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Sources2D.m
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Sources2D.m
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classdef Sources2D < handle
% This class is a wrapper of Constrained NMF for standard 2D data. It
% Both CNMF and CNMF-E model can be used.
% Author: Pengcheng Zhou, Columbia University, 2017
% zhoupc1988@gmail.com
%% properties
properties
% spatial
A; % spatial components of neurons
A_prev; % previous estimation of A
% temporal
C; % temporal components of neurons
C_prev; % previous estimation of C
C_raw; % raw traces of temporal components
S; % spike counts
kernel; % calcium dynamics. this one is less used these days.
% background
b; % spatial components of backgrounds
f; % temporal components of backgrounds
W; % a sparse weight matrix matrix
b0; % constant baselines for each pixel
b0_new; % correct the changes in b0 when we update A & C
% optiosn
options; % options for model fitting
P; % some estimated parameters or parameters relating to data
% data info
Fs = nan; % frame rate
file = '';
frame_range; % frame range of the data
%quality control
ids; % unique identifier for each neuron
tags; % tags bad neurons with multiple criterion using a 16 bits number
% ai indicates the bit value of the ith digits from
% right to the left
% a1 = 1 means that neuron has too few nonzero pixels
% a2 = 1 means that neuron has silent calcium transients
% a3 = 1 indicates that the residual after being deconvolved has
% much smaller std than the raw data, which is uaually the case
% when temporal traces are random noise
% a4 = 1 indicates that the CNMF-E fails in deconvolving temporal
% traces. this usually happens when neurons are false positives.
% a4 = 1 indicates that the neuron has small PNR
%others
Cn; % correlation image
PNR; % peak-to-noise ratio image.
Coor; % neuron contours
neurons_per_patch;
Df; % background for each component to normalize the filtered raw data
C_df; % temporal components of neurons and background normalized by Df
S_df; % spike counts of neurons normalized by Df
batches = cell(0); % results for each small batch data
file_id = []; % file id for each batch.
end
%% methods
methods
%% constructor and options setting
function obj = Sources2D(varargin)
obj.options = CNMFSetParms();
obj.P =struct('mat_file', [], 'mat_data', [], 'indicator', '', 'k_options', 0, ...
'k_snapshot', 0, 'k_del', 0, 'k_merge', 0, 'k_trim', 0, 'sn', [], ...
'kernel_pars',[], 'k_ids', 0);
if nargin>0
obj.options = CNMFSetParms(obj.options, varargin{:});
end
obj.kernel = create_kernel('exp2');
end
%------------------------------------------------------------------OPTIONS------------%
%% update parameters
function updateParams(obj, varargin)
obj.options = CNMFSetParms(obj.options, varargin{:});
end
%------------------------------------------------------------------PREPROCESSING-------%
%% data preprocessing
function Y = preprocess(obj,Y,p)
[obj.P,Y] = preprocess_data(Y,p,obj.options);
end
%------------------------------------------------------------------LOAD DATA-----------%
%% select data
function nam = select_data(obj, nam)
%% select file
if ~exist('nam', 'var') || isempty(nam) || ~exist(nam, 'file')
% choose files using graphical interfaces
try
load .dir.mat; %load previous path
catch
dir_nm = [cd(), filesep]; %use the current path
end
[file_nm, dir_nm] = uigetfile(fullfile(dir_nm, '*.tif;*.mat;*.h5;*.avi'));
