from 10 to None\n", - " 88018 [params.py: set():1006] [1655560] Changing key stride in group patch from 10 to None\n", - " 88021 [params.py: set():1006] [1655473] Changing key stride in group patch from 10 to None\n", - " 88021 [params.py: set():1006] [1655507] Changing key stride in group patch from 10 to None\n", - " 88027 [params.py: set():1006] [1655523] Changing key stride in group patch from 10 to None\n", - " 88027 [params.py: set():1006] [1655556] Changing key stride in group patch from 10 to None\n", - " 88027 [params.py: set():1006] [1655550] Changing key rf in group patch from 20 to None\n", - " 88028 [params.py: set():1006] [1655540] Changing key stride in group patch from 10 to None\n", - " 88029 [params.py: set():1006] [1655488] Changing key stride in group patch from 10 to None\n", - " 88029 [params.py: set():1006] [1655522] Changing key stride in group patch from 10 to None\n", - " 88032 [params.py: set():1006] [1655487] Changing key rf in group patch from 20 to None\n", - " 88037 [params.py: set():1006] [1655481] Changing key stride in group patch from 10 to None\n", - " 88039 [params.py: set():1006] [1655537] Changing key nb in group init from 2 to 1\n", - " 88039 [params.py: set():1006] [1655513] Changing key nb in group init from 2 to 1\n", - " 88041 [params.py: set():1006] [1655472] Changing key nb in group init from 2 to 1\n", - " 88038 [params.py: set():1006] [1655535] Changing key nb in group init from 2 to 1\n", - " 88038 [params.py: set():1006] [1655476] Changing key nb in group init from 2 to 1\n", - " 88040 [params.py: set():1006] [1655519] Changing key nb in group init from 2 to 1\n", - " 88040 [params.py: set():1006] [1655559] Changing key nb in group init from 2 to 1\n", - " 88039 [params.py: set():1006] [1655552] Changing key nb in group init from 2 to 1\n", - " 88041 [params.py: set():1006] [1655489] Changing key nb in group init from 2 to 1\n", - " 88042 [params.py: set():1006] [1655520] Changing key nb in group init from 2 to 1\n", - " 88041 [cnmf.py: fit():449] [1655514] (8000, 41, 41)\n", - " 88042 [params.py: set():1006] [1655553] Changing key nb in group init from 2 to 1\n", - " 88040 [params.py: set():1006] [1655531] Changing key nb in group init from 2 to 1\n", - " 88042 [params.py: set():1006] [1655530] Changing key nb in group temporal from 2 to 1\n", - " 88043 [params.py: set():1006] [1655511] Changing key nb in group init from 2 to 1\n", - " 88044 [params.py: set():1006] [1655509] Changing key stride in group patch from 10 to None\n", - " 88043 [params.py: set():1006] [1655554] Changing key nb in group init from 2 to 1\n", - " 88045 [params.py: set():1006] [1655497] Changing key nb in group init from 2 to 1\n", - " 88044 [params.py: set():1006] [1655496] Changing key nb in group init from 2 to 1\n", - " 88045 [params.py: set():1006] [1655490] Changing key nb in group init from 2 to 1\n", - " 87871 [params.py: set():1006] [1655545] Changing key rf in group patch from 20 to None\n", - " 88046 [params.py: set():1006] [1655542] Changing key stride in group patch from 10 to None\n", - " 88049 [params.py: set():1006] [1655501] Changing key rf in group patch from 20 to None\n", - " 87879 [params.py: set():1006] [1655495] Changing key rf in group patch from 20 to None\n", - " 88050 [params.py: set():1006] [1655484] Changing key rf in group patch from 20 to None\n", - " 88051 [params.py: set():1006] [1655475] Changing key stride in group patch from 10 to None\n", - " 88051 [params.py: set():1006] [1655536] Changing key stride in group patch from 10 to None\n", - " 88051 [params.py: set():1006] [1655561] Changing key stride in group patch from 10 to None\n", - " 88067 [params.py: set():1006] [1655543] Changing key nb in group init from 2 to 1\n", - " 88068 [params.py: set():1006] [1655474] Changing key stride in group patch from 10 to None\n", - " 88068 [params.py: set():1006] [1655499] Changing key nb in group init from 2 to 1\n", - " 88055 [map_reduce.py: cnmf_patches():78] [1660129] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51540_72060START\n", - " 88017 [params.py: set():1006] [1655503] Changing key rf in group patch from 20 to None\n", - " 88085 [params.py: set():1006] [1655528] Changing key rf in group patch from 20 to None\n", - " 87945 [params.py: set():1006] [1655494] Changing key rf in group patch from 20 to None\n", - " 88097 [params.py: set():1006] [1655563] Changing key rf in group patch from 20 to None\n", - " 88009 [params.py: set():1006] [1655538] Changing key rf in group patch from 20 to None\n", - " 88101 [params.py: set():1006] [1655504] Changing key rf in group patch from 20 to None\n", - " 88079 [params.py: set():1006] [1655505] Changing key rf in group patch from 20 to None\n", - " 88165 [params.py: set():1006] [1655503] Changing key stride in group patch from 10 to None\n", - " 88156 [params.py: set():1006] [1655553] Changing key nb in group temporal from 2 to 1\n", - " 88163 [params.py: set():1006] [1655474] Changing key nb in group init from 2 to 1\n", - " 88166 [params.py: set():1006] [1655563] Changing key stride in group patch from 10 to None\n", - " 88152 [params.py: set():1006] [1655481] Changing key nb in group init from 2 to 1\n", - " 88155 [params.py: set():1006] [1655520] Changing key nb in group temporal from 2 to 1\n", - " 88151 [params.py: set():1006] [1655487] Changing key stride in group patch from 10 to None\n", - " 88163 [params.py: set():1006] [1655561] Changing key nb in group init from 2 to 1\n", - " 88146 [params.py: set():1006] [1655549] Changing key nb in group init from 2 to 1\n", - " 88169 [params.py: set():1006] [1655493] Changing key stride in group patch from 10 to None\n", - " 88164 [params.py: set():1006] [1655499] Changing key nb in group temporal from 2 to 1\n", - " 88164 [params.py: set():1006] [1655543] Changing key nb in group temporal from 2 to 1\n", - " 88139 [params.py: set():1006] [1655532] Changing key nb in group init from 2 to 1\n", - " 88159 [params.py: set():1006] [1655490] Changing key nb in group temporal from 2 to 1\n", - " 88144 [params.py: set():1006] [1655483] Changing key nb in group init from 2 to 1\n", - " 88167 [params.py: set():1006] [1655538] Changing key stride in group patch from 10 to None\n", - " 88162 [params.py: set():1006] [1655475] Changing key nb in group init from 2 to 1\n", - " 88150 [params.py: set():1006] [1655522] Changing key nb in group init from 2 to 1\n", - " 88166 [params.py: set():1006] [1655494] Changing key stride in group patch from 10 to None\n", - " 88150 [params.py: set():1006] [1655488] Changing key nb in group init from 2 to 1\n", - " 88159 [params.py: set():1006] [1655496] Changing key nb in group temporal from 2 to 1\n", - " 88145 [params.py: set():1006] [1655525] Changing key nb in group init from 2 to 1\n", - " 88153 [params.py: set():1006] [1655472] Changing key nb in group temporal from 2 to 1\n", - " 88160 [params.py: set():1006] [1655545] Changing key stride in group patch from 10 to None\n", - " 88147 [params.py: set():1006] [1655560] Changing key nb in group init from 2 to 1\n", - " 88155 [params.py: set():1006] [1655513] Changing key nb in group temporal from 2 to 1\n", - " 88166 [params.py: set():1006] [1655528] Changing key stride in group patch from 10 to None\n", - " 88162 [params.py: set():1006] [1655536] Changing key nb in group init from 2 to 1\n", - " 88146 [params.py: set():1006] [1655541] Changing key nb in group init from 2 to 1\n", - " 88162 [params.py: set():1006] [1655484] Changing key stride in group patch from 10 to None\n", - " 88161 [params.py: set():1006] [1655542] Changing key nb in group init from 2 to 1\n", - " 88161 [params.py: set():1006] [1655495] Changing key stride in group patch from 10 to None\n", - " 88140 [params.py: set():1006] [1655470] Changing key stride in group patch from 10 to None\n", - " 88148 [params.py: set():1006] [1655473] Changing key nb in group init from 2 to 1\n", - " 88157 [params.py: set():1006] [1655531] Changing key nb in group temporal from 2 to 1\n", - " 88138 [params.py: set():1006] [1655482] Changing key stride in group patch from 10 to None\n", - " 88153 [params.py: set():1006] [1655535] Changing key nb in group temporal from 2 to 1\n", - " 88157 [params.py: set():1006] [1655530] Changing key nb in group spatial from 2 to 1\n", - " 88150 [params.py: set():1006] [1655550] Changing key stride in group patch from 10 to None\n", - " 88159 [params.py: set():1006] [1655497] Changing key nb in group temporal from 2 to 1\n", - " 88169 [params.py: set():1006] [1655477] Changing key stride in group patch from 10 to None\n", - " 88153 [params.py: set():1006] [1655476] Changing key nb in group temporal from 2 to 1\n", - " 88154 [params.py: set():1006] [1655519] Changing key nb in group temporal from 2 to 1\n", - " 88137 [params.py: set():1006] [1655544] Changing key stride in group patch from 10 to None\n", - " 88138 [params.py: set():1006] [1655479] Changing key nb in group init from 2 to 1\n", - " 88154 [params.py: set():1006] [1655559] Changing key nb in group temporal from 2 to 1\n", - " 88155 [params.py: set():1006] [1655489] Changing key nb in group temporal from 2 to 1\n", - " 88136 [params.py: set():1006] [1655491] Changing key stride in group patch from 10 to None\n", - " 88148 [params.py: set():1006] [1655523] Changing key nb in group init from 2 to 1\n", - " 88137 [params.py: set():1006] [1655506] Changing key stride in group patch from 10 to None\n", - " 88140 [params.py: set():1006] [1655502] Changing key nb in group init from 2 to 1\n", - " 88142 [params.py: set():1006] [1655469] Changing key nb in group init from 2 to 1\n", - " 88168 [params.py: set():1006] [1655510] Changing key stride in group patch from 10 to None\n", - " 88149 [params.py: set():1006] [1655540] Changing key nb in group init from 2 to 1\n", - " 88135 [params.py: set():1006] [1655524] Changing key stride in group patch from 10 to None\n", - " 88137 [params.py: set():1006] [1655500] Changing key nb in group init from 2 to 1\n", - " 88139 [params.py: set():1006] [1655527] Changing key nb in group init from 2 to 1\n", - " 88154 [params.py: set():1006] [1655552] Changing key nb in group temporal from 2 to 1\n", - " 88135 [params.py: set():1006] [1655498] Changing key stride in group patch from 10 to None\n", - " 88143 [params.py: set():1006] [1655547] Changing key nb in group init from 2 to 1\n", - " 88156 [cnmf.py: fit():466] [1655514] Using 1 processes\n", - " 88136 [params.py: set():1006] [1655533] Changing key nb in group init from 2 to 1\n", - " 88146 [params.py: set():1006] [1655508] Changing key nb in group init from 2 to 1\n", - " 88141 [params.py: set():1006] [1655480] Changing key stride in group patch from 10 to None\n", - " 88161 [params.py: set():1006] [1655501] Changing key stride in group patch from 10 to None\n", - " 88158 [params.py: set():1006] [1655511] Changing key nb in group temporal from 2 to 1\n", - " 88146 [params.py: set():1006] [1655546] Changing key nb in group init from 2 to 1\n", - " 88143 [params.py: set():1006] [1655548] Changing key stride in group patch from 10 to None\n", - " 88168 [params.py: set():1006] [1655505] Changing key stride in group patch from 10 to None\n", - " 88142 [params.py: set():1006] [1655564] Changing key nb in group init from 2 to 1\n", - " 88141 [params.py: set():1006] [1655529] Changing key nb in group init from 2 to 1\n", - " 88142 [params.py: set():1006] [1655478] Changing key stride in group patch from 10 to None\n", - " 88134 [params.py: set():1006] [1655521] Changing key nb in group init from 2 to 1\n", - " 88148 [params.py: set():1006] [1655507] Changing key nb in group init from 2 to 1\n", - " 88149 [params.py: set():1006] [1655556] Changing key nb in group init from 2 to 1\n", - " 88157 [params.py: set():1006] [1655554] Changing key nb in group temporal from 2 to 1\n", - " 88158 [params.py: set():1006] [1655509] Changing key nb in group init from 2 to 1\n", - " 88147 [params.py: set():1006] [1655516] Changing key nb in group init from 2 to 1\n", - " 88167 [params.py: set():1006] [1655504] Changing key stride in group patch from 10 to None\n", - " 88144 [params.py: set():1006] [1655492] Changing key nb in group init from 2 to 1\n", - " 88132 [params.py: set():1006] [1655517] Changing key nb in group init from 2 to 1\n", - " 88130 [params.py: set():1006] [1655534] Changing key nb in group init from 2 to 1\n", - " 88156 [params.py: set():1006] [1655537] Changing key nb in group temporal from 2 to 1\n", - " 88132 [params.py: set():1006] [1655562] Changing key stride in group patch from 10 to None\n", - " 88134 [params.py: set():1006] [1655557] Changing key stride in group patch from 10 to None\n", - " 88133 [params.py: set():1006] [1655486] Changing key nb in group init from 2 to 1\n", - " 88131 [params.py: set():1006] [1655526] Changing key stride in group patch from 10 to None\n", - " 88131 [params.py: set():1006] [1655485] Changing key nb in group init from 2 to 1\n", - " 88133 [params.py: set():1006] [1655512] Changing key nb in group init from 2 to 1\n", - " 88130 [params.py: set():1006] [1655551] Changing key nb in group init from 2 to 1\n", - " 88165 [map_reduce.py: cnmf_patches():80] [1660129] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51540_72060Read file\n", - " 88130 [params.py: set():1006] [1655539] Changing key stride in group patch from 10 to None\n", - " 88173 [params.py: set():1006] [1655518] Changing key rf in group patch from 20 to None\n", - " 88204 [map_reduce.py: cnmf_patches():78] [1660361] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66900_87420START\n", - " 88216 [params.py: set():1006] [1655538] Changing key nb in group init from 2 to 1\n", - " 88216 [params.py: set():1006] [1655543] Changing key nb in group spatial from 2 to 1\n", - " 88217 [params.py: set():1006] [1655541] Changing key nb in group temporal from 2 to 1\n", - " 88217 [params.py: set():1006] [1655563] Changing key nb in group init from 2 to 1\n", - " 88217 [params.py: set():1006] [1655553] Changing key nb in group spatial from 2 to 1\n", - " 88218 [params.py: set():1006] [1655493] Changing key nb in group init from 2 to 1\n", - " 88218 [params.py: set():1006] [1655487] Changing key nb in group init from 2 to 1\n", - " 88218 [params.py: set():1006] [1655561] Changing key nb in group temporal from 2 to 1\n", - " 88219 [params.py: set():1006] [1655482] Changing key nb in group init from 2 to 1\n", - " 88219 [params.py: set():1006] [1655474] Changing key nb in group temporal from 2 to 1\n", - " 88219 [params.py: set():1006] [1655473] Changing key nb in group temporal from 2 to 1\n", - " 88220 [params.py: set():1006] [1655475] Changing key nb in group temporal from 2 to 1\n", - " 88220 [params.py: set():1006] [1655499] Changing key nb in group spatial from 2 to 1\n", - " 88220 [params.py: set():1006] [1655528] Changing key nb in group init from 2 to 1\n", - " 88221 [params.py: set():1006] [1655488] Changing key nb in group temporal from 2 to 1\n", - " 88221 [params.py: set():1006] [1655522] Changing key nb in group temporal from 2 to 1\n", - " 88221 [params.py: set():1006] [1655481] Changing key nb in group temporal from 2 to 1\n", - " 88221 [params.py: set():1006] [1655470] Changing key nb in group init from 2 to 1\n", - " 88222 [params.py: set():1006] [1655560] Changing key nb in group temporal from 2 to 1\n", - " 88222 [params.py: set():1006] [1655494] Changing key nb in group init from 2 to 1\n", - " 88222 [params.py: set():1006] [1655513] Changing key nb in group spatial from 2 to 1\n", - " 88223 [params.py: set():1006] [1655495] Changing key nb in group init from 2 to 1\n", - " 88223 [params.py: set():1006] [1655549] Changing key nb in group temporal from 2 to 1\n", - " 88223 [params.py: set():1006] [1655544] Changing key nb in group init from 2 to 1\n", - " 88224 [params.py: set():1006] [1655490] Changing key nb in group spatial from 2 to 1\n", - " 88224 [params.py: set():1006] [1655532] Changing key nb in group temporal from 2 to 1\n", - " 88224 [params.py: set():1006] [1655496] Changing key nb in group spatial from 2 to 1\n", - " 88225 [params.py: set():1006] [1655531] Changing key nb in group spatial from 2 to 1\n", - " 88225 [params.py: set():1006] [1655542] Changing key nb in group temporal from 2 to 1\n", - " 88225 [params.py: set():1006] [1655483] Changing key nb in group temporal from 2 to 1\n", - " 88226 [params.py: set():1006] [1655502] Changing key nb in group temporal from 2 to 1\n", - " 88226 [params.py: set():1006] [1655472] Changing key nb in group spatial from 2 to 1\n", - " 88226 [params.py: set():1006] [1655476] Changing key nb in group spatial from 2 to 1\n", - " 88226 [params.py: set():1006] [1655550] Changing key nb in group init from 2 to 1\n", - " 88227 [params.py: set():1006] [1655546] Changing key nb in group temporal from 2 to 1\n", - " 88227 [params.py: set():1006] [1655484] Changing key nb in group init from 2 to 1\n", - " 88227 [params.py: set():1006] [1655530] Changing key p in group preprocess from 1 to 0\n", - " 88228 [params.py: set():1006] [1655525] Changing key nb in group temporal from 2 to 1\n", - " 88228 [params.py: set():1006] [1655519] Changing key nb in group spatial from 2 to 1\n", - " 88228 [params.py: set():1006] [1655510] Changing key nb in group init from 2 to 1\n", - " 88229 [params.py: set():1006] [1655540] Changing key nb in group temporal from 2 to 1\n", - " 88229 [params.py: set():1006] [1655535] Changing key nb in group spatial from 2 to 1\n", - " 88229 [params.py: set():1006] [1655545] Changing key nb in group init from 2 to 1\n", - " 88230 [params.py: set():1006] [1655536] Changing key nb in group temporal from 2 to 1\n", - " 88230 [params.py: set():1006] [1655489] Changing key nb in group spatial from 2 to 1\n", - " 88230 [params.py: set():1006] [1655479] Changing key nb in group temporal from 2 to 1\n", - " 88230 [params.py: set():1006] [1655497] Changing key nb in group spatial from 2 to 1\n", - " 88231 [params.py: set():1006] [1655552] Changing key nb in group spatial from 2 to 1\n", - " 88231 [params.py: set():1006] [1655507] Changing key nb in group temporal from 2 to 1\n", - " 88231 [params.py: set():1006] [1655556] Changing key nb in group temporal from 2 to 1\n", - " 88232 [params.py: set():1006] [1655511] Changing key nb in group spatial from 2 to 1\n", - " 88232 [params.py: set():1006] [1655547] Changing key nb in group temporal from 2 to 1\n", - " 88232 [params.py: set():1006] [1655506] Changing key nb in group init from 2 to 1\n", - " 88233 [params.py: set():1006] [1655500] Changing key nb in group temporal from 2 to 1\n", - " 88233 [params.py: set():1006] [1655469] Changing key nb in group temporal from 2 to 1\n", - " 88233 [params.py: set():1006] [1655501] Changing key nb in group init from 2 to 1\n", - " 88234 [cnmf.py: fit():477] [1655514] using 400 pixels per process\n", - " 88234 [params.py: set():1006] [1655478] Changing key nb in group init from 2 to 1\n", - " 88234 [params.py: set():1006] [1655491] Changing key nb in group init from 2 to 1\n", - " 88235 [params.py: set():1006] [1655529] Changing key nb in group temporal from 2 to 1\n", - " 88235 [params.py: set():1006] [1655554] Changing key nb in group spatial from 2 to 1\n", - " 88235 [params.py: set():1006] [1655534] Changing key nb in group temporal from 2 to 1\n", - " 88236 [params.py: set():1006] [1655521] Changing key nb in group temporal from 2 to 1\n", - " 88236 [params.py: set():1006] [1655559] Changing key nb in group spatial from 2 to 1\n", - " 88236 [params.py: set():1006] [1655477] Changing key nb in group init from 2 to 1\n", - " 88236 [params.py: set():1006] [1655508] Changing key nb in group temporal from 2 to 1\n", - " 88237 [params.py: set():1006] [1655523] Changing key nb in group temporal from 2 to 1\n", - " 88237 [params.py: set():1006] [1655537] Changing key nb in group spatial from 2 to 1\n", - " 88237 [params.py: set():1006] [1655564] Changing key nb in group temporal from 2 to 1\n", - " 88238 [params.py: set():1006] [1655520] Changing key nb in group spatial from 2 to 1\n", - " 88238 [params.py: set():1006] [1655509] Changing key nb in group temporal from 2 to 1\n", - " 88238 [params.py: set():1006] [1655480] Changing key nb in group init from 2 to 1\n", - " 88239 [params.py: set():1006] [1655524] Changing key nb in group init from 2 to 1\n", - " 88239 [params.py: set():1006] [1655533] Changing key nb in group temporal from 2 to 1\n", - " 88239 [params.py: set():1006] [1655516] Changing key nb in group temporal from 2 to 1\n", - " 88240 [params.py: set():1006] [1655504] Changing key nb in group init from 2 to 1\n", - " 88240 [params.py: set():1006] [1655548] Changing key nb in group init from 2 to 1\n", - " 88240 [params.py: set():1006] [1655498] Changing key nb in group init from 2 to 1\n", - " 88241 [params.py: set():1006] [1655505] Changing key nb in group init from 2 to 1\n", - " 88241 [params.py: set():1006] [1655492] Changing key nb in group temporal from 2 to 1\n", - " 88241 [params.py: set():1006] [1655557] Changing key nb in group init from 2 to 1\n", - " 88242 [params.py: set():1006] [1655512] Changing key nb in group temporal from 2 to 1\n", - " 88242 [params.py: set():1006] [1655503] Changing key nb in group init from 2 to 1\n", - " 88242 [params.py: set():1006] [1655485] Changing key nb in group temporal from 2 to 1\n", - " 88243 [params.py: set():1006] [1655551] Changing key nb in group temporal from 2 to 1\n", - " 88243 [params.py: set():1006] [1655517] Changing key nb in group temporal from 2 to 1\n", - " 88243 [params.py: set():1006] [1655486] Changing key nb in group temporal from 2 to 1\n", - " 88244 [params.py: set():1006] [1655562] Changing key nb in group init from 2 to 1\n", - " 88244 [params.py: set():1006] [1655526] Changing key nb in group init from 2 to 1\n", - " 88245 [params.py: set():1006] [1655527] Changing key nb in group temporal from 2 to 1\n", - " 88245 [params.py: set():1006] [1655539] Changing key nb in group init from 2 to 1\n", - " 88245 [params.py: set():1006] [1655518] Changing key stride in group patch from 10 to None\n", - " 88285 [params.py: set():1006] [1655539] Changing key nb in group temporal from 2 to 1\n", - " 88287 [params.py: set():1006] [1655526] Changing key nb in group temporal from 2 to 1\n", - " 88288 [params.py: set():1006] [1655562] Changing key nb in group temporal from 2 to 1\n", - " 88288 [params.py: set():1006] [1655480] Changing key nb in group temporal from 2 to 1\n", - " 88289 [params.py: set():1006] [1655551] Changing key nb in group spatial from 2 to 1\n", - " 88289 [params.py: set():1006] [1655517] Changing key nb in group spatial from 2 to 1\n", - " 88290 [params.py: set():1006] [1655485] Changing key nb in group spatial from 2 to 1\n", - " 88290 [params.py: set():1006] [1655563] Changing key nb in group temporal from 2 to 1\n", - " 88291 [params.py: set():1006] [1655486] Changing key nb in group spatial from 2 to 1\n", - " 88291 [params.py: set():1006] [1655527] Changing key nb in group spatial from 2 to 1\n", - " 88292 [params.py: set():1006] [1655503] Changing key nb in group temporal from 2 to 1\n", - " 88292 [params.py: set():1006] [1655489] Changing key p in group preprocess from 1 to 0\n", - " 88293 [params.py: set():1006] [1655501] Changing key nb in group temporal from 2 to 1\n", - " 88293 [params.py: set():1006] [1655475] Changing key nb in group spatial from 2 to 1\n", - " 88294 [params.py: set():1006] [1655553] Changing key p in group preprocess from 1 to 0\n", - " 88294 [params.py: set():1006] [1655510] Changing key nb in group temporal from 2 to 1\n", - " 88294 [params.py: set():1006] [1655525] Changing key nb in group spatial from 2 to 1\n", - " 88295 [params.py: set():1006] [1655509] Changing key nb in group spatial from 2 to 1\n", - " 88295 [params.py: set():1006] [1655536] Changing key nb in group spatial from 2 to 1\n", - " 88295 [params.py: set():1006] [1655535] Changing key p in group preprocess from 1 to 0\n", - " 88296 [params.py: set():1006] [1655557] Changing key nb in group temporal from 2 to 1\n", - " 88296 [params.py: set():1006] [1655492] Changing key nb in group spatial from 2 to 1\n", - " 88296 [params.py: set():1006] [1655507] Changing key nb in group spatial from 2 to 1\n", - " 88297 [params.py: set():1006] [1655469] Changing key nb in group spatial from 2 to 1\n", - " 88297 [params.py: set():1006] [1655512] Changing key nb in group spatial from 2 to 1\n", - " 88297 [params.py: set():1006] [1655543] Changing key p in group preprocess from 1 to 0\n", - " 88298 [params.py: set():1006] [1655528] Changing key nb in group temporal from 2 to 1\n", - " 88298 [params.py: set():1006] [1655511] Changing key p in group preprocess from 1 to 0\n", - " 88298 [params.py: set():1006] [1655540] Changing key nb in group spatial from 2 to 1\n", - " 88299 [params.py: set():1006] [1655545] Changing key nb in group temporal from 2 to 1\n", - " 88299 [params.py: set():1006] [1655549] Changing key nb in group spatial from 2 to 1\n", - " 88299 [params.py: set():1006] [1655547] Changing key nb in group spatial from 2 to 1\n", - " 88300 [params.py: set():1006] [1655516] Changing key nb in group spatial from 2 to 1\n", - " 88300 [params.py: set():1006] [1655521] Changing key nb in group spatial from 2 to 1\n", - " 88300 [params.py: set():1006] [1655559] Changing key p in group preprocess from 1 to 0\n", - " 88301 [params.py: set():1006] [1655532] Changing key nb in group spatial from 2 to 1\n", - " 88301 [params.py: set():1006] [1655519] Changing key p in group preprocess from 1 to 0\n", - " 88301 [params.py: set():1006] [1655491] Changing key nb in group temporal from 2 to 1\n", - " 88302 [params.py: set():1006] [1655506] Changing key nb in group temporal from 2 to 1\n", - " 88302 [params.py: set():1006] [1655564] Changing key nb in group spatial from 2 to 1\n", - " 88302 [params.py: set():1006] [1655552] Changing key p in group preprocess from 1 to 0\n", - " 88303 [params.py: set():1006] [1655533] Changing key nb in group spatial from 2 to 1\n", - " 88303 [params.py: set():1006] [1655482] Changing key nb in group temporal from 2 to 1\n", - " 88303 [params.py: set():1006] [1655470] Changing key nb in group temporal from 2 to 1\n", - " 88304 [params.py: set():1006] [1655500] Changing key nb in group spatial from 2 to 1\n", - " 88304 [params.py: set():1006] [1655518] Changing key nb in group init from 2 to 1\n", - " 88304 [params.py: set():1006] [1655554] Changing key p in group preprocess from 1 to 0\n", - " 88304 [params.py: set():1006] [1655524] Changing key nb in group temporal from 2 to 1\n", - " 88305 [params.py: set():1006] [1655546] Changing key nb in group spatial from 2 to 1\n", - " 88305 [params.py: set():1006] [1655530] Changing key p in group temporal from 1 to 0\n", - " 88305 [params.py: set():1006] [1655529] Changing key nb in group spatial from 2 to 1\n", - " 88306 [params.py: set():1006] [1655520] Changing key p in group preprocess from 1 to 0\n", - " 88306 [params.py: set():1006] [1655541] Changing key nb in group spatial from 2 to 1\n", - " 88306 [params.py: set():1006] [1655534] Changing key nb in group spatial from 2 to 1\n", - " 88307 [params.py: set():1006] [1655531] Changing key p in group preprocess from 1 to 0\n", - " 88307 [params.py: set():1006] [1655472] Changing key p in group preprocess from 1 to 0\n", - " 88307 [params.py: set():1006] [1655495] Changing key nb in group temporal from 2 to 1\n", - " 88308 [params.py: set():1006] [1655493] Changing key nb in group temporal from 2 to 1\n", - " 88308 [params.py: set():1006] [1655487] Changing key nb in group temporal from 2 to 1\n", - " 88308 [params.py: set():1006] [1655550] Changing key nb in group temporal from 2 to 1\n", - " 88309 [params.py: set():1006] [1655538] Changing key nb in group temporal from 2 to 1\n", - " 88309 [params.py: set():1006] [1655560] Changing key nb in group spatial from 2 to 1\n", - " 88309 [params.py: set():1006] [1655488] Changing key nb in group spatial from 2 to 1\n", - " 88310 [params.py: set():1006] [1655494] Changing key nb in group temporal from 2 to 1\n", - " 88310 [params.py: set():1006] [1655556] Changing key nb in group spatial from 2 to 1\n", - " 88310 [params.py: set():1006] [1655537] Changing key p in group preprocess from 1 to 0\n", - " 88311 [params.py: set():1006] [1655478] Changing key nb in group temporal from 2 to 1\n", - " 88311 [params.py: set():1006] [1655513] Changing key p in group preprocess from 1 to 0\n", - " 88311 [params.py: set():1006] [1655499] Changing key p in group preprocess from 1 to 0\n", - " 88312 [params.py: set():1006] [1655523] Changing key nb in group spatial from 2 to 1\n", - " 88312 [params.py: set():1006] [1655502] Changing key nb in group spatial from 2 to 1\n", - " 88312 [params.py: set():1006] [1655504] Changing key nb in group temporal from 2 to 1\n", - " 88312 [params.py: set():1006] [1655561] Changing key nb in group spatial from 2 to 1\n", - " 88313 [params.py: set():1006] [1655481] Changing key nb in group spatial from 2 to 1\n", - " 88313 [params.py: set():1006] [1655474] Changing key nb in group spatial from 2 to 1\n", - " 88313 [params.py: set():1006] [1655496] Changing key p in group preprocess from 1 to 0\n", - " 88314 [params.py: set():1006] [1655522] Changing key nb in group spatial from 2 to 1\n", - " 88314 [params.py: set():1006] [1655497] Changing key p in group preprocess from 1 to 0\n", - " 88314 [params.py: set():1006] [1655505] Changing key nb in group temporal from 2 to 1\n", - " 88315 [params.py: set():1006] [1655479] Changing key nb in group spatial from 2 to 1\n", - " 88315 [params.py: set():1006] [1655473] Changing key nb in group spatial from 2 to 1\n", - " 88315 [params.py: set():1006] [1655542] Changing key nb in group spatial from 2 to 1\n", - " 88316 [params.py: set():1006] [1655544] Changing key nb in group temporal from 2 to 1\n", - " 88316 [params.py: set():1006] [1655476] Changing key p in group preprocess from 1 to 0\n", - " 88316 [params.py: set():1006] [1655490] Changing key p in group preprocess from 1 to 0\n", - " 88316 [params.py: set():1006] [1655484] Changing key nb in group temporal from 2 to 1\n", - " 88317 [params.py: set():1006] [1655477] Changing key nb in group temporal from 2 to 1\n", - " 88317 [params.py: set():1006] [1655548] Changing key nb in group temporal from 2 to 1\n", - " 88318 [params.py: set():1006] [1655483] Changing key nb in group spatial from 2 to 1\n", - " 88318 [params.py: set():1006] [1655508] Changing key nb in group spatial from 2 to 1\n", - " 88322 [map_reduce.py: cnmf_patches():78] [1660550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82260_102780START\n", - " 88319 [map_reduce.py: cnmf_patches():80] [1660361] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66900_87420Read file\n", - " 88319 [params.py: set():1006] [1655498] Changing key nb in group temporal from 2 to 1\n", - " 88318 [cnmf.py: fit():478] [1655514] using 5000 block_size_temp\n", - " 88350 [params.py: set():1006] [1655538] Changing key nb in group spatial from 2 to 1\n", - " 88350 [params.py: set():1006] [1655495] Changing key nb in group spatial from 2 to 1\n", - " 88350 [params.py: set():1006] [1655493] Changing key nb in group spatial from 2 to 1\n", - " 88351 [params.py: set():1006] [1655550] Changing key nb in group spatial from 2 to 1\n", - " 88351 [params.py: set():1006] [1655556] Changing key p in group preprocess from 1 to 0\n", - " 88351 [params.py: set():1006] [1655489] Changing key p in group temporal from 1 to 0\n", - " 88352 [params.py: set():1006] [1655488] Changing key p in group preprocess from 1 to 0\n", - " 88352 [params.py: set():1006] [1655537] Changing key p in group temporal from 1 to 0\n", - " 88352 [params.py: set():1006] [1655486] Changing key p in group preprocess from 1 to 0\n", - " 88353 [params.py: set():1006] [1655501] Changing key nb in group spatial from 2 to 1\n", - " 88353 [params.py: set():1006] [1655472] Changing key p in group temporal from 1 to 0\n", - " 88354 [params.py: set():1006] [1655527] Changing key p in group preprocess from 1 to 0\n", - " 88354 [params.py: set():1006] [1655487] Changing key nb in group spatial from 2 to 1\n", - " 88354 [params.py: set():1006] [1655559] Changing key p in group temporal from 1 to 0\n", - " 88354 [params.py: set():1006] [1655503] Changing key nb in group spatial from 2 to 1\n", - " 88355 [params.py: set():1006] [1655517] Changing key p in group preprocess from 1 to 0\n", - " 88355 [params.py: set():1006] [1655516] Changing key p in group preprocess from 1 to 0\n", - " 88356 [params.py: set():1006] [1655536] Changing key p in group preprocess from 1 to 0\n", - " 88356 [params.py: set():1006] [1655519] Changing key p in group temporal from 1 to 0\n", - " 88356 [params.py: set():1006] [1655560] Changing key p in group preprocess from 1 to 0\n", - " 88356 [params.py: set():1006] [1655532] Changing key p in group preprocess from 1 to 0\n", - " 88357 [params.py: set():1006] [1655529] Changing key p in group preprocess from 1 to 0\n", - " 88357 [params.py: set():1006] [1655513] Changing key p in group temporal from 1 to 0\n", - " 88357 [params.py: set():1006] [1655518] Changing key nb in group temporal from 2 to 1\n", - " 88358 [params.py: set():1006] [1655504] Changing key nb in group spatial from 2 to 1\n", - " 88358 [params.py: set():1006] [1655551] Changing key p in group preprocess from 1 to 0\n", - " 88358 [params.py: set():1006] [1655491] Changing key nb in group spatial from 2 to 1\n", - " 88359 [params.py: set():1006] [1655478] Changing key nb in group spatial from 2 to 1\n", - " 88359 [params.py: set():1006] [1655563] Changing key nb in group spatial from 2 to 1\n", - " 88359 [params.py: set():1006] [1655509] Changing key p in group preprocess from 1 to 0\n", - " 88360 [params.py: set():1006] [1655521] Changing key p in group preprocess from 1 to 0\n", - " 88360 [params.py: set():1006] [1655500] Changing key p in group preprocess from 1 to 0\n", - " 88360 [params.py: set():1006] [1655543] Changing key p in group temporal from 1 to 0\n", - " 88360 [params.py: set():1006] [1655561] Changing key p in group preprocess from 1 to 0\n", - " 88361 [params.py: set():1006] [1655553] Changing key p in group temporal from 1 to 0\n", - " 88361 [params.py: set():1006] [1655470] Changing key nb in group spatial from 2 to 1\n", - " 88361 [params.py: set():1006] [1655494] Changing key nb in group spatial from 2 to 1\n", - " 88362 [params.py: set():1006] [1655479] Changing key p in group preprocess from 1 to 0\n", - " 88362 [params.py: set():1006] [1655499] Changing key p in group temporal from 1 to 0\n", - " 88362 [params.py: set():1006] [1655525] Changing key p in group preprocess from 1 to 0\n", - " 88363 [params.py: set():1006] [1655502] Changing key p in group preprocess from 1 to 0\n", - " 88363 [params.py: set():1006] [1655485] Changing key p in group preprocess from 1 to 0\n", - " 88363 [params.py: set():1006] [1655562] Changing key nb in group spatial from 2 to 1\n", - " 88364 [params.py: set():1006] [1655541] Changing key p in group preprocess from 1 to 0\n", - " 88364 [params.py: set():1006] [1655524] Changing key nb in group spatial from 2 to 1\n", - " 88364 [params.py: set():1006] [1655528] Changing key nb in group spatial from 2 to 1\n", - " 88365 [params.py: set():1006] [1655522] Changing key p in group preprocess from 1 to 0\n", - " 88367 [params.py: set():1006] [1655564] Changing key p in group preprocess from 1 to 0\n", - " 88365 [params.py: set():1006] [1655520] Changing key p in group temporal from 1 to 0\n", - " 88366 [params.py: set():1006] [1655511] Changing key p in group temporal from 1 to 0\n", - " 88367 [params.py: set():1006] [1655546] Changing key p in group preprocess from 1 to 0\n", - " 88370 [params.py: set():1006] [1655547] Changing key p in group preprocess from 1 to 0\n", - " 88367 [params.py: set():1006] [1655497] Changing key p in group temporal from 1 to 0\n", - " 88366 [params.py: set():1006] [1655557] Changing key nb in group spatial from 2 to 1\n", - " 88368 [params.py: set():1006] [1655552] Changing key p in group temporal from 1 to 0\n", - " 88366 [params.py: set():1006] [1655533] Changing key p in group preprocess from 1 to 0\n", - " 88365 [params.py: set():1006] [1655531] Changing key p in group temporal from 1 to 0\n", - " 88368 [params.py: set():1006] [1655540] Changing key p in group preprocess from 1 to 0\n", - " 88366 [params.py: set():1006] [1655512] Changing key p in group preprocess from 1 to 0\n", - " 88369 [params.py: set():1006] [1655534] Changing key p in group preprocess from 1 to 0\n", - " 88370 [cnmf.py: fit():436] [1655530] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88371 [params.py: set():1006] [1655506] Changing key nb in group spatial from 2 to 1\n", - " 88369 [params.py: set():1006] [1655545] Changing key nb in group spatial from 2 to 1\n", - " 88369 [params.py: set():1006] [1655475] Changing key p in group preprocess from 1 to 0\n", - " 88371 [params.py: set():1006] [1655535] Changing key p in group temporal from 1 to 0\n", - " 88372 [params.py: set():1006] [1655492] Changing key p in group preprocess from 1 to 0\n", - " 88370 [params.py: set():1006] [1655473] Changing key p in group preprocess from 1 to 0\n", - " 88371 [params.py: set():1006] [1655482] Changing key nb in group spatial from 2 to 1\n", - " 88369 [params.py: set():1006] [1655469] Changing key p in group preprocess from 1 to 0\n", - " 88368 [params.py: set():1006] [1655507] Changing key p in group preprocess from 1 to 0\n", - " 88372 [params.py: set():1006] [1655474] Changing key p in group preprocess from 1 to 0\n", - " 88373 [params.py: set():1006] [1655505] Changing key nb in group spatial from 2 to 1\n", - " 88372 [params.py: set():1006] [1655510] Changing key nb in group spatial from 2 to 1\n", - " 88381 [map_reduce.py: cnmf_patches():80] [1660550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82260_102780Read file\n", - " 88379 [params.py: set():1006] [1655498] Changing key nb in group spatial from 2 to 1\n", - " 88379 [cnmf.py: fit():481] [1655514] preprocessing ...