sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f2cf7d24174bd219e8fc7a47a1c2f67c2ebc9f55d6ab8eae75512587ec56df35 | MATLAB | 3,211 | 96 | function Image1and2_WholeImageWithFlow_and_WithFlowColoredByDirection(imageExportFolder, fileParams, flowFigureImageParams, imageA, vxMat, vyMat, relMat, numMicrons, imageNumber)
%% Check exist
if imageNumber == 1
fileName = 'WholeImage_WithFlow.png';
elseif imageNumber == 2
fileName = 'WholeImage_WithFlow_Colo... |
b3ab99bfb701935cd9db7986bf584442c450074c024ae6625224f66cd797071a | MATLAB | 3,212 | 115 | function [subj neighbor_idx] = create_spatial_avg_pat(subj, patname, maskname, varargin)
% Creates spatially averaged version of a given pattern.
%
%
% License:
%=====================================================================
%
% This is part of the Princeton MVPA toolbox, released under
% the GPL. See http://w... |
b531d9c37ed927b605ec04a8f5e2aed0a1aeb49404d76aba089499e9b35813db | MATLAB | 3,214 | 95 | % script to render surfaces for video
addpath(genpath('C:\Users\yonis\Documents\Laburo\PostDoc\Turbulence\RenderSurface\render_utils\'))
make_it_tight = true;
subplot = @(m,n,p)subtightplot(m, n, p, [0.01 0.05], [0.1 0.01], [0.1 0.01]);
if ~make_it_tight, clear subplot; end
% load surfaces for rendering on
basedir=... |
e73b4610abb19742266c8288a934ec9bd71f91b1508d1a5abdeaa393e07ec7e6 | MATLAB | 3,226 | 89 | % function model = ldatrain(labels,data,param)
%
% Compute Fisher LDA. Currently, only binary classification is implemented.
% INPUT (n = n_samples, p = n_features):
% labels: nx1 vector of labels
% data: pxn matrix of data
% param: struct variable with the following optional fields
% .shrinkag... |
fff1aa45091ac5d3724239c2eea535d684dbc13ac9bc5b87d46b6754225f6fdc | MATLAB | 3,226 | 114 | function [p1, p2, err] = extractButtondownPoints()
p1 = get(gca,'CurrentPoint'); % button down detected
finalRect = rbbox; % return figure units
p2 = abs(get(gca,'CurrentPoint'));
fprintf('Initial ...\n');
dispSelectedPts(p1, p2, finalRect);
[err, p1, p2] = errorCheck(p1, p2, finalRect);
if err<... |
07728ba77acbb61ef5a9d689066d62f2f465ba12a7a549a24af2e9b433034f0d | MATLAB | 3,227 | 99 | function Wbf=BF_type_cleaningFilt(X, L, A, alpha,gamma, Acomp, ncomp, typeReg, conds, smoothWithL)
% This function creates a beamforming-based data cleaning matrix, which can be used to filter out artifacts from EEG data provided that the topographies of the artifacts are known
%Input:
%
%X - input data (3D: chan... |
4f14f9b368358a20f83f04f59dea31d0e57748afeeba4a8b84687b8bfde896dc | MATLAB | 3,227 | 122 |
%% FF
all_cor=[];
region=["EC-DG","DG-CA3","CA3-CA1","CA1-EC"];
cd 'graph'
if ~exist('tunnel_cor', 'dir')
mkdir('tunnel_cor');
end
cd 'tunnel_cor'
Anova_cor=[];
Anova_name=[];
titl=region;
for regi = 1:4
all_n(regi)=0;
corr=[];
for fi=1:9
temp_corr=[];
temp=tab.rspin{1, fi}(1+(regi-1)*... |
86b08e4432d3f5c9465a2beef8e1ff2fcdb89e779eb5c42d203466f39be8dc37 | MATLAB | 3,227 | 83 | DataPath = '.\Data\Neuro\';
OutPath = '.\Data\PCA_SIM\';
addpath(genpath('.\functions\'))
%% === PCA ===
AMKSDs = matfile([DataPath,'AMKSDs_Sal.mat']); % trial-averaged KSDs
AMPSTHs = matfile([DataPath,'AMPSTHs_full_Sal.mat']); % single trial PSTHs
bws = AMKSDs.bws;
nBW = length(bws);
bins = AMPSTHs.bins;
nBins = le... |
3b9d2222e28363b0fd017e01f057487e98a510fb0330cb86f72b443d9fa02c78 | MATLAB | 3,234 | 131 | function stats = tmass_permutation(all_results, all_predictions, p)
%% Parameters
n_sim = 1000;
win_size = 1; % 1 equals no smoothing % 50 for EEG
n_sub = numel(all_predictions);
n_run = size(all_predictions(1).ang_pred,2);
n_tr = size(all_predictions(1).ang_pred,3);
parallel_pool(22)
%% Find significant clusters... |
542e74673ddde08aad3abf32ab891e07089f9cc1c4ed3e731eb3eaafb8bbdb5d | MATLAB | 3,237 | 77 | function visualize_contents()
% visualize_contents Render the Bo 2026 amygdala FIR temporal clusters
% into png_images/. See contents_description.md.