nam = [dir_nm, file_nm]; % full name of the data file
else
% file has been selected
nam = get_fullname(nam);
[dir_nm, ~, ~] = fileparts(nam);
end
if dir_nm~=0
try
save .dir.mat dir_nm;
catch
fprintf('You do not have the access to write file to the current folder');
end
else
fprintf('no file was selected. STOP!\n');
return;
end
obj.file = nam;
end
%% select multiple datasets
function nams = select_multiple_files(obj, nams)
if ~isempty(nams)
nams = unique(nams);
ind_keep = true(size(nams));
for m=1:length(nams)
if ~exist(nams{m}, 'file')
ind_keep = false;
else
nams{m} = get_fullname(nams{m});
end
end
nams = nams(ind_keep);
fprintf('\n----------------------------------------\n');
for m=1:length(nams)
fprintf('%d:\t%s\n', m, nams{m});
end
fprintf('\n----------------------------------------\n');
obj.file = nams;
return;
end
fprintf('\t-------------------------- GUIDE --------------------------\n');
fprintf('\ttype -i to remove the selected i-th file\n');
fprintf('\ttype 0 to stop the data selection\n');
fprintf('\ttype anything else to select more data\n');
fprintf('\t-------------------------- END --------------------------\n\n');
if ~exist('nams', 'var') || isempty(nams)
nams = cell(0);
end
while true
if isempty(nams)
fprintf('You haven''t selected any file yet\n');
else
ind_keep = true(size(nams));
for m=1:length(nams)
if ~exist(nams{m}, 'file')
ind_keep = false;
end
end
nams = nams(ind_keep);
fprintf('\n----------------------------------------\n');
for m=1:length(nams)
fprintf('%d:\t%s\n', m, nams{m});
end
fprintf('\n----------------------------------------\n');
end
% make choise
your_ans = input('make your choice: ');
if your_ans==0
break;
elseif your_ans<0 && round(abs(your_ans))<length(nams)
nams(round(abs(your_ans))) = [];
else
nams{end+1} = obj.select_data([]); %#ok<AGROW>
end
end
obj.file = unique(nams);
end
%% load the patched file into the memory
function map_data_to_memory(obj)
mat_file = obj.P.mat_file;
if exist(mat_file, 'file')
data_nam = sprintf('mat_data_%d', string2hash(mat_file));
if isempty(evalin('base', sprintf('whos(''%s'')', data_nam)))
% the data has not been mapped yet
evalin('base', sprintf('%s=load(''%s''); ', data_nam, mat_file));
end
end
end
%% load data within selected patch position and selected frames
function Ypatch = load_patch_data(obj, patch_pos, frame_range)
mat_data = obj.P.mat_data;
dims = mat_data.dims;
d1 = dims(1);
d2 = dims(2);
T = dims(3);
if ~exist('patch_pos', 'var') || isempty(patch_pos)
patch_pos = [1, d1, 1, d2];
end
if ~exist('frame_range', 'var') || isempty(frame_range)
frame_range = [1, T];
end
Ypatch = get_patch_data(mat_data, patch_pos, frame_range, false);
Ypatch(isnan(Ypatch)) = 0;
end
%% distribute data and be ready to run source extraction
function getReady(obj, pars_envs, filter_kernel)
% data and its extension
if iscell(obj.file)
nam = obj.file{1};
else
nam = obj.file;
end
% parameters for scaling things
if ~exist('pars_envs', 'var') || isempty(pars_envs)
memory_size_to_use = 16.0; %GB
memory_size_per_patch = 1.0; % GB;
patch_dims = [64, 64];
else
memory_size_to_use = pars_envs.memory_size_to_use;
memory_size_per_patch = pars_envs.memory_size_per_patch;
patch_dims = pars_envs.patch_dims;
end
if ~exist('filter_kernel', 'var')
filter_kernel = [];
end
% overlapping area
w_overlap = obj.options.ring_radius;
% distribute data
[data, dims, obj.P.folder_analysis] = distribute_data(nam,...
patch_dims, w_overlap, memory_size_per_patch, memory_size_to_use,...