\n", - " 88378 [params.py: set():1006] [1655508] Changing key p in group preprocess from 1 to 0\n", - " 88375 [params.py: set():1006] [1655554] Changing key p in group temporal from 1 to 0\n", - " 88377 [params.py: set():1006] [1655483] Changing key p in group preprocess from 1 to 0\n", - " 88377 [params.py: set():1006] [1655484] Changing key nb in group spatial from 2 to 1\n", - " 88378 [params.py: set():1006] [1655539] Changing key nb in group spatial from 2 to 1\n", - " 88377 [params.py: set():1006] [1655476] Changing key p in group temporal from 1 to 0\n", - " 88376 [params.py: set():1006] [1655549] Changing key p in group preprocess from 1 to 0\n", - " 88376 [params.py: set():1006] [1655477] Changing key nb in group spatial from 2 to 1\n", - " 88375 [params.py: set():1006] [1655544] Changing key nb in group spatial from 2 to 1\n", - " 88377 [params.py: set():1006] [1655548] Changing key nb in group spatial from 2 to 1\n", - " 88376 [params.py: set():1006] [1655542] Changing key p in group preprocess from 1 to 0\n", - " 88374 [params.py: set():1006] [1655481] Changing key p in group preprocess from 1 to 0\n", - " 88374 [params.py: set():1006] [1655496] Changing key p in group temporal from 1 to 0\n", - " 88375 [params.py: set():1006] [1655490] Changing key p in group temporal from 1 to 0\n", - " 88373 [params.py: set():1006] [1655526] Changing key nb in group spatial from 2 to 1\n", - " 88374 [params.py: set():1006] [1655480] Changing key nb in group spatial from 2 to 1\n", - " 88374 [params.py: set():1006] [1655523] Changing key p in group preprocess from 1 to 0\n", - " 88394 [map_reduce.py: cnmf_patches():100] [1660129] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51540_72060file loaded\n", - " 88412 [params.py: set():1006] [1655494] Changing key p in group preprocess from 1 to 0\n", - " 88413 [params.py: set():1006] [1655485] Changing key p in group temporal from 1 to 0\n", - " 88413 [params.py: set():1006] [1655470] Changing key p in group preprocess from 1 to 0\n", - " 88414 [params.py: set():1006] [1655562] Changing key p in group preprocess from 1 to 0\n", - " 88414 [cnmf.py: fit():436] [1655559] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88420 [params.py: set():1006] [1655502] Changing key p in group temporal from 1 to 0\n", - " 88421 [params.py: set():1006] [1655478] Changing key p in group preprocess from 1 to 0\n", - " 88417 [params.py: set():1006] [1655509] Changing key p in group temporal from 1 to 0\n", - " 88417 [params.py: set():1006] [1655486] Changing key p in group temporal from 1 to 0\n", - " 88420 [params.py: set():1006] [1655563] Changing key p in group preprocess from 1 to 0\n", - " 88421 [params.py: set():1006] [1655516] Changing key p in group temporal from 1 to 0\n", - " 88423 [params.py: set():1006] [1655561] Changing key p in group temporal from 1 to 0\n", - " 88424 [params.py: set():1006] [1655503] Changing key p in group preprocess from 1 to 0\n", - " 88423 [params.py: set():1006] [1655524] Changing key p in group preprocess from 1 to 0\n", - " 88422 [params.py: set():1006] [1655527] Changing key p in group temporal from 1 to 0\n", - " 88418 [cnmf.py: fit():436] [1655553] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88422 [params.py: set():1006] [1655501] Changing key p in group preprocess from 1 to 0\n", - " 88429 [cnmf.py: fit():436] [1655535] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88421 [cnmf.py: fit():436] [1655543] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88419 [params.py: set():1006] [1655517] Changing key p in group temporal from 1 to 0\n", - " 88428 [params.py: set():1006] [1655540] Changing key p in group temporal from 1 to 0\n", - " 88417 [params.py: set():1006] [1655536] Changing key p in group temporal from 1 to 0\n", - " 88426 [params.py: set():1006] [1655532] Changing key p in group temporal from 1 to 0\n", - " 88427 [params.py: set():1006] [1655545] Changing key p in group preprocess from 1 to 0\n", - " 88418 [cnmf.py: fit():436] [1655499] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88428 [params.py: set():1006] [1655551] Changing key p in group temporal from 1 to 0\n", - " 88427 [params.py: set():1006] [1655504] Changing key p in group preprocess from 1 to 0\n", - " 88426 [params.py: set():1006] [1655500] Changing key p in group temporal from 1 to 0\n", - " 88430 [params.py: set():1006] [1655512] Changing key p in group temporal from 1 to 0\n", - " 88416 [params.py: set():1006] [1655491] Changing key p in group preprocess from 1 to 0\n", - " 88430 [cnmf.py: fit():436] [1655472] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88425 [cnmf.py: fit():436] [1655497] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88424 [cnmf.py: fit():436] [1655519] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88416 [params.py: set():1006] [1655528] Changing key p in group preprocess from 1 to 0\n", - " 88427 [cnmf.py: fit():449] [1655530] (8000, 41, 40)\n", - " 88426 [cnmf.py: fit():436] [1655552] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88431 [params.py: set():1006] [1655493] Changing key p in group preprocess from 1 to 0\n", - " 88430 [params.py: set():1006] [1655533] Changing key p in group temporal from 1 to 0\n", - " 88425 [params.py: set():1006] [1655557] Changing key p in group preprocess from 1 to 0\n", - " 88424 [params.py: set():1006] [1655547] Changing key p in group temporal from 1 to 0\n", - " 88428 [params.py: set():1006] [1655560] Changing key p in group temporal from 1 to 0\n", - " 88422 [params.py: set():1006] [1655518] Changing key nb in group spatial from 2 to 1\n", - " 88419 [cnmf.py: fit():436] [1655513] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88418 [params.py: set():1006] [1655525] Changing key p in group temporal from 1 to 0\n", - " 88429 [cnmf.py: fit():436] [1655489] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88431 [params.py: set():1006] [1655546] Changing key p in group temporal from 1 to 0\n", - " 88415 [params.py: set():1006] [1655522] Changing key p in group temporal from 1 to 0\n", - " 88420 [params.py: set():1006] [1655479] Changing key p in group temporal from 1 to 0\n", - " 88432 [params.py: set():1006] [1655492] Changing key p in group temporal from 1 to 0\n", - " 88416 [params.py: set():1006] [1655541] Changing key p in group temporal from 1 to 0\n", - " 88431 [params.py: set():1006] [1655506] Changing key p in group preprocess from 1 to 0\n", - " 88415 [params.py: set():1006] [1655487] Changing key p in group preprocess from 1 to 0\n", - " 88432 [params.py: set():1006] [1655521] Changing key p in group temporal from 1 to 0\n", - " 88423 [params.py: set():1006] [1655529] Changing key p in group temporal from 1 to 0\n", - " 88429 [cnmf.py: fit():436] [1655511] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88432 [cnmf.py: fit():436] [1655531] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88434 [params.py: set():1006] [1655534] Changing key p in group temporal from 1 to 0\n", - " 88434 [cnmf.py: fit():436] [1655520] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88434 [params.py: set():1006] [1655564] Changing key p in group temporal from 1 to 0\n", - " 88435 [params.py: set():1006] [1655482] Changing key p in group preprocess from 1 to 0\n", - " 88435 [params.py: set():1006] [1655475] Changing key p in group temporal from 1 to 0\n", - " 88436 [params.py: set():1006] [1655488] Changing key p in group temporal from 1 to 0\n", - " 88435 [params.py: set():1006] [1655474] Changing key p in group temporal from 1 to 0\n", - " 88433 [cnmf.py: fit():436] [1655537] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88436 [params.py: set():1006] [1655469] Changing key p in group temporal from 1 to 0\n", - " 88433 [params.py: set():1006] [1655556] Changing key p in group temporal from 1 to 0\n", - " 88438 [cnmf.py: fit():436] [1655490] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88437 [params.py: set():1006] [1655507] Changing key p in group temporal from 1 to 0\n", - " 88437 [params.py: set():1006] [1655473] Changing key p in group temporal from 1 to 0\n", - " 88438 [params.py: set():1006] [1655510] Changing key p in group preprocess from 1 to 0\n", - " 88439 [params.py: set():1006] [1655526] Changing key p in group preprocess from 1 to 0\n", - " 88439 [params.py: set():1006] [1655548] Changing key p in group preprocess from 1 to 0\n", - " 88437 [params.py: set():1006] [1655550] Changing key p in group preprocess from 1 to 0\n", - " 88440 [cnmf.py: fit():436] [1655496] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88440 [params.py: set():1006] [1655480] Changing key p in group preprocess from 1 to 0\n", - " 88441 [params.py: set():1006] [1655495] Changing key p in group preprocess from 1 to 0\n", - " 88442 [cnmf.py: fit():436] [1655476] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88442 [params.py: set():1006] [1655477] Changing key p in group preprocess from 1 to 0\n", - " 88442 [params.py: set():1006] [1655542] Changing key p in group temporal from 1 to 0\n", - " 88441 [params.py: set():1006] [1655523] Changing key p in group temporal from 1 to 0\n", - " 88440 [params.py: set():1006] [1655544] Changing key p in group preprocess from 1 to 0\n", - " 88443 [params.py: set():1006] [1655539] Changing key p in group preprocess from 1 to 0\n", - " 88438 [params.py: set():1006] [1655505] Changing key p in group preprocess from 1 to 0\n", - " 88443 [params.py: set():1006] [1655549] Changing key p in group temporal from 1 to 0\n", - " 88444 [params.py: set():1006] [1655484] Changing key p in group preprocess from 1 to 0\n", - " 88443 [params.py: set():1006] [1655481] Changing key p in group temporal from 1 to 0\n", - " 88445 [cnmf.py: fit():436] [1655554] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88444 [params.py: set():1006] [1655483] Changing key p in group temporal from 1 to 0\n", - " 88444 [params.py: set():1006] [1655538] Changing key p in group preprocess from 1 to 0\n", - " 88445 [params.py: set():1006] [1655508] Changing key p in group temporal from 1 to 0\n", - " 88445 [pre_processing.py:interpolate_missing_data():41] [1655514] Checking for missing data entries (NaN)\n", - " 88446 [params.py: set():1006] [1655498] Changing key p in group preprocess from 1 to 0\n", - " 88477 [params.py: set():1006] [1655563] Changing key p in group temporal from 1 to 0\n", - " 88479 [params.py: set():1006] [1655524] Changing key p in group temporal from 1 to 0\n", - " 88477 [cnmf.py: fit():436] [1655516] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88482 [params.py: set():1006] [1655494] Changing key p in group temporal from 1 to 0\n", - " 88477 [params.py: set():1006] [1655503] Changing key p in group temporal from 1 to 0\n", - " 88479 [cnmf.py: fit():436] [1655536] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88478 [cnmf.py: fit():449] [1655535] (8000, 41, 41)\n", - " 88484 [cnmf.py: fit():466] [1655530] Using 1 processes\n", - " 88480 [params.py: set():1006] [1655493] Changing key p in group temporal from 1 to 0\n", - " 88481 [cnmf.py: fit():436] [1655509] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88476 [cnmf.py: fit():449] [1655543] (8000, 41, 41)\n", - " 88479 [params.py: set():1006] [1655545] Changing key p in group temporal from 1 to 0\n", - " 88482 [cnmf.py: fit():436] [1655512] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88482 [cnmf.py: fit():436] [1655533] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88483 [cnmf.py: fit():449] [1655499] (8000, 41, 41)\n", - " 88480 [params.py: set():1006] [1655501] Changing key p in group temporal from 1 to 0\n", - " 88487 [cnmf.py: fit():436] [1655551] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88489 [cnmf.py: fit():449] [1655519] (8000, 41, 41)\n", - " 88485 [cnmf.py: fit():436] [1655502] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88486 [cnmf.py: fit():436] [1655540] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88487 [cnmf.py: fit():436] [1655561] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88484 [params.py: set():1006] [1655528] Changing key p in group temporal from 1 to 0\n", - " 88486 [cnmf.py: fit():436] [1655560] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88481 [cnmf.py: fit():436] [1655532] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88483 [cnmf.py: fit():449] [1655472] (8000, 41, 41)\n", - " 88489 [params.py: set():1006] [1655557] Changing key p in group temporal from 1 to 0\n", - " 88486 [cnmf.py: fit():449] [1655497] (8000, 41, 40)\n", - " 88485 [cnmf.py: fit():436] [1655547] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88484 [cnmf.py: fit():436] [1655546] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88487 [cnmf.py: fit():436] [1655525] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88478 [cnmf.py: fit():436] [1655527] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88488 [cnmf.py: fit():436] [1655517] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88488 [cnmf.py: fit():436] [1655541] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88480 [cnmf.py: fit():436] [1655500] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88485 [params.py: set():1006] [1655491] Changing key p in group temporal from 1 to 0\n", - " 88490 [cnmf.py: fit():436] [1655492] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88489 [params.py: set():1006] [1655504] Changing key p in group temporal from 1 to 0\n", - " 88491 [cnmf.py: fit():436] [1655522] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88490 [cnmf.py: fit():449] [1655552] (8000, 41, 41)\n", - " 88490 [cnmf.py: fit():449] [1655513] (8000, 41, 41)\n", - " 88475 [cnmf.py: fit():449] [1655553] (8000, 41, 41)\n", - " 88491 [cnmf.py: fit():436] [1655479] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88475 [params.py: set():1006] [1655478] Changing key p in group temporal from 1 to 0\n", - " 88474 [cnmf.py: fit():436] [1655485] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88492 [params.py: set():1006] [1655518] Changing key p in group preprocess from 1 to 0\n", - " 88474 [cnmf.py: fit():436] [1655486] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88492 [cnmf.py: fit():436] [1655564] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88476 [params.py: set():1006] [1655470] Changing key p in group temporal from 1 to 0\n", - " 88493 [params.py: set():1006] [1655506] Changing key p in group temporal from 1 to 0\n", - " 88493 [cnmf.py: fit():449] [1655489] (8000, 41, 41)\n", - " 88476 [cnmf.py: fit():449] [1655559] (8000, 41, 41)\n", - " 88494 [cnmf.py: fit():436] [1655474] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88496 [params.py: set():1006] [1655487] Changing key p in group temporal from 1 to 0\n", - " 88496 [cnmf.py: fit():449] [1655520] (8000, 41, 41)\n", - " 88494 [cnmf.py: fit():449] [1655531] (8000, 41, 41)\n", - " 88495 [cnmf.py: fit():436] [1655534] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88495 [cnmf.py: fit():436] [1655521] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88495 [cnmf.py: fit():449] [1655537] (8000, 41, 41)\n", - " 88496 [cnmf.py: fit():449] [1655511] (8000, 41, 41)\n", - " 88497 [cnmf.py: fit():436] [1655469] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88494 [cnmf.py: fit():436] [1655529] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88497 [cnmf.py: fit():436] [1655475] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88498 [cnmf.py: fit():436] [1655488] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88473 [params.py: set():1006] [1655562] Changing key p in group temporal from 1 to 0\n", - " 88499 [cnmf.py: fit():449] [1655490] (8000, 41, 41)\n", - " 88499 [cnmf.py: fit():436] [1655556] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88498 [params.py: set():1006] [1655482] Changing key p in group temporal from 1 to 0\n", - " 88500 [cnmf.py: fit():436] [1655507] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88501 [params.py: set():1006] [1655480] Changing key p in group temporal from 1 to 0\n", - " 88502 [cnmf.py: fit():449] [1655496] (8000, 41, 41)\n", - " 88502 [params.py: set():1006] [1655548] Changing key p in group temporal from 1 to 0\n", - " 88503 [cnmf.py: fit():449] [1655476] (8000, 41, 41)\n", - " 88500 [params.py: set():1006] [1655526] Changing key p in group temporal from 1 to 0\n", - " 88501 [params.py: set():1006] [1655495] Changing key p in group temporal from 1 to 0\n", - " 88504 [params.py: set():1006] [1655550] Changing key p in group temporal from 1 to 0\n", - " 88503 [params.py: set():1006] [1655477] Changing key p in group temporal from 1 to 0\n", - " 88502 [cnmf.py: fit():436] [1655473] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88501 [params.py: set():1006] [1655510] Changing key p in group temporal from 1 to 0\n", - " 88505 [params.py: set():1006] [1655544] Changing key p in group temporal from 1 to 0\n", - " 88505 [cnmf.py: fit():436] [1655523] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88506 [cnmf.py: fit():436] [1655549] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88504 [cnmf.py: fit():436] [1655542] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88506 [params.py: set():1006] [1655539] Changing key p in group temporal from 1 to 0\n", - " 88508 [params.py: set():1006] [1655538] Changing key p in group temporal from 1 to 0\n", - " 88507 [cnmf.py: fit():449] [1655554] (8000, 41, 41)\n", - " 88506 [params.py: set():1006] [1655505] Changing key p in group temporal from 1 to 0\n", - " 88508 [cnmf.py: fit():436] [1655481] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88507 [params.py: set():1006] [1655484] Changing key p in group temporal from 1 to 0\n", - " 88508 [cnmf.py: fit():436] [1655483] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88509 [cnmf.py: fit():436] [1655508] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88527 [params.py: set():1006] [1655498] Changing key p in group temporal from 1 to 0\n", - " 88554 [cnmf.py: fit():477] [1655530] using 400 pixels per process\n", - " 88557 [cnmf.py: fit():449] [1655527] (8000, 41, 41)\n", - " 88552 [cnmf.py: fit():466] [1655499] Using 1 processes\n", - " 88555 [cnmf.py: fit():449] [1655540] (8000, 41, 41)\n", - " 88552 [cnmf.py: fit():449] [1655533] (8000, 41, 41)\n", - " 88554 [cnmf.py: fit():466] [1655472] Using 1 processes\n", - " 88557 [cnmf.py: fit():449] [1655547] (8000, 41, 41)\n", - " 88556 [cnmf.py: fit():466] [1655519] Using 1 processes\n", - " 88556 [cnmf.py: fit():436] [1655524] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88553 [cnmf.py: fit():436] [1655545] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88555 [cnmf.py: fit():436] [1655528] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88556 [cnmf.py: fit():466] [1655497] Using 1 processes\n", - " 88553 [cnmf.py: fit():449] [1655512] (8000, 41, 41)\n", - " 88559 [cnmf.py: fit():449] [1655541] (8000, 41, 41)\n", - " 88560 [cnmf.py: fit():449] [1655492] (8000, 41, 41)\n", - " 88546 [cnmf.py: fit():449] [1655536] (8000, 41, 41)\n", - " 88558 [cnmf.py: fit():449] [1655561] (8000, 41, 41)\n", - " 88558 [cnmf.py: fit():449] [1655525] (8000, 41, 41)\n", - " 88552 [cnmf.py: fit():466] [1655535] Using 1 processes\n", - " 88559 [cnmf.py: fit():449] [1655502] (8000, 41, 41)\n", - " 88558 [cnmf.py: fit():449] [1655516] (8000, 41, 41)\n", - " 88561 [cnmf.py: fit():436] [1655557] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88557 [cnmf.py: fit():449] [1655517] (8000, 41, 41)\n", - " 88561 [cnmf.py: fit():436] [1655501] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88560 [cnmf.py: fit():436] [1655504] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88564 [cnmf.py: fit():436] [1655563] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88562 [cnmf.py: fit():449] [1655546] (8000, 41, 41)\n", - " 88551 [cnmf.py: fit():466] [1655543] Using 1 processes\n", - " 88555 [cnmf.py: fit():449] [1655560] (8000, 41, 41)\n", - " 88563 [cnmf.py: fit():449] [1655551] (8000, 41, 41)\n", - " 88561 [cnmf.py: fit():449] [1655532] (8000, 41, 40)\n", - " 88554 [cnmf.py: fit():436] [1655503] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88560 [cnmf.py: fit():449] [1655500] (8000, 41, 40)\n", - " 88563 [cnmf.py: fit():436] [1655494] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88563 [cnmf.py: fit():466] [1655552] Using 1 processes\n", - " 88562 [cnmf.py: fit():466] [1655513] Using 1 processes\n", - " 88566 [cnmf.py: fit():449] [1655509] (8000, 41, 41)\n", - " 88564 [cnmf.py: fit():436] [1655491] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88566 [cnmf.py: fit():436] [1655506] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88568 [cnmf.py: fit():436] [1655470] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88569 [params.py: set():1006] [1655518] Changing key p in group temporal from 1 to 0\n", - " 88567 [cnmf.py: fit():449] [1655479] (8000, 41, 41)\n", - " 88569 [cnmf.py: fit():449] [1655486] (8000, 41, 41)\n", - " 88565 [cnmf.py: fit():449] [1655522] (8000, 41, 41)\n", - " 88568 [cnmf.py: fit():449] [1655485] (8000, 41, 41)\n", - " 88567 [cnmf.py: fit():466] [1655553] Using 1 processes\n", - " 88567 [cnmf.py: fit():466] [1655489] Using 1 processes\n", - " 88568 [cnmf.py: fit():449] [1655564] (8000, 41, 41)\n", - " 88570 [cnmf.py: fit():436] [1655478] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88546 [cnmf.py: fit():436] [1655493] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88571 [cnmf.py: fit():466] [1655559] Using 1 processes\n", - " 88573 [cnmf.py: fit():466] [1655537] Using 1 processes\n", - " 88572 [cnmf.py: fit():449] [1655474] (8000, 41, 40)\n", - " 88574 [cnmf.py: fit():436] [1655487] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88573 [cnmf.py: fit():449] [1655534] (8000, 41, 41)\n", - " 88573 [cnmf.py: fit():449] [1655521] (8000, 41, 41)\n", - " 88572 [cnmf.py: fit():466] [1655531] Using 1 processes\n", - " 88572 [cnmf.py: fit():466] [1655511] Using 1 processes\n", - " 88571 [cnmf.py: fit():466] [1655520] Using 1 processes\n", - " 88575 [cnmf.py: fit():449] [1655475] (8000, 41, 41)\n", - " 88575 [cnmf.py: fit():449] [1655469] (8000, 41, 41)\n", - " 88577 [cnmf.py: fit():449] [1655529] (8000, 41, 41)\n", - " 88576 [cnmf.py: fit():449] [1655507] (8000, 41, 41)\n", - " 88577 [cnmf.py: fit():466] [1655490] Using 1 processes\n", - " 88576 [cnmf.py: fit():436] [1655562] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88577 [cnmf.py: fit():436] [1655482] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88578 [cnmf.py: fit():449] [1655556] (8000, 41, 41)\n", - " 88578 [cnmf.py: fit():449] [1655488] (8000, 41, 41)\n", - " 88580 [cnmf.py: fit():436] [1655548] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88581 [cnmf.py: fit():436] [1655495] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88579 [cnmf.py: fit():436] [1655480] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88581 [cnmf.py: fit():466] [1655496] Using 1 processes\n", - " 88582 [cnmf.py: fit():436] [1655526] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88580 [cnmf.py: fit():436] [1655477] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88580 [cnmf.py: fit():466] [1655476] Using 1 processes\n", - " 88582 [cnmf.py: fit():436] [1655550] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88584 [cnmf.py: fit():449] [1655549] (8000, 41, 41)\n", - " 88582 [cnmf.py: fit():449] [1655473] (8000, 41, 41)\n", - " 88583 [cnmf.py: fit():436] [1655544] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88584 [cnmf.py: fit():436] [1655510] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88585 [cnmf.py: fit():449] [1655523] (8000, 41, 41)\n", - " 88584 [cnmf.py: fit():436] [1655539] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88585 [cnmf.py: fit():449] [1655542] (8000, 41, 41)\n", - " 88585 [cnmf.py: fit():436] [1655538] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88586 [cnmf.py: fit():449] [1655481] (8000, 41, 41)\n", - " 88586 [cnmf.py: fit():436] [1655484] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88587 [cnmf.py: fit():466] [1655554] Using 1 processes\n", - " 88587 [cnmf.py: fit():436] [1655505] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88587 [cnmf.py: fit():449] [1655483] (8000, 41, 41)\n", - " 88588 [cnmf.py: fit():449] [1655508] (8000, 41, 41)\n", - " 88588 [cnmf.py: fit():436] [1655498] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88589 [map_reduce.py: cnmf_patches():100] [1660361] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66900_87420file loaded\n", - " 88623 [cnmf.py: fit():466] [1655536] Using 1 processes\n", - " 88621 [cnmf.py: fit():466] [1655547] Using 1 processes\n", - " 88620 [cnmf.py: fit():466] [1655541] Using 1 processes\n", - " 88621 [cnmf.py: fit():466] [1655533] Using 1 processes\n", - " 88625 [cnmf.py: fit():466] [1655517] Using 1 processes\n", - " 88619 [cnmf.py: fit():449] [1655524] (8000, 41, 41)\n", - " 88622 [cnmf.py: fit():477] [1655499] using 400 pixels per process\n", - " 88624 [cnmf.py: fit():466] [1655540] Using 1 processes\n", - " 88624 [cnmf.py: fit():477] [1655472] using 400 pixels per process\n", - " 88622 [cnmf.py: fit():466] [1655525] Using 1 processes\n", - " 88623 [cnmf.py: fit():477] [1655535] using 400 pixels per process\n", - " 88622 [cnmf.py: fit():449] [1655528] (8000, 41, 41)\n", - " 88620 [cnmf.py: fit():466] [1655492] Using 1 processes\n", - " 88619 [cnmf.py: fit():477] [1655519] using 400 pixels per process\n", - " 88620 [cnmf.py: fit():449] [1655545] (8000, 41, 41)\n", - " 88624 [cnmf.py: fit():466] [1655561] Using 1 processes\n", - " 88627 [cnmf.py: fit():477] [1655497] using 400 pixels per process\n", - " 88625 [cnmf.py: fit():449] [1655504] (8000, 41, 41)\n", - " 88627 [cnmf.py: fit():449] [1655563] (8000, 41, 41)\n", - " 88626 [cnmf.py: fit():466] [1655560] Using 1 processes\n", - " 88628 [cnmf.py: fit():466] [1655532] Using 1 processes\n", - " 88625 [cnmf.py: fit():478] [1655530] using 5000 block_size_temp\n", - " 88627 [cnmf.py: fit():477] [1655552] using 400 pixels per process\n", - " 88621 [cnmf.py: fit():466] [1655512] Using 1 processes\n", - " 88625 [cnmf.py: fit():466] [1655546] Using 1 processes\n", - " 88626 [cnmf.py: fit():466] [1655516] Using 1 processes\n", - " 88629 [cnmf.py: fit():466] [1655551] Using 1 processes\n", - " 88629 [cnmf.py: fit():449] [1655501] (8000, 41, 41)\n", - " 88630 [cnmf.py: fit():466] [1655500] Using 1 processes\n", - " 88628 [cnmf.py: fit():477] [1655513] using 400 pixels per process\n", - " 88628 [cnmf.py: fit():449] [1655557] (8000, 41, 41)\n", - " 88630 [cnmf.py: fit():477] [1655543] using 400 pixels per process\n", - " 88631 [cnmf.py: fit():449] [1655503] (8000, 41, 41)\n", - " 88631 [cnmf.py: fit():466] [1655502] Using 1 processes\n", - " 88632 [cnmf.py: fit():449] [1655494] (8000, 41, 41)\n", - " 88618 [cnmf.py: fit():466] [1655527] Using 1 processes\n", - " 88632 [cnmf.py: fit():466] [1655509] Using 1 processes\n", - " 88634 [cnmf.py: fit():466] [1655485] Using 1 processes\n", - " 88633 [cnmf.py: fit():436] [1655518] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 88633 [cnmf.py: fit():449] [1655506] (8000, 41, 41)\n", - " 88634 [cnmf.py: fit():477] [1655553] using 400 pixels per process\n", - " 88635 [cnmf.py: fit():449] [1655491] (8000, 41, 41)\n", - " 88636 [cnmf.py: fit():466] [1655486] Using 1 processes\n", - " 88635 [cnmf.py: fit():449] [1655470] (8000, 41, 41)\n", - " 88636 [cnmf.py: fit():466] [1655522] Using 1 processes\n", - " 88637 [cnmf.py: fit():449] [1655478] (8000, 41, 41)\n", - " 88638 [cnmf.py: fit():477] [1655489] using 400 pixels per process\n", - " 88635 [cnmf.py: fit():466] [1655479] Using 1 processes\n", - " 88636 [cnmf.py: fit():466] [1655564] Using 1 processes\n", - " 88639 [cnmf.py: fit():466] [1655474] Using 1 processes\n", - " 88641 [cnmf.py: fit():466] [1655534] Using 1 processes\n", - " 88638 [cnmf.py: fit():477] [1655511] using 400 pixels per process\n", - " 88640 [cnmf.py: fit():477] [1655559] using 400 pixels per process\n", - " 88640 [cnmf.py: fit():449] [1655493] (8000, 41, 41)\n", - " 88639 [cnmf.py: fit():477] [1655531] using 400 pixels per process\n", - " 88640 [cnmf.py: fit():477] [1655537] using 400 pixels per process\n", - " 88642 [cnmf.py: fit():449] [1655487] (8000, 41, 41)\n", - " 88641 [cnmf.py: fit():466] [1655521] Using 1 processes\n", - " 88644 [cnmf.py: fit():477] [1655490] using 400 pixels per process\n", - " 88642 [cnmf.py: fit():477] [1655520] using 400 pixels per process\n", - " 88645 [cnmf.py: fit():449] [1655562] (8000, 41, 41)\n", - " 88644 [cnmf.py: fit():466] [1655529] Using 1 processes\n", - " 88643 [cnmf.py: fit():466] [1655469] Using 1 processes\n", - " 88643 [cnmf.py: fit():466] [1655475] Using 1 processes\n", - " 88646 [cnmf.py: fit():449] [1655548] (8000, 41, 40)\n", - " 88645 [cnmf.py: fit():466] [1655507] Using 1 processes\n", - " 88646 [cnmf.py: fit():466] [1655488] Using 1 processes\n", - " 88647 [cnmf.py: fit():449] [1655482] (8000, 41, 41)\n", - " 88647 [cnmf.py: fit():449] [1655495] (8000, 41, 41)\n", - " 88648 [cnmf.py: fit():449] [1655480] (8000, 41, 41)\n", - " 88649 [cnmf.py: fit():477] [1655496] using 400 pixels per process\n", - " 88648 [cnmf.py: fit():449] [1655477] (8000, 41, 41)\n", - " 88647 [cnmf.py: fit():466] [1655556] Using 1 processes\n", - " 88649 [cnmf.py: fit():477] [1655476] using 400 pixels per process\n", - " 88649 [cnmf.py: fit():449] [1655526] (8000, 41, 41)\n", - " 88650 [cnmf.py: fit():466] [1655549] Using 1 processes\n", - " 88651 [cnmf.py: fit():449] [1655510] (8000, 41, 41)\n", - " 88650 [cnmf.py: fit():466] [1655473] Using 1 processes\n", - " 88651 [cnmf.py: fit():449] [1655544] (8000, 41, 41)\n", - " 88651 [cnmf.py: fit():449] [1655550] (8000, 41, 41)\n", - " 88652 [cnmf.py: fit():449] [1655538] (8000, 41, 41)\n", - " 88653 [cnmf.py: fit():466] [1655481] Using 1 processes\n", - " 88653 [cnmf.py: fit():466] [1655542] Using 1 processes\n", - " 88653 [cnmf.py: fit():449] [1655484] (8000, 41, 41)\n", - " 88652 [cnmf.py: fit():449] [1655539] (8000, 41, 41)\n", - " 88654 [cnmf.py: fit():466] [1655523] Using 1 processes\n", - " 88654 [cnmf.py: fit():477] [1655554] using 400 pixels per process\n", - " 88655 [cnmf.py: fit():449] [1655505] (8000, 41, 41)\n", - " 88655 [cnmf.py: fit():466] [1655483] Using 1 processes\n", - " 88655 [cnmf.py: fit():466] [1655508] Using 1 processes\n", - " 88656 [cnmf.py: fit():449] [1655498] (8000, 41, 41)\n", - " 88682 [cnmf.py: fit():477] [1655540] using 400 pixels per process\n", - " 88685 [cnmf.py: fit():477] [1655541] using 400 pixels per process\n", - " 88681 [cnmf.py: fit():466] [1655528] Using 1 processes\n", - " 88684 [cnmf.py: fit():477] [1655560] using 400 pixels per process\n", - " 88681 [cnmf.py: fit():477] [1655547] using 400 pixels per process\n", - " 88685 [cnmf.py: fit():466] [1655545] Using 1 processes\n", - " 88684 [cnmf.py: fit():477] [1655532] using 400 pixels per process\n", - " 88682 [cnmf.py: fit():478] [1655535] using 5000 block_size_temp\n", - " 88686 [cnmf.py: fit():477] [1655546] using 400 pixels per process\n", - " 88683 [cnmf.py: fit():466] [1655524] Using 1 processes\n", - " 88681 [cnmf.py: fit():478] [1655472] using 5000 block_size_temp\n", - " 88686 [cnmf.py: fit():477] [1655512] using 400 pixels per process\n", - " 88684 [cnmf.py: fit():477] [1655533] using 400 pixels per process\n", - " 88686 [cnmf.py: fit():466] [1655563] Using 1 processes\n", - " 88688 [cnmf.py: fit():466] [1655504] Using 1 processes\n", - " 88683 [cnmf.py: fit():481] [1655530] preprocessing ...\n", - " 88688 [cnmf.py: fit():478] [1655497] using 5000 block_size_temp\n", - " 88687 [cnmf.py: fit():477] [1655551] using 400 pixels per process\n", - " 88688 [cnmf.py: fit():478] [1655499] using 5000 block_size_temp\n", - " 88687 [cnmf.py: fit():477] [1655561] using 400 pixels per process\n", - " 88687 [cnmf.py: fit():477] [1655536] using 400 pixels per process\n", - " 88689 [cnmf.py: fit():478] [1655552] using 5000 block_size_temp\n", - " 88680 [cnmf.py: fit():477] [1655525] using 400 pixels per process\n", - " 88683 [cnmf.py: fit():477] [1655492] using 400 pixels per process\n", - " 88689 [cnmf.py: fit():466] [1655557] Using 1 processes\n", - " 88685 [cnmf.py: fit():478] [1655519] using 5000 block_size_temp\n", - " 88690 [cnmf.py: fit():477] [1655500] using 400 pixels per process\n", - " 88689 [cnmf.py: fit():466] [1655501] Using 1 processes\n", - " 88690 [cnmf.py: fit():477] [1655516] using 400 pixels per process\n", - " 88691 [cnmf.py: fit():478] [1655543] using 5000 block_size_temp\n", - " 88691 [cnmf.py: fit():478] [1655513] using 5000 block_size_temp\n", - " 88692 [cnmf.py: fit():466] [1655503] Using 1 processes\n", - " 88692 [cnmf.py: fit():466] [1655494] Using 1 processes\n", - " 88693 [cnmf.py: fit():477] [1655502] using 400 pixels per process\n", - " 88694 [cnmf.py: fit():477] [1655485] using 400 pixels per process\n", - " 88697 [cnmf.py: fit():466] [1655478] Using 1 processes\n", - " 88695 [cnmf.py: fit():466] [1655506] Using 1 processes\n", - " 88694 [cnmf.py: fit():449] [1655518] (8000, 41, 41)\n", - " 88695 [cnmf.py: fit():466] [1655470] Using 1 processes\n", - " 88694 [cnmf.py: fit():477] [1655509] using 400 pixels per process\n", - " 88696 [cnmf.py: fit():477] [1655527] using 400 pixels per process\n", - " 88696 [cnmf.py: fit():466] [1655491] Using 1 processes\n", - " 88697 [cnmf.py: fit():478] [1655489] using 5000 block_size_temp\n", - " 88699 [cnmf.py: fit():478] [1655559] using 5000 block_size_temp\n", - " 88698 [cnmf.py: fit():478] [1655553] using 5000 block_size_temp\n", - " 88699 [cnmf.py: fit():477] [1655522] using 400 pixels per process\n", - " 88699 [cnmf.py: fit():478] [1655531] using 5000 block_size_temp\n", - " 88698 [cnmf.py: fit():477] [1655564] using 400 pixels per process\n", - " 88700 [cnmf.py: fit():477] [1655534] using 400 pixels per process\n", - " 88699 [cnmf.py: fit():478] [1655511] using 5000 block_size_temp\n", - " 88680 [cnmf.py: fit():477] [1655517] using 400 pixels per process\n", - " 88702 [cnmf.py: fit():477] [1655474] using 400 pixels per process\n", - " 88700 [cnmf.py: fit():477] [1655486] using 400 pixels per process\n", - " 88701 [cnmf.py: fit():477] [1655479] using 400 pixels per process\n", - " 88703 [cnmf.py: fit():478] [1655490] using 5000 block_size_temp\n", - " 88701 [cnmf.py: fit():478] [1655537] using 5000 block_size_temp\n", - " 88701 [cnmf.py: fit():466] [1655493] Using 1 processes\n", - " 88704 [cnmf.py: fit():477] [1655529] using 400 pixels per process\n", - " 88703 [cnmf.py: fit():466] [1655487] Using 1 processes\n", - " 88703 [cnmf.py: fit():477] [1655521] using 400 pixels per process\n", - " 88706 [cnmf.py: fit():477] [1655488] using 400 pixels per process\n", - " 88705 [cnmf.py: fit():466] [1655482] Using 1 processes\n", - " 88705 [cnmf.py: fit():477] [1655507] using 400 pixels per process\n", - " 88706 [cnmf.py: fit():466] [1655548] Using 1 processes\n", - " 88705 [cnmf.py: fit():478] [1655520] using 5000 block_size_temp\n", - " 88706 [cnmf.py: fit():466] [1655562] Using 1 processes\n", - " 88707 [cnmf.py: fit():477] [1655475] using 400 pixels per process\n", - " 88707 [cnmf.py: fit():477] [1655469] using 400 pixels per process\n", - " 88711 [cnmf.py: fit():466] [1655510] Using 1 processes\n", - " 88710 [cnmf.py: fit():466] [1655526] Using 1 processes\n", - " 88709 [cnmf.py: fit():477] [1655556] using 400 pixels per process\n", - " 88708 [cnmf.py: fit():478] [1655496] using 5000 block_size_temp\n", - " 88709 [cnmf.py: fit():466] [1655477] Using 1 processes\n", - " 88707 [cnmf.py: fit():466] [1655495] Using 1 processes\n", - " 88710 [cnmf.py: fit():477] [1655549] using 400 pixels per process\n", - " 88710 [cnmf.py: fit():478] [1655476] using 5000 block_size_temp\n", - " 88708 [cnmf.py: fit():466] [1655480] Using 1 processes\n", - " 88711 [cnmf.py: fit():466] [1655544] Using 1 processes\n", - " 88711 [cnmf.py: fit():477] [1655473] using 400 pixels per process\n", - " 88714 [cnmf.py: fit():466] [1655539] Using 1 processes\n", - " 88715 [cnmf.py: fit():478] [1655554] using 5000 block_size_temp\n", - " 88714 [cnmf.py: fit():477] [1655542] using 400 pixels per process\n", - " 88713 [cnmf.py: fit():477] [1655481] using 400 pixels per process\n", - " 88714 [cnmf.py: fit():477] [1655523] using 400 pixels per process\n", - " 88713 [cnmf.py: fit():466] [1655484] Using 1 processes\n", - " 88713 [cnmf.py: fit():466] [1655538] Using 1 processes\n", - " 88712 [cnmf.py: fit():466] [1655550] Using 1 processes\n", - " 88716 [cnmf.py: fit():477] [1655508] using 400 pixels per process\n", - " 88717 [cnmf.py: fit():466] [1655498] Using 1 processes\n", - " 88716 [cnmf.py: fit():477] [1655483] using 400 pixels per process\n", - " 88715 [cnmf.py: fit():466] [1655505] Using 1 processes\n", - " 88740 [cnmf.py: fit():478] [1655532] using 5000 block_size_temp\n", - " 88741 [cnmf.py: fit():481] [1655535] preprocessing ...\n", - " 88741 [cnmf.py: fit():478] [1655560] using 5000 block_size_temp\n", - " 88744 [cnmf.py: fit():481] [1655472] preprocessing ...\n", - " 88742 [cnmf.py: fit():478] [1655533] using 5000 block_size_temp\n", - " 88743 [cnmf.py: fit():477] [1655528] using 400 pixels per process\n", - " 88741 [cnmf.py: fit():478] [1655540] using 5000 block_size_temp\n", - " 88743 [cnmf.py: fit():478] [1655547] using 5000 block_size_temp\n", - " 88742 [cnmf.py: fit():477] [1655504] using 400 pixels per process\n", - " 88740 [cnmf.py: fit():477] [1655545] using 400 pixels per process\n", - " 88744 [cnmf.py: fit():481] [1655497] preprocessing ...\n", - " 88745 [cnmf.py: fit():478] [1655512] using 5000 block_size_temp\n", - " 88744 [cnmf.py: fit():481] [1655552] preprocessing ...\n", - " 88743 [cnmf.py: fit():481] [1655499] preprocessing ...\n", - " 88742 [cnmf.py: fit():478] [1655546] using 5000 block_size_temp\n", - " 88746 [cnmf.py: fit():478] [1655551] using 5000 block_size_temp\n", - " 88740 [cnmf.py: fit():478] [1655541] using 5000 block_size_temp\n", - " 88745 [cnmf.py: fit():478] [1655525] using 5000 block_size_temp\n", - " 88745 [pre_processing.py:interpolate_missing_data():41] [1655530] Checking for missing data entries (NaN)\n", - " 88747 [cnmf.py: fit():478] [1655536] using 5000 block_size_temp\n", - " 88746 [cnmf.py: fit():478] [1655492] using 5000 block_size_temp\n", - " 88747 [cnmf.py: fit():478] [1655561] using 5000 block_size_temp\n", - " 88748 [cnmf.py: fit():477] [1655557] using 400 pixels per process\n", - " 88746 [cnmf.py: fit():477] [1655563] using 400 pixels per process\n", - " 88749 [cnmf.py: fit():478] [1655500] using 5000 block_size_temp\n", - " 88749 [cnmf.py: fit():481] [1655519] preprocessing ...\n", - " 88749 [cnmf.py: fit():478] [1655516] using 5000 block_size_temp\n", - " 88748 [cnmf.py: fit():477] [1655501] using 400 pixels per process\n", - " 88750 [cnmf.py: fit():481] [1655513] preprocessing ...\n", - " 88750 [cnmf.py: fit():481] [1655543] preprocessing ...\n", - " 88754 [cnmf.py: fit():477] [1655506] using 400 pixels per process\n", - " 88751 [cnmf.py: fit():477] [1655503] using 400 pixels per process\n", - " 88752 [cnmf.py: fit():478] [1655502] using 5000 block_size_temp\n", - " 88754 [cnmf.py: fit():466] [1655518] Using 1 processes\n", - " 88753 [cnmf.py: fit():477] [1655494] using 400 pixels per process\n", - " 88753 [cnmf.py: fit():478] [1655509] using 5000 block_size_temp\n", - " 88752 [cnmf.py: fit():478] [1655485] using 5000 block_size_temp\n", - " 88755 [cnmf.py: fit():477] [1655491] using 400 pixels per process\n", - " 88754 [cnmf.py: fit():478] [1655527] using 5000 block_size_temp\n", - " 88755 [cnmf.py: fit():481] [1655559] preprocessing ...