diff --git a/caiman/motion_correction.py b/caiman/motion_correction.py index 12e9553ee..3dbbd9465 100644 --- a/caiman/motion_correction.py +++ b/caiman/motion_correction.py @@ -179,12 +179,15 @@ def __init__(self, fname, min_mov=None, dview=None, max_shifts=(6, 6), niter_rig """ if 'ndarray' in str(type(fname)): logging.info('Creating file for motion correction "tmp_mov_mot_corr.hdf5"') - cm.movie(fname).save('./tmp_mov_mot_corr.hdf5') + cm.movie(fname).save('./tmp_mov_mot_corr.hdf5') # FIXME don't write to the current directory! fname = ['./tmp_mov_mot_corr.hdf5'] if type(fname) is not list: fname = [fname] + if type(gSig_filt) is tuple: + gSig_filt = list(gSig_filt) # There are some serializers down the line that choke otherwise + self.fname = fname self.dview = dview self.max_shifts = max_shifts diff --git a/demos/obsolete/1_1/demo_OnACID_1_1.py b/demos/obsolete/1_1/demo_OnACID_1_1.py deleted file mode 100755 index 747e04d7f..000000000 --- a/demos/obsolete/1_1/demo_OnACID_1_1.py +++ /dev/null @@ -1,149 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Created on Wed Sep 20 17:52:23 2017 -Basic demo for the OnACID algorithm using CNMF initialization. For a more -complete demo check the script demo_OnACID_mesoscope.py - -@author: jfriedrich & epnev -""" - -import os -import numpy as np -import pylab as pl -import caiman as cm -from caiman.source_extraction import cnmf as cnmf -from caiman.utils.visualization import view_patches_bar, plot_contours -from copy import deepcopy -from scipy.special import log_ndtr -from caiman.paths import caiman_datadir - -#%% -def main(): - pass # For compatibility between running under Spyder and the CLI - -#%% load data - - fname = os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif') - Y = cm.load(fname).astype(np.float32) # - # used as a background image - Cn = cm.local_correlations(Y.transpose(1, 2, 0)) -#%% set up some parameters - - # frame rate (Hz) - fr = 10 - # approximate length of transient event in seconds - decay_time = 0.5 - # expected half size of neurons - gSig = [6, 6] - # order of AR indicator dynamics - p = 1 - # minimum SNR for accepting new components - min_SNR = 3.5 - # correlation threshold for new component inclusion - rval_thr = 0.90 - # number of background components - gnb = 3 - - # set up some additional supporting parameters needed for the algorithm (these are default values but change according to dataset characteristics) - - # number of shapes to be updated each time (put this to a finite small value to increase speed) - max_comp_update_shape = np.inf - # maximum number of expected components used for memory pre-allocation (exaggerate here) - expected_comps = 50 - # number of timesteps to consider when testing new neuron candidates - N_samples = np.ceil(fr * decay_time) - # exceptionality threshold - thresh_fitness_raw = log_ndtr(-min_SNR) * N_samples - # total length of file - T1 = Y.shape[0] - - # set up CNMF initialization parameters - - # merging threshold, max correlation allowed - merge_thresh = 0.8 - # number of frames for initialization (presumably from the first file) - initbatch = 400 - # size of patch - patch_size = 32 - # amount of overlap between patches - stride = 3 - # max number of components in each patch - K = 4 - -#%% obtain initial batch file used for initialization - # memory map file (not needed) - fname_new = Y[:initbatch].save(os.path.join(caiman_datadir(), 'example_movies', 'demo.mmap'), order='C') - Yr, dims, T = cm.load_memmap(fname_new) - images = np.reshape(Yr.T, [T] + list(dims), order='F') - Cn_init = cm.local_correlations(np.reshape(Yr, dims + (T,), order='F')) - - #%% RUN (offline) CNMF algorithm on the initial batch - pl.close('all') - cnm_init = cnmf.CNMF(2, k=K, gSig=gSig, merge_thresh=merge_thresh, fr=fr, - p=p, rf=patch_size // 2, stride=stride, skip_refinement=False, - normalize_init=False, options_local_NMF=None, - minibatch_shape=100, minibatch_suff_stat=5, - update_num_comps=True, rval_thr=rval_thr, - thresh_fitness_delta=-50, gnb=gnb, decay_time=decay_time, - thresh_fitness_raw=thresh_fitness_raw, - batch_update_suff_stat=False, max_comp_update_shape=max_comp_update_shape, - expected_comps=expected_comps, dview=None, - min_SNR=min_SNR) - - cnm_init = cnm_init.fit(images) - - print(('Number of components:' + str(cnm_init.estimates.A.shape[-1]))) - - pl.figure() - crd = plot_contours(cnm_init.estimates.A.tocsc(), Cn_init, thr=0.9) - -#%% run (online) OnACID algorithm - - cnm = deepcopy(cnm_init) - cnm.params.data['dims'] = (60, 80) - cnm._prepare_object(np.asarray(Yr), T1) - - t = initbatch - - Y_ = cm.load(fname)[initbatch:].astype(np.float32) - for frame_count, frame in enumerate(Y_): - cnm.fit_next(t, frame.copy().reshape(-1, order='F')) - t += 1 - -#%% extract the results - - C, f = cnm.estimates.C_on[gnb:cnm.M], cnm.estimates.C_on[:gnb] - A, b = cnm.estimates.Ab[:, gnb:cnm.M], cnm.estimates.Ab[:, :gnb] - print(('Number of components:' + str(A.shape[-1]))) - -#%% pass through the CNN classifier with a low threshold (keeps clearer neuron shapes and excludes processes) - use_CNN = True - if use_CNN: - # threshold for CNN classifier - thresh_cnn = 0.1 - from caiman.components_evaluation import evaluate_components_CNN - predictions, final_crops = evaluate_components_CNN( - A, dims, gSig, model_name=os.path.join(caiman_datadir(), 'model', 'cnn_model')) - A_exclude, C_exclude = A[:, predictions[:, 1] < - thresh_cnn], C[predictions[:, 1] < thresh_cnn] - A, C = A[:, predictions[:, 1] >= - thresh_cnn], C[predictions[:, 1] >= thresh_cnn] - noisyC = cnm.estimates.noisyC[gnb:cnm.M] - YrA = noisyC[predictions[:, 1] >= thresh_cnn] - C - else: - YrA = cnm.estimates.noisyC[gnb:cnm.M] - C - -#%% plot results - pl.figure() - crd = cm.utils.visualization.plot_contours(A, Cn, thr=0.9) - - view_patches_bar(Yr, A, C, b, f, - dims[0], dims[1], YrA, img=Cn) - -#%% -# This is to mask the differences between running this demo in Spyder -# versus from the CLI -if __name__ == "__main__": - main() diff --git a/demos/obsolete/1_1/demo_OnACID_mesoscope_1_1.py b/demos/obsolete/1_1/demo_OnACID_mesoscope_1_1.py deleted file mode 100755 index 47c23dc4d..000000000 --- a/demos/obsolete/1_1/demo_OnACID_mesoscope_1_1.py +++ /dev/null @@ -1,396 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Complete pipeline for online processing using OnACID. -@author: Andrea Giovannucci @agiovann and Eftychios Pnevmatikakis @epnev -Special thanks to Andreas Tolias and his lab at Baylor College of Medicine -for sharing their data used in this demo. -""" - -from copy import deepcopy -import glob -import numpy as np -import os -import pylab as pl -import scipy -import sys -from time import time - -try: - if __IPYTHON__: - print('Detected iPython') - # this is used for debugging purposes only. allows to reload classes when changed - get_ipython().magic('load_ext autoreload') - get_ipython().magic('autoreload 2') -except NameError: - pass - -import caiman as cm -from caiman.utils.visualization import view_patches_bar -from caiman.utils.utils import download_demo, load_object, save_object -from caiman.components_evaluation import evaluate_components_CNN -from caiman.motion_correction import motion_correct_iteration_fast -import cv2 -from caiman.utils.visualization import plot_contours -from caiman.source_extraction.cnmf.online_cnmf import bare_initialization -from caiman.source_extraction.cnmf.utilities import detrend_df_f_auto -from caiman.paths import caiman_datadir - -#%% -def main(): - pass # For compatibility between running under Spyder and the CLI - -#%% download and list all files to be processed - - # folder inside ./example_movies where files will be saved - fld_name = 'Mesoscope' - download_demo('Tolias_mesoscope_1.hdf5', fld_name) - download_demo('Tolias_mesoscope_2.hdf5', fld_name) - download_demo('Tolias_mesoscope_3.hdf5', fld_name) - - # folder where files are located - folder_name = os.path.join(caiman_datadir(), 'example_movies', fld_name) - extension = 'hdf5' # extension of files - # read all files to be processed - fls = glob.glob(folder_name + '/*' + extension) - - # your list of files should look something like this - print(fls) - -#%% Set up some parameters - - # frame rate (Hz) - fr = 15 - # approximate length of transient event in seconds - decay_time = 0.5 - # expected half size of neurons - gSig = (3, 3) - # order of AR indicator dynamics - p = 1 - # minimum SNR for accepting new components - min_SNR = 2.5 - # correlation threshold for new component inclusion - rval_thr = 0.85 - # spatial downsampling factor (increases speed but may lose some fine structure) - ds_factor = 1 - # number of background components - gnb = 2 - # recompute gSig if downsampling is involved - gSig = tuple(np.ceil(np.array(gSig) / ds_factor).astype('int')) - # flag for online motion correction - mot_corr = True - # maximum allowed shift during motion correction - max_shift = np.ceil(10. / ds_factor).astype('int') - - # set up some additional supporting parameters needed for the algorithm (these are default values but change according to dataset characteristics) - - # number of shapes to be updated each time (put this to a finite small value to increase speed) - max_comp_update_shape = np.inf - # number of files used for initialization - init_files = 1 - # number of files used for online - online_files = len(fls) - 1 - # number of frames for initialization (presumably from the first file) - initbatch = 200 - # maximum number of expected components used for memory pre-allocation (exaggerate here) - expected_comps = 300 - # initial number of components - K = 2 - # number of timesteps to consider when testing new neuron candidates - N_samples = np.ceil(fr * decay_time) - # exceptionality threshold - thresh_fitness_raw = scipy.special.log_ndtr(-min_SNR) * N_samples - # number of passes over the data - epochs = 2 - # upper bound for number of frames in each file (used right below) - len_file = 1000 - # total length of all files (if not known use a large number, then truncate at the end) - T1 = len(fls) * len_file * epochs - -#%% Initialize movie - - # load only the first initbatch frames and possibly downsample them - if ds_factor > 1: - Y = cm.load(fls[0], subindices=slice(0, initbatch, None)).astype( - np.float32).resize(1. / ds_factor, 1. / ds_factor) - else: - Y = cm.load(fls[0], subindices=slice( - 0, initbatch, None)).astype(np.float32) - - if mot_corr: # perform motion correction on the first initbatch frames - mc = Y.motion_correct(max_shift, max_shift) - Y = mc[0].astype(np.float32) - borders = np.max(mc[1]) - else: - Y = Y.astype(np.float32) - - # minimum value of movie. Subtract it to make the data non-negative - img_min = Y.min() - Y -= img_min - img_norm = np.std(Y, axis=0) - # normalizing factor to equalize the FOV - img_norm += np.median(img_norm) - Y = Y / img_norm[None, :, :] # normalize data - - _, d1, d2 = Y.shape - dims = (d1, d2) # dimensions of FOV - Yr = Y.to_2D().T # convert data into 2D array - - Cn_init = Y.local_correlations(swap_dim=False) # compute correlation image - #pl.imshow(Cn_init) - #pl.title('Correlation Image on initial batch') - #pl.colorbar() - - bnd_Y = np.percentile(Y,(0.001,100-0.001)) # plotting boundaries for Y - -#%% initialize OnACID with bare initialization - - cnm_init = bare_initialization(Y[:initbatch].transpose(1, 2, 0), init_batch=initbatch, k=K, gnb=gnb, - gSig=gSig, p=p, minibatch_shape=100, minibatch_suff_stat=5, - update_num_comps=True, rval_thr=rval_thr, - thresh_fitness_raw=thresh_fitness_raw, - batch_update_suff_stat=True, max_comp_update_shape=max_comp_update_shape, - deconv_flag=False, use_dense=False, - simultaneously=False, n_refit=0, - max_num_added=3, min_num_trial=3, - sniper_mode=False, use_peak_max=False, - expected_comps=expected_comps) - -#%% Plot initialization results - - crd = plot_contours(cnm_init.estimates.A.tocsc(), Cn_init, thr=0.9) - A, C, b, f, YrA, sn = cnm_init.estimates.A, cnm_init.estimates.C, cnm_init.estimates.b, cnm_init.estimates.f, \ - cnm_init.estimates.YrA, cnm_init.estimates.sn - view_patches_bar(Yr, scipy.sparse.coo_matrix( - A.tocsc()[:, :]), C[:, :], b, f, dims[0], dims[1], YrA=YrA[:, :], img=Cn_init) - - bnd_AC = np.percentile(A.dot(C),(0.001,100-0.005)) - bnd_BG = np.percentile(b.dot(f),(0.001,100-0.001)) - -#%% create a function for plotting results in real time if needed - - def create_frame(cnm2, img_norm, captions): - cnm2_est = cnm2.estimates - A, b = cnm2_est.Ab[:, gnb:], cnm2_est.Ab[:, :gnb].toarray() - C, f = cnm2_est.C_on[gnb:cnm2.M, :], cnm2_est.C_on[:gnb, :] - # inferred activity due to components (no background) - frame_plot = (frame_cor.copy() - bnd_Y[0])/np.diff(bnd_Y) - comps_frame = A.dot(C[:, t - 1]).reshape(cnm2.dims, order='F') - bgkrnd_frame = b.dot(f[:, t - 1]).reshape(cnm2.dims, order='F') # denoised frame (components + background) - denoised_frame = comps_frame + bgkrnd_frame - denoised_frame = (denoised_frame.copy() - bnd_Y[0])/np.diff(bnd_Y) - comps_frame = (comps_frame.copy() - bnd_AC[0])/np.diff(bnd_AC) - - if show_residuals: - #all_comps = np.reshape(cnm2.Yres_buf.mean(0), cnm2.dims, order='F') - all_comps = np.reshape(cnm2_est.mean_buff, cnm2.dims, order='F') - all_comps = np.minimum(np.maximum(all_comps, 0)*2 + 0.25, 255) - else: - all_comps = np.array(A.sum(-1)).reshape(cnm2.dims, order='F') - # spatial shapes - frame_comp_1 = cv2.resize(np.concatenate([frame_plot, all_comps * 1.], axis=-1), - (2 * np.int(cnm2.dims[1] * resize_fact), np.int(cnm2.dims[0] * resize_fact))) - frame_comp_2 = cv2.resize(np.concatenate([comps_frame, denoised_frame], axis=-1), - (2 * np.int(cnm2.dims[1] * resize_fact), np.int(cnm2.dims[0] * resize_fact))) - frame_pn = np.concatenate([frame_comp_1, frame_comp_2], axis=0).T - vid_frame = np.repeat(frame_pn[:, :, None], 3, axis=-1) - vid_frame = np.minimum((vid_frame * 255.), 255).astype('u1') - - if show_residuals and cnm2_est.ind_new: - add_v = np.int(cnm2.dims[1]*resize_fact) - for ind_new in cnm2_est.ind_new: - cv2.rectangle(vid_frame,(int(ind_new[0][1]*resize_fact),int(ind_new[1][1]*resize_fact)+add_v), - (int(ind_new[0][0]*resize_fact),int(ind_new[1][0]*resize_fact)+add_v),(255,0,255),2) - - cv2.putText(vid_frame, captions[0], (5, 20), fontFace=5, fontScale=0.8, color=( - 0, 255, 0), thickness=1) - cv2.putText(vid_frame, captions[1], (np.int( - cnm2.dims[0] * resize_fact) + 5, 20), fontFace=5, fontScale=0.8, color=(0, 255, 0), thickness=1) - cv2.putText(vid_frame, captions[2], (5, np.int( - cnm2.dims[1] * resize_fact) + 20), fontFace=5, fontScale=0.8, color=(0, 255, 0), thickness=1) - cv2.putText(vid_frame, captions[3], (np.int(cnm2.dims[0] * resize_fact) + 5, np.int( - cnm2.dims[1] * resize_fact) + 20), fontFace=5, fontScale=0.8, color=(0, 255, 0), thickness=1) - cv2.putText(vid_frame, 'Frame = ' + str(t), (vid_frame.shape[1] // 2 - vid_frame.shape[1] // - 10, vid_frame.shape[0] - 20), fontFace=5, fontScale=0.8, color=(0, 255, 255), thickness=1) - return vid_frame - -#%% Prepare object for OnACID - cnm2 = deepcopy(cnm_init) - - save_init = False # flag for saving initialization object. Useful if you want to check OnACID with different parameters but same initialization - if save_init: - cnm_init.dview = None - save_object(cnm_init, fls[0][:-4] + '_DS_' + str(ds_factor) + '.pkl') - cnm_init = load_object(fls[0][:-4] + '_DS_' + str(ds_factor) + '.pkl') - - cnm2._prepare_object(np.asarray(Yr), T1, idx_components=None) - - cnm2.thresh_CNN_noisy = 0.5 - -#%% Run OnACID and optionally plot results in real time - epochs = 1 - cnm2.estimates.Ab_epoch = [] # save the shapes at the end of each epoch - t = initbatch # current timestep - tottime = [] - Cn = Cn_init.copy() - - # flag for removing components with bad shapes - remove_flag = False - T_rm = 650 # remove bad components every T_rm frames - rm_thr = 0.1 # CNN classifier removal threshold - # flag for plotting contours of detected components at the end of each file - plot_contours_flag = False - # flag for showing results video online (turn off flags for improving speed) - play_reconstr = True - # flag for saving movie (file could be quite large..) - save_movie = False - movie_name = os.path.join(folder_name, 'sniper_meso_0.995_new.avi') # name of movie to be saved - resize_fact = 1.2 # image resizing factor - - if online_files == 0: # check whether there are any additional files - process_files = fls[:init_files] # end processing at this file - init_batc_iter = [initbatch] # place where to start - end_batch = T1 - else: - process_files = fls[:init_files + online_files] # additional files - # where to start reading at each file - init_batc_iter = [initbatch] + [0] * online_files - - - shifts = [] - show_residuals = True - if show_residuals: - caption = 'Mean Residual Buffer' - else: - caption = 'Identified Components' - captions = ['Raw Data', 'Inferred Activity', caption, 'Denoised Data'] - if save_movie and play_reconstr: - fourcc = cv2.VideoWriter_fourcc('8', 'B', 'P', 'S') - # fourcc = cv2.VideoWriter_fourcc(*'XVID') - out = cv2.VideoWriter(movie_name, fourcc, 30.0, tuple( - [int(2 * x * resize_fact) for x in cnm2.dims])) - - for iter in range(epochs): - if iter > 0: - # if not on first epoch process all files from scratch - process_files = fls[:init_files + online_files] - init_batc_iter = [0] * (online_files + init_files) - - # np.array(fls)[np.array([1,2,3,4,5,-5,-4,-3,-2,-1])]: - for file_count, ffll in enumerate(process_files): - print('Now processing file ' + ffll) - Y_ = cm.load(ffll, subindices=slice( - init_batc_iter[file_count], T1, None)) - - # update max-correlation (and perform offline motion correction) just for illustration purposes - if plot_contours_flag: - if ds_factor > 1: - Y_1 = Y_.resize(1. / ds_factor, 1. / ds_factor, 1) - else: - Y_1 = Y_.copy() - if mot_corr: - templ = (cnm2.estimates.Ab.data[:cnm2.estimates.Ab.indptr[1]] * cnm2.estimates.C_on[0, t - 1]).reshape(cnm2.estimates.dims, order='F') * img_norm - newcn = (Y_1 - img_min).motion_correct(max_shift, max_shift, - template=templ)[0].local_correlations(swap_dim=False) - Cn = np.maximum(Cn, newcn) - else: - Cn = np.maximum(Cn, Y_1.local_correlations(swap_dim=False)) - - old_comps = cnm2.N # number of existing components - for frame_count, frame in enumerate(Y_): # now process each file - if np.isnan(np.sum(frame)): - raise Exception('Frame ' + str(frame_count) + ' contains nan') - if t % 100 == 0: - print('Epoch: ' + str(iter + 1) + '. ' + str(t) + ' frames have beeen processed in total. ' + str(cnm2.N - - old_comps) + ' new components were added. Total number of components is ' + str(cnm2.estimates.Ab.shape[-1] - gnb)) - old_comps = cnm2.N - - t1 = time() # count time only for the processing part - frame_ = frame.copy().astype(np.float32) # - if ds_factor > 1: - frame_ = cv2.resize( - frame_, img_norm.shape[::-1]) # downsampling - - frame_ -= img_min # make data non-negative - - if mot_corr: # motion correct - templ = cnm2.estimates.Ab.dot( - cnm2.estimates.C_on[:cnm2.M, t - 1]).reshape(cnm2.dims, order='F') * img_norm - frame_cor, shift = motion_correct_iteration_fast( - frame_, templ, max_shift, max_shift) - shifts.append(shift) - else: - templ = None - frame_cor = frame_ - - frame_cor = frame_cor / img_norm # normalize data-frame - cnm2.fit_next(t, frame_cor.reshape(-1, order='F')) # run OnACID on this frame - # store time - tottime.append(time() - t1) - - t += 1 - - if t % T_rm == 0 and remove_flag: - prd, _ = evaluate_components_CNN(cnm2.estimates.Ab[:, gnb:], dims, gSig) - ind_rem = np.where(prd[:, 1] < rm_thr)[0].tolist() - cnm2.remove_components(ind_rem) - print('Removing '+str(len(ind_rem))+' components') - - if t % 1000 == 0 and plot_contours_flag: - pl.cla() - A = cnm2.estimates.Ab[:, gnb:] - # update the contour plot every 1000 frames - crd = cm.utils.visualization.plot_contours(A, Cn, thr=0.9) - pl.pause(1) - - if play_reconstr: # generate movie with the results - vid_frame = create_frame(cnm2, img_norm, captions) - if save_movie: - out.write(vid_frame) - if t-initbatch < 100: - #for rp in np.int32(np.ceil(np.exp(-np.arange(1,100)/30)*20)): - for rp in range(len(cnm2.estimates.ind_new)*2): - out.write(vid_frame) - cv2.imshow('frame', vid_frame) - if t-initbatch < 100: - for rp in range(len(cnm2.estimates.ind_new)*2): - cv2.imshow('frame', vid_frame) - if cv2.waitKey(1) & 0xFF == ord('q'): - break - - print('Cumulative processing speed is ' + str((t - initbatch) / - np.sum(tottime))[:5] + ' frames per second.') - # save the shapes at the end of each epoch - cnm2.estimates.Ab_epoch.append(cnm2.estimates.Ab.copy()) - - if save_movie: - out.release() - cv2.destroyAllWindows() - -#%% save results (optional) - save_results = False - - if save_results: - np.savez('results_analysis_online_MOT_CORR.npz', - Cn=Cn, Ab=cnm2.estimates.Ab, Cf=cnm2.estimates.C_on, b=cnm2.estimates.b, f=cnm2.estimates.f, - dims=cnm2.dims, tottime=tottime, noisyC=cnm2.estimates.noisyC, shifts=shifts) - - #%% extract results from the objects and do some plotting - A, b = cnm2.estimates.Ab[:, gnb:], cnm2.estimates.Ab[:, :gnb].toarray() - C, f = cnm2.estimates.C_on[gnb:cnm2.M, t - t // - epochs:t], cnm2.estimates.C_on[:gnb, t - t // epochs:t] - noisyC = cnm2.estimates.noisyC[:, t - t // epochs:t] - b_trace = [osi.b for osi in cnm2.estimates.OASISinstances] if hasattr( - cnm2, 'OASISinstances') else [0] * C.shape[0] - - pl.figure() - crd = cm.utils.visualization.plot_contours(A, Cn, thr=0.9) - view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, :]), C[:, :], b, f, - dims[0], dims[1], YrA=noisyC[gnb:cnm2.M] - C, img=Cn) - -#%% -# This is to mask the differences between running this demo in Spyder -# versus from the CLI -if __name__ == "__main__": - main() diff --git a/demos/obsolete/1_1/demo_caiman_basic_1_1.py b/demos/obsolete/1_1/demo_caiman_basic_1_1.py deleted file mode 100755 index 972b270fe..000000000 --- a/demos/obsolete/1_1/demo_caiman_basic_1_1.py +++ /dev/null @@ -1,174 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Stripped demo for running the CNMF source extraction algorithm with CaImAn and -evaluation the components. The analysis can be run either in the whole FOV -or in patches. For a complete pipeline (including motion correction) -check demo_pipeline.py -Data courtesy of W. Yang, D. Peterka and R. Yuste (Columbia University) - -This demo is designed to be run under spyder or jupyter; its plotting functions -are tailored for that environment. - -@authors: @agiovann and @epnev - -""" - -from __future__ import print_function -from builtins import range -import cv2 - -try: - cv2.setNumThreads(0) -except(): - pass - -try: - if __IPYTHON__: - print("Detected iPython") - get_ipython().magic('load_ext autoreload') - get_ipython().magic('autoreload 2') -except NameError: - pass - -import numpy as np -import os -import glob -import matplotlib.pyplot as plt -from copy import deepcopy - -import caiman as cm -from caiman.source_extraction.cnmf import cnmf as cnmf -from caiman.paths import caiman_datadir -from caiman.source_extraction.cnmf import params as params - -#%% -def main(): - pass # For compatibility between running under Spyder and the CLI - -#%% start a cluster - - c, dview, n_processes =\ - cm.cluster.setup_cluster(backend='local', n_processes=None, - single_thread=False) - -#%% save files to be processed - - # This datafile is distributed with Caiman - fnames = [os.path.join(caiman_datadir(), 'example_movies', 'demoMovie.tif')] - # location of dataset (can actually be a list of filed to be concatenated) - add_to_movie = -np.min(cm.load(fnames[0], subindices=range(200))).astype(float) - # determine minimum value on a small chunk of data - add_to_movie = np.maximum(add_to_movie, 0) - # if minimum is negative subtract to make the data non-negative - base_name = 'Yr' - fname_new = cm.save_memmap(fnames, dview=dview, base_name=base_name, - order='C', - add_to_movie=add_to_movie) - -#%% LOAD MEMORY MAPPABLE FILE - Yr, dims, T = cm.load_memmap(fname_new) - d1, d2 = dims - images = np.reshape(Yr.T, [T] + list(dims), order='F') - -#%% play movie, press q to quit - play_movie = False - if play_movie: - cm.movie(images).play(fr=50, magnification=4, gain=3.) - -#%% correlation image. From here infer neuron size and density - Cn = cm.movie(images).local_correlations(swap_dim=False) - plt.imshow(Cn, cmap='gray') - plt.title('Correlation Image') - -#%% set up some parameters - - is_patches = True # flag for processing in patches or not - fr = 10 # approximate frame rate of data - decay_time = 5.0 # length of transient - - if is_patches: # PROCESS IN PATCHES AND THEN COMBINE - rf = 10 # half size of each patch - stride = 4 # overlap between patches - K = 4 # number of components in each patch - else: # PROCESS THE WHOLE FOV AT ONCE - rf = None # setting these parameters to None - stride = None # will run CNMF on the whole FOV - K = 30 # number of neurons expected (in the whole FOV) - - gSig = [6, 6] # expected half size of neurons - merge_thresh = 0.80 # merging threshold, max correlation allowed - p = 2 # order of the autoregressive system - gnb = 2 # global background order - - params_dict = {'fnames': fnames, - 'fr': fr, - 'decay_time': decay_time, - 'rf': rf, - 'stride': stride, - 'K': K, - 'gSig': gSig, - 'merge_thr': merge_thresh, - 'p': p, - 'nb': gnb} - -# opts = params.CNMFParams(dims=dims, -# method_init='greedy_roi', gSig=gSig, -# merge_thresh=merge_thresh, p=p, gnb=gnb, k=K, -# rf=rf, stride=stride, rolling_sum=False, -# fr=fr, decay_time=decay_time) - opts = params.CNMFParams(params_dict=params_dict) -#%% Now RUN CNMF - cnm = cnmf.CNMF(n_processes, params=opts, dview=dview) - cnm = cnm.fit(images) - -#%% plot contour plots of components - cnm.estimates.plot_contours(img=Cn) - -#%% copy into a new object and refit - cnm.dview = None - cnm2 = deepcopy(cnm) - cnm2.dview = dview - cnm2.params.set('patch', {'rf': None}) - cnm2 = cnm2.fit(images) - -#%% COMPONENT EVALUATION - # the components are evaluated in three ways: - # a) the shape of each component must be correlated with the data - # b) a minimum peak SNR is required over the length of a transient - # c) each shape passes a CNN based classifier (this will pick up only neurons - # and filter out active processes) - - min_SNR = 2.5 # peak SNR for accepted components (if above this, acept) - rval_thr = 0.90 # space correlation threshold (if above this, accept) - use_cnn = True # use the CNN classifier - min_cnn_thr = 0.95 # if cnn classifier predicts below this value, reject - - cnm2.params.set('quality', {'min_SNR': min_SNR, - 'rval_thr': rval_thr, - 'use_cnn': use_cnn, - 'min_cnn_thr': min_cnn_thr}) - - cnm2.estimates.evaluate_components(images, cnm2.params, dview=dview) -#%% visualize selected and rejected components - cnm2.estimates.plot_contours(img=Cn, idx=cnm2.estimates.idx_components) - -#%% visualize selected components - cnm2.estimates.view_components(images, idx=cnm2.estimates.idx_components, img=Cn) - -#%% play movie with results - cnm2.estimates.play_movie(images, magnification=4) - -#%% STOP CLUSTER and clean up log files - cm.stop_server(dview=dview) - - log_files = glob.glob('Yr*_LOG_*') - for log_file in log_files: - os.remove(log_file) - -#%% -# This is to mask the differences between running this demo in Spyder -# versus from the CLI -if __name__ == "__main__": - main() diff --git a/demos/obsolete/1_1/demo_pipeline_1_1.py b/demos/obsolete/1_1/demo_pipeline_1_1.py deleted file mode 100755 index eacab4e0b..000000000 --- a/demos/obsolete/1_1/demo_pipeline_1_1.py +++ /dev/null @@ -1,247 +0,0 @@ -#!/usr/bin/env python - -""" -Complete demo pipeline for motion correction, source extraction, and -deconvolution of two-photon calcium imaging data using the CaImAn package. - -Demo is also available as a jupyter notebook (see demo_pipeline.ipynb) -Dataset couresy of Sue Ann Koay and David Tank (Princeton University) - -This demo pertains to two photon data. For a complete analysis pipeline for -one photon microendoscopic data see demo_pipeline_cnmfE.py - -copyright GNU General Public License v2.0 -authors: @agiovann and @epnev -""" - -from __future__ import division -from __future__ import print_function -from builtins import range - -import os -import cv2 -import glob - -try: - cv2.setNumThreads(0) -except(): - pass - -try: - if __IPYTHON__: - print("Running under iPython") - # this is used for debugging purposes only. allows to reload classes - # when changed - get_ipython().magic('load_ext autoreload') - get_ipython().magic('autoreload 2') -except NameError: - pass - -import matplotlib.pyplot as plt -import numpy as np - -import caiman as cm -from caiman.utils.utils import download_demo -from caiman.source_extraction.cnmf import cnmf as cnmf -from caiman.motion_correction import MotionCorrect -from caiman.source_extraction.cnmf import params as params -from copy import deepcopy - -#%% -def main(): - pass # For compatibility between running under Spyder and the CLI - -#%% First setup some parameters for data and motion correction - - # dataset dependent parameters - fname = ['Sue_2x_3000_40_-46.tif'] # filename to be processed - fr = 30 # imaging rate in frames per second - decay_time = 0.4 # length of a typical transient in seconds - dxy = (2., 2.) # spatial resolution in x and y in (um per pixel) - max_shift_um = (12., 12.) # maximum shift in um - patch_motion_um = (100., 100.) # patch size for non-rigid motion correction in um - - # motion correction parameters - pwrigid_motion_correct = True # flag to select rigid vs pw_rigid motion correction - max_shifts = tuple([int(a/b) for a, b in zip(max_shift_um, dxy)]) - # maximum allow rigid shift in pixels - # for parallelization split the movies in num_splits chuncks across time - splits_rig = 56 - # start a new patch for pw-rigid motion correction every x pixels - strides = tuple([int(a/b) for a, b in zip(patch_motion_um, dxy)]) - # overlap between pathes (size of patch strides+overlaps) - overlaps = (24, 24) - # for parallelization split the movies in num_splits chuncks across time - splits_els = 56 - upsample_factor_grid = 4 # upsample factor to avoid smearing when merging patches - # maximum deviation allowed for patch with respect to rigid shifts - max_deviation_rigid = 3 - -#%% download the dataset if it's not present in your folder - if fname[0] in ['Sue_2x_3000_40_-46.tif', 'demoMovie.tif']: - fname = [download_demo(fname[0])] - -#%% play the movie - # playing the movie using opencv. It requires loading the movie in memory. - # To close the video press q - display_images = False - - if display_images: - m_orig = cm.load_movie_chain(fname) - downsample_ratio = 0.2 - moviehandle = m_orig.resize(1, 1, downsample_ratio) - moviehandle.play(q_max=99.5, fr=60, magnification=2) - -#%% start a cluster for parallel processing - c, dview, n_processes = cm.cluster.setup_cluster( - backend='local', n_processes=None, single_thread=False) - - -#%%% MOTION CORRECTION - # first we create a motion correction object with the parameters specified - min_mov = cm.load(fname[0], subindices=range(200)).min() - # this will be subtracted from the movie to make it non-negative - - mc = MotionCorrect(fname, min_mov, dview=dview, max_shifts=max_shifts, - splits_rig=splits_rig, - strides=strides, overlaps=overlaps, - splits_els=splits_els, border_nan='copy', - upsample_factor_grid=upsample_factor_grid, - max_deviation_rigid=max_deviation_rigid, - shifts_opencv=True, nonneg_movie=True) - # note that the file is not loaded in memory - -#%% Run piecewise-rigid motion correction using NoRMCorre - if pwrigid_motion_correct: - mc.motion_correct_pwrigid(save_movie=True) - m_els = cm.load(mc.fname_tot_els) - bord_px_els = np.ceil(np.maximum(np.max(np.abs(mc.x_shifts_els)), - np.max(np.abs(mc.y_shifts_els)))).astype(np.int) - fnames = mc.fname_tot_els # name of the pw-rigidly corrected file. - - else: - mc.motion_correct_rigid(save_movie=True) - m_els = cm.load(mc.fname_tot_rig) - bord_px_els = np.ceil(np.max(np.abs(mc.shifts_rig))).astype(np.int) - fnames = mc.fname_tot_rig # name of the rigidly corrected file. - - # maximum shift to be used for trimming against NaNs -#%% compare with original movie - if display_images: - downsample_ratio = 0.2 - moviehandle = cm.concatenate([m_orig.resize(1, 1, downsample_ratio) - min_mov, - m_els.resize(1, 1, downsample_ratio)], - axis=2) - moviehandle.play(fr=60, q_max=99.5, magnification=2) # press q to exit - -#%% MEMORY MAPPING - # memory map the file in order 'C' - border_to_0 = bord_px_els # exclude borders due to motion correction -# border_to_0 = 0 if mc.border_nan is 'copy' else bord_px_els - # you can include boundaries if you used the 'copy' option in the motion - # correction, although be careful abou the components near the boundaries - fname_new = cm.save_memmap(fnames, base_name='memmap_', order='C', - border_to_0=border_to_0) # exclude borders - - # now load the file - Yr, dims, T = cm.load_memmap(fname_new) - images = np.reshape(Yr.T, [T] + list(dims), order='F') - # load frames in python format (T x X x Y) - -#%% restart cluster to clean up memory - cm.stop_server(dview=dview) - c, dview, n_processes = cm.cluster.setup_cluster( - backend='local', n_processes=None, single_thread=False) - -#%% parameters for source extraction and deconvolution - p = 1 # order of the autoregressive system - gnb = 2 # number of global background components - merge_thresh = 0.8 # merging threshold, max correlation allowed - # half-size of the patches in pixels. e.g., if rf=25, patches are 50x50 - rf = 15 - stride_cnmf = 6 # amount of overlap between the patches in pixels - K = 4 # number of components per patch - gSig = [4, 4] # expected half size of neurons - # initialization method (if analyzing dendritic data using 'sparse_nmf') - method_init = 'greedy_roi' - - # parameters for component evaluation - - opts = params.CNMFParams(dims=dims, fr=fr, decay_time=decay_time, - method_init=method_init, gSig=gSig, - merge_thresh=merge_thresh, p=p, gnb=gnb, k=K, - rf=rf, stride=stride_cnmf, rolling_sum=True) - -#%% RUN CNMF ON PATCHES - - # First extract spatial and temporal components on patches and combine them - # for this step deconvolution is turned off (p=0) - - opts.set('temporal', {'p': 0}) - cnm = cnmf.CNMF(n_processes, params=opts, dview=dview) - cnm = cnm.fit(images) - -#%% plot contours of found components - Cn = cm.local_correlations(images.transpose(1, 2, 0)) - Cn[np.isnan(Cn)] = 0 - cnm.estimates.plot_contours(img=Cn) - plt.title('Contour plots of found components') - -#%% COMPONENT EVALUATION - # the components are evaluated in three ways: - # a) the shape of each component must be correlated with the data - # b) a minimum peak SNR is required over the length of a transient - # c) each shape passes a CNN based classifier - min_SNR = 2.5 # signal to noise ratio for accepting a component - rval_thr = 0.8 # space correlation threshold for accepting a component - cnn_thr = 0.8 # threshold for CNN based classifier - cnm.params.set('quality', {'fr': fr, - 'decay_time': decay_time, - 'min_SNR': min_SNR, - 'rval_thr': rval_thr, - 'use_cnn': True, - 'min_cnn_thr': cnn_thr}) - cnm.estimates.evaluate_components(images, cnm.params, dview=dview) - -#%% PLOT COMPONENTS - cnm.estimates.plot_contours(img=Cn, idx=cnm.estimates.idx_components) - -#%% VIEW TRACES (accepted and rejected) - - if display_images: - cnm.estimates.view_components(images, img=Cn, idx=cnm.estimates.idx_components) - cnm.estimates.view_components(images, img=Cn, idx=cnm.estimates.idx_components_bad) - -#%% RE-RUN seeded CNMF on accepted patches to refine and perform deconvolution - - cnm.dview = None - cnm2 = deepcopy(cnm) - cnm2.dview = dview - cnm2.params.set('patch', {'rf': None}) - cnm2.params.set('temporal', {'p': p}) - cnm2 = cnm2.fit(images) - -#%% Extract DF/F values - cnm2.estimates.detrend_df_f(quantileMin=8, frames_window=250) - -#%% Show final traces - cnm2.estimates.view_components(Yr, img=Cn) - -#%% reconstruct denoised movie (press q to exit) - if display_images: - cnm2.estimates.play_movie(images, q_max=99.9, gain_res=2, - magnification=2, - bpx=border_to_0, - include_bck=True) - -#%% STOP CLUSTER and clean up log files - cm.stop_server(dview=dview) - log_files = glob.glob('*_LOG_*') - for log_file in log_files: - os.remove(log_file) - -#%% -# This is to mask the differences between running this demo in Spyder -# versus from the CLI -if __name__ == "__main__": - main() diff --git a/demos/obsolete/1_1/demo_pipeline_cnmfE_1_1.py b/demos/obsolete/1_1/demo_pipeline_cnmfE_1_1.py deleted file mode 100755 index 4424696cf..000000000 --- a/demos/obsolete/1_1/demo_pipeline_cnmfE_1_1.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python - -""" -Complete demo pipeline for motion correction, source extraction, and deconvolution -of one photon microendoscopic calcium imaging data using the CaImAn package. - -Demo is also available as a jupyter notebook (see demo_pipeline_cnmfE.ipynb) -""" - -from __future__ import division -from __future__ import print_function - - -import matplotlib.pyplot as plt -import numpy as np - -try: - if __IPYTHON__: - print('Detected iPython') - # this is used for debugging purposes only. allows to reload classes when changed - get_ipython().magic('load_ext autoreload') - get_ipython().magic('autoreload 2') -except NameError: - pass - -import caiman as cm -from caiman.source_extraction import cnmf -from caiman.utils.utils import download_demo -from caiman.utils.visualization import inspect_correlation_pnr -from caiman.motion_correction import motion_correct_oneP_rigid, motion_correct_oneP_nonrigid -from caiman.source_extraction.cnmf import params as params -from copy import deepcopy - -#%% -def main(): - pass # For compatibility between running under Spyder and the CLI - -#%% First setup some parameters - - # dataset dependent parameters - display_images = False # Set to true to show movies and images - fnames = ['data_endoscope.tif'] # filename to be processed - fr = 10 # movie frame rate - decay_time = 0.4 # length of a typical transient in seconds - - # motion correction parameters - do_motion_correction_nonrigid = False - do_motion_correction_rigid = True # choose motion correction type - - gSig_filt = (3, 3) # size of filter, in general gSig (see below), - # change this one if algorithm does not work - max_shifts = (5, 5) # maximum allowed rigid shift - strides = (48, 48) # start a new patch for pw-rigid motion correction every x pixels - overlaps = (24, 24) # overlap between pathes (size of patch strides+overlaps) - # for parallelization split the movies in num_splits chuncks across time - # (make sure that length_movie/num_splits_to_process_rig>100) - splits_rig = 10 - splits_els = 10 - upsample_factor_grid = 4 # upsample factor to avoid smearing when merging patches - # maximum deviation allowed for patch with respect to rigid shifts - max_deviation_rigid = 3 - -#%% start the cluster - try: - cm.stop_server() # stop it if it was running - except(): - pass - - c, dview, n_processes = cm.cluster.setup_cluster(backend='local', - n_processes=24, # number of process to use, if you go out of memory try to reduce this one - single_thread=False) - -#%% download demo file - fnames = [download_demo(fnames[0])] - filename_reorder = fnames - -#%% MOTION CORRECTION - if do_motion_correction_nonrigid or do_motion_correction_rigid: - # do motion correction rigid - mc = motion_correct_oneP_rigid(fnames, - gSig_filt=gSig_filt, - max_shifts=max_shifts, - dview=dview, - splits_rig=splits_rig, - save_movie=not(do_motion_correction_nonrigid), - border_nan='copy' - ) - - new_templ = mc.total_template_rig - - plt.subplot(1, 2, 1); plt.imshow(new_templ) # % plot template - plt.subplot(1, 2, 2); plt.plot(mc.shifts_rig) # % plot rigid shifts - plt.legend(['x shifts', 'y shifts']) - plt.xlabel('frames'); plt.ylabel('pixels') - - # borders to eliminate from movie because of motion correction - bord_px = np.ceil(np.max(np.abs(mc.shifts_rig))).astype(np.int) - filename_reorder = mc.fname_tot_rig - - # do motion correction nonrigid - if do_motion_correction_nonrigid: - mc = motion_correct_oneP_nonrigid( - fnames, - gSig_filt=gSig_filt, - max_shifts=max_shifts, - strides=strides, - overlaps=overlaps, - splits_els=splits_els, - upsample_factor_grid=upsample_factor_grid, - max_deviation_rigid=max_deviation_rigid, - dview=dview, - splits_rig=None, - save_movie=True, # whether to save movie in memory mapped format - new_templ=new_templ, # template to initialize motion correction - border_nan='copy' - ) - - filename_reorder = mc.fname_tot_els - bord_px = np.ceil( - np.maximum(np.max(np.abs(mc.x_shifts_els)), - np.max(np.abs(mc.y_shifts_els)))).astype(np.int) - - # create memory mappable file in the right order on the hard drive (C order) - fname_new = cm.save_memmap( - filename_reorder, - base_name='memmap_', - order='C', - border_to_0=bord_px, - dview=dview) - - # load memory mappable file - Yr, dims, T = cm.load_memmap(fname_new) - Y = Yr.T.reshape((T,) + dims, order='F') - -#%% parameters for source extraction and deconvolution - - p = 1 # order of the autoregressive system - K = None # upper bound on number of components per patch, in general None - gSig = (3, 3) # gaussian width of a 2D gaussian kernel, which approximates a neuron - gSiz = (13, 13) # average diameter of a neuron, in general 4*gSig+1 - Ain = None # possibility to seed with predetermined binary masks - merge_thresh = .7 # merging threshold, max correlation allowed - rf = 40 # half-size of the patches in pixels. e.g., if rf=40, patches are 80x80 - stride_cnmf = 20 # amount of overlap between the patches in pixels - # (keep it at least large as gSiz, i.e 4 times the neuron size gSig) - tsub = 2 # downsampling factor in time for initialization, - # increase if you have memory problems - ssub = 1 # downsampling factor in space for initialization, - # increase if you have memory problems - # you can pass them here as boolean vectors - low_rank_background = None # None leaves background of each patch intact, - # True performs global low-rank approximation if gnb>0 - gnb = 0 # number of background components (rank) if positive, - # else exact ring model with following settings - # gnb= 0: Return background as b and W - # gnb=-1: Return full rank background B - # gnb<-1: Don't return background - nb_patch = 0 # number of background components (rank) per patch if gnb>0, - # else it is set automatically - min_corr = .8 # min peak value from correlation image - min_pnr = 10 # min peak to noise ration from PNR image - ssub_B = 2 # additional downsampling factor in space for background - ring_size_factor = 1.4 # radius of ring is gSiz*ring_size_factor - - # parameters for component evaluation - min_SNR = 3 # adaptive way to set threshold on the transient size - r_values_min = 0.85 # threshold on space consistency (if you lower more components - # will be accepted, potentially with worst quality) - - opts = params.CNMFParams(dims=dims, fr=fr, decay_time=decay_time, - method_init='corr_pnr', # use this for 1 photon - k=K, - gSig=gSig, - gSiz=gSiz, - merge_thresh=merge_thresh, - p=p, - tsub=tsub, - ssub=ssub, - rf=rf, - stride=stride_cnmf, - only_init_patch=True, # set it to True to run CNMF-E - gnb=gnb, - nb_patch=nb_patch, - method_deconvolution='oasis', # could use 'cvxpy' alternatively - low_rank_background=low_rank_background, - update_background_components=True, # sometimes setting to False improve the results - min_corr=min_corr, - min_pnr=min_pnr, - normalize_init=False, # just leave as is - center_psf=True, # leave as is for 1 photon - ssub_B=ssub_B, - ring_size_factor=ring_size_factor, - del_duplicates=True, # whether to remove duplicates from initialization - border_pix=bord_px) # number of pixels to not consider in the borders) - -#%% compute some summary images (correlation and peak to noise) - # change swap dim if output looks weird, it is a problem with tiffile - cn_filter, pnr = cm.summary_images.correlation_pnr(Y, gSig=gSig[0], swap_dim=False) - # inspect the summary images and set the parameters - inspect_correlation_pnr(cn_filter, pnr) - # print parameters set above, modify them if necessary based on summary images - print(min_corr) # min correlation of peak (from correlation image) - print(min_pnr) # min peak to noise ratio - -#%% RUN CNMF ON PATCHES - cnm = cnmf.CNMF(n_processes=n_processes, dview=dview, Ain=Ain, params=opts) - cnm.fit(Y) - -#%% DISCARD LOW QUALITY COMPONENTS - cnm.params.set('quality', {'min_SNR': min_SNR, - 'rval_thr': r_values_min, - 'use_cnn': False}) - cnm.estimates.evaluate_components(Y, cnm.params, dview=dview) - - print(' ***** ') - print('Number of total components: ', len(cnm.estimates.C)) - print('Number of accepted components: ', len(cnm.estimates.idx_components)) - -#%% PLOT COMPONENTS - cnm.dims = dims - if display_images: - cnm.estimates.plot_contours(img=cn_filter, idx=cnm.estimates.idx_components) - cnm.estimates.view_components(Y, idx=cnm.estimates.idx_components) - -#%% MOVIES - if display_images: - # fully reconstructed movie - cnm.estimates.play_movie(Y, q_max=99.9, magnification=2, - include_bck=True, gain_res=10, bpx=bord_px) - # movie without background - cnm.estimates.play_movie(Y, q_max=99.9, magnification=2, - include_bck=False, gain_res=4, bpx=bord_px) - -#%% STOP SERVER - cm.stop_server(dview=dview) -# This is to mask the differences between running this demo in Spyder -# versus from the CLI -if __name__ == "__main__": - main() diff --git a/demos/obsolete/caiman_gui.py b/demos/obsolete/caiman_gui.py deleted file mode 100644 index 7ca340bce..000000000 --- a/demos/obsolete/caiman_gui.py +++ /dev/null @@ -1,119 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- -""" - -author: Pengcheng Zhou -email: zhoupc1988@gmail.com -created: 6/16/17 -last edited: -""" - -import os -from collections import OrderedDict -from PyQt5.QtWidgets import QFileDialog, QWidget, QLabel, QPushButton, \ - QLineEdit, QGridLayout, QApplication - -# --------------------------------CLASSES-------------------------------- - - -class FileOpen(QWidget): - - def __init__(self, parent=None, pars=None, directory='.'): - super(FileOpen, self).__init__(parent) - - if not pars: - self.file_name = None - self.dir_folder = os.path.realpath(directory) - self.name = None - self.type = None - self.fr = 10.0 - self.pixel_size = [1.0] - else: - self.file_name = pars.file_name - if directory != '.': - self.dir_folder = directory - else: - self.dir_folder = pars.dir_folder - self.name = pars.name - self.type = pars.type - self.fr = pars.fr - self.pixel_size = pars.pixel_size - - self.open_button = QPushButton("Open") - self.open_button.show() - self.open_button.clicked.connect(self.load_from_file) - - self.close_button = QPushButton("Close") - self.close_button.show() - self.close_button.clicked.connect(self.done) - - # directory - dir_label = QLabel("Directory") - self.dir_line = QLineEdit() - - # name - name_label = QLabel("Name") - self.name_line = QLineEdit() - - # type - type_label = QLabel("Type") - self.type_line = QLineEdit() - - # frame rate - fs_label = QLabel("Frame rate (Hz)") - self.fr_line = QLineEdit(str(self.fr)) - - # pixel size - pixel_size_label = QLabel("Pixel size (um)") - self.pixel_size_line = QLineEdit('1') - - layout = QGridLayout() - layout.addWidget(self.open_button, 0, 0) - layout.addWidget(dir_label, 1, 0) - layout.addWidget(self.dir_line, 1, 1) - layout.addWidget(name_label) - layout.addWidget(self.name_line) - layout.addWidget(type_label) - layout.addWidget(self.type_line) - layout.addWidget(fs_label) - layout.addWidget(self.fr_line) - layout.addWidget(pixel_size_label) - layout.addWidget(self.pixel_size_line) - layout.addWidget(self.close_button) - - self.setLayout(layout) - self.setWindowTitle("choose video data for processing") - - def load_from_file(self): - self.file_name, _ = QFileDialog.getOpenFileName(QFileDialog(), "open file", self.dir_folder) - self.dir_folder, file_name = os.path.split(self.file_name) - self.name, self.type = os.path.splitext(file_name) - self.dir_line.setText(self.dir_folder) - self.name_line.setText(self.name) - self.type_line.setText((self.type[1:])) - - def done(self): - self.fr = float(self.fr_line.text()) - self.pixel_size = [float(i) for i in self.pixel_size_line.text().split(',')] - self.close() - - -# -------------------------------FUNCTIONS------------------------------- - - -def open_file(directory='.'): - app = QApplication([]) - file_ui = FileOpen(directory=directory) - file_ui.show() - # sys.exit(app.exec_()) - app.exec_() - file_ui.fr = float(file_ui.fr_line.text()) - temp = file_ui.pixel_size_line.text() - file_ui.pixel_size = [float(i) for i in temp.split(',')] - - file_info = OrderedDict([('file_name', file_ui.file_name), ('dir', file_ui.dir_folder), ('name', file_ui.name), - ('type', file_ui.type), ('Fs', file_ui.fr), ('pixel_size', file_ui.pixel_size)]) - return file_info - - -#----------------------------------RUN---------------------------------- diff --git a/demos/obsolete/demo_caiman.py b/demos/obsolete/demo_caiman.py deleted file mode 100755 index 06c005ef8..000000000 --- a/demos/obsolete/demo_caiman.py +++ /dev/null @@ -1,250 +0,0 @@ -#!/usr/bin/env python - -from __future__ import print_function -# %% -from builtins import str -from builtins import range - -import caiman.source_extraction.cnmf.params - -try: - if __IPYTHON__: - # this is used for debugging purposes only. allows to reload classes when changed - get_ipython().magic('load_ext autoreload') - get_ipython().magic('autoreload 2') -except NameError: - print('Not launched under iPython') - pass - -import sys -import numpy as np -from time import time -from scipy.sparse import coo_matrix -import psutil -import glob -import os -import scipy -from ipyparallel import Client -# mpl.use('Qt5Agg') -import pylab as pl -pl.ion() -#%% -import caiman as cm -from caiman.components_evaluation import evaluate_components -from caiman.utils.visualization import plot_contours, view_patches_bar -from caiman.base.rois import extract_binary_masks_blob -from caiman.source_extraction import cnmf -#%% -# backend='SLURM' -backend = 'local' -if backend == 'SLURM': - n_processes = np.int(os.environ.get('SLURM_NPROCS')) -else: - # roughly number of cores on your machine minus 1 - n_processes = np.maximum(np.int(psutil.cpu_count()), 1) -print(('using ' + str(n_processes) + ' processes')) -#%% start cluster for efficient computation -single_thread = False - -if single_thread: - dview = None -else: - try: - c.close() - except: - print('C was not existing, creating one') - print("Stopping cluster to avoid unnencessary use of memory....") - sys.stdout.flush() - if backend == 'SLURM': - try: - stop_server(is_slurm=True) - except: - print('Nothing to stop') - # todocument - slurm_script = '/mnt/xfs1/home/agiovann/SOFTWARE/Constrained_NMF/SLURM/slurmStart.sh' - cm.start_server(slurm_script=slurm_script) - pdir, profile = os.environ['IPPPDIR'], os.environ['IPPPROFILE'] - c = Client(ipython_dir=pdir, profile=profile) - else: - cm.stop_server() - cm.start_server() - c = Client() - - print(('Using ' + str(len(c)) + ' processes')) - dview = c[:len(c)] -#%% FOR LOADING ALL TIFF FILES IN A FILE AND SAVING THEM ON A SINGLE MEMORY MAPPABLE FILE -fnames = [] -base_folder = './example_movies/' # folder containing the demo files -for file in glob.glob(os.path.join(base_folder, '*.tif')): - if file.endswith("ie.tif"): - fnames.append(os.path.abspath(file)) - -fnames.sort() -if len(fnames) == 0: - # todocument raise - raise Exception("Could not find any tiff file") - -print(fnames) -fnames = fnames -# %% -# idx_x=slice(12,500,None) -# idx_y=slice(12,500,None) -# idx_xy=(idx_x,idx_y) -add_to_movie = 0 # of movie too negative need to add a baseline -downsample_factor = 1 # use .2 or .1 if file is large and you want a quick answer -idx_xy = None -base_name = 'Yr' -name_new = cm.save_memmap_each(fnames, dview=dview, base_name=base_name, resize_fact=( - 1, 1, downsample_factor), remove_init=0, idx_xy=idx_xy, add_to_movie=add_to_movie) -# todocument sort -name_new.sort() -print(name_new) - -#%% -# todocument return -fname_new = cm.save_memmap_join( - name_new, base_name='Yr', n_chunks=12, dview=dview) -#%% -Yr, dims, T = cm.load_memmap(fname_new) -Y = np.reshape(Yr, dims + (T,), order='F') -#%% visualize correlation image -Cn = cm.local_correlations(Y) -pl.imshow(Cn, cmap='gray') -#%% parameters of experiment -K = 30 # number of neurons expected per patch -gSig = [7, 7] # expected half size of neurons -merge_thresh = 0.8 # merging threshold, max correlation allowed -p = 2 # order of the autoregressive system -options = caiman.source_extraction.cnmf.params.CNMFParams(dims, K=K, gSig=gSig, ssub=2, tsub=2, p=p, nb=1, normalize_init=True) -options['preprocess_params']['noise_method'] = 'mean' -#%% PREPROCESS DATA AND INITIALIZE COMPONENTS -t1 = time() -Yr, sn, g, psx = cm.source_extraction.cnmf.pre_processing.preprocess_data( - Yr, dview=dview, **options['preprocess_params']) -print((time() - t1)) -#%% -t1 = time() -Atmp, Ctmp, b_in, f_in, center = cm.source_extraction.cnmf.initialization.initialize_components( - Y, **options['init_params']) -print((time() - t1)) -#%% Refine manually component by clicking on neurons -refine_components = False -if refine_components: - Ain, Cin = cm.source_extraction.cnmf.utilities.manually_refine_components( - Y, options['init_params']['gSig'], coo_matrix(Atmp), Ctmp, Cn, thr=0.9) -else: - Ain, Cin = Atmp, Ctmp -#%% plot estimated component -pl.figure() -crd = plot_contours(coo_matrix(Ain), Cn) -pl.show() -#%% UPDATE SPATIAL COMPONENTS -# pl.close() -t1 = time() -A, b, Cin, f_in = cm.source_extraction.cnmf.spatial.update_spatial_components( - Yr, Cin, f_in, Ain, sn=sn, dview=dview, dims=dims, **options['spatial_params']) -t_elSPATIAL = time() - t1 -pl.figure() -crd = plot_contours(A, Cn) - -#%% update_temporal_components -# pl.close() -t1 = time() -# set this to zero for fast updating without deconvolution -options['temporal_params']['p'] = 0 -C, A, b, f, S, bl, c1, neurons_sn, g, YrA = cm.source_extraction.cnmf.temporal.update_temporal_components( - Yr, A, b, Cin, f_in, dview=dview, bl=None, c1=None, sn=None, g=None, **options['temporal_params']) -t_elTEMPORAL = time() - t1 -print(t_elTEMPORAL) -#%% merge components corresponding to the same neuron -t1 = time() -A_m, C_m, nr_m, merged_ROIs, S_m, bl_m, c1_m, sn_m, g_m = cm.source_extraction.cnmf.merging.merge_components( - Yr, A, b, C, f, S, sn, options['temporal_params'], options['spatial_params'], dview=dview, bl=bl, c1=c1, sn=neurons_sn, g=g, thr=merge_thresh, mx=50, fast_merge=True) -t_elMERGE = time() - t1 -print(t_elMERGE) - - -#%% -# plt.figure() -#crd = cm.source_extraction.cnmf.plot_contours(A_m,Cn,thr=0.9) -#%% refine spatial and temporal -# pl.close() -t1 = time() -A2, b2, C2, f = cm.source_extraction.cnmf.spatial.update_spatial_components( - Yr, C_m, f, A_m, sn=sn, dview=dview, dims=dims, **options['spatial_params']) -# set it back to original value to perform full deconvolution -options['temporal_params']['p'] = p -C2, A2, b2, f2, S2, bl2, c12, neurons_sn2, g21, YrA = cm.source_extraction.cnmf.temporal.update_temporal_components( - Yr, A2, b2, C2, f, dview=dview, bl=None, c1=None, sn=None, g=None, **options['temporal_params']) -print((time() - t1)) - -pl.figure() -crd = plot_contours(A2.tocsc()[:, :], Cn, thr=0.9) - -#%% -#%% -final_frate = 10 - -Npeaks = 10 -traces = C + YrA -# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5]) -# traces_b=np.diff(traces,axis=1) -fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = \ - evaluate_components(Y, traces, A, C, b, f, final_frate, remove_baseline=True, - N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, thresh_C=0.3) - -idx_components_r = np.where(r_values >= .85)[0] -idx_components_raw = np.where(fitness_raw < -40)[0] -idx_components_delta = np.where(fitness_delta < -40)[0] - - -#min_radius = gSig[0] - 2 -# masks_ws, idx_blobs, idx_non_blobs = extract_binary_masks_blob( -# A.tocsc(), min_radius, dims, num_std_threshold=1, -# minCircularity=0.7, minInertiaRatio=0.2, minConvexity=.5) - -idx_components = np.union1d(idx_components_r, idx_components_raw) -idx_components = np.union1d(idx_components, idx_components_delta) -#idx_blobs = np.intersect1d(idx_components, idx_blobs) -idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components) - -print(' ***** ') -print((len(traces))) -print((len(idx_components))) -# print((len(idx_blobs))) - - -min_radius = gSig[0] - 2 -masks_ws, idx_blobs, idx_non_blobs = extract_binary_masks_blob( - A2.tocsc(), min_radius, dims, num_std_threshold=1, - minCircularity=0.6, minInertiaRatio=0.2, minConvexity=.8) - - -idx_components = np.union1d(idx_components_r, idx_components_raw) -idx_components = np.union1d(idx_components, idx_components_delta) -idx_blobs = np.intersect1d(idx_components, idx_blobs) -idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components) - -print(' ***** ') -print((len(traces))) -print((len(idx_components))) -print((len(idx_blobs))) -#%% visualize components -# pl.figure(); -pl.subplot(1, 3, 1) -crd = plot_contours(A2.tocsc()[:, idx_components], Cn, thr=0.9) -pl.subplot(1, 3, 2) -crd = plot_contours(A2.tocsc()[:, idx_blobs], Cn, thr=0.9) -pl.subplot(1, 3, 3) -crd = plot_contours(A2.tocsc()[:, idx_components_bad], Cn, thr=0.9) -#%% -view_patches_bar(Yr, scipy.sparse.coo_matrix(A2.tocsc()[ - :, idx_components]), C2[idx_components, :], b2, f2, dims[0], dims[1], YrA=YrA[idx_components, :], img=Cn) -#%% -view_patches_bar(Yr, scipy.sparse.coo_matrix(A2.tocsc()[ - :, idx_components_bad]), C2[idx_components_bad, :], b2, f2, dims[0], dims[1], YrA=YrA[idx_components_bad, :], img=Cn) -#%% STOP CLUSTER -pl.close() -if not single_thread: - c.close() - cm.stop_server() diff --git a/demos/obsolete/demo_caiman_cnmf.ipynb b/demos/obsolete/demo_caiman_cnmf.ipynb deleted file mode 100755 index 1e0b9da39..000000000 --- a/demos/obsolete/demo_caiman_cnmf.ipynb +++ /dev/null @@ -1,684 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "

Here we will be focusing more on the cnmf part and its main functions

\n", - "" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "try:\n", - " if __IPYTHON__:\n", - " # this is used for debugging purposes only. allows to reload classes when changed\n", - " get_ipython().magic(u'load_ext autoreload')\n", - " get_ipython().magic(u'autoreload 2')\n", - "except NameError: \n", - " print('Not IPYTHON') \n", - " pass\n", - "\n", - "import sys\n", - "import numpy as np\n", - "from time import time\n", - "from scipy.sparse import coo_matrix\n", - "import psutil\n", - "import glob\n", - "import os\n", - "import scipy\n", - "from ipyparallel import Client\n", - "import pylab as pl\n", - "import caiman as cm\n", - "from caiman.components_evaluation import evaluate_components\n", - "from caiman.utils.visualization import plot_contours,view_patches_bar,nb_plot_contour,nb_view_patches\n", - "from caiman.base.rois import extract_binary_masks_blob\n", - "import caiman.source_extraction.cnmf as cnmf\n", - "from caiman.utils.utils import download_demo" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "#import bokeh.plotting as bp\n", - "import bokeh.plotting as bpl\n", - "try:\n", - " from bokeh.io import vform, hplot\n", - "except:\n", - " # newer version of bokeh does not use vform & hplot, instead uses column & row\n", - " from bokeh.layouts import column as vform\n", - " from bokeh.layouts import row as hplot\n", - "from bokeh.models import CustomJS, ColumnDataSource, Slider\n", - "from IPython.display import display, clear_output\n", - "import matplotlib as mpl\n", - "import matplotlib.cm as cmap\n", - "import numpy as np\n", - "\n", - "bpl.output_notebook()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Using the workload manager SLURM

\n", - "to have an extensive use of the machine. \n", - "\n", - "

we want to operate this the faster possible. Thanks to the segmentation of the video in patches we can parallelize ou algorithm. We are using python integrated methods to get this parallelization to work on one machine as well as on clusters of machines

\n", - "\n", - "\n", - " \n", - "\n", - "

learn more : https://slurm.schedmd.com/overview.html

" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "# frame rate in Hz\n", - "final_frate=10 \n", - "#backend='SLURM'\n", - "backend='local'\n", - "if backend == 'SLURM':\n", - " n_processes = np.int(os.environ.get('SLURM_NPROCS'))\n", - "else:\n", - " # roughly number of cores on your machine minus 1\n", - " n_processes = np.maximum(np.int(psutil.cpu_count()),1) \n", - "print('using ' + str(n_processes) + ' processes')\n", - "#%% start cluster for efficient computation\n", - "single_thread=False\n", - "\n", - "if single_thread:\n", - " dview=None\n", - "else: \n", - " try:\n", - " c.close()\n", - " except:\n", - " print('C was not existing, creating one')\n", - " print(\"Stopping cluster to avoid unnencessary use of memory....\")\n", - " sys.stdout.flush() \n", - " if backend == 'SLURM':\n", - " try:\n", - " cm.stop_server(is_slurm=True)\n", - " except:\n", - " print('Nothing to stop')\n", - " slurm_script='/mnt/xfs1/home/agiovann/SOFTWARE/Constrained_NMF/SLURM/slurmStart.sh'\n", - " cm.start_server(slurm_script=slurm_script)\n", - " pdir, profile = os.environ['IPPPDIR'], os.environ['IPPPROFILE']\n", - " c = Client(ipython_dir=pdir, profile=profile) \n", - " else:\n", - " cm.stop_server()\n", - " cm.start_server() \n", - " c=Client()\n", - "\n", - " print('Using '+ str(len(c)) + ' processes')\n", - " dview=c[:len(c)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can see here that the number of processes are the number of core your computer possess.
Your computer can be seen as a node that possess X cores " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Memory mapping files in F order

\n", - "

see : http://localhost:8888/notebooks/CaImAn/demo_caiman_pipeline.ipynb

\n", - "

We want the parallel processes to access and our video matrix without having it in memory and duplicating it, as explained already on the demo_pipeline notebook

\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "#%% FOR LOADING ALL TIFF FILES IN A FILE AND SAVING THEM ON A SINGLE MEMORY MAPPABLE FILE\n", - "fnames=['demoMovieJ.tif']\n", - "base_folder='./example_movies/' # folder containing the demo files\n", - "# %% download movie if not there \n", - "if fnames[0] in ['Sue_2x_3000_40_-46.tif','demoMovieJ.tif']:\n", - " download_demo(fnames[0])\n", - " fnames = [os.path.join('example_movies',fnames[0])]\n", - "m_orig = cm.load_movie_chain(fnames[:1])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "downsample_factor=1 # use .2 or .1 if file is large and you want a quick answer\n", - "final_frate=final_frate*downsample_factor\n", - "name_new=cm.save_memmap_each(fnames\n", - " , dview=dview,base_name='Yr', resize_fact=(1, 1, downsample_factor)\n", - " , remove_init=0,idx_xy=None )\n", - "name_new.sort()\n", - "fname_new=cm.save_memmap_join(name_new,base_name='Yr', n_chunks=12, dview=dview)\n", - "print(fnames)\n", - "print(fname_new)\n", - "print (\"\\n we can see we are loading the file (line1) into a memorymapped object (line2)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

the correlation image

\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "Yr,dims,T=cm.load_memmap(fname_new)\n", - "Y=np.reshape(Yr,dims+(T,),order='F')\n", - "#%% visualize correlation image\n", - "Cn = cm.local_correlations(Y)\n", - "pl.imshow(Cn,cmap='gray') \n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " CNMFSetParms define Dictionaries of CNMF parameters.\n", - " Any parameter that is not set get a default value specified.\n", - " \n", - " each dictionnary is used by different part of the CNMF process : \n", - " - init_paramters\n", - " - pre_processing_parameters\n", - " - patch_parameters\n", - " - spatial_parameters\n", - " - temporal_parameters\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "K=30 # number of neurons expected per patch\n", - "gSig=[6,6] # expected half size of neurons\n", - "merge_thresh=0.8 # merging threshold, max correlation allowed\n", - "p=2 #order of the autoregressive system\n", - "options = cnmf.utilities.CNMFSetParms(Y\n", - " ,n_processes,p=p,gSig=gSig,K=K,ssub=2,tsub=2, normalize_init=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Preprocessing of the datas and initialization of the components

\n", - "\n", - "

see More : NMF AND ROI :http://www.cell.com/neuron/fulltext/S0896-6273(15)01084-3

\n", - "Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data by Eftychios A. Pnevmatikakis & al. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "Yr,sn,g,psx = cnmf.pre_processing.preprocess_data(Yr\n", - " ,dview=dview\n", - " ,n_pixels_per_process=100, noise_range = [0.25,0.5]\n", - " ,noise_method = 'logmexp', compute_g=False, p = 2,\n", - " lags = 5, include_noise = False, pixels = None\n", - " ,max_num_samples_fft=3000, check_nan = True)\n", - "\n", - "Ain, Cin, b_in, f_in, center=cnmf.initialization.initialize_components(Y\n", - " ,K=30, gSig=[5, 5], gSiz=None, ssub=1, tsub=1, nIter=5, maxIter=5, nb=1\n", - " , use_hals=False, normalize_init=True, img=None, method='greedy_roi'\n", - " , max_iter_snmf=500, alpha_snmf=10e2, sigma_smooth_snmf=(.5, .5, .5)\n", - " , perc_baseline_snmf=20)\n", - "p1=nb_plot_contour(Cn,Ain,dims[0],dims[1],thr=0.9,face_color=None\n", - " , line_color='black',alpha=0.4,line_width=2)\n", - "bpl.show(p1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

HALS

\n", - "we want to minimize\n", - "\n", - "updating parameters\n", - "\n", - "

HALS : (Keigo Kimura et al.) http://proceedings.mlr.press/v39/kimura14.pdf

\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "Ain, Cin, b_in, f_in = cnmf.initialization.hals(Y, Ain, Cin, b_in, f_in, maxIter=5)\n", - "p1=nb_plot_contour(Cn,Ain,dims[0],dims[1],thr=0.9,face_color=None\n", - " , line_color='black',alpha=0.4,line_width=2)\n", - "bpl.show(p1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

CNMF process

\n", - "\n", - "*** We are considering the video as a matrix called Y of dimension height x widht x frames ***\n", - "\n", - " we now want to find A, C and B such that Y = A x C + B\n", - " \n", - " B being the Background, composed of its spatial b and temporal f component\n", - " A being the spatial component of the neurons (also seen as their shape)\n", - " C being the temporal component of the neurons (also seen as their calcium activity or traces)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Update spatial

\n", - "\n", - "** will consider C as fixed and try to update A. **\n", - "\n", - " the process will be the following : \n", - " \n", - " - intialization of each parameters \n", - " - testing of the input values\n", - " \n", - " - finding relevant pixels in that should belong to the neuron using either an iterative structure or an ellipse to look around the center of mass of the neuron ( cm found in the initialization )\n", - " - this will be define a first shape of the neuron \n", - " - /!\\ pixels are usually unlinked\n", - " \n", - " - computing the distance indicator (a map of the distances of each relevant pixels to the center of mass of the neuron)\n", - " \n", - " - memory mapping the matrices C and Y (info before)\n", - " \n", - " - updating the components in parallel : \n", - " - using ipyparralel\n", - " - solving this problem for each pixel of the component\n", - " $$ arg\\min_{A_i,B_i}\\sum A_i $$\n", - " subject to\n", - " $$|| Y_i - A_i\\times C + b_i\\times f || <= std_{noise}(i)\\times \\sqrt(T)$$\n", - " - using the lasso lars method from scikit learn toolbox\n", - " https://en.wikipedia.org/wiki/Least-angle_regression,
\n", - " https://en.wikipedia.org/wiki/Lasso_(statistics),
\n", - " http://scikit-learn.org/stable/modules/linear_model.html#lars-lasso\n", - " \n", - " \n", - " - then, the newly refined components are thresholded (the C of the CNMF, one of the constrained here is that the matrix needs to be sparse) :\n", - " \n", - " - first by applicating a median filtering https://en.wikipedia.org/wiki/Median_filter\n", - " - then by thresholding using a normalized user defined value \n", - " - continuing with a morphological closing of the components, using openCv functions https://www.mathworks.com/help/images/ref/imclose.html (the matlab version)\n", - " - we remove the unconnected pixels (we keep the large connected components )\n", - " \n", - " \n", - " - finnaly we compute the residuals (also called the background) which is computed as B=Y-AC\n", - " \n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "options['spatial_params']['n_pixels_per_process'] = 2000" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "A,b,Cin,f_in = cnmf.spatial.update_spatial_components(Yr, Cin, f_in, Ain, sn=sn, dview=dview,**options['spatial_params'])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "p1=nb_plot_contour(Cn,A.todense(),dims[0],dims[1],thr=0.9,face_color=None,\n", - " line_color='black',alpha=0.4,line_width=2)\n", - "bpl.show(p1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Update temporal

\n", - "\n", - "** Will consider A as fixed and try to update C. **\n", - "\n", - " the process will be the following : \n", - " \n", - " - Intialization of each parameters \n", - " - Testing of the input values\n", - " \n", - " - Generating residuals s.t. $$Yres_A = YA - (A^T AC)^T$$\n", - " \n", - " - Creating groups of components that can be processed in parallel\n", - " - Ones that are composed of not overlapping components\n", - " - Using a simple greedy method\n", - " \n", - " - Updating Calcium traces ( C ) \n", - " - Using Oasis. which will deconvolve the spikes of each neurons from the Calcium traces matrix C. learn more : (Friedrich & al) http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005423\n", - " see the demo here : https://github.com/j-friedrich/OASIS/blob/master/examples/Demo.ipynb\n", - " - To infer the true shape of the calcium traces using an autoregressive framework\n", - " - To infer the most likely spike train ( also called particular events). It will find the probability of a spike train according to the mean and std of the trace. \n", - " - If it is superior to a threshold it will be defined as a particular event/neural spike\n", - " \n", - " - This will give us a matrix which is itself constrained ( C from CNMF ) \n", - " - This is done in parallel using ipyparallel. \n", - " - We finally update the background" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "options['temporal_params']['block_size'] = 2000" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "options['temporal_params']['p'] = 0 # fast updating without deconvolution\n", - "C,A,b,f,S,bl,c1,neurons_sn,g,YrA,lam = cnmf.temporal.update_temporal_components(\n", - " Yr,A,b,Cin,f_in,bl=None,c1=None,sn=None,g=None,**options['temporal_params']) \n", - "clear_output(wait=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

Merging components

\n", - "\n", - "**merge the components that overlaps and have a high temporal correlation **\n", - "\n", - " the process will be the following : \n", - " \n", - " - intialization of each parameters \n", - " - testing of the input values\n", - " \n", - " - find a graph of overlapping components\n", - " - we look for connected ones\n", - " - we keep the one that are \"connected enough\" (above a threshold)\n", - " \n", - " \n", - " - On Each groups : \n", - " - We normalize the components to be able to compare them\n", - " - We sum them together\n", - " - we process a rank one NMF\n", - " - we compute the traces (deconvolution)\n", - " - We replace the neurons by the merged one\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "A_m,C_m,nr_m,merged_ROIs,S_m,bl_m,c1_m,sn_m,g_m=cnmf.merging.merge_components(\n", - " Yr,A,b,C,f,S,sn,options['temporal_params'], options['spatial_params'],\n", - " dview=dview, bl=bl, c1=c1, sn=neurons_sn, g=g, thr=merge_thresh,\n", - " mx=50, fast_merge = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# A refining step\n", - "refine spatial and temporal components" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "A2,b2,C2,f = cnmf.spatial.update_spatial_components(Yr, C_m, f, A_m,\n", - " sn=sn,dview=dview, **options['spatial_params'])\n", - "options['temporal_params']['p'] = p # set it back to perform full deconvolution\n", - "\n", - "C2,A2,b2,f2,S2,bl2,c12,neurons_sn2,g21,YrA, lam = cnmf.temporal.update_temporal_components(\n", - " Yr,A2,b2,C2,f,dview=dview, bl=None,c1=None,sn=None,g=None,**options['temporal_params'])\n", - "clear_output(wait=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

DISCARD LOW QUALITY COMPONENT

\n", - "

The patch dubdivision creates several spurious components that are not neurons

\n", - "

We select the components according to criteria examining spatial and temporal components

\n", - "\n", - "\n", - "

Temporal components, for each trace:

\n", - "\n", - "
  • compute the robust mode, corresponding to the baseline value
    • \n", - "
  • use the values under the mode to estimate noise variance
    • \n", - "
  • compute the probability of having large transients given the noise distribution estimated
    • \n", - "
  • Threshold on this probability s.t. some of the component are discarded because lacking large enough positive transients
    • \n", - "\n", - "

    Spatial components, for each components:

    \n", - "\n", - "
  • average the frames in the moveie where the neurons is active (from temporal component), this provides a nice image of the neuron
    • \n", - "
  • compare this image with the corresponding spatial component (Person's correlation coefficient)
    • \n", - "
  • threshold the correlation coefficient
    • \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "#evaluation\n", - "fitness_raw, fitness_delta, erfc_raw,erfc_delta, r_values, significant_samples = evaluate_components(Y, C2+YrA, A2, C2, b2, f2, final_frate,\n", - " remove_baseline=True,N=5, robust_std=False,\n", - " Athresh=0.1, Npeaks=10, thresh_C=0.3)\n", - "#different thresholding ( needs to pass at least one of them )\n", - "traces = C2 + YrA\n", - "idx_components_r=np.where(r_values>=.6)[0]\n", - "idx_components_raw=np.where(fitness_raw<-60)[0] \n", - "idx_components_delta=np.where(fitness_delta<-20)[0] \n", - "\n", - "#merging to have all that have passed at least one threshold.\n", - "idx_components=np.union1d(idx_components_r,idx_components_raw)\n", - "idx_components=np.union1d(idx_components,idx_components_delta) \n", - "#finding the bad components\n", - "idx_components_bad=np.setdiff1d(range(len(traces)),idx_components)\n", - "\n", - "clear_output(wait=True)\n", - "print(' ***** ')\n", - "print(len(traces))\n", - "print(len(idx_components))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "fg=pl.figure(figsize=(12,20))\n", - "pl.subplot(1,2,1)\n", - "crd = plot_contours(A2.tocsc()[:,idx_components],Cn,thr=0.9)\n", - "\n", - "pl.subplot(1,2,2)\n", - "crd = plot_contours(A2.tocsc()[:,idx_components_bad],Cn,thr=0.9)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "p2=nb_plot_contour(Cn,A2.tocsc()[:,idx_components].todense(),dims[0],dims[1],thr=0.9,face_color='purple', line_color='black',alpha=0.3,line_width=2)\n", - "bpl.show(p2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# accepted components" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "discard_traces_fluo=nb_view_patches(Yr,A2.tocsc()[:,idx_components],C2[idx_components],b2,f2,dims[0],dims[1],thr = 0.8,image_neurons=Cn, denoised_color='red')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# discarded components" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "discard_traces_fluo=nb_view_patches(Yr,A2.tocsc()[:,idx_components_bad],C2[idx_components_bad],b2,f2,dims[0],dims[1],thr = 0.8,image_neurons=Cn, denoised_color='red')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "cm.stop_server()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.4" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/demos/obsolete/demo_caiman_cnmf.py b/demos/obsolete/demo_caiman_cnmf.py deleted file mode 100755 index 7bdf01c5e..000000000 --- a/demos/obsolete/demo_caiman_cnmf.py +++ /dev/null @@ -1,260 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Created on Wed Feb 24 18:39:45 2016 - -@author: Andrea Giovannucci - -For explanation consult at https://github.com/agiovann/Constrained_NMF/releases/download/v0.4-alpha/Patch_demo.zip -and https://github.com/agiovann/Constrained_NMF -""" - -from __future__ import print_function -from builtins import str -from builtins import range - -try: - if __IPYTHON__: - print('Debugging!') - get_ipython().magic('load_ext autoreload') - get_ipython().magic('autoreload 2') -except NameError: - print('Not launched under iPython') - -import numpy as np -import glob -import os -import scipy -from ipyparallel import Client -# mpl.use('Qt5Agg') -import pylab as pl -pl.ion() -#%% - -import caiman as cm -from caiman.source_extraction.cnmf import cnmf as cnmf -from caiman.source_extraction.cnmf.utilities import extract_DF_F -from caiman.components_evaluation import evaluate_components -from caiman.utils.visualization import plot_contours, view_patches_bar -from caiman.utils.utils import download_demo -from caiman.cluster import setup_cluster -# %% RUN ANALYSIS -c, dview, n_processes = setup_cluster( - backend='local', n_processes=None, single_thread=False) -#%% -is_patches = True -is_dendrites = False - -if is_dendrites == True: - # THIS METHOd CAN GIVE POSSIBLY INCONSISTENT RESULTS ON SOMAS WHEN NOT USED WITH PATCHES - init_method = 'sparse_nmf' - alpha_snmf = 10e1 # this controls sparsity -else: - init_method = 'greedy_roi' - alpha_snmf = None # 10e2 # this controls sparsity - -#%% FOR LOADING ALL TIFF FILES IN A FILE AND SAVING THEM ON A SINGLE MEMORY MAPPABLE FILE -fnames = ['demoMovieJ.tif'] -base_folder = './example_movies/' # folder containing the demo files - -download_demo(fnames[0]) -fnames = [os.path.abspath(os.path.join(base_folder, fnames[0]))] -# TODO: todocument -m_orig = cm.load_movie_chain(fnames[:1]) -print(fnames) -fnames = fnames -#%% -# idx_x=slice(12,500,None) -# idx_y=slice(12,500,None) -# idx_xy=(idx_x,idx_y) - -add_to_movie = 300 # the movie must be positive!!! -downsample_factor = 1 # use .2 or .1 if file is large and you want a quick answer -idx_xy = None -base_name = 'Yr' -name_new = cm.save_memmap_each(fnames, dview=dview, base_name=base_name, resize_fact=( - 1, 1, downsample_factor), remove_init=0, idx_xy=idx_xy, add_to_movie=add_to_movie) -name_new.sort() -print(name_new) -#%% -if len(name_new) > 1: - fname_new = cm.save_memmap_join( - name_new, base_name='Yr', n_chunks=12, dview=dview) -else: - print('One file only, not saving!') - fname_new = name_new[0] -#%% -# fname_new='Yr_d1_501_d2_398_d3_1_order_F_frames_369_.mmap' -Yr, dims, T = cm.load_memmap(fname_new) -d1, d2 = dims -images = np.reshape(Yr.T, [T] + list(dims), order='F') -Y = np.reshape(Yr, dims + (T,), order='F') -#%% -if np.min(images) < 0: - raise Exception('Movie too negative, add_to_movie should be larger') -if np.sum(np.isnan(images)) > 0: - raise Exception('Movie contains nan! You did not remove enough borders') -#%% -Cn = cm.local_correlations(Y[:, :, :3000]) -pl.imshow(Cn, cmap='gray') - -#%% -if not is_patches: - K = 35 # number of neurons expected per patch - gSig = [7, 7] # expected half size of neurons - merge_thresh = 0.8 # merging threshold, max correlation allowed - p = 2 # order of the autoregressive system - cnm = cnmf.CNMF(n_processes, method_init=init_method, k=K, gSig=gSig, merge_thresh=merge_thresh, - p=p, dview=dview, Ain=None, method_deconvolution='oasis', skip_refinement=False) - cnm = cnm.fit(images) - crd = plot_contours(cnm.A, Cn, thr=0.9) - C_dff = extract_DF_F(Yr, cnm.A, cnm.C, cnm.bl, - quantileMin=8, frames_window=200, dview=dview) - pl.figure() - pl.plot(C_dff.T) -else: - rf = 14 # half-size of the patches in pixels. rf=25, patches are 50x50 - stride = 6 # amounpl.it of overlap between the patches in pixels - K = 6 # number of neurons expected per patch - gSig = [6, 6] # expected half size of neurons - merge_thresh = 0.8 # merging threshold, max correlation allowed - p = 1 # order of the autoregressive system - save_results = False - - cnm = cnmf.CNMF(n_processes, k=K, gSig=gSig, merge_thresh=0.8, p=0, dview=dview, Ain=None, rf=rf, stride=stride, memory_fact=1, - method_init=init_method, alpha_snmf=alpha_snmf, only_init_patch=True, gnb=2, method_deconvolution='oasis', low_rank_background=True) - cnm = cnm.fit(images) - - A_tot = cnm.A - C_tot = cnm.C - YrA_tot = cnm.YrA - b_tot = cnm.b - f_tot = cnm.f - sn_tot = cnm.sn - - print(('Number of components:' + str(A_tot.shape[-1]))) - pl.figure() - crd = plot_contours(A_tot, Cn, thr=0.9) - final_frate = 10 # approx final rate (after eventual downsampling ) - Npeaks = 10 - traces = C_tot + YrA_tot - # traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5]) - # traces_b=np.diff(traces,axis=1) - fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = evaluate_components( - Y, traces, A_tot, C_tot, b_tot, f_tot, final_frate, remove_baseline=True, N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, thresh_C=0.3) - - idx_components_r = np.where(r_values >= .5)[0] - idx_components_raw = np.where(fitness_raw < -40)[0] - idx_components_delta = np.where(fitness_delta < -20)[0] - - idx_components = np.union1d(idx_components_r, idx_components_raw) - idx_components = np.union1d(idx_components, idx_components_delta) - idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components) - - print(('Keeping ' + str(len(idx_components)) + - ' and discarding ' + str(len(idx_components_bad)))) - pl.figure() - crd = plot_contours(A_tot.tocsc()[:, idx_components], Cn, thr=0.9) - A_tot = A_tot.tocsc()[:, idx_components] - C_tot = C_tot[idx_components] - save_results = True - if save_results: - np.savez('results_analysis_patch.npz', A_tot=A_tot, C_tot=C_tot, - YrA_tot=YrA_tot, sn_tot=sn_tot, d1=d1, d2=d2, b_tot=b_tot, f=f_tot) - - cnm = cnmf.CNMF(n_processes, k=A_tot.shape, gSig=gSig, merge_thresh=merge_thresh, p=p, dview=dview, Ain=A_tot, Cin=C_tot, b_in=b_tot, - f_in=f_tot, rf=None, stride=None, method_deconvolution='oasis', gnb=2, low_rank_background=True) - cnm = cnm.fit(images) - -#% -A, C, b, f, YrA, sn = cnm.A, cnm.C, cnm.b, cnm.f, cnm.YrA, cnm.sn - -#%% -final_frate = 10 - -Npeaks = 10 -traces = C + YrA - -idx_components, idx_components_bad, fitness_raw, fitness_delta, r_values = cm.components_evaluation.estimate_components_quality(traces, Y, A, C, b, f, final_frate=final_frate, - Npeaks=10, r_values_min=.85, - fitness_min=-30, fitness_delta_min=-30, return_all=True, N=5, - remove_baseline=True, dview=dview, robust_std=False, Athresh=0.1, thresh_C=0.3, num_traces_per_group=20) -#%% -from caiman.components_evaluation import evaluate_components_CNN -predictions, final_crops = evaluate_components_CNN( - A, dims, gSig, model_name='model/cnn_model') -#%% -threshold = .95 -from caiman.utils.visualization import matrixMontage -pl.figure() -matrixMontage(np.squeeze( - final_crops[np.where(predictions[:, 1] >= threshold)[0]])) -pl.figure() -matrixMontage(np.squeeze( - final_crops[np.where(predictions[:, 0] >= threshold)[0]])) -#%% -thresh = .95 -idx_components_cnn = np.where(predictions[:, 1] >= thresh)[0] - -print(' ***** ') -print((len(final_crops))) -print((len(idx_components_cnn))) -# print((len(idx_blobs))) -#% -idx_components_r = np.where((r_values >= .99))[0] -idx_components_raw = np.where(fitness_raw < -60)[0] -idx_components_delta = np.where(fitness_delta < -60)[0] - -bad_comps = np.where((r_values <= .2) | (fitness_raw >= -4) - | (predictions[:, 1] <= .05))[0] - -#idx_and_condition_1 = np.where((r_values >= .65) & ((fitness_raw < -20) | (fitness_delta < -20)) )[0] - -idx_components = np.union1d(idx_components_r, idx_components_raw) -idx_components = np.union1d(idx_components, idx_components_delta) -idx_components = np.union1d(idx_components, idx_components_cnn) -idx_components = np.setdiff1d(idx_components, bad_comps) -#idx_components = np.intersect1d(idx_components,idx_size_neuro) -#idx_components = np.union1d(idx_components, idx_and_condition_1) -#idx_components = np.union1d(idx_components, idx_and_condition_2) - -#idx_blobs = np.intersect1d(idx_components, idx_blobs) -#idx_components = idx_components_cnn -idx_components_bad = np.setdiff1d(list(range(len(r_values))), idx_components) - - -print(' ***** ') -print((len(r_values))) -print((len(idx_components))) -#%% -save_results = True -if save_results: - np.savez(os.path.join(os.path.split(fname_new)[0], 'results_analysis.npz'), Cn=Cn, A=A.todense( - ), C=C, b=b, f=f, YrA=YrA, sn=sn, d1=d1, d2=d2, idx_components=idx_components, idx_components_bad=idx_components_bad) - -#%% visualize components -# pl.figure(); -pl.subplot(1, 2, 1) -crd = plot_contours(A.tocsc()[:, idx_components], Cn, thr=0.9) -#pl.subplot(1, 3, 2) -#crd = plot_contours(A.tocsc()[:, idx_blobs], Cn, thr=0.9) -pl.subplot(1, 2, 2) -crd = plot_contours(A.tocsc()[:, idx_components_bad], Cn, thr=0.9) -#%% -view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, idx_components]), C[ - idx_components, :], b, f, dims[0], dims[1], YrA=YrA[idx_components, :], img=Cn) -#%% -view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, idx_components_bad]), C[ - idx_components_bad, :], b, f, dims[0], dims[1], YrA=YrA[idx_components_bad, :], img=Cn) -#%% -C_dff = extract_DF_F(Yr, A.tocsc()[:, idx_components], C[idx_components, :], - cnm.bl[idx_components], quantileMin=8, frames_window=200, dview=dview) -pl.plot(C_dff.T) - -#%% STOP CLUSTER and clean up log files -cm.stop_server() - -log_files = glob.glob('*_LOG_*') -for log_file in log_files: - os.remove(log_file) diff --git a/demos/obsolete/demo_caiman_cnmf_JAN.py b/demos/obsolete/demo_caiman_cnmf_JAN.py deleted file mode 100644 index 8db79dc4a..000000000 --- a/demos/obsolete/demo_caiman_cnmf_JAN.py +++ /dev/null @@ -1,304 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Created on Wed Feb 24 18:39:45 2016 - -@author: Andrea Giovannucci - -For explanation consult at https://github.com/agiovann/Constrained_NMF/releases/download/v0.4-alpha/Patch_demo.zip -and https://github.com/agiovann/Constrained_NMF - -""" - -from __future__ import print_function -#%% -from builtins import str -from builtins import range -try: - if __IPYTHON__: - # this is used for debugging purposes only. allows to reload classes when changed - get_ipython().magic('load_ext autoreload') - get_ipython().magic('autoreload 2') -except NameError: - print('Not launched under iPython') - -import sys -import numpy as np -import psutil -import glob -import os -import scipy -from ipyparallel import Client -# mpl.use('Qt5Agg') - - -import pylab as pl -pl.ion() -#%% -import caiman as cm -from caiman.components_evaluation import evaluate_components -from caiman.utils.visualization import plot_contours, view_patches_bar -from caiman.base.rois import extract_binary_masks_blob -from caiman.source_extraction import cnmf as cnmf -#%% -pl.close('all') -#%% -is_dendrites = False -init_method = 'greedy_roi' -alpha_snmf = None # 10e2 # this controls sparsity - -#%% -# backend='SLURM' -backend = 'local' -if backend == 'SLURM': - n_processes = np.int(os.environ.get('SLURM_NPROCS')) -else: - # roughly number of cores on your machine minus 1 - n_processes = np.maximum(np.int(psutil.cpu_count()), 1) -print(('using ' + str(n_processes) + ' processes')) -#%% start cluster for efficient computation -single_thread = False - -if single_thread: - dview = None -else: - try: - c.close() - except: - print('C was not existing, creating one') - print("Stopping cluster to avoid unnencessary use of memory....") - sys.stdout.flush() - if backend == 'SLURM': - try: - cm.stop_server(is_slurm=True) - except: - print('Nothing to stop') - slurm_script = '/mnt/xfs1/home/agiovann/SOFTWARE/Constrained_NMF/SLURM/slurmStart.sh' - cm.start_server(slurm_script=slurm_script) - pdir, profile = os.environ['IPPPDIR'], os.environ['IPPPROFILE'] - c = Client(ipython_dir=pdir, profile=profile) - else: - cm.stop_server() - cm.start_server() - c = Client() - - print(('Using ' + str(len(c)) + ' processes')) - dview = c[:len(c)] -#%% FOR LOADING ALL TIFF FILES IN A FILE AND SAVING THEM ON A SINGLE MEMORY MAPPABLE FILE - -fnames = [] -base_folder = './' # folder containing the demo files -for file in glob.glob(os.path.join(base_folder, '*.tif')): - if file.endswith("/All_BL.tif"): - fnames.append(os.path.abspath(file)) -fnames.sort() -if len(fnames) == 0: - raise Exception("Could not find any tiff file") - -print(fnames) -fnames = fnames -#%% -# idx_x=slice(12,500,None) -# idx_y=slice(12,500,None) -# idx_xy=(idx_x,idx_y) -border_to_0 = 6 -add_to_movie = 300 # the movie must be positive!!! -downsample_factor = 1 # use .2 or .1 if file is large and you want a quick answer -idx_xy = None -base_name = 'Yr' -name_new = cm.save_memmap_each(fnames, dview=dview, base_name=base_name, resize_fact=( - 1, 1, downsample_factor), remove_init=0, idx_xy=idx_xy, add_to_movie=add_to_movie, border_to_0=border_to_0) -name_new.sort() -print(name_new) -#%% -fname_new = cm.save_memmap_join( - name_new, base_name='Yr', n_chunks=12, dview=dview) -#%% -# fname_new='Yr_d1_501_d2_398_d3_1_order_F_frames_369_.mmap' -Yr, dims, T = cm.load_memmap(fname_new) -d1, d2 = dims -images = np.reshape(Yr.T, [T] + list(dims), order='F') -Y = np.reshape(Yr, dims + (T,), order='F') -#%% -if np.min(images) < 0: - raise Exception('Movie too negative, add_to_movie should be larger') -#%% -Cn = cm.local_correlations(Y[:, :, :3000]) -pl.imshow(Cn, cmap='gray') - -#%% - -#%% -rf = 20 # half-size of the patches in pixels. rf=25, patches are 50x50 -stride = 5 # amounpl.it of overlap between the patches in pixels -K = 4 # number of neurons expected per patch -gSig = [6, 6] # expected half size of neurons -merge_thresh = 0.8 # merging threshold, max correlation allowed -p = 2 # order of the autoregressive system -memory_fact = 1 # unitless number accounting how much memory should be used. You will need to try different values to see which one would work the default is OK for a 16 GB system -save_results = False -#%% RUN ALGORITHM ON PATCHES - -cnm = cnmf.CNMF(n_processes, k=K, gSig=gSig, merge_thresh=0.8, p=0, dview=c[:], Ain=None, rf=rf, stride=stride, memory_fact=memory_fact, - method_init=init_method, alpha_snmf=alpha_snmf, only_init_patch=True, gnb=1, method_deconvolution='oasis') -cnm = cnm.fit(images) - -A_tot = cnm.A -C_tot = cnm.C -YrA_tot = cnm.YrA -b_tot = cnm.b -f_tot = cnm.f -sn_tot = cnm.sn - -print(('Number of components:' + str(A_tot.shape[-1]))) - -#%% -final_frate = 10 # approx final rate (after eventual downsampling ) -tB = np.minimum(-2, np.floor(-5. / 30 * final_frate)) -tA = np.maximum(5, np.ceil(25. / 30 * final_frate)) -Npeaks = 10 -traces = C_tot + YrA_tot -# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5]) -# traces_b=np.diff(traces,axis=1) -fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = evaluate_components( - Y, traces, A_tot, C_tot, b_tot, f_tot, remove_baseline=True, N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, tB=tB, tA=tA, thresh_C=0.3) - -idx_components_r = np.where(r_values >= .4)[0] -idx_components_raw = np.where(fitness_raw < -20)[0] -idx_components_delta = np.where(fitness_delta < -10)[0] - -idx_components = np.union1d(idx_components_r, idx_components_raw) -idx_components = np.union1d(idx_components, idx_components_delta) -idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components) - -print(('Keeping ' + str(len(idx_components)) + - ' and discarding ' + str(len(idx_components_bad)))) -#%% -pl.figure() -crd = plot_contours(A_tot.tocsc()[:, idx_components], Cn, thr=0.9) -#%% -A_tot = A_tot.tocsc()[:, idx_components] -C_tot = C_tot[idx_components] -#%% -save_results = True -if save_results: - np.savez('results_analysis_patch.npz', A_tot=A_tot, C_tot=C_tot, - YrA_tot=YrA_tot, sn_tot=sn_tot, d1=d1, d2=d2, b_tot=b_tot, f=f_tot) -#%% if you have many components this might take long! -pl.figure() -crd = plot_contours(A_tot, Cn, thr=0.9) -#%% -cnm = cnmf.CNMF(n_processes, k=A_tot.shape, gSig=gSig, merge_thresh=merge_thresh, p=p, dview=dview, Ain=A_tot, Cin=C_tot, - f_in=f_tot, rf=None, stride=None) -cnm = cnm.fit(images) - -#%% -A, C, b, f, YrA, sn = cnm.A, cnm.C, cnm.b, cnm.f, cnm.YrA, cnm.sn -#%% -final_frate = 1 -tB = np.minimum(-2, np.floor(-5. / 30 * final_frate)) -tA = np.maximum(5, np.ceil(25. / 30 * final_frate)) -Npeaks = 10 -traces = C + YrA -# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5]) -# traces_b=np.diff(traces,axis=1) -fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = \ - evaluate_components(Y, traces, A, C, b, f, remove_baseline=True, - N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, tB=tB, tA=tA, thresh_C=0.3) - -idx_components_r = np.where(r_values >= .5)[0] -idx_components_raw = np.where(fitness_raw < -40)[0] -idx_components_delta = np.where(fitness_delta < -20)[0] - - -min_radius = gSig[0] -# masks_ws, idx_blobs, idx_non_blobs = extract_binary_masks_blob( -# A.tocsc(), min_radius, dims, num_std_threshold=1, -# minCircularity=0.5, minInertiaRatio=0.2, minConvexity=.7) - -#% LOOK FOR BLOB LIKE STRUCTURES! -masks_ws, is_blob, is_non_blob = cm.base.rois.extract_binary_masks_blob_parallel(A.tocsc(), min_radius, dims, num_std_threshold=1, - minCircularity=0.5, minInertiaRatio=0.2, minConvexity=.7, dview=dview) - -idx_blobs = np.where(is_blob)[0] -idx_non_blobs = np.where(is_non_blob)[0] - -idx_components = np.union1d(idx_components_r, idx_components_raw) -idx_components = np.union1d(idx_components, idx_components_delta) -idx_blobs = np.intersect1d(idx_components, idx_blobs) -idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components) - -print(' ***** ') -print((len(traces))) -print((len(idx_components))) -print((len(idx_blobs))) -#%% -save_results = True -if save_results: - np.savez(os.path.join(os.path.split(fname_new)[0], 'results_analysis.npz'), Cn=Cn, A=A.todense( - ), C=C, b=b, f=f, YrA=YrA, sn=sn, d1=d1, d2=d2, idx_components=idx_components, idx_components_bad=idx_components_bad) - np.savez(os.path.join(os.path.split(fname_new)[0], 'results_blobs.npz'), spatial_comps=A.tocsc().toarray().reshape( - dims + (-1,), order='F').transpose([2, 0, 1]), masks=masks_ws, idx_components=idx_components, idx_blobs=idx_blobs, idx_components_bad=idx_components_bad) -#%% visualize components -# pl.figure(); -pl.subplot(1, 3, 1) -crd = plot_contours(A.tocsc()[:, idx_components], Cn, thr=0.9) -pl.subplot(1, 3, 2) -crd = plot_contours(A.tocsc()[:, idx_blobs], Cn, thr=0.9) -pl.subplot(1, 3, 3) -crd = plot_contours(A.tocsc()[:, idx_components_bad], Cn, thr=0.9) -#%% -view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, idx_components]), C[ - idx_components, :], b, f, dims[0], dims[1], YrA=YrA[idx_components, :], img=Cn) -#%% -view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, idx_components_bad]), C[ - idx_components_bad, :], b, f, dims[0], dims[1], YrA=YrA[idx_components_bad, :], img=Cn) -#%% STOP CLUSTER and clean up log files -pl.close() -if not single_thread: - c.close() - cm.stop_server(is_slurm=(backend == 'SLURM')) - del c - del dview - -log_files = glob.glob('Yr*_LOG_*') -for log_file in log_files: - os.remove(log_file) -#%% LOAD RESULTS -try: - if __IPYTHON__: - # this is used for debugging purposes only. allows to reload classes when changed - get_ipython().magic('load_ext autoreload') - get_ipython().magic('autoreload 2') -except NameError: - print('Not launched under iPython') - -import sys -import numpy as np -import psutil -import glob -import os -import scipy -from ipyparallel import Client -import caiman as cm -from caiman.components_evaluation import evaluate_components -from caiman.utils.visualization import plot_contours, view_patches_bar -from caiman.base.rois import extract_binary_masks_blob -from caiman.source_extraction import cnmf as cnmf - - -import pylab as pl -pl.ion() - -with np.load('results_analysis.npz') as ld: - locals().update(ld) - -A = scipy.sparse.coo_matrix(A) -fname_new = 'Yr_d1_512_d2_512_d3_1_order_C_frames_3201_.mmap' -Yr, dims, T = cm.load_memmap(fname_new) -d1, d2 = dims -images = np.reshape(Yr.T, [T] + list(dims), order='F') -Y = np.reshape(Yr, dims + (T,), order='F') -gSig = [6, 6] # expected half size of neurons -final_frate = 1 diff --git a/demos/obsolete/demo_caiman_cnmf_Yi.py b/demos/obsolete/demo_caiman_cnmf_Yi.py deleted file mode 100644 index ae16419a2..000000000 --- a/demos/obsolete/demo_caiman_cnmf_Yi.py +++ /dev/null @@ -1,308 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Created on Wed Feb 24 18:39:45 2016 - -@author: Andrea Giovannucci - -For explanation consult at https://github.com/agiovann/Constrained_NMF/releases/download/v0.4-alpha/Patch_demo.zip -and https://github.com/agiovann/Constrained_NMF - -""" - -from __future__ import print_function -#%% -from builtins import str -from builtins import range - -try: - if __IPYTHON__: - # this is used for debugging purposes only. allows to reload classes when changed - get_ipython().magic('load_ext autoreload') - get_ipython().magic('autoreload 2') -except NameError: - print('Not launched under iPython') - -import sys -import numpy as np -import psutil -import glob -import os -import scipy -from ipyparallel import Client -# mpl.use('Qt5Agg') - - -import pylab as pl -pl.ion() -#%% -import caiman as cm -from caiman.components_evaluation import evaluate_components -from caiman.utils.visualization import plot_contours, view_patches_bar -from caiman.base.rois import extract_binary_masks_blob -from caiman.source_extraction import cnmf as cnmf -#%% -pl.close('all') -#%% -is_dendrites = False -init_method = 'greedy_roi' -alpha_snmf = None # 10e2 # this controls sparsity - -#%% -# backend='SLURM' -backend = 'local' -if backend == 'SLURM': - n_processes = np.int(os.environ.get('SLURM_NPROCS')) -else: - # roughly number of cores on your machine minus 1 - n_processes = np.maximum(np.int(psutil.cpu_count()), 1) -print(('using ' + str(n_processes) + ' processes')) -#%% start cluster for efficient computation -single_thread = False - -if single_thread: - dview = None -else: - try: - c.close() - except: - print('C was not existing, creating one') - print("Stopping cluster to avoid unnencessary use of memory....") - sys.stdout.flush() - if backend == 'SLURM': - try: - cm.stop_server(is_slurm=True) - except: - print('Nothing to stop') - slurm_script = '/mnt/xfs1/home/agiovann/SOFTWARE/Constrained_NMF/SLURM/slurmStart.sh' - cm.start_server(slurm_script=slurm_script) - pdir, profile = os.environ['IPPPDIR'], os.environ['IPPPROFILE'] - c = Client(ipython_dir=pdir, profile=profile) - else: - cm.stop_server() - cm.start_server() - c = Client() - - print(('Using ' + str(len(c)) + ' processes')) - dview = c[:len(c)] -#%% FOR LOADING ALL TIFF FILES IN A FILE AND SAVING THEM ON A SINGLE MEMORY MAPPABLE FILE - -fnames = [] -base_folder = './' # folder containing the demo files -for file in glob.glob(os.path.join(base_folder, '*.tif')): - if file.endswith("data2_BL.tif"): # or data1_BL.tif - fnames.append(os.path.abspath(file)) -fnames.sort() -if len(fnames) == 0: - raise Exception("Could not find any tiff file") - -print(fnames) -fnames = fnames -#%% -# idx_x=slice(12,500,None) -# idx_y=slice(12,500,None) -# idx_xy=(idx_x,idx_y) -border_to_0 = 30 # 28 for data2!!!! -add_to_movie = 500 # the movie must be positive!!! -downsample_factor = 1 # use .2 or .1 if file is large and you want a quick answer -idx_xy = None -base_name = 'Yr' -name_new = cm.save_memmap_each(fnames, dview=dview, base_name=base_name, resize_fact=( - 1, 1, downsample_factor), remove_init=0, idx_xy=idx_xy, add_to_movie=add_to_movie, border_to_0=border_to_0) -name_new.sort() -print(name_new) -#%% -fname_new = cm.save_memmap_join( - name_new, base_name='Yr', n_chunks=12, dview=dview) -#%% -# fname_new='Yr_d1_501_d2_398_d3_1_order_F_frames_369_.mmap' -Yr, dims, T = cm.load_memmap(fname_new) -d1, d2 = dims -images = np.reshape(Yr.T, [T] + list(dims), order='F') -Y = np.reshape(Yr, dims + (T,), order='F') -#%% -if np.min(images) < 0: - raise Exception('Movie too negative, add_to_movie should be larger') -#%% -Cn = cm.local_correlations(Y[:, :, :3000]) -pl.imshow(Cn, cmap='gray') - -#%% - -#%% -rf = 20 # half-size of the patches in pixels. rf=25, patches are 50x50 -stride = 5 # amounpl.it of overlap between the patches in pixels -K = 3 # number of neurons expected per patch -gSig = [8, 8] # expected half size of neurons -merge_thresh = 0.8 # merging threshold, max correlation allowed -p = 2 # order of the autoregressive system -memory_fact = 1 # unitless number accounting how much memory should be used. You will need to try different values to see which one would work the default is OK for a 16 GB system -save_results = False -#%% RUN ALGORITHM ON PATCHES - -cnm = cnmf.CNMF(n_processes, k=K, gSig=gSig, merge_thresh=0.8, p=0, dview=c[:], Ain=None, rf=rf, stride=stride, memory_fact=memory_fact, - method_init=init_method, alpha_snmf=alpha_snmf, only_init_patch=True, gnb=1, method_deconvolution='oasis') -cnm = cnm.fit(images) - -A_tot = cnm.A -C_tot = cnm.C -YrA_tot = cnm.YrA -b_tot = cnm.b -f_tot = cnm.f -sn_tot = cnm.sn - -print(('Number of components:' + str(A_tot.shape[-1]))) - -#%% -final_frate = 1 # approx final rate (after eventual downsampling ) -tB = np.minimum(-2, np.floor(-5. / 30 * final_frate)) -tA = np.maximum(5, np.ceil(25. / 30 * final_frate)) -Npeaks = 10 -traces = C_tot + YrA_tot -# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5]) -# traces_b=np.diff(traces,axis=1) -fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = evaluate_components( - Y, traces, A_tot, C_tot, b_tot, f_tot, remove_baseline=True, N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, tB=tB, tA=tA, thresh_C=0.3) - -idx_components_r = np.where(r_values >= .4)[0] -idx_components_raw = np.where(fitness_raw < -20)[0] -idx_components_delta = np.where(fitness_delta < -10)[0] - -idx_components = np.union1d(idx_components_r, idx_components_raw) -idx_components = np.union1d(idx_components, idx_components_delta) -idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components) - -print(('Keeping ' + str(len(idx_components)) + - ' and discarding ' + str(len(idx_components_bad)))) -#%% -pl.figure() -crd = plot_contours(A_tot.tocsc()[:, idx_components], Cn, thr=0.9) -#%% -A_tot = A_tot.tocsc()[:, idx_components] -C_tot = C_tot[idx_components] -#%% -save_results = True -if save_results: - np.savez('results_analysis_patch.npz', A_tot=A_tot, C_tot=C_tot, - YrA_tot=YrA_tot, sn_tot=sn_tot, d1=d1, d2=d2, b_tot=b_tot, f=f_tot) -#%% if you have many components this might take long! -pl.figure() -crd = plot_contours(A_tot, Cn, thr=0.9) -#%% -cnm = cnmf.CNMF(n_processes, k=A_tot.shape, gSig=gSig, merge_thresh=merge_thresh, p=p, dview=dview, Ain=A_tot, Cin=C_tot, - f_in=f_tot, rf=None, stride=None) -cnm = cnm.fit(images) - -#%% -A, C, b, f, YrA, sn = cnm.A, cnm.C, cnm.b, cnm.f, cnm.YrA, cnm.sn -#%% -final_frate = 1 -tB = np.minimum(-2, np.floor(-5. / 30 * final_frate)) -tA = np.maximum(5, np.ceil(25. / 30 * final_frate)) -Npeaks = 10 -traces = C + YrA -# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5]) -# traces_b=np.diff(traces,axis=1) -fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = \ - evaluate_components(Y, traces, A, C, b, f, remove_baseline=True, - N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, tB=tB, tA=tA, thresh_C=0.3) - -idx_components_r = np.where(r_values >= .5)[0] -idx_components_raw = np.where(fitness_raw < -40)[0] -idx_components_delta = np.where(fitness_delta < -20)[0] - - -min_radius = gSig[0] -# masks_ws, idx_blobs, idx_non_blobs = extract_binary_masks_blob( -# A.tocsc(), min_radius, dims, num_std_threshold=1, -# minCircularity=0.5, minInertiaRatio=0.2, minConvexity=.7) - -#% LOOK FOR BLOB LIKE STRUCTURES! -masks_ws, is_blob, is_non_blob = cm.base.rois.extract_binary_masks_blob_parallel(A.tocsc(), min_radius, dims, num_std_threshold=1, - minCircularity=0.5, minInertiaRatio=0.2, minConvexity=.7, dview=dview) - -idx_blobs = np.where(is_blob)[0] -idx_non_blobs = np.where(is_non_blob)[0] - -idx_components = np.union1d(idx_components_r, idx_components_raw) -idx_components = np.union1d(idx_components, idx_components_delta) -idx_blobs = np.intersect1d(idx_components, idx_blobs) -idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components) - -print(' ***** ') -print((len(traces))) -print((len(idx_components))) -print((len(idx_blobs))) -#%% -save_results = True -if save_results: - np.savez(os.path.join(os.path.split(fname_new)[0], 'results_analysis.npz'), Cn=Cn, A=A.todense( - ), C=C, b=b, f=f, YrA=YrA, sn=sn, d1=d1, d2=d2, idx_components=idx_components, idx_components_bad=idx_components_bad) - np.savez(os.path.join(os.path.split(fname_new)[0], 'results_blobs.npz'), spatial_comps=A.tocsc().toarray().reshape( - dims + (-1,), order='F').transpose([2, 0, 1]), masks=masks_ws, idx_components=idx_components, idx_blobs=idx_blobs, idx_components_bad=idx_components_bad) -#%% visualize components -# pl.figure(); -pl.subplot(1, 3, 1) -crd = plot_contours(A.tocsc()[:, idx_components], Cn, thr=0.9) -pl.subplot(1, 3, 2) -crd = plot_contours(A.tocsc()[:, idx_blobs], Cn, thr=0.9) -pl.subplot(1, 3, 3) -crd = plot_contours(A.tocsc()[:, idx_components_bad], Cn, thr=0.9) -#%% -view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, idx_components]), C[ - idx_components, :], b, f, dims[0], dims[1], YrA=YrA[idx_components, :], img=Cn) -#%% -view_patches_bar(Yr, scipy.sparse.coo_matrix(A.tocsc()[:, idx_components_bad]), C[ - idx_components_bad, :], b, f, dims[0], dims[1], YrA=YrA[idx_components_bad, :], img=Cn) -#%% STOP CLUSTER and clean up log files -pl.close() -if not single_thread: - c.close() - cm.stop_server(is_slurm=(backend == 'SLURM')) - del c - del dview - -log_files = glob.glob('Yr*_LOG_*') -for log_file in log_files: - os.remove(log_file) -#%% LOAD RESULTS -try: - if __IPYTHON__: - # this is used for debugging purposes only. allows to reload classes when changed - get_ipython().magic('load_ext autoreload') - get_ipython().magic('autoreload 2') -except NameError: - print('Not IPYTHON') - pass - -import sys -import numpy as np -import psutil -import glob -import os -import scipy -from ipyparallel import Client -import caiman as cm -from caiman.components_evaluation import evaluate_components -from caiman.utils.visualization import plot_contours, view_patches_bar -from caiman.base.rois import extract_binary_masks_blob -from caiman.source_extraction import cnmf as cnmf - - -import pylab as pl -pl.ion() - -with np.load('results_analysis.npz') as ld: - locals().update(ld) - -A = scipy.sparse.coo_matrix(A) -fname_new = 'Yr_d1_512_d2_512_d3_1_order_C_frames_3201_.mmap' -Yr, dims, T = cm.load_memmap(fname_new) -d1, d2 = dims -images = np.reshape(Yr.T, [T] + list(dims), order='F') -Y = np.reshape(Yr, dims + (T,), order='F') -gSig = [8, 8] # expected half size of neurons -final_frate = 1 -with np.load('results_blobs.npz') as ld: - locals().update(ld) diff --git a/demos/obsolete/demo_caiman_cnmfe.ipynb b/demos/obsolete/demo_caiman_cnmfe.ipynb deleted file mode 100755 index 048cb19b4..000000000 --- a/demos/obsolete/demo_caiman_cnmfe.ipynb +++ /dev/null @@ -1,864 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "

    Here we will be focusing more on the cnmf part and its main functions

    " - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "try:\n", - " if __IPYTHON__:\n", - " # this is used for debugging purposes only. allows to reload classes when changed\n", - " get_ipython().magic(u'load_ext autoreload')\n", - " get_ipython().magic(u'autoreload 2')\n", - "except NameError: \n", - " print('Not IPYTHON') \n", - " pass\n", - "\n", - "import sys\n", - "import numpy as np\n", - "from time import time\n", - "from scipy.sparse import coo_matrix\n", - "import psutil\n", - "import glob\n", - "import os\n", - "import scipy\n", - "from ipyparallel import Client\n", - "#import matplotlib as mpl\n", - "#mpl.use('TkAgg')\n", - "\n", - "import pylab as pl\n", - "#pl.ion()\n", - "\n", - "import caiman as cm\n", - "from caiman.components_evaluation import evaluate_components\n", - "from caiman.utils.visualization import plot_contours,view_patches_bar,nb_plot_contour,nb_view_patches\n", - "from caiman.base.rois import extract_binary_masks_blob\n", - "import caiman.source_extraction.cnmf as cnmf" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
    \n", - " \n", - " Loading BokehJS ...\n", - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "\n", - "(function(global) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " var force = true;\n", - "\n", - " if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", - " window._bokeh_onload_callbacks = [];\n", - " window._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - "\n", - " \n", - " if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n", - " window._bokeh_timeout = Date.now() + 5000;\n", - " window._bokeh_failed_load = false;\n", - " }\n", - "\n", - " var NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
    \\n\"+\n", - " \"

    \\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

    \\n\"+\n", - " \"
      \\n\"+\n", - " \"
    • re-rerun `output_notebook()` to attempt to load from CDN again, or
      • \\n\"+\n", - " \"
    • use INLINE resources instead, as so:
      • \\n\"+\n", - " \"
    \\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
    \"}};\n", - "\n", - " function display_loaded() {\n", - " if (window.Bokeh !== undefined) {\n", - " var el = document.getElementById(\"03c53c4b-aed9-4828-b983-6716f4dcbfa3\");\n", - " el.textContent = \"BokehJS \" + Bokeh.version + \" successfully loaded.\";\n", - " } else if (Date.now() < window._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", - " delete window._bokeh_onload_callbacks\n", - " console.info(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(js_urls, callback) {\n", - " window._bokeh_onload_callbacks.push(callback);\n", - " if (window._bokeh_is_loading > 0) {\n", - " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " window._bokeh_is_loading = js_urls.length;\n", - " for (var i = 0; i < js_urls.length; i++) {\n", - " var url = js_urls[i];\n", - " var s = document.createElement('script');\n", - " s.src = url;\n", - " s.async = false;\n", - " s.onreadystatechange = s.onload = function() {\n", - " window._bokeh_is_loading--;\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", - " run_callbacks()\n", - " }\n", - " };\n", - " s.onerror = function() {\n", - " console.warn(\"failed to load library \" + url);\n", - " };\n", - " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", - " }\n", - " };var element = document.getElementById(\"03c53c4b-aed9-4828-b983-6716f4dcbfa3\");\n", - " if (element == null) {\n", - " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '03c53c4b-aed9-4828-b983-6716f4dcbfa3' but no matching script tag was found. \")\n", - " return false;\n", - " }\n", - "\n", - " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.js\"];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " document.getElementById(\"03c53c4b-aed9-4828-b983-6716f4dcbfa3\").textContent = \"BokehJS is loading...\";\n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.5.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " \n", - " if ((window.Bokeh !== undefined) || (force === true)) {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i](window.Bokeh);\n", - " }if (force === true) {\n", - " display_loaded();\n", - " }} else if (Date.now() < window._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!window._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " window._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " var cell = $(document.getElementById(\"03c53c4b-aed9-4828-b983-6716f4dcbfa3\")).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - "\n", - " }\n", - "\n", - " if (window._bokeh_is_loading === 0) {\n", - " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(js_urls, function() {\n", - " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(this));" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#import bokeh.plotting as bp\n", - "import bokeh.plotting as bpl\n", - "try:\n", - " from bokeh.io import vform, hplot\n", - "except:\n", - " # newer version of bokeh does not use vform & hplot, instead uses column & row\n", - " from bokeh.layouts import column as vform\n", - " from bokeh.layouts import row as hplot\n", - "from bokeh.models import CustomJS, ColumnDataSource, Slider\n", - "from IPython.display import display, clear_output\n", - "import matplotlib as mpl\n", - "import matplotlib.cm as cmap\n", - "import numpy as np\n", - "\n", - "bpl.output_notebook()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