[], filter_kernel);
obj.P.mat_data = data;
obj.P.mat_file = data.Properties.Source;
obj.updateParams('d1', dims(1), 'd2', dims(2));
obj.P.numFrames = dims(3);
% estimate the noise level for each pixel
try
obj.P.sn = obj.P.mat_data.sn;
catch
obj.P.sn = obj.estimate_noise();
mat_data = obj.P.mat_data;
mat_data.Properties.Writable = true;
mat_data.sn = obj.P.sn;
mat_data.Properties.Writable = false;
end
%% map the data to the memory
temp = cast(0, data.dtype);
temp = whos('temp');
data_space = prod(data.dims) * temp.bytes / (2^30);
if data_space < memory_size_to_use
obj.map_data_to_memory();
end
end
%% distribute data and be ready to run batch mode source extraction
function getReady_batch(obj, pars_envs, log_folder)
%% parameters for scaling things
if ~exist('pars_envs', 'var') || isempty(pars_envs)
pars_envs = struct('memory_size_to_use', 4, ... % GB, memory space you allow to use in MATLAB
'memory_size_per_patch', 0.3, ... % GB, space for loading data within one patch
'patch_dims', [64, 64], ... %GB, patch size
'batch_frames', []); % number of frames per batch
end
batch_frames = pars_envs.batch_frames;
if ~exist('log_folder', 'var')||isempty(log_folder)||(~exist(log_folder, 'dir'))
obj.P.log_folder = [cd(), filesep];
else
obj.P.log_folder = log_folder;
end
%% distribute all files
nams = unique(obj.file);
obj.file = nams;
% pre-allocate spaces for saving results of multiple patches
batches_ = cell(100, 1);
file_id_ = zeros(100,1, 'like', uint8(1));
numFrames = zeros(100,1);
k_batch = 0;
for m=1:length(nams)
neuron = obj.copy();
neuron.file = nams{m};
neuron.getReady(pars_envs);
if m==1
obj.options = neuron.options;
end
T = neuron.P.numFrames;
if isempty(batch_frames)
batch_frames = T;
end
nbatches = round(T/batch_frames);
if nbatches<=1
frame_range_t = [1, T];
else
frame_range_t = round(linspace(1, T, nbatches+1));
end
nbatches = length(frame_range_t) -1;
for n=1:nbatches
k_batch = k_batch +1;
tmp_neuron = neuron.copy();
tmp_neuron.frame_range = frame_range_t(n:(n+1))-[0, 1*(n~=nbatches)];
batch_i.neuron = tmp_neuron;
batch_i.shifts = []; % shifts allowed
file_id_(k_batch) = m;
batches_{k_batch} = batch_i;
numFrames(k_batch) = diff(tmp_neuron.frame_range)+1;
end
end
obj.batches = batches_(1:k_batch);
obj.file_id = file_id_(1:k_batch);
obj.P.numFrames = numFrames(1:k_batch);
end
%% estimate noise
function sn = estimate_noise(obj, frame_range, method)
mat_data = obj.P.mat_data;
dims = mat_data.dims;
T = dims(3);
if ~exist('frame_range', 'var') || isempty(frame_range)
frame_range = [1, min(T, 3000)];
end
T = diff(frame_range)+1;
if ~exist('method', 'var') || isempty(method)
method = 'psd';
end
if strcmpi(method, 'hist')
% fit the histogram of with a parametric gaussian shape.
% it works badly when the
% most frames are involved in calcium transients.
foo = @(Y) GetSn_hist(Y, false);
elseif strcmpi(method, 'std')
% simly use the standard deviation. works badly when
% neurons have large calcium transients
foo = @(Y) std(Y, 0, 2);
else
% default options is using the power spectrum method.
% works badly when noises have temporal correlations
foo = @GetSn;
end
block_idx_r = mat_data.block_idx_r;
block_idx_c = mat_data.block_idx_c;
nr_block = length(block_idx_r) - 1;
nc_block = length(block_idx_c) - 1;
sn = cell(nr_block, nc_block);
fprintf('estimating the nosie level for every pixel.......\n');
tic;
for mblock=1:(nr_block*nc_block)
[m, n] = ind2sub([nr_block, nc_block], mblock);
r0 = block_idx_r(m); %#ok<PFBNS>
r1 = block_idx_r(m+1);
c0 = block_idx_c(n); %#ok<PFBNS>
c1 = block_idx_c(n+1);
Ypatch = get_patch_data(mat_data, [r0, r1, c0, c1], frame_range, false);
Ypatch = double(reshape(Ypatch, [], T));
tmp_sn = reshape(foo(Ypatch), r1-r0+1, c1-c0+1);
if m~=nr_block
tmp_sn(end-1, :) = [];
end
if n~=nc_block
tmp_sn(:, end-1) = [];
end
sn{mblock} = tmp_sn;
end
fprintf('Time cost for estimating the nosie levels: %.3f \n\n', toc);
sn = cell2mat(sn);
end
%% pick parameters
[gSig, gSiz, ring_radius, min_corr, min_pnr] = set_parameters(obj)
%------------------------------------------------------------------INITIALIZATION-----------%
%% fast initialization, for 2P data, CNMF
function [center] = initComponents(obj, Y, K, tau)
if nargin<4 ; tau = []; end
[obj.A, obj.C, obj.b, obj.f, center] = initialize_components(Y, K, tau, obj.options);
end
%% initialize neurons using patch method