\n", - " 88756 [cnmf.py: fit():478] [1655522] using 5000 block_size_temp\n", - " 88739 [cnmf.py: fit():477] [1655524] using 400 pixels per process\n", - " 88756 [cnmf.py: fit():481] [1655553] preprocessing ...\n", - " 88757 [cnmf.py: fit():481] [1655489] preprocessing ...\n", - " 88756 [cnmf.py: fit():477] [1655478] using 400 pixels per process\n", - " 88755 [cnmf.py: fit():477] [1655470] using 400 pixels per process\n", - " 88760 [cnmf.py: fit():478] [1655479] using 5000 block_size_temp\n", - " 88759 [cnmf.py: fit():478] [1655486] using 5000 block_size_temp\n", - " 88757 [cnmf.py: fit():481] [1655531] preprocessing ...\n", - " 88757 [cnmf.py: fit():481] [1655511] preprocessing ...\n", - " 88761 [cnmf.py: fit():478] [1655517] using 5000 block_size_temp\n", - " 88760 [cnmf.py: fit():481] [1655490] preprocessing ...\n", - " 88759 [cnmf.py: fit():478] [1655564] using 5000 block_size_temp\n", - " 88759 [cnmf.py: fit():478] [1655474] using 5000 block_size_temp\n", - " 88760 [cnmf.py: fit():481] [1655537] preprocessing ...\n", - " 88758 [cnmf.py: fit():478] [1655534] using 5000 block_size_temp\n", - " 88761 [cnmf.py: fit():477] [1655493] using 400 pixels per process\n", - " 88762 [cnmf.py: fit():478] [1655529] using 5000 block_size_temp\n", - " 88762 [cnmf.py: fit():477] [1655487] using 400 pixels per process\n", - " 88762 [cnmf.py: fit():478] [1655521] using 5000 block_size_temp\n", - " 88767 [cnmf.py: fit():478] [1655488] using 5000 block_size_temp\n", - " 88769 [cnmf.py: fit():477] [1655548] using 400 pixels per process\n", - " 88770 [cnmf.py: fit():478] [1655475] using 5000 block_size_temp\n", - " 88770 [cnmf.py: fit():477] [1655562] using 400 pixels per process\n", - " 88770 [cnmf.py: fit():478] [1655469] using 5000 block_size_temp\n", - " 88769 [cnmf.py: fit():477] [1655482] using 400 pixels per process\n", - " 88769 [cnmf.py: fit():481] [1655520] preprocessing ...\n", - " 88768 [cnmf.py: fit():478] [1655507] using 5000 block_size_temp\n", - " 88779 [cnmf.py: fit():477] [1655480] using 400 pixels per process\n", - " 88780 [cnmf.py: fit():478] [1655473] using 5000 block_size_temp\n", - " 88779 [cnmf.py: fit():481] [1655496] preprocessing ...\n", - " 88778 [cnmf.py: fit():477] [1655510] using 400 pixels per process\n", - " 88778 [cnmf.py: fit():481] [1655476] preprocessing ...\n", - " 88778 [cnmf.py: fit():478] [1655556] using 5000 block_size_temp\n", - " 88778 [cnmf.py: fit():478] [1655549] using 5000 block_size_temp\n", - " 88779 [cnmf.py: fit():477] [1655544] using 400 pixels per process\n", - " 88777 [cnmf.py: fit():477] [1655495] using 400 pixels per process\n", - " 88777 [cnmf.py: fit():477] [1655477] using 400 pixels per process\n", - " 88777 [cnmf.py: fit():477] [1655526] using 400 pixels per process\n", - " 88783 [cnmf.py: fit():477] [1655538] using 400 pixels per process\n", - " 88783 [cnmf.py: fit():477] [1655484] using 400 pixels per process\n", - " 88782 [cnmf.py: fit():478] [1655523] using 5000 block_size_temp\n", - " 88783 [cnmf.py: fit():477] [1655550] using 400 pixels per process\n", - " 88782 [cnmf.py: fit():481] [1655554] preprocessing ...\n", - " 88782 [cnmf.py: fit():478] [1655481] using 5000 block_size_temp\n", - " 88781 [cnmf.py: fit():477] [1655539] using 400 pixels per process\n", - " 88781 [cnmf.py: fit():478] [1655542] using 5000 block_size_temp\n", - " 88785 [cnmf.py: fit():477] [1655505] using 400 pixels per process\n", - " 88785 [cnmf.py: fit():478] [1655483] using 5000 block_size_temp\n", - " 88784 [cnmf.py: fit():477] [1655498] using 400 pixels per process\n", - " 88784 [cnmf.py: fit():478] [1655508] using 5000 block_size_temp\n", - " 88800 [cnmf.py: fit():481] [1655536] preprocessing ...\n", - " 88800 [cnmf.py: fit():481] [1655540] preprocessing ...\n", - " 88795 [map_reduce.py: cnmf_patches():100] [1660550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82260_102780file loaded\n", - " 88799 [cnmf.py: fit():481] [1655533] preprocessing ...\n", - " 88816 [cnmf.py: fit():481] [1655532] preprocessing ...\n", - " 88815 [cnmf.py: fit():481] [1655547] preprocessing ...\n", - " 88801 [cnmf.py: fit():478] [1655545] using 5000 block_size_temp\n", - " 88800 [pre_processing.py:interpolate_missing_data():41] [1655472] Checking for missing data entries (NaN)\n", - " 88815 [pre_processing.py:interpolate_missing_data():41] [1655552] Checking for missing data entries (NaN)\n", - " 88816 [pre_processing.py:interpolate_missing_data():41] [1655497] Checking for missing data entries (NaN)\n", - " 88798 [pre_processing.py:interpolate_missing_data():41] [1655535] Checking for missing data entries (NaN)\n", - " 88799 [cnmf.py: fit():481] [1655560] preprocessing ...\n", - " 88816 [pre_processing.py:interpolate_missing_data():41] [1655499] Checking for missing data entries (NaN)\n", - " 88802 [cnmf.py: fit():478] [1655528] using 5000 block_size_temp\n", - " 88818 [cnmf.py: fit():481] [1655492] preprocessing ...\n", - " 88817 [cnmf.py: fit():481] [1655512] preprocessing ...\n", - " 88801 [cnmf.py: fit():478] [1655504] using 5000 block_size_temp\n", - " 88817 [cnmf.py: fit():481] [1655546] preprocessing ...\n", - " 88801 [cnmf.py: fit():481] [1655551] preprocessing ...\n", - " 88818 [cnmf.py: fit():481] [1655541] preprocessing ...\n", - " 88817 [cnmf.py: fit():481] [1655525] preprocessing ...\n", - " 88819 [cnmf.py: fit():481] [1655561] preprocessing ...\n", - " 88819 [cnmf.py: fit():478] [1655557] using 5000 block_size_temp\n", - " 88820 [cnmf.py: fit():478] [1655563] using 5000 block_size_temp\n", - " 88821 [cnmf.py: fit():481] [1655516] preprocessing ...\n", - " 88821 [cnmf.py: fit():481] [1655500] preprocessing ...\n", - " 88822 [pre_processing.py:interpolate_missing_data():41] [1655543] Checking for missing data entries (NaN)\n", - " 88821 [cnmf.py: fit():478] [1655501] using 5000 block_size_temp\n", - " 88825 [cnmf.py: fit():478] [1655506] using 5000 block_size_temp\n", - " 88824 [cnmf.py: fit():477] [1655518] using 400 pixels per process\n", - " 88825 [pre_processing.py:interpolate_missing_data():41] [1655513] Checking for missing data entries (NaN)\n", - " 88824 [cnmf.py: fit():481] [1655485] preprocessing ...\n", - " 88824 [cnmf.py: fit():478] [1655491] using 5000 block_size_temp\n", - " 88827 [cnmf.py: fit():478] [1655524] using 5000 block_size_temp\n", - " 88825 [cnmf.py: fit():478] [1655494] using 5000 block_size_temp\n", - " 88822 [pre_processing.py:interpolate_missing_data():41] [1655519] Checking for missing data entries (NaN)\n", - " 88823 [cnmf.py: fit():481] [1655509] preprocessing ...\n", - " 88826 [cnmf.py: fit():478] [1655478] using 5000 block_size_temp\n", - " 88828 [pre_processing.py:interpolate_missing_data():41] [1655489] Checking for missing data entries (NaN)\n", - " 88828 [pre_processing.py:interpolate_missing_data():41] [1655559] Checking for missing data entries (NaN)\n", - " 88828 [cnmf.py: fit():481] [1655522] preprocessing ...\n", - " 88827 [cnmf.py: fit():481] [1655527] preprocessing ...\n", - " 88829 [cnmf.py: fit():481] [1655479] preprocessing ...\n", - " 88826 [cnmf.py: fit():481] [1655502] preprocessing ...\n", - " 88829 [cnmf.py: fit():478] [1655503] using 5000 block_size_temp\n", - " 88829 [cnmf.py: fit():478] [1655470] using 5000 block_size_temp\n", - " 88826 [pre_processing.py:interpolate_missing_data():41] [1655553] Checking for missing data entries (NaN)\n", - " 88830 [cnmf.py: fit():481] [1655486] preprocessing ...\n", - " 88831 [pre_processing.py:interpolate_missing_data():41] [1655511] Checking for missing data entries (NaN)\n", - " 88831 [cnmf.py: fit():481] [1655517] preprocessing ...\n", - " 88830 [pre_processing.py:interpolate_missing_data():41] [1655490] Checking for missing data entries (NaN)\n", - " 88830 [pre_processing.py:interpolate_missing_data():41] [1655531] Checking for missing data entries (NaN)\n", - " 88831 [cnmf.py: fit():481] [1655474] preprocessing ...\n", - " 88832 [pre_processing.py:interpolate_missing_data():41] [1655537] Checking for missing data entries (NaN)\n", - " 88832 [cnmf.py: fit():481] [1655564] preprocessing ...\n", - " 88832 [cnmf.py: fit():481] [1655534] preprocessing ...\n", - " 88833 [cnmf.py: fit():481] [1655521] preprocessing ...\n", - " 88834 [cnmf.py: fit():478] [1655487] using 5000 block_size_temp\n", - " 88833 [cnmf.py: fit():481] [1655529] preprocessing ...\n", - " 88833 [cnmf.py: fit():478] [1655493] using 5000 block_size_temp\n", - " 88835 [cnmf.py: fit():481] [1655488] preprocessing ...\n", - " 88841 [pre_processing.py:interpolate_missing_data():41] [1655520] Checking for missing data entries (NaN)\n", - " 88841 [cnmf.py: fit():481] [1655507] preprocessing ...\n", - " 88841 [cnmf.py: fit():478] [1655482] using 5000 block_size_temp\n", - " 88840 [cnmf.py: fit():478] [1655562] using 5000 block_size_temp\n", - " 88840 [cnmf.py: fit():481] [1655475] preprocessing ...\n", - " 88840 [cnmf.py: fit():478] [1655548] using 5000 block_size_temp\n", - " 88839 [cnmf.py: fit():481] [1655469] preprocessing ...\n", - " 88846 [cnmf.py: fit():478] [1655526] using 5000 block_size_temp\n", - " 88844 [cnmf.py: fit():478] [1655477] using 5000 block_size_temp\n", - " 88845 [cnmf.py: fit():481] [1655473] preprocessing ...\n", - " 88844 [cnmf.py: fit():478] [1655510] using 5000 block_size_temp\n", - " 88845 [cnmf.py: fit():478] [1655480] using 5000 block_size_temp\n", - " 88845 [cnmf.py: fit():478] [1655544] using 5000 block_size_temp\n", - " 88845 [cnmf.py: fit():478] [1655495] using 5000 block_size_temp\n", - " 88843 [cnmf.py: fit():481] [1655556] preprocessing ...\n", - " 88843 [pre_processing.py:interpolate_missing_data():41] [1655476] Checking for missing data entries (NaN)\n", - " 88844 [cnmf.py: fit():481] [1655549] preprocessing ...\n", - " 88843 [pre_processing.py:interpolate_missing_data():41] [1655496] Checking for missing data entries (NaN)\n", - " 88849 [cnmf.py: fit():478] [1655539] using 5000 block_size_temp\n", - " 88848 [cnmf.py: fit():478] [1655538] using 5000 block_size_temp\n", - " 88848 [cnmf.py: fit():481] [1655523] preprocessing ...\n", - " 88848 [pre_processing.py:interpolate_missing_data():41] [1655554] Checking for missing data entries (NaN)\n", - " 88848 [cnmf.py: fit():481] [1655481] preprocessing ...\n", - " 88847 [cnmf.py: fit():478] [1655550] using 5000 block_size_temp\n", - " 88849 [cnmf.py: fit():481] [1655542] preprocessing ...\n", - " 88847 [cnmf.py: fit():478] [1655484] using 5000 block_size_temp\n", - " 88853 [cnmf.py: fit():478] [1655498] using 5000 block_size_temp\n", - " 88853 [cnmf.py: fit():481] [1655483] preprocessing ...\n", - " 88854 [cnmf.py: fit():481] [1655508] preprocessing ...\n", - " 88853 [cnmf.py: fit():478] [1655505] using 5000 block_size_temp\n", - " 88916 [pre_processing.py:interpolate_missing_data():41] [1655512] Checking for missing data entries (NaN)\n", - " 88916 [pre_processing.py:interpolate_missing_data():41] [1655547] Checking for missing data entries (NaN)\n", - " 88936 [pre_processing.py:interpolate_missing_data():41] [1655536] Checking for missing data entries (NaN)\n", - " 88936 [pre_processing.py:interpolate_missing_data():41] [1655492] Checking for missing data entries (NaN)\n", - " 88937 [pre_processing.py:interpolate_missing_data():41] [1655560] Checking for missing data entries (NaN)\n", - " 88935 [pre_processing.py:interpolate_missing_data():41] [1655551] Checking for missing data entries (NaN)\n", - " 88936 [pre_processing.py:interpolate_missing_data():41] [1655540] Checking for missing data entries (NaN)\n", - " 88937 [cnmf.py: fit():481] [1655504] preprocessing ...\n", - " 88937 [cnmf.py: fit():481] [1655545] preprocessing ...\n", - " 88938 [pre_processing.py:interpolate_missing_data():41] [1655546] Checking for missing data entries (NaN)\n", - " 88938 [pre_processing.py:interpolate_missing_data():41] [1655525] Checking for missing data entries (NaN)\n", - " 88915 [pre_processing.py:interpolate_missing_data():41] [1655532] Checking for missing data entries (NaN)\n", - " 88951 [pre_processing.py:interpolate_missing_data():41] [1655541] Checking for missing data entries (NaN)\n", - " 88953 [cnmf.py: fit():481] [1655557] preprocessing ...\n", - " 88952 [pre_processing.py:interpolate_missing_data():41] [1655561] Checking for missing data entries (NaN)\n", - " 88955 [pre_processing.py:interpolate_missing_data():41] [1655500] Checking for missing data entries (NaN)\n", - " 88973 [cnmf.py: fit():481] [1655501] preprocessing ...\n", - " 88973 [cnmf.py: fit():481] [1655563] preprocessing ...\n", - " 88972 [pre_processing.py:interpolate_missing_data():41] [1655516] Checking for missing data entries (NaN)\n", - " 88988 [cnmf.py: fit():478] [1655518] using 5000 block_size_temp\n", - " 88899 [pre_processing.py:interpolate_missing_data():41] [1655533] Checking for missing data entries (NaN)\n", - " 88988 [cnmf.py: fit():481] [1655491] preprocessing ...\n", - " 88989 [pre_processing.py:interpolate_missing_data():41] [1655485] Checking for missing data entries (NaN)\n", - " 88988 [pre_processing.py:interpolate_missing_data():41] [1655509] Checking for missing data entries (NaN)\n", - " 89019 [cnmf.py: fit():481] [1655478] preprocessing ...\n", - " 89020 [pre_processing.py:interpolate_missing_data():41] [1655479] Checking for missing data entries (NaN)\n", - " 88989 [cnmf.py: fit():481] [1655494] preprocessing ...\n", - " 89035 [pre_processing.py:interpolate_missing_data():41] [1655527] Checking for missing data entries (NaN)\n", - " 88954 [cnmf.py: fit():481] [1655528] preprocessing ...\n", - " 89051 [cnmf.py: fit():481] [1655524] preprocessing ...\n", - " 88971 [cnmf.py: fit():481] [1655506] preprocessing ...\n", - " 89003 [pre_processing.py:interpolate_missing_data():41] [1655522] Checking for missing data entries (NaN)\n", - " 89052 [pre_processing.py:interpolate_missing_data():41] [1655502] Checking for missing data entries (NaN)\n", - " 89052 [cnmf.py: fit():481] [1655470] preprocessing ...\n", - " 89083 [cnmf.py: fit():481] [1655503] preprocessing ...\n", - " 89084 [pre_processing.py:interpolate_missing_data():41] [1655486] Checking for missing data entries (NaN)\n", - " 89099 [pre_processing.py:interpolate_missing_data():41] [1655517] Checking for missing data entries (NaN)\n", - " 89100 [pre_processing.py:interpolate_missing_data():41] [1655564] Checking for missing data entries (NaN)\n", - " 89115 [pre_processing.py:interpolate_missing_data():41] [1655534] Checking for missing data entries (NaN)\n", - " 89100 [pre_processing.py:interpolate_missing_data():41] [1655474] Checking for missing data entries (NaN)\n", - " 89124 [cnmf.py: fit():481] [1655487] preprocessing ...\n", - " 89125 [pre_processing.py:interpolate_missing_data():41] [1655529] Checking for missing data entries (NaN)\n", - " 89125 [cnmf.py: fit():481] [1655493] preprocessing ...\n", - " 89124 [pre_processing.py:interpolate_missing_data():41] [1655521] Checking for missing data entries (NaN)\n", - " 89137 [cnmf.py: fit():481] [1655482] preprocessing ...\n", - " 89137 [cnmf.py: fit():481] [1655562] preprocessing ...\n", - " 89151 [pre_processing.py:interpolate_missing_data():41] [1655469] Checking for missing data entries (NaN)\n", - " 89137 [pre_processing.py:interpolate_missing_data():41] [1655507] Checking for missing data entries (NaN)\n", - " 89136 [pre_processing.py:interpolate_missing_data():41] [1655475] Checking for missing data entries (NaN)\n", - " 89138 [cnmf.py: fit():481] [1655548] preprocessing ...\n", - " 89136 [pre_processing.py:interpolate_missing_data():41] [1655488] Checking for missing data entries (NaN)\n", - " 89162 [cnmf.py: fit():481] [1655526] preprocessing ...\n", - " 89162 [pre_processing.py:interpolate_missing_data():41] [1655556] Checking for missing data entries (NaN)\n", - " 89180 [cnmf.py: fit():481] [1655480] preprocessing ...\n", - " 89162 [cnmf.py: fit():481] [1655495] preprocessing ...\n", - " 89179 [pre_processing.py:interpolate_missing_data():41] [1655549] Checking for missing data entries (NaN)\n", - " 89161 [cnmf.py: fit():481] [1655477] preprocessing ...\n", - " 89162 [pre_processing.py:interpolate_missing_data():41] [1655473] Checking for missing data entries (NaN)\n", - " 89161 [cnmf.py: fit():481] [1655544] preprocessing ...\n", - " 89161 [cnmf.py: fit():481] [1655510] preprocessing ...\n", - " 89201 [cnmf.py: fit():481] [1655550] preprocessing ...\n", - " 89201 [pre_processing.py:interpolate_missing_data():41] [1655481] Checking for missing data entries (NaN)\n", - " 89201 [cnmf.py: fit():481] [1655539] preprocessing ...\n", - " 89201 [cnmf.py: fit():481] [1655538] preprocessing ...\n", - " 89219 [pre_processing.py:interpolate_missing_data():41] [1655542] Checking for missing data entries (NaN)\n", - " 89220 [cnmf.py: fit():481] [1655484] preprocessing ...\n", - " 89200 [pre_processing.py:interpolate_missing_data():41] [1655523] Checking for missing data entries (NaN)\n", - " 89228 [pre_processing.py:interpolate_missing_data():41] [1655483] Checking for missing data entries (NaN)\n", - " 89228 [pre_processing.py:interpolate_missing_data():41] [1655508] Checking for missing data entries (NaN)\n", - " 89229 [cnmf.py: fit():481] [1655505] preprocessing ...\n", - " 89228 [cnmf.py: fit():481] [1655498] preprocessing ...\n", - " 89315 [pre_processing.py:interpolate_missing_data():41] [1655545] Checking for missing data entries (NaN)\n", - " 89299 [pre_processing.py:interpolate_missing_data():41] [1655504] Checking for missing data entries (NaN)\n", - " 89460 [pre_processing.py:interpolate_missing_data():41] [1655557] Checking for missing data entries (NaN)\n", - " 89460 [pre_processing.py:interpolate_missing_data():41] [1655501] Checking for missing data entries (NaN)\n", - " 89508 [pre_processing.py:interpolate_missing_data():41] [1655491] Checking for missing data entries (NaN)\n", - " 89507 [cnmf.py: fit():481] [1655518] preprocessing ...\n", - " 89459 [pre_processing.py:interpolate_missing_data():41] [1655563] Checking for missing data entries (NaN)\n", - " 89527 [pre_processing.py:interpolate_missing_data():41] [1655478] Checking for missing data entries (NaN)\n", - " 89563 [pre_processing.py:interpolate_missing_data():41] [1655494] Checking for missing data entries (NaN)\n", - " 89579 [pre_processing.py:interpolate_missing_data():41] [1655528] Checking for missing data entries (NaN)\n", - " 89624 [pre_processing.py:interpolate_missing_data():41] [1655503] Checking for missing data entries (NaN)\n", - " 89623 [pre_processing.py:interpolate_missing_data():41] [1655470] Checking for missing data entries (NaN)\n", - " 89592 [pre_processing.py:interpolate_missing_data():41] [1655506] Checking for missing data entries (NaN)\n", - " 89592 [pre_processing.py:interpolate_missing_data():41] [1655524] Checking for missing data entries (NaN)\n", - " 89755 [pre_processing.py:interpolate_missing_data():41] [1655493] Checking for missing data entries (NaN)\n", - " 89724 [pre_processing.py:interpolate_missing_data():41] [1655487] Checking for missing data entries (NaN)\n", - " 89799 [pre_processing.py:interpolate_missing_data():41] [1655548] Checking for missing data entries (NaN)\n", - " 89760 [pre_processing.py:interpolate_missing_data():41] [1655482] Checking for missing data entries (NaN)\n", - " 89760 [pre_processing.py:interpolate_missing_data():41] [1655562] Checking for missing data entries (NaN)\n", - " 89881 [pre_processing.py:interpolate_missing_data():41] [1655510] Checking for missing data entries (NaN)\n", - " 89880 [pre_processing.py:interpolate_missing_data():41] [1655526] Checking for missing data entries (NaN)\n", - " 89880 [pre_processing.py:interpolate_missing_data():41] [1655477] Checking for missing data entries (NaN)\n", - " 89880 [pre_processing.py:interpolate_missing_data():41] [1655495] Checking for missing data entries (NaN)\n", - " 89881 [pre_processing.py:interpolate_missing_data():41] [1655544] Checking for missing data entries (NaN)\n", - " 89879 [pre_processing.py:interpolate_missing_data():41] [1655480] Checking for missing data entries (NaN)\n", - " 89912 [pre_processing.py:interpolate_missing_data():41] [1655538] Checking for missing data entries (NaN)\n", - " 89911 [pre_processing.py:interpolate_missing_data():41] [1655550] Checking for missing data entries (NaN)\n", - " 89912 [pre_processing.py:interpolate_missing_data():41] [1655484] Checking for missing data entries (NaN)\n", - " 89896 [pre_processing.py:interpolate_missing_data():41] [1655539] Checking for missing data entries (NaN)\n", - " 89976 [pre_processing.py:interpolate_missing_data():41] [1655498] Checking for missing data entries (NaN)\n", - " 89975 [pre_processing.py:interpolate_missing_data():41] [1655505] Checking for missing data entries (NaN)\n", - " 90095 [pre_processing.py:interpolate_missing_data():41] [1655518] Checking for missing data entries (NaN)\n", - " 90698 [params.py: set():1006] [1660129] Changing key rf in group patch from 20 to None\n", - " 90983 [params.py: set():1006] [1660129] Changing key stride in group patch from 10 to None\n", - " 91245 [params.py: set():1006] [1660361] Changing key rf in group patch from 20 to None\n", - " 91291 [params.py: set():1006] [1660129] Changing key nb in group init from 2 to 1\n", - " 91519 [params.py: set():1006] [1660361] Changing key stride in group patch from 10 to None\n", - " 91539 [params.py: set():1006] [1660129] Changing key nb in group temporal from 2 to 1\n", - " 91867 [params.py: set():1006] [1660361] Changing key nb in group init from 2 to 1\n", - " 91871 [params.py: set():1006] [1660129] Changing key nb in group spatial from 2 to 1\n", - " 92135 [params.py: set():1006] [1660361] Changing key nb in group temporal from 2 to 1\n", - " 92136 [params.py: set():1006] [1660129] Changing key p in group preprocess from 1 to 0\n", - " 92234 [params.py: set():1006] [1660550] Changing key rf in group patch from 20 to None\n", - " 92465 [params.py: set():1006] [1660129] Changing key p in group temporal from 1 to 0\n", - " 92466 [params.py: set():1006] [1660361] Changing key nb in group spatial from 2 to 1\n", - " 92591 [params.py: set():1006] [1660550] Changing key stride in group patch from 10 to None\n", - " 92707 [cnmf.py: fit():484] [1655514] initializing ...\n", - " 92747 [params.py: set():1006] [1660361] Changing key p in group preprocess from 1 to 0\n", - " 92754 [cnmf.py: fit():436] [1660129] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 92851 [params.py: set():1006] [1660550] Changing key nb in group init from 2 to 1\n", - " 93066 [initialization.py:initialize_components():292] [1655514] Variance Normalization\n", - " 93079 [params.py: set():1006] [1660361] Changing key p in group temporal from 1 to 0\n", - " 93080 [cnmf.py: fit():449] [1660129] (8000, 41, 41)\n", - " 93155 [params.py: set():1006] [1660550] Changing key nb in group temporal from 2 to 1\n", - " 93375 [cnmf.py: fit():466] [1660129] Using 1 processes\n", - " 93376 [cnmf.py: fit():436] [1660361] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 93467 [params.py: set():1006] [1660550] Changing key nb in group spatial from 2 to 1\n", - " 93663 [cnmf.py: fit():449] [1660361] (8000, 41, 41)\n", - " 93664 [cnmf.py: fit():477] [1660129] using 400 pixels per process\n", - " 93771 [params.py: set():1006] [1660550] Changing key p in group preprocess from 1 to 0\n", - " 93900 [cnmf.py: fit():478] [1660129] using 5000 block_size_temp\n", - " 93907 [cnmf.py: fit():466] [1660361] Using 1 processes\n", - " 93987 [params.py: set():1006] [1660550] Changing key p in group temporal from 1 to 0\n", - " 94056 [map_reduce.py: cnmf_patches():78] [1655514] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97620_118140START\n", - " 93751 [cnmf.py: fit():484] [1655530] initializing ...\n", - " 94191 [cnmf.py: fit():477] [1660361] using 400 pixels per process\n", - " 94195 [cnmf.py: fit():481] [1660129] preprocessing ...\n", - " 94210 [cnmf.py: fit():484] [1655497] initializing ...\n", - " 94250 [cnmf.py: fit():484] [1655531] initializing ...\n", - " 94299 [cnmf.py: fit():436] [1660550] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 94214 [cnmf.py: fit():484] [1655489] initializing ...\n", - " 94346 [cnmf.py: fit():484] [1655535] initializing ...\n", - " 94348 [cnmf.py: fit():484] [1655559] initializing ...\n", - " 94355 [map_reduce.py: cnmf_patches():80] [1655514] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97620_118140Read file\n", - " 94314 [cnmf.py: fit():484] [1655532] initializing ...\n", - " 94360 [cnmf.py: fit():484] [1655537] initializing ...\n", - " 94397 [cnmf.py: fit():484] [1655519] initializing ...\n", - " 94399 [cnmf.py: fit():484] [1655511] initializing ...\n", - " 94405 [cnmf.py: fit():484] [1655499] initializing ...\n", - " 94405 [initialization.py:initialize_components():292] [1655530] Variance Normalization\n", - " 94420 [cnmf.py: fit():484] [1655554] initializing ...\n", - " 94435 [pre_processing.py:interpolate_missing_data():41] [1660129] Checking for missing data entries (NaN)\n", - " 94441 [cnmf.py: fit():484] [1655553] initializing ...\n", - " 94441 [cnmf.py: fit():478] [1660361] using 5000 block_size_temp\n", - " 94451 [cnmf.py: fit():484] [1655552] initializing ...\n", - " 94476 [cnmf.py: fit():484] [1655543] initializing ...\n", - " 94480 [initialization.py:initialize_components():292] [1655497] Variance Normalization\n", - " 94492 [cnmf.py: fit():484] [1655490] initializing ...\n", - " 94493 [cnmf.py: fit():484] [1655513] initializing ...\n", - " 94167 [cnmf.py: fit():484] [1655520] initializing ...\n", - " 94424 [cnmf.py: fit():484] [1655474] initializing ...\n", - " 94531 [cnmf.py: fit():484] [1655472] initializing ...\n", - " 94533 [cnmf.py: fit():484] [1655496] initializing ...\n", - " 94459 [cnmf.py: fit():484] [1655540] initializing ...\n", - " 94523 [cnmf.py: fit():484] [1655541] initializing ...\n", - " 94542 [cnmf.py: fit():484] [1655486] initializing ...\n", - " 94480 [cnmf.py: fit():484] [1655547] initializing ...\n", - " 94463 [cnmf.py: fit():484] [1655546] initializing ...\n", - " 94555 [initialization.py:initialize_components():292] [1655531] Variance Normalization\n", - " 94467 [cnmf.py: fit():484] [1655479] initializing ...\n", - " 94556 [cnmf.py: fit():449] [1660550] (8000, 41, 41)\n", - " 94439 [cnmf.py: fit():484] [1655485] initializing ...\n", - " 94445 [cnmf.py: fit():484] [1655512] initializing ...\n", - " 94564 [cnmf.py: fit():484] [1655476] initializing ...\n", - " 94450 [cnmf.py: fit():484] [1655561] initializing ...\n", - " 94365 [cnmf.py: fit():484] [1655522] initializing ...\n", - " 94574 [cnmf.py: fit():484] [1655492] initializing ...\n", - " 94574 [initialization.py:initialize_components():292] [1655489] Variance Normalization\n", - " 94608 [initialization.py:initialize_components():292] [1655559] Variance Normalization\n", - " 94608 [initialization.py:initialize_components():292] [1655535] Variance Normalization\n", - " 94611 [cnmf.py: fit():484] [1655516] initializing ...\n", - " 94614 [cnmf.py: fit():484] [1655560] initializing ...\n", - " 94629 [cnmf.py: fit():484] [1655536] initializing ...\n", - " 94630 [initialization.py:initialize_components():292] [1655532] Variance Normalization\n", - " 94631 [initialization.py:initialize_components():292] [1655537] Variance Normalization\n", - " 94563 [cnmf.py: fit():484] [1655488] initializing ...\n", - " 94632 [cnmf.py: fit():484] [1655500] initializing ...\n", - " 94606 [cnmf.py: fit():484] [1655517] initializing ...\n", - " 94634 [cnmf.py: fit():484] [1655509] initializing ...\n", - " 94642 [cnmf.py: fit():484] [1655521] initializing ...\n", - " 94645 [cnmf.py: fit():484] [1655502] initializing ...\n", - " 94652 [cnmf.py: fit():484] [1655533] initializing ...\n", - " 94654 [cnmf.py: fit():484] [1655551] initializing ...\n", - " 94603 [cnmf.py: fit():484] [1655556] initializing ...\n", - " 94655 [initialization.py:initialize_components():292] [1655519] Variance Normalization\n", - " 94659 [initialization.py:initialize_components():292] [1655511] Variance Normalization\n", - " 94662 [cnmf.py: fit():484] [1655475] initializing ...\n", - " 94663 [initialization.py:initialize_components():292] [1655499] Variance Normalization\n", - " 94676 [initialization.py:initialize_components():292] [1655554] Variance Normalization\n", - " 94692 [initialization.py:initialize_components():292] [1655553] Variance Normalization\n", - " 94692 [cnmf.py: fit():481] [1660361] preprocessing ...\n", - " 94696 [initialization.py:initialize_components():292] [1655552] Variance Normalization\n", - " 94699 [cnmf.py: fit():484] [1655473] initializing ...\n", - " 94715 [initialization.py:initialize_components():292] [1655543] Variance Normalization\n", - " 94696 [cnmf.py: fit():484] [1655508] initializing ...\n", - " 94723 [cnmf.py: fit():484] [1655527] initializing ...\n", - " 94727 [initialization.py:initialize_components():292] [1655490] Variance Normalization\n", - " 94728 [initialization.py:initialize_components():292] [1655513] Variance Normalization\n", - " 94730 [cnmf.py: fit():484] [1655549] initializing ...\n", - " 94731 [initialization.py:initialize_components():292] [1655520] Variance Normalization\n", - " 94733 [cnmf.py: fit():484] [1655529] initializing ...\n", - " 94650 [cnmf.py: fit():484] [1655503] initializing ...\n", - " 94736 [cnmf.py: fit():484] [1655523] initializing ...\n", - " 94737 [initialization.py:initialize_components():292] [1655474] Variance Normalization\n", - " 94737 [initialization.py:initialize_components():292] [1655472] Variance Normalization\n", - " 94738 [initialization.py:initialize_components():292] [1655496] Variance Normalization\n", - " 94419 [cnmf.py: fit():484] [1655525] initializing ...\n", - " 94739 [initialization.py:initialize_components():292] [1655540] Variance Normalization\n", - " 94740 [initialization.py:initialize_components():292] [1655541] Variance Normalization\n", - " 94740 [initialization.py:initialize_components():292] [1655486] Variance Normalization\n", - " 94742 [cnmf.py: fit():484] [1655469] initializing ...\n", - " 94743 [initialization.py:initialize_components():292] [1655547] Variance Normalization\n", - " 94743 [initialization.py:initialize_components():292] [1655546] Variance Normalization\n", - " 94753 [cnmf.py: fit():484] [1655507] initializing ...\n", - " 94753 [initialization.py:initialize_components():292] [1655479] Variance Normalization\n", - " 94763 [cnmf.py: fit():466] [1660550] Using 1 processes\n", - " 94764 [initialization.py:initialize_components():292] [1655512] Variance Normalization\n", - " 94765 [initialization.py:initialize_components():292] [1655476] Variance Normalization\n", - " 94765 [initialization.py:initialize_components():292] [1655485] Variance Normalization\n", - " 94766 [initialization.py:initialize_components():292] [1655561] Variance Normalization\n", - " 94766 [initialization.py:initialize_components():292] [1655522] Variance Normalization\n", - " 94769 [cnmf.py: fit():484] [1655545] initializing ...\n", - " 94770 [cnmf.py: fit():484] [1655534] initializing ...\n", - " 94771 [initialization.py:initialize_components():292] [1655492] Variance Normalization\n", - " 94783 [cnmf.py: fit():484] [1655564] initializing ...\n", - " 94622 [cnmf.py: fit():484] [1655491] initializing ...\n", - " 94792 [cnmf.py: fit():484] [1655481] initializing ...\n", - " 94794 [cnmf.py: fit():484] [1655542] initializing ...\n", - " 94796 [cnmf.py: fit():484] [1655548] initializing ...\n", - " 94708 [cnmf.py: fit():484] [1655494] initializing ...\n", - " 94808 [initialization.py:initialize_components():292] [1655516] Variance Normalization\n", - " 94823 [initialization.py:initialize_components():292] [1655560] Variance Normalization\n", - " 94826 [cnmf.py: fit():484] [1655495] initializing ...\n", - " 94827 [initialization.py:initialize_components():292] [1655536] Variance Normalization\n", - " 94840 [initialization.py:initialize_components():292] [1655488] Variance Normalization\n", - " 94850 [cnmf.py: fit():484] [1655524] initializing ...\n", - " 94851 [initialization.py:initialize_components():292] [1655517] Variance Normalization\n", - " 94851 [initialization.py:initialize_components():292] [1655500] Variance Normalization\n", - " 94852 [initialization.py:initialize_components():292] [1655509] Variance Normalization\n", - " 94852 [initialization.py:initialize_components():292] [1655521] Variance Normalization\n", - " 94855 [cnmf.py: fit():484] [1655544] initializing ...\n", - " 94861 [cnmf.py: fit():484] [1655501] initializing ...\n", - " 94862 [cnmf.py: fit():484] [1655483] initializing ...\n", - " 94863 [initialization.py:initialize_components():292] [1655502] Variance Normalization\n", - 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" 94813 [cnmf.py: fit():484] [1655498] initializing ...\n", - " 94928 [initialization.py:initialize_components():292] [1655473] Variance Normalization\n", - " 94937 [cnmf.py: fit():484] [1655563] initializing ...\n", - " 94939 [cnmf.py: fit():484] [1655487] initializing ...\n", - " 94953 [initialization.py:initialize_components():292] [1655508] Variance Normalization\n", - " 94961 [cnmf.py: fit():484] [1655477] initializing ...\n", - " 95029 [cnmf.py: fit():484] [1655528] initializing ...\n", - " 95028 [cnmf.py: fit():484] [1655470] initializing ...\n", - " 94963 [cnmf.py: fit():484] [1655506] initializing ...\n", - " 94984 [initialization.py:initialize_components():292] [1655549] Variance Normalization\n", - " 94985 [initialization.py:initialize_components():292] [1655523] Variance Normalization\n", - " 94991 [initialization.py:initialize_components():292] [1655503] Variance Normalization\n", - " 95007 [initialization.py:initialize_components():292] [1655529] Variance Normalization\n", - " 95008 [initialization.py:initialize_components():292] [1655525] Variance Normalization\n", - " 95017 [cnmf.py: fit():484] [1655480] initializing ...\n", - " 95035 [initialization.py:initialize_components():292] [1655469] Variance Normalization\n", - " 95114 [initialization.py:initialize_components():292] [1655542] Variance Normalization\n", - " 95114 [initialization.py:initialize_components():292] [1655494] Variance Normalization\n", - " 95051 [cnmf.py: fit():477] [1660550] using 400 pixels per process\n", - " 95052 [initialization.py:initialize_components():292] [1655507] Variance Normalization\n", - " 95079 [initialization.py:initialize_components():292] [1655534] Variance Normalization\n", - " 95080 [initialization.py:initialize_components():292] [1655545] Variance Normalization\n", - " 94952 [cnmf.py: fit():484] [1655518] initializing ...\n", - " 95095 [initialization.py:initialize_components():292] [1655564] Variance Normalization\n", - " 95104 [map_reduce.py: cnmf_patches():78] [1655530] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 112980_133500START\n", - " 95104 [initialization.py:initialize_components():292] [1655491] Variance Normalization\n", - " 95105 [initialization.py:initialize_components():292] [1655481] Variance Normalization\n", - " 95112 [initialization.py:initialize_components():292] [1655548] Variance Normalization\n", - " 95113 [cnmf.py: fit():484] [1655526] initializing ...\n", - " 95263 [initialization.py:initialize_components():292] [1655550] Variance Normalization\n", - " 94859 [cnmf.py: fit():484] [1655484] initializing ...\n", - " 95256 [initialization.py:initialize_components():292] [1655482] Variance Normalization\n", - " 95141 [cnmf.py: fit():484] [1655505] initializing ...\n", - " 95171 [initialization.py:initialize_components():292] [1655495] Variance Normalization\n", - " 95180 [cnmf.py: fit():484] [1655538] initializing ...\n", - " 94779 [cnmf.py: fit():484] [1655510] initializing ...\n", - " 95196 [initialization.py:initialize_components():292] [1655524] Variance Normalization\n", - " 95227 [initialization.py:initialize_components():292] [1655544] Variance Normalization\n", - " 95236 [initialization.py:initialize_components():292] [1655501] Variance Normalization\n", - " 95236 [initialization.py:initialize_components():292] [1655483] Variance Normalization\n", - " 95268 [initialization.py:initialize_components():292] [1655562] Variance Normalization\n", - " 95273 [initialization.py:initialize_components():292] [1655478] Variance Normalization\n", - " 95274 [initialization.py:initialize_components():292] [1655504] Variance Normalization\n", - " 95274 [initialization.py:initialize_components():292] [1655539] Variance Normalization\n", - " 95278 [initialization.py:initialize_components():292] [1655493] Variance Normalization\n", - " 95315 [initialization.py:initialize_components():292] [1655498] Variance Normalization\n", - " 95319 [initialization.py:initialize_components():292] [1655487] Variance Normalization\n", - " 95319 [initialization.py:initialize_components():292] [1655563] Variance Normalization\n", - " 95328 [initialization.py:initialize_components():292] [1655477] Variance Normalization\n", - " 95335 [initialization.py:initialize_components():292] [1655557] Variance Normalization\n", - " 95336 [initialization.py:initialize_components():292] [1655506] Variance Normalization\n", - " 95420 [cnmf.py: fit():478] [1660550] using 5000 block_size_temp\n", - " 95387 [initialization.py:initialize_components():292] [1655470] Variance Normalization\n", - " 95388 [initialization.py:initialize_components():292] [1655480] Variance Normalization\n", - " 95408 [initialization.py:initialize_components():292] [1655528] Variance Normalization\n", - " 95477 [initialization.py:initialize_components():292] [1655526] Variance Normalization\n", - " 95447 [initialization.py:initialize_components():292] [1655518] Variance Normalization\n", - " 95463 [map_reduce.py: cnmf_patches():80] [1655530] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 112980_133500Read file\n", - " 95506 [initialization.py:initialize_components():292] [1655505] Variance Normalization\n", - " 95516 [initialization.py:initialize_components():292] [1655538] Variance Normalization\n", - 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" 95712 [map_reduce.py: cnmf_patches():78] [1655554] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235860_256380START\n", - " 95780 [cnmf.py: fit():481] [1660550] preprocessing ...