%
% Writes:
% Bo2026_AmygdalaFIR_4clusters_montage.png standard CANlab atlas montage
% Bo2026_AmygdalaFIR_4clusters_isosurface.png 3-D isosurface
% Bo2026_A... |
e189282d44764abd32e40fb4e5148d04f8ce41e8ba1d910451b80b0a022eb210 | MATLAB | 3,237 | 123 | clear;
% INT549:thr=7
prompt={'tau','threshold_scaling'};
answer=inputdlg(prompt);
% set to folder with tiffs
root = 'D:\McCormick_Data_May27_2021-\2021\Jun\4004_210610_E266_meso_am_1\2P';
file='4004_210610_E266_0_00012_00001.tif';
fs = dir(fullfile(root,file ));
fname = fs(1).name;
fname = fullfile(root, fn... |
56fda7f0496e2b58dbbf93554279e4734d71370995d1aa25855c42dfab187952 | MATLAB | 3,238 | 93 | function [transformationMatrix] = ...
optical_flow_linear_registration3d(moving, fixed, varargin)
% OPTICAL_FLOW_LINEAR_REGISTRATION3D Estimates a transformation matrix using optical flow
%
% INPUT ARGUMENTS
% moving - Moving iamge
% fixed - Fixed image
%
% OUTPUT ARGUMENTS
% transfor... |
c71f4f0d963d42a9961a60f445e062a152e7bd7aaaa4b6b9f3e8d94c96400f59 | MATLAB | 3,238 | 75 | function mv_print_regression_info(cfg, X, Y, X2, Y2)
% Prints information regarding the regression procedure, including the
% cross-validation procedure and the dimensions of the input data. This
% function is called by mv_regress.
%
% Usage:
% mv_print_regression_info(cfg, <X, Y, X2, Y2>)
%
%Parameters:
% cfg - ... |
e54ad53cc73e834e10ee643d5d403bb9be17c0b382251865c1c007450b6515e6 | MATLAB | 3,238 | 170 | %%
clear
REPEATS = 10000;
T = 100;
L = 2;
for ii = 1:REPEATS
response = randn(1,T);
group = mod(1:T,L);
if mean(response(find(group)))>mean(response(find(~group)))
pos = 1;
else
pos = 0;
end
[~,~,~,AUC(ii)] = perfcurve(group,response,pos);
end
figure;plotHistForFC(AUC,0:0... |
2efa506ebed5f97878fa337a926fe520cfba312e3c87c469edcd2ff8842f5718 | MATLAB | 3,240 | 96 | % Regression unit test
%
% Model: ridge regression
rng(42)
tol = 10e-10;
mf = mfilename;
% Generate regression data
nfeatures = 100;
X = randn(1000, nfeatures);
y = X * rand(nfeatures, 1) + 10.01*randn(1000,1); % univariate model
Y = X * rand(nfeatures, 10) +10.01*randn(1000,10); % multivariate model
%... |
c677fa5c31aa4343b218d36ed237b3f0c1d673f879f2675bc50ea59b43edf74a | MATLAB | 3,246 | 120 | function create_quadrature_filters_for_phase_difference_registration3d(varargin)
% CREATE_QUADRATURE_FILTERS_FOR_PHASE_DIFFERENCE_REGISTRATION3D Creates 3D quadrature filters for usage in linear phase-difference registration
%
% create_quadrature_filters_for_phase_difference_registration3d()
%
% INPUT ARGUMENTS
% N/A
%... |
08f99eba6fd57530a55165331717b1a3f448636c436aae433a59ab50ddeba21d | MATLAB | 3,251 | 124 | % Clean red and blue LEDs position of a 'led' file