% for 1P and 2P data, CNMF and CNMF-E
[center, Cn, PNR] = initComponents_parallel(obj, K, frame_range, save_avi, use_parallel, use_prev)
%% initialize neurons for multiple batches
% for 1P and 2P data, CNMF and CNMF-E
[center, Cn, PNR] = initComponents_batch(obj, K, frame_range, save_avi, use_parallel)
%% fast initialization for microendoscopic data
[center, Cn, pnr] = initComponents_endoscope(obj, Y, K, patch_sz, debug_on, save_avi);
[center] = initComponents_2p(obj,Y, K, options, sn, debug_on, save_avi);
%% pick neurons from the residual
% for 1P and 2P data, CNMF and CNMF-E
[center, Cn, PNR] = initComponents_residual_parallel(obj, K, save_avi, use_parallel, min_corr, min_pnr, seed_method)
%------------------------------------------------------------------UPDATE MODEL VARIABLES---%
%% update background components in parallel
% for 1P and 2P data, CNMF and CNMF-E
update_background_parallel(obj, use_parallel)
%% update background for all batches
update_background_batch(obj, use_parallel)
%% update spatial components in parallel
% for 1P and 2P data, CNMF and CNMF-E
update_spatial_parallel(obj, use_parallel, update_sn)
%% update spatial components in batch mode
update_spatial_batch(obj, use_parallel);
%% update temporal components in parallel
% for 1P and 2P data, CNMF and CNMF-E
update_temporal_parallel(obj, use_parallel, use_c_hat)
%% update temporal components in batch mode
% for 1P and 2P data, CNMF and CNMF-E
update_temporal_batch(obj, use_parallel)
%% update spatial components, 2P data, CNMF
function updateSpatial(obj, Y)
[obj.A, obj.b, obj.C] = update_spatial_components(Y, ...
obj.C, obj.f, obj.A, obj.P, obj.options);
end
%% udpate spatial components without background, no background components
function updateSpatial_nb(obj, Y)
[obj.A, obj.C] = update_spatial_components_nb(Y, ...
obj.C, obj.A, obj.P, obj.options);
end
%% udpate spatial components using NNLS and thresholding
function updateSpatial_nnls(obj, Y)
[obj.A, obj.C] = update_spatial_nnls(Y, ...
obj.C, obj.A, obj.P, obj.options);
end
%% update temporal components for endoscope data
updateSpatial_endoscope(obj, Y, numIter, method, IND_thresh);
%% update temporal components
function updateTemporal(obj, Y)
[obj.C, obj.f, obj.P, obj.S] = update_temporal_components(...
Y, obj.A, obj.b, obj.C, obj.f, obj.P, obj.options);
end
%% udpate temporal components with fast deconvolution
updateTemporal_endoscope(obj, Y, allow_deletion)
updateTemporal_endoscope_parallel(obj, Y, smin)
%% update temporal components without background
function updateTemporal_nb(obj, Y)
[obj.C, obj.P, obj.S] = update_temporal_components_nb(...
Y, obj.A, obj.b, obj.C, obj.f, obj.P, obj.options);
end
%------------------------------------------------------------------MERGE NEURONS ---------%
%% merge found components
function [nr, merged_ROIs] = merge(obj, Y)
[obj.A, obj.C, nr, merged_ROIs, obj.P, obj.S] = merge_components(...
Y,obj.A, [], obj.C, [], obj.P,obj.S, obj.options);
end
%% quick merge neurons based on spatial and temporal correlation
[merged_ROIs, newIDs] = quickMerge(obj, merge_thr)
%% quick merge neurons based on spatial and temporal correlation
[merged_ROIs, newIDs] = MergeNeighbors(obj, dmin, method)
%------------------------------------------------------------------EXPORT---%
function save_neurons(obj, folder_nm, with_craw)
if ~exist('folder_nm', 'var') || isempty(folder_nm)
folder_nm = [obj.P.log_folder, 'neurons'];
end
if ~exist('with_craw', 'var')||isempty(with_craw)
with_craw = true;
end
if exist(folder_nm, 'dir')
temp = cd();
cd(folder_nm);
delete *;
cd(temp);
else
mkdir(folder_nm);
end
if with_craw
obj.viewNeurons([], obj.C_raw, folder_nm);
else
obj.viewNeurons([], [], folder_nm);
end
end
%% export the result as a video
avi_filename = show_demixed_video(obj,save_avi, kt, frame_range, center_ac, range_ac, range_Y, multi_factor, use_craw) %% compute the residual
% function [Y_res] = residual(obj, Yr)
% Y_res = Yr - obj.A*obj.C - obj.b*obj.f;
% end
%% take the snapshot of current results
function [A, C, b, f, P, S] = snapshot(obj)
A = obj.A;
C = obj.C;
b = obj.b;
f = obj.f;
P = obj.P;
try
S = obj.S;
catch
S = [];
end
end
%% extract DF/F signal after performing NMF
function [C_df, Df, S_df] = extractDF_F(obj, Y, i)
if ~exist('i', 'var')
i = size(obj.A, 2) + 1;
end
[obj.C_df, obj.Df, obj.S_df] = extract_DF_F(Y, [obj.A, obj.b],...