\n", - " 95814 [map_reduce.py: cnmf_patches():100] [1655514] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97620_118140file loaded\n", - " 95901 [map_reduce.py: cnmf_patches():80] [1655552] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 159060_179580Read file\n", - " 95902 [map_reduce.py: cnmf_patches():80] [1655490] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174420_194940Read file\n", - " 95902 [map_reduce.py: cnmf_patches():80] [1655520] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143700_164220Read file\n", - " 95902 [map_reduce.py: cnmf_patches():80] [1655497] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128340_148860Read file\n", - " 95904 [map_reduce.py: cnmf_patches():80] [1655472] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220500_241020Read file\n", - " 95904 [map_reduce.py: cnmf_patches():80] [1655499] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205140_225660Read file\n", - " 95904 [map_reduce.py: cnmf_patches():80] [1655553] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189780_210300Read file\n", - " 95905 [map_reduce.py: cnmf_patches():80] [1655554] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235860_256380Read file\n", - " 95935 [map_reduce.py: cnmf_patches():78] [1655475] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236884_256892START\n", - " 95936 [pre_processing.py:interpolate_missing_data():41] [1660550] Checking for missing data entries (NaN)\n", - " 96044 [map_reduce.py: cnmf_patches():78] [1655511] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5490_26010START\n", - " 96045 [map_reduce.py: cnmf_patches():78] [1655559] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20850_41370START\n", - " 96046 [map_reduce.py: cnmf_patches():78] [1655513] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 36210_56730START\n", - " 96047 [map_reduce.py: cnmf_patches():78] [1655532] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51570_72090START\n", - " 96048 [map_reduce.py: cnmf_patches():78] [1655496] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66930_87450START\n", - " 96049 [map_reduce.py: cnmf_patches():78] [1655537] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82290_102810START\n", - " 96050 [map_reduce.py: cnmf_patches():78] [1655500] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97650_118170START\n", - " 96051 [map_reduce.py: cnmf_patches():78] [1655531] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 113010_133530START\n", - " 96051 [map_reduce.py: cnmf_patches():78] [1655533] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128370_148890START\n", - " 96052 [map_reduce.py: cnmf_patches():78] [1655543] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143730_164250START\n", - " 96053 [map_reduce.py: cnmf_patches():78] [1655535] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 159090_179610START\n", - " 96054 [map_reduce.py: cnmf_patches():78] [1655527] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174450_194970START\n", - " 96054 [map_reduce.py: cnmf_patches():78] [1655534] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189810_210330START\n", - " 96056 [map_reduce.py: cnmf_patches():78] [1655486] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205170_225690START\n", - " 96056 [map_reduce.py: cnmf_patches():78] [1655489] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220530_241050START\n", - " 96058 [map_reduce.py: cnmf_patches():78] [1655519] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235890_256410START\n", - " 96059 [map_reduce.py: cnmf_patches():78] [1655492] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236914_256922START\n", - " 96059 [map_reduce.py: cnmf_patches():78] [1655541] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5520_26040START\n", - " 96060 [map_reduce.py: cnmf_patches():78] [1655536] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20880_41400START\n", - " 96061 [map_reduce.py: cnmf_patches():78] [1655516] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 36240_56760START\n", - " 96062 [map_reduce.py: cnmf_patches():78] [1655561] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51600_72120START\n", - " 96062 [map_reduce.py: cnmf_patches():78] [1655522] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66960_87480START\n", - " 96063 [map_reduce.py: cnmf_patches():78] [1655512] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82320_102840START\n", - " 96064 [map_reduce.py: cnmf_patches():78] [1655525] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97680_118200START\n", - " 96065 [map_reduce.py: cnmf_patches():78] [1655551] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 113040_133560START\n", - " 96065 [map_reduce.py: cnmf_patches():78] [1655488] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128400_148920START\n", - " 96066 [map_reduce.py: cnmf_patches():78] [1655507] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143760_164280START\n", - " 96067 [map_reduce.py: cnmf_patches():78] [1655540] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 159120_179640START\n", - " 96069 [map_reduce.py: cnmf_patches():78] [1655476] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174480_195000START\n", - " 96069 [map_reduce.py: cnmf_patches():78] [1655474] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189840_210360START\n", - " 96070 [map_reduce.py: cnmf_patches():78] [1655564] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205200_225720START\n", - " 96070 [map_reduce.py: cnmf_patches():78] [1655469] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220560_241080START\n", - " 96071 [map_reduce.py: cnmf_patches():78] [1655524] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235920_256440START\n", - " 96074 [map_reduce.py: cnmf_patches():78] [1655544] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236944_256952START\n", - " 96092 [map_reduce.py: cnmf_patches():80] [1655475] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236884_256892Read file\n", - " 96095 [map_reduce.py: cnmf_patches():78] [1655485] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5550_26070START\n", - " 96096 [map_reduce.py: cnmf_patches():78] [1655502] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20910_41430START\n", - " 96097 [map_reduce.py: cnmf_patches():78] [1655560] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 36270_56790START\n", - " 96098 [map_reduce.py: cnmf_patches():78] [1655547] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51630_72150START\n", - " 96098 [map_reduce.py: cnmf_patches():78] [1655529] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 66990_87510START\n", - " 96099 [map_reduce.py: cnmf_patches():78] [1655556] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82350_102870START\n", - " 96100 [map_reduce.py: cnmf_patches():78] [1655521] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97710_118230START\n", - " 96101 [map_reduce.py: cnmf_patches():78] [1655479] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 113070_133590START\n", - " 96102 [map_reduce.py: cnmf_patches():78] [1655549] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128430_148950START\n", - " 96102 [map_reduce.py: cnmf_patches():78] [1655523] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143790_164310START\n", - " 96103 [map_reduce.py: cnmf_patches():78] [1655545] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 159150_179670START\n", - " 96104 [map_reduce.py: cnmf_patches():78] [1655503] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174510_195030START\n", - " 96105 [map_reduce.py: cnmf_patches():78] [1655546] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189870_210390START\n", - " 96106 [map_reduce.py: cnmf_patches():78] [1655509] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205230_225750START\n", - " 96107 [map_reduce.py: cnmf_patches():78] [1655548] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220590_241110START\n", - " 96107 [map_reduce.py: cnmf_patches():78] [1655481] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235950_256470START\n", - " 96108 [map_reduce.py: cnmf_patches():78] [1655557] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236974_256982START\n", - " 96109 [map_reduce.py: cnmf_patches():78] [1655517] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5580_26100START\n", - " 96110 [map_reduce.py: cnmf_patches():78] [1655483] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20940_41460START\n", - " 96111 [map_reduce.py: cnmf_patches():78] [1655501] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 36300_56820START\n", - " 96112 [map_reduce.py: cnmf_patches():78] [1655494] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51660_72180START\n", - " 96113 [map_reduce.py: cnmf_patches():78] [1655528] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 67020_87540START\n", - " 96113 [map_reduce.py: cnmf_patches():78] [1655510] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82380_102900START\n", - " 96114 [map_reduce.py: cnmf_patches():78] [1655550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97740_118260START\n", - " 96115 [map_reduce.py: cnmf_patches():78] [1655484] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 113100_133620START\n", - " 96128 [map_reduce.py: cnmf_patches():78] [1655477] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 5582_26101START\n", - " 96127 [map_reduce.py: cnmf_patches():78] [1655508] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 237004_257012START\n", - " 96126 [map_reduce.py: cnmf_patches():78] [1655487] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235980_256500START\n", - " 96125 [map_reduce.py: cnmf_patches():78] [1655473] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220620_241140START\n", - " 96124 [map_reduce.py: cnmf_patches():78] [1655562] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205260_225780START\n", - " 96123 [map_reduce.py: cnmf_patches():78] [1655478] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189900_210420START\n", - " 96123 [map_reduce.py: cnmf_patches():78] [1655482] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174540_195060START\n", - " 96122 [map_reduce.py: cnmf_patches():78] [1655542] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 159180_179700START\n", - " 96121 [map_reduce.py: cnmf_patches():78] [1655470] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143820_164340START\n", - " 96117 [map_reduce.py: cnmf_patches():78] [1655480] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128460_148980START\n", - " 96129 [map_reduce.py: cnmf_patches():78] [1655495] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20942_41461START\n", - " 96130 [map_reduce.py: cnmf_patches():78] [1655504] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 36302_56821START\n", - " 96131 [map_reduce.py: cnmf_patches():78] [1655539] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 51662_72181START\n", - " 96131 [map_reduce.py: cnmf_patches():78] [1655538] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 67022_87541START\n", - " 96141 [map_reduce.py: cnmf_patches():100] [1655530] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 112980_133500file loaded\n", - " 96134 [map_reduce.py: cnmf_patches():78] [1655498] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 97742_118261START\n", - " 96133 [map_reduce.py: cnmf_patches():78] [1655505] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82382_102901START\n", - " 96152 [map_reduce.py: cnmf_patches():78] [1655506] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 113102_133621START\n", - " 96153 [map_reduce.py: cnmf_patches():78] [1655563] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 128462_148981START\n", - " 96154 [map_reduce.py: cnmf_patches():78] [1655491] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143822_164341START\n", - " 96154 [map_reduce.py: cnmf_patches():78] [1655493] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 159182_179701START\n", - " 96155 [map_reduce.py: cnmf_patches():78] [1655526] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 174542_195061START\n", - " 96158 [map_reduce.py: cnmf_patches():78] [1655518] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 189902_210421START\n", - " 96187 [map_reduce.py: cnmf_patches():80] [1655486] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205170_225690Read file\n", - " 96188 [map_reduce.py: cnmf_patches():80] [1655536] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 20880_41400Read file\n", - " 96188 [map_reduce.py: cnmf_patches():80] [1655469] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220560_241080Read file\n", - " 96188 [map_reduce.py: cnmf_patches():80] [1655512] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 82320_102840Read file\n", - 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" 97692 [map_reduce.py: cnmf_patches():100] [1655487] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235980_256500file loaded\n", - " 97692 [map_reduce.py: cnmf_patches():100] [1655524] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235920_256440file loaded\n", - " 97693 [map_reduce.py: cnmf_patches():100] [1655557] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236974_256982file loaded\n", - " 97696 [map_reduce.py: cnmf_patches():100] [1655562] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205260_225780file loaded\n", - " 97696 [map_reduce.py: cnmf_patches():100] [1655544] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 236944_256952file loaded\n", - " 97703 [map_reduce.py: cnmf_patches():100] [1655508] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 237004_257012file loaded\n", - " 97287 [map_reduce.py: cnmf_patches():100] [1655470] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 143820_164340file loaded\n", - " 99605 [cnmf.py: fit():484] [1660129] initializing ...\n", - " 99892 [initialization.py:initialize_components():292] [1660129] Variance Normalization\n", - " 99970 [cnmf.py: fit():484] [1660361] initializing ...\n", - " 100519 [initialization.py:initialize_components():292] [1660361] Variance Normalization\n", - " 101308 [cnmf.py: fit():484] [1660550] initializing ...\n", - " 101590 [initialization.py:initialize_components():292] [1660550] Variance Normalization\n", - " 111320 [map_reduce.py: cnmf_patches():78] [1660129] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205262_225781START\n", - " 112031 [params.py: set():1006] [1655508] Changing key rf in group patch from 20 to None\n", - " 112936 [params.py: set():1006] [1655541] Changing key rf in group patch from 20 to None\n", - " 112068 [params.py: set():1006] [1655536] Changing key rf in group patch from 20 to None\n", - " 112019 [params.py: set():1006] [1655486] Changing key rf in group patch from 20 to None\n", - " 112891 [params.py: set():1006] [1655547] Changing key rf in group patch from 20 to None\n", - " 113276 [params.py: set():1006] [1655533] Changing key rf in group patch from 20 to None\n", - " 113472 [map_reduce.py: cnmf_patches():80] [1660129] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205262_225781Read file\n", - " 113584 [params.py: set():1006] [1655535] Changing key rf in group patch from 20 to None\n", - " 113669 [params.py: set():1006] [1655546] Changing key rf in group patch from 20 to None\n", - " 113693 [params.py: set():1006] [1655502] Changing key rf in group patch from 20 to None\n", - " 113680 [params.py: set():1006] [1655536] Changing key stride in group patch from 10 to None\n", - " 111255 [params.py: set():1006] [1655519] Changing key rf in group patch from 20 to None\n", - " 113736 [params.py: set():1006] [1655486] Changing key stride in group patch from 10 to None\n", - " 113751 [params.py: set():1006] [1655541] Changing key stride in group patch from 10 to None\n", - " 113770 [params.py: set():1006] [1655524] Changing key rf in group patch from 20 to None\n", - " 113775 [params.py: set():1006] [1655508] Changing key stride in group patch from 10 to None\n", - " 113767 [params.py: set():1006] [1655547] Changing key stride in group patch from 10 to None\n", - " 113800 [map_reduce.py: cnmf_patches():78] [1660550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235982_256501START\n", - " 113687 [params.py: set():1006] [1655532] Changing key rf in group patch from 20 to None\n", - " 113903 [params.py: set():1006] [1655537] Changing key rf in group patch from 20 to None\n", - " 113679 [params.py: set():1006] [1655522] Changing key rf in group patch from 20 to None\n", - " 113934 [params.py: set():1006] [1655524] Changing key stride in group patch from 10 to None\n", - " 113936 [params.py: set():1006] [1655508] Changing key nb in group init from 2 to 1\n", - " 113931 [params.py: set():1006] [1655486] Changing key nb in group init from 2 to 1\n", - " 113932 [params.py: set():1006] [1655541] Changing key nb in group init from 2 to 1\n", - " 113937 [params.py: set():1006] [1655547] Changing key nb in group init from 2 to 1\n", - " 113929 [params.py: set():1006] [1655519] Changing key stride in group patch from 10 to None\n", - " 113928 [params.py: set():1006] [1655502] Changing key stride in group patch from 10 to None\n", - " 113927 [params.py: set():1006] [1655536] Changing key nb in group init from 2 to 1\n", - " 113926 [params.py: set():1006] [1655535] Changing key stride in group patch from 10 to None\n", - " 113964 [params.py: set():1006] [1655494] Changing key rf in group patch from 20 to None\n", - " 113967 [params.py: set():1006] [1655521] Changing key rf in group patch from 20 to None\n", - " 113927 [params.py: set():1006] [1655546] Changing key stride in group patch from 10 to None\n", - " 113627 [params.py: set():1006] [1655525] Changing key rf in group patch from 20 to None\n", - " 114005 [params.py: set():1006] [1655514] Changing key rf in group patch from 20 to None\n", - " 113834 [params.py: set():1006] [1655523] Changing key rf in group patch from 20 to None\n", - " 113924 [params.py: set():1006] [1655533] Changing key stride in group patch from 10 to None\n", - " 113551 [map_reduce.py: cnmf_patches():78] [1663745] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220622_241141START\n", - " 113647 [params.py: set():1006] [1655543] Changing key rf in group patch from 20 to None\n", - " 114016 [params.py: set():1006] [1655497] Changing key rf in group patch from 20 to None\n", - " 113982 [params.py: set():1006] [1655476] Changing key rf in group patch from 20 to None\n", - " 113871 [params.py: set():1006] [1655482] Changing key rf in group patch from 20 to None\n", - " 114013 [params.py: set():1006] [1655511] Changing key rf in group patch from 20 to None\n", - " 114042 [map_reduce.py: cnmf_patches():78] [1660361] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 237006_257013START\n", - " 114072 [params.py: set():1006] [1655520] Changing key rf in group patch from 20 to None\n", - " 114061 [params.py: set():1006] [1655506] Changing key rf in group patch from 20 to None\n", - " 114041 [params.py: set():1006] [1655559] Changing key rf in group patch from 20 to None\n", - " 113439 [params.py: set():1006] [1655474] Changing key rf in group patch from 20 to None\n", - " 114081 [params.py: set():1006] [1655531] Changing key rf in group patch from 20 to None\n", - " 114064 [params.py: set():1006] [1655552] Changing key rf in group patch from 20 to None\n", - " 114020 [params.py: set():1006] [1655509] Changing key rf in group patch from 20 to None\n", - " 114100 [map_reduce.py: cnmf_patches():80] [1660550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235982_256501Read file\n", - " 114109 [params.py: set():1006] [1655548] Changing key rf in group patch from 20 to None\n", - " 113920 [params.py: set():1006] [1655526] Changing key rf in group patch from 20 to None\n", - " 114112 [params.py: set():1006] [1655481] Changing key rf in group patch from 20 to None\n", - " 114085 [params.py: set():1006] [1655493] Changing key rf in group patch from 20 to None\n", - " 114122 [params.py: set():1006] [1655484] Changing key rf in group patch from 20 to None\n", - " 114113 [params.py: set():1006] [1655553] Changing key rf in group patch from 20 to None\n", - " 114154 [params.py: set():1006] [1655538] Changing key rf in group patch from 20 to None\n", - " 114157 [params.py: set():1006] [1655470] Changing key rf in group patch from 20 to None\n", - " 114151 [params.py: set():1006] [1655549] Changing key rf in group patch from 20 to None\n", - " 114162 [params.py: set():1006] [1655527] Changing key rf in group patch from 20 to None\n", - " 114198 [params.py: set():1006] [1655475] Changing key rf in group patch from 20 to None\n", - " 114200 [params.py: set():1006] [1655503] Changing key rf in group patch from 20 to None\n", - " 114193 [params.py: set():1006] [1655472] Changing key rf in group patch from 20 to None\n", - " 113809 [params.py: set():1006] [1655488] Changing key rf in group patch from 20 to None\n", - " 114207 [params.py: set():1006] [1655517] Changing key rf in group patch from 20 to None\n", - " 114161 [params.py: set():1006] [1655518] Changing key rf in group patch from 20 to None\n", - " 114213 [params.py: set():1006] [1655510] Changing key rf in group patch from 20 to None\n", - " 114136 [params.py: set():1006] [1655477] Changing key rf in group patch from 20 to None\n", - " 114070 [params.py: set():1006] [1655554] Changing key rf in group patch from 20 to None\n", - " 114196 [params.py: set():1006] [1655534] Changing key rf in group patch from 20 to None\n", - " 114222 [params.py: set():1006] [1655499] Changing key rf in group patch from 20 to None\n", - " 114187 [params.py: set():1006] [1655544] Changing key rf in group patch from 20 to None\n", - " 114211 [params.py: set():1006] [1655489] Changing key rf in group patch from 20 to None\n", - " 114191 [params.py: set():1006] [1655557] Changing key rf in group patch from 20 to None\n", - " 114251 [params.py: set():1006] [1655563] Changing key rf in group patch from 20 to None\n", - " 114234 [params.py: set():1006] [1655487] Changing key rf in group patch from 20 to None\n", - " 114253 [params.py: set():1006] [1655561] Changing key rf in group patch from 20 to None\n", - " 114249 [params.py: set():1006] [1655500] Changing key rf in group patch from 20 to None\n", - " 114267 [params.py: set():1006] [1655478] Changing key rf in group patch from 20 to None\n", - " 114284 [params.py: set():1006] [1655529] Changing key rf in group patch from 20 to None\n", - " 114290 [params.py: set():1006] [1655551] Changing key rf in group patch from 20 to None\n", - " 114132 [params.py: set():1006] [1655504] Changing key rf in group patch from 20 to None\n", - " 114169 [params.py: set():1006] [1655490] Changing key rf in group patch from 20 to None\n", - " 114245 [params.py: set():1006] [1655556] Changing key rf in group patch from 20 to None\n", - " 114183 [params.py: set():1006] [1655479] Changing key rf in group patch from 20 to None\n", - " 114289 [params.py: set():1006] [1655491] Changing key rf in group patch from 20 to None\n", - " 114295 [params.py: set():1006] [1655550] Changing key rf in group patch from 20 to None\n", - " 114296 [params.py: set():1006] [1655498] Changing key rf in group patch from 20 to None\n", - " 114303 [params.py: set():1006] [1655480] Changing key rf in group patch from 20 to None\n", - " 114273 [params.py: set():1006] [1655483] Changing key rf in group patch from 20 to None\n", - " 114308 [params.py: set():1006] [1655495] Changing key rf in group patch from 20 to None\n", - " 114314 [params.py: set():1006] [1655532] Changing key stride in group patch from 10 to None\n", - " 114315 [params.py: set():1006] [1655537] Changing key stride in group patch from 10 to None\n", - " 114315 [params.py: set():1006] [1655522] Changing key stride in group patch from 10 to None\n", - " 114316 [params.py: set():1006] [1655508] Changing key nb in group temporal from 2 to 1\n", - " 114317 [params.py: set():1006] [1655541] Changing key nb in group temporal from 2 to 1\n", - " 114317 [params.py: set():1006] [1655486] Changing key nb in group temporal from 2 to 1\n", - " 114317 [params.py: set():1006] [1655547] Changing key nb in group temporal from 2 to 1\n", - " 114318 [params.py: set():1006] [1655519] Changing key nb in group init from 2 to 1\n", - " 114318 [params.py: set():1006] [1655502] Changing key nb in group init from 2 to 1\n", - " 114318 [params.py: set():1006] [1655536] Changing key nb in group temporal from 2 to 1\n", - " 114319 [params.py: set():1006] [1655546] Changing key nb in group init from 2 to 1\n", - " 114319 [params.py: set():1006] [1655535] Changing key nb in group init from 2 to 1\n", - " 114320 [params.py: set():1006] [1655521] Changing key stride in group patch from 10 to None\n", - " 114320 [params.py: set():1006] [1655524] Changing key nb in group init from 2 to 1\n", - " 114320 [params.py: set():1006] [1655494] Changing key stride in group patch from 10 to None\n", - " 114321 [params.py: set():1006] [1655525] Changing key stride in group patch from 10 to None\n", - " 114321 [params.py: set():1006] [1655514] Changing key stride in group patch from 10 to None\n", - " 114322 [params.py: set():1006] [1655533] Changing key nb in group init from 2 to 1\n", - " 114322 [params.py: set():1006] [1655523] Changing key stride in group patch from 10 to None\n", - " 114322 [map_reduce.py: cnmf_patches():80] [1663745] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220622_241141Read file\n", - " 114323 [params.py: set():1006] [1655543] Changing key stride in group patch from 10 to None\n", - " 114323 [params.py: set():1006] [1655497] Changing key stride in group patch from 10 to None\n", - " 114324 [params.py: set():1006] [1655476] Changing key stride in group patch from 10 to None\n", - " 114324 [params.py: set():1006] [1655482] Changing key stride in group patch from 10 to None\n", - " 114324 [params.py: set():1006] [1655511] Changing key stride in group patch from 10 to None\n", - " 114325 [map_reduce.py: cnmf_patches():80] [1660361] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 237006_257013Read file\n", - " 114325 [params.py: set():1006] [1655520] Changing key stride in group patch from 10 to None\n", - " 114326 [params.py: set():1006] [1655506] Changing key stride in group patch from 10 to None\n", - " 114326 [params.py: set():1006] [1655559] Changing key stride in group patch from 10 to None\n", - " 114327 [params.py: set():1006] [1655474] Changing key stride in group patch from 10 to None\n", - " 114327 [params.py: set():1006] [1655531] Changing key stride in group patch from 10 to None\n", - " 114328 [params.py: set():1006] [1655552] Changing key stride in group patch from 10 to None\n", - " 114328 [params.py: set():1006] [1655509] Changing key stride in group patch from 10 to None\n", - " 114329 [params.py: set():1006] [1655548] Changing key stride in group patch from 10 to None\n", - " 114329 [params.py: set():1006] [1655481] Changing key stride in group patch from 10 to None\n", - " 114329 [params.py: set():1006] [1655526] Changing key stride in group patch from 10 to None\n", - " 114330 [params.py: set():1006] [1655484] Changing key stride in group patch from 10 to None\n", - " 114330 [params.py: set():1006] [1655553] Changing key stride in group patch from 10 to None\n", - " 114331 [params.py: set():1006] [1655493] Changing key stride in group patch from 10 to None\n", - " 114332 [params.py: set():1006] [1655538] Changing key stride in group patch from 10 to None\n", - " 114333 [params.py: set():1006] [1655470] Changing key stride in group patch from 10 to None\n", - " 114333 [params.py: set():1006] [1655549] Changing key stride in group patch from 10 to None\n", - " 114334 [params.py: set():1006] [1655527] Changing key stride in group patch from 10 to None\n", - " 114336 [params.py: set():1006] [1655503] Changing key stride in group patch from 10 to None\n", - " 114336 [params.py: set():1006] [1655472] Changing key stride in group patch from 10 to None\n", - " 114336 [params.py: set():1006] [1655475] Changing key stride in group patch from 10 to None\n", - " 114337 [params.py: set():1006] [1655517] Changing key stride in group patch from 10 to None\n", - " 114337 [params.py: set():1006] [1655488] Changing key stride in group patch from 10 to None\n", - " 114339 [params.py: set():1006] [1655518] Changing key stride in group patch from 10 to None\n", - " 114339 [params.py: set():1006] [1655510] Changing key stride in group patch from 10 to None\n", - " 114340 [params.py: set():1006] [1655477] Changing key stride in group patch from 10 to None\n", - " 114340 [params.py: set():1006] [1655554] Changing key stride in group patch from 10 to None\n", - " 114341 [params.py: set():1006] [1655534] Changing key stride in group patch from 10 to None\n", - " 114341 [params.py: set():1006] [1655499] Changing key stride in group patch from 10 to None\n", - " 114342 [params.py: set():1006] [1655544] Changing key stride in group patch from 10 to None\n", - " 114343 [map_reduce.py: cnmf_patches():100] [1660129] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 205262_225781file loaded\n", - " 114344 [params.py: set():1006] [1655489] Changing key stride in group patch from 10 to None\n", - " 114344 [params.py: set():1006] [1655557] Changing key stride in group patch from 10 to None\n", - " 114345 [params.py: set():1006] [1655563] Changing key stride in group patch from 10 to None\n", - " 114346 [params.py: set():1006] [1655561] Changing key stride in group patch from 10 to None\n", - " 114346 [params.py: set():1006] [1655487] Changing key stride in group patch from 10 to None\n", - " 114347 [params.py: set():1006] [1655500] Changing key stride in group patch from 10 to None\n", - " 114347 [params.py: set():1006] [1655478] Changing key stride in group patch from 10 to None\n", - " 114349 [params.py: set():1006] [1655507] Changing key rf in group patch from 20 to None\n", - " 114143 [params.py: set():1006] [1655562] Changing key rf in group patch from 20 to None\n", - " 114219 [params.py: set():1006] [1655473] Changing key rf in group patch from 20 to None\n", - " 114350 [params.py: set():1006] [1655529] Changing key stride in group patch from 10 to None\n", - " 114301 [params.py: set():1006] [1655505] Changing key rf in group patch from 20 to None\n", - " 114149 [params.py: set():1006] [1655542] Changing key rf in group patch from 20 to None\n", - " 114351 [params.py: set():1006] [1655551] Changing key stride in group patch from 10 to None\n", - " 114352 [params.py: set():1006] [1655504] Changing key stride in group patch from 10 to None\n", - " 114363 [params.py: set():1006] [1655469] Changing key rf in group patch from 20 to None\n", - " 114364 [params.py: set():1006] [1655490] Changing key stride in group patch from 10 to None\n", - " 114364 [params.py: set():1006] [1655556] Changing key stride in group patch from 10 to None\n", - " 114365 [params.py: set():1006] [1655479] Changing key stride in group patch from 10 to None\n", - " 114365 [params.py: set():1006] [1655491] Changing key stride in group patch from 10 to None\n", - " 114366 [params.py: set():1006] [1655498] Changing key stride in group patch from 10 to None\n", - " 114366 [params.py: set():1006] [1655550] Changing key stride in group patch from 10 to None\n", - " 114367 [params.py: set():1006] [1655480] Changing key stride in group patch from 10 to None\n", - " 114367 [params.py: set():1006] [1655483] Changing key stride in group patch from 10 to None\n", - " 114369 [params.py: set():1006] [1655539] Changing key rf in group patch from 20 to None\n", - " 114382 [params.py: set():1006] [1655484] Changing key nb in group init from 2 to 1\n", - " 114383 [params.py: set():1006] [1655509] Changing key nb in group init from 2 to 1\n", - " 114380 [params.py: set():1006] [1655563] Changing key nb in group init from 2 to 1\n", - " 114381 [params.py: set():1006] [1655494] Changing key nb in group init from 2 to 1\n", - " 114382 [params.py: set():1006] [1655527] Changing key nb in group init from 2 to 1\n", - " 114387 [params.py: set():1006] [1655526] Changing key nb in group init from 2 to 1\n", - " 114382 [params.py: set():1006] [1655500] Changing key nb in group init from 2 to 1\n", - " 114381 [params.py: set():1006] [1655523] Changing key nb in group init from 2 to 1\n", - " 114386 [params.py: set():1006] [1655475] Changing key nb in group init from 2 to 1\n", - " 114380 [params.py: set():1006] [1655549] Changing key nb in group init from 2 to 1\n", - " 114385 [params.py: set():1006] [1655476] Changing key nb in group init from 2 to 1\n", - " 114391 [params.py: set():1006] [1655482] Changing key nb in group init from 2 to 1\n", - " 114383 [params.py: set():1006] [1655534] Changing key nb in group init from 2 to 1\n", - " 114391 [params.py: set():1006] [1655517] Changing key nb in group init from 2 to 1\n", - " 114387 [params.py: set():1006] [1655544] Changing key nb in group init from 2 to 1\n", - " 114389 [params.py: set():1006] [1655538] Changing key nb in group init from 2 to 1\n", - " 114383 [params.py: set():1006] [1655497] Changing key nb in group init from 2 to 1\n", - " 114385 [params.py: set():1006] [1655489] Changing key nb in group init from 2 to 1\n", - " 114389 [params.py: set():1006] [1655535] Changing key nb in group temporal from 2 to 1\n", - " 114384 [params.py: set():1006] [1655520] Changing key nb in group init from 2 to 1\n", - " 114385 [params.py: set():1006] [1655477] Changing key nb in group init from 2 to 1\n", - " 114390 [params.py: set():1006] [1655474] Changing key nb in group init from 2 to 1\n", - " 114388 [params.py: set():1006] [1655536] Changing key nb in group spatial from 2 to 1\n", - " 114387 [params.py: set():1006] [1655557] Changing key nb in group init from 2 to 1\n", - " 114384 [params.py: set():1006] [1655472] Changing key nb in group init from 2 to 1\n", - " 114386 [params.py: set():1006] [1655531] Changing key nb in group init from 2 to 1\n", - " 114388 [params.py: set():1006] [1655503] Changing key nb in group init from 2 to 1\n", - " 114388 [params.py: set():1006] [1655543] Changing key nb in group init from 2 to 1\n", - " 114386 [params.py: set():1006] [1655499] Changing key nb in group init from 2 to 1\n", - " 114389 [params.py: set():1006] [1655546] Changing key nb in group temporal from 2 to 1\n", - " 114391 [params.py: set():1006] [1655510] Changing key nb in group init from 2 to 1\n", - " 114390 [params.py: set():1006] [1655511] Changing key nb in group init from 2 to 1\n", - " 114392 [params.py: set():1006] [1655514] Changing key nb in group init from 2 to 1\n", - " 114390 [params.py: set():1006] [1655525] Changing key nb in group init from 2 to 1\n", - " 114393 [params.py: set():1006] [1655561] Changing key nb in group init from 2 to 1\n", - " 114388 [params.py: set():1006] [1655533] Changing key nb in group temporal from 2 to 1\n", - " 114394 [params.py: set():1006] [1655518] Changing key nb in group init from 2 to 1\n", - " 114392 [params.py: set():1006] [1655552] Changing key nb in group init from 2 to 1\n", - " 114384 [params.py: set():1006] [1655521] Changing key nb in group init from 2 to 1\n", - " 114391 [params.py: set():1006] [1655553] Changing key nb in group init from 2 to 1\n", - " 114393 [params.py: set():1006] [1655519] Changing key nb in group temporal from 2 to 1\n", - " 114394 [params.py: set():1006] [1655554] Changing key nb in group init from 2 to 1\n", - " 114392 [params.py: set():1006] [1655470] Changing key nb in group init from 2 to 1\n", - " 114377 [params.py: set():1006] [1655495] Changing key stride in group patch from 10 to None\n", - " 114393 [params.py: set():1006] [1655481] Changing key nb in group init from 2 to 1\n", - " 114380 [params.py: set():1006] [1655488] Changing key nb in group init from 2 to 1\n", - " 114394 [params.py: set():1006] [1655541] Changing key nb in group spatial from 2 to 1\n", - " 114394 [params.py: set():1006] [1655502] Changing key nb in group temporal from 2 to 1\n", - " 114385 [params.py: set():1006] [1655548] Changing key nb in group init from 2 to 1\n", - " 114399 [params.py: set():1006] [1655537] Changing key nb in group init from 2 to 1\n", - " 114381 [params.py: set():1006] [1655506] Changing key nb in group init from 2 to 1\n", - " 114399 [params.py: set():1006] [1655524] Changing key nb in group temporal from 2 to 1\n", - " 114400 [params.py: set():1006] [1655487] Changing key nb in group init from 2 to 1\n", - " 114401 [params.py: set():1006] [1655522] Changing key nb in group init from 2 to 1\n", - " 114400 [params.py: set():1006] [1655559] Changing key nb in group init from 2 to 1\n", - " 114402 [params.py: set():1006] [1655493] Changing key nb in group init from 2 to 1\n", - " 114401 [params.py: set():1006] [1655508] Changing key nb in group spatial from 2 to 1\n", - " 114401 [params.py: set():1006] [1655486] Changing key nb in group spatial from 2 to 1\n", - " 114402 [params.py: set():1006] [1655547] Changing key nb in group spatial from 2 to 1\n", - " 114403 [params.py: set():1006] [1655532] Changing key nb in group init from 2 to 1\n", - " 114404 [params.py: set():1006] [1655478] Changing key nb in group init from 2 to 1\n", - " 114420 [params.py: set():1006] [1655564] Changing key rf in group patch from 20 to None\n", - " 114423 [params.py: set():1006] [1655507] Changing key stride in group patch from 10 to None\n", - " 114428 [params.py: set():1006] [1655528] Changing key rf in group patch from 20 to None\n", - " 114430 [params.py: set():1006] [1655562] Changing key stride in group patch from 10 to None\n", - " 114432 [params.py: set():1006] [1655473] Changing key stride in group patch from 10 to None\n", - " 114432 [params.py: set():1006] [1655529] Changing key nb in group init from 2 to 1\n", - " 114433 [params.py: set():1006] [1655505] Changing key stride in group patch from 10 to None\n", - " 114434 [params.py: set():1006] [1655542] Changing key stride in group patch from 10 to None\n", - " 114444 [params.py: set():1006] [1655551] Changing key nb in group init from 2 to 1\n", - " 114443 [params.py: set():1006] [1655545] Changing key rf in group patch from 20 to None\n", - " 114447 [map_reduce.py: cnmf_patches():100] [1663745] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 220622_241141file loaded\n", - " 114447 [params.py: set():1006] [1655504] Changing key nb in group init from 2 to 1\n", - " 114491 [map_reduce.py: cnmf_patches():100] [1660361] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 237006_257013file loaded\n", - " 114490 [map_reduce.py: cnmf_patches():100] [1660550] Yr_d1_512_d2_512_d3_1_order_C_frames_8000__LOG_ 235982_256501file loaded\n", - " 114518 [params.py: set():1006] [1655520] Changing key nb in