    Using the workload manager SLURM

    \n", - "to have an extensive use of the machine. \n", - "

    This is to be used when working with a cluster of machines \n", - "\n", - "

    This will put dispatch and manage the workload gave by the algorithm :

    \n", - "

    learn more : https://slurm.schedmd.com/overview.html

    " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "using 4 processes\n", - "C was not existing, creating one\n", - "Stopping cluster to avoid unnencessary use of memory....\n", - "Stopping cluster...\n", - "NOT SLURM\n", - "Waiting for cluster to stop....... done\n", - "Starting cluster...Waiting for connection file: ~/.ipython/profile_default/security/ipcontroller-client.json\n", - "......Using 4 processes\n" - ] - } - ], - "source": [ - "# frame rate in Hz\n", - "final_frate=10 \n", - "#backend='SLURM'\n", - "backend='local'\n", - "if backend == 'SLURM':\n", - " n_processes = np.int(os.environ.get('SLURM_NPROCS'))\n", - "else:\n", - " n_processes = np.maximum(np.int(psutil.cpu_count()),1) # roughly number of cores on your machine minus 1\n", - "print('using ' + str(n_processes) + ' processes')\n", - "#%% start cluster for efficient computation\n", - "single_thread=False\n", - "\n", - "if single_thread:\n", - " dview=None\n", - "else: \n", - " try:\n", - " c.close()\n", - " except:\n", - " print('C was not existing, creating one')\n", - " print(\"Stopping cluster to avoid unnencessary use of memory....\")\n", - " sys.stdout.flush() \n", - " if backend == 'SLURM':\n", - " try:\n", - " cm.stop_server(is_slurm=True)\n", - " except:\n", - " print('Nothing to stop')\n", - " slurm_script='/mnt/xfs1/home/agiovann/SOFTWARE/Constrained_NMF/SLURM/slurmStart.sh'\n", - " cm.start_server(slurm_script=slurm_script)\n", - " pdir, profile = os.environ['IPPPDIR'], os.environ['IPPPROFILE']\n", - " c = Client(ipython_dir=pdir, profile=profile) \n", - " else:\n", - " cm.stop_server()\n", - " cm.start_server() \n", - " c=Client()\n", - "\n", - " print('Using '+ str(len(c)) + ' processes')\n", - " dview=c[:len(c)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " We can see here that the number of processes are the number of core your computer possess.
    Your computer can be seen as a node that possess X cores     " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

    Memory mapping files in F order

    \n", - "

    see : https://github.com/flatironinstitute/CaImAn/blob/master/demo_caiman_pipeline.ipynb

    \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('/Users/zhoupc/Dropbox/Liam/caiman/example_movies/Yr0000_d1_128_d2_128_d3_1_order_C_frames_1000_.mmap', 1000)\n", - "/Users/zhoupc/Dropbox/Liam/caiman/example_movies/Yr_d1_128_d2_128_d3_1_order_C_frames_1000_.mmap\n", - "Deleting big mov\n", - "['/Users/zhoupc/Dropbox/Liam/caiman/example_movies/data_endoscope.tif']\n", - "/Users/zhoupc/Dropbox/Liam/caiman/example_movies/Yr_d1_128_d2_128_d3_1_order_C_frames_1000_.mmap\n", - "\n", - " we can see we are loading the file (line1) into a memorymapped object (line2)\n" - ] - } - ], - "source": [ - "#%% FOR LOADING ALL TIFF FILES IN A FILE AND SAVING THEM ON A SINGLE MEMORY MAPPABLE FILE\n", - "fnames= []\n", - "base_folder='./example_movies/' # folder containing the demo files\n", - "for file in glob.glob(os.path.join(base_folder,'*.tif')):\n", - " if file.endswith(\"endoscope.tif\"):\n", - " fnames.append(os.path.abspath(file))\n", - "fnames.sort()\n", - "fnames=fnames\n", - "downsample_factor=1 # use .2 or .1 if file is large and you want a quick answer\n", - "final_frate=final_frate*downsample_factor\n", - "idx_xy=None\n", - "base_name='Yr'\n", - "name_new=cm.save_memmap_each(fnames\n", - " , dview=dview,base_name=base_name, resize_fact=(1, 1, downsample_factor)\n", - " , remove_init=0,idx_xy=idx_xy )\n", - "name_new.sort()\n", - "fname_new=cm.save_memmap_join(name_new,base_name='Yr', n_chunks=12, dview=dview)\n", - "print(fnames)\n", - "print(fname_new)\n", - "print(\"\\n we can see we are loading the file (line1) into a memorymapped object (line2)\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