[obj.C; obj.f], obj.S, i);
C_df = obj.C_df;
Df = obj.Df;
S_df = obj.S_df;
end
%% extract DF/F signal for microendoscopic data
function [C_df,C_raw_df, Df] = extract_DF_F_endoscope(obj, Ybg)
options_ = obj.options;
A_ = obj.A;
C_ = obj.C;
[K,T] = size(obj.C); % number of frames
Ybg = bsxfun(@times, A_, 1./sum(A_.^2, 1))' * Ybg; % estimate the background for each neurons
if isempty(options_.df_window) || (options_.df_window > T)
% use quantiles of the whole recording session as the
% baseline
if options_.df_prctile == 50
Df = median(Ybg,2);
else
Df = prctile(Ybg,options_.df_prctile,2);
end
C_df = spdiags(Df,0,K,K)\C_;
C_raw_df = spdiags(Df,0,K,K)\obj.C_raw;
else
% estimate the baseline for each short period
if options_.df_prctile == 50
Df = medfilt1(Ybg,options_.df_window,[],2,'truncate');
else
Df = zeros(size(Ybg));
for i = 1:size(Df,1)
df_temp = running_percentile(Ybg(i,:), options_.df_window, options_.df_prctile);
Df(i,:) = df_temp(:)';
end
end
C_df = C_./Df;
C_raw_df = obj.C_raw./Df;
end
end
%% order_ROIs
function [srt] = orderROIs(obj, srt)
%% order neurons
% srt: sorting order
nA = sqrt(sum(obj.A.^2));
nr = length(nA);
if nargin<2
srt='srt';
end
K = size(obj.C, 1);
if ischar(srt)
if strcmpi(srt, 'decay_time')
% time constant
if K<0
disp('Are you kidding? You extracted 0 neurons!');
return;
else
taud = zeros(K, 1);
for m=1:K
temp = ar2exp(obj.P.kernel_pars(m));
taud(m) = temp(1);
end
[~, srt] = sort(taud);
end
elseif strcmp(srt, 'mean')
if obj.options.deconv_flag
temp = mean(obj.C,2)'.*sum(obj.A);
else
temp = mean(obj.C,2)'.*sum(obj.A)./obj.P.neuron.sn';
end
[~, srt] = sort(temp, 'descend');
elseif strcmp(srt, 'sparsity_spatial')
temp = sqrt(sum(obj.A.^2, 1))./sum(abs(obj.A), 1);
[~, srt] = sort(temp);
elseif strcmp(srt, 'sparsity_temporal')
temp = sqrt(sum(obj.C_raw.^2, 2))./sum(abs(obj.C_raw), 2);
[~, srt] = sort(temp, 'descend');
elseif strcmp(srt, 'circularity')
% order neurons based on its circularity
tmp_circularity = zeros(K,1);
for m=1:K
[w, r] = nnmf(obj.reshape(obj.A(:, m),2), 1);
ky = sum(w>max(w)*0.3);
kx = sum(r>max(r)*0.3);
tmp_circularity(m) = abs((kx-ky+0.5)/((kx+ky)^2));
end
[~, srt] = sort(tmp_circularity, 'ascend');
elseif strcmpi(srt, 'pnr')
pnrs = max(obj.C, [], 2)./std(obj.C_raw-obj.C, 0, 2);
[~, srt] = sort(pnrs, 'descend');
elseif strcmpi(srt, 'temporal_cluster')
obj.orderROIs('pnr');
dd = pdist(obj.C_raw, 'cosine');
tree = linkage(dd, 'complete');
srt = optimalleaforder(tree, dd);
elseif strcmpi(srt, 'spatial_cluster')
obj.orderROIs('pnr');
A_ = bsxfun(@times, obj.A, 1./sqrt(sum(obj.A.^2, 1)));
temp = 1-A_' * A_;
dd = temp(tril(true(size(temp)), -1));
dd = reshape(dd, 1, []);
tree = linkage(dd, 'complete');
srt = optimalleaforder(tree, dd);
else %if strcmpi(srt, 'snr')
snrs = var(obj.C, 0, 2)./var(obj.C_raw-obj.C, 0, 2);
[~, srt] = sort(snrs, 'descend');
end
end
obj.A = obj.A(:, srt);
obj.C = obj.C(srt, :);
try
obj.C_raw = obj.C_raw(srt,:);