group temporal from 2 to 1\n", - " 114517 [params.py: set():1006] [1655563] Changing key nb in group temporal from 2 to 1\n", - " 114515 [params.py: set():1006] [1655554] Changing key nb in group temporal from 2 to 1\n", - " 114519 [params.py: set():1006] [1655495] Changing key nb in group init from 2 to 1\n", - " 114520 [params.py: set():1006] [1655477] Changing key nb in group temporal from 2 to 1\n", - " 114519 [params.py: set():1006] [1655488] Changing key nb in group temporal from 2 to 1\n", - " 114520 [params.py: set():1006] [1655484] Changing key nb in group temporal from 2 to 1\n", - " 114510 [params.py: set():1006] [1655476] Changing key nb in group temporal from 2 to 1\n", - " 114517 [params.py: set():1006] [1655518] Changing key nb in group temporal from 2 to 1\n", - " 114525 [params.py: set():1006] [1655478] Changing key nb in group temporal from 2 to 1\n", - " 114507 [params.py: set():1006] [1655497] Changing key nb in group temporal from 2 to 1\n", - " 114511 [params.py: set():1006] [1655510] Changing key nb in group temporal from 2 to 1\n", - " 114527 [params.py: set():1006] [1655473] Changing key nb in group init from 2 to 1\n", - " 114520 [params.py: set():1006] [1655537] Changing key nb in group temporal from 2 to 1\n", - " 114506 [params.py: set():1006] [1655509] Changing key nb in group temporal from 2 to 1\n", - " 114515 [params.py: set():1006] [1655481] Changing key nb in group temporal from 2 to 1\n", - " 114517 [params.py: set():1006] [1655519] Changing key nb in group spatial from 2 to 1\n", - " 114508 [params.py: set():1006] [1655534] Changing key nb in group temporal from 2 to 1\n", - " 114509 [params.py: set():1006] [1655523] Changing key nb in group temporal from 2 to 1\n", - " 114515 [params.py: set():1006] [1655499] Changing key nb in group temporal from 2 to 1\n", - " 114516 [params.py: set():1006] [1655521] Changing key nb in group temporal from 2 to 1\n", - " 114527 [params.py: set():1006] [1655562] Changing key nb in group init from 2 to 1\n", - " 114526 [params.py: set():1006] [1655528] Changing key stride in group patch from 10 to None\n", - " 114511 [params.py: set():1006] [1655543] Changing key nb in group temporal from 2 to 1\n", - " 114531 [params.py: set():1006] [1655504] Changing key nb in group temporal from 2 to 1\n", - " 114519 [params.py: set():1006] [1655502] Changing key nb in group spatial from 2 to 1\n", - " 114527 [params.py: set():1006] [1655529] Changing key nb in group temporal from 2 to 1\n", - " 114524 [params.py: set():1006] [1655493] Changing key nb in group temporal from 2 to 1\n", - " 114529 [params.py: set():1006] [1655545] Changing key stride in group patch from 10 to None\n", - " 114509 [params.py: set():1006] [1655517] Changing key nb in group temporal from 2 to 1\n", - " 114511 [params.py: set():1006] [1655475] Changing key nb in group temporal from 2 to 1\n", - " 114516 [params.py: set():1006] [1655552] Changing key nb in group temporal from 2 to 1\n", - " 114517 [params.py: set():1006] [1655525] Changing key nb in group temporal from 2 to 1\n", - " 114514 [params.py: set():1006] [1655511] Changing key nb in group temporal from 2 to 1\n", - " 114501 [params.py: set():1006] [1655539] Changing key stride in group patch from 10 to None\n", - " 114510 [params.py: set():1006] [1655538] Changing key nb in group temporal from 2 to 1\n", - " 114526 [params.py: set():1006] [1655507] Changing key nb in group init from 2 to 1\n", - " 114509 [params.py: set():1006] [1655546] Changing key nb in group spatial from 2 to 1\n", - " 114523 [params.py: set():1006] [1655506] Changing key nb in group temporal from 2 to 1\n", - " 114524 [params.py: set():1006] [1655532] Changing key nb in group temporal from 2 to 1\n", - " 114506 [params.py: set():1006] [1655549] Changing key nb in group temporal from 2 to 1\n", - " 114513 [params.py: set():1006] [1655489] Changing key nb in group temporal from 2 to 1\n", - " 114507 [params.py: set():1006] [1655535] Changing key nb in group spatial from 2 to 1\n", - " 114518 [params.py: set():1006] [1655541] Changing key p in group preprocess from 1 to 0\n", - " 114510 [params.py: set():1006] [1655482] Changing key nb in group temporal from 2 to 1\n", - " 114515 [params.py: set():1006] [1655561] Changing key nb in group temporal from 2 to 1\n", - " 114512 [params.py: set():1006] [1655536] Changing key p in group preprocess from 1 to 0\n", - " 114524 [params.py: set():1006] [1655547] Changing key p in group preprocess from 1 to 0\n", - " 114514 [params.py: set():1006] [1655503] Changing key nb in group temporal from 2 to 1\n", - " 114528 [params.py: set():1006] [1655505] Changing key nb in group init from 2 to 1\n", - " 114528 [params.py: set():1006] [1655542] Changing key nb in group init from 2 to 1\n", - " 114513 [params.py: set():1006] [1655544] Changing key nb in group temporal from 2 to 1\n", - " 114518 [params.py: set():1006] [1655514] Changing key nb in group temporal from 2 to 1\n", - " 114510 [params.py: set():1006] [1655474] Changing key nb in group temporal from 2 to 1\n", - " 114512 [params.py: set():1006] [1655557] Changing key nb in group temporal from 2 to 1\n", - " 114523 [params.py: set():1006] [1655524] Changing key nb in group spatial from 2 to 1\n", - " 114508 [params.py: set():1006] [1655494] Changing key nb in group temporal from 2 to 1\n", - " 114529 [params.py: set():1006] [1655551] Changing key nb in group temporal from 2 to 1\n", - " 114522 [params.py: set():1006] [1655522] Changing key nb in group temporal from 2 to 1\n", - " 114525 [params.py: set():1006] [1655564] Changing key stride in group patch from 10 to None\n", - " 114501 [params.py: set():1006] [1655483] Changing key nb in group init from 2 to 1\n", - " 114508 [params.py: set():1006] [1655500] Changing key nb in group temporal from 2 to 1\n", - " 114516 [params.py: set():1006] [1655470] Changing key nb in group temporal from 2 to 1\n", - " 114507 [params.py: set():1006] [1655527] Changing key nb in group temporal from 2 to 1\n", - " 114523 [params.py: set():1006] [1655559] Changing key nb in group temporal from 2 to 1\n", - " 114507 [params.py: set():1006] [1655526] Changing key nb in group temporal from 2 to 1\n", - " 114512 [params.py: set():1006] [1655472] Changing key nb in group temporal from 2 to 1\n", - " 114500 [params.py: set():1006] [1655491] Changing key nb in group init from 2 to 1\n", - " 114499 [params.py: set():1006] [1655498] Changing key nb in group init from 2 to 1\n", - " 114500 [params.py: set():1006] [1655480] Changing key nb in group init from 2 to 1\n", - " 114512 [params.py: set():1006] [1655531] Changing key nb in group temporal from 2 to 1\n", - " 114522 [params.py: set():1006] [1655486] Changing key p in group preprocess from 1 to 0\n", - " 114498 [params.py: set():1006] [1655469] Changing key stride in group patch from 10 to None\n", - " 114523 [params.py: set():1006] [1655508] Changing key p in group preprocess from 1 to 0\n", - " 114521 [params.py: set():1006] [1655487] Changing key nb in group temporal from 2 to 1\n", - " 114521 [params.py: set():1006] [1655548] Changing key nb in group temporal from 2 to 1\n", - " 114513 [params.py: set():1006] [1655533] Changing key nb in group spatial from 2 to 1\n", - " 114499 [params.py: set():1006] [1655479] Changing key nb in group init from 2 to 1\n", - " 114500 [params.py: set():1006] [1655550] Changing key nb in group init from 2 to 1\n", - " 114498 [params.py: set():1006] [1655556] Changing key nb in group init from 2 to 1\n", - " 114514 [params.py: set():1006] [1655553] Changing key nb in group temporal from 2 to 1\n", - " 114497 [params.py: set():1006] [1655490] Changing key nb in group init from 2 to 1\n", - " 114627 [params.py: set():1006] [1655499] Changing key nb in group spatial from 2 to 1\n", - " 114628 [params.py: set():1006] [1655557] Changing key nb in group spatial from 2 to 1\n", - " 114640 [params.py: set():1006] [1655553] Changing key nb in group spatial from 2 to 1\n", - " 114638 [params.py: set():1006] [1655562] Changing key nb in group temporal from 2 to 1\n", - " 114633 [params.py: set():1006] [1655535] Changing key p in group preprocess from 1 to 0\n", - " 114641 [params.py: set():1006] [1655490] Changing key nb in group temporal from 2 to 1\n", - " 114628 [params.py: set():1006] [1655510] Changing key nb in group spatial from 2 to 1\n", - " 114618 [params.py: set():1006] [1655517] Changing key nb in group spatial from 2 to 1\n", - " 114635 [params.py: set():1006] [1655483] Changing key nb in group temporal from 2 to 1\n", - " 114631 [params.py: set():1006] [1655549] Changing key nb in group spatial from 2 to 1\n", - " 114636 [params.py: set():1006] [1655486] Changing key p in group temporal from 1 to 0\n", - " 114640 [params.py: set():1006] [1655556] Changing key nb in group temporal from 2 to 1\n", - " 114629 [params.py: set():1006] [1655514] Changing key nb in group spatial from 2 to 1\n", - " 114635 [params.py: set():1006] [1655472] Changing key nb in group spatial from 2 to 1\n", - " 114630 [params.py: set():1006] [1655470] Changing key nb in group spatial from 2 to 1\n", - " 114630 [params.py: set():1006] [1655476] Changing key nb in group spatial from 2 to 1\n", - " 114624 [params.py: set():1006] [1655484] Changing key nb in group spatial from 2 to 1\n", - " 114620 [params.py: set():1006] [1655497] Changing key nb in group spatial from 2 to 1\n", - " 114637 [params.py: set():1006] [1655491] Changing key nb in group temporal from 2 to 1\n", - " 114630 [params.py: set():1006] [1655488] Changing key nb in group spatial from 2 to 1\n", - " 114625 [params.py: set():1006] [1655547] Changing key p in group temporal from 1 to 0\n", - " 114626 [params.py: set():1006] [1655545] Changing key nb in group init from 2 to 1\n", - " 114630 [params.py: set():1006] [1655522] Changing key nb in group spatial from 2 to 1\n", - " 114636 [params.py: set():1006] [1655498] Changing key nb in group temporal from 2 to 1\n", - " 114636 [params.py: set():1006] [1655480] Changing key nb in group temporal from 2 to 1\n", - " 114626 [params.py: set():1006] [1655544] Changing key nb in group spatial from 2 to 1\n", - " 114637 [params.py: set():1006] [1655533] Changing key p in group preprocess from 1 to 0\n", - " 114633 [params.py: set():1006] [1655526] Changing key nb in group spatial from 2 to 1\n", - " 114639 [params.py: set():1006] [1655536] Changing key p in group temporal from 1 to 0\n", - " 114639 [params.py: set():1006] [1655487] Changing key nb in group spatial from 2 to 1\n", - " 114619 [params.py: set():1006] [1655475] Changing key nb in group spatial from 2 to 1\n", - " 114632 [params.py: set():1006] [1655548] Changing key nb in group spatial from 2 to 1\n", - " 114625 [params.py: set():1006] [1655554] Changing key nb in group spatial from 2 to 1\n", - " 114620 [params.py: set():1006] [1655519] Changing key p in group preprocess from 1 to 0\n", - " 114621 [params.py: set():1006] [1655477] Changing key nb in group spatial from 2 to 1\n", - " 114623 [params.py: set():1006] [1655502] Changing key p in group preprocess from 1 to 0\n", - " 114620 [params.py: set():1006] [1655543] Changing key nb in group spatial from 2 to 1\n", - " 114637 [params.py: set():1006] [1655534] Changing key nb in group spatial from 2 to 1\n", - " 114623 [params.py: set():1006] [1655528] Changing key nb in group init from 2 to 1\n", - " 114638 [params.py: set():1006] [1655479] Changing key nb in group temporal from 2 to 1\n", - " 114634 [params.py: set():1006] [1655527] Changing key nb in group spatial from 2 to 1\n", - " 114624 [params.py: set():1006] [1655482] Changing key nb in group spatial from 2 to 1\n", - " 114631 [params.py: set():1006] [1655509] Changing key nb in group spatial from 2 to 1\n", - " 114626 [params.py: set():1006] [1655564] Changing key nb in group init from 2 to 1\n", - " 114626 [params.py: set():1006] [1655561] Changing key nb in group spatial from 2 to 1\n", - " 114634 [params.py: set():1006] [1655531] Changing key nb in group spatial from 2 to 1\n", - " 114619 [params.py: set():1006] [1655503] Changing key nb in group spatial from 2 to 1\n", - " 114634 [params.py: set():1006] [1655495] Changing key nb in group temporal from 2 to 1\n", - " 114633 [params.py: set():1006] [1655551] Changing key nb in group spatial from 2 to 1\n", - " 114625 [params.py: set():1006] [1655511] Changing key nb in group spatial from 2 to 1\n", - " 114619 [params.py: set():1006] [1655507] Changing key nb in group temporal from 2 to 1\n", - " 114623 [params.py: set():1006] [1655474] Changing key nb in group spatial from 2 to 1\n", - " 114637 [params.py: set():1006] [1655469] Changing key nb in group init from 2 to 1\n", - " 114622 [params.py: set():1006] [1655500] Changing key nb in group spatial from 2 to 1\n", - " 114618 [params.py: set():1006] [1655473] Changing key nb in group temporal from 2 to 1\n", - " 114617 [params.py: set():1006] [1655506] Changing key nb in group spatial from 2 to 1\n", - " 114629 [params.py: set():1006] [1655542] Changing key nb in group temporal from 2 to 1\n", - " 114640 [params.py: set():1006] [1655520] Changing key nb in group spatial from 2 to 1\n", - " 114617 [params.py: set():1006] [1655524] Changing key p in group preprocess from 1 to 0\n", - " 114628 [params.py: set():1006] [1655559] Changing key nb in group spatial from 2 to 1\n", - " 114629 [params.py: set():1006] [1655529] Changing key nb in group spatial from 2 to 1\n", - " 114627 [params.py: set():1006] [1655552] Changing key nb in group spatial from 2 to 1\n", - " 114624 [params.py: set():1006] [1655504] Changing key nb in group spatial from 2 to 1\n", - " 114621 [params.py: set():1006] [1655523] Changing key nb in group spatial from 2 to 1\n", - " 114633 [params.py: set():1006] [1655537] Changing key nb in group spatial from 2 to 1\n", - " 114638 [params.py: set():1006] [1655508] Changing key p in group temporal from 1 to 0\n", - " 114622 [params.py: set():1006] [1655478] Changing key nb in group spatial from 2 to 1\n", - " 114622 [params.py: set():1006] [1655525] Changing key nb in group spatial from 2 to 1\n", - " 114618 [params.py: set():1006] [1655493] Changing key nb in group spatial from 2 to 1\n", - " 114627 [params.py: set():1006] [1655538] Changing key nb in group spatial from 2 to 1\n", - " 114622 [params.py: set():1006] [1655505] Changing key nb in group temporal from 2 to 1\n", - " 114621 [params.py: set():1006] [1655546] Changing key p in group preprocess from 1 to 0\n", - " 114632 [params.py: set():1006] [1655539] Changing key nb in group init from 2 to 1\n", - " 114635 [params.py: set():1006] [1655550] Changing key nb in group temporal from 2 to 1\n", - " 114632 [params.py: set():1006] [1655494] Changing key nb in group spatial from 2 to 1\n", - " 114617 [params.py: set():1006] [1655532] Changing key nb in group spatial from 2 to 1\n", - " 114616 [params.py: set():1006] [1655489] Changing key nb in group spatial from 2 to 1\n", - " 114618 [params.py: set():1006] [1655518] Changing key nb in group spatial from 2 to 1\n", - " 114616 [params.py: set():1006] [1655563] Changing key nb in group spatial from 2 to 1\n", - " 114616 [params.py: set():1006] [1655541] Changing key p in group temporal from 1 to 0\n", - " 114631 [params.py: set():1006] [1655481] Changing key nb in group spatial from 2 to 1\n", - " 114615 [params.py: set():1006] [1655521] Changing key nb in group spatial from 2 to 1\n", - " 114719 [params.py: set():1006] [1660129] Changing key rf in group patch from 20 to None\n", - " 114778 [params.py: set():1006] [1655479] Changing key nb in group spatial from 2 to 1\n", - " 114774 [params.py: set():1006] [1655544] Changing key p in group preprocess from 1 to 0\n", - " 114777 [params.py: set():1006] [1655563] Changing key p in group preprocess from 1 to 0\n", - " 114782 [params.py: set():1006] [1655521] Changing key p in group preprocess from 1 to 0\n", - " 114778 [params.py: set():1006] [1655489] Changing key p in group preprocess from 1 to 0\n", - " 114776 [params.py: set():1006] [1655493] Changing key p in group preprocess from 1 to 0\n", - " 114779 [params.py: set():1006] [1655537] Changing key p in group preprocess from 1 to 0\n", - " 114774 [params.py: set():1006] [1655546] Changing key p in group temporal from 1 to 0\n", - " 114777 [params.py: set():1006] [1655538] Changing key p in group preprocess from 1 to 0\n", - " 114775 [params.py: set():1006] [1655502] Changing key p in group temporal from 1 to 0\n", - " 114778 [params.py: set():1006] [1655551] Changing key p in group preprocess from 1 to 0\n", - " 114780 [params.py: set():1006] [1655500] Changing key p in group preprocess from 1 to 0\n", - " 114776 [params.py: set():1006] [1655523] Changing key p in group preprocess from 1 to 0\n", - " 114770 [params.py: set():1006] [1655526] Changing key p in group preprocess from 1 to 0\n", - " 114768 [params.py: set():1006] [1655542] Changing key nb in group spatial from 2 to 1\n", - " 114776 [params.py: set():1006] [1655550] Changing key nb in group spatial from 2 to 1\n", - " 114768 [params.py: set():1006] [1655487] Changing key p in group preprocess from 1 to 0\n", - " 114770 [params.py: set():1006] [1655476] Changing key p in group preprocess from 1 to 0\n", - " 114778 [params.py: set():1006] [1655518] Changing key p in group preprocess from 1 to 0\n", - " 114766 [cnmf.py: fit():436] [1655547] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114779 [params.py: set():1006] [1655532] Changing key p in group preprocess from 1 to 0\n", - " 114773 [params.py: set():1006] [1655524] Changing key p in group temporal from 1 to 0\n", - " 114777 [cnmf.py: fit():436] [1655508] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114767 [params.py: set():1006] [1655533] Changing key p in group temporal from 1 to 0\n", - " 114775 [params.py: set():1006] [1655528] Changing key nb in group temporal from 2 to 1\n", - " 114781 [params.py: set():1006] [1655510] Changing key p in group preprocess from 1 to 0\n", - " 114765 [params.py: set():1006] [1655549] Changing key p in group preprocess from 1 to 0\n", - " 114782 [params.py: set():1006] [1655545] Changing key nb in group temporal from 2 to 1\n", - " 114779 [params.py: set():1006] [1655505] Changing key nb in group spatial from 2 to 1\n", - " 114771 [params.py: set():1006] [1655475] Changing key p in group preprocess from 1 to 0\n", - " 114772 [params.py: set():1006] [1655539] Changing key nb in group temporal from 2 to 1\n", - " 114772 [params.py: set():1006] [1655504] Changing key p in group preprocess from 1 to 0\n", - " 114774 [params.py: set():1006] [1655534] Changing key p in group preprocess from 1 to 0\n", - " 114769 [params.py: set():1006] [1655548] Changing key p in group preprocess from 1 to 0\n", - " 114764 [params.py: set():1006] [1655519] Changing key p in group temporal from 1 to 0\n", - " 114765 [params.py: set():1006] [1655553] Changing key p in group preprocess from 1 to 0\n", - " 114765 [params.py: set():1006] [1655557] Changing key p in group preprocess from 1 to 0\n", - " 114780 [cnmf.py: fit():436] [1655541] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114764 [params.py: set():1006] [1655561] Changing key p in group preprocess from 1 to 0\n", - " 114763 [params.py: set():1006] [1655554] Changing key p in group preprocess from 1 to 0\n", - " 114780 [params.py: set():1006] [1655509] Changing key p in group preprocess from 1 to 0\n", - " 114769 [params.py: set():1006] [1655473] Changing key nb in group spatial from 2 to 1\n", - " 114781 [params.py: set():1006] [1655481] Changing key p in group preprocess from 1 to 0\n", - " 114770 [params.py: set():1006] [1655527] Changing key p in group preprocess from 1 to 0\n", - " 114763 [params.py: set():1006] [1655507] Changing key nb in group spatial from 2 to 1\n", - " 114766 [params.py: set():1006] [1655556] Changing key nb in group spatial from 2 to 1\n", - " 114781 [params.py: set():1006] [1655499] Changing key p in group preprocess from 1 to 0\n", - " 114765 [params.py: set():1006] [1655484] Changing key p in group preprocess from 1 to 0\n", - " 114773 [params.py: set():1006] [1655470] Changing key p in group preprocess from 1 to 0\n", - " 114768 [params.py: set():1006] [1655529] Changing key p in group preprocess from 1 to 0\n", - " 114773 [params.py: set():1006] [1655525] Changing key p in group preprocess from 1 to 0\n", - " 114767 [params.py: set():1006] [1655543] Changing key p in group preprocess from 1 to 0\n", - " 114767 [params.py: set():1006] [1655506] Changing key p in group preprocess from 1 to 0\n", - " 114768 [params.py: set():1006] [1655469] Changing key nb in group temporal from 2 to 1\n", - " 114760 [params.py: set():1006] [1655522] Changing key p in group preprocess from 1 to 0\n", - " 114769 [params.py: set():1006] [1655482] Changing key p in group preprocess from 1 to 0\n", - " 114764 [params.py: set():1006] [1655488] Changing key p in group preprocess from 1 to 0\n", - " 114763 [params.py: set():1006] [1655559] Changing key p in group preprocess from 1 to 0\n", - " 114773 [params.py: set():1006] [1655494] Changing key p in group preprocess from 1 to 0\n", - " 114762 [params.py: set():1006] [1655483] Changing key nb in group spatial from 2 to 1\n", - " 114771 [params.py: set():1006] [1655503] Changing key p in group preprocess from 1 to 0\n", - " 114766 [params.py: set():1006] [1655562] Changing key nb in group spatial from 2 to 1\n", - " 114759 [params.py: set():1006] [1655490] Changing key nb in group spatial from 2 to 1\n", - " 114771 [params.py: set():1006] [1655474] Changing key p in group preprocess from 1 to 0\n", - " 114759 [cnmf.py: fit():436] [1655486] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114771 [params.py: set():1006] [1655491] Changing key nb in group spatial from 2 to 1\n", - " 114761 [params.py: set():1006] [1655514] Changing key p in group preprocess from 1 to 0\n", - " 114775 [params.py: set():1006] [1655478] Changing key p in group preprocess from 1 to 0\n", - " 114761 [params.py: set():1006] [1655535] Changing key p in group temporal from 1 to 0\n", - " 114760 [params.py: set():1006] [1655480] Changing key nb in group spatial from 2 to 1\n", - " 114761 [params.py: set():1006] [1655517] Changing key p in group preprocess from 1 to 0\n", - " 114759 [params.py: set():1006] [1655495] Changing key nb in group spatial from 2 to 1\n", - " 114762 [params.py: set():1006] [1655477] Changing key p in group preprocess from 1 to 0\n", - " 114762 [params.py: set():1006] [1655511] Changing key p in group preprocess from 1 to 0\n", - " 114760 [params.py: set():1006] [1655497] Changing key p in group preprocess from 1 to 0\n", - " 114758 [params.py: set():1006] [1655498] Changing key nb in group spatial from 2 to 1\n", - " 114761 [cnmf.py: fit():436] [1655536] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114772 [params.py: set():1006] [1655520] Changing key p in group preprocess from 1 to 0\n", - " 114758 [params.py: set():1006] [1655531] Changing key p in group preprocess from 1 to 0\n", - " 114757 [params.py: set():1006] [1655552] Changing key p in group preprocess from 1 to 0\n", - " 114758 [params.py: set():1006] [1655564] Changing key nb in group temporal from 2 to 1\n", - " 114757 [params.py: set():1006] [1655472] Changing key p in group preprocess from 1 to 0\n", - " 114750 [params.py: set():1006] [1663745] Changing key rf in group patch from 20 to None\n", - " 114812 [params.py: set():1006] [1660361] Changing key rf in group patch from 20 to None\n", - " 114808 [params.py: set():1006] [1660550] Changing key rf in group patch from 20 to None\n", - " 114860 [cnmf.py: fit():449] [1655536] (8000, 41, 41)\n", - " 114858 [params.py: set():1006] [1655564] Changing key nb in group spatial from 2 to 1\n", - " 114859 [params.py: set():1006] [1655477] Changing key p in group temporal from 1 to 0\n", - " 114858 [params.py: set():1006] [1655552] Changing key p in group temporal from 1 to 0\n", - " 114861 [params.py: set():1006] [1660361] Changing key stride in group patch from 10 to None\n", - " 114855 [cnmf.py: fit():436] [1655535] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114857 [params.py: set():1006] [1655559] Changing key p in group temporal from 1 to 0\n", - " 114857 [params.py: set():1006] [1655498] Changing key p in group preprocess from 1 to 0\n", - " 114853 [params.py: set():1006] [1655561] Changing key p in group temporal from 1 to 0\n", - " 114844 [params.py: set():1006] [1655549] Changing key p in group temporal from 1 to 0\n", - " 114856 [params.py: set():1006] [1655480] Changing key p in group preprocess from 1 to 0\n", - " 114856 [params.py: set():1006] [1655490] Changing key p in group preprocess from 1 to 0\n", - " 114858 [params.py: set():1006] [1655531] Changing key p in group temporal from 1 to 0\n", - " 114852 [params.py: set():1006] [1655474] Changing key p in group temporal from 1 to 0\n", - " 114855 [params.py: set():1006] [1655503] Changing key p in group temporal from 1 to 0\n", - " 114851 [params.py: set():1006] [1655473] Changing key p in group preprocess from 1 to 0\n", - " 114852 [params.py: set():1006] [1655482] Changing key p in group temporal from 1 to 0\n", - " 114852 [params.py: set():1006] [1655499] Changing key p in group temporal from 1 to 0\n", - " 114859 [params.py: set():1006] [1655514] Changing key p in group temporal from 1 to 0\n", - " 114847 [params.py: set():1006] [1655505] Changing key p in group preprocess from 1 to 0\n", - " 114859 [params.py: set():1006] [1655511] Changing key p in group temporal from 1 to 0\n", - " 114858 [params.py: set():1006] [1655483] Changing key p in group preprocess from 1 to 0\n", - " 114842 [cnmf.py: fit():449] [1655541] (8000, 41, 41)\n", - " 114860 [params.py: set():1006] [1655476] Changing key p in group temporal from 1 to 0\n", - " 114851 [params.py: set():1006] [1655484] Changing key p in group temporal from 1 to 0\n", - " 114854 [params.py: set():1006] [1655497] Changing key p in group temporal from 1 to 0\n", - " 114851 [params.py: set():1006] [1655537] Changing key p in group temporal from 1 to 0\n", - " 114853 [cnmf.py: fit():449] [1655486] (8000, 41, 41)\n", - " 114850 [cnmf.py: fit():436] [1655519] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114862 [params.py: set():1006] [1660550] Changing key stride in group patch from 10 to None\n", - " 114844 [params.py: set():1006] [1655475] Changing key p in group temporal from 1 to 0\n", - " 114849 [params.py: set():1006] [1655557] Changing key p in group temporal from 1 to 0\n", - " 114850 [params.py: set():1006] [1655478] Changing key p in group temporal from 1 to 0\n", - " 114842 [params.py: set():1006] [1655528] Changing key nb in group spatial from 2 to 1\n", - " 114848 [params.py: set():1006] [1655494] Changing key p in group temporal from 1 to 0\n", - " 114839 [params.py: set():1006] [1655487] Changing key p in group temporal from 1 to 0\n", - " 114854 [params.py: set():1006] [1655507] Changing key p in group preprocess from 1 to 0\n", - " 114843 [params.py: set():1006] [1655510] Changing key p in group temporal from 1 to 0\n", - " 114846 [params.py: set():1006] [1655481] Changing key p in group temporal from 1 to 0\n", - " 114849 [params.py: set():1006] [1655488] Changing key p in group temporal from 1 to 0\n", - " 114845 [params.py: set():1006] [1655556] Changing key p in group preprocess from 1 to 0\n", - " 114854 [params.py: set():1006] [1655491] Changing key p in group preprocess from 1 to 0\n", - " 114854 [params.py: set():1006] [1655520] Changing key p in group temporal from 1 to 0\n", - " 114860 [params.py: set():1006] [1655472] Changing key p in group temporal from 1 to 0\n", - " 114847 [params.py: set():1006] [1655532] Changing key p in group temporal from 1 to 0\n", - " 114839 [params.py: set():1006] [1655500] Changing key p in group temporal from 1 to 0\n", - " 114841 [params.py: set():1006] [1655538] Changing key p in group temporal from 1 to 0\n", - " 114849 [params.py: set():1006] [1655529] Changing key p in group temporal from 1 to 0\n", - " 114844 [params.py: set():1006] [1655548] Changing key p in group temporal from 1 to 0\n", - " 114856 [params.py: set():1006] [1655517] Changing key p in group temporal from 1 to 0\n", - " 114841 [params.py: set():1006] [1655542] Changing key p in group preprocess from 1 to 0\n", - " 114853 [params.py: set():1006] [1655527] Changing key p in group temporal from 1 to 0\n", - " 114840 [cnmf.py: fit():436] [1655533] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114845 [params.py: set():1006] [1655554] Changing key p in group temporal from 1 to 0\n", - " 114842 [params.py: set():1006] [1655543] Changing key p in group temporal from 1 to 0\n", - " 114849 [params.py: set():1006] [1655470] Changing key p in group temporal from 1 to 0\n", - " 114843 [params.py: set():1006] [1655539] Changing key nb in group spatial from 2 to 1\n", - " 114838 [params.py: set():1006] [1655489] Changing key p in group temporal from 1 to 0\n", - " 114845 [params.py: set():1006] [1655562] Changing key p in group preprocess from 1 to 0\n", - " 114840 [cnmf.py: fit():436] [1655502] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114846 [params.py: set():1006] [1655553] Changing key p in group temporal from 1 to 0\n", - " 114838 [params.py: set():1006] [1655523] Changing key p in group temporal from 1 to 0\n", - " 114839 [cnmf.py: fit():449] [1655547] (8000, 41, 41)\n", - " 114861 [params.py: set():1006] [1663745] Changing key stride in group patch from 10 to None\n", - " 114850 [params.py: set():1006] [1655506] Changing key p in group temporal from 1 to 0\n", - " 114840 [params.py: set():1006] [1655504] Changing key p in group temporal from 1 to 0\n", - " 114846 [params.py: set():1006] [1655534] Changing key p in group temporal from 1 to 0\n", - " 114836 [cnmf.py: fit():436] [1655546] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114847 [params.py: set():1006] [1655469] Changing key nb in group spatial from 2 to 1\n", - " 114845 [params.py: set():1006] [1655550] Changing key p in group preprocess from 1 to 0\n", - " 114836 [params.py: set():1006] [1655521] Changing key p in group temporal from 1 to 0\n", - " 114839 [params.py: set():1006] [1655551] Changing key p in group temporal from 1 to 0\n", - " 114837 [params.py: set():1006] [1655526] Changing key p in group temporal from 1 to 0\n", - " 114848 [params.py: set():1006] [1655509] Changing key p in group temporal from 1 to 0\n", - " 114843 [cnmf.py: fit():436] [1655524] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114835 [params.py: set():1006] [1655544] Changing key p in group temporal from 1 to 0\n", - " 114838 [params.py: set():1006] [1655493] Changing key p in group temporal from 1 to 0\n", - " 114848 [params.py: set():1006] [1655522] Changing key p in group temporal from 1 to 0\n", - " 114857 [params.py: set():1006] [1655495] Changing key p in group preprocess from 1 to 0\n", - " 114835 [params.py: set():1006] [1655545] Changing key nb in group spatial from 2 to 1\n", - " 114837 [params.py: set():1006] [1655518] Changing key p in group temporal from 1 to 0\n", - " 114836 [cnmf.py: fit():449] [1655508] (8000, 41, 40)\n", - " 114837 [params.py: set():1006] [1655479] Changing key p in group preprocess from 1 to 0\n", - " 114841 [params.py: set():1006] [1655525] Changing key p in group temporal from 1 to 0\n", - " 114835 [params.py: set():1006] [1655563] Changing key p in group temporal from 1 to 0\n", - " 114826 [params.py: set():1006] [1660129] Changing key stride in group patch from 10 to None\n", - " 114931 [cnmf.py: fit():436] [1655509] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114924 [cnmf.py: fit():436] [1655532] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114929 [cnmf.py: fit():436] [1655525] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114929 [cnmf.py: fit():449] [1655535] (8000, 41, 41)\n", - " 114930 [params.py: set():1006] [1655545] Changing key p in group preprocess from 1 to 0\n", - " 114933 [params.py: set():1006] [1655495] Changing key p in group temporal from 1 to 0\n", - " 114931 [cnmf.py: fit():436] [1655526] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114929 [cnmf.py: fit():449] [1655524] (8000, 41, 41)\n", - " 114930 [params.py: set():1006] [1655550] Changing key p in group temporal from 1 to 0\n", - " 114930 [params.py: set():1006] [1655469] Changing key p in group preprocess from 1 to 0\n", - " 114927 [cnmf.py: fit():436] [1655518] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114933 [params.py: set():1006] [1660129] Changing key nb in group init from 2 to 1\n", - " 114927 [cnmf.py: fit():436] [1655553] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114918 [cnmf.py: fit():436] [1655520] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114912 [cnmf.py: fit():436] [1655484] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114925 [cnmf.py: fit():436] [1655521] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114932 [cnmf.py: fit():436] [1655561] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114922 [cnmf.py: fit():466] [1655547] Using 1 processes\n", - " 114925 [cnmf.py: fit():436] [1655543] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114908 [params.py: set():1006] [1655490] Changing key p in group temporal from 1 to 0\n", - " 114920 [cnmf.py: fit():436] [1655529] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114920 [params.py: set():1006] [1655473] Changing key p in group temporal from 1 to 0\n", - " 114926 [cnmf.py: fit():436] [1655522] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114928 [cnmf.py: fit():436] [1655544] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114921 [cnmf.py: fit():436] [1655548] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114928 [cnmf.py: fit():466] [1655536] Using 1 processes\n", - " 114907 [cnmf.py: fit():436] [1655482] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114916 [cnmf.py: fit():436] [1655557] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114920 [cnmf.py: fit():436] [1655481] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114928 [cnmf.py: fit():436] [1655500] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114919 [params.py: set():1006] [1655491] Changing key p in group temporal from 1 to 0\n", - " 114918 [cnmf.py: fit():436] [1655510] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114932 [params.py: set():1006] [1655479] Changing key p in group temporal from 1 to 0\n", - " 114919 [params.py: set():1006] [1655556] Changing key p in group temporal from 1 to 0\n", - " 114919 [cnmf.py: fit():436] [1655504] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114922 [cnmf.py: fit():436] [1655534] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114927 [cnmf.py: fit():436] [1655493] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114925 [params.py: set():1006] [1655539] Changing key p in group preprocess from 1 to 0\n", - " 114913 [cnmf.py: fit():436] [1655476] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114921 [cnmf.py: fit():436] [1655488] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114909 [params.py: set():1006] [1655483] Changing key p in group temporal from 1 to 0\n", - " 114932 [cnmf.py: fit():436] [1655551] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114912 [params.py: set():1006] [1655507] Changing key p in group temporal from 1 to 0\n", - " 114933 [cnmf.py: fit():436] [1655563] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114931 [cnmf.py: fit():466] [1655508] Using 1 processes\n", - " 114910 [cnmf.py: fit():436] [1655497] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114918 [cnmf.py: fit():436] [1655554] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114926 [cnmf.py: fit():436] [1655489] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114909 [cnmf.py: fit():436] [1655499] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114910 [cnmf.py: fit():436] [1655511] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114911 [cnmf.py: fit():436] [1655527] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114916 [cnmf.py: fit():436] [1655517] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114923 [cnmf.py: fit():436] [1655472] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114912 [cnmf.py: fit():436] [1655487] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114923 [cnmf.py: fit():436] [1655523] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114921 [cnmf.py: fit():449] [1655533] (8000, 41, 41)\n", - " 114924 [params.py: set():1006] [1655542] Changing key p in group temporal from 1 to 0\n", - " 114923 [cnmf.py: fit():436] [1655538] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114913 [cnmf.py: fit():436] [1655478] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114917 [cnmf.py: fit():436] [1655494] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114911 [params.py: set():1006] [1655528] Changing key p in group preprocess from 1 to 0\n", - " 114922 [cnmf.py: fit():449] [1655502] (8000, 41, 41)\n", - " 114926 [cnmf.py: fit():449] [1655546] (8000, 41, 41)\n", - " 114911 [cnmf.py: fit():466] [1655541] Using 1 processes\n", - " 114915 [cnmf.py: fit():436] [1655503] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114911 [cnmf.py: fit():449] [1655519] (8000, 41, 41)\n", - " 114914 [cnmf.py: fit():436] [1655549] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114908 [cnmf.py: fit():436] [1655531] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114924 [params.py: set():1006] [1655562] Changing key p in group temporal from 1 to 0\n", - " 114910 [cnmf.py: fit():436] [1655537] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114913 [cnmf.py: fit():436] [1655470] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114907 [params.py: set():1006] [1655480] Changing key p in group temporal from 1 to 0\n", - " 114907 [cnmf.py: fit():466] [1655486] Using 1 processes\n", - " 114916 [cnmf.py: fit():436] [1655474] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114908 [cnmf.py: fit():436] [1655477] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114917 [cnmf.py: fit():436] [1655506] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114915 [params.py: set():1006] [1655505] Changing key p in group temporal from 1 to 0\n", - " 114906 [cnmf.py: fit():436] [1655552] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114917 [params.py: set():1006] [1663745] Changing key nb in group init from 2 to 1\n", - " 114909 [params.py: set():1006] [1655498] Changing key p in group temporal from 1 to 0\n", - " 114914 [cnmf.py: fit():436] [1655475] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114914 [params.py: set():1006] [1660550] Changing