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demo_large_data_1p.m
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demo_large_data_1p.m
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%% clear the workspace and select data
% clear; clc; close all;
%% choose data
neuron = Sources2D();
nam = get_fullname('./data_1p.tif'); % this demo data is very small, here we just use it as an example
nam = neuron.select_data(nam); %if nam is [], then select data interactively
%% parameters
% ------------------------- COMPUTATION ------------------------- %
pars_envs = struct('memory_size_to_use', 8, ... % GB, memory space you allow to use in MATLAB
'memory_size_per_patch', 0.6, ... % GB, space for loading data within one patch
'patch_dims', [64, 64]); %GB, patch size
% ------------------------- SPATIAL ------------------------- %
gSig = 3; % pixel, gaussian width of a gaussian kernel for filtering the data. 0 means no filtering
gSiz = 13; % pixel, neuron diameter
ssub = 1; % spatial downsampling factor
with_dendrites = false; % with dendrites or not
if with_dendrites
% determine the search locations by dilating the current neuron shapes
updateA_search_method = 'dilate'; %#ok<UNRCH>
updateA_bSiz = 5;
updateA_dist = neuron.options.dist;
else
% determine the search locations by selecting a round area
updateA_search_method = 'ellipse'; %#ok<UNRCH>
updateA_dist = 5;
updateA_bSiz = neuron.options.dist;
end
spatial_constraints = struct('connected', true, 'circular', false); % you can include following constraints: 'circular'
spatial_algorithm = 'hals_thresh';
% ------------------------- TEMPORAL ------------------------- %
Fs = 10; % frame rate
tsub = 1; % temporal downsampling factor
deconv_flag = true; % run deconvolution or not
deconv_options = struct('type', 'ar1', ... % model of the calcium traces. {'ar1', 'ar2'}
'method', 'foopsi', ... % method for running deconvolution {'foopsi', 'constrained', 'thresholded'}
'smin', -5, ... % minimum spike size. When the value is negative, the actual threshold is abs(smin)*noise level
'optimize_pars', true, ... % optimize AR coefficients
'optimize_b', true, ...% optimize the baseline);
'max_tau', 100); % maximum decay time (unit: frame);
nk = 3; % detrending the slow fluctuation. usually 1 is fine (no detrending)
% when changed, try some integers smaller than total_frame/(Fs*30)
detrend_method = 'spline'; % compute the local minimum as an estimation of trend.
% ------------------------- BACKGROUND ------------------------- %
bg_model = 'ring'; % model of the background {'ring', 'svd'(default), 'nmf'}
nb = 1; % number of background sources for each patch (only be used in SVD and NMF model)
ring_radius = 18; % when the ring model used, it is the radius of the ring used in the background model.
%otherwise, it's just the width of the overlapping area
num_neighbors = []; % number of neighbors for each neuron
bg_ssub = 2; % downsample background for a faster speed
% ------------------------- MERGING ------------------------- %
show_merge = false; % if true, manually verify the merging step
merge_thr = 0.65; % thresholds for merging neurons; [spatial overlap ratio, temporal correlation of calcium traces, spike correlation]
method_dist = 'max'; % method for computing neuron distances {'mean', 'max'}
dmin = 5; % minimum distances between two neurons. it is used together with merge_thr
dmin_only = 2; % merge neurons if their distances are smaller than dmin_only.
merge_thr_spatial = [0.8, 0.4, -inf]; % merge components with highly correlated spatial shapes (corr=0.8) and small temporal correlations (corr=0.1)
% ------------------------- INITIALIZATION ------------------------- %
K = []; % maximum number of neurons per patch. when K=[], take as many as possible.
min_corr = 0.8; % minimum local correlation for a seeding pixel
min_pnr = 8; % minimum peak-to-noise ratio for a seeding pixel
min_pixel = gSig^2; % minimum number of nonzero pixels for each neuron
bd = 0; % number of rows/columns to be ignored in the boundary (mainly for motion corrected data)
frame_range = []; % when [], uses all frames
save_initialization = false; % save the initialization procedure as a video.
use_parallel = true; % use parallel computation for parallel computing
show_init = true; % show initialization results
choose_params = true; % manually choose parameters
center_psf = true; % set the value as true when the background fluctuation is large (usually 1p data)
% set the value as false when the background fluctuation is small (2p)
% ------------------------- Residual ------------------------- %
min_corr_res = 0.7;
min_pnr_res = 6;
seed_method_res = 'auto'; % method for initializing neurons from the residual
update_sn = true;
% ---------------------- WITH MANUAL INTERVENTION -------------------- %
with_manual_intervention = false;
% ------------------------- FINAL RESULTS ------------------------- %
save_demixed = true; % save the demixed file or not
kt = 3; % frame intervals
% ------------------------- UPDATE ALL ------------------------- %
neuron.updateParams('gSig', gSig, ... % -------- spatial --------
'gSiz', gSiz, ...