obj.S = obj.S(srt,:);
obj.P.kernel_pars = obj.P.kernel_pars(srt, :);
obj.P.neuron_sn = obj.P.neuron_sn(srt);
obj.ids = obj.ids(srt);
obj.tags = obj.tags(srt);
end
end
%% view contours
function [Coor, json_file] = viewContours(obj, Cn, contour_threshold, display, ind, ln_wd)
if or(isempty(Cn), ~exist('Cn', 'var') )
Cn = reshape(obj.P.sn, obj.options.d1, obj.options.d2);
end
if (~exist('display', 'var') || isempty(display)); display=0; end
if (~exist('ind', 'var') || isempty(ind)); ind=1:size(obj.A, 2); end
if (~exist('ln_wd', 'var') || isempty(ln_wd)); ln_wd = 1; end
[obj.Coor, json_file] = plot_contours(obj.A(:, ind), Cn, ...
contour_threshold, display, [], [], ln_wd);
Coor = obj.Coor;
end
%% plot components
function plotComponents(obj, Y, Cn)
if ~exist('Cn', 'var')
Cn = [];
end
view_components(Y, obj.A, obj.C, obj.b, obj.f, Cn, obj.options);
end
%% plot components GUI
function plotComponentsGUI(obj, Y, Cn)
if ~exist('Cn', 'var')
Cn = [];
end
plot_components_GUI(Y,obj.A,obj.C,obj.b,obj.f,Cn,obj.options)
end
%% make movie
function makePatchVideo(obj, Y)
make_patch_video(obj.A, obj.C, obj.b, obj.f, Y, obj.Coor,...
obj.options);
end
%% down sample data and initialize it
[obj_ds, Y_ds] = downSample(obj, Y)
%% up-sample the results
upSample(obj, obj_ds, T);
%% copy the objects
function obj_new = copy(obj)
% Instantiate new object of the same class.
obj_new = feval(class(obj));
% Copy all non-hidden properties.
p = properties(obj);
for i = 1:length(p)
obj_new.(p{i}) = obj.(p{i});
end
end
%% concatenate results from multi patches
function concatenate_temporal_batch(obj)
T_k= obj.P.numFrames;
T = sum(T_k);
K = size(obj.A,2);
C_ = zeros(K, T);
C_raw_ = zeros(K, T);
deconv_flag = obj.options.deconv_flag;
if deconv_flag
S_ = zeros(K, T);
end
t = 0;
nbatches = length(obj.batches);
for mbatch=1:nbatches
neuron_k = obj.batches{mbatch}.neuron;
try
C_(:, t+(1:T_k(mbatch))) = neuron_k.C;
catch
pause;
end
C_raw_(:, t+(1:T_k(mbatch))) = neuron_k.C_raw;
if deconv_flag
S_(:, t+(1:T_k(mbatch))) = neuron_k.S;
end
t = t+T_k(mbatch);
end
obj.C = C_;
obj.C_raw = C_raw_;
if deconv_flag
obj.S = S_;
end
end
%% quick view
ind_del = viewNeurons(obj, ind, C2, folder_nm);
displayNeurons(obj, ind, C2, folder_nm);
%% function remove false positives
function ids = remove_false_positives(obj, show_delete)
if ~exist('show_delete', 'var')
show_delete = false;
end
tags_ = obj.tag_neurons_parallel(); % find neurons with fewer nonzero pixels than min_pixel and silent calcium transients
ids = find(tags_);
if isempty(ids)
fprintf('all components are good \n');
else
if show_delete
obj.viewNeurons(ids, obj.C_raw);
else
obj.delete(ids);
end
end
end
%% delete neurons
function delete(obj, ind)
% write the deletion into the log file
if ~exist('ind', 'var') || isempty(ind)
return;
end
try
% folders and files for saving the results
log_file = obj.P.log_file;
flog = fopen(log_file, 'a');