    Correlation image

    \n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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s5Anw/c8l33mwZ86/c4e59yOpDyGEuwH81wB+c+/vAOB7APzB3iEfBPD3j3KN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\njx/Dte/G9GX7HgAfAxAwDUzpqzG6SX04CeAr2LMz0fZjHQ9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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#%%\n", - "Yr,dims,T=cm.load_memmap(fname_new)\n", - "Y=np.reshape(Yr,dims+(T,),order='F')\n", - "#%% visualize correlation image\n", - "Cn = cm.local_correlations(Y)\n", - "pl.imshow(Cn,cmap='gray') \n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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WoaW0gyUJFDaoeSlqkt9f80rLUlJ4GEhQdZryKm5vu2vdQAFl1jpjLBAAB6cD\nQEIeuuLSqUdshWkdXsV3zN8NADgUrFlrSPdojK5Yw45nnLE3pbqoq9K+3S34uqcSDkxUpDErbUmg\nrCXoVLrHBnYbF55XCfouoCqWJrG1q7F151XNpbFKEIgFrHqNqqVqOed+vrvHwd53Lh3C5hZv87wC\nnSlejdhMpGiIWPo5oAKJLE+nwoF1wa1Jf64VsNYnALg1ZuvNHQtsMTo338GplPtjn7+Fo8GqvW73\nvGXwSHso5FkSrbFdVNyAAI7Ga9bdx/3A7TJZeC1KsCVWzIEmeIWJ+gQKWYmaTMC6n+C1nYcAcGLD\nl4dHAAAb47p91iMh8znElNtM0TqN0RJ7/R4l/eZ1bZtW0xb005ipGpO9krsmQAQd+lBJhvg0WyO8\nmTq0EjoCw7gRlm4zf1C6koJBAb/L8oJS40byrPWnCFTJkSSWgrwZ2Uw+ygqEm2IiKIqSn0eO1SAU\nJgMs04DwAo37PF+iroKq8CEZ65I/EGvLfA1po7Q+pWJRSTqE5qLhpCqtSCV3U4V7KYfdH3Qrbjld\nbj//i1RkFctZqO3vJtBapXqHxcVyqxFZa5a9fwprtcpqsOdlDT4pWk+ghM6AigL5FD+ktfBp7OS0\nMvdUpXvfk58KGo+P+Lv15dUDyMy35LWcYJTkHlYflmDzs8omHlCF3yqQTMG0XvYRU9EIHc6MBN+3\nNLJWaaqmrLQaGYzOSbLTeIyXRKVsPR/jaYJWlSzLipUQ4AQHQ/+ivdKy5jdTdCT7e1/MsqubxFjZ\nwzK/vz9E87hfWlYvA5MuvyZDiXJwcLiqmOTsFgcHB4dLYZLl10QoURqsYc9FPayBNeZeEqIn3E9b\nA7YeDLMAENKvF8Tn0JF4lRvqK2iIKm/MfseHc2hKwMFK0sJY4qvmQraMHIw2rCVLUWF5orppjEdG\n7MdNx8IzQtpqwqn2rQXK/AwpQ9twFRURziScc5vLau9AewOHAragzakBQlkavX7qAdw/5LiXr46Z\nC2lKPYpDvhCzAXgkZQvaiRGvWqaCAabkudfzJh4e7wMAPDDYD1+WuCYAPNE+zqVs4eoWMbZk2WBi\nyWKV2t9n/W2E8ruhhqgi18rGSi1nbdzVZSvLX5+4kftlsYFiilcd7dYQx/awxaUTlDFfhg6h4ZdB\n6eOiHIJfHe+3v8+Lte2Qv4kZZZ6L3+E+b9n2Qao9BHLdmHKM5BlW8pY8dw0AB4mPtIeNjMfXlKya\nZv1tJLJ2vXiwAAAgAElEQVTCz7W2y1ZDZVDNyl3PY4wkYKGhMyzmHCR6YlBaCY+F5+zxW0KyYgLq\njzbXsMcrOcUSEQwh5WjJ+Gmp0ipqxkxAecl+vwtokE0qcLi6KGIfkHg6f3uESEgSC08CkhWVVsUK\nwW0eKaQdkSdrMv+0tnQGQGlw2b6O5/FoWllLgDcGaqt8j+apETwJMjfklNrzADHSqkzDF9LoRILc\nvWHJZaU0wVgxxtNCZBmQjS3KQyCvlVanwbyM4y3DQ0Vl0HSFxNo3tEthhXGcsIPFv2o1AjjA3ATn\nq35pgTLnh/0CWaxs/5i4LFCVKkDuGwBppxJbJN/l+rJYkfqJjSHTyrNkloZiwRsXNpgchbZ0B2G3\nQLbGjfzq1DwAYGnYwqlNjuUNvBxfdz0njNzW5MDuxfEUPjriF5IM26gtle2yELnkjcu4Mi/RloLA\nWiRrhaXK0QVZfioUxnykLcnvT5x8Cz587KM4H/9k8Wu4LW1YdvRwW9ug/yI0XF1krWZpsyRQJdKI\nhabFUOUQaXixUGi0QqTTMbR3+crQpMuviVCiQMz+3QpG1sWRaWWDHvtDHmzbSWQzmqrZdwFlVhl4\nXJSN471ZrA74g+mrAkfbHPBrgrJn/J79ECsUWCFWVo6rWQzHfD9SpWnWuOiCPLNcVUbxmvb71tWV\naA8bItmSjCdk7KVoEysOezyNQEb0P6idsIrLOXHn3Ts+iM2i/Hj+XZ/JFxdHPCFvaZ6z7sQTySwe\nGLDi0ctC7I/ZjRBJHy2nbWwJHW/TG2NWiFNM8HUOsorVUtq2rlIANvvOEymYat+6Dh8e7sXdK6z8\njdclm7CVYXaOr/+y+VOYEvfUeiIZdVmImXAg725kr7uWNuwzmEDMm+rn8MKIg8XnvAJNSRAwik1K\nBRrEM3mtaFgXXkoZ1kWxubPPhKmDIsQLYlZstvK65egyLlyPNLbk6xFTat+NQUxk+3s5b2Eg7tdE\ne7h3wHyMRnH/uplH0JIxOdC+TXJIc76+yRwFWKEzY4b7+YlKUuO8d/B0MMkruWsBRaAwXGgiXh3Z\nchtEAcI1Vo4LyajSHiwvEmnYYO7CB7K6ybTjceNvjWG0EJUAay/isT3YJwrQlEbeEKW/p2zWFhUx\n2sd3MpbnrRhZo+RxMtlvwWbJY2U/4FQqGKathU87Mt9saZJhmQVnlJWwq0uFKyq3GyJLf4P5tAD5\nYBu9Jy+DqU1beodL9+d4itB/Ac/7oME/s7UaOvcLj9RyXpZq0coGuJvz+zOErF4qA6Y94TbP2eFC\nw97XH2YVRU/aqsi6WiktQDn/XjvTR/Mxlt/nFH9/zgUa+w7yN+e1+x61svWGkAmYR0WAg1O8uHu4\n2YT3uLJ9a5QUT5SloFcgF0Ux6Oe2XWbsUA6EoqxoDWgJ2jck975fwBfuw/tP7sf75spMvZ9dvQkA\n8IlPvpQPjrR1B/qD8j3Y6h9p+b60XypX6XaIM33uA0MoPM58FKLQqZwD93erCk2y/JoMJcrBweGq\nQWOyGX8dHBwcLoZJl18To0QZc6WxEOyNy8DaM2IlqgcpZiVlf97rWvfQSAdYFzfNyT670ha3Otja\nElt3QWhHvPQyQdcL/oYNmk61by1cNS9Fs8YWgJFo751oiEFu6BCUDcxOK+Q9iWX+juz2TNx5vTSy\nrhsFICD+fa/n42USNH2vMASfSmesO/Dx0QxOyfOYkiwtb4SVjPtjOW1bF95MOLCWJmOhW8zK0iUv\nqp22AdiGW2qkPdwvbsTPd2/AneOjANg9avrJWOsS7WNZXIuP9mbRHfBquTHPS8eb55bwwvZZAGxB\ne6jPZu3jW2z56Y4izDb52M16DXsjtpqtjRtYHrDrbSbme72q9Sj2ynKsSaFlBi9sEHxJvTDSAQrp\n5xFleDTh+35mlRnJ1/p1fKnBVrNhFlhm+5va5YpwU9yyCiPbN1WYbQ01tgHv63nTWtnmo5JB3rhq\nx1qXjONebvvFrKj6OrS8XmGVqkOExbaOsCZWtdWsBTV6OkKktKY5XB0UAWE45yOr19E4JRbuUema\nrS0ay1DdWqpGM561yICAwttJBqbSwl4jmY4wlkDi4UFJ+pgeA7LSzxEgG4pcqpO9R76nIe1TSJvl\nflPclsTl4/c1goEp7aEx7piaZ9KWXEOnlQBuE7euAZNrYdL0a+uFdcdlMVmLian5aagHAJb7Wu4R\nVBjDu4eeaJnp3ZDiBUfZqvy1s1wKrADhr258AQBg4xP7UVsRy55XWqCMy1QlgG8qMGwBtXVJODGl\nYJqetbb5dQW/L/N2W+phDtIy6J/IUkZQrrHnfrFsSwbB1qvGuHmaZczBcMNSrhjrc90boxXKSygI\nw3lp10YZzG84sZK2V5bOUR6iDZZhgRznDwjJiA84vG/dUuuYZKfYS62M7S418fN/9yYAwP8x8Mog\ndGGwV6OS3oIqXFqmfqlKSisk5ShZ0Td9nFri742S0IM8VfDOsbyM1oU+Ylc8UZMtvyZCiaIcCDcV\n7t/cbxWbmWiAusQGtaVmW+RlVnGKKUVfs732TDptlaBb2jzJbmotYXGWXWCnulM24+6ExDvN+9tQ\nunSTGNdJxx9iTuoImY9gyx9bxahOY9Rldhq3WTXOZVQE1v1iMjU2xnXrZjrkrcFQpATk4aAlqCtj\naT45uBUA8MXlQ/DlA3ygvmX327aoBEpI83p5ZBU983M7iy0v0c3hko0tCsQ8XaBAQLz//uFBPLC1\nFwCwlcTWv+0rk6WnMc592V/DvilWgm6bZrfboXgdG5IZeff2ITyywv087EomyNBDt8muv83pGOtN\nVnCbwRjrfeG3MqV7/B5iQyhI5YdlJLXq+oW2fRBSbpVGRYUVVi2JxVpDHdsJtyHLPcsBZt5dqj0b\n29ZSCUJ5dxU5j46JIaMtTMn1E+1Z0lNTYqZBCUbSrgTqCX78gPIdK6oUhkS2sEr2SKTlmXQajyfc\nh6dGM/ZDshtM+kruWkDhA8NZQhYrKHGh1xe1/egWsXGlEYrQuLKw43fjXjKvarAvAmkem/6gKN1p\nLSH5rSVWqdeV80hz4V6gzDzzRoWNeUrrVJZHMfo7lcqNN9ZW8VGVGnxFmy+QR1SWdaEyI84W124S\nGktS7LimLIGliWfaPhxad5/2Sndf0CvvZZSwcKPCO1WZTnWJd10INtE8wHP91180jdZHpDCxX2a3\nWY4lDRujVV/NLanpYJ9kHTfKMiZUAIE8u4lLo2FSZudFQZm1l2tES6w4FzezSyuopTY8oeUN7bfA\nyJqYUqyNWF5+zSsexNE6u/vu217AvfdzxnNxt2/72IwTb6xB4j81/FZpI8TaDD/D0WPruL3FZaxM\niECqPdzpcQzrYH+AzU2+L4YeCikGrxp8rWzgw0tKtcBEIJj3QXlJsFkEBMsN7QG0LLJeFLNwDIRb\n8nu3QDIV2Tiuy8Gky6+JUKIcHByuLiZ5Jefg4OBwKUyy/JoMJUpWM8M0wNI6u4xWWgMc6TADaizs\n0nmhrEtp0Z/GmbR0V724xtr3MQncm1EZZDGFz42O4GPrLwQAPNRjd0/HH1prREC5dVt1vKHN3DKM\n6bNRDzM+Wx1a3tBmUsWynPOgbWC4YQ0HSo6kwMutm+mQv2mD4BWUDZaeExPooWDNuug8VWA65ra0\nZUmgqLDne0rb38eFb60zZ0fcln4e2v0dldvstirb9j6xdN0QL+NLxG6v5UHL8p6MMt+2JRZLkSKN\nPXHf9h0APDKYt67Hx9enMR4JE3IkRVEbCWoR93fo59bK2AlGmGtxPzVkW7+IMJKoyLrOLWv7ugRy\nrhehdYvNeV3UZfnYUGXA+dJMxz5jXcZPoPIdnGAAu8os35jXR2Aydkx2HgiprOi6Bdmsv8fG89gU\nspz9IVsJ+zq01s2VvIWlMY9VE1jezWMsy/keNPqyxG57I7tq3Cz4mg+O9uPMmJ+h0GpHaYzLhdY0\n0Su5awFUsBvGWFUAIG1H8Acy3ipuDGOgVLkuy5yMS0tSf68piYJKoLNCfUmsGYd4MPRSBepJ4dsB\nQYj/4Y800lbJ9QOwG8hcy0sqLq7UBLZTWZzXI0Sbkt1nOJw8Qm64rrzKdT3gCTkRVYvRmRHyummL\nWMCDMqsLBAg1GqLtAr64FG3wfaHRP1BmmxnuwIf6nJW8FdXxSH+Oj/U0/LFYVroa/pDlheHS8kcE\nJYHn4UaC/kG+1rgtrqqKeyoYFAg3JHt7m2WvbsRltHZW0j/qwLPbDe9XP1fWir+Z162cNIk8X+nt\nx09e/18AAK+rVRJH9t6Dn5/hZKL/W38DAKDxmGczFP0BQYkLV6pKoXk6gxZeu7/pvwh/e+g6AEAc\nS5LLIEK+Jck5PQ++yXxMCOMZsZR6fD5lygaRa1VaJE0wu8pLd168rm3QfeEByYz0jRnfY0K4aTIY\nc/T3+TtcuU8Vky6/JkKJ0qHG6HCCtx/5HD4c3w4AeODUPhvrYpQRIo1z8mHqZTfa81/WfBwvk2yu\nGc9kWkW2ztk/qD2O9Ta70z67yVlbp0YzqImPZFz4mA54JtdVYl1BBoejMjU9oNy6HEfyEdzM63h0\nxErSdlazKfwvnzlpt5m6cl9N9kKJ625KlT6aFZFKXx3vx/K4Zbcbha4p16yrBGGlnlwVJi5sK+UZ\ntzGu2/tWYcrKFBWKTQ+FdacFXm7juYwSNUoCZKIMNMIEoREQokh0s8i6TJXS2DPN/XW0wxkqs1Hf\n1kI09wM4Bk11+PfFAbtf/757A6ZEsh7yt7AiioXJDgwpx5yQUc55CWZUmTMegPv0iNSzu9s7bF3A\nh2vrlv7BZC2upk1LXxFTCg8r0kDj7tOWBPRMNo3jY37PK0nLXtegX0SWBuLB0QIe2ZR4MKnBeLwx\na8dOQHmpDNO6JT1dlIVBlRz2utoqPlEh09sNrgZZHRG9EcB7wYXWfkNr/XPn7T8M4IMApuSYd0tN\numsOup0jf+MmNtYaaH2F58HMAxqm3ku4zvPY72coAt6WxYRQSCeziGz9tLQpH7GKdM7qpbur/TDP\nw9GcssSHKgWiDflgbeUYT5d18gBWrDxRMPyhtq7D8ZQQTtaApF3SFijJLDauuGCYQ4ks0BVqhh1U\nApVadEFPlLC8JMgsotKFZzO8SjYF/mCb2Ni1TLYVGEuNODVS2OzxvF2TcICtNMZiTxYb3cC2RXtk\nSSvjsyyL8noIJdmKaask5bUuq0zbcjLR6hhqILQ5HVkYa40irGTUDo2CXNjU/c5xPqd3sI6/jflb\nszFft/JyXYh337rvSzuVpwre1mZi5t+cY9oB9UgLZvYXQaU+oCrdlOa+lIcYrfG3btyU2CoAoYwT\nf1i+pyIASEhAx7Pyjpo5kmlzrEJtSdtjAYAyDV8UKx5HfGx/QSHcKDM9ASBe1WidFkVuzkce7ayl\neDm4WmSbROQBuBPAotb6W4noOgAfApMpfRHAf6u1vmQwxeSqdw4ODhMLET6/CuBNAG4F8D1EdOt5\nh/0YgD/UWr8UwHcD+LVntpUODg4Ol8T/DOCByt8/D+AXtdbHAGwA+IEnu8BEWKJIaUTNMf5R8xEc\nFoLBX8jegOOPcaBzHLI2e6C5hS0h8rpvfT/2SlHft0x/EVPKlNAQawjIuqQ6ysNtMbv7HgiZk2g9\nqWNGFiOnB1N4pGCT8KHGBvoZr3yMRSmm1AauKxQ2mPNUysyfD/X3YU1WGJ1wZIsVm1Ix3byGJXFD\nPjjaby0qDTW21/jyNnMOPbq1B9uS+dauj6z7yZRGmfL6Ngi+SvzZ8QbWb2yCwk8uz+ArdXnetm+z\nzArLE6Vtod9HR/M412Or1eH2RkkUOSqtS4FkK+5rbONFrTPSBpOVGFrrFQAcbDIf161Nztg7GJYc\nVOt505JeAmVGpnF73bV2GI/2+H2EXoZEAtpvanOR3te3HsCMLKf2epG1rI11CuOs28zL65ugzrTw\nkEtmpCFi3c5qWEn4uR+kkuxzs+DVa1+Hto9T7WPWlwKjzYHNtKm+D7NtKW1bslhdCTDfEMvduPCR\nibt4VATYG7BLsGo5vF7G0S3xoiXT2w04nvaKxxS8CsAjWuvjAEBEHwLwFgBfOe/WxqTWAXDmSjdi\nUnCkvoZfe/Hv4uPd2/DxhZsBAFvDvdjzAFu1TUCtGmcIpQ4rFT7SuvDi1QjaN24U3q8rfE2FD+sH\nNCVTph/Q1t/mJRr+sLRsGKOvLY+SaevOi7Zy61JMhTtKe6W1IW0R0r6xKhgGTrLZdWnLs27IPKoU\nUV4TMt2zY3hSJLmI/IrVqcI5ZbZVlvEq1ZYs02YFZoXNUlMjsmVq7j/Lc5VI223hugfKSjckybPT\nUMiLlUJRE7eV1gjE6hSvS5hCUsCTsi+qP0I+wzIka1StT3L9rCiTBVDycQWb/L73f1ZhZcAWsjsX\nWtYt2jzK8/xdN118KhgS30ysgVGBHQXIbQKBPB9U+W4byzmUyFFjWcyjkhdMpdpycWV1YDQnz7Wf\nLf9TjZH1OHSpg3h1JyeVSoFok28WbiYYzUug/Iq2CQZhryzJYzJCtSd8X7tIzrtK8gtEdBDAtwD4\ndwD+ORERgG8A8L1yyAcBvAfAr1/qOhOhRDk4OFxN0G7M4bNEdGfl7/drrd9f+fsAgFOVv08DuOO8\na7wHwMeJ6EcANAB80+U2wsHB4fmOXckv4Mll2C8B+FcAzMp1D4BNrbWJOzkNlnOXxMQoUURAXQV4\nRcQxNN+y7z78xiYvYrf6bBU51Nq0aaNr26WlodAKqpK2z9jZ6SYYfEpqD6SFZy1NvsqxOeZ7PLi5\nF11hLJ9rsMp+U30Jc2KBAGB5mr7a49XQid4MahKtd7S2hkMBP0PVemXYq+/pHsCJrdsAcKxMMt4Z\nMZwnHuKmxD8FqY3PMiVuGmpsg6f7AEYFr1q6RVxSG0hMVDbw8egWM+g+PDsPSNB9S2KqUhBOmBic\ncRtbEm/wuCYbB2tWQ43aGNdPsTXpVZ0TNubIBEcvZR2cDco4ngO1khkeKK1B56OXRUgkwGJaeKIe\nWpvDGUkw8P0CNSnkbCxR+7xtzEkgpIKy73ygc5ySwM0HBxx4upnUbFzZWtoorZMyDmpegr70Wy+P\n8MCIebPMpG15I2tFO+BvWIoDRRqbwmJv2OxHRWgDxOsqwf4OjxnzDm9vn7bFo08nM7i/y+PnCxtH\ncH3TFFGW0jv+aCdX19NYiHGK8GVfYFVr/YpL7L/QBc9/yd8D4Le01v+BiF4N4HeI6EVa6wsHgzyH\n0STCa2KFV0Rfwv6Ax/7P3/5m7BG73GjeFLMtu02legcnjzHkUkWEVTvUBBeHJduJtRDEayl8saIU\ngQdPSlYZpmsQ8yDx9csA+Kqly7BP5zkhNzVqDQE4AeE2n9TSGklTeKhCQrTFDW4e57muxim0Knnx\nvJEppVJa2myMjUY5krSG3+drVVPh66vC59TxMRRPQyoWJZUQ4m2Zqyc1goGp8cJlXKooYt9SPxRR\nyfBu3onnEUTMAr73hGK5TOcg3FBZYZMFkpkYueGtkf6K1lMc+nOe60U9xGiWX96Z7+WO/fQIeG2M\nC+J3V1/NTXiYx4zKAVOPXVUSEOxYKrT9XSUFgoHhr+LdXJ5Fvo8hwRgX/aG2FkHzpLP1ATLDVRc3\nkbQlQWhUlqAx10qmQ1vQubGUWauTTVYICJSWVql8F0HlwK7lF3AJGUZE3wpgWWt9FxG9zmy+yO0v\niclQokiDSENBIRZeoDvqj+DjM7cAAE6uC4kXaev6adTGSCTQ8aFkH24KHuJryQBjtiZDfFhgJHwr\n5iMaqcy6yG5ortpssbODDnriTgs8+TgXoQ0Y7hchziZSj07cfjU/xY0tDki+pbZo6+gZ105RUeiy\nQmF9ixXAtBcibLPCdHiWMxHT3MO2uIEiL7OlQoxClmuFTc2T61zawT19dgNup7Ed/Ge7osykCv1x\nyaVlgpoNl1K1bYdr6+g0+aO+sjiFqb0SuN3p7eg3YGdwvSGKDChH2zccTKXt2bg+q+d40DbAuxmV\nQfx7w7K6+OMBv/Ms93BsmvvWZEiOtI/1gp+hTjkGkkDwlWQPPt3lMgYPSxbmMAuwt8bPkhQ+hpJi\n1JGRX/cSW79PkcZqykqpIW3dV+vixogTARb8LlpkXKGAh+GOZ+TnZCG+N9iy9zXK+o3REg74/J73\nBZsYiAL62eXrcMbjMbVQ4y/k2UEHD3fZ1v5gfR+wXN5jN7gKZRNOAzhU+fsgnuiu+wEAbwQArfVn\niSgGMAtg+Uo3ZlIQUYA3Nx8EAPzOkTswnON3aD7IeVS6slSmrQJClawn86YKcNkzPr/qouOfpEsO\npKrSobIC3prwFtUkk+9wHYkErId9ANnOgOHq56MIS+4nE5xNFb4of1Ag2hByye3Elj+x50cBqDDE\nml7ppqx8bYyiqL3yXtoneIn0x0iSLkYZQlkgNxeVJX3MY8NZ9URSTQAIzm3ZjDnzDGqUgYwSFZTv\nwQTfU0HIWiKvsoqeX0k6M267rBmid5Dnb+8AYSyZaUZMxis+9n22vL8JtA/vY9n9w9H34Vuvvx8A\n8J1TX7CLr/ee+yZ85vMcWnj4c9zH20d9mzFXbW8mruBws1RgiqDsQxPgnYceRKxBq7K/uMwOX2u4\nxc+9XGuWz+1pOy6Mm9UfaSQt4+otXaIqKeAlZK9r2kLauJMB0jRJZV9eA+DbiejNAGJw2MEvAZgi\nIl+sUReSaU+ACyx3cLjGYQp4Xs7/p4AvADhGRNcRUQgOHP/wececBPCNAEBEt4CF1coVfDQHB4dr\nHLuRX08mw7TW/1prfVBrfRQsu/5Sa/19AP4KwNvksHcA+LMna99kWKI02eDbXDTygHJMRbLSl3TU\nlj/GlFARvGTuDE73OSX+xGgWDwptwBFxu8WU27TQ1TzAiZRXhNsZW5mGeWCDojveEHGdtX5jTQHY\nEmSQVsq6GMvFfMRWmoX2pi25YoKMgTIlfyntYCzLsPm4hyPz7O7basd44R5ut7Gw3bO2gCgQa0at\na10DxsIx0oENmv7qcD+Od9ldpzVZd5gB5WRLAKTas8zexh0YU4a6LGvn2l2c3MNB7p9ab2Kccnun\n6vwO0tyzLs8vbB9BLkGLxqI0yCNrcSkysjQNZjBHcYoYJki+j0PiIpvxerY4s2HtvjFewmdDprA4\n0Z9BTyx+p0bcvr+jY1gINuxzPjLmBIR7tg/gTK/khwKAmdrAMq37KreUDIYyYiurITPjwB9Yi99J\nsCUq12Qtiy3SiCUwPYW2xbJN4Hm/CBEbiwOUdatWJ3RLlX2/XyKMFWl0Ez72nCxlk9y3/GiFVqCn\n6QArrvB6SWudEdE/A/AxcBb1B7TW9xPRTwO4U2v9YQD/AsB/JKIfBa/n36m1flLz+HMdZ4Q+I809\nKGMpUiWVgGF/zj1Cb0GoBLqVbhELAgd7l4zm1QLBvBHwxKKkSYK45V7GE2XcT4VXWkm8RFsLlvnJ\ngetyeV0pzSI/vFFuz88jzwaJAwCUKa5sTDtA3ozssfYZqu4xc69KsHEREMZSgiXoCsVBP7XWn8Ir\nLSLitUcelwVzVU4YzAv32loIGnNH6kDcmGleOmcq3W3cY1mtnCNqHKIIvR37VaaRintr6/oAfabV\nQzKXIppiGdFu8M/VlTbOetzIhU/37TPM3c0vf2PYwR8/xhQGf9h5pTU5Nh8MceAR4dYTa1zQ82Hy\nTQq/dO2NpDRPvDQG1cScF5CllDBs83mtpM8gvZOl3iQ5mASD7XoNXiD3Xw8wnjbuOHF5JhX28oLH\nhbmX39tpkezv9bD2Gt72ulsegoLG4387xG5wpeXXJfC/AvgQEf0MgLsB/KcnO2EylCgARUHYKkZY\nEbPlvaND6KWluwxgwsm6qalWG1slZyVp4a967Po7EPLHdcrr22uvZG1LcmaUlUIrm5Xlq9xmax2K\n1y3/j/m4Nr0RtgsWjFt53X6AjwUcozPj96xLZy1r2t9PjlnBWRq37X3NBx0A9jXL+oDGfaQB7G+w\nInhr8wz2SdaW4RHazOs4m7DyuDpu2ky8Y61lTEucz7Z8kDdn6hglPOkXx1M2tsdwMNVVZt1TsZfg\njg7Xo3pwbh5rXVbUTB+HKkcvKV1KD/kcc9SSIlynBtN4vMvPsLLZRDqQWZvw4J9e2MLX7H8cAPCN\nna/geqnjN6cytITnKdVSRkc9bu+zmtyORzekhEzGz3Ju1LKZbbGfYmXIJujTa1PoNHmS3ji1attv\n+ryXRgg9Q3rH73N51MK81GlcCDatojkSYTkufFteCICNvRsVBZYli8bUuIspsecHlCORLJclkYDH\nkznLN6aowOKY+2t9ULP19Q43efy+ePo0bq9xPxzwevjowi3YLbRmZfBKQzifPnLetp+o/P4VsNn8\neYPlvI9P9F4OAFh5fBpHTH22dpmlZD7gKtMYC8Fl1iiJCU1NNK0AEVHI6mQ/5rIOQNDXpWKUFpUY\nGdiSJKbsjD8q469UopHWK5xNAuNuK5QuGUEF1ZBGlRcoRDnTgbLKkXX5kUYRymKj5VUUtfKa1UWB\n4azqz3v2PlnNxOLkCNZZXkVToa3Pl0n7KSP7Uec+k+eqBba2XVW5M+SnWgGZuMByaasm2DipvO6X\nmXi5LJYWIgxnpR7qYY1sj9RbnRrh6B5eGNsap9EYxzMOKdg6U0fnYX6GeIXl0/7VEbpH+UUOZ33L\nDRavasQr/I3zt1m2Rm0fSVv4+uYAU8JQxD2yhm9jwbJ6aN1tpswPE4w+cf5rIsQbQm4qsXODOEIm\nCp3uZMiktqPXM+WDyJK+euNKTF9a2HIwi6+TcjZvuQf/6fBndtzzVWEPl4urJb/K6+tPAfiU/H4c\nnHn8lDExSpSDg8PVwy4DMx0cHByedUyy/JoIJYr6CvHnmnhd8C4ocT9pTTYzzBS7XUmaWByxFcan\nAjc32RUWUI6RREguJry6X8Q0UlHFU+3Z0hzXRxzTei7r4IvbXOhxO6nhcGPdXqst1hXz4sZFgJ6k\nxh7mq9YAACAASURBVAzy0AZ7Gx6olhrajL3F8TTOSrkO425sBSPUarxq6WYReklot28Iv9TJTT52\npj7ErVJE+fpwBTNiuai6E01AclJ42BOxxe2wZDUCZRB4szFCp8bPsp42cP+Q7c9LPt9rb7BpLSMx\npWhLwPmL95zByZj70ayszP0AoJdEOC3s4gZfPbPX/t5qDhG0d7oW++MQn1m8jp+l8HBo/lMAgI4q\nS98YvqcZleHmkPmlPuHdikxWgt2xFBIulHX/emlk39PCzBY6Uhn9WJPfc66VLfWzldTQCHiVtzwq\n+ZjmxRV7Q7Bs3W0G9w0P4kTCruCj/oYNjl/PA1vqx7Cvt72Rdf0N1BD76zxuH9tmi+RjwzlbvmAr\nq+Fvzt3A/blZx8H9/P5e2FwEAHxT4wEsyMo9pghBsHvGco4pcOGPVxP39Wdw6999P5JxAFpkWbFw\np0a4yXOKRD5RUZp+VF7y92R1oH9AgncH4ipfKqxlpsoT5BvOn0xXyrYoBFsyVyuuNuOS8gc5SNL/\nsrqy7jibnRdqm52HgmwmYLVMhynEW2gFeOV4MteiwlCXK+tSymJljSDVosPVEjQmUH44S+V2eZSs\n4cOTea+90u1pjsvj0rLnjTT8kVhWIg+Qci+QtlBaQKWSELMxgidJN8b6VIQKSYvPWb85Qh5HO/oo\n3NLoHRbL/Qu20JCs4djPrMy14Qtehpq4+HoHQ0x91UTSS/vT3PJTaRXY56qfS6FSE/ltrFMJ0kal\njZI3ZCx7actHJtcdzShr1TIuPC+xBRjYOmU8tYW2lr1oXaygXcLmq8Xb0x5hOOA+KArjjvYsmzzl\nFd6svEDvKFug9MtZ7p1vhdotJl1+TYQS5eDgcHUxyQU8HRwcHC6FSZZfu1aiiOgQgN8GsA+cjft+\nrfV7iWgGwB8AOArgBIDv0lpvXOw6AGvKzcUC61/uIA0N90YOr83qc09WIieKPehK+n8rHuPVU48C\nAF5ZO26vtS7xKctZy/5eaIW6BHybeKdUezjZ5UDllW4DXYm/2lvrljQK/tgeawLS2xX+HltDTwfW\nOrQ0buNkj604LbHi3Nxawg0xW0Y+kx+zcUr9MLK0BAbtaISDIVslWmqIUMLjTcBz6vk2mDtUuS1y\n2c1jnBmzdWhzJLxaU5t46RTzIU77fZtGa4LUl9IpPJ5wvNEgjywrbMMbW26lQK7f8BIbo7aZ17A2\nFH++PEuhCS87zPf6Nwc+guuCnZHQfz9q42PCj/VYfw/+aIPdznv2/A1mPW/HsTm0LSp8rL6ME3V+\nT+eEN6znRTaGaKYxQCvkfm4HI2udNH24njURVxjRN4WJfehzuw83NnBTzFavBX+IlgSOx1KLcTOv\n42TClqR7xwu4WRIYHkrnsSUB/oYLK6a0ZI1XKW6or8h74mfJNeF+4RZ7dGsWS6d5nFCUoynPYAph\nT6kCMXF/+/BAF+HZeip4Gjwr1zSupAwLVhT2vT9mNm6ZM/4gt6v+cEuKlSe+tdwM5wNsvpLf+2tu\nehR3dB4DAMsp9/+deSE27mQLb/PUTk4pwLBPGwZtDRqJScdXNpjaQGUaWoLQsxps6rmJFypCIK8L\nvUBGyMOdtffyyEMmwcteWlirlPYrZFbKxHcFloKgCkuboCuFlfuVcU2lxa1KA7F6u3DhHQHSaUnf\n7xlrHdA6Jf3dLxB0hZsvK8pgfmmLoorVqpsi6LEF2peA/PG+Jvr7+RmG87pC/yBWnjmyFAv9rRq+\n/oX8/TnRn7HVGkxCzTANkCSS0BJrLL+Ct+/9vPANao1whb0I3rCMNS0izyYDVOPaasupPEto34n5\ndOQhWeb7tEk2Zs7Gl8Uoi1MX/P4BjsOzY0ksf8N9Gq0Oy/7p+hDnTG29QKghQmXvX18egsYSY+Z5\nGEv81OGZS06Vy8aky6+nY4nKAPwLrfUXiagF4C4i+gsA7wTwSa31zxHRuwG8GxzxfvEL1YD1WxXq\nL19FXwbUVJTaci/NkAVQzU+x3q/b3w9IhtacGqMhE2afx8HTC/4Wzoi75dFk3hIi5hkPttW0hY0B\nj6bBWh2PdXn/yXAGtTqPqL0tHnmtYGTNiVEjs+6bshyIh7HMuFQrHGhwysMLpeTJ7bWTlptpqd7B\nl4kJHXtpiOva/LGfjvjjuVDbsh/lkHL7Ua6qWkZ5KzTZEjVf6e3H6R4rUWPhz7qptYTXNJg/a4/X\nRyDndUXhW8nbuHtwBADw92tHbSbf4cYG2kIQaXiVCpBVrDZHNTTFLbYlfdhqjPCGPcx78oKAEIkC\n4IlS8o21MW4NPw0A+O3g5fj0Kmff/YH/cvzjzl0AgJ1cbPzHTfFZnJ5iZWN5m4Vp8mAb/X18/05t\nZF1308EA04ZLSt7HVl6zyl9vHNrx5Xnls+7z+X21SCGSauizoqTdHJ2xxaX/YuOFuC9ml+h2FiMS\n5SgSBbeamQnAZlYaPrJuHuOc2OILTfCllEuzMbJ9f0bc0d0aoSMfOv9py4/JNoc/i7hiMkwrQtrw\nmAfKZHvVFYKecPlsScLB+tBm0W28NsLbbv8iAOAX9t39hGv+m9kH8fbOawEAX/jzF2H6QeHkqZRy\nUeJOUaO0zEZDaNftyihLlcy4QJHNcjOKTd4o7KK1SBVyyVQzH+fCCyzfkj/SiFck5CHwQLksJoVQ\nMm2qHYHohhBUt00wOBBIEHJtvUBmOIj6ZdHgYMDX3DoaYvNmOfaGbTRkXvb6fK/tdgTzGZv7UmqV\nO9KVci0mYzZWUOPSL6pr4s5rlG49o2BoH9BehSAKQBFoFL60bzHE3fuYzHpPbYC1gRR/70pm5iAA\nxhKkfjBFLoWLa+ssw6buWoaOpKTWMLMuR+0pFIH0eSD70wLBtnwD1zwUvtFGyz42Lry0XkkCqK67\nbLYmLJEqFWWmXSylWjbncrQlO3yQBijynXKDikp/hB60uEy9cY7GEvft8WVedOJmXCFMtvzadcu0\n1me11l+U37vgIn4HwPWzPiiHfRDAW59uIx0cHJ4eCtBl/X8+wMkwB4fnBi5Xfj2TMuyKxEQR0VEA\nLwXw9wD2aq3PAiykiGj+yS8A5JHGgfY2uhLM1wrHOFgXHh1RqceFj64wyu6tb+OQz1acKVVaEOpi\nbQmQYSRB02te02qyJgV9NuhiWjiQerUYxUg4VtYC9CK+RzfgVYPfStGS1Pl2OLRM5CNJuV9NW1hP\neSUyyELc3GLqgxfV2L21z9u2fEzXR8uoi4VNkbYFeo11SVFh3W4jHSCRpaLZ3y8i2x+KNBb7bG3T\nmizT+eEO99tt9VPYJ8HvMypDbAJOxTIyo0Y4F/D5W+PYckMdqG9aq5VJLe1lIU5sz9hXdrTFFj9D\nP7E6bFg3YUCetUAZeKSwX3hTvqN9Nz6/cRTA/8/em8bakl3nYd/eu6ZTZ7zzfWO/fkM3e2Cz2U2J\nDBWKphQqsqTEcCDHiQUhgZwICILAhhHEsP84QBLY/uMkgIMYihPYcGJJ1jw51EhFFEU1KZLdZM/9\n+vWb353vmU/NOz/W2qvOZXez2ZN13DwLaNzX955Tw66qXWt/61vfB/yz5z6Bsx+lbT0WEqk6VKU8\nBGe8Y3xvm8q1TwXUCJD6Fn5E+7q/c4jNgM4x1PkJA1+ASOyObN5tJIJEJSzBMCt9IZNrpYTkbvjR\nOOdNhTT6zcNTeLoiFLHXSHChRfffgUf7bOpUGiPc/8//bJuZXMe9Tlu2G5gSI5aPeHFC5Zs/9K/g\nB2NCEbdMJcTOdxLvd4vwByHeiznMqWA7ZEX5kJqLSZ0sdyDlmnSjxGc7z37bzf7UJpFzv3jlEo4V\noRyuNNO8p+CN6flTpRUisrIWYNNd5QjgpYVyhrmW7FIAQOdOesHCYwSiVAZ5mzWGwlqewJHMg6FF\neOTQEo2CtZ2SXq0i7ojhwaSqTYbd9JNZcG8O4nuplP68xBPyuSsZDR4EmpepKejK2r5QJO7yvHe1\n3BCNo8ooeIyK5d1IpAtc6LwS1AyoEagZm+iqqjbRxRwbYR6REpQRwPEXSebl+CNDzI742hzSsfgK\nyNcZfWzkKJiwnnbqY1I5l1+NgvXmymWMRInRcGUQsFRGMChEwsBJR+jSzinM10iUWLpY1HY0fSvX\nUZW1mXWyyh+OSrH/shbI+b2oMmd3o+qSq1YoucRrPU3lawCNp+i9+Q8fuYK/vfYK3m0s+vz1rpMo\npVQLwC8B+JvW2qFS39nJKqV+GsBPA4DXW4FVQGRypKwZ1JoTvZzPKl1XVqhLRHw1tVLwlbv7+UHW\nFj1LE0nPTOQiNJ3Yoc5xf4de3pVVSLgEdhS3YKdsAzKibRWlwphfjsOsgeOCEibnr7abdjAp6IHs\n+okIb65pKi21dQ6ei7DhDdFhqxOtLO4LD/ioeftVQ0Q63U86bhaktAY+J2RaVchLI9s63WF9qQ4l\nZmf8YxmjSCn435LY9HQpHYYAJImaFKHwwoYZPVCjPMT+gB6Os2t9PNSkfVxs0PH/0s3H8cyEkpy/\n2r4n12E+XGJ12Q/xg+svAgBuDbv414eP0fFuU3n2tBkhdtdWp9Kh2OTks7+V4oFTVMJ7uHVXks69\nrIPdtCPjAQAPNHdwJqTtnon6eDagJOiFHUpWxkUoHnjbdgKft2Xe4D42usJKRNfhwe5urf0llkCh\nlG19VSDmN4kTitsputJl6c0J5VirRFj2Hju/fy5/BF8JqZvxsdZtpMdvYrT1HcYiw+F/3vFezGFB\nvEJikIE6kTTYuVISwOsXpzda1EKub/S8AMCjvEDwghLFg1TyT0ZMHWj50Fx/6kzzEzwn5ZJuThpU\nXsK4vwcazpmJq99QpZIO0DAoMGzTPZ11aF7zpvXLt/KAvFW/9NOu4+PQtqyuy3LVnGCoe/maFNLh\ndXJAIf5q2TqP23aCRzaIh3guPq6pFDzfGq+sO/ryCnmXS4odM9e1xwvU0kq5D56Wc3DHjzkhSmVR\n12lc/qtqGxTrWWSsadj40w6CLi+Ihnz8PQu4jrbcQKWcgI5dhmPr+wCoEydVJ5Bu3ExSIV1jOkqk\nEbDVSuq0x7R6QzHeeQ6dK6mazCLe4UV4WY/3jMuYauIhc/dOpUSUSmUugQbMjAbJdTUCLsnm7uoX\naF782f/js3jqxy8AAH758u++/gDfRizy/PWujkwp5YMmn//HWvvL/OtdpdQp/vspvIlPlrX2Z6y1\nH7PWfkw3m2/0kWUsYxnvQbxPti8fiHiv5jAvWs5hy1jG+xHvh+3LexnvpjtPgSTRX7DW/qO5P/06\nyHPmH+A79J5xMc7rbrWsMqLz5Ix+s9IgZeRlUgTY4e67LTNEzl1s8wiCQyjaOoHvMRzKS41hFWEz\npFXeoFGv8rdbIyFmv8YEuWrmIwjoe86oeD463gwd7/Vy9k7pumtTzOPDq2GtoeQQJldmzKyRrq/d\nvCOfc+hWrDP4fA6hLgVxMbrCRkSIjdPEaqoMkXIdeQY+L6ncWJWo0S5fV1hrsTKwLpBWJ1fGx9OG\nLJzafopzft1BCAA/Xz6Bl0aE7gw2M6xpV4Z8fZ7uK4O/3CYS+s/7T4oiuX+q5G1Wsi6fWoVbOV0H\nhyaurkzw5MpNOq68KZ2Ro7y+jlshoXLn/CNBss74tc7TzoRKcKMslO1vmwk0My0N7+uo9KVUuxLN\n8O+uUUfOxXAPd3NWHGdksrJKtt9UmZRinZ7U05PzuDujf2elh3NtJp77M2zz8YZcEn12dBpf3SUS\n+zMHp9G47VqFlvFexXs5h6kK8FLL1ASnSzRfWlFzn2VNoHsavz74KADgE9E33nC71xhxubK1L12m\n9yY0L9zLN5DeqTu5lJOyrgAbuNKdrX/HHXs6LVHxv+MDRnZGGjmX9tpxChOzAnZE+w/6tcFv0QCK\nJiP+vpLyZW2GXGs/QdV/dyiOLuquwjKqu/4AwLDuVdalD5/a7OPhNqHel8NdeeaugrTbitRD68hZ\n3yhRbdeZndOcquS8VUb7rcL6eXKfm22o+rhRl8Osc4FWqB2hAZQtnkcHHrINtmiZcDlvpFC6azA0\niA7oOjX2XJ3Tl+47KCW2MCav5jomuQS4YlBENQro0DJ/Vh+LN50fAz4+pxOV1gRyf1qJvU3aUXOW\nNrydkYYMQalgEq78HNPP9q1Syq9ZR8Of8P2zZepmBd6mN7M4+vsXAAAf3/ivMD6rcH1v/jH7YMS7\nKed9H4CfBPBNpdTT/Lu/C5p4/pVS6q+DDEj/ylttSFnASxTGWYgxC6BNsgAZ17pdUnOcNKTkdLW/\njt+PHwEAtHtfxTkunbgkalRZHM51z7nW8aaqnxLXNaWVFRuRU42BWMBssVjivWlXMtvNaHSi2woA\nfK+U0s24jMTOw5Vu9v0jbLCn337Rka4uT5di5+KSqI5JMGKLmbTycJBRouhe5BvBSF60ACVP7hxc\nODkHX5VziLRCBSfvwIKRlSft+6VV0nE3KkIk3JXnrEsaQe2tsNUYikjnFv98cus2vrZH3Sr/enI/\nfoInvjcuUgBtTrIsgJRFVZ2nHwD0uWX4C9PL+MLxFQB1m+tDazvCC7s+XcNNtps52+7j0TZJEzwU\n0c8z3jEi8R1McJ6tbzabzJebxXhuSse9YYbo833iEvA/mV7B88fEfcgrjQFLJOwUXfl3myUvTvvH\n2DAj3pePl5JzAIBfvvs4AODW3ioMJ/Pn14+l7Pqhxl3pNHX3wcONO/h6izonX5uu4alo/U1G8juL\n7xay+NuM93AOs9CZhTY0lwEgb7uMn08WeVSVhR7Tfb7xdICfvUxSH/73lPivV78MAAh54XGjUPjb\nV/8qAOBi+xAPNIlr+ZS9AAC4G67JC62MPJTOKmU6t9DTTk1zjgsUeVIyCoa8uOz7mIxpvoyjTDKI\nMuZFWg44VxdrIC/10lfgNYy8vN25A8TVci9tV26c79yzRoldjDexUl48fJS2v9GYSMftpApFombK\nySXGPnz+jpnmAFjUtJhLWgt3Dco6qfS0cMhcJ2Ax0iJ6ma6q+kDrVseT7iks41I2LFSDhTOZmxv2\nLfyx+7BC+zZ38u7RwVrfwPK1UWUJMI3FKiW7Tdu1bIEbY1XMleZSJ7SqEA5YpFrXi1YVO95bbSUU\nDHMUDW7Ps2QdBJDcAcBCr8pJKwDBgH7fuVF75PUvcSk3BLwJfTZZA4omH4/P5cJMIes4kVkgb9sT\nNkNvJxZ5/nrHSZS19o/xRoY8FD/4Tre7jGUs472NRddZ+fOK5Ry2jGUsfiz6/LUYiuXcPeCbUkQF\nJ2mAe5XT1KGPJZmPLKXMdpTF+KUxrfCvn13DR7tU3pknSjuUp2emaDOBM+FUOLNGslutrIhtRl4D\na7y0eaBJVIhT0VDE1LbDWsfJaThNy1B0opLKx1FGqNGdGek2XfU3xUpmVga4w+Thc82+kI4dAtHT\nUySGNY68GK9N2DKErUO2mzEeYkHJnl8LTR7NYil/ui65EkqKiCUsKl6FTfnn9XwVz/SpZDSYNhB3\n2WE8jTFICGXrRrUFjtNmOc5iKVWNuFPx/sYBPj98AADwucNH8fHoOo2hT9+ZL+uVtkJq65VxxWO7\nX3KJrcrxlRmRqn/19kdwPKaS42aHxn01mOJ2QujTS0cbOD6k7zX8HHGXVuE1wfukXYortzkdrMNZ\njGtTQnly+6ighI6g/tS98xjtExrYWJnhpQaVLPuNBjbYTNMJe257AyFA7hcdXE/omjk0b2N1iCs9\nEuD8UHNXLIg2vKGgcA41871j6Qj9TMvij89cwbuJRSZmfiCCUSfraUEwVGXZ/BXwnNv9LIee0HVt\n3LLY/gO6j3/24PvxC5eptOeoB8OjJsDkZVwABlyuvjVkkdZMSxmnaPmCLvlGwXMWMG4GqGpNKGuU\nIFiOvNzYs5ht0+tg1g7gHk9XwrOauvLcuTqkqQxrOxcnTmmSujsvb2pwtVuEKk1WYbrFem1pJbpX\nJqmk1KkY1poVPvaY1rDiTXA9oWfV2WSFuwaNI+5QtBAdKFVWUA79c4R6pU4gc4aviXs/RwrIWvR3\nnc/9QRjaqNEpXQ+tN1HIx3TyDhXzx4DPBOzoMIe/Twi3ZbSw8jQ0i6OqJIdiwWFTWVhDsJNDyNKe\nkdKcV9SokiulpV0jJs5xWUlZNRzUSFo44HEpLPwxi5POFCabJ6EhVQA+o09h38oQ3Ps0/WydHYmI\nKACMXVdi36BsMBrGWlpVBOQpV5NO54Ct//Z2Y5Hnr8VIopaxjGW8f/FdRhZfxjKW8QGKBZ+/FiKJ\nUpZWL5MsgM9K0lkBMZ5db1F6v1N4CEJadcwyg+IOIRRfOr6C57a3T3z2fOsY5xuEEFwKd6WV2Gk8\n7RVtUbVeCaY4TGi5dH2wKsjBlTahBpHOxWYkUoWgG+5nBYUha+3n1mA9JISiNjD20Oe/Xx2u494x\nraw8XQmiEzPzL7IFMl6V7JuaWH7ISu2b8QirzBGITYp7rPN0Z9hBUp68nIn1kVtaEqa2QsYrsn1e\n1nx9egE3B7Si802JtWgixx0aWiXNWAvreBSj2KeV8NPqDL7YfkDGFgC2/AEeO3dbxvBX2rSq/i9W\nSJF5RUfQjNwVKPG1lDSnssJDm02S7zBp9Ovj+/C71x6kz96LYbnGPmLDz+vjNbG7STIfNqf7ZJr7\nwplw17lfNYTfNKwiHBWEKrmxWolmgjK+MNzGJKexub1Px1LtRkDEZPEok8aCjpfgfEj8Kqd4nluD\n/ZKu2X7RxpUGjc333P8aAOBisIdtXqKXFsh5PBJrMHVIpnU6MyXWNK1eY13ABO/GgHixOQUfhFCl\nhTfKYZUvKI3OLfwR8x9HdN1VUnMLUZRYeYa4cO3rEWbbNAc5vlE7s+hforngxmwb12O+B9h1obFj\nEIx4DvLVCYVqVTlUwM0JtUaSzirAkbn5toj3S4yPGDVY8QWFcabERUOhfdsJBNX7yWNzgq9D25+z\nHGnMfZ7BhLSjRTU7GGmEfUbpikrQnaBP+39tb0206DaiMa4OCYka32Z0atfCZzX4KjBz2khz97vj\nRFVVTeZGjVoFLHtgUg+a+bf2psbwMs9XjF7ZYF6uu9ZOat61AvM1mKgfDkrRTfL3p7Cs0VVGc3O0\nI5B3YzJMBuCNM5gp86eUG0MtOk4mswj7J3l2utDCK2vspLVemDMy1gp6RmNUtsI5FFEJF8qhiLoE\ngj594OiJEn//M78AAPhP2q+3cvlHRxfx63dJnubGq5v1dXa6WlYh3aRzWT81QCdKcBS+gbTFW8Si\nz18LkUTpHIh3LXbvrCBeZWKvqQTWdslI4BXiOxe1UiSu8ywoRTjTCSCebxxJslFB49WM9PIG3I22\nm3eEELwdDnEY0gS2M2hjwmKbrizX86dIGdc+VjES57XEd95xHstnGybHwzGRmk8zWdioCiMmIXe8\n+/G54UMACJLe3aAk5nH2ajPKIrF0XLFOhfDukrjNcIxzTI4elhGmxUUAwDQJkfExTplQP6oijBS/\ntFHIC9qVzQ7zui37THcgpOzjPMa9hJKzMYtA5pknAn1lbvDckDzguis07vcFB/jMGolD/svJx/D5\nfUqy3Bj/pfZz6DKZ/G5h8QsH3wMAONjrYH2TSPf/9Pr30TW4sSbE0NaFAS6u0DUNOLFLSh97UzqH\nTpwg5sTa15XA/S5xCnUu989R0RLbFWcFE5hSBPzGeSgJtLMHsmGF9ikqET+2fhcfblOieG6uWcD5\nG+6VbewVHbl2lwIq150WPzyNUNEbp0KFxNL3prZAznC1sycCgMhpmin7rrzzgMXmFHwQQlUWZpIC\nWkGVzgqjgjek+19eaEbDxnOaX05wUdWdUjkTgtOOQsgvtHhXI13hZgy+lI19i2DEyZKBJCBmmou1\njPivFZDyFqyVF3wS+7LNiKYVpFs+rO9KYfS7MlIi8hgOSmjueAsmGlnPdcTRZ72ZhWvutQqiSeVe\n9HlLSZnQpLWOk9dPUHG5Kzqkz852G3hBUwn92fIUyj49P62bhs/LIm/Td/RcZ5uX1E017h8qsScS\nDFOefKZUUc01APgoGqyVxSKiWVfVJctUIdpn7cGdHGyTCX9I19PMctF26n9iRXIv0aEqAavp7+Gw\nguZjqUKD4HDGn6FjiT0trP1gXMEfs04gJ39eqOX7sJCESWeO7K6QbdFcb42S8ZitarhXAL8y4I2B\nITly4W986nfeMHly8bdWr+FBbuD5H4ofw96ra+4Q6PitQrBBc9+llQM0TI5v6refRAGLPX8tRBK1\njGUs4/2LRSdmLmMZy1jGm8Wiz1+LkURZ0g5RqcZ0wES1OMOUicyHlkowcTNFyNYE40kEw6ul7dUh\nfmj7BQDAE/F1AEQsdlIBN/M1ISe+PCFEqrIal5q0fFjxJtLSD9TQoc9LqH4eC6nzOI3xikfb2Ipq\nEnvGS69QF0I8P+8xVK9zaN78af8YdxJCn75++yxeTUjvpGRYNdZWSntNnUrZykkMrAVjIU1n1shx\nJ8MQs1X6t1uDTaoQfbaKAGaCRLlSV8Pk2G7TOZxvHmPTH8p4OMV4p9t1s1xF1aVjOb0xwOkGaVHd\nF5Bi+bbXF82rz2y/gt+4/igA4J9NPw4AeGl7Gx9t3QAAPDM5h8+/REiVv+vjcESlvfge7cvvWjzx\n/S8BAH5q6ws459G+pnz8L2an8JUxEc+fOTqDYVGjddfGhERdH9OqaFb4dQMBrJQn5y1XXjiilW6a\ne7Vti1M77uZ4lBWTP969hgs+3TObZvw6Q+d+GYusxRl/jA2Wwmhrd219aL428/B0hdpo+pC1zyKd\nCclcozzZF/4OYpEnoQ9EVBYqyaF9A8/WqICU7zyHIikp46iiqn/vKWTNuqUdIHTJoVMmBRp7dL81\nDrj5YFzUGktWwWPtJTNOka8S4u60hnReQTFVwkxyIXCbjBGdsi5FTfsa2bYrBfHpeTVCpiqDkP8c\n9sta7sCV9Wxtkuy0g+TcAWRzzkzhMeBN6xKnI1tHx4zC3DZIZwSXaAv4jIYXDObplqrV4NOKLLr+\nTAAAIABJREFUSoIAktUASY/Lnsd0sK1XBqKVla5ForDtMQHcEc0BwB+V6JBrF4p9buPvKZR8rv7Y\nonOToLfgcCboDzL6mW93sf84y/XcX0Cz2Xjl7H8yDTNmKseOQXhUyxVoRv/9I0JxoqyEN2aF9rQU\nuQqHNpqkRtB0UpBkAiCfK9aaSFe4nOgrQQaTNYWixUR+d7lzhfaHCPn/b3rX8FZ63D8aE9L625s3\n8Juv0ZyrmF6hM4UGVwkuxvSecO/UtxuLPH8tRBJlPSBZ1QBKcb6ujmOYGb98PkzJyMe2b4mo5XOD\nU7i2Sy9MrSy6niuZ0E/nhwYAHT3DAejJdeWxtp+I3lJlNfoJJRvJLIDu8QuWoce8qpOV20c9GJ6M\nOtt0A51vHMlFzq2Wcprzy2vPOaifwRjnGnQ+z+gzOEjppekShMhmsi1flVgLJyeO22lUAUC/bEq5\nDba+0Zx/XG6NlLXmw3UC+qoUAb+GzkT7qWcmOOcf8jnQOL7Y3cSYE9xz7T6e4ITIfa6nMzR5u4/F\nt3B4hia+33uZrLw/N3oIf9K6AAAYDGOYHTruolnBH3GZgCeS9LEZfmrrCwCAj0dDROqk0ORl/zY+\nFJDG0qngEXxu52EAdG3W2jReM9YTO9rtQM34RdUq0OrR+HWZh7U/baLPXlFVVVtfWH65tHsjfG+P\nOE0fCu+iw+XRcK7rzyWl7roDpHnlxt7wRKShhRdGkxOL9VmL6zndy09PSBtKK4tHG1Q6/FCwIx2M\n7ySc4u8y3s84aeMh4Wxf2PNzcqYh+XDQL+Afc/erp2tfNrfuUaj5VUXdSedKcVaR7QpAPCeTulqR\nQhnzSzNwiQJgOMFQZQnvmJ7rkBOJymh4E+bwDEPkXMKqwlp/yHV9FRHAuRn8YYGAn99kpb5HnYYR\n2jVXywlp6lLN+cLV5acq9KTk2NjLeFxCRIfcBb2tUIhu1ZwOFCeEOq+w/zgN3vByBbVKY2uZWxu9\nsoazf0DzQ7LmIeeuQn/Gi7dpbRWjcwuTMF1kRMfXullIOdCM0xM6T2rK9j1cnh1cbmBykb4Xr08R\n8SLYPcd5aZBxeXZiYsREnyT7lG/x/FNZAf/4DZIPx9UydRlTWVuXjqXTsbYfshpIOnwdW1YSXym5\nWsCwoOi9coqzXuv1+32DuBAdwnqsebjHC9UMGA7perw42kJgSqHFvJ1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nTUa/a+5UaN3i\nzuesrPXIwrpUJ/Y/UyXei6qs76W3G4s+f71zBb9lLGMZy1jGMpaxjO/iWAwkCqRi6+lKTG4BoCdm\nhW+MRJW8AipLjeiIyXA3CI991duAz47RQVCgGVJ6PGVisbU5IiZCn24M8TQTy1Wisd0jNONTPUJG\nLvgHouOUzDHjYu58a3qpWL00dYqMEQSn51RaJb8z0NLR9sNbz+Fah0pzI/YxmFd0jVRtGuzMjnfS\njnS8tbozbDfpWDeCMca8PHTkaWfWDBCi0uXy5Vk2MN40I0T30xi8ONnGU1Mii3fU83iACeE+l1Ar\nVJjwsRwlTXjcbeK0sM56QKzqkmDF5csNxnYfjHfwanODx3uAJxqELjWVFtQr5tXaT65+Cf/88JMA\ngN87elgI3J9tkrXPlqmQ87X/7elFfKH/gIxdz6dzXAu4C8/vixH0tArR9EhB/ZmDMwAAT1dY4yV4\nPoc3qy4ddytIkfF5l1CIeOmWwOC4YH0rLtH1GglKJovOSh+32d5nxChjYEpM+dpVuUbc4e4Zq3DE\n23JIF0AEfYDQSbwztwSJReYUfFBClRXmq8xFpGD9k7ZNW9FIrJwqP4EJXMcdoJ3qNYf1jFh3IAwE\ngRJT4NCg6NXoUsQ6PaoC/CGjWYz+lA1vTisI0qFVdB1SpaF2GcnSmNNL4oMJKsy2Gd0daSkPDc8r\nROw00Dgq5VwcepO1NXLWYwqG8yfnyNpB3emmNaRdjKMKTE1in1NoL5RDp4CSu9VOfUHh/z1H5af4\n/gwPRuRq0GaU91a2Js0+ZmyguWSYrrGeUrOA8ufKnyWXwNh4eVYotDr0/I7OauQdRwa3wArNF0Ic\n930MLtP1WP26Rp+R9WSLS7XaopwwsrfvoXWTTnF0ob4mbih0ajBltEwXgOV5NnDXuLDSGGWNQtF0\n6BDfG6GeI89bGW5bKXFzcN2/Jq/gJY7SoKQj01kChcdFjTymOcA6Y15ikbVcSZG+E+9YmLTWnyrf\njdTdAs9fC5NELWMZy3i/YrG7W5axjGUs481jseevxUiiFFAGFr4pZZVVFPrb1horVLjFyEueeXDc\nYz2sT8nVgZWyOGJZAncxPnXxVfEme6xxE7fvI9TgG6ML6IW0crkSEIcoUiX2mf9yL++hX9DONhjt\n2PKHYgrc0UlNROb0/6hsIWeT3FhnQsD+cHQL20yOfykh7ahxGQrq5KsSOSNTY0tp/J1pDzNGM06v\nDLDFekeRzjFkNMv5xpWlxrSoeVUOaXH+gqtmik83X5S/udbV37aPIuHfn2X0qgLwdHIBAHA4iRF4\njPIxEcJAndB0cuvJiKUSOnomyN1mMMKqTvgctcgpOPLgZc/ik20iWv9M//vxLw6/FwDwWzHxt4pK\nizL5cNSQVdhnr7yI7ZDGc501Yq6EOzhtaqPopENjuzvryD5jRst20i58RtjWVmo9qWAOHaqvbSAk\n87WQxvN881jur9vTHnYmHTlegAySnWdfL57hcodIrpfjPUGi/uj4AT6+tsgeXGntvevC+yKv5D4o\nYY2GygshPasKQOUaNOgenZU+mq75IGuIREk0qGB9RpJYnVrlBYoNvofaNcHaH7M0yyMBUpq2oCzg\nD3lbR7WieNSnfwTDHNYJbltdSyC0WeU80kgYwY53lbTw5OyzpoxFxXyhoqnBdD/kXQvF91bAcin+\npETRqInWbl9zPT/fMnCMoihFxOi5qILas0+XVraVBzUC536XtRSC36AB+Rc/8HE8fp4chNus8TfI\nInzzNiHQnWsQIrSA/3NSA9BW1LwdOboMFUrWSypDCBJl2wXiJkvnFI7HqJB3GNnzFFaeZVTrOvt0\nBqT9BQDd1wocX2GeW1iJ3IBJuKmnACZnat2vko2Hm/d43PsZipj5v416olBMXA8GhRg2W8/KPWlz\nDcN6eA4lNBk1GQBAdFir1Hsz5tglpUhOJKfbojNmFRAOGJljg2l/YkUm4vBRhepMAhu9M2LUIs9f\ni5FEVURCGyYhFHcGlJUGN3hIaYho2a7jIsPnx48DAPJ+CAQOzmQBzO0pHtqiJGh32sbd60S2vnCJ\niND/3srz+GS0K4cw2v4zAMCdURe3hpRMODuPnp7iWkplwudHp6STzelYxTqFk4csoaQTzyUY0zLE\nTkaT4Zo/kSRqw0ykw8sJNx4XsRDKu2Ym1jR7DAffG7WFkOipSgQ05z/jynhVqaW8dJA2RbvJRaRK\ntLmj7zOt5/E7I0pSnhmexYsTEn/cDCkBGRURvnFIUPh4p4XmFpXAXs7ocx8JriKcV9jjKLkuMKwa\nUvaKdSYvldxWYsvgokIlxsYPdndxML0fAHD1JUo0zVRDu26WUxkunafr3DSpJK4bnESd84ZYdVol\ngBgXn41pm9fGa9IoAEASlxVOjE5FA7mepdVS30isL0nfVkgz0NngSJKso6yJIuRuKyHkB4h4vD+2\ncgMPcLkht57cB3cUlwDTUEjorV4K67/z7rxFV/z9YISiOoauu8VMbqG4JOS04QqrcWtC1/jG4Sp8\nFjaM781et8V8u4dsleaC8baHlO06nFCmeaKPJzbpHtqdtfHaXZrj7LORiBzmTb6HNBAeseVVXkGz\nyKcJuFMrqjDNuFtt4qN9g57PlKZAVMpKh2i6YkVLSJV1cuR++hMga7Ep8ZqqxTa5cdbMytqsWUOM\ndIGq/j3/LBrzpai5RZrrCPQhSRYAsc7Z+tUAzz1MCxInZKlyhc5V7hr+8gB5j+ZxjwWLy5aCzVzH\n25xi6Nyj40ygrT+nrTQ37bkFHaySOaoyCu0dTlb5WL1JiaDPti2eRsCLf39Yz21Oq65sAHnTddrV\nBxQM2Vx6apDHfO3iWjRVpvu5cYM/d7CFgs/78Mec2F+qxzsYUiIEAK27TJz3NKo2DfJ0wxNNKGuA\nYOA6MlkDbFxhdIYXyIEV8dG3G4s+fy1GErWMZSzj/Qtbv5uWsYxlLOPfqljw+WsxkihFujyBV2IQ\n15n8LpsX9pw8gFJImbn5dNrDb90lLaFox0PFMKxigt8nz72Gx9sE5/7C7AkEK5SW/8ipZ+lnfAst\nXTPdPhsTs++5My/iF1/8KADgF/c+BgB4qL2Du0wSvjvpImWEoN9k00hvgj1GkkqjT5CDATL8dW3w\nkyLER3hfkSrRVHS87juVVVJ2C3UO39LvnZXLYFgb5jb9VKQPjvMYt0Z0jLMJrax6vQmaDGUfJk2s\nsOp6fkJvi35umyk+GpP0wkvjLbxwRMjbsxWhP4NxhHxE21WNEklCy5U/7l8GADwe3cDDLA+goZEz\nE3pQ0c/ryTqOUxqv3BpMGUOfqByoaqkJANivLK5ltN+DtAXDq7veGUJ8JrMAl7eoFPZAZw8r3H97\nc7aKHovSOPJ+T9clRQDYYpKpK/s9e3wKOwldu/Vggh6P0RYjcGeDIzS5Z9eoSkp4kyoUCQOnBxbr\nFDmjbVvhEOcbRyfO6+n+WQSMRK14E3S4pBmpHGe8Y94fIXAvt07Jvh6M7gFLnajFDgXAMyQzwCWp\n8LhAcEir9v0m3WNp7mHGOlD2Zoy153n1fve4Jlv3aK7IVgJMNlkuYbMuW21+iuyw/sv7viC7fy3d\nEBT1m8k5xNfp+UxXGNWYGTQY7VJ5heiQ5xM2/fYbORRrzmUdT/SjfK6E54CQ4IvYihGwmSrRBXJR\n+aouH5m6nCcGxmkhOlA28AQ1UxWAwkmG12RyGeIK0E6B3ThNrHpcqqC2bcmbGqe+WCM9ALXkB3ts\nLt8K4LGURLzDhu+RRhk5ZMWKrIjTi4oOLfwJl0f7BgUbBRdRhTQ5Wau0MyNIUrxfa4d5+2zfFRgk\nG2wRY5SQ8svQIG87xW/aVrZSyTmqtDaqTpmu0tiHqM2rOcTGlVznza3pF4wi5hr8akXWq3WkytCp\n1CtBzpwlUedGhrxNG8466gRZPFln5Iz1xHSha9mLHCj6AVC+s3lokeevxUiiQDXVTpBiZ51usigo\ncLMgYcSuJsi6AvBKTi/3f7X3PbjFJTrdq18wzpMNgPBM7h128eBpKt39Jfbha+noBIenq+ku+9Hu\n0/j/Vigx+MpzpGv0/MY2Yu7ui/1cSmRXR7T/hsmw7iT4rRKLFley0qpCP6MH5m7exZ0OYeQXvEPp\nvnOhlZWXZ6AKlFwmPGRhzzI1WGVtqJVgJt18Nycr2NmnMqRN66TB0zQ23WAmZbPcdZtZhZyx6In1\npIPwcnNfyngudpIOsrI+1gF3nF0fkqjIr0ZPIunS2J4xYxG4+9MZjeWLoy2xuLiRrOFpnwQoL/gH\nMl6uA/JWvobnpsRdCE2B0y06X5eARCbHJRbN2/IHkkhen61JAtrmBCWc41zltgTPkdj0aZutIEU/\npWvT9lLcz/YGZwJKaja9oWg30TjReY3LSMquIWtijaqG3HMAcCZ0XYH0uUCXMoaRyue4aQliPof7\nvKGMi9OnauuZeGO9k7BYbE7BByasrXWOAHiTAvGO66ije3987CMYsP/nDYuVp0kTzvqecKHEe8/U\nL6kyAtJt+vvfvfhbAICH+R4FgEeCu2IRNckD3N7j7tNJXfZy4owqr9C5Qffs+D46vmKj9l+rAit8\noeiQfjeeedCsa2ajEkXMJaVjJbpArplaZ1b+Pb+enE+20lOdepzGTlioTkBdQqkzKx52JrfyUnce\nepVf+7M5gU/6AN4wXIdj3gmke23lZU62VIiEtZ+qwEpZjWX10L2WUSkSQOtOCcVZyiz3kHddF5pL\nWhWad5hiohWyLpdgExqQZCOUJGy+HBkfVEhY4DR5gDfpWdnu/LnPj4E3rWT3rtTprGKqORFSlWkZ\nGpMq0X+SV6Hl/YHKkC6Rc/dD3jInxVUd5uEBRWPueAGUFjXtQoNKie9gGlr0+etd60QMh6E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WVI6wH8yjipw+S6KSd1yVEVQHWBroMT+V3TKbqarpGPTDxM/7D7AG5oSqL8I5qLmkqJwGUZaint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3Lt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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from caiman.summary_images import correlation_pnr_filtered\n", - "cn, pnr, _ = correlation_pnr_filtered(np.transpose(Y, [2,0,1]))\n", - "pl.figure(figsize=(10, 5))\n", - "pl.subplot(1,2,1)\n", - "pl.imshow(cn, vmin=0, vmax=1)\n", - "pl.colorbar()\n", - "pl.title('corr. image')\n", - "pl.subplot(1,2,2)\n", - "pl.imshow(pnr, vmin=0, vmax=50)\n", - "pl.colorbar()\n", - "pl.title('PNR image')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "using 4 processes\n", - "using 1000 pixels per process\n", - "using 1000 block_size\n" - ] - } - ], - "source": [ - "#%% parameters of experiment\n", - "K=[] # number of neurons expected per patch\n", - "gSig=[3, 3] # expected half size of neurons\n", - "gSiz = [10, 10] # size of the gaussian kernel \n", - "p=2 #order of the autoregressive system\n", - "\n", - "options = cnmf.utilities.CNMFSetParms(Y\n", - " ,n_processes,p=p,gSig=gSig,K=K,ssub=2,tsub=2, normalize_init=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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IwAmFqiJwx48fl3gFq1hE0k1Cu1FymO0aDKIcDOLN6ziW37Cm4DjOGIAx9++U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\n5QouX74s78Z86ZYwBcdxDgI46P49DMB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0GFIyamlCiUgOHN61Cw2qrHmuvx/pdNpzRgQq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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# from caiman.source_extraction.cnmf.initialization import greedyROI_corr\n", - "# Ain, Cin, C_raw, S, center, b, f = greedyROI_corr(Y, g_size=10, g_sig=3, min_corr=0.85, min_pnr=20)\n", - "\n", - "pl.imshow(cn, cmap='gray')\n", - "pl.plot(center[0], center[1], '.r')" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "m = 1\n", - "pl.subplot(2,2,1)\n", - "pl.imshow(Ain[:, m].reshape(128, 128))\n", - "pl.subplot(2,1,2)\n", - "pl.plot(C_raw[m])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " CNMFSetParms define Dictionaries of CNMF parameters.\n", - " Any parameter that is not set get a default value specified.\n", - " \n", - " each dictionnary is used by different part of the CNMF process : \n", - " - init_paramters\n", - " - pre_processing_parameters\n", - " - patch_parameters\n", - " - spatial_parameters\n", - " - temporal_parameters\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

    Preprocessing of the datas and initialization of the components

    \n", - "
    • here, we compute the mean of the noise spectral density
      • \n", - "
    • then, we initialize each component with a greedy ROI algorithm on component that have been spatially filter using a gaussian kernel
      • \n", - "
    • we then further update the component using Hals method on the newly obtained nmf paramters
    \n", - "

    see More : NMF AND ROI :http://www.cell.com/neuron/fulltext/S0896-6273(15)01084-3

    \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "Yr,sn,g,psx = cnmf.pre_processing.preprocess_data(Yr\n", - " ,dview=dview\n", - " ,n_pixels_per_process=100, noise_range = [0.25,0.5]\n", - " ,noise_method = 'logmexp', compute_g=False, p = 2,\n", - " lags = 5, include_noise = False, pixels = None\n", - " ,max_num_samples_fft=3000, check_nan = True)\n", - "\n", - "Ain, Cin, b_in, f_in, center=cnmf.initialization.initialize_components(Y\n", - " ,K=30, gSig=[5, 5], gSiz=None, ssub=1, tsub=1, nIter=5, maxIter=5, nb=1\n", - " , use_hals=False, normalize_init=True, img=None, method='greedy_roi'\n", - " , max_iter_snmf=500, alpha_snmf=10e2, sigma_smooth_snmf=(.5, .5, .5)\n", - " , perc_baseline_snmf=20)\n", - "p1=nb_plot_contour(Cn,Ain,dims[0],dims[1],thr=0.9,face_color=None\n", - " , line_color='black',alpha=0.4,line_width=2)\n", - "bpl.show(p1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

    HALS

    \n", - "we want to minimize\n", - "\n", - "updating parameters\n", - "\n", - "