'ring_radius', ring_radius, ...
'ssub', ssub, ...
'search_method', updateA_search_method, ...
'bSiz', updateA_bSiz, ...
'dist', updateA_bSiz, ...
'spatial_constraints', spatial_constraints, ...
'spatial_algorithm', spatial_algorithm, ...
'tsub', tsub, ... % -------- temporal --------
'deconv_flag', deconv_flag, ...
'deconv_options', deconv_options, ...
'nk', nk, ...
'detrend_method', detrend_method, ...
'background_model', bg_model, ... % -------- background --------
'nb', nb, ...
'ring_radius', ring_radius, ...
'num_neighbors', num_neighbors, ...
'bg_ssub', bg_ssub, ...
'merge_thr', merge_thr, ... % -------- merging ---------
'dmin', dmin, ...
'method_dist', method_dist, ...
'min_corr', min_corr, ... % ----- initialization -----
'min_pnr', min_pnr, ...
'min_pixel', min_pixel, ...
'bd', bd, ...
'center_psf', center_psf);
neuron.Fs = Fs;
%% distribute data and be ready to run source extraction
neuron.getReady(pars_envs);
%% initialize neurons from the video data within a selected temporal range
if choose_params
% change parameters for optimized initialization
[gSig, gSiz, ring_radius, min_corr, min_pnr] = neuron.set_parameters();
end
[center, Cn, PNR] = neuron.initComponents_parallel(K, frame_range, save_initialization, use_parallel);
neuron.compactSpatial();
if show_init
figure();
ax_init= axes();
imagesc(Cn, [0, 1]); colormap gray;
hold on;
plot(center(:, 2), center(:, 1), '.r', 'markersize', 10);
end
%% estimate the background components
neuron.update_background_parallel(use_parallel);
neuron_init = neuron.copy();
%% merge neurons and update spatial/temporal components
neuron.merge_neurons_dist_corr(show_merge);
neuron.merge_high_corr(show_merge, merge_thr_spatial);
%% update spatial components
%% pick neurons from the residual
[center_res, Cn_res, PNR_res] =neuron.initComponents_residual_parallel([], save_initialization, use_parallel, min_corr_res, min_pnr_res, seed_method_res);
if show_init
axes(ax_init);
plot(center_res(:, 2), center_res(:, 1), '.g', 'markersize', 10);
end
neuron_init_res = neuron.copy();
%% udpate spatial&temporal components, delete false positives and merge neurons
% update spatial
if update_sn
neuron.update_spatial_parallel(use_parallel, true);
udpate_sn = false;
else
neuron.update_spatial_parallel(use_parallel);
end
% merge neurons based on correlations
neuron.merge_high_corr(show_merge, merge_thr_spatial);
for m=1:2
% update temporal
neuron.update_temporal_parallel(use_parallel);
% delete bad neurons
neuron.remove_false_positives();
% merge neurons based on temporal correlation + distances
neuron.merge_neurons_dist_corr(show_merge);
end
%% add a manual intervention and run the whole procedure for a second time
neuron.options.spatial_algorithm = 'nnls';
if with_manual_intervention
show_merge = true;
neuron.orderROIs('snr'); % order neurons in different ways {'snr', 'decay_time', 'mean', 'circularity'}
neuron.viewNeurons([], neuron.C_raw);
% merge closeby neurons
neuron.merge_close_neighbors(true, dmin_only);
% delete neurons
tags = neuron.tag_neurons_parallel(); % find neurons with fewer nonzero pixels than min_pixel and silent calcium transients
ids = find(tags>0);
if ~isempty(ids)
neuron.viewNeurons(ids, neuron.C_raw);
end
end
%% run more iterations
neuron.update_background_parallel(use_parallel);
neuron.update_spatial_parallel(use_parallel);
neuron.update_temporal_parallel(use_parallel);
K = size(neuron.A,2);
tags = neuron.tag_neurons_parallel(); % find neurons with fewer nonzero pixels than min_pixel and silent calcium transients
neuron.remove_false_positives();
neuron.merge_neurons_dist_corr(show_merge);
neuron.merge_high_corr(show_merge, merge_thr_spatial);
if K~=size(neuron.A,2)
neuron.update_spatial_parallel(use_parallel);
neuron.update_temporal_parallel(use_parallel);
neuron.remove_false_positives();
end
%% save the workspace for future analysis
neuron.orderROIs('snr');
cnmfe_path = neuron.save_workspace();
%% show neuron contours
Coor = neuron.show_contours(0.6);
%% create a video for displaying the
amp_ac = 140;
range_ac = 5+[0, amp_ac];
multi_factor = 10;
range_Y = 1300+[0, amp_ac*multi_factor];
avi_filename = neuron.show_demixed_video(save_demixed, kt, [], amp_ac, range_ac, range_Y, multi_factor);
%% save neurons shapes
neuron.save_neurons();