key nb in group init from 2 to 1\n", - " 114906 [params.py: set():1006] [1660361] Changing key nb in group init from 2 to 1\n", - " 114906 [cnmf.py: fit():436] [1655514] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114905 [params.py: set():1006] [1655564] Changing key p in group preprocess from 1 to 0\n", - " 114905 [cnmf.py: fit():436] [1655559] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 115003 [cnmf.py: fit():449] [1655559] (8000, 41, 41)\n", - " 115000 [cnmf.py: fit():449] [1655503] (8000, 41, 41)\n", - " 115002 [cnmf.py: fit():477] [1655541] using 400 pixels per process\n", - " 115001 [cnmf.py: fit():436] [1655505] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 115003 [params.py: set():1006] [1655564] Changing key p in group temporal from 1 to 0\n", - " 114996 [cnmf.py: fit():449] [1655477] (8000, 40, 41)\n", - " 115002 [cnmf.py: fit():466] [1655535] Using 1 processes\n", - " 114978 [cnmf.py: fit():449] [1655521] (8000, 41, 41)\n", - " 114984 [cnmf.py: fit():449] [1655517] (8000, 41, 41)\n", - " 114985 [cnmf.py: fit():436] [1655483] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 115000 [cnmf.py: fit():466] [1655502] Using 1 processes\n", - " 114999 [cnmf.py: fit():449] [1655475] (8000, 41, 40)\n", - " 114982 [cnmf.py: fit():436] [1655495] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114996 [cnmf.py: fit():449] [1655549] (8000, 41, 41)\n", - " 115001 [cnmf.py: fit():449] [1655514] (8000, 41, 41)\n", - " 114991 [cnmf.py: fit():449] [1655474] (8000, 41, 41)\n", - " 114986 [cnmf.py: fit():449] [1655482] (8000, 41, 41)\n", - " 114985 [cnmf.py: fit():449] [1655522] (8000, 41, 41)\n", - " 114989 [cnmf.py: fit():436] [1655507] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114996 [cnmf.py: fit():449] [1655523] (8000, 41, 41)\n", - " 114991 [cnmf.py: fit():477] [1655536] using 400 pixels per process\n", - " 114992 [cnmf.py: fit():466] [1655546] Using 1 processes\n", - " 114977 [cnmf.py: fit():449] [1655532] (8000, 41, 41)\n", - " 114997 [cnmf.py: fit():449] [1655487] (8000, 41, 41)\n", - " 114994 [cnmf.py: fit():466] [1655533] Using 1 processes\n", - " 114989 [cnmf.py: fit():449] [1655500] (8000, 41, 41)\n", - " 114998 [cnmf.py: fit():449] [1655531] (8000, 41, 41)\n", - " 114985 [cnmf.py: fit():436] [1655542] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114981 [cnmf.py: fit():477] [1655547] using 400 pixels per process\n", - " 114987 [cnmf.py: fit():436] [1655550] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114986 [cnmf.py: fit():449] [1655497] (8000, 41, 41)\n", - " 114992 [cnmf.py: fit():449] [1655476] (8000, 41, 41)\n", - " 114999 [cnmf.py: fit():436] [1655562] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114995 [cnmf.py: fit():449] [1655544] (8000, 41, 40)\n", - " 114979 [cnmf.py: fit():449] [1655548] (8000, 41, 41)\n", - " 114991 [cnmf.py: fit():449] [1655499] (8000, 41, 41)\n", - " 114986 [cnmf.py: fit():449] [1655511] (8000, 41, 41)\n", - " 114982 [cnmf.py: fit():449] [1655518] (8000, 40, 41)\n", - " 114988 [cnmf.py: fit():449] [1655525] (8000, 41, 41)\n", - " 114992 [cnmf.py: fit():466] [1655519] Using 1 processes\n", - " 114980 [cnmf.py: fit():449] [1655484] (8000, 41, 41)\n", - " 114998 [cnmf.py: fit():449] [1655506] (8000, 40, 41)\n", - " 114978 [cnmf.py: fit():449] [1655543] (8000, 41, 41)\n", - " 114983 [cnmf.py: fit():449] [1655534] (8000, 41, 41)\n", - " 114981 [params.py: set():1006] [1655539] Changing key p in group temporal from 1 to 0\n", - " 114989 [cnmf.py: fit():449] [1655552] (8000, 41, 41)\n", - " 114995 [cnmf.py: fit():449] [1655470] (8000, 41, 41)\n", - " 114995 [cnmf.py: fit():449] [1655537] (8000, 41, 41)\n", - " 114983 [cnmf.py: fit():449] [1655551] (8000, 41, 41)\n", - " 114983 [cnmf.py: fit():449] [1655529] (8000, 41, 41)\n", - " 115003 [params.py: set():1006] [1655528] Changing key p in group temporal from 1 to 0\n", - " 114987 [cnmf.py: fit():449] [1655538] (8000, 40, 41)\n", - " 114990 [cnmf.py: fit():449] [1655493] (8000, 40, 41)\n", - " 114981 [params.py: set():1006] [1655469] Changing key p in group temporal from 1 to 0\n", - " 115001 [params.py: set():1006] [1660129] Changing key nb in group temporal from 2 to 1\n", - " 114993 [cnmf.py: fit():449] [1655472] (8000, 41, 41)\n", - " 114988 [cnmf.py: fit():436] [1655556] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114979 [cnmf.py: fit():449] [1655557] (8000, 41, 40)\n", - " 114987 [cnmf.py: fit():449] [1655489] (8000, 41, 41)\n", - " 114994 [cnmf.py: fit():449] [1655478] (8000, 41, 41)\n", - " 114978 [cnmf.py: fit():449] [1655526] (8000, 40, 41)\n", - " 114977 [cnmf.py: fit():449] [1655504] (8000, 40, 41)\n", - " 114976 [cnmf.py: fit():436] [1655491] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114988 [params.py: set():1006] [1655545] Changing key p in group temporal from 1 to 0\n", - " 114997 [cnmf.py: fit():449] [1655494] (8000, 41, 41)\n", - " 114997 [cnmf.py: fit():449] [1655509] (8000, 41, 41)\n", - " 114982 [cnmf.py: fit():436] [1655473] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 115000 [params.py: set():1006] [1663745] Changing key nb in group temporal from 2 to 1\n", - " 114999 [params.py: set():1006] [1660550] Changing key nb in group temporal from 2 to 1\n", - " 114980 [cnmf.py: fit():436] [1655479] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114998 [cnmf.py: fit():436] [1655498] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114993 [cnmf.py: fit():449] [1655488] (8000, 41, 41)\n", - " 115002 [params.py: set():1006] [1660361] Changing key nb in group temporal from 2 to 1\n", - " 114977 [cnmf.py: fit():449] [1655563] (8000, 40, 41)\n", - " 114984 [cnmf.py: fit():449] [1655561] (8000, 41, 41)\n", - " 114994 [cnmf.py: fit():477] [1655486] using 400 pixels per process\n", - " 114980 [cnmf.py: fit():449] [1655527] (8000, 41, 41)\n", - " 114990 [cnmf.py: fit():436] [1655490] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114992 [cnmf.py: fit():436] [1655480] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 114993 [cnmf.py: fit():449] [1655553] (8000, 41, 41)\n", - " 114976 [cnmf.py: fit():449] [1655481] (8000, 41, 41)\n", - " 114984 [cnmf.py: fit():449] [1655554] (8000, 41, 41)\n", - " 114979 [cnmf.py: fit():477] [1655508] using 400 pixels per process\n", - " 114990 [cnmf.py: fit():449] [1655510] (8000, 41, 41)\n", - " 114975 [cnmf.py: fit():449] [1655520] (8000, 41, 41)\n", - " 114975 [cnmf.py: fit():466] [1655524] Using 1 processes\n", - " 115044 [cnmf.py: fit():466] [1655482] Using 1 processes\n", - " 115044 [cnmf.py: fit():477] [1655502] using 400 pixels per process\n", - " 115044 [cnmf.py: fit():436] [1655564] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 115045 [cnmf.py: fit():466] [1655549] Using 1 processes\n", - " 115045 [cnmf.py: fit():449] [1655505] (8000, 40, 41)\n", - " 115045 [cnmf.py: fit():466] [1655474] Using 1 processes\n", - " 115046 [cnmf.py: fit():466] [1655475] Using 1 processes\n", - " 115046 [cnmf.py: fit():466] [1655517] Using 1 processes\n", - " 115046 [cnmf.py: fit():466] [1655506] Using 1 processes\n", - " 115047 [cnmf.py: fit():436] [1655528] Parallel processing in a single patch is not available for loaded in memory or sliced data.\n", - " 115047 [cnmf.py: fit():466] [1655499] Using 1 processes\n", - " 115047 [cnmf.py: fit():466] [1655477] Using 1 processes\n", - " 115047 [cnmf.py: fit():449] [1655542] (8000, 41, 41)\n", - " 115048 [cnmf.py: fit():466] [1655522] Using 1 processes\n", - " 115048 [cnmf.py: fit():478] [1655541] using 5000 block_size_temp\n", - " 115048 [cnmf.py: fit():478] [1655536] using 5000 block_size_temp\n", - " 115049 [cnmf.py: fit():466] [1655511] Using 1 processes\n", - " 115049 [cnmf.py: fit():477] [1655519] using 400 pixels per process\n", - " 115049 [cnmf.py: fit():466] [1655476] Using 1 processes\n", - " 115049 [cnmf.py: fit():466] [1655544] Using 1 processes\n", - "IOPub message rate exceeded.\n", - "The Jupyter server will temporarily stop sending output\n", - "to the client in order to avoid crashing it.\n", - "To change this limit, set the config variable\n", - "`--ServerApp.iopub_msg_rate_limit`.\n", - "\n", - "Current values:\n", - "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n", - "ServerApp.rate_limit_window=3.0 (secs)\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "fname_new = params_movie['fname']\n", - "\n", - "\n", - "# %% RUN ANALYSIS\n", "c, dview, n_processes = cm.cluster.setup_cluster(\n", - " backend='local', n_processes=None, single_thread=False)\n", - "\n", - "\n", - "# %% LOAD MEMMAP FILE\n", + " backend='local', n_processes=None, single_thread=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load MEMMAP File" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "# fname_new='Yr_d1_501_d2_398_d3_1_order_F_frames_369_.mmap'\n", "Yr, dims, T = cm.load_memmap(fname_new)\n", "d1, d2 = dims\n", "images = np.reshape(Yr.T, [T] + list(dims), order='F')\n", - "# TODO: needinfo\n", "Y = np.reshape(Yr, dims + (T,), order='F')\n", - "m_images = cm.movie(images)\n", - "\n", - "# TODO: show screenshot 10\n", - "# %% correlation image\n", + "m_images = cm.movie(images)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Correlation image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "if m_images.shape[0] < 10000:\n", " Cn = m_images.local_correlations(\n", " swap_dim=params_movie['swap_dim'], frames_per_chunk=1500)\n", @@ -5093,33 +247,14 @@ "else:\n", " Cn = np.array(cm.load(('/'.join(fname_new.split('/') \n", " [:-3] + ['projections', 'correlation_image_better.tif'])))).squeeze()\n", - "pl.imshow(Cn, cmap='gray', vmax=.95)" + "plt.imshow(Cn, cmap='gray', vmax=.95)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<>:24: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n", - "<>:24: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n", - "/tmp/ipykernel_1655335/2106226496.py:24: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\n", - " if params_movie['init_method'] is not 'sparse_nmf':\n", - " 62256 [params.py: set():1006] [1655335] Changing key init_batch in group online from 200 to 8000\n", - " 62258 [cnmf.py: fit():449] [1655335] (8000, 512, 512)\n", - " 62258 [cnmf.py: fit():466] [1655335] Using 1 processes\n", - " 62258 [cnmf.py: fit():477] [1655335] using 4000 pixels per process\n", - " 62258 [cnmf.py: fit():478] [1655335] using 5000 block_size_temp\n", - " 62259 [params.py: set():1006] [1655335] Changing key n_pixels_per_process in group preprocess from 4000 to 400\n", - " 62259 [params.py: set():1006] [1655335] Changing key n_pixels_per_process in group spatial from 4000 to 400\n", - " 62277 [map_reduce.py: run_CNMF_patches():227] [1655335] Patch size: (40, 40)\n" - ] - } - ], + "outputs": [], "source": [ "\n", "# %% some parameter settings\n", @@ -5172,7 +307,8 @@ "# TODO: show screenshot 12`\n", "# TODO : change the way it is used\n", "crd = plot_contours(A_tot, Cn, thr=params_display['thr_plot'])\n", - "# %% DISCARD LOW QUALITY COMPONENT\n", + "\n", + "# DISCARD LOW QUALITY COMPONENT\n", "final_frate = params_movie['final_frate']\n", "# threshold on space consistency\n", "r_values_min = params_movie['r_values_min_patch']\n", @@ -5252,17 +388,24 @@ " dims[1], YrA=YrA[idx_components_bad, :], img=Cn)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Data" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# Load Data\n", "params_display = {\n", " 'downsample_ratio': .2,\n", " 'thr_plot': 0.8\n", "}\n", + "\n", "try:\n", " fname_new = fname_new[()]\n", "except:\n", @@ -5284,18 +427,22 @@ "metadata": {}, "outputs": [], "source": [ - "from caiman.components_evaluation import evaluate_components_CNN\n", "predictions, final_crops = evaluate_components_CNN(\n", " A, dims, gSig, model_name='model/cnn_model')\n", - "#%%\n", + "\n", "cm.movie(final_crops).play(gain=3, magnification=6, fr=5)\n", - "#%%\n", "cm.movie(np.squeeze(final_crops[np.where(predictions[:, 1] >= 0.5)[0]])).play(\n", " gain=2., magnification=5, fr=5)\n", - "#%%\n", "cm.movie(np.squeeze(final_crops[np.where(predictions[:, 0] >= 0.5)[0]])).play(\n", - " gain=2., magnification=5, fr=5)\n", - "#%%\n", + " gain=2., magnification=5, fr=5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "thresh = .5\n", "idx_components_cnn = np.where(predictions[:, 1] >= thresh)[0]\n", "idx_components_bad_cnn = np.where(predictions[:, 0] > (1 - thresh))[0]\n", @@ -5303,15 +450,15 @@ "print(' ***** ')\n", "print((len(final_crops)))\n", "print((len(idx_components_cnn)))\n", - "# print((len(idx_blobs)))\n", - "#%%\n", + "\n", "idx_components_r = np.where((r_values >= .5))[0]\n", "idx_components_raw = np.where(fitness_raw < -5)[0]\n", "idx_components_delta = np.where(fitness_delta < -5)[0]\n", "#idx_and_condition_1 = np.where((r_values >= .65) & ((fitness_raw < -20) | (fitness_delta < -20)) )[0]\n", "\n", "idx_components = np.union1d(idx_components_r, idx_components_raw)\n", - "idx_components = np.union1d(idx_components, idx_components_delta)" + "idx_components = np.union1d(idx_components, idx_components_delta)\n", + "idx_components_bad = np.setdiff1d(list(range(len(r_values))), idx_components)" ] }, { @@ -5320,31 +467,43 @@ "metadata": {}, "outputs": [], "source": [ - "idx_components_bad = np.setdiff1d(list(range(len(r_values))), idx_components)\n", - "\n", "print(' ***** ')\n", "print((len(r_values)))\n", "print((len(idx_components)))\n", - "#%%\n", - "pl.subplot(1, 2, 1)\n", + "\n", + "plt.subplot(1, 2, 1)\n", "crd = plot_contours(A.tocsc()[:, idx_components],\n", " Cn, thr=params_display['thr_plot'], vmax=0.35)\n", - "pl.subplot(1, 2, 2)\n", + "plt.subplot(1, 2, 2)\n", "crd = plot_contours(A.tocsc()[:, idx_components_bad],\n", - " Cn, thr=params_display['thr_plot'], vmax=0.35)\n", - "#%%\n", + " Cn, thr=params_display['thr_plot'], vmax=0.35)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run Analysis " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "c, dview, n_processes = cm.cluster.setup_cluster(\n", " backend='local', n_processes=None, single_thread=False)\n", "\n", - "#%%\n", "gt_file = os.path.join(os.path.split(fname_new)[0], os.path.split(\n", " fname_new)[1][:-4] + 'match_masks.npz')\n", + "\n", "with np.load(gt_file) as ld:\n", " print(ld.keys())\n", " locals().update(ld)\n", " A_gt = scipy.sparse.coo_matrix(A_gt[()])\n", " dims = (d1, d2)\n", - "#%%\n", + "\n", "view_patches_bar(Yr, scipy.sparse.coo_matrix(A_gt.toarray()[\n", " :, idx_components_gt]), C_gt[idx_components_gt], b, f, dims[0], dims[1], YrA=YrA_gt[idx_components_gt], img=Cn)" ] @@ -5355,37 +514,46 @@ "metadata": {}, "outputs": [], "source": [ - "from sklearn.preprocessing import normalize\n", - "\n", "dist_A = (normalize(A_gt.tocsc()[:, idx_components_gt], axis=0).T.dot(\n", " normalize(A.tocsc()[:, :], axis=0))).toarray()\n", "dist_C = normalize(C_gt[idx_components_gt], axis=1).dot(\n", " normalize(C[:], axis=1).T)\n", "dist_A = dist_A * (dist_A > 0)\n", "\n", - "pl.figure(figsize=(30, 20))\n", + "plt.figure(figsize=(30, 20))\n", "tp_gt, tp_comp, fn_gt, fp_comp, performance_cons_off = cm.base.rois.nf_match_neurons_in_binary_masks(A_gt.toarray()[:, idx_components_gt].reshape([dims[0], dims[1], -1], order='F').transpose([2, 0, 1]),\n", " A.toarray()[:, :].reshape([dims[0], dims[1], -1], order='F').transpose([2, 0, 1]), thresh_cost=.7, min_dist=10,\n", " print_assignment=False, plot_results=True, Cn=Cn, labels=['GT', 'Offline'], D=[1 - dist_A * (dist_C > .8)])\n", - "pl.rcParams['pdf.fonttype'] = 42\n", + "plt.rcParams['pdf.fonttype'] = 42\n", "font = {'family': 'Myriad Pro',\n", " 'weight': 'regular',\n", " 'size': 20}\n", - "pl.rc('font', **font)\n", - "#%%\n", - "# TODO: needinfo\n", + "plt.rc('font', **font)\n", + "\n", "idx_final = tp_comp[np.where(dist_A[tp_gt, tp_comp] > 0.7)[0]]\n", "view_patches_bar(Yr, scipy.sparse.coo_matrix(A.toarray()[\n", " :, idx_final]), C[idx_final], b, f, dims[0], dims[1], YrA=YrA[idx_final], img=Cn)\n", - "#%%\n", + "\n", "view_patches_bar(Yr, scipy.sparse.coo_matrix(A.toarray()[\n", " :, fp_comp]), C[fp_comp], b, f, dims[0], dims[1], YrA=YrA[fp_comp], img=Cn)\n", - "#%%\n", + "\n", "view_patches_bar(Yr, scipy.sparse.coo_matrix(A_gt.toarray()[\n", " :, fn_gt]), C_gt[fn_gt], b_gt, f_gt, dims[0], dims[1], YrA=YrA_gt[fn_gt], img=Cn)\n", - "#%%\n", - "pl.hist(r_values[tp_comp], 30)\n", - "#%%\n", + "\n", + "plt.hist(r_values[tp_comp], 30)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "np.savez(os.path.join(os.path.split(fname_new)[0], os.path.split(fname_new)[1][:-4] + '_training_set.npz'), fname_new=fname_new,\n", " A_seeded=A_gt.tocsc()[\n", " :, idx_components_gt], C_seeded=C_gt[idx_components_gt], YrA_seeded=YrA_gt[idx_components_gt],\n", @@ -5397,13 +565,20 @@ " :, fp_comp], C_negative=C[fp_comp], YrA_negative=YrA[fp_comp],\n", " r_values=r_values, fitness_delta=fitness_delta, fitness_raw=fitness_raw, Cn=Cn, dims=dims\n", ")\n", - "#%%\n", + "\n", "with np.load(os.path.join(os.path.split(fname_new)[0], os.path.split(fname_new)[1][:-4] + '_training_set.npz')) as ld:\n", " print(ld.keys())\n", " locals().update(ld)\n", " fname_new = fname_new[()]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plots" + ] + }, { "cell_type": "code", "execution_count": null, @@ -5411,27 +586,39 @@ "outputs": [], "source": [ "thr = 0.98\n", - "# TODO: show screenshot 14\n", "pl.subplot(1, 3, 1)\n", "crd = plot_contours(A_matched[()], Cn, thr=thr)\n", "pl.subplot(1, 3, 2)\n", "crd = plot_contours(A_unmatched[()], Cn, thr=thr)\n", "pl.subplot(1, 3, 3)\n", "crd = plot_contours(A_negative[()], Cn, thr=thr)\n", - "#%%\n", - "pl.subplot(1, 3, 1)\n", + "\n", + "plt.subplot(1, 3, 1)\n", "crd = pl.imshow(A_matched[()].sum(1).reshape(\n", " dims, order='F'), vmax=A_matched[()].max() * .2)\n", - "pl.subplot(1, 3, 2)\n", + "plt.subplot(1, 3, 2)\n", "crd = pl.imshow(A_unmatched[()].sum(1).reshape(\n", " dims, order='F'), vmax=A_unmatched[()].max() * .2)\n", - "pl.subplot(1, 3, 3)\n", + "plt.subplot(1, 3, 3)\n", "crd = pl.imshow(A_negative[()].sum(1).reshape(\n", - " dims, order='F'), vmax=A_negative[()].max() * .2)\n", - "#%%\n", + " dims, order='F'), vmax=A_negative[()].max() * .2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Maskings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "masks_sue = scipy.io.loadmat('/mnt/xfs1/home/agiovann/Downloads/yuste_sue_masks.mat')\n", "\n", - "#%%\n", "with h5py.File('/mnt/xfs1/home/agiovann/Downloads/yuste_1.protoroi.mat')as f:\n", " print(f.keys())\n", " print(list(f['repository']))\n", diff --git a/caiman/train/prepare_training_set.ipynb b/caiman/train/prepare_training_set.ipynb index 622720957..b1e59d592 100644 --- a/caiman/train/prepare_training_set.ipynb +++ b/caiman/train/prepare_training_set.ipynb @@ -7,20 +7,26 @@ "outputs": [], "source": [ "import cv2\n", + "import itertools\n", "import numpy as np\n", "import os\n", + "from sklearn.preprocessing import normalize\n", "\n", "import caiman as cm\n", "from caiman.utils.utils import download_demo\n", - "from caiman.base.rois import extract_binary_masks_blob\n", + "from caiman.base.rois import com, extract_binary_masks_blob\n", "from caiman.utils.visualization import plot_contours, view_patches_bar\n", "from caiman.source_extraction.cnmf import cnmf as cnmf\n", "from caiman.motion_correction import MotionCorrect, tile_and_correct, motion_correction_piecewise \n", "from caiman.components_evaluation import estimate_components_quality, evaluate_components\n", - "from caiman.tests.comparison import comparison\n", - "\n", - "from caiman.base.rois import com\n", - "from sklearn.preprocessing import normalize" + "from caiman.tests.comparison import comparison" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading up the Ground Truth Files" ] }, { @@ -50,6 +56,13 @@ " 'gSig': [7, 7]} ]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Data and Analysis using match_masks.npz file " + ] + }, { "cell_type": "code", "execution_count": null, @@ -62,7 +75,7 @@ " gSig = dc['gSig']\n", " gt_file = os.path.join(os.path.split(fname)[0], os.path.split(fname)[\n", " 1][:-4] + 'match_masks.npz')\n", - " # LOAD DATA\n", + " \n", " #analysis_file = '/mnt/ceph/neuro/jeremie_analysis/neurofinder.03.00.test/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_._results_analysis.npz'\n", " with np.load(os.path.join(os.path.split(fname)[0], os.path.split(fname)[1][:-4] + 'results_analysis.npz'), encoding='latin1') as ld:\n", " print(ld.keys())\n", @@ -108,6 +121,13 @@ " r_values=r_values, fitness_delta=fitness_delta, fitness_raw=fitness_raw, Cn=Cn, dims=dims, gSig=gSig)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Obtain Training Files" + ] + }, { "cell_type": "code", "execution_count": null, @@ -118,13 +138,27 @@ "print(training_files)\n", "crop_size = 50\n", "half_crop = crop_size // 2\n", - "from sklearn.preprocessing import normalize\n", "id_file = 0\n", "reference_gSig_neuron = 5\n", "#folder = '/mnt/xfs1/home/agiovann/SOFTWARE/CaImAn/images_examples'\n", "all_masks_gt = []\n", "labels_gt = []\n", - "traces_gt = []\n", + "traces_gt = []" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Training Files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "for fl in training_files:\n", "\n", " with np.load(fl) as ld:\n", @@ -150,7 +184,7 @@ " A_matched = normalize(A_matched, axis=0)\n", " A_unmatched = normalize(A_unmatched, axis=0)\n", " A_negative = normalize(A_negative, axis=0)\n", - "\n", + " \n", " masks_gt = np.concatenate([A_matched.reshape(tuple(dims) + (-1,), order='F').transpose([2, 0, 1]), A_unmatched.reshape(tuple(\n", " dims) + (-1,), order='F').transpose([2, 0, 1]), A_negative.reshape(tuple(dims) + (-1,), order='F').transpose([2, 0, 1])], axis=0)\n", " labels_gt = np.concatenate([labels_gt, np.ones(\n", @@ -182,7 +216,7 @@ " borders = np.array(crop_img.shape)\n", " img_tmp = np.zeros_like(crop_img)\n", " crop_img = cv2.resize(crop_img, dsize=None, fx=zoom, fy=zoom)\n", - "\n", + " \n", " deltaw = (half_crop * 2 - crop_img.shape[0]) // 2\n", " deltah = (half_crop * 2 - crop_img.shape[1]) // 2\n", " img_tmp[deltaw:deltaw + crop_img.shape[0],\n", @@ -206,7 +240,7 @@ " vertical_flip=True,\n", " random_mult_range=[.25, 2]\n", " )\n", - "\n", + " \n", " count_neuro += 1\n", " for x_batch, y_batch in datagen.flow(np.repeat(crop_img[np.newaxis, :, :], 10, 0)[:, :, :, None], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0], batch_size=10):\n", " print(y_batch)\n", @@ -234,23 +268,30 @@ "outputs": [], "source": [ "all_masks_gt = np.vstack(all_masks_gt)\n", - "#%%\n", "cm.movie(np.squeeze(all_masks_gt[labels_gt == 0])).play(\n", " gain=3., magnification=10)\n", - "#%%\n", "np.savez('ground_truth_components_minions.npz',\n", " all_masks_gt=all_masks_gt, labels_gt=labels_gt, traces_gt=traces_gt)\n", - "#%%\n", - "import itertools\n", - "\n", "\n", "def grouper(n, iterable, fillvalue=None):\n", " \"grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx\"\n", " args = [iter(iterable)] * n\n", - " return itertools.zip_longest(*args, fillvalue=fillvalue)\n", - "\n", - "\n", - "#%% curate once more. Remove wrong negatives\n", + " return itertools.zip_longest(*args, fillvalue=fillvalue)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Curate Once More. Remove Wrong Negatives" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "negatives = np.where(labels_gt == 1)[0]\n", "wrong = []\n", "count = 0\n", @@ -276,21 +317,49 @@ " wrong.append(np.array(a)[np.ravel_multi_index(\n", " [inp[:, 1], inp[:, 0]], shps_img_mont)])\n", " np.save('temp_label_pos_minions.npy', wrong)\n", - " pl.close()\n", - "#%%\n", - "pl.imshow(montage2d(all_masks_gt[np.concatenate(wrong)].squeeze()))\n", - "#%%\n", + " plt.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot Masks" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.imshow(montage2d(all_masks_gt[np.concatenate(wrong)].squeeze()))\n", + "\n", "lab_pos_wrong = np.load('temp_label_pos_minions.npy')\n", "lab_neg_wrong = np.load('temp_label_neg_plus_minions.npy')\n", "\n", "labels_gt_cur = labels_gt.copy()\n", "labels_gt_cur[np.concatenate(lab_pos_wrong)] = 0\n", - "labels_gt_cur[np.concatenate(lab_neg_wrong)] = 1\n", - "\n", + "labels_gt_cur[np.concatenate(lab_neg_wrong)] = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save the file to train the network" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ "np.savez('ground_truth_comoponents_curated_minions.npz',\n", " all_masks_gt=all_masks_gt, labels_gt_cur=labels_gt_cur)\n", - "#%%\n", - "pl.imshow(montage2d(all_masks_gt[labels_gt_cur == 0].squeeze()))" + "\n", + "plt.imshow(montage2d(all_masks_gt[labels_gt_cur == 0].squeeze()))" ] } ], diff --git a/caiman/train/train_cnn_model_pytorch.py b/caiman/train/train_cnn_model_helper.py similarity index 67% rename from caiman/train/train_cnn_model_pytorch.py rename to caiman/train/train_cnn_model_helper.py index bb2a72b97..8bc589e91 100644 --- a/caiman/train/train_cnn_model_pytorch.py +++ b/caiman/train/train_cnn_model_helper.py @@ -1,6 +1,10 @@ import numpy as np import os import keras +from keras.layers import Input, Conv2D, Activation, MaxPooling2D, Dropout, Flatten, Dense +from keras.models import save_model, load_model +from sklearn.model_selection import train_test_split +from sklearn.utils import class_weight as cw import torch import torch.nn as nn import torch.nn.functional as F @@ -8,6 +12,9 @@ import caiman as cm from caiman.paths import caiman_datadir +from caiman.utils.image_preprocessing_keras import ImageDataGenerator + +os.environ["KERAS_BACKEND"] = "torch" class cnn_model_pytorch(torch.nn.Module): def __init__(self, in_channels, num_classes): @@ -61,7 +68,6 @@ def get_batch_accuracy(output, y, N): return correct / N def train(model, train_loader, loss_function, optimizer, train_N, augment): - loss = 0 accuracy = 0 @@ -78,7 +84,6 @@ def train(model, train_loader, loss_function, optimizer, train_N, augment): print('Train - Loss: {:.4f} Accuracy: {:.4f}'.format(loss, accuracy)) def validate(model, valid_loader, loss_function, optimizer, valid_N, augment): - loss = 0 accuracy = 0 @@ -91,22 +96,37 @@ def validate(model, valid_loader, loss_function, optimizer, valid_N, augment): accuracy += get_batch_accuracy(output, y, valid_N) print('Valid - Loss: {:.4f} Accuracy: {:.4f}'.format(loss, accuracy)) -if __name__ == "__main__": - batch_size = 128 - num_classes = 2 - epochs = 5000 - test_fraction = 0.25 - augmentation = True - img_rows, img_cols = 50, 50 # input image dimensions - - with np.load('/mnt/ceph/data/neuro/caiman/data_minions/ground_truth_components_curated_minions.npz') as ld: - all_masks_gt = ld['all_masks_gt'] - labels_gt = ld['labels_gt_cur'] - - in_channels = 1 - cnn_model = pytorch_cnn_model(in_channels, num_classes) - - # model_path = save_model_pytorch(cnn_model) - # loaded_model = load_model_pytorch(model_path) - # print(loaded_model) +def cnn_model_keras(input_shape, num_classes): + sequential_model = keras.Sequential([ + Input(shape=input_shape, dtype="float32"), + Conv2D(filters=32, kernel_size=(3,3), strides=(1, 1), + activation="relu"), + Conv2D(filters=32, kernel_size=(3,3), strides=(1, 1), + activation="relu"), + MaxPooling2D(pool_size=(2, 2)), + Dropout(rate=0.25), + Conv2D(filters=64, kernel_size=(3,3), strides=(1, 1), + padding="same", activation="relu"), + Conv2D(filters=64, kernel_size=(3,3), strides=(1, 1), + activation="relu"), + MaxPooling2D(pool_size=(2, 2)), + Dropout(rate=0.25), + Flatten(), + Dense(units=512, activation="relu"), + Dropout(rate=0.5), + Dense(units=num_classes, activation="relu"), + ]) + return sequential_model + +def save_model_keras(model, name: str): + model_name = os.path.join(caiman_datadir(), 'model', name) + model_path = model_name + ".keras" + model.save(model_path) + print('Saved trained model at %s ' % model_path) + return model_path + +def load_model_keras(model_path: str): + loaded_model = load_model(model_path) + print('Load trained model at %s ' % model_path) + return loaded_model diff --git a/caiman/train/train_cnn_model_keras.ipynb b/caiman/train/train_cnn_model_keras.ipynb index e908a26c2..ac882fe29 100644 --- a/caiman/train/train_cnn_model_keras.ipynb +++ b/caiman/train/train_cnn_model_keras.ipynb @@ -37,7 +37,7 @@ "\n", "import caiman as cm\n", "from caiman.paths import caiman_datadir\n", - "from caiman.train.train_cnn_model_keras import cnn_model_keras, save_model_keras, load_model_keras\n", + "from caiman.train.train_cnn_model_helper import cnn_model_keras, save_model_keras, load_model_keras\n", "\n", "os.environ[\"KERAS_BACKEND\"] = \"torch\"" ] diff --git a/caiman/train/train_cnn_model_keras.py b/caiman/train/train_cnn_model_keras.py deleted file mode 100644 index 1d8167b32..000000000 --- a/caiman/train/train_cnn_model_keras.py +++ /dev/null @@ -1,97 +0,0 @@ -import numpy as np -import os -import keras -from keras.layers import Input, Conv2D, Activation, MaxPooling2D, Dropout, Flatten, Dense -from keras.models import save_model, load_model -from sklearn.model_selection import train_test_split -from sklearn.utils import class_weight as cw - -import caiman as cm -from caiman.paths import caiman_datadir -from caiman.utils.image_preprocessing_keras import ImageDataGenerator - -os.environ["KERAS_BACKEND"] = "torch" - -def cnn_model_keras(input_shape, num_classes): - sequential_model = keras.Sequential([ - Input(shape=input_shape, dtype="float32"), - Conv2D(filters=32, kernel_size=(3,3), strides=(1, 1), - activation="relu"), - Conv2D(filters=32, kernel_size=(3,3), strides=(1, 1), - activation="relu"), - MaxPooling2D(pool_size=(2, 2)), - Dropout(rate=0.25), - Conv2D(filters=64, kernel_size=(3,3), strides=(1, 1), - padding="same", activation="relu"), - Conv2D(filters=64, kernel_size=(3,3), strides=(1, 1), - activation="relu"), - MaxPooling2D(pool_size=(2, 2)), - Dropout(rate=0.25), - Flatten(), - Dense(units=512, activation="relu"), - Dropout(rate=0.5), - Dense(units=num_classes, activation="relu"), - ]) - return sequential_model - -def save_model_keras(model, name: str): - model_name = os.path.join(caiman_datadir(), 'model', name) - model_path = model_name + ".keras" - model.save(model_path) - print('Saved trained model at %s ' % model_path) - return model_path - -def load_model_keras(model_path: str): - loaded_model = load_model(model_path) - print('Load trained model at %s ' % model_path) - return loaded_model - -if __name__ == "__main__": - batch_size = 128 - num_classes = 2 - epochs = 5000 - test_fraction = 0.25 - augmentation = True - img_rows, img_cols = 50, 50 # input image dimensions - - with np.load('/mnt/ceph/data/neuro/caiman/data_minions/ground_truth_components_curated_minions.npz') as ld: - all_masks_gt = ld['all_masks_gt'] - labels_gt = ld['labels_gt_cur'] - - x_train, x_test, y_train, y_test = train_test_split( - all_masks_gt, labels_gt, test_size=test_fraction) - - class_weight = cw.compute_class_weight(class_weight='balanced', - classes=np.unique(y_train), y=y_train) - - if keras.config.image_data_format() == 'channels_first': - x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) - x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) - input_shape = (1, img_rows, img_cols) - else: - x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) - x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) - input_shape = (img_rows, img_cols, 1) - - x_train = x_train.astype('float32') - x_test = x_test.astype('float32') - print('x_train shape:', x_train.shape) - print(x_train.shape[0], 'train samples') - print(x_test.shape[0], 'test samples') - - # convert class vectors to binary class matrices - y_train = keras.utils.to_categorical(y_train, num_classes) - y_test = keras.utils.to_categorical(y_test, num_classes) - - cnn_model_cifar = keras_cnn_model_cifar(input_shape, num_classes) - cnn_model_cifar.summary() - - opt = keras.optimizers.RMSprop(learning_rate=0.0001, weight_decay=1e-6) - cnn_model_cifar.compile(loss=keras.losses.categorical_crossentropy, - optimizer=opt, metrics=['accuracy']) #don't need this - - #Augmentations - - score = cnn_model_cifar.evaluate(x_test, y_test, verbose=0) - print('Test loss:', score[0]) - print('Test accuracy:', score[1]) \ No newline at end of file diff --git a/caiman/train/train_cnn_model_pytorch.ipynb b/caiman/train/train_cnn_model_pytorch.ipynb index 00cf973e0..c1eff0728 100644 --- a/caiman/train/train_cnn_model_pytorch.ipynb +++ b/caiman/train/train_cnn_model_pytorch.ipynb @@ -42,7 +42,6 @@ "import numpy as np\n", "import os\n", "import keras \n", - "\n", "import torch\n", "from torch.optim import Adam\n", "from torch.utils.data import Dataset, TensorDataset, DataLoader\n", @@ -50,8 +49,9 @@ "\n", "import caiman as cm\n", "from caiman.paths import caiman_datadir\n", - "from caiman.train.train_cnn_model_pytorch import cnn_model_pytorch, get_batch_accuracy, load_model_pytorch, save_model_pytorch\n", - "from caiman.train.train_cnn_model_pytorch import train_test_split, train, validate \n", + "from caiman.train.train_cnn_model_helper import cnn_model_pytorch, get_batch_accuracy, load_model_pytorch, save_model_pytorch\n", + "from caiman.train.train_cnn_model_helper import train_test_split, train, validate \n", + "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "torch.cuda.is_available()" ] diff --git a/caiman/train/training.md b/caiman/train/training.md index 65f000773..d3b2ef033 100644 --- a/caiman/train/training.md +++ b/caiman/train/training.md @@ -1,6 +1,6 @@ -HOW TO GENERATE GROUND TRUTH DATA +HOW TO GENERATE GROUND TRUTH DATA TO TRAIN THE NETWORK -Step 1 go to script ground_truth_cnmf_seeded.py and generate new ground truth.This will generate a file ending in *match_masks.npz -Step 2 If you want to match the cnmf-seeded components from GT with the results of a CNMF run you can use the script match_seeded_gt.py -Step 3 You might want to clean up the components, you can use the prepare_training_set.py -Step 4 In order to train the network use either the train_net_minst.py or train_net_cifar.py \ No newline at end of file +Step 1: Go to ground_truth_cnmf_seeded.py and generate new ground truth. This generates a file ending in match_masks.npz +Step 2: Go to match_seeded_gt.py IF you want to match the cnmf-seeded components from GT with the results of a CNMF run +Step 3: Go to prepare_training_set.py IF you might want to clean up the components +Step 4: Train the network from train_cnn_model_pytorch.ipynb (train_cnn_model_keras.ipynb not in use) \ No newline at end of file From 3d166be0fecc3468184dd6eb2408a6ed08e45d69 Mon Sep 17 00:00:00 2001 From: Manuel Paez <79882298+mannypaeza@users.noreply.github.com> Date: Fri, 31 Jan 2025 16:56:34 -0500 Subject: [PATCH 4/4] local device changes --- Jenkinsfile | 2 +- caiman/base/__init__.py | 3 + caiman/base/movies.py | 9 +- caiman/base/timeseries.py | 4 +- caiman/base/traces.py | 3 +- caiman/components_evaluation.py | 68 +-- caiman/source_extraction/cnmf/online_cnmf.py | 74 ++-- caiman/tests/test_mrcnn_pytorch.py | 22 + caiman/tests/test_pytorch.py | 47 +-- caiman/train/__init__.py | 3 +- caiman/train/ground_truth_cnmf_seeded.ipynb | 393 +++++++++++++----- .../{train_cnn_model_helper.py => helper.py} | 3 +- caiman/train/match_seeded_gt.ipynb | 170 +++++--- caiman/train/prepare_training_set.ipynb | 63 ++- caiman/train/train_cnn_model_pytorch.ipynb | 73 ++-- caiman/train/training.md | 4 +- caiman/utils/nn_models.py | 19 +- environment-minimal.yml | 5 +- environment.yml | 3 +- 19 files changed, 643 insertions(+), 325 deletions(-) create mode 100644 caiman/tests/test_mrcnn_pytorch.py rename caiman/train/{train_cnn_model_helper.py => helper.py} (99%) diff --git a/Jenkinsfile b/Jenkinsfile index 4c4fff6e5..a9bbc4ec6 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -27,7 +27,7 @@ pipeline { sh 'mamba env create -q -f environment.yml -p $CONDA_ENV' sh '''#!/bin/bash -ex source activate $CONDA_ENV - export KERAS_BACKEND=tensorflow + export KERAS_BACKEND=torch pip install . TEMPDIR=$(mktemp -d) export CAIMAN_DATA=$TEMPDIR/caiman_data diff --git a/caiman/base/__init__.py b/caiman/base/__init__.py index e69de29bb..b46d51dfe 100644 --- a/caiman/base/__init__.py +++ b/caiman/base/__init__.py @@ -0,0 +1,3 @@ +#!/usr/bin/env python + +from caiman.base.timeseries import timeseries \ No newline at end of file diff --git a/caiman/base/movies.py b/caiman/base/movies.py index f0a9a5796..6f6bc368b 100644 --- a/caiman/base/movies.py +++ b/caiman/base/movies.py @@ -37,12 +37,15 @@ import caiman.utils.sbx_utils import caiman.utils.visualization +from caiman.base.timeseries import timeseries +from caiman.base.traces import trace + try: cv2.setNumThreads(0) except: pass -class movie(caiman.base.timeseries.timeseries): +class movie(timeseries): """ Class representing a movie. This class subclasses timeseries, that in turn subclasses ndarray @@ -895,7 +898,7 @@ def partition_FOV_KMeans(self, fovs = cv2.resize(np.uint8(fovs), (w1, h1), 1. / fx, 1. / fy, interpolation=cv2.INTER_NEAREST) return np.uint8(fovs), mcoef, distanceMatrix - def extract_traces_from_masks(self, masks: np.ndarray) -> caiman.base.traces.trace: + def extract_traces_from_masks(self, masks: np.ndarray) -> trace: """ Args: masks: array, 3D with each 2D slice bein a mask (integer or fractional) @@ -914,7 +917,7 @@ def extract_traces_from_masks(self, masks: np.ndarray) -> caiman.base.traces.tra pixelsA = np.sum(A, axis=1) A = A / pixelsA[:, None] # obtain average over ROI - traces = caiman.base.traces.trace(np.dot(A, np.transpose(Y)).T, **self.__dict__) + traces = trace(np.dot(A, np.transpose(Y)).T, **self.