    HALS : http://proceedings.mlr.press/v39/kimura14.pdf

    \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "Ain, Cin, b_in, f_in = cnmf.initialization.hals(Y, Ain, Cin, b_in, f_in, maxIter=5)\n", - "p1=nb_plot_contour(Cn,Ain,dims[0],dims[1],thr=0.9,face_color=None\n", - " , line_color='black',alpha=0.4,line_width=2)\n", - "bpl.show(p1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "#%% UPDATE SPATIAL COMPONENTS\n", - "pl.close()\n", - "t1 = time()\n", - "A,b,Cin,f_in = cnmf.spatial.update_spatial_components(Yr, Cin, f_in, Ain, sn=sn, dview=dview,**options['spatial_params'])\n", - "t_elSPATIAL = time() - t1\n", - "print t_elSPATIAL \n", - "#clear_output(wait=True)\n", - "print('DONE!')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "pl.figure(num=None, figsize=(9, 7), dpi=100, facecolor='w', edgecolor='k')\n", - "crd = plot_contours(A,Cn,thr=0.9)\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "p1=nb_plot_contour(Cn,A.todense(),dims[0],dims[1],thr=0.9,face_color=None, line_color='black',alpha=0.4,line_width=2)\n", - "bpl.show(p1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "pl.close()\n", - "t1 = time()\n", - "options['temporal_params']['p'] = 0 # set this to zero for fast updating without deconvolution\n", - "C,A,b,f,S,bl,c1,neurons_sn,g,YrA = cnmf.temporal.update_temporal_components(Yr,A,b,Cin,f_in,bl=None,c1=None,sn=None,g=None,**options['temporal_params'])\n", - "t_elTEMPORAL = time() - t1\n", - "print t_elTEMPORAL \n", - "clear_output(wait=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "#%% merge components corresponding to the same neuron\n", - "t1 = time()\n", - "A_m,C_m,nr_m,merged_ROIs,S_m,bl_m,c1_m,sn_m,g_m=cnmf.merging.merge_components(Yr,A,b,C,f,S,sn,options['temporal_params'], options['spatial_params'],dview=dview, bl=bl, c1=c1, sn=neurons_sn, g=g, thr=merge_thresh, mx=50, fast_merge = True)\n", - "t_elMERGE = time() - t1\n", - "print t_elMERGE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "#refine spatial and temporal components\n", - "pl.close()\n", - "t1 = time()\n", - "A2,b2,C2,f = cnmf.spatial.update_spatial_components(Yr, C_m, f, A_m, sn=sn,dview=dview, **options['spatial_params'])\n", - "options['temporal_params']['p'] = p # set it back to original value to perform full deconvolution\n", - "C2,A2,b2,f2,S2,bl2,c12,neurons_sn2,g21,YrA = cnmf.temporal.update_temporal_components(Yr,A2,b2,C2,f,dview=dview, bl=None,c1=None,sn=None,g=None,**options['temporal_params'])\n", - "clear_output(wait=True)\n", - "print time() - t1 # 100 seconds\n", - "print('DONE!')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "tB = np.minimum(-2,np.floor(-5./30*final_frate))\n", - "tA = np.maximum(5,np.ceil(25./30*final_frate))\n", - "Npeaks=10\n", - "traces=C2+YrA\n", - "# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5])\n", - "# traces_b=np.diff(traces,axis=1)\n", - "fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = \\\n", - " evaluate_components(Y, traces, A2, C2, b2, f2, final_frate, remove_baseline=True,\n", - " N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, thresh_C=0.3)\n", - " \n", - "idx_components_r=np.where(r_values>=.6)[0]\n", - "idx_components_raw=np.where(fitness_raw<-60)[0] \n", - "idx_components_delta=np.where(fitness_delta<-20)[0] \n", - "\n", - "\n", - "min_radius=gSig[0]-2\n", - "masks_ws,idx_blobs,idx_non_blobs=extract_binary_masks_blob(\n", - "A2.tocsc(), min_radius, dims, num_std_threshold=1, \n", - "minCircularity= 0.6, minInertiaRatio = 0.2,minConvexity =.8)\n", - "\n", - "\n", - "\n", - "\n", - "idx_components=np.union1d(idx_components_r,idx_components_raw)\n", - "idx_components=np.union1d(idx_components,idx_components_delta) \n", - "idx_blobs=np.intersect1d(idx_components,idx_blobs) \n", - "idx_components_bad=np.setdiff1d(range(len(traces)),idx_components)\n", - "clear_output(wait=True)\n", - "print(' ***** ')\n", - "print len(traces)\n", - "print(len(idx_components))\n", - "print(len(idx_blobs))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "fg=pl.figure(figsize=(12,20))\n", - "pl.subplot(1,2,1)\n", - "crd = plot_contours(A2.tocsc()[:,idx_components],Cn,thr=0.9)\n", - "pl.subplot(1,2,2)\n", - "crd = plot_contours(A2.tocsc()[:,idx_components_bad],Cn,thr=0.9)\n", - "print(dims)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "p2=nb_plot_contour(Cn,A2.tocsc()[:,idx_components].todense(),dims[0],dims[1],thr=0.9,face_color='purple', line_color='black',alpha=0.3,line_width=2)\n", - "bpl.show(p2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "discard_traces_fluo=nb_view_patches(Yr,A2.tocsc()[:,idx_components],C2[idx_components],b2,f2,dims[0],dims[1],thr = 0.8,image_neurons=Cn, denoised_color='red')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "discard_traces_fluo=nb_view_patches(Yr,A2.tocsc()[:,idx_components_bad],C2[idx_components_bad],b2,f2,dims[0],dims[1],thr = 0.8,image_neurons=Cn, denoised_color='red')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "scrolled": true - }, - "outputs": [], - "source": [ - "#%% STOP CLUSTER\n", - "cm.stop_server()" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.4" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/demos/obsolete/demo_caiman_patches.py b/demos/obsolete/demo_caiman_patches.py deleted file mode 100755 index 814f599ad..000000000 --- a/demos/obsolete/demo_caiman_patches.py +++ /dev/null @@ -1,264 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Created on Wed Feb 24 18:39:45 2016 - -@author: Andrea Giovannucci - -For explanation consult at https://github.com/agiovann/Constrained_NMF/releases/download/v0.4-alpha/Patch_demo.zip -and https://github.com/agiovann/Constrained_NMF - -""" - -from __future__ import division -from __future__ import print_function -#%% -from builtins import str -from builtins import range -from past.utils import old_div - -import caiman.source_extraction.cnmf.params - -try: - if __IPYTHON__: - # this is used for debugging purposes only. allows to reload classes when changed - get_ipython().magic('load_ext autoreload') - get_ipython().magic('autoreload 2') -except NameError: - print('Not launched under iPython') - -import sys -import numpy as np -from time import time -from scipy.sparse import coo_matrix -import psutil -import glob -import os -import scipy -from ipyparallel import Client -import matplotlib as mpl -# mpl.use('Qt5Agg') - - -import pylab as pl -pl.ion() -#%% -import caiman as cm -from caiman.components_evaluation import evaluate_components -from caiman.utils.visualization import plot_contours, view_patches_bar -from caiman.base.rois import extract_binary_masks_blob -import caiman.source_extraction.cnmf as cnmf -#%% -# frame rate in Hz -final_frate = 10 - -#%% -# backend='SLURM' -backend = 'local' -if backend == 'SLURM': - n_processes = np.int(os.environ.get('SLURM_NPROCS')) -else: - # roughly number of cores on your machine minus 1 - n_processes = np.maximum(np.int(psutil.cpu_count()), 1) -print(('using ' + str(n_processes) + ' processes')) -#%% start cluster for efficient computation -single_thread = False - -if single_thread: - dview = None -else: - try: - c.close() - except: - print('C was not existing, creating one') - print("Stopping cluster to avoid unnencessary use of memory....") - sys.stdout.flush() - if backend == 'SLURM': - try: - cm.stop_server(is_slurm=True) - except: - print('Nothing to stop') - slurm_script = '/mnt/xfs1/home/agiovann/SOFTWARE/Constrained_NMF/SLURM/slurmStart.sh' - cm.start_server(slurm_script=slurm_script) - pdir, profile = os.environ['IPPPDIR'], os.environ['IPPPROFILE'] - c = Client(ipython_dir=pdir, profile=profile) - else: - cm.stop_server() - cm.start_server() - c = Client() - - print(('Using ' + str(len(c)) + ' processes')) - dview = c[:len(c)] -#%% FOR LOADING ALL TIFF FILES IN A FILE AND SAVING THEM ON A SINGLE MEMORY MAPPABLE FILE -fnames = [] -base_folder = './example_movies/' # folder containing the demo files -for file in glob.glob(os.path.join(base_folder, '*.tif')): - if file.endswith("ie.tif"): - fnames.append(os.path.abspath(file)) -fnames.sort() -if len(fnames) == 0: - raise Exception("Could not find any tiff file") -print(fnames) -fnames = fnames -#%% -# idx_x=slice(12,500,None) -# idx_y=slice(12,500,None) -# idx_xy=(idx_x,idx_y) -downsample_factor = 1 # use .2 or .1 if file is large and you want a quick answer -idx_xy = None -base_name = 'Yr' -name_new = cm.save_memmap_each(fnames, dview=dview, base_name=base_name, resize_fact=( - 1, 1, downsample_factor), remove_init=0, idx_xy=idx_xy) -name_new.sort() -print(name_new) -#%% -name_new = cm.save_memmap_each(fnames, dview=dview, base_name='Yr', resize_fact=( - 1, 1, 1), remove_init=0, idx_xy=None) -name_new.sort() -#%% -fname_new = cm.save_memmap_join( - name_new, base_name='Yr', n_chunks=12, dview=dview) -#%% -Yr, dims, T = cm.load_memmap(fname_new) -d1, d2 = dims -Y = np.reshape(Yr, dims + (T,), order='F') -#%% visualize correlation image -Cn = cm.local_correlations(Y) -pl.imshow(Cn, cmap='gray') -#%% -rf = 10 # half-size of the patches in pixels. rf=25, patches are 50x50 -stride = 2 # amounpl.it of overlap between the patches in pixels -K = 3 # number of neurons expected per patch -gSig = [7, 7] # expected half size of neurons -merge_thresh = 0.8 # merging threshold, max correlation allowed -p = 2 # order of the autoregressive system -memory_fact = 1 # unitless number accounting how much memory should be used. You will need to try different values to see which one would work the default is OK for a 16 GB system -save_results = True -#%% RUN ALGORITHM ON PATCHES -options_patch = caiman.source_extraction.cnmf.params.CNMFParams(dims, K=K, gSig=gSig, ssub=1, tsub=4, p=0, thr=merge_thresh) -A_tot, C_tot, YrA_tot, b, f, sn_tot, optional_outputs = cnmf.map_reduce.run_CNMF_patches(fname_new, (d1, d2, T), options_patch, rf=rf, stride=stride, - dview=dview, memory_fact=memory_fact, gnb=1) -print(('Number of components:' + str(A_tot.shape[-1]))) -#%% -if save_results: - np.savez('results_analysis_patch.npz', A_tot=A_tot.todense(), - C_tot=C_tot, sn_tot=sn_tot, d1=d1, d2=d2, b=b, f=f) -#%% if you have many components this might take long! -pl.figure() -crd = plot_contours(A_tot, Cn, thr=0.9) -# %% set parameters for full field of view analysis -options = caiman.source_extraction.cnmf.params.CNMFParams(dims, K=A_tot.shape[-1], gSig=gSig, p=0, thr=merge_thresh) -pix_proc = np.minimum(np.int((d1 * d2) / n_processes / (old_div(T, 2000.))), - np.int(old_div((d1 * d2), n_processes))) # regulates the amount of memory used -options['spatial_params']['n_pixels_per_process'] = pix_proc -options['temporal_params']['n_pixels_per_process'] = pix_proc -#%% merge spatially overlaping and temporally correlated components -A_m, C_m, nr_m, merged_ROIs, S_m, bl_m, c1_m, sn_m, g_m = cnmf.merging.merge_components(Yr, A_tot, [], np.array(C_tot), [], np.array( - C_tot), [], options['temporal_params'], options['spatial_params'], dview=dview, thr=options['merging']['thr'], mx=np.Inf) -#%% update temporal to get Y_r -options['temporal_params']['p'] = 0 -# change ifdenoised traces time constant is wrong -options['temporal_params']['fudge_factor'] = 0.96 -options['temporal_params']['backend'] = 'ipyparallel' -C_m, A_m, b, f_m, S_m, bl_m, c1_m, neurons_sn_m, g2_m, YrA_m = cnmf.temporal.update_temporal_components( - Yr, A_m, b, C_m, f, dview=dview, bl=None, c1=None, sn=None, g=None, **options['temporal_params']) - -#%% get rid of evenrually noisy components. -# But check by visual inspection to have a feeling fot the threshold. Try to be loose, you will be able to get rid of more of them later! -tB = np.minimum(-2, np.floor(-5. / 30 * final_frate)) -tA = np.maximum(5, np.ceil(25. / 30 * final_frate)) -Npeaks = 10 -traces = C_m + YrA_m -# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5]) -# traces_b=np.diff(traces,axis=1) -fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples =\ - evaluate_components(Y, traces, A_m, C_m, b, f_m, - remove_baseline=True, N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, tB=tB, tA=tA, thresh_C=0.3) - -idx_components_r = np.where(r_values >= .5)[0] -idx_components_raw = np.where(fitness_raw < -20)[0] -idx_components_delta = np.where(fitness_delta < -10)[0] - - -idx_components = np.union1d(idx_components_r, idx_components_raw) -idx_components = np.union1d(idx_components, idx_components_delta) -idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components) - -print(' ***** ') -print((len(traces))) -print((len(idx_components))) - -#%% -A_m = A_m[:, idx_components] -C_m = C_m[idx_components, :] - -#%% display components DO NOT RUN IF YOU HAVE TOO MANY COMPONENTS -pl.figure() -crd = plot_contours(A_m, Cn, thr=0.9) -#%% -print(('Number of components:' + str(A_m.shape[-1]))) -#%% UPDATE SPATIAL OCMPONENTS -t1 = time() -A2, b2, C2, f = cnmf.spatial.update_spatial_components( - Yr, C_m, f, A_m, sn=sn_tot, dview=dview, dims=dims, **options['spatial_params']) -print((time() - t1)) -#%% UPDATE TEMPORAL COMPONENTS -options['temporal_params']['p'] = p -# change ifdenoised traces time constant is wrong -options['temporal_params']['fudge_factor'] = 0.96 -C2, A2, b2, f2, S2, bl2, c12, neurons_sn2, g21, YrA = cnmf.temporal.update_temporal_components( - Yr, A2, b2, C2, f, dview=dview, bl=None, c1=None, sn=None, g=None, **options['temporal_params']) -#%% stop server and remove log files -log_files = glob.glob('Yr*_LOG_*') -for log_file in log_files: - os.remove(log_file) -#%% order components according to a quality threshold and only select the ones wiht qualitylarger than quality_threshold. -B = np.minimum(-2, np.floor(-5. / 30 * final_frate)) -tA = np.maximum(5, np.ceil(25. / 30 * final_frate)) -Npeaks = 10 -traces = C2 + YrA -# traces_a=traces-scipy.ndimage.percentile_filter(traces,8,size=[1,np.shape(traces)[-1]/5]) -# traces_b=np.diff(traces,axis=1) -fitness_raw, fitness_delta, erfc_raw, erfc_delta, r_values, significant_samples = evaluate_components( - Y, traces, A2, C2, b2, f2, remove_baseline=True, N=5, robust_std=False, Athresh=0.1, Npeaks=Npeaks, tB=tB, tA=tA, thresh_C=0.3) - -idx_components_r = np.where(r_values >= .6)[0] -idx_components_raw = np.where(fitness_raw < -60)[0] -idx_components_delta = np.where(fitness_delta < -20)[0] - - -min_radius = gSig[0] - 2 -masks_ws, idx_blobs, idx_non_blobs = extract_binary_masks_blob( - A2.tocsc(), min_radius, dims, num_std_threshold=1, - minCircularity=0.6, minInertiaRatio=0.2, minConvexity=.8) - - -idx_components = np.union1d(idx_components_r, idx_components_raw) -idx_components = np.union1d(idx_components, idx_components_delta) -idx_blobs = np.intersect1d(idx_components, idx_blobs) -idx_components_bad = np.setdiff1d(list(range(len(traces))), idx_components) - -print(' ***** ') -print((len(traces))) -print((len(idx_components))) -print((len(idx_blobs))) -#%% visualize components -# pl.figure(); -pl.subplot(1, 3, 1) -crd = plot_contours(A2.tocsc()[:, idx_components], Cn, thr=0.9) -pl.subplot(1, 3, 2) -crd = plot_contours(A2.tocsc()[:, idx_blobs], Cn, thr=0.9) -pl.subplot(1, 3, 3) -crd = plot_contours(A2.tocsc()[:, idx_components_bad], Cn, thr=0.9) -#%% -view_patches_bar(Yr, scipy.sparse.coo_matrix(A2.tocsc()[ - :, idx_components]), C2[idx_components, :], b2, f2, dims[0], dims[1], YrA=YrA[idx_components, :], img=Cn) -#%% -view_patches_bar(Yr, scipy.sparse.coo_matrix(A2.tocsc()[ - :, idx_components_bad]), C2[idx_components_bad, :], b2, f2, dims[0], dims[1], YrA=YrA[idx_components_bad, :], img=Cn) -#%% STOP CLUSTER -pl.close() -if not single_thread: - c.close() - cm.stop_server() diff --git a/demos/obsolete/demo_cnmfE_2D.py b/demos/obsolete/demo_cnmfE_2D.py deleted file mode 100644 index fbab5426f..000000000 --- a/demos/obsolete/demo_cnmfE_2D.py +++ /dev/null @@ -1,223 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Created on Thu Sep 7 13:29:49 2017 - -@author: agiovann -""" - -#%% -try: - get_ipython().magic(u'load_ext autoreload') - get_ipython().magic(u'autoreload 2') -except: - print('Not launched under iPython') - -import numpy as np -import matplotlib.pyplot as plt -from matplotlib.widgets import Slider -from scipy.sparse import coo_matrix -import caiman as cm -from caiman.source_extraction import cnmf -import scipy - - -#%% -def show_img(ax, img): - from mpl_toolkits.axes_grid1 import make_axes_locatable - im = ax.imshow(img) - divider = make_axes_locatable(ax) - cax = divider.append_axes("right", size="5%", pad=0.05) - plt.colorbar(im, cax=cax) - - -#%% -fname = './example_movies/data_endoscope.tif' -gSig = 3 # gaussian width of a 2D gaussian kernel, which approximates a neuron -gSiz = 10 # average diameter of a neuron -min_corr = .8 -min_pnr = 10 -fname = '/opt/local/Data/1photon/3168_PAG_TIFF.tif' -#fname = '/opt/local/Data/1photon/Yr_d1_190_d2_198_d3_1_order_F_frames_35992_.mmap' -gSig = 3 # gaussian width of a 2D gaussian kernel, which approximates a neuron -gSiz = 16 # average diameter of a neuron -min_corr = .6 -min_pnr = 10 - -# If True, the background can be roughly removed. This is useful when the background is strong. -center_psf = True - - -Y = cm.load(fname) -T, d1, d2 = Y.shape -print('The dimension of data is ', Y.shape) - -ax = plt.axes() -ax.axis('off') -show_img(ax, Y[100, ]) - - -#%% -# show correlation image of the raw data; show correlation image and PNR image of the filtered data -cn_raw = cm.summary_images.max_correlation_image( - Y, swap_dim=False, bin_size=3000) -#%% TAKES MEMORY!!! -cn_filter, pnr = cm.summary_images.correlation_pnr( - Y, gSig=gSig, center_psf=center_psf, swap_dim=False) -plt.figure(figsize=(10, 5)) -#%% -for i, (data, title) in enumerate(((Y.mean(0), 'Mean image (raw)'), - (Y.max(0), 'Max projection (raw)'), - (cn_raw[1:-1, 1:-1], 'Correlation (raw)'), - (cn_filter, 'Correlation (filtered)'), - (pnr, 'PNR (filtered)'), - (cn_filter * pnr, 'Correlation*PNR (filtered)'))): - plt.subplot(2, 3, 1 + i) - plt.imshow(data, cmap='jet', aspect='equal') - plt.axis('off') - plt.colorbar() - plt.title(title) - - -#%% -# pick thresholds -fig = plt.figure(figsize=(10, 4)) -plt.axes([0.05, 0.2, 0.4, 0.7]) -im_cn = plt.imshow(cn_filter, cmap='jet') -plt.title('correlation image') -plt.colorbar() -plt.axes([0.5, 0.2, 0.4, 0.7]) -im_pnr = plt.imshow(pnr, cmap='jet') -plt.title('PNR') -plt.colorbar() - -s_cn_max = Slider(plt.axes([0.05, 0.01, 0.35, 0.03]), 'vmax', - cn_filter.min(), cn_filter.max(), valinit=cn_filter.max()) -s_cn_min = Slider(plt.axes([0.05, 0.07, 0.35, 0.03]), 'vmin', - cn_filter.min(), cn_filter.max(), valinit=cn_filter.min()) -s_pnr_max = Slider(plt.axes([0.5, 0.01, 0.35, 0.03]), 'vmax', - pnr.min(), pnr.max(), valinit=pnr.max()) -s_pnr_min = Slider(plt.axes([0.5, 0.07, 0.35, 0.03]), 'vmin', - pnr.min(), pnr.max(), valinit=pnr.min()) - - -def update(val): - im_cn.set_clim([s_cn_min.val, s_cn_max.val]) - im_pnr.set_clim([s_pnr_min.val, s_pnr_max.val]) - fig.canvas.draw_idle() - - -s_cn_max.on_changed(update) -s_cn_min.on_changed(update) -s_pnr_max.on_changed(update) -s_pnr_min.on_changed(update) - - -#%% -c, dview, n_processes = cm.cluster.setup_cluster( - backend='local', n_processes=None, single_thread=False) - - -#%% -cnm = cnmf.CNMF(n_processes=n_processes, method_init='corr_pnr', k=35, gSig=(3, 3), gSiz=(10, 10), merge_thresh=.8, - p=1, dview=dview, tsub=1, ssub=1, Ain=None, rf=(25, 25), stride=(25, 25), - only_init_patch=True, gnb=5, nb_patch=3, method_deconvolution='oasis', - low_rank_background=False, update_background_components=False, min_corr=min_corr, - min_pnr=min_pnr, normalize_init=False, deconvolve_options_init=None, - ring_size_factor=1.5, center_psf=True) - -#%% -# cnm = cnmf.CNMF(n_processes=2, method_init='corr_pnr', k=155, gSig=(3, 3), gSiz=(10, 10), merge_thresh=.8, -# p=1, dview=None, tsub=1, ssub=1, Ain=None, rf=(64, 64), stride=(0, 0), -# only_init_patch=True, gnb=10, nb_patch=3, method_deconvolution='oasis', -# low_rank_background=False, update_background_components=False, min_corr=.8, -# min_pnr=10, normalize_init=False, deconvolve_options_init=None, -# ring_size_factor=1.5, center_psf=True) - -#%% -#cnm.options['init_params']['gSiz'] = (10, 10) -#cnm.options['init_params']['gSig'] = (3, 3) -#cnm.options['init_params']['min_corr'] = .85 -#cnm.options['init_params']['min_pnr'] = 20 -# cnm.options['init_params']['normalize_init']=False - - -#%% -memmap = True # must be True for patches -if memmap: - if '.mmap' in fname: - fname_new = fname - else: - fname_new = cm.save_memmap([fname], base_name='Yr') - Yr, dims, T = cm.load_memmap(fname_new) - cnm.fit(Yr.T.reshape((T,) + dims, order='F')) -else: - cnm.fit(Y) -# %% -#A_tot, C_tot, b_tot, f_tot, YrA_tot, sn = cnm.A, cnm.C, cnm.b, cnm.f, cnm.YrA, cnm.sn -# %% -#crd = cm.utils.visualization.plot_contours(A_tot, cn_filter, thr=.95, vmax=0.95) -# %% -#plt.imshow(A_tot.sum(-1).reshape(dims, order='F')) -# -# -# %% DISCARD LOW QUALITY COMPONENT -#final_frate = 10 -# r_values_min = 0.1 # threshold on space consistency -# fitness_min = - 20 # threshold on time variability -# threshold on time variability (if nonsparse activity) -#fitness_delta_min = - 20 -#Npeaks = 10 -#traces = C_tot + YrA_tot -## TODO: todocument -# idx_components, idx_components_bad = cm.components_evaluation.estimate_components_quality( -# traces, Yr, A_tot, C_tot, b_tot, f_tot, final_frate=final_frate, Npeaks=Npeaks, -# r_values_min=r_values_min, fitness_min=fitness_min, fitness_delta_min=fitness_delta_min) -# -#print(('Keeping ' + str(len(idx_components)) + ' and discarding ' + str(len(idx_components_bad)))) -# %% -#plt.subplot(1, 2, 1) -#crd = cm.utils.visualization.plot_contours(A_tot.tocsc()[:, idx_components], cn_filter, thr=.95) -#plt.subplot(1, 2, 2) -# crd = cm.utils.visualization.plot_contours( -# A_tot.tocsc()[:, idx_components_bad], cn_filter, thr=.95) -# %% -# cm.utils.visualization.view_patches_bar( -# Yr, coo_matrix(A_tot.tocsc()[:, idx_components]), C_tot[idx_components, :], -# b_tot, f_tot, dims[0], dims[1], YrA=YrA_tot[idx_components, :], img=cn_filter) -# %% -# cm.utils.visualization.view_patches_bar( -# Yr, coo_matrix(A_tot.tocsc()[:, idx_components_bad]), C_tot[idx_components_bad, :], -# b_tot, f_tot, dims[0], dims[1], YrA=YrA_tot[idx_components_bad, :], img=cn_filter) -# -# -# %% rerun updating the components to refine -# cnm = cnmf.CNMF(n_processes=1, k=A_tot.shape, gSig=[gSig, gSig], merge_thresh=0.8, p=1, -# dview=dview, Ain=A_tot, Cin=C_tot, b_in=b_tot, -# f_in=f_tot, rf=None, stride=None, method_deconvolution='oasis', gnb=None, -# low_rank_background=False, update_background_components=False) -# -# memmap = True # must be True for patches -# if memmap: -# fname_new = cm.save_memmap([fname], base_name='Yr') -# Yr, dims, T = cm.load_memmap(fname_new) -# cnm.fit(Yr.T.reshape((T,) + dims, order='F')) -# else: -# cnm.fit(Y) - - -#%% -A, C, b, f, YrA, sn = cnm.A, cnm.C, cnm.b, cnm.f, cnm.YrA, cnm.sn -#%% -pl.figure() -crd = cm.utils.visualization.plot_contours( - A.tocsc()[:, idx_components], cn_filter, thr=.9) - -#%% -plt.imshow(A.sum(-1).reshape(dims, order='F'), vmax=200) -#%% -idx_components = np.arange(A.shape[-1]) -cm.utils.visualization.view_patches_bar( - Yr, coo_matrix(A.tocsc()[:, idx_components]), C[idx_components], - b, f, dims[0], dims[1], YrA=YrA[idx_components], img=cn_filter) diff --git a/demos/obsolete/demo_cnmfE_2D_new.py b/demos/obsolete/demo_cnmfE_2D_new.py deleted file mode 100644 index 472b88b0d..000000000 --- a/demos/obsolete/demo_cnmfE_2D_new.py +++ /dev/null @@ -1,131 +0,0 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -""" -Created on Thu Sep 7 13:29:49 2017 - -@author: agiovann -""" - -#%% -try: - get_ipython().magic(u'load_ext autoreload') - get_ipython().magic(u'autoreload 