log_data = matfile(obj.P.log_data, 'Writable', true); %#ok<NASGU>
ids_del = obj.ids(ind);
if isempty(ids_del)
fprintf('no neurons to be deleted\n');
fclose(flog);
return;
end
fprintf(flog, '[%s]\b', get_minute());
fprintf('Deleted %d neurons: ', length(ids_del));
fprintf(flog, 'Deleted %d neurons: \n', length(ids_del));
for m=1:length(ids_del)
fprintf('%2d, ', ids_del(m));
fprintf(flog, '%2d, ', ids_del(m));
end
fprintf('\n');
fprintf(flog, '\n');
fprintf('\nThe IDS of these neurons were saved as intermediate_results.ids_del_%s\n\n', tmp_str);
if obj.options.save_intermediate
tmp_str = get_date();
tmp_str=strrep(tmp_str, '-', '_');
eval(sprintf('log_data.ids_del_%s = ids_del;', tmp_str));
fprintf(flog, '\tThe IDS of these neurons were saved as intermediate_results.ids_del_%s\n\n', tmp_str);
end
fclose(flog);
end
obj.A(:, ind) = [];
obj.C(ind, :) = [];
if ~isempty(obj.S)
try obj.S(ind, :) = []; catch; end
end
if ~isempty(obj.C_raw)
try obj.C_raw(ind, :) = []; catch; end
end
if isfield(obj.P, 'kernel_pars')&&( ~isempty(obj.P.kernel_pars))
try obj.P.kernel_pars(ind, :) = []; catch; end
end
try obj.ids(ind) = []; catch; end
try obj.tags(ind) =[]; catch; end
% save the log
end
%% merge neurons
%% estimate neuron centers
function [center] = estCenter(obj)
center = com(obj.A, obj.options.d1, obj.options.d2);
end
%% update A & C using HALS
function [obj, IDs] = HALS_AC(obj, Y)
%update A,C,b,f with HALS
Y = obj.reshape(Y, 1);
[obj.A, obj.C, obj.b, obj.f, IDs] = HALS_2d(Y, obj.A, obj.C, obj.b,...
obj.f, obj.options);
end
%% update backgrounds
[B, F] = updateBG(obj, Y, nb, model)
%% reshape data
function Y = reshape(obj, Y, dim)
% reshape the imaging data into diffrent dimensions
d1 = obj.options.d1;
d2 = obj.options.d2;
if dim==1
Y=reshape(Y, d1*d2, []); %each frame is a vector
else
Y = reshape(full(Y), d1, d2, []); %each frame is an image
end
end
%% deconvolve all temporal components
C_ = deconvTemporal(obj, use_parallel, method_noise)
%% decorrelate all tmeporal components
C_ = decorrTemporal(obj, wd);
%% play movie
function playMovie(obj, Y, min_max, col_map, avi_nm, t_pause)
Y = double(Y);
d1 = obj.options.d1;
d2 = obj.options.d2;
% play movies
figure('papersize', [d2,d1]/max(d1,d2)*5);
width = d2/max(d1,d2)*500;
height =d1/max(d1,d2)*500;
set(gcf, 'position', [500, 200, width, height]);
axes('position', [0,0, 1, 1]);
if ~exist('col_map', 'var') || isempty(col_map)
col_map = jet;
end
if exist('avi_nm', 'var') && ischar(avi_nm)
avi_file = VideoWriter(avi_nm);
if ~isnan(obj.Fs)
avi_file.FrameRate = obj.Fs;
end
avi_file.open();
avi_flag = true;
else
avi_flag = false;
end
if ismatrix(Y); Y=obj.reshape(Y, 2); end
[~, ~, T] = size(Y);
if (nargin<3) || (isempty(min_max))
temp = Y(:, :, randi(T, min(100, T), 1));
min_max = quantile(temp(:), [0.2, 0.9999]);
min_max(1) = max(min_max(1), 0);
min_max(2) = max(min_max(2), min_max(1)+0.1);
end
if ~exist('t_pause', 'var'); t_pause=0.01; end
for t=1:size(Y,3)