__dict__) return traces def resize(self, fx=1, fy=1, fz=1, interpolation=cv2.INTER_AREA): diff --git a/caiman/base/timeseries.py b/caiman/base/timeseries.py index 334b2d786..92ad10250 100644 --- a/caiman/base/timeseries.py +++ b/caiman/base/timeseries.py @@ -33,7 +33,7 @@ pass -class timeseries(np.ndarray): +class timeseries(np.ndarray): """ Class representing a time series. """ @@ -88,7 +88,7 @@ def __array_prepare__(self, out_arr, context=None): if context is not None: inputs = context[1] for inp in inputs: - if isinstance(inp, timeseries): + if isinstance(inp, timeseries): if frRef is None: frRef = inp.fr else: diff --git a/caiman/base/traces.py b/caiman/base/traces.py index 973f1fca1..ad3b90f4b 100644 --- a/caiman/base/traces.py +++ b/caiman/base/traces.py @@ -8,6 +8,7 @@ plt.ion() import caiman.base.timeseries +from caiman.base.timeseries import timeseries try: cv2.setNumThreads(0) @@ -18,7 +19,7 @@ # This holds the trace class, which is a specialised Caiman timeseries class. -class trace(caiman.base.timeseries.timeseries): +class trace(timeseries): """ Class representing a trace. diff --git a/caiman/components_evaluation.py b/caiman/components_evaluation.py index d0a74668d..c6486e90e 100644 --- a/caiman/components_evaluation.py +++ b/caiman/components_evaluation.py @@ -273,35 +273,35 @@ def evaluate_components_CNN(A, if not isGPU and 'CAIMAN_ALLOW_GPU' not in os.environ: print("GPU run not requested, disabling use of GPUs") os.environ['CUDA_VISIBLE_DEVICES'] = '-1' - try: - os.environ["KERAS_BACKEND"] = "torch" - from keras.models import model_load - use_keras = True - logging.info('Using Keras') - except (ModuleNotFoundError): - use_keras = False - logging.info('Using Torch') + # try: + # os.environ["KERAS_BACKEND"] = "torch" + # from keras.models import model_load + # use_keras = True + # logging.info('Using Keras') + # except (ModuleNotFoundError): + # use_keras = False + logging.info('Using Torch') if loaded_model is None: - if use_keras: - if os.path.isfile(os.path.join(caiman_datadir(), model_name + ".keras")): - model_file = os.path.join(caiman_datadir(), model_name + ".keras") - elif os.path.isfile(model_name + ".keras"): - model_file = model_name + ".keras" - else: - raise FileNotFoundError(f"File for requested model {model_name} not found") - - print(f"USING MODEL (keras API): {model_file}") - loaded_model = model_load(model_file) + # if use_keras: + # if os.path.isfile(os.path.join(caiman_datadir(), model_name + ".keras")): + # model_file = os.path.join(caiman_datadir(), model_name + ".keras") + # elif os.path.isfile(model_name + ".keras"): + # model_file = model_name + ".keras" + # else: + # raise FileNotFoundError(f"File for requested model {model_name} not found") + # + # print(f"USING MODEL (keras API): {model_file}") + # loaded_model = model_load(model_file) + #else: + if os.path.isfile(os.path.join(caiman_datadir(), model_name + ".pt")): + model_file = os.path.join(caiman_datadir(), model_name + ".pt") + elif os.path.isfile(model_name + ".pt"): + model_file = model_name + ".pt" else: - if os.path.isfile(os.path.join(caiman_datadir(), model_name + ".pt")): - model_file = os.path.join(caiman_datadir(), model_name + ".pt") - elif os.path.isfile(model_name + ".pt"): - model_file = model_name + ".pt" - else: - raise FileNotFoundError(f"File for requested model {model_name} not found") - print(f"USING MODEL (tensorflow API): {model_file}") - loaded_model = torch.load(model_file) + raise FileNotFoundError(f"File for requested model {model_name} not found") + print(f"USING MODEL (PyTorch API): {model_file}") + loaded_model = torch.load(model_file) logging.debug("Loaded model from disk") @@ -315,14 +315,14 @@ def evaluate_components_CNN(A, half_crop[1]:com[1] + half_crop[1]] for mm, com in zip(A.tocsc().T, coms) ] final_crops = np.array([cv2.resize(im / np.linalg.norm(im), (patch_size, patch_size)) for im in crop_imgs]) - if use_keras: - predictions = loaded_model.predict(final_crops[:, :, :, np.newaxis], batch_size=32, verbose=1) - else: - final_crops = torch.tensor(final_crops, dtype=torch.float32) - final_crops = torch.reshape(final_crops, (-1, final_crops.shape[-1], - final_crops.shape[1], final_crops.shape[2])) - with torch.no_grad(): - prediction = loaded_model(final_crops[:, np.newaxis, :, :]) + # if use_keras: + # predictions = loaded_model.predict(final_crops[:, :, :, np.newaxis], batch_size=32, verbose=1) + # else: + final_crops = torch.tensor(final_crops, dtype=torch.float32) + final_crops = torch.reshape(final_crops, (-1, final_crops.shape[-1], + final_crops.shape[1], final_crops.shape[2])) + with torch.no_grad(): + predictions = loaded_model(final_crops[:, np.newaxis, :, :]) return predictions, final_crops diff --git a/caiman/source_extraction/cnmf/online_cnmf.py b/caiman/source_extraction/cnmf/online_cnmf.py index 1fe25d75d..2e008fae0 100644 --- a/caiman/source_extraction/cnmf/online_cnmf.py +++ b/caiman/source_extraction/cnmf/online_cnmf.py @@ -15,7 +15,7 @@ @url http://papers.nips.cc/paper/6832-onacid-online-analysis-of-calcium-imaging-data-in-real-time Implemented in PyTorch -Date: July 18, 2024 +Date: January 7th, 2025 """ import cv2 @@ -323,29 +323,26 @@ def _prepare_object(self, Yr, T, new_dims=None, idx_components=None): if self.params.get('online', 'path_to_model') is None or self.params.get('online', 'sniper_mode') is False: loaded_model = None self.params.set('online', {'sniper_mode': False}) - self.tf_in = None - self.tf_out = None - # self.use_torch = None + # self.tf_in = None + # self.tf_out = None else: - try: - from keras.models import load_model - logging.info('Using Keras') - use_keras = True - except(ModuleNotFoundError): - use_keras = False - logging.info('Using Torch') + # try: + # from keras.models import load_model + # use_keras = True + # logging.info('Using Keras') + # use_keras = True + # except(ModuleNotFoundError): + # use_keras = False + logging.info('Using Torch') path = self.params.get('online', 'path_to_model').split(".")[:-1] - if use_keras: - # uses online model -> be careful - model_path = ".".join(path + ["keras"]) - loaded_model = model_load(model_path) - # self.use_torch = False - else: - model_path = '.'.join(path + ['pt']) - loaded_model = load_graph(model_path) - loaded_model = torch.load(model_file) - # self.use_torch = True + # if use_keras: + # model_path = ".".join(path + ["keras"]) + # loaded_model = model_load(model_path) + + model_path = '.'.join(path + ['pt']) + loaded_model = load_graph(model_path) + # loaded_model = torch.load(model_file) self.loaded_model = loaded_model @@ -547,8 +544,7 @@ def fit_next(self, t, frame_in, num_iters_hals=3): sniper_mode=self.params.get('online', 'sniper_mode'), use_peak_max=self.params.get('online', 'use_peak_max'), mean_buff=self.estimates.mean_buff, - tf_in=self.tf_in, tf_out=self.tf_out, - # use_torch=self.use_torch, + # tf_in=self.tf_in, tf_out=self.tf_out, ssub_B=ssub_B, W=self.estimates.W if self.is1p else None, b0=self.estimates.b0 if self.is1p else None, corr_img=self.estimates.corr_img if use_corr else None, @@ -2002,9 +1998,9 @@ 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, patch_size=50, loaded_model=None, test_both=False, thresh_CNN_noisy=0.5, use_peak_max=False, - thresh_std_peak_resid = 1, mean_buff=None, - tf_in=None, tf_out=None): - # use_torch=None): + thresh_std_peak_resid = 1, mean_buff=None #, + ): # tf_in=None, tf_out=None): + """ Extract new candidate components from the residual buffer and test them using space correlation or the CNN classifier. The function runs the CNN @@ -2085,19 +2081,18 @@ def get_candidate_components(sv, dims, Yres_buf, min_num_trial=3, gSig=(5, 5), Ain2 /= np.std(Ain2,axis=1)[:,None] Ain2 = np.reshape(Ain2,(-1,) + tuple(np.diff(ijSig_cnn).squeeze()),order= 'F') Ain2 = np.stack([cv2.resize(ain,(patch_size ,patch_size)) for ain in Ain2]) - if use_torch is None: - predictions = loaded_model.predict(Ain2[:,:,:,np.newaxis], batch_size=min_num_trial, verbose=0) - keep_cnn = list(np.where(predictions[:, 0] > thresh_CNN_noisy)[0]) - else: - final_crops = torch.tensor(Ain2, dtype=torch.float32) - final_crops = torch.reshape(Ain2, (-1, Ain2.shape[-1], - Ain2.shape[1], Ain2.shape[2])) - with torch.no_grad(): - prediction = loaded_model(Ain2[:, np.newaxis, :, :]) + # if use_torch is None: + # predictions = loaded_model.predict(Ain2[:,:,:,np.newaxis], batch_size=min_num_trial, verbose=0) + # keep_cnn = list(np.where(predictions[:, 0] > thresh_CNN_noisy)[0]) + # else: + final_crops = torch.tensor(Ain2, dtype=torch.float32) + final_crops = torch.reshape(Ain2, (-1, Ain2.shape[-1], + Ain2.shape[1], Ain2.shape[2])) + with torch.no_grad(): + predictions = loaded_model(Ain2[:, np.newaxis, :, :]) keep_cnn = list(torch.where(predictions[:, 0] > thresh_CNN_noisy)[0]) cnn_pos = Ain2[keep_cnn] #Make sure this works - # tensor.numpy() also works else: keep_cnn = [] # list(range(len(Ain_cnn))) @@ -2146,8 +2141,8 @@ def update_num_components(t, sv, Ab, Cf, Yres_buf, Y_buf, rho_buf, corr_img=None, first_moment=None, second_moment=None, crosscorr=None, col_ind=None, row_ind=None, corr_img_mode=None, max_img=None, downscale_matrix=None, upscale_matrix=None, - tf_in=None, tf_out=None): - # torch_in=None, torch_out=None): + ): # tf_in=None, tf_out=None): + """ Checks for new components in the residual buffer and incorporates them if they pass the acceptance tests """ @@ -2177,8 +2172,7 @@ def update_num_components(t, sv, Ab, Cf, Yres_buf, Y_buf, rho_buf, sniper_mode=sniper_mode, rval_thr=rval_thr, patch_size=50, loaded_model=loaded_model, thresh_CNN_noisy=thresh_CNN_noisy, use_peak_max=use_peak_max, test_both=test_both, mean_buff=mean_buff, - tf_in=tf_in, tf_out=tf_out) - #torch_in=torch_in, torch_out=torch_out) + ) # tf_in=tf_in, tf_out=tf_out) ind_new_all = ijsig_all diff --git a/caiman/tests/test_mrcnn_pytorch.py b/caiman/tests/test_mrcnn_pytorch.py new file mode 100644 index 000000000..883f6d76c --- /dev/null +++ b/caiman/tests/test_mrcnn_pytorch.py @@ -0,0 +1,22 @@ +#!/usr/bin/env python + +import numpy as np +import os +import torch + +import caiman as cm +from caiman.paths import caiman_datadir +from caiman.utils.utils import download_model, download_demo +from caiman.source_extraction.volpy.mrcnn import neurons +import caiman.source_extraction.volpy.mrcnn.model as modellib + +def mrcnn(img, size_range, weights_path): + + return + +def test_mrcnn(): + weights_path = download_model('mask_rcnn') + summary_images = cm.load(download_demo('demo_voltage_imaging_summary_images.tif')) + ROIs = mrcnn(img=summary_images.transpose([1, 2, 0]), size_range=[5, 22], + weights_path=weights_path) + assert ROIs.shape[0] == 14, 'fail to infer correct number of neurons' \ No newline at end of file diff --git a/caiman/tests/test_pytorch.py b/caiman/tests/test_pytorch.py index 900aa702a..aa09f416b 100644 --- a/caiman/tests/test_pytorch.py +++ b/caiman/tests/test_pytorch.py @@ -2,48 +2,41 @@ import numpy as np import os -import keras from caiman.paths import caiman_datadir from caiman.utils.utils import load_graph -try: - os.environ["KERAS_BACKEND"] = "torch" - from keras.models import load_model - use_keras = True -except(ModuleNotFoundError): - import torch - use_keras = False +import torch def test_torch(): os.environ['CUDA_VISIBLE_DEVICES'] = '-1' try: model_name = os.path.join(caiman_datadir(), 'model', 'cnn_model') - if use_keras: - model_file = model_name + ".keras" - print('USING MODEL:' + model_file) - - loaded_model = load_model(model_file) - loaded_model.compile('sgd', 'mse') - elif use_keras == True: - model_file = model_name + ".pth" - loaded_model = torch.load(model_file) + # if use_keras: + # model_file = model_name + ".keras" + # print('USING MODEL:' + model_file) + # + # loaded_model = load_model(model_file) + # loaded_model.compile('sgd', 'mse') + # elif use_keras == True: + model_file = model_name + ".pth" + loaded_model = torch.load(model_file) except: - raise Exception(f'NN model could not be loaded. use_keras = {use_keras}') + raise Exception(f'NN model could not be loaded.') #use_keras = {use_keras}') A = np.random.randn(10, 50, 50, 1) try: - if use_keras == False: - predictions = loaded_model.predict(A, batch_size=32) - elif use_keras == True: - A = torch.tensor(A, dtype=torch.float32) - A = torch.reshape(A, (-1, A.shape[-1], A.shape[1], A.shape[2])) - with torch.no_grad(): - predictions = loaded_model(A) - pass + # if use_keras == False: + # predictions = loaded_model.predict(A, batch_size=32) + # elif use_keras == True: + A = torch.tensor(A, dtype=torch.float32) + A = torch.reshape(A, (-1, A.shape[-1], A.shape[1], A.shape[2])) + with torch.no_grad(): + predictions = loaded_model(A) + # pass except: - raise Exception('NN model could not be deployed. use_keras = ' + str(use_keras)) + raise Exception('NN model could not be deployed.') #use_keras = + str(use_keras)) if __name__ == "__main__": test_torch() \ No newline at end of file diff --git a/caiman/train/__init__.py b/caiman/train/__init__.py index 4612a75d7..8700f901b 100644 --- a/caiman/train/__init__.py +++ b/caiman/train/__init__.py @@ -1,6 +1,7 @@ #!/usr/bin/env python import pkg_resources -from caiman.train.train_cnn_model_helper import cnn_model_pytorch, train_test_split, train, validate, get_batch_accuracy, save_model_pytorch, load_model_pytorch, cnn_model_keras, save_model_keras, load_model_keras +from caiman.train.helper import cnn_model_pytorch, get_batch_accuracy, load_model_pytorch +from caiman.train.helper import save_model_pytorch, train_test_split, train, validate __version__ = pkg_resources.get_distribution('caiman').version \ No newline at end of file diff --git a/caiman/train/ground_truth_cnmf_seeded.ipynb b/caiman/train/ground_truth_cnmf_seeded.ipynb index 18db196a6..d914f7798 100644 --- a/caiman/train/ground_truth_cnmf_seeded.ipynb +++ b/caiman/train/ground_truth_cnmf_seeded.ipynb @@ -4,12 +4,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Prepare ground truth built by matching with the results of CNMF" + "# Prepare ground truth built by matching with the results of CNMF\n", + "\n", + "User/programmer guide to understand and try the code. Currently being retooled. \n", + "\n", + "Details: all of other usefull functions (demos available on jupyter notebook) \n", + "-*- coding: utf-8 -*-\n", + "\n", + "Version: 1.0\n", + "\n", + "Copyright: GNU General Public License v2.0\n", + "\n", + "Created on Mon Nov 21 15:53:15 2016\n", + "\n", + "Updated on Thu Jan 09 13:50:00 2025\n", + "\n", + "Authors: agiovann, mpaez" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -17,6 +32,7 @@ "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", + "import scipy \n", "\n", "import caiman as cm\n", "from caiman.utils.utils import download_demo\n", @@ -37,13 +53,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# neurofinder.03.00.test\n", - "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.03.00.test/images/final_map/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_.mmap'],\n", - " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.03.00.test/regions/joined_consensus_active_regions.npy'],\n", + "params_movie = {'fname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_03_00/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_.mmap'],\n", + " 'gtname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_03_00/joined_consensus_active_regions.npy'],\n", " 'p': 1, # order of the autoregressive system\n", " 'merge_thresh': 1, # merging threshold, max correlation allow\n", " 'final_frate': 10,\n", @@ -67,8 +83,8 @@ " }\n", "\n", "# neurofinder.04.00.test\n", - "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.04.00.test/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_3000_.mmap'],\n", - " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.04.00.test/regions/joined_consensus_active_regions.npy'],\n", + "params_movie = {'fname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_04_00_test/Yr_d1_512_d2_512_d3_1_order_C_frames_3000_.mmap'],\n", + " 'gtname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_04_00_test/joined_consensus_active_regions.npy'],\n", " 'p': 1, # order of the autoregressive system\n", " 'merge_thresh': 1, # merging threshold, max correlation allow\n", " 'final_frate': 10,\n", @@ -93,8 +109,8 @@ " }\n", "\n", "# Yi not clear neurons\n", - "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/Yi.data.001/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_7826_.mmap'],\n", - " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/Yi.data.001/regions/joined_consensus_active_regions.npy'],\n", + "params_movie = {'fname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/Yi_data_001/Yr_d1_512_d2_512_d3_1_order_C_frames_7826_.mmap'],\n", + " 'gtname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/Yi_data_001/joined_consensus_active_regions.npy'],\n", " 'p': 1, # order of the autoregressive system\n", " 'merge_thresh': 1, # merging threshold, max correlation allow\n", " 'final_frate': 30,\n", @@ -117,8 +133,8 @@ " }\n", "\n", "# neurofinder.02.00\n", - "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.02.00/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_8000_.mmap'],\n", - " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.02.00/regions/joined_consensus_active_regions.npy'],\n", + "params_movie = {'fname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_02_01/Yr_d1_512_d2_512_d3_1_order_C_frames_8000_.mmap'],\n", + " 'gtname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_02_01/joined_consensus_active_regions.npy'],\n", " 'merge_thresh': .8, # merging threshold, max correlation allow\n", " 'final_frate': 10,\n", " 'gnb': 1,\n", @@ -130,8 +146,8 @@ " }\n", "\n", "# yuste: used kernel = np.ones((radius//4,radius//4),np.uint8)\n", - "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/yuste.Single_150u/images/final_map/Yr_d1_200_d2_256_d3_1_order_C_frames_3000_.mmap'],\n", - " 'gtname': ['/mnt/ceph/data/neuro/caiman//labeling/yuste.Single_150u/regions/joined_consensus_active_regions.npy'],\n", + "params_movie = {'fname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/yuste_single_150u/Yr_d1_200_d2_256_d3_1_order_C_frames_3000_.mmap'],\n", + " 'gtname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/yuste_single_150/joined_consensus_active_regions.npy'],\n", " 'p': 1, # order of the autoregressive system\n", " 'merge_thresh': 1, # merging threshold, max correlation allow\n", " 'final_frate': 10,\n", @@ -144,8 +160,8 @@ " }\n", "\n", "# neurofinder 00 00\n", - "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.00.00/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_2936_.mmap'],\n", - " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.00.00/regions/joined_consensus_active_regions.npy'],\n", + "params_movie = {'fname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_00_00/Yr_d1_512_d2_512_d3_1_order_C_frames_2936_.mmap'],\n", + " 'gtname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_00_00/joined_consensus_active_regions.npy'],\n", " 'p': 1, # order of the autoregressive system\n", " 'merge_thresh': 1, # merging threshold, max correlation allow\n", " 'final_frate': 10,\n", @@ -169,25 +185,25 @@ " }\n", "\n", "# k53\n", - "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/k53_20160530/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_116043_.mmap'],\n", - " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/k53_20160530/regions/joined_consensus_active_regions.npy'],\n", - " 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/k53_20160530/regions/joined_consensus_active_regions.npy'],\n", - " 'p': 1, # order of the autoregressive system\n", - " 'merge_thresh': 1, # merging threshold, max correlation allow\n", - " 'final_frate': 30,\n", - " 'gnb': 1,\n", - " # whether to update the background components in the spatial phase\n", - " 'update_background_components': True,\n", - " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", - " #(to be used with one background per patch)\n", - " 'swap_dim': False, # for some movies needed\n", - " 'kernel': None\n", - " }\n", + "# params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/k53_20160530/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_116043_.mmap'],\n", + "# 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/k53_20160530/regions/joined_consensus_active_regions.npy'],\n", + "# 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/k53_20160530/regions/joined_consensus_active_regions.npy'],\n", + "# 'p': 1, # order of the autoregressive system\n", + "# 'merge_thresh': 1, # merging threshold, max correlation allow\n", + "# 'final_frate': 30,\n", + "# 'gnb': 1,\n", + "# # whether to update the background components in the spatial phase\n", + "# 'update_background_components': True,\n", + "# 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + "# #(to be used with one background per patch)\n", + "# 'swap_dim': False, # for some movies needed\n", + "# 'kernel': None\n", + "# }\n", "\n", "# neurofinder: 01.01\n", - "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.01.01/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_1825_.mmap'],\n", - " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.01.01/regions/joined_consensus_active_regions.npy'],\n", - " 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/neurofinder.01.01/regions/joined_consensus_active_regions.npy'],\n", + "params_movie = {'fname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_01_01/Yr_d1_512_d2_512_d3_1_order_C_frames_1825_.mmap'],\n", + " 'gtname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_01_01/joined_consensus_active_regions.npy'],\n", + " 'seed_name': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_01_01/joined_consensus_active_regions.npy'],\n", " 'p': 1, # order of the autoregressive system\n", " 'merge_thresh': 1, # merging threshold, max correlation allow\n", " 'final_frate': 10,\n", @@ -201,25 +217,25 @@ " }\n", "\n", "# J115: 01.01\n", - "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/J115_2015-12-09_L01_ELS/images/final_map/Yr_d1_463_d2_472_d3_1_order_C_frames_90000_.mmap'],\n", - " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/J115_2015-12-09_L01_ELS/regions/joined_consensus_active_regions.npy'],\n", - " 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/J115_2015-12-09_L01_ELS/regions/joined_consensus_active_regions.npy'],\n", - " 'p': 1, # order of the autoregressive system\n", - " 'merge_thresh': 1, # merging threshold, max correlation allow\n", - " 'final_frate': 10,\n", - " 'gnb': 1,\n", - " # whether to update the background components in the spatial phase\n", - " 'update_background_components': True,\n", - " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", - " #(to be used with one background per patch)\n", - " 'swap_dim': False, # for some movies needed\n", - " 'kernel': None\n", - " }\n", + "# params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/J115_2015-12-09_L01_ELS/images/final_map/Yr_d1_463_d2_472_d3_1_order_C_frames_90000_.mmap'],\n", + "# 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/J115_2015-12-09_L01_ELS/regions/joined_consensus_active_regions.npy'],\n", + "# 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/J115_2015-12-09_L01_ELS/regions/joined_consensus_active_regions.npy'],\n", + "# 'p': 1, # order of the autoregressive system\n", + "# 'merge_thresh': 1, # merging threshold, max correlation allow\n", + "# 'final_frate': 10,\n", + "# 'gnb': 1,\n", + "# # whether to update the background components in the spatial phase\n", + "# 'update_background_components': True,\n", + "# 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + "# #(to be used with one background per patch)\n", + "# 'swap_dim': False, # for some movies needed\n", + "# 'kernel': None\n", + "# }\n", "\n", "# J123\n", - "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/J123_2015-11-20_L01_0/images/final_map/Yr_d1_458_d2_477_d3_1_order_C_frames_41000_.mmap'],\n", - " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/J123_2015-11-20_L01_0/regions/joined_consensus_active_regions.npy'],\n", - " 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/J123_2015-11-20_L01_0/regions/joined_consensus_active_regions.npy'],\n", + "params_movie = {'fname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/J123/Yr_d1_458_d2_477_d3_1_order_C_frames_41000_.mmap'],\n", + " 'gtname': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/J123/joined_consensus_active_regions.npy'],\n", + " 'seed_name': ['/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/J123/joined_consensus_active_regions.npy'],\n", " 'p': 1, # order of the autoregressive system\n", " 'merge_thresh': 1, # merging threshold, max correlation allow\n", " 'final_frate': 10,\n", @@ -232,38 +248,38 @@ " 'kernel': None\n", " }\n", "# Jan-AMG\n", - "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/Jan-AMG_exp3_001/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_115897_.mmap'],\n", - " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/Jan-AMG_exp3_001/regions/joined_consensus_active_regions.npy'],\n", - " 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/Jan-AMG_exp3_001/regions/joined_consensus_active_regions.npy'],\n", - " 'p': 1, # order of the autoregressive system\n", - " 'merge_thresh': 1, # merging threshold, max correlation allow\n", - " 'final_frate': 10,\n", - " 'gnb': 1,\n", - " # whether to update the background components in the spatial phase\n", - " 'update_background_components': True,\n", - " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", - " #(to be used with one background per patch)\n", - " 'swap_dim': False, # for some movies needed\n", - " 'kernel': None,\n", - " 'crop_pix': 8,\n", - " }\n", + "# params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/Jan-AMG_exp3_001/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_115897_.mmap'],\n", + "# 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/Jan-AMG_exp3_001/regions/joined_consensus_active_regions.npy'],\n", + "# 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/Jan-AMG_exp3_001/regions/joined_consensus_active_regions.npy'],\n", + "# 'p': 1, # order of the autoregressive system\n", + "# 'merge_thresh': 1, # merging threshold, max correlation allow\n", + "# 'final_frate': 10,\n", + "# 'gnb': 1,\n", + "# # whether to update the background components in the spatial phase\n", + "# 'update_background_components': True,\n", + "# 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + "# #(to be used with one background per patch)\n", + "# 'swap_dim': False, # for some movies needed\n", + "# 'kernel': None,\n", + "# 'crop_pix': 8,\n", + "# }\n", "\n", "# sue k37, not nice because few events\n", - "params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/k37_20160109_AM_150um_65mW_zoom2p2_00001_1-16/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_48000_.mmap'],\n", - " 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/k37_20160109_AM_150um_65mW_zoom2p2_00001_1-16/regions/joined_consensus_active_regions.npy'],\n", - " 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/k37_20160109_AM_150um_65mW_zoom2p2_00001_1-16/regions/joined_consensus_active_regions.npy'],\n", - " 'p': 1, # order of the autoregressive system\n", - " 'merge_thresh': 1, # merging threshold, max correlation allow\n", - " 'final_frate': 30,\n", - " 'gnb': 2,\n", - " # whether to update the background components in the spatial phase\n", - " 'update_background_components': True,\n", - " 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", - " #(to be used with one background per patch)\n", - " 'swap_dim': False, # for some movies needed\n", - " 'kernel': None,\n", - " 'crop_pix': 7,\n", - " }" + "# params_movie = {'fname': ['/mnt/ceph/data/neuro/caiman/labeling/k37_20160109_AM_150um_65mW_zoom2p2_00001_1-16/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_48000_.mmap'],\n", + "# 'gtname': ['/mnt/ceph/data/neuro/caiman/labeling/k37_20160109_AM_150um_65mW_zoom2p2_00001_1-16/regions/joined_consensus_active_regions.npy'],\n", + "# 'seed_name': ['/mnt/ceph/data/neuro/caiman/labeling/k37_20160109_AM_150um_65mW_zoom2p2_00001_1-16/regions/joined_consensus_active_regions.npy'],\n", + "# 'p': 1, # order of the autoregressive system\n", + "# 'merge_thresh': 1, # merging threshold, max correlation allow\n", + "# 'final_frate': 30,\n", + "# 'gnb': 2,\n", + "# # whether to update the background components in the spatial phase\n", + "# 'update_background_components': True,\n", + "# 'low_rank_background': True, # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", + "# #(to be used with one background per patch)\n", + "# 'swap_dim': False, # for some movies needed\n", + "# 'kernel': None,\n", + "# 'crop_pix': 7,\n", + "# }" ] }, { @@ -275,7 +291,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -297,9 +313,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The local backend is an alias for the multiprocessing backend, and the alias may be removed in some future version of Caiman\n" + ] + } + ], "source": [ "c, dview, n_processes = cm.cluster.setup_cluster(\n", " backend='local', n_processes=None, single_thread=False)\n", @@ -320,16 +344,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "File request:[/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/projections/correlation_image_better.tif] not found!\n" + ] + }, + { + "ename": "Exception", + "evalue": "File /Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/projections/correlation_image_better.tif not found!", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m Cn[np\u001b[38;5;241m.\u001b[39misnan(Cn)] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m#Saved as a tif file\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m Cn \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\u001b[43mcm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams_movie\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mgtname\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mprojections\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcorrelation_image_better.tif\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m)\u001b[38;5;241m.\u001b[39msqueeze() \n\u001b[1;32m 10\u001b[0m plt\u001b[38;5;241m.\u001b[39mimshow(Cn, cmap\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgray\u001b[39m\u001b[38;5;124m'\u001b[39m, vmax\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.95\u001b[39m)\n", + "File \u001b[0;32m/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/base/movies.py:1540\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file_name, fr, start_time, meta_data, subindices, shape, var_name_hdf5, in_memory, is_behavior, bottom, top, left, right, channel, outtype, is3D)\u001b[0m\n\u001b[1;32m 1538\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1539\u001b[0m logger\u001b[38;5;241m.\u001b[39merror(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFile request:[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] not found!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1540\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFile \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 1542\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m movie(input_arr\u001b[38;5;241m.\u001b[39mastype(outtype),\n\u001b[1;32m 1543\u001b[0m fr\u001b[38;5;241m=\u001b[39mfr,\n\u001b[1;32m 1544\u001b[0m start_time\u001b[38;5;241m=\u001b[39mstart_time,\n\u001b[1;32m 1545\u001b[0m file_name\u001b[38;5;241m=\u001b[39mos\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msplit(file_name)[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 1546\u001b[0m meta_data\u001b[38;5;241m=\u001b[39mmeta_data)\n", + "\u001b[0;31mException\u001b[0m: File /Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/projections/correlation_image_better.tif not found!" + ] + } + ], "source": [ "if m_images.shape[0] < 10000:\n", " Cn = m_images.local_correlations(\n", " swap_dim=params_movie['swap_dim'], frames_per_chunk=1500)\n", " Cn[np.isnan(Cn)] = 0\n", "else:\n", - " #Saved as a tif file\n", " Cn = np.array(cm.load(('/'.join(params_movie['gtname'][0].split('/')[:-2] + [\n", " 'projections', 'correlation_image_better.tif'])))).squeeze() \n", "\n", @@ -338,9 +381,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "9\n", + "(183, 458, 477)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "if not '.mat' in params_movie['seed_name'][0]:\n", " roi_cons = np.load(params_movie['seed_name'][0])\n", @@ -357,9 +429,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'Cn' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[7], line 11\u001b[0m\n\u001b[1;32m 8\u001b[0m A_in \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mreshape(roi_cons\u001b[38;5;241m.\u001b[39mtranspose(\n\u001b[1;32m 9\u001b[0m [\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m]), (\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, roi_cons\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]), order\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 10\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure()\n\u001b[0;32m---> 11\u001b[0m crd \u001b[38;5;241m=\u001b[39m plot_contours(A_in, \u001b[43mCn\u001b[49m, thr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.99999\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'Cn' is not defined" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "if params_movie['kernel'] is not None: # kernel usually two\n", " kernel = np.ones(\n", @@ -383,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -402,9 +495,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "spatial support for each components given by the user\n", + "estimating f\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/deconvolution.py:1004: FutureWarning: Beginning in SciPy 1.17, multidimensional input will be treated as a batch, not `ravel`ed. To preserve the existing behavior and silence this warning, `ravel` arguments before passing them to `toeplitz`.\n", + " A = scipy.linalg.toeplitz(xc[lags + np.arange(lags)],\n", + "/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/deconvolution.py:1004: FutureWarning: Beginning in SciPy 1.17, multidimensional input will be treated as a batch, not `ravel`ed. To preserve the existing behavior and silence this warning, `ravel` arguments before passing them to `toeplitz`.\n", + " A = scipy.linalg.toeplitz(xc[lags + np.arange(lags)],\n", + "/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/deconvolution.py:1004: FutureWarning: Beginning in SciPy 1.17, multidimensional input will be treated as a batch, not `ravel`ed. To preserve the existing behavior and silence this warning, `ravel` arguments before passing them to `toeplitz`.\n", + " A = scipy.linalg.toeplitz(xc[lags + np.arange(lags)],\n", + "/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/deconvolution.py:1004: FutureWarning: Beginning in SciPy 1.17, multidimensional input will be treated as a batch, not `ravel`ed. To preserve the existing behavior and silence this warning, `ravel` arguments before passing them to `toeplitz`.\n", + " A = scipy.linalg.toeplitz(xc[lags + np.arange(lags)],\n", + "/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/deconvolution.py:1004: FutureWarning: Beginning in SciPy 1.17, multidimensional input will be treated as a batch, not `ravel`ed. To preserve the existing behavior and silence this warning, `ravel` arguments before passing them to `toeplitz`.\n", + " A = scipy.linalg.toeplitz(xc[lags + np.arange(lags)],\n", + "/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/deconvolution.py:1004: FutureWarning: Beginning in SciPy 1.17, multidimensional input will be treated as a batch, not `ravel`ed. To preserve the existing behavior and silence this warning, `ravel` arguments before passing them to `toeplitz`.\n", + " A = scipy.linalg.toeplitz(xc[lags + np.arange(lags)],\n", + "/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/deconvolution.py:1004: FutureWarning: Beginning in SciPy 1.17, multidimensional input will be treated as a batch, not `ravel`ed. To preserve the existing behavior and silence this warning, `ravel` arguments before passing them to `toeplitz`.\n", + " A = scipy.linalg.toeplitz(xc[lags + np.arange(lags)],\n", + "/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/deconvolution.py:1004: FutureWarning: Beginning in SciPy 1.17, multidimensional input will be treated as a batch, not `ravel`ed. To preserve the existing behavior and silence this warning, `ravel` arguments before passing them to `toeplitz`.\n", + " A = scipy.linalg.toeplitz(xc[lags + np.arange(lags)],\n", + "/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/deconvolution.py:1004: FutureWarning: Beginning in SciPy 1.17, multidimensional input will be treated as a batch, not `ravel`ed. To preserve the existing behavior and silence this warning, `ravel` arguments before passing them to `toeplitz`.\n", + " A = scipy.linalg.toeplitz(xc[lags + np.arange(lags)],\n", + "/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/deconvolution.py:1004: FutureWarning: Beginning in SciPy 1.17, multidimensional input will be treated as a batch, not `ravel`ed. To preserve the existing behavior and silence this warning, `ravel` arguments before passing them to `toeplitz`.\n", + " A = scipy.linalg.toeplitz(xc[lags + np.arange(lags)],\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'CNMF' object has no attribute 'A'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 10\u001b[0m\n\u001b[1;32m 6\u001b[0m cnm \u001b[38;5;241m=\u001b[39m cnmf\u001b[38;5;241m.\u001b[39mCNMF(check_nan\u001b[38;5;241m=\u001b[39mcheck_nan, n_processes\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, k\u001b[38;5;241m=\u001b[39mA_in\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], gSig\u001b[38;5;241m=\u001b[39m[radius, radius], merge_thresh\u001b[38;5;241m=\u001b[39mparams_movie[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmerge_thresh\u001b[39m\u001b[38;5;124m'\u001b[39m], p\u001b[38;5;241m=\u001b[39mparams_movie[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;124m'\u001b[39m], Ain\u001b[38;5;241m=\u001b[39mA_in\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mbool\u001b[39m),\n\u001b[1;32m 7\u001b[0m dview\u001b[38;5;241m=\u001b[39mdview, rf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, stride\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, gnb\u001b[38;5;241m=\u001b[39mparams_movie[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgnb\u001b[39m\u001b[38;5;124m'\u001b[39m], method_deconvolution\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moasis\u001b[39m\u001b[38;5;124m'\u001b[39m, border_pix\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m, low_rank_background\u001b[38;5;241m=\u001b[39mparams_movie[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlow_rank_background\u001b[39m\u001b[38;5;124m'\u001b[39m], n_pixels_per_process\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000\u001b[39m)\n\u001b[1;32m 8\u001b[0m cnm \u001b[38;5;241m=\u001b[39m cnm\u001b[38;5;241m.\u001b[39mfit(images)\n\u001b[0;32m---> 10\u001b[0m A \u001b[38;5;241m=\u001b[39m \u001b[43mcnm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mA\u001b[49m\n\u001b[1;32m 11\u001b[0m C \u001b[38;5;241m=\u001b[39m cnm\u001b[38;5;241m.\u001b[39mC\n\u001b[1;32m 12\u001b[0m YrA \u001b[38;5;241m=\u001b[39m cnm\u001b[38;5;241m.\u001b[39mYrA\n", + "\u001b[0;31mAttributeError\u001b[0m: 'CNMF' object has no attribute 'A'" + ] + } + ], "source": [ "if images.shape[0] > 10000:\n", " check_nan = False\n", @@ -435,9 +574,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'A' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[15], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# TODO: needinfo\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m view_patches_bar(Yr, scipy\u001b[38;5;241m.\u001b[39msparse\u001b[38;5;241m.\u001b[39mcoo_matrix(\u001b[43mA\u001b[49m\u001b[38;5;241m.