2') -except: - print('Not launched under iPython') - -import numpy as np -import matplotlib.pyplot as pl -from matplotlib.widgets import Slider -from scipy.sparse import coo_matrix -import caiman as cm -from caiman.source_extraction import cnmf -from caiman.utils.visualization import inspect_correlation_pnr -import scipy - -#%% -#fname = './example_movies/data_endoscope.tif' -# gSig = [3,3] # gaussian width of a 2D gaussian kernel, which approximates a neuron -# gSiz = [10,10] # average diameter of a neuron -#min_corr = .8 -#min_pnr = 10 - -#fname = '/opt/local/Data/1photon/3168_PAG_TIFF.tif' -fname = '/opt/local/Data/1photon/Yr_d1_190_d2_198_d3_1_order_F_frames_35992_.mmap' -gSig = [3, 3] # gaussian width of a 2D gaussian kernel, which approximates a neuron -gSiz = [16, 16] # average diameter of a neuron -min_corr = .6 -min_pnr = 10 - -# If True, the background can be roughly removed. This is useful when the background is strong. -center_psf = True - - -if 'mmap' in fname: - Yr, dims, T = cm.load_memmap(fname) - Y = Yr.T.reshape((T,) + dims, order='F') -else: - Y = cm.load(fname) -T, d1, d2 = Y.shape -print('The dimension of data is ', Y.shape) - -ax = pl.axes() -ax.axis('off') -pl.imshow(Y[100]) -#%% -# show correlation image of the raw data; show correlation image and PNR image of the filtered data -cn_raw = cm.summary_images.max_correlation_image( - Y, swap_dim=False, bin_size=3000) -#%% TAKES MEMORY!!! -cn_filter, pnr = cm.summary_images.correlation_pnr( - Y, gSig=gSig, center_psf=center_psf, swap_dim=False) - -#%% -pl.figure(figsize=(10, 5)) -for i, (data, title) in enumerate(((Y.mean(0), 'Mean image (raw)'), - (Y.max(0), 'Max projection (raw)'), - (cn_raw[1:-1, 1:-1], 'Correlation (raw)'), - (cn_filter, 'Correlation (filtered)'), - (pnr, 'PNR (filtered)'), - (cn_filter * pnr, 'Correlation*PNR (filtered)'))): - pl.subplot(2, 3, 1 + i) - pl.imshow(data, cmap='jet', aspect='equal') - pl.axis('off') - pl.colorbar() - pl.title(title) - - -#%% -# pick thresholds -inspect_correlation_pnr(cn_filter, pnr) - -#%% start cluster -try: - dview.terminate() - dview = None -except: - pass -c, dview, n_processes = cm.cluster.setup_cluster( - backend='local', n_processes=None, single_thread=False) -#%% -cnm = cnmf.CNMF(n_processes=n_processes, method_init='corr_pnr', k=10, - gSig=gSig, gSiz=gSiz, merge_thresh=.8, p=1, dview=dview, - tsub=1, ssub=1, Ain=None, rf=(50, 50), stride=(32, 32), only_init_patch=True, - gnb=16, nb_patch=16, method_deconvolution='oasis', low_rank_background=True, - update_background_components=True, min_corr=min_corr, min_pnr=min_pnr, - normalize_init=False, deconvolve_options_init=None, - ring_size_factor=1.5, center_psf=center_psf, del_duplicates=True) - -cnm.fit(Y) -# %% DISCARD LOW QUALITY COMPONENT -final_frate = 10 -r_values_min = 0.9 # threshold on space consistency -fitness_min = -100 # threshold on time variability -# threshold on time variability (if nonsparse activity) -fitness_delta_min = - 100 -Npeaks = 5 -traces = cnm.C + cnm.YrA -# TODO: todocument -idx_components, idx_components_bad = cm.components_evaluation.estimate_components_quality( - traces, Yr, cnm.A, cnm.C, cnm.b, cnm.f, final_frate=final_frate, Npeaks=Npeaks, - r_values_min=r_values_min, fitness_min=fitness_min, fitness_delta_min=fitness_delta_min, dview=dview) - -print(('Keeping ' + str(len(idx_components)) + - ' and discarding ' + str(len(idx_components_bad)))) - - -#%% -A_, C_, YrA_, b_, f_ = cnm.A[:, - idx_components], cnm.C[idx_components], cnm.YrA[idx_components], cnm.b, cnm.f -#%% -pl.figure() -crd = cm.utils.visualization.plot_contours( - A_.tocsc()[:, idx_components], cn_filter, thr=.9) - -#%% -pl.imshow(A_.sum(-1).reshape(dims, order='F'), vmax=200) -#%% -idx_components = np.arange(A_.shape[-1]) -cm.utils.visualization.view_patches_bar( - Yr, coo_matrix(A_.tocsc()[:, idx_components]), C_[idx_components], - b_, f_, dims[0], dims[1], YrA=YrA_[idx_components], img=cn_filter) diff --git a/demos/obsolete/demo_cnmfe_2d.ipynb b/demos/obsolete/demo_cnmfe_2d.ipynb deleted file mode 100644 index 64bc60c4b..000000000 --- a/demos/obsolete/demo_cnmfe_2d.ipynb +++ /dev/null @@ -1,4064 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Debugging!\n", - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "try:\n", - " get_ipython().magic(u'load_ext autoreload')\n", - " get_ipython().magic(u'autoreload 2')\n", - "except:\n", - " print('NOT IPYTHON')\n", - "\n", - "import numpy as np\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "from matplotlib.widgets import Slider\n", - "import caiman as cm\n", - "from caiman.source_extraction import cnmf\n", - "import bokeh\n", - "import bokeh.plotting as bpl\n", - "from bokeh.models import CustomJS, ColumnDataSource, Range1d\n", - "from bokeh.io import output_notebook, reset_output\n", - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def show_img(ax, img):\n", - " from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - " im = ax.imshow(img)\n", - " divider = make_axes_locatable(ax)\n", - " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", - " plt.colorbar(im, cax=cax)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# step 1: load data " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('The dimension of data is ', (1000, 128, 128))\n" - ] - }, - { - "data": { - "image/png": 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QiwY81SlWQqb6lexmAfqp2RXZeAoGl9GIxoiyoupCkcHHXY5EWzq6ZVO7r1lR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\nnntACkLL++I67jl0K7QjO75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CUma6JQgCdWTtQpVJvipB3dGN/hztHnWVrd/pPGaSNVg7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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fname = './example_movies/data_endoscope.tif'\n", - "Y = cm.load(fname)\n", - "T, d1, d2 = Y.shape\n", - "print('The dimension of data is ', Y.shape)\n", - "\n", - "ax = plt.axes()\n", - "ax.axis('off')\n", - "show_img(ax, Y[100])" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1000, 128, 128)\n" - ] - }, - { - "data": { - "image/png": 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YaPjL0NB2jTGfUP/+kHPuQ/d6spevpHJ66K9zzpg78CApORET2iCDDPLGy5cB\n27jpnPuG19h/XyqnA7jGxYaNMRcAXL9bR0/EhKYBrbwiph3keNHEqFy7JmRi6lBZSbE1sPbFmlkg\n7aslUCB1NJvAlCF+OLjWNQAgj0J19S6EQ0M/WKZxgXrsgb23PdDXs2+s9nJ8KaOATb5Lq/MkLuSa\nnJ94G7SaF3UsFdkPvV+wcUb8duxM3oiXku3Awm2OTakqc4fHrumkAdJWBKDaYcAA2rxmPI7zDjkj\nS+kigVWc9ewiiByOnieU/O8//k4AwPdtfQJP5/QO/62vfxYA8EevfB0dfj3FjScooMC5vImpccqT\nX+rCLUeO/EbcQ843XboUORii4fy2RMC24tBvNOV6m7hRa66ai01nGQDB11a6ONCIK8XiekH04Fw/\n9WiVYSuje+CADufX8jWAMO4lAtuG1r4DpxtnSdA1j+sMn51fQFcczOtJ8HhfKqcD+E0APwjS7n4Q\nwL+/W0dOxISmVeKREPG1EezWONhOqkrpIhyW7SR2przRoifM7gMHQgWhvJVJ4F/aiM2ukJCMTj8S\nV4tTdgP0cj4yvo2Pnqf2dj7rS67d8Okxj9U42qeI2eEmfYTv2LgqJmeX6G87XYipxGlOQDBD+Hca\nFfLic7+1g5zHlKN561boFkXQGo57PqVLjqjP1aypHIU840kizzy0j8XnCXP2L/+EAgF/433P4es8\nu+x37jwDAPh/H3kXACDdiyQViCN9ia17C0hkGpncutFLfQ/aVO4mvVvb9KrvhqyHSMaZ76lxVlKk\nEvA7GdoPRKR0zOViG6/OfJ0GT2KwWCWB+mpKTMtsls6bVAJaUkjbGMG3bUgFqRpRZzz05C8BGSWu\n09e/jNzHyukfBPDrxpgfAfAlAN93t76ciAltkEEGeaPl9S00fJ8qp98C8L4vpx8nYkLTtQx5JeJV\ni3M5N+2yV73GGocmooeyX/oVr04l7L2O/z6Q+vlVFkZSzBMTtDHrNZC5x251+fuBtmYpDloXkpMv\nXqQV9/AcQRB8eiXmkUOce43Bm7vHVYYLCWOTstY+C4e9sk18uZksBcuWqqCE5LGatnmUo5RxYCe+\nJlZk0fTWLCEDwIrGwlrtskmCCST0OsrE923w+L3r9FX8wQUiD9j5M3qe/+zR78UvPfV/AAD2fU4k\nd6vcaiT7gZO4t6LFaxYOZtEEjmUHhmPR9MxtPW5R514KFYjQFZ4YzqNzT+l8HUCha86qDDNP+TRb\neoKDVYx9/nc8AAAgAElEQVRR6rM5fIbApWXAj/I4cxs7NlggAf8XoESJDyRpDbabfQP45PQHMFPg\nRExogwwyyBsvA2PtfRINPOyusrr6ufiA/GpI2lX7eO2ADQwSkPPmnRVMaxilAiwmHWK94FAPEil/\nE/tVtE/p7dsUlPnIu4hgL3uFrm0WEao00CwD7VWU/XG8Om8ncxx62MONkqtwGwkUbI04O6ASXxH3\nh7WQxFSiQQmhoUVPc5k57f/qF+9g7ZG1vHWi61DyM+D+XMz3cf6dNC4H10hzLf7gPH5x+2/RNp+r\nyo915/HbeHSy57vbf7bcH2uaQLUj/leo4+l3paAaXeDtRFdX989WU/50qYISU0uuL4uu59qlSZrV\nqfhCPecjJuMCWznnC3NgJuQncz4yhzq0VrWwtO1seqQK/LS1sdpZnM/6bBvOmUFDG2SQQR4MoaDA\nUPXpvgiDaFeIW6uflsxWIUdTRadYixl53Sm35Vr4AECpUPw3+2MaZ+DdcK08v8a1+8ERvwYm+NpM\n0D7Yt8Qr5M1yQ1JXnnyUNJIXcJYad4BpPC/WkjSSRZ3gZkUh/bpz7Z14Jv7Dm0vS0Io6Rux9Sxz9\nK10kK3XXnNCRSuYBi+BadSr52uJD800w/CExFeYNt6FeHcmZbEcXuxAPgCJz33jmZQDA7/9npF0V\nn9rBRy89Tvcy8nxyU2rrie097HQi1zUMjjyF9VVPaDiNCqFcn6roH4toTqpcHo+RLlTDx62UphX2\ntcktAfS46/QkIVq9f7/nVSqwkVOTuWrCQ0m8T5T9pABwys78NemYwyqT49i3uBkve7m7nHOc2bKl\nMQcZagrcN9GEjFwnMrLtBNxMUamwVI3twTvmdSofuCCqXcCyrUOYj02bQ797DBCyAYomaX0A3Iam\niOH+8KS45WtjjjbpQ5uOCpSVryK14ty7kdS/7E6iiallAuZ8v1vLiVAWTf3EuWwSGctuPiPta0+U\njTLX2RSKTCMQhC6NUemiMNn5SUzz2aMDA1jnbE8Q6ih828UXAAAfnr9DPliukHV219cEzY+x43FZ\noSpSmCjZVE9sLQnwnzm62Np3LjuUiYn7+qXFrkB8GMl/1OS9TIEwiTkseaFBO8d1nRw1ueTM8jjk\nUYnzk8PWcQerkRSivrHwhKAl3d/bdq7jQt4+vnEGC8+YzBCeS8sd5GO6L343c8VOnJs7BQUGH9og\ngwzygMhA8HifRFcK59W3q41pMkeBYdgAFm0FA9hWYm1JoAyBiYM1KV3HQKib4Xr5cqwTFEjaIFvf\nbzYTWKua1RmOubaovxc2p96yfVOu+cwNcowflLmih+7XYuQxksyIxrYovbmvoVZjX3tgM1Ej5Pn+\nQt6mu6PJXqoK55p8Uggh/Sjx8ymaMFa5wD1CPdHTnm77HRevtmA0APDomPIZ3zK+1tMwCpXPyNrV\n5WIHeyuCUKy8iccaz0GZ47zXdJi2PDYNtjzljg4UsXnNIrmfppaMglBrtAlEnUJZ5MHOLtAwMbxm\nK1mKpnqrmMg1bFc7ndC4PL1xCeeSg1Y/GljJpvm0e1jaZ+2Uqde7FFRdeZ0zBU6MnIgJbZBBBnnj\nZSiS8gYIg2I5UBArYOHRKtQrBIi+OFSKDiskr5pcHedY1fisO1ob0VC3H6wG23YLUiSmVkVDfEXs\nJlubCrTvfRz7Ps2F05FCKB7Ik5Dvx5oZMydopzprESsPvmycQRq1QcKZIv9j0T4h1j40v1nwnfH4\nBI6vVKX98FhwWpOuTBX8mKEAiezz2gxTiBdNLIwgLLo4DoOEH89Ji92O5i1fIkAOb9vJZyyaGI+O\nCN7BWs3LBQF4P398Fp+8RdrMOKExeM/OKwGGwW2YBHnUTj/ThVl4LHXgqjZtcsuGCTNhBeSaKTaZ\nmwVpUqzRV0rT5vfir28TE89DyW0B8TI85bBJcb2kQMht/35VjcWm8qPS9UPAg+m7tTgHlGsqyr/Z\n5URMaOtK2TMRHk9sRRNj5p2hcQc9DaiCwKoNnrTGnZcUCJEnnReozZ7uB6MxO+u4+dex5BYVHXdc\n+H5HnuxwsSHH7OQ06U6ilVyDUfU8Eb9abIvZwrKZLbHrE7R1Gb7uBNwtO6fHh5Ka7+zYXkkwgH7n\ndSaTLpuQe9VEcgq3ojYbcGkilHUb58YlBgHg4TGZi5txKPbMZqAwzKqyejM1mc69w/35OUWOE1vj\n7TlRZ/EkwMSG1jhcndEksJvTmE2jQt6HU/GxjCOTN/Lkr+sOHNahnB9AUcmQjRKS2IH2+6LrV7Cp\nebxi/JyTCY2zYriy1yvlKclP5QntoB7hi7Mzcn2AFoTTCd0Xv4c6cNKlFgLY5BwmtEEGGeQBkSFT\n4D6Jps8OqxWtfByaPlyNZNvMr2CLOhHzc9tTryQmFPHtmioNAlWQaHcNxGzQAYMuN3twtjfKSR5g\nBLdWU7kGAFxfTnHoV2FW+dnpe+14KjihjYy0iSyqeihvxp69PNuRNhjOEttGjmeNLjFVYLpYAz1h\nYYxVAyuc+bxt6QLHPZtWR0oz2TBLPx50zT1MxCHNwprahl1IjQXW7BajFF84JA2Dn+P59FDuoZtf\n2cAGk1AFjEJ+JGlUF0f7PSofvqexXeFrT78KIJi0DQzOJhQoYJxWhEa0wADb6LwvWE8R1HTgQ0sX\nCwU896tqLPaW9D4zNOPUaI7ItuE9fJ9fWuwKawa3e2MVxvrxCdW0OJse9epidF0PXRlgG4MMMsgD\nJIPJed8ksFxYce5n3odxvSF/UwODnZSpnmllWdQJVv549jElcd/Jr8Pyof1KzmPfRY4+ADHrADKL\nJunlSRZI5PrsI9kvRrh+i/w2TeEJJDe8NpZWKL0CxVrb4ThXfi9/T77fh0UuDtyE2Uey5drUlXVB\nDIB8e7nt31+3GAgQAizWa2+sXSWoJIeTfTt5XuL5JVG97wnzRECmdzM/tpM53rp5w1/T+3tcLDxy\nXTmqR3JPl4tt2c75iexIP6xyHDV561yuZv5Qelv8ZC8WZ1r90WJN8Nd1YQ+li8XfyPtSF5hJVh1Q\ncalgRhwkubTcxt6MNDQOECVRLdXiL+R0T+zz1f5YphU/kx6LRcPHTaNlqxIaEFhiChe3/Mta7rFe\nwJtKTsSEdqvkikqNmJWMhubycEAgreN9WVTJQ6/WmAYBExSS1PlFZod7DYsdbyIJJU0T9/j32QzQ\nyexcPHfeBMqiReUnttkYzcr3xfvdG5/utCpj1LWvnRDTMYdlLuXOpNqTSoFZ+cyC2psnu6MqZESA\no5axOOt5UhFCS9P0AgA6sb9mnNuaugGRYOCa3kSQmEqiijwe64gDtTnI+DOW22UgrbyQ7Lf6Nndh\nDIRaCrYXyHnp6LTsfyTfa11z1mQ9mh/qZyT7ASA3RmVTeFNS11/o1FpYIRA7snuDJ7gGpreA3FxO\nsVz4xXVKC0NqK5zLfFaEJ7nkZ7h0IbOlVa2qs2DrUn7C8MwBnSaSYtZaKMo55HIOMsggD4AMwNr7\nKAJJiErRYpjsjokeJ0khq0/mc/BmdSbH8UodoemZYkJfrJLZ+WFqHBqHtyPTb0MqCqntkh9oKsFW\nbfgao9M8gz3lTUe/KoMdxscJUPn2/Or5ueqswDy4DTZZpmkhTuSqDquzmCbs0G8S0Swlf5BzOtH0\nUl2IE5/GhmtakmkV6j3S+AWHc6D4DmZMVyNjh3Ruy+Bk9+O3Gx9Lu9yf4yrr1XrQAZeAs2tnUACQ\n2gIHZS6ZAqLB2+BW4LxNfk5ju5KxCZRLDfbrQNDZFXGNyLsQggJSC5aJOOu0N951Y5GkdPwko2d3\nfnQkkIvu+I3NqtePxplWTQi9XQsn6De2XzhaznkATc4Hzys4yCCD3FU4ynkv/9+LGGO+yxjznDHm\neV/lvLt/xxjzG8aYTxtjPuYrpvO+f2KMedYY84wx5l8bY3K//aeNMZeNMZ/0/3/P3fpxIjQ0YUww\nTa/wRxZ7n1BcynFSZ1PVptSV1nVhFUAXyIjEd8ar1kj5OQI4NgnVfxRbBV97K/V1IpXWxu1tJKQh\nfc3WLdxcksZwzVBg4+iYtKDoVoJ45n1zZ+n+3K0JXnqejq+fJP/KI2conzGLKiEozHxmwU66WA9L\n6TBSaOqYbqBAI/9fixtLs3MwwJMd8LkpBWjMwr6mlYtblYkA4Ex81CPlPEjGvXMLVVmp6/A+qnPZ\ntuOBuNlGFXybHi7BzC2jqBQKomyN0z+Qd/arW2ntrTt+kaL27lI+1bDynjI4fFElGOe+9uYG+Qo3\nVNaE5LuqIkD8HINPNGjEWtOddzI45io75o4a2usU5TTGRAD+OYDvBJWg+7gx5jedc7py+k8C+KRz\n7v3GmKf88e8zxlwE8N8DeKdzbmGM+XVQ1ah/6c/7Befcz91rXwYNbZBBvgrFOYPK2Xv6/x7kvQCe\nd8694MvU/RsA39s55p0APkLXdp8D8LgxhiuhxwBGxpgYwBjAq1/pfZ0IDU370FgYTsD8X5kNzBCF\n73bRRMhs38fARH+8amoWja4QwaOPqHnfS9HEyH3hinlHo9uKFxJVmqtVnK/JfptZlcnLwH6+4xlp\nNbYwmF7yaVZpWD1Pf4a27ReksVxNfFGOvBBg7ZkJ+VtGUSkawIZvIrOl5K8G+mwGemYhJ9MDie+0\nnAnDCOdycv4oot5qf+Ty3jVZItOgNExyGIDKQjCp/GXiBxTNjK69GxfydwC0huMZjnFKXfdiervV\nDypw0q6RmZpKIrraB9ity1kLyDgKUWL/Y00jxy3RBmJHaCQKvd+Mpa3Ep79xlXQdCV0HuWmkj6EY\nTCgq1P98u1TwBBJf/5l/GUGBu1VOvwjgFfXvSwC+qdPGp0A1N//QGPNeAI8BeNg596fGmJ8D8DKA\nBYDfdc79rjrvx4wxPwDgEwB+3Dl3G68hJ2JCC/ie8EEcqYpHAL2IbC6y0xcIGB+WxgWzq5d07oyY\nLYEkbwJ4+JKme7El94lzDEMmgrxwNoT2xXnrL7nXTLDykxBjpdghvDhdo7lE+y78EZ13+20xjh+m\nF4zfs7L05mOUIPWm99SbtBpCIcESW0pCue1MLhNb9CaSdcR/6wgBGXvWOCsfYMhZNL2kd51UzyYT\nY9QSU2Mrokl538M8bpUTMQmZFDETk99JniSLhZPnyM7vCGE8QsaHd18Yo/BlwVzsTp6NSkDvQlxq\nZ3qJ/MCda1uWLhL8F7tDNtJCClYzBCkzVa9vXMdgqSArQgflmvA3Vxpr0lY9DN1W7axMrFq+zEyB\nu1VOvxf5IIBfNMZ8EsBnAPw5gNoYswPS5p4AsA/g3xpj/hvn3L8C8EsAfsZ392cA/DyAH36ti5yI\nCW2QQQZ54+V1hG1cBvCI+vfDfpuIr5L+QwBgjDEAXgTwAoC/DeBF59wNv+//BvAtAP6Vc+4an2+M\n+RUAv3W3jpyoCa2BkcrpvKqxSbkZL0R15t9pvJKK3Cy1s+Iw1nUAAFpZGf5weU50NmUTSeifV76j\nMiDOH/MgTdb2lipgIHAGuFCX02sO15YbArJl7ncmeKy2Ihw+SdrJhlfUdz5f4ZXv8sGMHc+r7/uw\nXKTINhk64QG5qoapjJ8GgXY0DGsapGjLuuTk2hlxjgeHuBdTw7o2rTlVFPfXdW2tsIueB4Cb1VQh\n/3fCvXTQ7Nqp3TWNc1sicm1NJDVVT7OswbmcRcu8BdCqPSoQF8W/361eXyIwa6zLk9QV1rvCOavT\npMDZnEzkLjsGsF5j5r7V6nHOayaVDOaofj/5XnhflzIeeN1xaB8H8FZjzBOgiewDAP6ePsAYsw1g\n7n1sfx/AHzjnDo0xLwP4ZmPMGGRyvg9kXsIYc8E5d8U38X4Az9ytIydqQhtkkEHeOHm9cGjOucoY\n848BfBi0/v2qc+5ZY8w/8vt/GcA7APyaoQIKzwL4Eb/vT4wx/w7AnwGoQKYo++d+1hjzNMjkfAnA\nj96tLydiQtPOel7hpl6riVUIu+r4CUZ2JedKqFs5rtkxWnh/2XGd4YWjXQDA5dukoT25e0tSZVgr\neKk6jaV3hLP/gfM9t6KFqqDdBrYCQevZTEI4nouZTFLPyzY1OHqE+n3lW8m3NLlk4LzGdWaHtE7J\nBT1KUIw8tXfpw/P5cQ9aoldq4RPr8LpRH0OwhB35AhBdw48WtI9YnM5SYEV9E+xjEjiBS3sVmI7q\nXNKQWMPciefCg6YhCwD54IoOaWENI+1q/x37CNOOL437DgTfotayGPKQuCpwrinqbT6vWzRGS3fb\nVhxgNcwIkkeVWCCBISXAUrr5o6mpenVkdUDEKir4iO/V39a6ildanEOPwv0vI8653wbw251tv6z+\n/iiAt93h3J8C8FNrtn//l9uPEzGhsSzqBLbbJf8sYlPLy6DrCIgZilCclyewRGHTAODqchMvXSOC\nwSimfU9vX8LX5VRWjc2QL8zO4vKMJjx2wj86ouBKbkuVXxe+5kYmEnopn5pexStLb1L5AEc+9SXu\n4il2x/QBv5rR8fvnxkjGdH+csN4ceo7+ucXK54XOSrq3UVT2PoTa2B46nT+qyLheqTWLBvART+bh\nT00t0cSQeB1qCnQjmbULJJGhLoGn0mmMtMVMsGNbtIoD05iFiYfvZeIzKGYmC3mdQu0TTHy+du1M\nf6JR/daTsr4O9YMnaYuUCQoU86zc62ugnHTOLACUTYprBS1ITNg4jYve5BLBqRJ77Ta1+4TftWOV\ngN/N26X7omsxNjBaYx6zDKlPgwwyyAMhQy7nfZTSazdVE92xpsCoA88AaGVlk7CQlTfwvDNinJ38\nz147j+qQNJy3vv0SAOC9ky/ifEwm3tKvlLvZsbB8RJ1sA7puu44BXCOmF2tq02iJhzzFDWuRDDtp\nnJUMB86EuBTVGHnsW+63xUceHnJksHqI2h15wsTdJORECk0NStmmCRsBYGKKnoahCR4nov0Gxol1\nwvcuFEq2DNTUaGcujG0hWhtLbsue1jC2hWKrCP0FgE276OdE6voIjANzWcsRTmMQ6L+5jXB8ILKU\nKk7GwDKVlLSlMGpek2TNVWunXert4yrDtSVliBwWND7jeCVmtsb6de+vfU2fJeEyP1arXmZGDdsr\nJqzrCNxJS3PDhDbIIIM8KPIgJqefiAmNc+5itZKIZhZpzYhWw5FlrjLT80loUGeoD+mLfBzmQErb\n/to2ZVecjo4VbTLnza0kQ4Fz7Tj4sA7qYI2TsLr4ZlzccvzqfoyiUlbqc/lRuJcOzTajMpIjoPLb\nRnGAjPTqlJqmpxERaxcd04Ub1M5If4VhA01PQwvVkQyS1/gGBJQK1nJqGS/x5a0Br2o+r26Gg0bo\n67HlPq7UuIsG5a91UI9a/ad2QyEaPl7705h1JFUAXOpreD9a75hpw2l437ViM2hmvtLUqXQuAO0Q\ntHFCpLlOM5ZghspS6FYdI9JR1jzbGQuZqdZqaM4NPrRBBhnkgREj1FwPkpyICY3Dx40xaPyKxDAJ\nBiVO4gKl50pj7SazlajNoZBGoD4O9SI9nCCv5G+uoH252sG2pYjjc8VDAIAX5rvSBvNnafVcgzmB\n9ordSr/xGgIXRNH95tQX1kS30wUOyzaFdHqbrjm62SAae59Swqk+Tq7fhWq07t3/Lp3F2IRiIABp\nHbzaS94rQlXywCTBwGTX0qb43stORG3sx5PObWsM3I5utzZhbLu+IAA9OMbSpdJfPj4xlWjrHA3l\nvm5Ec+mj1tryqJ23yW3TrweoGg39CPmU1EaYFG5W5C9j9uUXD09hWdI1375NgPdz6WEP2Ds2q1Bb\nliE0HJ13RvoT/H2RQDk0D14ZtZlAdCpet/gOy+BDu0/C+W2rJpJag1P/onLi+iQuWkVlAYJq8CTB\nTtzjOkNVt/FqjNQ/vX2MG7fpxXvugOo5TqJCiBK5ctOqjnA6o4+Sw+aMk6Lr++pDhmsANApGEKhz\n2CSQX0sf051eMDZr9ws6bnQzTB7jtI0rYxwWoHBlsAoDxgn5YXLSEA7uV0iIpjFdh1bXmQCcRWDl\nPqOeScMfqzZ/+fmkplbFcH31KWclKNE1mSa2QAcpgtRULZxV9/5YtIktpIkKO8iO/FQweCWizqTP\nomm/Q7HqqGdqciHhmwdTob7mHOXINGjW1oHggEU740IHB/iYzJb3VOFJv3vrakkMVZ8GGWSQB0cc\n+dEeNDlRE9okXgXnZ0caZ8Q8Czl1AZTIDnUN/ZAq7P7fp0dzLFfeaXtImtp/Kt4iFXh4vZokKzw6\nWc9SEiFoKXDKrONu23DcxJs0XNdynWnaKurCNT0PaZU/deTN7vMRdvPAeMHjMXN9uuguaR9rV7kt\ng2mjVnQ+Xq/oTQfiwPebmrpfUV45+fsBif62ZZMIGl8obuAE2Msm1iYzTqgxEyAuyh5zyLJJVIUp\nZhzpg2M1pIQhEdI3l/QYWsJ5VQ+Um5hKKksxJOfKnMC0q4MMJmVNi9qcK5YYof82qx77CWuppQva\no6aMZ+HgUaLM/tC3YDGsoxMHhijnIIMM8oCIG4IC90+YqvpUNsck4grltGoVDef0xQKilXxPW/TA\nlIWNe/xPHGC4ON4XOMYtf82jIuvBJbayhVA7h4IhId2Jr9Wo8m68urKT+qjO5YXh8m7ijLdND3Jh\nTSNh/sV+OzhQjUPghAG/B/VYKpRLyF6V2Atl3gKUQlLG/LZNu2xVUef7bVxfs2GRoIDSljSltz5G\nwyHWaT7rNIduUKCG7fmzqJ/t8athVQqRh2N4OISu18n1RLVviYMIN6oN8bF1fYmpqXuwinmTCc8b\n+84Ol9RWej18Wi89Qul2zdRKsRYe29TU8ncXgrQRLUTDvdpsyTGsjWWKR03o5jvcdPUaaBPLYHLe\nJ+Foy7xKBXfGbK/ryB+l3iaMmH2vVWOQJ4PIOGz5KOGOrwuwauJWFA8AJtGq5+jmyNJ2NO8VtM1N\n2TOVNRHkzZJedj5mI1qKGcoBhqqJesnC1cjTCa2AK4dkyuyMF76twO/PL3ZuwuQik8CanETN8x8i\nhyFhXaJtEmAw0gZHSjl6mdtSzMJALtifyEK9y1omLZ3NwKYVZy5wW3oi5InyqMkUfo73hXoU6ybi\nbtAGCE53NoGBQK7I43LocWlLl8gkp4Mw/F5IkMRX7ppcAqqxL4jtaaQWdYLNuG3mUVDFtdoImL1G\nSBx58p01mcrWCFkN/Iy6k/TYFnckoRyinIMMMsgDIc4NE9p9k0en5IA/KHPMvOOUswZ4NVrUiazA\nUqexqQSzwytl40zP4S7qva0E+c8r2bJJBOvG2LDE1D1aIqmcbhJhOWCZN1motuP7Ea2hROKaoxEa\ngaPw77XFBuY+YJFuUD9m5/3qXwLzOfXx/AZlFpRNLJQx2jzqEvyxJKbq8c2XLsZEYcZorBThIDNP\nWMjxXbOrVvTj3G43qHCn9hmVT/fQdtCvq0gkOZSwmHTM1cg0rWeq+zNRLBfMTKHhI9z/M/FRK6eV\njms/a91+6SJ5fpx7vFp5KIgDvDUq+bqjqGxlvtA9pcglm6NdOWrpkh4dkNb8Q92AMKZd6EoEh+Ud\nJq4BtjHIIIM8MDL40O6TsOO/aqyQOLLzPuVai2WOuPZO+2Thz4t7FW2scUpLamsIWntj30yERtF9\nh5C3OJi9lsIOeMrb7ITQXdRDaB/XubTBq/feioID+3YkPkHu/+35CMdeC4tjz//1sNdSVyZU3E76\ndTaDgzlUMuo6tTXsQMuh9weG3EXX4j/jbQCQKoc9t78u/zBU2+qSfntt1u9nosfSRShjX8UpOvb3\n1M4OoPa8n8isen4yjfZn/1q6RqthWTaJ+MdY+06jqhfYWNc+S+kiyTiZexLHLPMA70eBauKfcRWq\nlEUd2EvhLBrOlFDaI0AaWPc+c7NCatrBHU23Xne0rlL56LQ4GDRDlPP+CGcHxLYRlP/SO1JtHKo+\nsbrO6UjjaBVUbPW+8UsWdyaZyAQTTyYvWyNDO9vAwoWiw52Pe92LrVH7nFqzbBKZrNiMZhbc5SqY\nWnNP2FiUcSj35MWcoRfcIRQY1oEFFjFBVAJ6t6JRAh2x7TvtV8qM6U5SORNJou4h+VvmbmcfYaDa\nxIorF+NmSRhANuvLJpL6AnlO7bFpaE0jkwvf27zJUDZtzJn+aNPOcy9d1HtuKxf12HqJlfbOE5ng\n4Px7soVFL7ujrn2Q6uECSd7GkEUmEFPyxKkjvaHKE2czxL2ote6TzuDg+9/070V3UVonr6eCZoz5\nLgC/CKJk/d+dcx/s7N8B8KsAvgbAEsAPO+ee8fv+CajOgANVhPoh59zSGHMKwP8J4HEQBff33a2M\n3YM3RQ8yyCB3Fx8UuJf/7yaqcvp3gwoK/11jzDs7h3Hl9K8F8AOgyQ+qcvo3OOfeDZoQP+DP+QkA\nv+eceyuA3/P/fk05ERraqlaEdpwwbFzrN0YjGhpvy0wVHNbeRMlshbpqJ6V32wIg+ZvLJuk5RyPT\nCBxE8/UDpOmsQ/lzuP/YF/kcR8ERvZAqPUEjOV7RaryqvEY6TxlhgcSb2xJEiBrEvkAta7NLFwum\nSjBIKhAgmovUAAhr17JJe8elSnOoO9kDkqQOuzYbgEWS5C07uYP5yvuem5+X498z/RIAgiK8XBBW\na68iiEviiyznKFXAhUzyo3oktUK7eDQgaCetzIE1WKyuyT6xK7lnoQpSyeHde4/QYMPDgDjbhCWf\nrDDKaBx2R1Th6VQyE21eU2tLP9EmlaxhlQbN5n/TgxSVLoYvI9vH4plqbYAFwOupoknldAAwxnDl\n9L9Qx7wTVJsTzrnPGWPWVU4v0a6c/r0Avt3//WsA/iOAf/paHRk0tEEG+SqV10tDw/rK6Rc7x3Dl\ndHQqp18GwJXTrwA4UJXTz6kydlcBnMNd5ERoaOyvqhorFZJYG2MnZ+XioOn4pWVsV8EnYQITh/gb\nhBY7+CtEW9OwDNaMVN3FLqOBpmrp1uUEwkou4FI4HPh9ds1SWJTsT6N7MhZIvUN5Iyf/EUM1nDOI\nfOKriesAACAASURBVE1PLpJSNnErb5R/1zErdCW3wUfTpf7R99pl7oABLNr5hhpwmndQ9ompYb2P\n82q1DQBYNCneNaEatI8ktwAAN6pNHMek2TLyntuPmkZ8Rkd18D32AMRKusVaIrgemWRqajSdKvBE\n1d3WBsNY9DXTxFTYTSiIcW5EcJq5r/vaOIOpr/J1xtfi5IwVun4V+tghB50roG/3ujrXliUyTS9A\nIMGEO2hhDsECuAfZNcZ8Qv37Q865D93x6PXylVROD/11zvkSeK8pJ2JCG2SQQd5gcegFoV5Dbjrn\nvuE19t+XyukArnGxYWPMBQDX79bREzGhrXxks6jjANewzJEW6Lm7YNsGBjlrVZ576rjOej6lwL9l\ne9W9N6Jlr/ZhDdPzOwiBpItlZbMqAtX1w1nTSCSS+dgOF5S+VFRxL80pzUpsjun4bc+scTsjjcQY\ngBcnJxGzwKLQZVgAVNRSgWlDGlFgZshlRQ/95whj0NqU5iDarPfROae0vFVrrGpnpbjHlRVpaCO7\nwmkPzUh9mtOGXQgshjU0fiZn4kNpX5drCxHKAHFgLUUivAj7uNK6vs9u5HPf59wC6KW36erjwT9p\nxSd2ISd9nIHeN5ZTeV95W27LFkCb76lLxsnv73Gdyz1LLrHSznifjmR2NdalS9Zql8DrikO7L5XT\nAfwmgB8EaXc/CODf360jJ2JCY4hG1VhxejN2SyOrGa+W2OBo1hMNQB+CfvmAMLHVzgqUguEVhYvR\nDaVrCbCNYHrw8VyYWF9D0Oe2kn5seh75K6AJrWysEDbOOC+1MRIguLWgD4snL6vgLDt+sqP+MlOt\nxiGtd4uu275sUsmr5IlPQy/KDsZvbAtVe8BPFrYQLJuQRKrx5o//4ZSKOb/ozuC55QVpD6AkeW73\n0ENu9EfIJiwTalplWmvYSfoaCkcLfQ9yZfDkELJMbO/jD/RKdUjk923MmkzeC4YBcSbKLTNB7us/\njGzIQOGJWwtnSUj1KcmdDbVGNZlBIEpYgwGUd4LN6aiHTRN5nSa0+1g5/YMAft0Y8yMAvgTg++7W\nlxMxoQ0yyCBvtNyzw/+e5D5VTr8F0tjuWU7EhJZFDEBMhMqHAwBMjtc4KwwctaPVp2j6wMPE1Dj0\n9TgncagOBQBb8UL+Zk2tRCTXL1SOHosV2IMPLMCJmXFQkSb14mIXRz4f9JRn8TiTHgEK/AqEVVa/\nSJz7VxYxqsznAzKyfOkR7FmFxucDpqo+qcAOFDe/mJgMWuUq6aoilBAJImkBaqkt24MzsNa5ES2k\nXqZGvG938kEDiFbBWjzMYiNa4vk50Z/zM3hq9Co+OXsUALBf0rN7+/gq3a/SjAIspOzlYerriaaD\nvpmpmVXmNbNasFkeCCy3PAxHg29Zo+N+zJu0p8VeLwJoeDcluMbpZCb97mpQUSvzpJ03umGXvSDP\nSpmXGjydqoAWjUEY+zuCa4fUp0EGGeSBEAe4e49yvmnkRExoRgCnFglX8/GOdPZNWNMIid6NJf0e\nRCGMLwwcUVjlulTcmqaZZV3lKJ37J0BFv1Lersb4D5ffDQC4+SnSNKIVsNqma5x+kjIz3r17RXJO\n2R84U2lOAkcpmc4baHzaTOXHo5l7aMcixuich0TEbd8R9dE75Z0TraRbOCOC62kAYxN8Ysx8UcPc\nEfrROCugXIbJaAruLuCzUfxpvG8nnuFcdgggaFefX16Q6vZvm1Ag60xMx1A6UtDMqI+2pXnyGHTv\nTwcH+B0Qv1mdtOARLOxB5HHMbUitWjZtH1fZxL0UucMV3cd2uiAtHWjVZ2Wf5frc0PbnSH47Ll4T\n/MJd7bSBRe3f/y6VeXqHupwkw4R2X0Qcu1WErYzR7+3kbSAECDgn8rjMJFrI5tw0KSTbgF+8qZ8E\nFnUSqHzEbGhaOZz8y2YlO/5fnO8CAP7olSeQ/DGZFW/55U/R8dtbWD5Fju5L306I908/DbzjNH2c\nXHruqPDYpjqSnD+etADAZH7y9hFbO/cfbWEEUsiMvpFpeh+HzquMJCIcqi11pUTUS9qe2KIVHQQg\nk9J+E6KA3N7KtbMMgHbFqS5mamILPJoR/oyfz141xdMbhMt8KKEFQWcphLzHUDYwZGuEUnuzZiLX\n0ONBJIqQcwHgWrklbYSJ0qAWJzxHTBn3p2o/+PvjknUAcGVJjLLMOnxhdLh2ItGIf4AKFUtdhA7m\nkXJz+3Zhd+KLTCMklTzx8W9q6haBZUsGk3OQQQZ5YGSY0O6PsJa1mRc46xHXguzmlbiJZLUae40r\nbgI2TQr2JgtVGYl+uSJPYmqpFaBXXP6b8zsbBOc6h/Sf2yfzsnluCib4uPYDX0vnTw0YwVE9Tmbm\nxc1D7GaEtzrwju6FzwooS6UV8UsVOWxNSZNjE3yRk0ZkqqDlMCf9NFr20PJrueO9ZrJyQeNizcU6\nu1aLYK2qCwvQTBQtR3uHcidaA4MJ2DSDs96c5G1jW2DbwxlCYeR+geJGBQX4b11BSuoLCOwhmF9L\n3/W5Op5NQe53Zhp53hwgakwb2gEABz7o9OLstLyTR97UnBV03KxOlaYVslLWZTawBNp0+iGWkPUZ\nClpWLhZtVweNANI0u8cD+HKBtW8aORET2iCDDPLGy0DweJ+EV5WtbNEjo2O/FkMwgOB7WSktK1Hw\nDWvbPFSBdSFQa+tsAinyoaAOrF1wG5yHtzpXoX6SNJyL524CAN66eUN8fRzEeCjbl0yBaxEBap9J\nyM92vD/CeNPzVkV0TTOPMFvS6r418eDZzDt9SyN8aNz+cZ23GBVYulWwgvZkMbEePqBgGyyi6cAJ\ncwi3xah5DfSsG62VcaZCO8+TziEJmpELRVik6v2qlV0AtKuCd7WxxFS996R2NvgUVaAAAIo6kXHI\nVB5uF0Q7b9KgrVmGBtG9HNW5gH5fOCZ/allHOJ3TmDLbxtgzbMyrVDT/UMvUoJHK807uKZVMD67g\nHmqNsrRozU2b4jwxFSLvd+1W8UpQryV4pIMGDe2+yFbmP6ColJQnNhs4WV0XEBa0fzHCRkoTQ9iX\nCDKfKYViJvBTBHviNIeTiYcDAUUTPoD9ksw+ji5+4ztewGNjQr0/mtHvQ8ltXK3IKcz0OPMmlXbZ\nCf6es+T4/mj1uGQKLI7pI7VljOWMJrTc74t8oVqcWrUyBIAOkl4l0ndL0IWCtmUvG0A7i/m8SLXL\nE9lS1UmIOpMoJRy1U3dYSherNuiaZ+KjOwYRdLvaXDvy/VyqSlNd7Na6yKy+Px6HU/Gx9Icd/jxp\nzetM2ummVuW2RBm1i1oDIRrPi+uOz5hKbYV9j1Pc8Ti0rWghk1dI0wqmrC49R+OzvoRfIIxsn7dO\n7lTCDgDunur95pMTMaENMsggb7A4DEGB+yWxckwnYi54M8Mj8CtnMUF7NTTGiequ63Z2KYJYclsK\nboilcDGsa1M8AwGuwZWgOGn+djHG+Zyc2lsRrbzb0Uw0tKqDWwPCisrU4Q9vHWA7XbT23ai2kKSk\nDTCZYz7yofe4EpNX14Hs0oPr1TzqQDk27KIFu2Bh/BlrM6ej4xY1ELXVT3DXmQJdJD/3a6bqZ7LT\nf2ILhcEKWiEnhnc1S21Ocxuli+Ra3VxUaqPtLhjbQjIVNm3I3lh6anQ+PrelmJp8701Le6Tnw3Ud\nbhdjXPGEAxzY4ueUR5VoWpxNEEXNWvwZa6/sAhAiUBf13td1GteyCXCkbs7qnRLTATMEBQYZZJAH\nSAYN7f4IA2YncSH+MfYLZJ6poHKRaG2aeofpuxdeY6BwufXntskiE1P3WCcod7FdsEJDGTgPD165\n+ezBeVxd0qq8nNAKf6ueSgUj9gGS07kdLuf2Y9tIlSAZg62lBAOYGFACF3GFbZ8jGqAaTrQZ0dSg\nq0+xg5kd+6PgC/P3t1+PVY1Jn5VgE6XhsG9MjZffp2m8+VzWSbXv6ozXqrTfbOmCc/9OImBaBM0s\n+NwMVmjni1rT9O5F0w4FIHAAVosvChwgUoEnYeJgdg4biqN4v+3NxRT7S583nNAzY2tj1UQY3Yn6\nutXHUKwlkDL2rY026WObJjxCqA7FoGjx/ZnV2spc/iYfODkRE9oggwzyBsuAQ7t/IpqX8g+w/2HC\nUc461BfkFXKpqqkzE0celeKrEiCuDQVRdHQT8IBMiRaFPMhuWgzvqzYj7PsydBzRPJseiXYwVtxX\nfP0u+WMelbhdeJ+RD51vjpc4Nz5u3cvC+pJ7USVa5qzmiF+M3MMl2P+Um1JyLJOIKZ6p/cMmlz6y\n32ydf4XGPRBAAu20KX5CmkCSYR6sCXTTcHQ/6BoMownX5b6wf5T3EU14m/1DizBrKK19XRERvnfN\nyNGtpZqYSjSh4Evj9kNN17MZgb8X0wS3lqSZ8zNjX2sWVULsyNHuyDTSX47+RnC9lMpu7VgtNQwy\niYbyNqv29yPNd8rlHKKc90mY1FGHn2PTDpvXzgjinieIgyJH6vE/scdsIQr7JYnYBIqcpJMBoK+h\nKztJHqOwgtK+7XguZqWE+5tUrqWLFfMHzWhzcXhHpSTRp7H/mBTfPMMCuEDtJFm1gh4AUReN0306\n3vfjUnkKb8uppgSzwvIHNG8ymfj4fjftQvrEk9BBPRFoAzvQ9STQdUpHayZFdtRrEkV2jOvzl3Uo\nNNx1lmsnv+RVyqQUh6BHB3cHBEiEZt6ddWoEtPcH8kQmQiq69EQ2bONrT6IVmqz9rmn3yU7CboJg\nWndziSNTy99ZB3pSNAnmCJCZVn9UH/W5aceMr51dG4gA8ED60IaqT4MMMsgDIydCQ9OMAt1