imagesc(Y(:, :, t), min_max); colormap(col_map);
axis equal; axis off tight;
if isnan(obj.Fs)
title(sprintf('Frame %d', t));
else
text(1, 10, sprintf('Time = %.2f', t/obj.Fs), 'fontsize', 15, 'color', 'w');
end
pause(t_pause);
if avi_flag
temp = getframe(gcf);
temp.cdata = imresize(temp.cdata, [height, width]);
avi_file.writeVideo(temp);
end
end
if avi_flag
avi_file.close();
end
end
%% export AVI
function exportAVI(obj, Y, min_max, avi_nm, col_map)
% export matrix data to movie
%min_max: 1*2 vector, scale
%avi_nm: string, file name
%col_map: colormap
T = size(Y, ndims(Y));
Y = Y(:);
if ~exist('col_map', 'var') || isempty(col_map)
col_map = jet;
end
if ~exist('avi_nm', 'var') || isempty(avi_nm)
avi_nm = 'a_movie_with_no_name.avi';
end
if ~exist('min_max', 'var') || isempty(min_max)
min_max = [min(Y(1:10:end)), max(Y(1:10:end))];
end
Y = uint8(64*(Y-min_max(1))/diff(min_max));
Y(Y<1) = 1;
Y(Y>64) = 64;
col_map = uint8(255*col_map);
Y = reshape(col_map(Y, :), obj.options.d1, [], T, 3);
Y = permute(Y, [1,2,4,3]);
avi_file = VideoWriter(avi_nm);
avi_file.open();
for m=1:T
% temp.cdata = squeeze(Y(:, :, :, m));
% temp.colormap = [];
avi_file.writeVideo(squeeze(Y(:, :, :, m)));
end
avi_file.close();
end
%% trim spatial components
function [ind_small] = trimSpatial(obj, thr, sz)
% remove small nonzero pixels
if nargin<2; thr = 0.01; end
if nargin<3; sz = 5; end
se = strel('square', sz);
ind_small = false(size(obj.A, 2), 1);
for m=1:size(obj.A,2)
ai = obj.A(:,m);
ai_open = imopen(obj.reshape(ai,2), se);
temp = full(ai_open>max(ai)*thr);
l = bwlabel(obj.reshape(temp,2), 4); % remove disconnected components
[~, ind_max] = max(ai_open(:));
ai(l(:)~=l(ind_max)) = 0;
if sum(ai(:)>0) < obj.options.min_pixel %the ROI is too small
ind_small(m) = true;
end
obj.A(:, m) = ai(:);
end
% ind_small = find(ind_small);
% obj.delete(ind_small);
end
%% keep spatial shapes compact
function compactSpatial(obj)
for m=1:size(obj.A, 2)
ai = obj.reshape(obj.A(:, m), 2);
ai = circular_constraints(ai);
obj.A(:, m) = ai(:);
end
end
%% solve A & C with regression
function [ind_delete] = regressAC(obj, Y)
if ~ismatrix(Y); Y=obj.reshape(Y,2); end
maxIter = 1;
A2 = obj.A; % initial spatial components
for miter=1:maxIter
ind_nonzero = obj.trimSpatial(50); % remove tiny nonzero pixels
active_pixel = determine_search_location(A2, 'dilate', obj.options);
K = size(A2, 2); %number of neurons
tmp_C = (A2'*A2)\(A2'*Y); % update temporal components
tmp_C(tmp_C<0) = 0;
% % tmp_C(bsxfun(@gt, quantile(tmp_C, 0.95, 2), tmp_C)) = 0;
% tmp_C(bsxfun(@gt, mean(tmp_C, 2)+std(tmp_C, 0.0, 2), tmp_C)) = 0;
A2 = (Y*tmp_C')/(tmp_C*tmp_C'); % update spatial components
A2(or(A2<0, ~active_pixel)) = 0;
for m=1:K
% remove disconnected pixels
img = obj.reshape(A2(:,m), 2);
lb = bwlabel(img>max(img(:))/50, 4);
img(lb~=mode(lb(ind_nonzero(:, m)))) = 0 ; %find pixels connected to the original components
A2(:,m) = img(:);
end