\u001b[39mtocsc()[:, :]), C[:, :], b, f, dims[\u001b[38;5;241m0\u001b[39m], dims[\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m 3\u001b[0m YrA\u001b[38;5;241m=\u001b[39mYrA[:, :], img\u001b[38;5;241m=\u001b[39mCn)\n\u001b[1;32m 5\u001b[0m c, dview, n_processes \u001b[38;5;241m=\u001b[39m cm\u001b[38;5;241m.\u001b[39mcluster\u001b[38;5;241m.\u001b[39msetup_cluster(\n\u001b[1;32m 6\u001b[0m backend\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocal\u001b[39m\u001b[38;5;124m'\u001b[39m, n_processes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, single_thread\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 8\u001b[0m min_size_neuro \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mpi\n", + "\u001b[0;31mNameError\u001b[0m: name 'A' is not defined" + ] + } + ], "source": [ "# TODO: needinfo\n", "view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, :]), C[:, :], b, f, dims[0], dims[1],\n", @@ -475,9 +626,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'A_thr' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m30\u001b[39m, \u001b[38;5;241m20\u001b[39m))\n\u001b[0;32m----> 2\u001b[0m tp_gt, tp_comp, fn_gt, fp_comp, performance_cons_off \u001b[38;5;241m=\u001b[39m cm\u001b[38;5;241m.\u001b[39mbase\u001b[38;5;241m.\u001b[39mrois\u001b[38;5;241m.\u001b[39mnf_match_neurons_in_binary_masks(roi_cons, \u001b[43mA_thr\u001b[49m[:, :]\u001b[38;5;241m.\u001b[39mreshape([dims[\u001b[38;5;241m0\u001b[39m], dims[\u001b[38;5;241m1\u001b[39m], \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m], order\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mF\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mtranspose([\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m]) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m1.\u001b[39m, thresh_cost\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.7\u001b[39m, min_dist\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m,\n\u001b[1;32m 3\u001b[0m print_assignment\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, plot_results\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, Cn\u001b[38;5;241m=\u001b[39mCn, labels\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGT\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOffline\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m 4\u001b[0m plt\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpdf.fonttype\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m42\u001b[39m\n\u001b[1;32m 5\u001b[0m font \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfamily\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMyriad Pro\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mweight\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mregular\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 7\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msize\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;241m20\u001b[39m}\n", + "\u001b[0;31mNameError\u001b[0m: name 'A_thr' is not defined" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(30, 20))\n", "tp_gt, tp_comp, fn_gt, fp_comp, performance_cons_off = cm.base.rois.nf_match_neurons_in_binary_masks(roi_cons, A_thr[:, :].reshape([dims[0], dims[1], -1], order='F').transpose([2, 0, 1]) * 1., thresh_cost=.7, min_dist=10,\n", @@ -498,9 +670,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'Cn' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m np\u001b[38;5;241m.\u001b[39msavez(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msplit(fname_new)[\u001b[38;5;241m0\u001b[39m], os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msplit(fname_new)[\u001b[38;5;241m1\u001b[39m][:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m4\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatch_masks.npz\u001b[39m\u001b[38;5;124m'\u001b[39m), Cn\u001b[38;5;241m=\u001b[39m\u001b[43mCn\u001b[49m,\n\u001b[1;32m 2\u001b[0m tp_gt\u001b[38;5;241m=\u001b[39mtp_gt, tp_comp\u001b[38;5;241m=\u001b[39mtp_comp, fn_gt\u001b[38;5;241m=\u001b[39mfn_gt, fp_comp\u001b[38;5;241m=\u001b[39mfp_comp, performance_cons_off\u001b[38;5;241m=\u001b[39mperformance_cons_off, idx_size_neuro_gt\u001b[38;5;241m=\u001b[39midx_size_neuro, A_thr\u001b[38;5;241m=\u001b[39mA_thr,\n\u001b[1;32m 3\u001b[0m A_gt\u001b[38;5;241m=\u001b[39mA, C_gt\u001b[38;5;241m=\u001b[39mC, b_gt\u001b[38;5;241m=\u001b[39mb, f_gt\u001b[38;5;241m=\u001b[39mf, YrA_gt\u001b[38;5;241m=\u001b[39mYrA, d1\u001b[38;5;241m=\u001b[39md1, d2\u001b[38;5;241m=\u001b[39md2, idx_components_gt\u001b[38;5;241m=\u001b[39midx_size_neuro[\n\u001b[1;32m 4\u001b[0m tp_comp],\n\u001b[1;32m 5\u001b[0m idx_components_bad_gt\u001b[38;5;241m=\u001b[39midx_size_neuro[fp_comp], fname_new\u001b[38;5;241m=\u001b[39mfname_new)\n", + "\u001b[0;31mNameError\u001b[0m: name 'Cn' is not defined" + ] + } + ], "source": [ "np.savez(os.path.join(os.path.split(fname_new)[0], os.path.split(fname_new)[1][:-4] + 'match_masks.npz'), Cn=Cn,\n", " tp_gt=tp_gt, tp_comp=tp_comp, fn_gt=fn_gt, fp_comp=fp_comp, performance_cons_off=performance_cons_off, idx_size_neuro_gt=idx_size_neuro, A_thr=A_thr,\n", @@ -508,13 +692,20 @@ " tp_comp],\n", " idx_components_bad_gt=idx_size_neuro[fp_comp], fname_new=fname_new)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "caiman_pytorch", + "display_name": "caiman_pytorch_2", "language": "python", - "name": "caiman_pytorch" + "name": "caiman_pytorch_2" }, "language_info": { "codemirror_mode": { @@ -526,7 +717,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/caiman/train/train_cnn_model_helper.py b/caiman/train/helper.py similarity index 99% rename from caiman/train/train_cnn_model_helper.py rename to caiman/train/helper.py index 8bc589e91..dc914ef2e 100644 --- a/caiman/train/train_cnn_model_helper.py +++ b/caiman/train/helper.py @@ -128,5 +128,4 @@ def save_model_keras(model, name: str): def load_model_keras(model_path: str): loaded_model = load_model(model_path) print('Load trained model at %s ' % model_path) - return loaded_model - + return loaded_model \ No newline at end of file diff --git a/caiman/train/match_seeded_gt.ipynb b/caiman/train/match_seeded_gt.ipynb index 8bf16e0d4..9d15c5b27 100644 --- a/caiman/train/match_seeded_gt.ipynb +++ b/caiman/train/match_seeded_gt.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -39,11 +39,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "params_movie = {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.03.00.test/images/final_map/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_.mmap',\n", + "#Neurofinder 03.00.test \n", + "params_movie = {'fname': '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_03_00/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_.mmap',\n", " 'p': 1, # order of the autoregressive system\n", " 'merge_thresh': 0.8, # merging threshold, max correlation allow\n", " 'rf': 25, # half-size of the patches in pixels. rf=25, patches are 50x50 20\n", @@ -73,37 +74,8 @@ " #(to be used with one background per patch)\n", " }\n", "\n", - "params_movie = {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.04.00.test/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_3000_.mmap',\n", - " 'p': 1, # order of the autoregressive system\n", - " 'merge_thresh': 0.8, # merging threshold, max correlation allow\n", - " 'rf': 20, # half-size of the patches in pixels. rf=25, patches are 50x50 20\n", - " 'stride_cnmf': 10, # amounpl.it of overlap between the patches in pixels\n", - " 'K': 5, # number of components per patch\n", - " # if dendritic. In this case you need to set init_method to sparse_nmf\n", - " 'is_dendrites': False,\n", - " 'init_method': 'greedy_roi',\n", - " 'gSig': [5, 5], # expected half size of neurons\n", - " 'alpha_snmf': None, # this controls sparsity\n", - " 'final_frate': 10,\n", - " 'r_values_min_patch': .5, # threshold on space consistency\n", - " 'fitness_min_patch': -10, # threshold on time variability\n", - " # threshold on time variability (if nonsparse activity)\n", - " 'fitness_delta_min_patch': -10,\n", - " 'Npeaks': 5,\n", - " 'r_values_min_full': .8,\n", - " 'fitness_min_full': - 40,\n", - " 'fitness_delta_min_full': - 40,\n", - " 'only_init_patch': True,\n", - " 'gnb': 2,\n", - " 'memory_fact': 1,\n", - " 'n_chunks': 10,\n", - " # whether to update the background components in the spatial phase\n", - " 'update_background_components': True,\n", - " 'low_rank_background': True # whether to update the using a low rank approximation. In the False case all the nonzero elements of the background components are updated using hals\n", - " #(to be used with one background per patch)\n", - " }\n", - "\n", - "params_movie = {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.04.00.test/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_3000_.mmap',\n", + "#Neurofinder 04.00.test \n", + "params_movie = {'fname': '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_04_00_test/Yr_d1_512_d2_512_d3_1_order_C_frames_3000_.mmap',\n", " 'p': 1, # order of the autoregressive system\n", " 'merge_thresh': 0.8, # merging threshold, max correlation allow\n", " 'rf': 20, # half-size of the patches in pixels. rf=25, patches are 50x50 20\n", @@ -134,7 +106,7 @@ " }\n", "\n", "# neurofinder 02.00\n", - "params_movie = {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.02.00/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_8000_.mmap',\n", + "params_movie = {'fname': '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_00_00/Yr_d1_512_d2_512_d3_1_order_C_frames_2936_.mmap',\n", " 'p': 1, # order of the autoregressive system\n", " 'merge_thresh': 0.8, # merging threshold, max correlation allow\n", " 'rf': 20, # half-size of the patches in pixels. rf=25, patches are 50x50 20\n", @@ -176,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -198,9 +170,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The local backend is an alias for the multiprocessing backend, and the alias may be removed in some future version of Caiman\n" + ] + } + ], "source": [ "c, dview, n_processes = cm.cluster.setup_cluster(\n", " backend='local', n_processes=None, single_thread=False)" @@ -215,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -236,9 +216,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "if m_images.shape[0] < 10000:\n", " Cn = m_images.local_correlations(\n", @@ -252,9 +253,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:24: SyntaxWarning: \"is not\" with 'str' literal. Did you mean \"!=\"?\n", + "<>:24: SyntaxWarning: \"is not\" with 'str' literal. Did you mean \"!=\"?\n", + "/var/folders/2r/g94ddsvn0_gbc2zf01hj0zn00000gn/T/ipykernel_75234/246607138.py:24: SyntaxWarning: \"is not\" with 'str' literal. Did you mean \"!=\"?\n", + " if params_movie['init_method'] is not 'sparse_nmf':\n" + ] + }, + { + "ename": "TypeError", + "evalue": "unsupported operand type(s) for /: 'NoneType' and 'float'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRemoteTraceback\u001b[0m Traceback (most recent call last)", + "\u001b[0;31mRemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/multiprocessing/pool.py\", line 125, in worker\n result = (True, func(*args, **kwds))\n ^^^^^^^^^^^^^^^^^^^\n File \"/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/multiprocessing/pool.py\", line 48, in mapstar\n return list(map(*args))\n ^^^^^^^^^^^^^^^^\n File \"/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/map_reduce.py\", line 112, in cnmf_patches\n cnm = cnm.fit(images)\n ^^^^^^^^^^^^^^^\n File \"/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/cnmf.py\", line 486, in fit\n self.initialize(Y)\n File \"/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/cnmf.py\", line 955, in initialize\n initialize_components(Y, sn=estim.sn, options_total=self.params.to_dict(),\n File \"/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/initialization.py\", line 300, in initialize_components\n alpha_snmf /= np.mean(img) # normalize alpha for sparse nmf\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypeError: unsupported operand type(s) for /: 'NoneType' and 'float'\n\"\"\"", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 37\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;66;03m# TODO: todocument\u001b[39;00m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;66;03m# TODO: warnings 3\u001b[39;00m\n\u001b[1;32m 32\u001b[0m cnm \u001b[38;5;241m=\u001b[39m cnmf\u001b[38;5;241m.\u001b[39mCNMF(n_processes\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, k\u001b[38;5;241m=\u001b[39mK, gSig\u001b[38;5;241m=\u001b[39mgSig, merge_thresh\u001b[38;5;241m=\u001b[39mparams_movie[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmerge_thresh\u001b[39m\u001b[38;5;124m'\u001b[39m], p\u001b[38;5;241m=\u001b[39mparams_movie[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mp\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 33\u001b[0m dview\u001b[38;5;241m=\u001b[39mdview, rf\u001b[38;5;241m=\u001b[39mrf, stride\u001b[38;5;241m=\u001b[39mstride_cnmf, memory_fact\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 34\u001b[0m method_init\u001b[38;5;241m=\u001b[39minit_method, alpha_snmf\u001b[38;5;241m=\u001b[39malpha_snmf, only_init_patch\u001b[38;5;241m=\u001b[39mparams_movie[\n\u001b[1;32m 35\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124monly_init_patch\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 36\u001b[0m gnb\u001b[38;5;241m=\u001b[39mparams_movie[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgnb\u001b[39m\u001b[38;5;124m'\u001b[39m], method_deconvolution\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moasis\u001b[39m\u001b[38;5;124m'\u001b[39m, border_pix\u001b[38;5;241m=\u001b[39mparams_movie[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcrop_pix\u001b[39m\u001b[38;5;124m'\u001b[39m], low_rank_background\u001b[38;5;241m=\u001b[39mparams_movie[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlow_rank_background\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m---> 37\u001b[0m cnm \u001b[38;5;241m=\u001b[39m \u001b[43mcnm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimages\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 39\u001b[0m A_tot \u001b[38;5;241m=\u001b[39m cnm\u001b[38;5;241m.\u001b[39mA\n\u001b[1;32m 40\u001b[0m C_tot \u001b[38;5;241m=\u001b[39m cnm\u001b[38;5;241m.\u001b[39mC\n", + "File \u001b[0;32m/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/cnmf.py:580\u001b[0m, in \u001b[0;36mCNMF.fit\u001b[0;34m(self, images, indices)\u001b[0m\n\u001b[1;32m 575\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(images, np\u001b[38;5;241m.\u001b[39mmemmap):\n\u001b[1;32m 576\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m(\n\u001b[1;32m 577\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYou need to provide a memory mapped file as input if you use patches!!\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 579\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimates\u001b[38;5;241m.\u001b[39mA, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimates\u001b[38;5;241m.\u001b[39mC, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimates\u001b[38;5;241m.\u001b[39mYrA, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimates\u001b[38;5;241m.\u001b[39mb, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimates\u001b[38;5;241m.\u001b[39mf, \\\n\u001b[0;32m--> 580\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimates\u001b[38;5;241m.\u001b[39msn, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimates\u001b[38;5;241m.\u001b[39moptional_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mrun_CNMF_patches\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 581\u001b[0m \u001b[43m \u001b[49m\u001b[43mimages\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdims\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 582\u001b[0m \u001b[43m \u001b[49m\u001b[43mdview\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdview\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmemory_fact\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpatch\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mmemory_fact\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 583\u001b[0m \u001b[43m \u001b[49m\u001b[43mgnb\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43minit\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnb\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mborder_pix\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpatch\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mborder_pix\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 584\u001b[0m \u001b[43m \u001b[49m\u001b[43mlow_rank_background\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpatch\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlow_rank_background\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 585\u001b[0m \u001b[43m \u001b[49m\u001b[43mdel_duplicates\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mpatch\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mdel_duplicates\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 586\u001b[0m \u001b[43m \u001b[49m\u001b[43mindices\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindices\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 588\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimates\u001b[38;5;241m.\u001b[39mbl, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimates\u001b[38;5;241m.\u001b[39mc1, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimates\u001b[38;5;241m.\u001b[39mg, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimates\u001b[38;5;241m.\u001b[39mneurons_sn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 589\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmerging\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/caiman/source_extraction/cnmf/map_reduce.py:231\u001b[0m, in \u001b[0;36mrun_CNMF_patches\u001b[0;34m(file_name, shape, params, gnb, dview, memory_fact, border_pix, low_rank_background, del_duplicates, indices)\u001b[0m\n\u001b[1;32m 229\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dview \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmultiprocessing\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mtype\u001b[39m(dview)):\n\u001b[0;32m--> 231\u001b[0m file_res \u001b[38;5;241m=\u001b[39m \u001b[43mdview\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_async\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcnmf_patches\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs_in\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m4294967\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 232\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 233\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", + "File \u001b[0;32m/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/multiprocessing/pool.py:774\u001b[0m, in \u001b[0;36mApplyResult.get\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 772\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_value\n\u001b[1;32m 773\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 774\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_value\n", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'NoneType' and 'float'" + ] + } + ], "source": [ "\n", "# %% some parameter settings\n", @@ -344,9 +373,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'CNMF' object has no attribute 'A'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m A, C, b, f, YrA, sn \u001b[38;5;241m=\u001b[39m \u001b[43mcnm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mA\u001b[49m, cnm\u001b[38;5;241m.\u001b[39mC, cnm\u001b[38;5;241m.\u001b[39mb, cnm\u001b[38;5;241m.\u001b[39mf, cnm\u001b[38;5;241m.\u001b[39mYrA, cnm\u001b[38;5;241m.\u001b[39msn\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# %% again recheck quality of components, stricter criteria\u001b[39;00m\n\u001b[1;32m 3\u001b[0m final_frate \u001b[38;5;241m=\u001b[39m params_movie[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfinal_frate\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", + "\u001b[0;31mAttributeError\u001b[0m: 'CNMF' object has no attribute 'A'" + ] + } + ], "source": [ "A, C, b, f, YrA, sn = cnm.A, cnm.C, cnm.b, cnm.f, cnm.YrA, cnm.sn\n", "# %% again recheck quality of components, stricter criteria\n", @@ -397,9 +438,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_00_00/Yr_d1_512_d2_512_d3_1_order_C_frames_2936_.results_analysis.npz'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[10], line 11\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m#analysis_file = '/mnt/ceph/neuro/jeremie_analysis/neurofinder.03.00.test/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_._results_analysis.npz'\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname_new\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname_new\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresults_analysis.npz\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m ld:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28mprint\u001b[39m(ld\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28mlocals\u001b[39m()\u001b[38;5;241m.\u001b[39mupdate(ld)\n", + "File \u001b[0;32m/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/numpy/lib/npyio.py:427\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding, max_header_size)\u001b[0m\n\u001b[1;32m 425\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 427\u001b[0m fid \u001b[38;5;241m=\u001b[39m stack\u001b[38;5;241m.\u001b[39menter_context(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mos_fspath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 428\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 430\u001b[0m \u001b[38;5;66;03m# Code to distinguish from NumPy binary files and pickles.\u001b[39;00m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_00_00/Yr_d1_512_d2_512_d3_1_order_C_frames_2936_.results_analysis.npz'" + ] + } + ], "source": [ "params_display = {\n", " 'downsample_ratio': .2,\n", @@ -423,9 +477,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'A' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m predictions, final_crops \u001b[38;5;241m=\u001b[39m evaluate_components_CNN(\n\u001b[0;32m----> 2\u001b[0m \u001b[43mA\u001b[49m, dims, gSig, model_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel/cnn_model\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 4\u001b[0m cm\u001b[38;5;241m.\u001b[39mmovie(final_crops)\u001b[38;5;241m.\u001b[39mplay(gain\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, magnification\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m6\u001b[39m, fr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\n\u001b[1;32m 5\u001b[0m cm\u001b[38;5;241m.\u001b[39mmovie(np\u001b[38;5;241m.\u001b[39msqueeze(final_crops[np\u001b[38;5;241m.\u001b[39mwhere(predictions[:, \u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.5\u001b[39m)[\u001b[38;5;241m0\u001b[39m]]))\u001b[38;5;241m.\u001b[39mplay(\n\u001b[1;32m 6\u001b[0m gain\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2.\u001b[39m, magnification\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, fr\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'A' is not defined" + ] + } + ], "source": [ "predictions, final_crops = evaluate_components_CNN(\n", " A, dims, gSig, model_name='model/cnn_model')\n", @@ -634,9 +700,9 @@ ], "metadata": { "kernelspec": { - "display_name": "caiman_pytorch", + "display_name": "caiman_pytorch_2", "language": "python", - "name": "caiman_pytorch" + "name": "caiman_pytorch_2" }, "language_info": { "codemirror_mode": { @@ -648,7 +714,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/caiman/train/prepare_training_set.ipynb b/caiman/train/prepare_training_set.ipynb index b1e59d592..ed0bed3e8 100644 --- a/caiman/train/prepare_training_set.ipynb +++ b/caiman/train/prepare_training_set.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -31,29 +31,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ - "inputs = [{'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.03.00.test/images/final_map/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_.mmap', 'gSig': [8, 8]},\n", - " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.04.00.test/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_3000_.mmap',\n", + "inputs = [{'fname': '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_03_00/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_.mmap', \n", + " 'gSig': [8, 8]},\n", + " {'fname': '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_04_00_test/Yr_d1_512_d2_512_d3_1_order_C_frames_3000_.mmap',\n", " 'gSig': [5, 5]},\n", - " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.02.00/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_8000_.mmap',\n", + " {'fname': '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_02_01/Yr_d1_512_d2_512_d3_1_order_C_frames_8000_.mmap',\n", " 'gSig': [5, 5]},\n", - " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/yuste.Single_150u/images/final_map/Yr_d1_200_d2_256_d3_1_order_C_frames_3000_.mmap',\n", + " {'fname': '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/yuste_single_150u/Yr_d1_200_d2_256_d3_1_order_C_frames_3000_.mmap',\n", " 'gSig': [5, 5]},\n", - " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.00.00/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_2936_.mmap',\n", - " 'gSig': [6, 6]},\n", - " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/neurofinder.01.01/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_1825_.mmap',\n", + " {'fname': '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_00_00/Yr_d1_512_d2_512_d3_1_order_C_frames_2936_.mmap',\n", " 'gSig': [6, 6]},\n", - " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/k53_20160530/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_116043_.mmap',\n", + " {'fname': '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_01_01/Yr_d1_512_d2_512_d3_1_order_C_frames_1825_.mmap',\n", " 'gSig': [6, 6]},\n", - " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/J115_2015-12-09_L01_ELS/images/final_map/Yr_d1_463_d2_472_d3_1_order_C_frames_90000_.mmap',\n", - " 'gSig': [7, 7]},\n", - " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/J123_2015-11-20_L01_0/images/final_map/Yr_d1_458_d2_477_d3_1_order_C_frames_41000_.mmap',\n", - " 'gSig': [12, 12]},\n", - " {'fname': '/mnt/ceph/data/neuro/caiman/labeling/Jan-AMG_exp3_001/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_115897_.mmap',\n", - " 'gSig': [7, 7]} ]" + " # {'fname': '/mnt/ceph/data/neuro/caiman/labeling/k53_20160530/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_116043_.mmap',\n", + " # 'gSig': [6, 6]},\n", + " # {'fname': '/mnt/ceph/data/neuro/caiman/labeling/J115_2015-12-09_L01_ELS/images/final_map/Yr_d1_463_d2_472_d3_1_order_C_frames_90000_.mmap',\n", + " # 'gSig': [7, 7]},\n", + " {'fname': '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/J123/Yr_d1_458_d2_477_d3_1_order_C_frames_41000_.mmap',\n", + " 'gSig': [12, 12]} ]\n", + " # {'fname': '/mnt/ceph/data/neuro/caiman/labeling/Jan-AMG_exp3_001/images/final_map/Yr_d1_512_d2_512_d3_1_order_C_frames_115897_.mmap',\n", + " # 'gSig': [7, 7]} ]" ] }, { @@ -65,9 +66,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_03_00/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_.mmap\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_03_00/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_.results_analysis.npz'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[5], line 9\u001b[0m\n\u001b[1;32m 5\u001b[0m gt_file \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msplit(fname)[\u001b[38;5;241m0\u001b[39m], os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39msplit(fname)[\n\u001b[1;32m 6\u001b[0m \u001b[38;5;241m1\u001b[39m][:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m4\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatch_masks.npz\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m#analysis_file = '/mnt/ceph/neuro/jeremie_analysis/neurofinder.03.00.test/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_._results_analysis.npz'\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresults_analysis.npz\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mlatin1\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m ld:\n\u001b[1;32m 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(ld\u001b[38;5;241m.\u001b[39mkeys())\n\u001b[1;32m 11\u001b[0m \u001b[38;5;28mlocals\u001b[39m()\u001b[38;5;241m.\u001b[39mupdate(ld)\n", + "File \u001b[0;32m/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/numpy/lib/npyio.py:427\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding, max_header_size)\u001b[0m\n\u001b[1;32m 425\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 427\u001b[0m fid \u001b[38;5;241m=\u001b[39m stack\u001b[38;5;241m.\u001b[39menter_context(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mos_fspath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 428\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 430\u001b[0m \u001b[38;5;66;03m# Code to distinguish from NumPy binary files and pickles.\u001b[39;00m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/Users/manuelpaez/Documents/Flatiron/Caiman/data/source_components/neurofinder_03_00/Yr_d1_498_d2_467_d3_1_order_C_frames_2250_.results_analysis.npz'" + ] + } + ], "source": [ "for dc in inputs[:]:\n", " fname = dc['fname']\n", @@ -365,9 +386,9 @@ ], "metadata": { "kernelspec": { - "display_name": "caiman_pytorch", + "display_name": "caiman_pytorch_2", "language": "python", - "name": "caiman_pytorch" + "name": "caiman_pytorch_2" }, "language_info": { "codemirror_mode": { @@ -379,7 +400,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/caiman/train/train_cnn_model_pytorch.ipynb b/caiman/train/train_cnn_model_pytorch.ipynb index c1eff0728..b10f52cf1 100644 --- a/caiman/train/train_cnn_model_pytorch.ipynb +++ b/caiman/train/train_cnn_model_pytorch.ipynb @@ -8,7 +8,11 @@ "\n", "This notebook will help to demonstrate how to train the CNN Model used in CaImAn to evaluate the shape of (2p) spatial components using the Torch API.\n", "\n", - "The basic function for this is caiman.train.train_cnn_model_keras.cnn_model_pytorch(). It takes it the number of classes to build of a CNN model (based on a tutorial on the CIFAR dataset). The other functions, caiman.train.train_cnn_model.save_model_file() and caiman.train.train_cnn_model.load_model_file() save and retrieve the model and weights of the model. " + "The basic function for this is caiman.train.train_cnn_model_keras.cnn_model_pytorch(). It takes in the number of classes to build a CNN model. \n", + "\n", + "The other functions, caiman.train.helper.save_model_file() and caiman.train.helper.load_model_file() save and retrieve the model and weights of the model. \n", + "\n", + "Author: agiovanni, Manuel Paez" ] }, { @@ -16,17 +20,6 @@ "execution_count": 1, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-08-06 23:34:38.644002: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n", - "2024-08-06 23:34:38.675416: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", - "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "/mnt/home/mpaez/miniconda3/envs/caiman_pytorch/lib/python3.11/site-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML\n", - " warnings.warn(\"Can't initialize NVML\")\n" - ] - }, { "data": { "text/plain": [ @@ -41,7 +34,6 @@ "source": [ "import numpy as np\n", "import os\n", - "import keras \n", "import torch\n", "from torch.optim import Adam\n", "from torch.utils.data import Dataset, TensorDataset, DataLoader\n", @@ -49,8 +41,8 @@ "\n", "import caiman as cm\n", "from caiman.paths import caiman_datadir\n", - "from caiman.train.train_cnn_model_helper import cnn_model_pytorch, get_batch_accuracy, load_model_pytorch, save_model_pytorch\n", - "from caiman.train.train_cnn_model_helper import train_test_split, train, validate \n", + "from caiman.train.helper import cnn_model_pytorch, get_batch_accuracy, load_model_pytorch, save_model_pytorch\n", + "from caiman.train.helper import train_test_split, train, validate \n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "torch.cuda.is_available()" @@ -60,7 +52,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Initalizing the Parameters for the Model (based on MNIST dataset) " + "## Initalizing the Parameters for the Model" ] }, { @@ -82,17 +74,32 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Loading the Dataset of the Model " + "## Loading the Dataset of the Model \n", + "\n", + "Note: do not use minions" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/mnt/ceph/data/neuro/caiman/data_minions/ground_truth_components_curated_minions.npz'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m/mnt/ceph/data/neuro/caiman/data_minions/ground_truth_components_curated_minions.npz\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m ld: \n\u001b[1;32m 2\u001b[0m all_masks_gt \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(ld[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mall_masks_gt\u001b[39m\u001b[38;5;124m'\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat32)\n\u001b[1;32m 3\u001b[0m labels_gt \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(ld[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels_gt_cur\u001b[39m\u001b[38;5;124m'\u001b[39m], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong)\n", + "File \u001b[0;32m/opt/anaconda3/envs/caiman_pytorch_2/lib/python3.12/site-packages/numpy/lib/npyio.py:427\u001b[0m, in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding, max_header_size)\u001b[0m\n\u001b[1;32m 425\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 426\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 427\u001b[0m fid \u001b[38;5;241m=\u001b[39m stack\u001b[38;5;241m.\u001b[39menter_context(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mos_fspath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 428\u001b[0m own_fid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m 430\u001b[0m \u001b[38;5;66;03m# Code to distinguish from NumPy binary files and pickles.\u001b[39;00m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/mnt/ceph/data/neuro/caiman/data_minions/ground_truth_components_curated_minions.npz'" + ] + } + ], "source": [ - "with np.load('/mnt/ceph/data/neuro/caiman/data_minions/ground_truth_components_curated_minions.npz') as ld:\n", - " all_masks_gt = torch.tensor(ld['all_masks_gt'], dtype=torch.float32)\n", + "with np.load('/mnt/ceph/data/neuro/caiman/data_minions/ground_truth_components_curated_minions.npz') as ld: \n", + " all_masks_gt = torch.tensor(ld['all_masks_gt'], dtype=torch.float32) #define\n", " labels_gt = torch.tensor(ld['labels_gt_cur'], dtype=torch.long)" ] }, @@ -105,9 +112,21 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'all_masks_gt' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m all_masks_gt \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mreshape(\u001b[43mall_masks_gt\u001b[49m, (\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, in_channels, img_rows, img_cols))\n\u001b[1;32m 2\u001b[0m dataset \u001b[38;5;241m=\u001b[39m TensorDataset(all_masks_gt, labels_gt) \n\u001b[1;32m 4\u001b[0m train_dataset, test_dataset \u001b[38;5;241m=\u001b[39m train_test_split(dataset, test_fraction)\n", + "\u001b[0;31mNameError\u001b[0m: name 'all_masks_gt' is not defined" + ] + } + ], "source": [ "all_masks_gt = torch.reshape(all_masks_gt, (-1, in_channels, img_rows, img_cols))\n", "dataset = TensorDataset(all_masks_gt, labels_gt) \n", @@ -212,7 +231,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualize Results" + "## Visualize the Results" ] }, { @@ -330,9 +349,9 @@ ], "metadata": { "kernelspec": { - "display_name": "caiman_pytorch", + "display_name": "caiman_pytorch_2", "language": "python", - "name": "caiman_pytorch" + "name": "caiman_pytorch_2" }, "language_info": { "codemirror_mode": { @@ -344,7 +363,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.0" } }, "nbformat": 4, diff --git a/caiman/train/training.md b/caiman/train/training.md index d3b2ef033..3b8db54d4 100644 --- a/caiman/train/training.md +++ b/caiman/train/training.md @@ -2,5 +2,5 @@ HOW TO GENERATE GROUND TRUTH DATA TO TRAIN THE NETWORK Step 1: Go to ground_truth_cnmf_seeded.py and generate new ground truth. This generates a file ending in match_masks.npz Step 2: Go to match_seeded_gt.py IF you want to match the cnmf-seeded components from GT with the results of a CNMF run -Step 3: Go to prepare_training_set.py IF you might want to clean up the components -Step 4: Train the network from train_cnn_model_pytorch.ipynb (train_cnn_model_keras.ipynb not in use) \ No newline at end of file +Step 3: Go to prepare_training_set.py IF you want to clean up the components +Step 4: Train the network from train_cnn_model_pytorch.ipynb \ No newline at end of file diff --git a/caiman/utils/nn_models.py b/caiman/utils/nn_models.py index adc3f02f0..1adc82904 100644 --- a/caiman/utils/nn_models.py +++ b/caiman/utils/nn_models.py @@ -5,16 +5,8 @@ one photon data using a "ring-CNN" background model. """ -import numpy as np -import os -os.environ["KERAS_BACKEND"] = "torch" -import time - -import torch -import torch.nn.functional as F -import torch.nn as nn import keras -import keras.ops as ops +from keras import ops from keras.constraints import Constraint from keras.layers import Input, Dense, Reshape, Layer, Activation from keras.models import Model @@ -23,6 +15,15 @@ from keras.initializers import Constant, RandomUniform from keras.utils import Sequence +import os +os.environ["KERAS_BACKEND"] = "torch" +import numpy as np + +import time +import torch +import torch.nn.functional as F +import torch.nn as nn + import caiman.base.movies from caiman.paths import caiman_datadir diff --git a/environment-minimal.yml b/environment-minimal.yml index 81fa6898c..86106e708 100644 --- a/environment-minimal.yml +++ b/environment-minimal.yml @@ -9,6 +9,7 @@ dependencies: - ipython - ipyparallel - ipywidgets +- keras - matplotlib - moviepy - pytest @@ -22,7 +23,9 @@ dependencies: - scikit-image >=0.19.0 - scikit-learn >=1.2 - scipy >= 1.10.1 -- tensorflow >=2.4.0,<2.16 +# - tensorflow >=2.4.0,<2.16 - tifffile +- torch +- torchvision - tqdm - zarr diff --git a/environment.yml b/environment.yml index 014c3532c..e7d4944fd 100644 --- a/environment.yml +++ b/environment.yml @@ -13,6 +13,7 @@ dependencies: - ipyparallel - jupyter - jupyter_bokeh +- keras - matplotlib - moviepy - mypy @@ -30,7 +31,7 @@ dependencies: - scikit-image >=0.19.0 - scikit-learn >=1.2 - scipy >= 1.10.1 -- tensorflow >=2.4.0,<2.16 +# - tensorflow >=2.4.0,<2.16 - tifffile - tk - tqdm