qTy/N\nfb3GYtxj2NyfjYSZYuSBr3lUYeG1r0lUtI6vnRWwrV7ZuySRtcpx7HHLRyvJ+dSrfc+8QKhmzVW+\neRVf1olQCTG1dlVHOLCeyrpmoke/z1khPjzlSQMXdYLrJQUn2HzeKyc4Ske+39QP1kz2qqloOkzN\nE8EpZH5A0nMgoUtJHimoCLdVwwi9j4yFaAlxywkPkLbXBbTuVROcium+WGuaKaWiS0xJNOiBeQNo\nO+1ZJjYEE7hilA78dBlIVi6S6y9dmyCzUXTv/Pw5VxcIwG5+f7fihQC1NVEma8zcj41o0TMt+Z07\nqvMW9RCPD7fHz6Bb7Z3H427yepqcX2nldGPM20HV0VmeBPDPnHP/mzHmpwH8AwA3/L6f9My4d5QT\nMaENMsggb7A4vG6pT6py+neCanJ+3Bjzm845XWiYK6e/3xjzlD/+fc655wA8rdq5DOA31Hm/4Jz7\nuXvty4ma0DQDBjvNr82JzPHMeCbQhStzArE2jYVhIG7DOWxGtLtF3YZGJKaWYipaK9R1PgFa3ZbK\nPwYEjW4aLVXVa/aNmVbBFNoWSAjZ0cxa0HEZKlOlnlq7rCMcLGhlZgpultkqxU1L/qbthNO6gFeX\n236bh0bYSuAjLOuAmJqiWsCfrDkg+GD2vY9rXZGNXPn0umDbcP645ecBPJcZFxTxPjqipm7n3UJx\nfkleovKn1ioNyh8UclRdn0q9CxFJFd128GeFd4KrRMk9WQUlEW1pJc+Unydr6LvxUSuHmK5jWpXC\nqI1gRXQDBrpv1ob+LDtjmpjqjgDaO5M74kRUTnfOXVPHvA/AF51zX/pKO3IiJjRJNncRLh3TR3p7\nTibC+Q2KKH399suCUdsr6EMbZStB1bMcl5lMUGwSSOaAcfKAA09+QI6LCm8aXC+JxPHVBU2eW56k\n8Ux6hChpJw+Po0I+Uh05qzvOXjZHyibqBS6OFxliHyBYrbypuWLG2hTHo/aE+dh0T+oujCI67pF8\nTyaQFxZnAABTn9S/Gx+3iiXzfVoE0xEAoBz/Ia8yXJsxYdosFQxb084RvVluKDoeOuZscoQnU7Ig\nuDAxVmES6jq19bWCiWV7WQwTu+rlgWrpJbPD9jIbtNnaCzY4IPftc9bBysXYicNYAiEf2BoneLJU\n4cY0PgxoZyIc1Vw9KwSxwj03cs1ADqkWlbsUFl4nr6PJua5y+jd1juHK6X+oK6cD0BPaBwD86855\nP2aM+QFQabsfd87dfq2ODEGBQQb5ahV3j//7yunq/3/4FVztgwC2feX0H4OvnM47jTEpgP8SwL9V\n5/wSyKf2NIArAH7+bhc5ERoao+ZvL0e4vkeO7p0tchI/feoSAFoxmb6F5fRkLqsUm5yxaVQVqf58\nzSskBxO0iAnpLKbeGcuZCEfGo9ttLdTLXAT5QnYgx+cRF+VVLAodk3YUl7h8SPfJ5mVVWQl6MKbK\nHXvTsDaYTej+XvDa2zQp8NCIittWChPG98fwEb7mQT1S9UcD/Qw7xs/EpAkTdVKg0NZjVjvTgwAs\nm1S0uxsV3RNrGgCkDgTjBb9x+qIEJfZq2rd0ico5ZUd62xQG2pRCjIhvo+VNa1tLs+tgtjS6XrRN\nc+f3JTel5Fquc7h3MW2NM9L+Srk5uuwZ2jUhdU25kLF6R8MzqXsm9diuMDaLVhtSDHnNNUXuXUO7\nn5XTWb4bwJ9pE1T/bYz5FQC/dbeODhraIIN8FYpx9/7/PYhUTvea1gdAVc/D9YzZ9vsAVTldHfJ3\n0TE3jTEX1D/fD+CZu3XkRGhon79K/p7q2hjmFGk6f/0MaWYXPHj0Zrkh3GQMuk2T4P/iqkHLOsZR\nSdoUA2A597MxpofaX8c5lZhafCjnRjTmrI3Vzojvan9F/TmbHQnNt1Rkb2LRkritY88csr8cYeGp\nmleFZ3WojSqS4p3VpXcqHxrUhc+IWNCq/PLGDp7euuT7FIITLOyzYm2pcQZbzBPHjCBmJbAK1tSW\nLlFaWJvFI7GNCijQNffrsTixnz2+CACYeY07iyq8fLwDADgzChAH1rReKIjW/GY5xeP5TWjRmlkX\nnlC6qFVEpSvrHOTdTAQY28+hRLtuZ1e6VOdHTd5jwwg02hYJ2lkm62qe5lipzImqtQ8u5OZK1Sr1\n/mqwrQZQA8DYN7F8LfjG6xTl/MtUTgcAY8wEFCH90U7TP2uMeRqkS760Zn9PTsSENsggg7zx8nri\n0P6SldNnAE6v2f79X24/TsSElnyKfCnV+Qbf+pYvAgC+Y+tzAIjJAgAOqxy3inbe3DQug/bFqSdx\nLXUw+XfhNd2NeClaE0uzJsdQ//1ITkGVuYeAHFYjMHfsrQVpaP/f4knsHfvcTF+erixiRK/Samke\n97xpG3TmzVsbcF7Tiqbe1zUtMM1JOz2cex/UwvN07Rk0PrlxtUFv4f6psURPz8XkS9urp7hdt/2M\nuiRad1ubhaGdu0pj0C42AhcpsHDQUj5z9DCNw8tPAAB2N+l+H924jRdeJi3syib519679RL2Knqm\nzy9o30PZfivvU/d31mTiu+qyyeq/Vy4SgG8XokH5jO1yfrq0nR6XAEHpUpnbHsfY7XIimvkj+Z4/\nL0TRC8f5vYX0v+68ryW0Jso8ft4Pp3x6fO116U6NM0htO92LpWiSO7OaPICpTydiQlu8nR7C97zr\nGXzf6T8BAEz8C74swgNkc2FRMfmewdST7TF9y3Y6x7E3Odns4klrZFeS18miQ9+N0OZEyJjnvQPH\nmEYFJjHnlNIL9/z+LlZfoo/09Kd8DYKlQ0HID9xOabKbPUZtpHkJ5L4uwakDf50A8zheZtwhamvu\nwFZUlXvH90GG5+c0IZzamEl/ukGBQ1+haBIXPfwS1FBos0ubmECAYzSwkvXAcrua4I8vPwYAKDyF\n+N98K9Urfdf4Mp69QQn8R/s0Bp84eEzgNKc8NdOFZL9X7UlDEzhQwPtWLsKE829Vv3VdUqAN35gJ\ndZKTe+HivMH0NJIo3sXelS7CgafUZvR+6aJe4GmlTODu5DJrsl4upzVOJreuqZyYqgdPyWygUg/Z\nGv3EfO5HZsuemwUAcO/+sTeVnIgJbZBBBvkrkGFCuz/yP733dwAA3zx6AbsefDr3q8ojCRUY2cun\nAnLl6tRAqBjFq9D+atyjQw5VgCLEHuyoSSJHnUrr1jhE7Jjv1qY0jYBx3zq5LttvnCN1rNgmjagq\ngIO30L7sUdJIvuXhlwCQNnZ9SRodwzwA4KanouG6nbxg57ed1BybXeR9Btc8/fPRJBQiEdobrmSl\nNFA2j9h0W7m4B2TVWo4QPWoYh1/sNXxjMfOUOxPSkv/mxnNyzFtPE4j2OefrmiLQ8HDAZ10VJ50B\nILm24AI6SU/jym3Zc7iH4Ecp5qV2snNAZB3hpGiMfhwPqrFAcYKWFTSfY2/qM/yFcnk7EA1nhWqp\nmz1yp37zcYkfjwSNbJsq90Co2tWueDW2Rc+0ZnmtJII3qwywjUEGGeSBkROhof0XU1rRz0QZLEjD\nuVnTSva4d3gn4xewV7UBrUUVIBpLD1BtnMFWtpS/AeU3UbTLvApmVuXBrSEc7KbREKiThEGrT02v\nYvvt1N+XHj4FgKAZT0wI8vGeLcoK2YgCaLRsCOJwVIWVeul9gxxYSEpfyu+wkqCAJ6XAqjTi87tc\nEDRiK15ITum1YlPuDyB/Io/b2ZT6lZoqpD5xAMCYnlaqtbaVV6SC1uZg4/ZSvxINoxFH98iX5ttK\nFjibkhaj6392GSOkDVv2GDuo7x4grfIxNWgWCGUMdcoR3681TchHbUKbq44ZJoBZWyF3IXUNoLHt\nFidh0alVrAU3zmDutcEAr9C5nFHrN0LT0rC5XW1xADyO7bzlsWGWjmottAXAYHLeLzllPW7JJJg3\nq84+T9lijjGffh4AcGNF5tozewF3N/VBAablAULUiHFjqa38dBk+dCr+6k1NBPMlINXbL2xiarRd\nzzSxvX18FQDwlnFITdMoc90WXIzCfxScJZHaCkcem1Yv6bGMPNtgul/g+BHqebbvJ+fUCp/9lYRM\n8fFkJSYSR4I3LedcWmx4Jt/Wvfg+iTO7obxIOqddW4Boe+hcxp6NowKnfFbHjWvUj4/NvgYA8N7/\nv713i7EsS8sDv7X27VzjlpGZlXWvwk1fwHQDFh6PJdsaZAvb0iBbIwwPBhuYMQ8gHvxghofRPLYs\nWzYPFojBWDx4hC1LIyObwdK0ZYNxy+rGNNAXoKurL5lVWZkZGRkRJ+Kcs8++rHlY//+vf+99sjIN\nleXo6PVLoYg4Z5+9176ctf7L93/f9Eu4vfC9uecr/wW+np9jn2blXMI/tyU843ElA/R7jiE2Ld8y\n8XExQXc/aNM8/f58ndI+4PAPcpy+SHXlEunS4Pc0HVM/hEzQKrwYPfNa8NgNJ2I23kfZZtIvGt4L\nqQM+/rYG/Y7FokC0aNGulMUJ7dnYF8jlaVAJ3/1LRNh4kFBy29XYI8I+1gxYlrmg6m9ReJcljRAk\nckcBe2CbNhVO/jF5IaltZXWV3jhThpBzyyXqhxc+3GENSL9f3SsYSBHpCXLAERUFSuoV3ZsuMcoo\nKV13s7imcXCU2V1dp9CwgNANrSd+H+d1gb4mqaawYRLF5yiM9wrk/n32uCqX4iFh7qY92AGcggoo\nL4i7Ov6/L3ps5C9//jsBAF97bR/v3PEh+OEtf8yb2dlAldyPt+uhteRhdOEkdP1Mu1XpPXQ7dDsn\nrBnqYbaKmlpj75Kep8XHL1TRgamFqiYZPDv6XjOeTPdqNr1o4LSeyPEDrqxL4KjHNbEbuX7aU9zm\ngbJtU5UHECe0aNGiXQ0zuJpVzksxof2ju38JgM87vDTxyPy/uvsZAMCe9V7Fxjm80/gczfEmkBgy\nQwV3BUzSDc4p8cqe2QERQ5ZNirQHPC3bVMC2DMQFuslgIKyQrQtJ2VJBIxqV4/CfrwdsDuzxvFHt\n4BHlv3j82azFNUqavZ35JD/jYNvUIj+l1X6Xcnt7DfYo0c7n/mAzw01SJOJzZ2GZW8WpnBvnV5rW\nSl4q8KJZycdsQ9wzuFnnvF4hlPzoJTo2QWLuLncxOfDn9GdvvUn7N0pZnfKHphnk6/jaaYDrhetC\nNTrjViDXoEgVOgckN8YcaMqj0cdcuy5URcMq2IPioo3fznR+v5tpkGuSBLGePsSGj5OZZiAG0zgz\nyP3pXKjtJcbWLkPVbPmaxxzas7NPfsm3zMzma7QUUv1O7tHnlbtDv1P89tK/dufCJ5qNcZjQlzqg\n/A1GhCvbSOhJD7M1ksTVIULh6EutqqF91R+dXOXJS4cImmMfAOBS9WD6/TNW6P5mjiUVAIosTBrc\nAWHoYV9f9/t89OGJrKbNiJLg+2uMs24osZOWQmO0Svz+uSCi23p4HHO7EuZXniRyU3ZUnoAwEeem\nUd0G/rpcuEIm/f0pN4V5q1uLazM/oR1mHos3MlWHVLNvEmZxMt4MJ7fM1EpLILRFPS7sap0Fl5qq\nLUy43DGwbjPZR59pNzO1bM+Ty6N6MugokDRDG9IQbL5qSQ3l4GcuHVQyW5l0VYGgCR0U4XrQOSFM\n5jzB60LOuxUGrppdigktWrRo/x0sTmjPxrgHd3+yEgT/Zy+eBwDBnj2qJ/jciYdp3Fv4hPrN+Tl2\n865XkCp9S4ZEMMngOKmwopWLiwLcJQBs13jsdT9iZGoRnxUyP7gBP70OrRK6zEGtx4keJ6/YZZti\nWXWP1k79NucvZQEjd817N0URtD136BrspCs5JqsQ7dE5rZpcwuybxoefPtTrYrcy9Rp7H2vlrQYM\nVDgnvg6pdFcQrXhZ4NrEpwwYqrGXLBXRZCBxlKZwGocmaex7QQmc3KtWYBJDjNo2SxQOjU3f78d5\nMyNbYQLvqXLhymMYtyeitHfGY1sjw9yw6LB/j4tIQIBytEwLhODddzUwut0x1jiBEvUxhI/1zhBD\nzmjRol0lixPas7HnD31P316xkgT3IyJPfHNxCMCXyJnp4fqMVv1iKfmyHRIDWTWZoO+vFRed45Rt\nijMigmRGjuujc1llrxOCfd1myJKuFyErn+kSDcp7QodMUAclZsFJXPYKZkkpvaJlFW4Bw01cTcdK\nKbdzs5YmtdHIr9hF2mCv8J7ZLA06obsk4MF05S2Cjiav6OKBwUrBQif5BZbAlNP85DsM8mvaXAkT\nNQAAIABJREFUXpyd0PWgrgbb4MWRf22qKHTkfFUBpQ9VYIrtzDRyTDavBRogHIC/Z5wHHMBl0Ao7\nB59lq8kWtWoV7ZbHITTeWyiL5sla6WsGEC//7j8nM1t1wLL8Xt+r0nAWvs67VBxbu1ypiXmPTkM5\n2DHkffRzgWIuVjmjRYt2lSx6aM/Gnp/5nM6mSSQPw+IkJyvi88o32B+RR0IcaAf5UrwqzkUc11NU\nXCUib49Vzc+qkZAyNopt41rhK3G84iUYah+yeU/Aj5HzQhWC18YeiHUOfR4qXkUndiMkjuulP+bx\nzgQnC3+u9oz6Uid+3GbSYDTz57xPVcPMttgljU5WiC9sLZVV1iR9izRM9/IVirH37li7s7QZdhN/\nDuLdONPJjwHBS9FtSDrfyLAXVnXnfM9hdi7j4daqi7YYyNLpKvE26wNDR6YKFUrlMTIpY99bqVwy\naAWyaDtQFb8vO/QUt5AjSl+oaYUbjT1z8VxNI321mbTZKQJRGpwWX8l6M0yD0FfLavOtC7lZ3cIm\nnG6q9Yr38TjNzphDe0Y2p3CxTuwgAfzC3E92qW2kuZpv1jxdS4ilkfrXcw8RuL3a7+yrbq2oLBUk\n8Hu6GctDM6VxMPc+EEIx/mJWCqsm5XWXIOlBM/TDy5/VTKs1hZUcXq7qDPVDP6GNHtBrL9CXarfC\nbExjo8b7SboR0WFNhsjj+72HvoCyoB7Ks+lICgUMoVg0o4Hw7bLNcUSapHyd94mI8SC5GLDGNjDS\nZM5N52wTu5H97tA2euLahohn4+eAsWdAgFnkSaNCwNDE/m4mzffqmK3CqbFZ0yWY5InE92F2w+0E\n7vFKU7AykbEt23zQDbBGNiBs5DCxUpoCTE/ksWwBksHbMWypr5OglcAG9h5OaMaY7wHwM/A35Bec\ncx/vvb8P4BcBfBOANYAfds591hjzQQD/Qm36OoD/wzn3j40xB/Teq/CaAt8XdTmjRYs2tKfV5HyK\nSc8YkwD4J/BSdB8B8APGmI/0NvtpAJ9xzn0bgB+En/zgnPsD59zHnHMfA/CdAJYA/h/6zE8B+IRz\n7gMAPkH/v6tdCg+NbZpsME5CghsIak6Vs0hbv/pwj2bdWgkTtQo2s3Fw6MNwhb18hWMKOdnGaYXT\njT/G/bX3TIpJLZ4fr7Ls+YxsJSupTmD3PZ12ixai8MK7FClR7tRp6AtMLsgDIXGv9XMEJM0aKSKw\nvTg5kTBbJ7dbQ90R1HXAXtZOscat0RldD+8llG0mtNJsX14dSpjK149D/G/fuY1DJjBUPZ263xHA\nwOPQ1jrb6b/sWz90XzRjeU+U300m6uWJ4+JAjX6XAdvElkqtyo8pNw2yno7B2mVSvOAQOVBQhbBy\nSae1bPOg2CRFpAAj0apMgKdm6hcztHfP4+fruGuW4ZwVNXk/HG5gB+ccIEOuoxHLZvCehpzfBeAN\n59ybAGCM+WUA3wvg82qbj8CLDcM59/vGmFeNMTe19iaA7wbwJefcV+n/7wXwF+jvXwLwHwD8vXcb\nSPTQokX7BrX/Bl3OJymnvwDgtvr/Dr2m7XcA/HUAMMZ8F4BX4AWJtX0/utqcN51zd+nvdwDcfNI5\nXSoPbdVkuKBkdiormJ9zGc6hrYHFKYmAMEyhbkMZnHNz7KGNkwrHI++R3Ft4byxLGilErKkQcbyZ\nCqEi55tKBtOiBljMROU6eFUdCRAyMEJwvkeDV5PE/21IHTu1Ldqc/qYUXkoem3PBW9ojEO0sKcVL\n4t/LJpfrcYv0RA9HJEiSL/Fy4enMecW+v5mLiMo98k6PVxPJLzJw94w82K+uryEbcy9syKVpr8SP\np5H/Tyn3c3ezK+M+SINGJ18P9ozW7FnQ+SaKKUPnuvq5SmuG7CZ90RRtG5cM4CAtrOqr7HKfef3W\nbuvT2lWDBL32jJi3TBL0Lhtsn2wpOoiSO2xg1FDP/2AfimetkVxa8JKXPSUwsaf30J6knP409nEA\nP2OM+QyA3wPw2+D+OQAkQvw/A/jft33YOedI0/Nd7VJMaFyFrNpERGoZwS8YqzYRUkQuDtRtghW5\n4heEPStsjR0SIOaHQKs+3Zr4L/r5hoSBVyM8N/dhlJAtJo1Mgsuk665XbSqobFGEQni4WNg3U0y4\nUolDCA2ZKmhF5JbLKkNb+C+us9Qr+pAog17OkFB1k4kbj6upVHj5i3Naj2Vi5wLAi0XIoXJ18/7G\nT16fevCyKExtNv6a7s+X+JadB51r+mDlQ/jfPnoBxzt+Qfi2+VsAPP6KOyf6uK475X6H3RXwk+nD\nyo+DJ6DddCXn0CfF1Mn+kSwa4bX1oJejy0rrx5VKUUDjydpegJKZBo1RmDR1LhqnF7oaHMrHsPtW\nLh1UuUemkv13sWZdqUEOL3X1Vy+UfeWoUmHbpENBSCPt+1EUeAvAS+r/F+m1cCivkv63AcAYYwB8\nGcCbapO/DOC/9kLQe8aYW865u6Sifh9PsBhyRov2jWhPGW4+ZZ7tUwA+YIx5jTyt7wfwK3oDY8we\nvQcAPwrg12mSY/sBdMNN0D5+iP7+IQD/+kkDuRQeGtuqyZDTSs500UzpU9ha4cnCPNyHcoyTSjwy\nTRHkLRfP7+W591zeODkUb22PcG7rOsOKiCXPyUsRBSkb7vAIjLJvOqV2wFMVcWGBTbwPW2Fv7N87\nfuS9lYfHM9i539/Fi+yh+c+1xwUWU+428N7V2WaEZubPcy8LYRKTX7KnxiHofrrEO6UP+37jrdcB\nAIuv7gJ0Po68w3a2EsYOvrbnRGt+/2hH8HN8n755en9AP81h41k1Eg3OD4z9wnt3syd6B8wEsmlT\nVGnXk0tYPKFVfYzkJCU2UHbrQkCALISwz3+sldcCVKNBMiBgVD28dK+4ELBuM4Fw6O3Zg5qq7fg9\n9paYCtxfaAz2weOUFAIzZih/YxvZ41rBRwJLSddHaWAej/F7jzw051xtjPlxAP8OHrbxi865zxlj\nfoze/zkAHwbwSxQ2fg7Aj/DnjTFTAH8RwN/p7frjAP6lMeZHAHwVwPc9aSyXakKLFi3a+2fvZeuT\nc+5XAfxq77WfU39/EsA3P+azFwCubXn9IXzl86ntUkxonGexppWeTOlFA+dnrHgMvIqvmgxTgnkw\nar6wNU6qoBbOnwVIoYju4o0R9W3OUxxRjoiZPkZJJd5DQTm0qQlq2RqECvhkPHuPrLiuCwVJD8KR\nmQTXKVn/pcbrVZqTDO7Anws7CglJEOXHFqdzn7tarql/b7TBhPKL7FHN0kCZzV4Nk2FmpsGdpeeR\nq36LVKIeASffQrmZCeW4yhy/ec97cBURGSbslZrQ2fDmqe+xvTU6w2FGgNqWzy+wQTAU5kNjX6x6\nMT/GbkqdGYSkf1RPMKH7yB4Os6zspkthqNDq6oFPjDpL2kR6T/vFgM0Wz0t7YxrlrxPyejyt8gA5\nZ7Vsc/FON73e1sexefTZTVpoLr3ue9a0W+jeFfLfhe+NLkb0z7PfK8oWOwWekYWbn8hE0vaawltn\n5OERYWAYaGJH/s0VsqpXMdtJV6oi57d/brxAThiik5JEgl2CJSH4ObmumW75mNxc3cBKMzXbPFmr\nql93QvModb8Pt6ZQ69iioqKEEU0BOt97Bm3mv/zV8/TWaCOTFoeZLUz4AkoI7ieKVZPjD+77yfOl\nf08FhnGC+rv9QvDagWed/f23b+L+bT8J7b3mw/IPHXhFq/98OoV7x4eciznLsG0kYc0TH0/06ybF\nvaXf13+2Xgnqz+/9IV7K/LFs7q/LF1YvdBS3gBAqT5Kyw9bKv0dSfVRSha5bDNDv8bOQU5ogs4Ek\nUprIMcSwsTVOsftSN8G6zcJ4CS+n8YpsLF1X2GogBFy5BEUvfNbPS7+woM9vIotnKsUJ/mTmwoTW\nlwgE8NSg2a83uxQTWrRo0f47WJzQno1xKJkYN9DSZLPq6p9Roj4xQSuRQ9Msa6RZOzQiq0S+rPZh\nJU4Lv5px+LqoC6E85t81hXdlmyq9gdAcHDoEgtIQewhNr88vM00glsxCI7XgzpiC+xoVNx55Lw0A\nzg7Io8tq5Or4/nq04tmwJ/AS8f2/sbyB9SkVFkrv0dnMSrHj9dkRAODzb7+K+R0/jv1v8d4b358s\na1CT+DE390+SUjwXDhP5ul8fnUunx2cf3KIxOvz5PS8s/VwS1Ke+uvYpFC42FD0tB39tQ7P8Nnrr\nZOjM0PWpB2G/17JsBtux17Z2XbhOglZooLZR8ugeX8B7b9y4zudQtlnoCWaIC0zAJ9phONzvUd24\nVKi3tbZAP5TWeLRtRYH3uFPg0tilmNCiRYv2/ptpr96MdukmtH75mTsGrGklF2ZVEUHT3gCUBG17\n+S+r+uw4cW1D4nqWdDsKVk0mXp10LNAxS5PCkge1T9Q7y6ZQJXfIOLRGpx8vi3Eksl/DvZw7LezG\nnwM7Dtw5UE+NjNtQbq9qrIBsOX9zMzvDPiXcGbbBzBmn1Rigz65v+O3Pb6V4ae9taDv4XYODz/sk\n/xe/03tNN1+jAspZgRmBfbMPBw+qT/bIZIcvzo6FEeRziffQfvfBLQFPf9vOW3T9ciHeXJJQzJ/c\n9ePaTZYh78TAZNjQQ9kBnBJavg9d2JKHAiDc76GTw4UOBddN1GemDn2Vmg3DMZXQELwqrBgSFaQD\nSuyJ3QiQtp+HHZkqiJ70uiD8Z0Mxgz2yWa8QtUY2OCaAmEOLFi3a1bIYcj4jY29o1WTiuWjPjK1P\n2KhzY+yFlW0q+TeGgAQiQTPIs4yTSryrCbEvlEWKE0sVz14PaadlhXNLKoe29fx6T05mGuxl3pOa\nzInBAYBjkOuxz40wL1qbANU+vUfe4arM8XBN0AZq9dpNVnIuA8GSqpB83dnL/vqsboTK8Se+5iFC\nO8sW9sKP6fr/6/f/yf/JVyinX8xR7vntP3p4T86FPQWbBQYJwAuiMKTjT8z9tVrVGb7wwPcY36cK\n6OH4XMbLzB7cCpWYVnnt/te6zQLTBHk3U7sRJg2GabBXo9udAjFlePQ1mafuIQW6kJt+r6h/ntiD\n6iqi69Y3LeTCAiia3FL3ZOrjJMrr1NavhmamBnrbBc/y8c9l9NCesRVJ6AZIew9D2aYSYpU1N+cG\n+MOCNAIerqeSLK9HF7JfALDZSvBO/OBVzqDlUAIBcc9UO9zPyKYnQGmCttWQuM+lAl8Y9YR7E7QS\n5t7c8V/4O1WC/bmf5I4zCpHP/YSSnxpw1GRG9PAnrWgQSPgMI+HWASHt+f+dbI2DG/6cFq/6UHJ0\nH/jSv/WT1Y1P+/E0oxa3/4rHmOULgo18hWAYhy1e+qjHk706edi5Bv48/TXgMPfuZg9fvvDH+oMj\nDxl5Zf8Rrk2XnWu6aVNMKFRmDYKRatqWRmvV68jIfP7iXrS5JPlFVFgwWbWCswR8mdwPnsTQSvjH\nuLJcQSPYNFSIz5/xhzxh+kWuq9ikuwc4KG5gkCnSAiCEnhuXdsSs2fhZy2VS1x0FTsbG/z9O+Sl6\naNGiRbs6Fie0Z2N9IkYgrFZnRCdkTQtLqw73Zp7VYwHDnq2DJ7UzojCOks+pKgBwbyaDUfX+2Aob\nyvwMTNVho3DK0xMxS9aBZQFhNQz6nX1ldiOJ8xem3iM5vpiIEvrzB96Tuv2O7w5I1kYKBK4h2ERj\nsSY68ftr3+kwT4MKEUdK7E1M0w0+fM2TFfzXP+GvS/blOW7835/12y28p3j0k/8j8Gf9mCwxgqTk\nEb80X+B/OPwygKCnkJlGPFYmY/ydU0+FdbSaCfHh4sy/98UqxSvXPJTkkDzom8WZ3Huma2ICR638\nzVAKTWgonppSceqHqJVLFcGjIoEUr4uJEkMXSCg6hJBtSc8ipz7GSTUI6azy3sV7I49qlqwHik3v\nlq7Y5lmNTCXPk4aWBH0BIos0qjCyDSvsoupTtGjRrohFHNozNM4BNc4IiJON82a5aaUn8wF5JIuy\nAHO+7U/8in59fC6g1UYxcACUP+kBEMs2HSRQdfGABVPYG2uVio7+HRLGgWgv5NMIWIsAkuSV/DXK\nRZ3sTXCy9l7M4dh7KW8/5/NKazsOq+yGPIwmLLsiGqM8gHNWHFKr/3HpPb7VA/97/7hF/VGfQ3v0\nIX/sxQcafBPl9T6861ueatrnPF13PDM2zk99aX0dAPDVU98rujMq8ZdufsHv99DDMj5xN/QnM6TD\nGide8jZvpd/juHS53FutE8rj6LdKtbBBD5O+xPqecUnA37sh4SJbH1A7spVAc/jY54pMkc+lQADT\nBr63cBwGyIY8LHnVNvTmssfYIZqkIlbeef66nHSVSx4vIOOu3ox2KSY0LgBUW7jPdSWTQ8hV5R+e\neVHKl+6jU88APLIV7lWeJueNlU9En2z8l7VI6tDnRxPCpk2ls4AxXKVLJawQmhfBvjnVFB7CVv6y\nab7+ovcg6YmTK6oZFQA+OL+H26mfCA5ySppT3+Ybo0OcU8iGksZhHW5M/cTHlcFFMwoN1DRBMGZu\nnGxETyE/9vt48O1A+jf8F2s29p0CeDjHl27763a+8df7tR0fIqamEZJIfe7cZP72yl/3W0SY+bG9\nO/gz0y8CAHaouf+sHuErVCiYKiKCk8qPjZltq8yPcTdZynXjMK1sMyxNt1jTqjCU2Wx11bCfXPfd\nBlwt5KR92B+H0dLvaVq5nxx6Lpug4sRdAdu6CErp+TWdiaxvfeLGEQIOTRojXJisRDLRtPJaXwuh\ncinsYyau6KFFixbtalgE1j47Yze8tg1aSnqz18Y59saZwC5B2pQ7+RrfMfMCMR/KPZygUYwTHHKw\nVsA4qST8ZKxa1SaobDdMa2DD6odhwYKNV97M1AN8m+/R62KOdEjbP/fddIWTtKvA9PLUs13s5mvc\n3fGkiHdOPAXQZpNgRaj6dUoh3/khbhAtEZ/fUTmTcy8rYnh4wXuHr7xwhD+57xH5TLn0m49mSN72\n1+Ed6q+siXDyfF7gJPfbMTU6X0Mg9L2yx3iYLcQze5F0Cr51+hbePPewkPulh3d8ZPa2UApxyMbX\nLDGtfPG0DmpI8hO/v2K+0OEZWx8usU18ODONFAoGrBtu2C85SQLTCO9fK1/1i0H6s+HZaeSzVc+7\nO2kmA4zayCqvjWzZFuKVsgXiS7PVawRiUSBatGhXyOKE9owtNQ3StAuoZS+rbhMhYGSP4M5iD/8x\n+SAA4Hg2k/28Ve519vs8KSBpcKJ4S7aWxK7mSusnpwWqocj0NOPDVl1EgXdA9uv34eRvBkfuJisR\nPWERESFuTEockHo5c7e98eAQtx/4nNujKXGH5RVOKV8oniV5Rqlt8MIusVvk3osYpxU+9eBlAMDJ\nufcO200CQ16xXVNRo1KoevLCzgnI/KicYJz6/R0U3sviIg8ATMizHdFOv3P0FfzW5BUAobjzqJri\nVu7HNsnOaHvKTanE+KL13puGaHDOaGSrTiKcrzOwnVMMGBYgPGNHH/JBHpUrxHuUZL/Khw0IFm3I\n0TFPW2aaDhsL4POeGowLBLiPVa9JPzCC4vtGdU70laCCZmwAC3fM4T0tCvxRldPpvT0AvwDgW2lk\nP+yc+6Qx5v8E8L8CeEC7+Wlixn2sXYoJjR+8DA1WIhzcfQh9p4C/aVwUeHA6w1t3DgAA/2niq3W3\nrp1ilvsvwaszn8zeSVk0WGGKFIaHE+gcjmZ2iA3iSW/Z5gFrxHoGxg46BeY2YML6SW0dvuhQgbd7\ntPGTS0bH2cuW0hp0nTj67+S7OLnw+3t06hPp57slnr92SmOjxvXSfwnfOZ9jnzQTrpEI8fmmENUn\nltXLJhtUVEnlrHFVURFhOR2QAaw3GTaFf38n96kAxpIBwAmd83W6Pq+kDV6nAgR3YZRtiqN61rnO\nozSEkn3anpGtBl90jZYXHQC6x2uXyfbM7z9SZItnrV8EPFFnlwmXzRoHEM5Opx8exxQ7tysZd5jY\nwv5EsyApBy1SbF4G0B+TQ9uJ1RRBocjErL78mr5WjyOtfK+KAko5/S/Ca3J+yhjzK845LTTMyul/\nzRjzIdqe6bV/BsCvOef+FxJS0bmXf+Sc+wdPO5ao+hQt2jequaf8ebKJcrpzbgOAldO1fQTAvwe8\ncjqAV40xN40xuwD+HIB/Su9tnHMnf9RTuhQeWinKTWF+zXor5DQtpczP3PlFWuN268OuauVXpPun\nMzQ71FEwptOjX4UJ9Nkh5KzE+xG6HxVWcui21r18g+bgJlDFiMvvZIXWjchsCYVU/W0AYENFjC+e\neVzX9fG5nDtfK90P+aj2yfXqtMBDElK+MT+XcQDA3dMdLEix6fqOfy+xreyHw7JFUuCErqVZ+GOV\np/5zD6oUCaUE6o2/PiZxKKijgFMC3NDfuoD/akAwFWPxwZEv4BxVMzk2d3AwjCWEULUS/Q0alX0e\n/rwT/nWhC0DwoHRDd9sLUVtnZYkfaGraSp6FBXmd82Q9IJrUmLYgEkzHUV0kW2mx+VhKl4LHyBGF\nfk6CLqeVzoo+3GjtttMHvcfA2m3K6X+6tw0rp/9GTzm9gQ8p/5kx5qMAfgvAT5JwCgD8hDHmBwF8\nGsDfdc49wrtY9NCiRftGNOdg2qf7AXBojPm0+vnf/ghH/DiAPVJO/wkE5fQUwHcA+Fnn3LcDuADw\nU/SZnwXwOoCPAbgL4B8+6SCXykPTlNpBiCSsLtxXuTfxk/St0Slen3uk/YJW+NYZoR7i/TIco9OP\nyfTVthkIdHTpZAL5n/+/6bzfN/bQzpvRQO+x6Xig3eTtsikGXsHR0ntbj9ZjyX8xONYYh92xz5u4\nQ6LnPhujpr5LvgbXqF9ymm7wzoX35M5Lv9o7ZzAt6JqKJmkKkHDL5C0a/4yS0NcMmnF3DZxM1tgl\nGM26R5u+bHM8qD3c5A8UyPUdAj7zdanbZJCz4gKAzn+xB51pHVTavmyzDu203y54OgJxppyULizw\nvnQBQkgXOVGvEu8a0sNeEo+Nz2Ptsi1qXyE3q5kyJMe1BdYjYF4FKu7nd9cuhW35+bedcXj90T+2\nLueRc+5Pvcv7fxzl9AmAO865/0Kb/ivQhKZV1I0x/xeAf/OkgUYPLVq0b1C7DMrpzrl3ANw2xnyQ\n3vtuAJ+nz9xSu/hrAD77pIFcCg+NzcKJZyavqfwTV5cEQGkU60IaaLTLnpo6V0fP6pFAIRgIyyur\nPlZma1nl+zmSBqbTcsJju1d5T6RKQvlcVldq2WIBk8PsPEi9kXfwsJoKuHVDXqmj8T88nmIx8h4L\nVyOd8zkwAJiQl2X3WuyR1/by7FHnunjOMb8d56vuX8zwcOEhIsuNH0eeNkh2Sfrulr+2nJ5KLhLY\nM/La9hoZ4zsL7/mtS7+PR7v+PF7bGYv3+yD12xzVc1FwZ7Gb1yYP5T708z2aylx7VFz9Y9P3kQFW\nE1XZ1LJxADpQhsCQkQ68FsmzIRmQOE6ScqAByvtN4KRdifOvE7ORZ3KDYZ5vG5kje1d8fmWbYUHw\nkZFqs2Prt1Ytt7QTAvDn+R5pCvxxldPhQ9B/ThPemyBPDsDfN8Z8jEb7FQyV1Qd2qSY0oNu7CYQJ\nZZxsQuM1fRHGSSWTnNbl5L/7TeQNwqSo+eGZykeHNvyQFPKw0xfNBWFYHYbyPvQXiz/DfY/cA3h/\nM5dJ9rxmDctMigGMtH9xl8LRdY6SIBrF1O9/XGywQ/CURNherdD1MG+/bvznHtFDgn5M0g1uW4/Z\ne0Q4tEdHc2mAx9xfK0NMtG5j4c7pkWFSxKRFToWC5ZJ6Ou/6Qs29h7v43alfZG9SkaJ1BkuC3XAY\nfbNYyDUqVOKfjZuwE3pcj9upLATcYTBL1iEMFYoe1g4tBxNa05pBuAhgELb2NS70Plpn0ZhukCN9\nm87KM8Hn4hlruyy6ulDUn3wy0wy7TEy4xzpM53Gvt0yKj7X3sPXpj6mc/hkAg5DWOfc3/1vHcekm\ntGjRor0/FpvTn7FVzgpcg0Oli1onO4nRIgkrkyQ8TUD5s/fDv8e0Ak/sRpL8UoiwbWfVBvyKGqiV\nhwnVfpixbAsJLzXRHnsFvJK+UPgw8NOnr+LBaibHB4BRUknPKYecTCX+wsEpjnIfGq7W3gNwzuDm\nxKPqNT0SF0f6+qbTZCMAYz633WyF8Z7/7KOx99D+ENexvkvU3/eIIHNFl7gBmN2pvEbnPi2wf91v\nsHfTF2juL/y5XVyMsLhLhYjbPiR3iZMOhAc3SbWoTfD63INtXyEd0V2iFgJ6pIwATuvQ4/h85iFL\nU1viIYFzjxs/fisQnRoZtz8gl32wB9UoKE8l7BrdxHuDoL+ALdAJDgNLuueFqRXBZ4gUpIeTPt+h\n5R6AeVt5vkOXSTvoKa1cMoga2Nub2M1W2IY/jas3o12qCS1atGjvk0W2jWdnG9V6xNAMXplS1a7U\nL1efVJPO+/y5tLcdeytlmwqEgym4NWcZr56JdZ0SPgCUipmBIQUdVW83BE/yqsk9gMz1dVxOMKc2\noTnlyx6sZ7hHyXUWPymIkjtLWqSUN9mjvNY036AQanF/7HGywUHeJWDUEAP2Tnn1bp1BySpRI8rD\nPV/iD8ce0Ptg6sfbPCCvYmHA+WehBL9IcafxObMR5femI7qH8yUuUmr/YUJKZ9BOaLxjaierMunr\nfK44k+vM59Hn+DpILwbezMSUaBK/X/bQAsFmjtOmy2SiwdOsrdnCDfQt2ZZtHjwty6I3zWAcuqWJ\neeIYS6BVzCXnpVhC2EIOMBUmGJ3T00wd/Jrk4kyXl6/BsC8ZYGDt1ZvRLsWEFkR9G/SxW4w6L2zV\nmZgAP9nxQ76ihPs42cjN1dJ2vE2hHkb/nqp2qcomH4sn1t3EJ595Musb70UTPB5T5fNeuUOv+XN5\nffYQL1FoxaFK4wzKGeHmiFhxRI3l+8VSmtKnNOGPk0pCSM1Pf9BjlO1zzesxZqaR408ROhGya/4z\nqz0fQp6/HqqiHJaPGf3eJCgpDN5QlbOuw+TFiZpdUrSa5hUORv7va4Ufa4uAHZSOATPOq3O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xsFv2GKbE2+yNePe0DLNpNiQ5YwLMhu7RRg2qd+UcNuUXwSe488NOdcbYz5cQD/Dj7T+YvOuc8Z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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Cn = Y.local_correlations(swap_dim=False)\n", - "ax = plt.axes()\n", - "ax.axis('off')\n", - "show_img(ax, Cn)\n", - "print(Y.shape)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From the example above, we can see that 1-photon data typically have large and blurring background that contaminates the detection of neural signal. This makes it very hard to detect neurons and extract their temporal activities. CNMF-E uses spatially filtering to remove the low spatial frequency term and visualize neurons via two summary images: local correlation image and peak-to-noise ratios (PNR) image. Local correlation image highlights the locally correlated spatial structures, and PNR image highlights neurons' calcium transients. \n", - "\n", - "The spatial filtering that roughly removes the background is the key to the sucess of CNMF-E. The kernel is simply generated from a truncated Gaussian kernel. However, the important part is centering the kernel to make its mean equal to 0. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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AVvld4u8tOvwCatxhmaFcUmvusC2l+gxLMXyD0XBMkuakDUtDoqhZsSdBh2hs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\ngMllt8AaMyWwCGAKKN3yISm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01d/Bi254dWD35JLzdUkMWavpVxDJggZr4J9XS/KYjh+f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bgxI3l0SWOp9xKbewmCH2e8QF/HNT4aspj2WOrPw3VO16YX9GW2/TSweBZN+/HHof3dih4Lnluf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A3RAYiOR365MUgHY3YmqbhLzZg6DB2d64jTO7r3Ml1mx+CwBr2aKhuXjdOG19sg67O3eL6r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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# parameters \n", - "gSig = 3 # gaussian width of a 2D gaussian kernel, which approximates a neuron \n", - "gSiz = 10 # average diameter of a neuron \n", - "center_psf = True # If True, the background can be roughly removed. This is useful when the background is strong. \n", - "\n", - "# show correlation image of the raw data; show correlation image and PNR image of the filtered data\n", - "cn_raw = cm.summary_images.local_correlations_fft(Y, swap_dim=False)\n", - "cn_filter, pnr = cm.summary_images.correlation_pnr(Y, gSig=gSig, center_psf=center_psf, swap_dim=False)\n", - "plt.figure(figsize=(10, 5))\n", - "\n", - "for i, (data, title) in enumerate(((Y.mean(0), 'Mean image (raw)'),\n", - " (Y.max(0), 'Max projection (raw)'),\n", - " (cn_raw[1:-1,1:-1], 'Correlation (raw)'),\n", - " (cn_filter, 'Correlation (filtered)'),\n", - " (pnr, 'PNR (filtered)'),\n", - " (cn_filter*pnr, 'Correlation*PNR (filtered)'))):\n", - " plt.subplot(2,3,1+i)\n", - " plt.imshow(data, cmap='jet', aspect='equal')\n", - " plt.axis('off')\n", - " plt.colorbar() \n", - " plt.title(title);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, the summary images resulted from the raw data poorly display isolated neurons. While spatial filtering significanly improves the visualization of single neurons. Our initialization procedure roots from this observation. We pick pixels with large local correlation coefficients and large PNR values, then we use them as seed pixels for initialization spatial & temporal components of all single neurons. \n", - "\n", - "To do initialization, we have to specify two thresholds for detecting seed pixels: the mininum local correlation and the minimum PNR. All pxiels satisfying these two requirements are chosen as our seed pixels. The initialization procedure stops when there are no seed pixels. \n", - "\n", - "When specifying the thresholds, we have to make sure most neurons have seed pixels above the thresholds. Otherwise, we may miss lots of neurons in this step. In the meanwhile, we want to make the thresholds to screen out most pixels that are out of neurons' ROIs. But don't be too picky about the parameter selection. CNMF-E can still pick neurons from the residual after we estimate the background and subtract it from the raw video in the later phase. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# step 2: initialization " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": [ - "/* Put everything inside the global mpl namespace */\n", - "window.mpl = {};\n", - "\n", - "\n", - "mpl.get_websocket_type = function() {\n", - " if (typeof(WebSocket) !== 'undefined') {\n", - " return WebSocket;\n", - " } else if (typeof(MozWebSocket) !== 'undefined') {\n", - " return MozWebSocket;\n", - " } else {\n", - " alert('Your browser does not have WebSocket support.' +\n", - " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", - " 'Firefox 4 and 5 are also supported but you ' +\n", - " 'have to enable WebSockets in about:config.');\n", - " };\n", - "}\n", - "\n", - "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", - " this.id = figure_id;\n", - "\n", - " this.ws = websocket;\n", - "\n", - " this.supports_binary = (this.ws.binaryType != undefined);\n", - "\n", - " if (!this.supports_binary) {\n", - " var warnings = document.getElementById(\"mpl-warnings\");\n", - " if (warnings) {\n", - " warnings.style.display = 'block';\n", - " warnings.textContent = (\n", - " \"This browser does not support binary websocket messages. \" +\n", - " \"Performance may be slow.\");\n", - " }\n", - " }\n", - "\n", - " this.imageObj = new Image();\n", - "\n", - " this.context = undefined;\n", - " this.message = undefined;\n", - " this.canvas = undefined;\n", - " this.rubberband_canvas = undefined;\n", - " this.rubberband_context = undefined;\n", - " this.format_dropdown = undefined;\n", - "\n", - " this.image_mode = 'full';\n", - "\n", - " this.root = $('
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    This is to be used when working with a cluster of machines : This will put dispatch and manage the workload gave by the algorithm :
    \n", - "