%
% USAGE
%
% Process_DetectLED(videoFile,<options>)
%
% videoFile path to Basler video file, including the '.avi'
% extension or not
% <options> optional list of property-value pairs (see table below)
%
% ============... |
089d9303c7d2c4c629a31c604bbdf40c2b8370a8fad4599e4aaf5b836f87f17e | MATLAB | 3,253 | 128 | %%
[~, sids] = sort(Quality(Quality.^2>0.3), 'ascend');
x = dtest(sids, :);
x = x./max(x, [], 2);
figure;
imagesc(x); colorbar
title(sprintf('%d-%d %d', ii, f, bctype));
% time
Fz = 9.47;
StmDur = 3; % in second
BasDur = 0.5;
StmDurFrm = round(StmDur*Fz);
BasDurFrm = round(BasDur*Fz);
SpnDurFrm = StmDurFr... |
92ab33ed697b4d0636abeaca890c5f3c956f6d7d6ce2487583de83528ee26245 | MATLAB | 3,255 | 79 | %% Plotting alpha and tau paramaters of line fitting tuning curve analysis
%% Code
load('tuning_curve_line_fitting_params.mat')
% load('tuning_amplitude.mat')
%%
load('MI_split_RFTrials.mat')
split = ["sens cued","LR cued"];
mySplit = ["cuedSens","cuedBias"]; fig_id = ["g","f"];
COL(1,:) = [1,0,0]; COL(2... |
df054dca7da876c93f5cb8505db3e34c79a67527d5d66c34e2c2237c65267cbe | MATLAB | 3,257 | 91 | function this = isivAnalysis(this)
%ISIVANALYSIS Perform nonparametric ISIV analysis on an ACTI object
%
% $Author: Peng Li
% $Date: Jun 15, 2020
% $Modif.: Mar 8, 2026
% Revise to enable multiscale and max IS calculation
% Default output will be max IS at scale 60 or first scal... |
ab0b43957fd0f5e21113cb7a01c07e6949dc647f20dafec12b58c5a165849118 | MATLAB | 3,258 | 83 | % function p = stats_binomial(n_correct,n_samples,chancelevel,tail)
%
% Fast function to calculate multiple binomial tests, but only for discrete
% values of n (otherwise please use myBinomTest on Matlab file exchange).
% This function does not require the Statistics Toolbox.
%
% INPUT:
% n_correct: how... |
07da147a16718f4e7bef4b4163f1766aec17dc68caaa5e0a4df674e2d62b02b4 | MATLAB | 3,261 | 110 | % Clean red and blue LEDs position of a 'led' file
%
% USAGE
%
% Process_DetectLED(videoFile,<options>)
%
% videoFile path to Basler video file, including the '.avi'
% extension or not
% <options> optional list of property-value pairs (see table below)
%
% ============... |
3f91d6e1c9597eaf6269029672c085efe684a345d094148c7c3ff81943a2dccc | MATLAB | 3,262 | 105 | close all
labelIO = true;
modelName = 'ligandScreen';
network = IO([modelName '-Model.tsv']);
annotation = readtable([modelName '-Annotation.tsv'], 'FileType', 'text');
inputNodes = annotation.code(ismember(annotation.ligand, 'True'));
outputNodes = annotation.code(ismember(annotation.TF, 'True'));
uniprot2gene = conta... |
95585f958e45fe9bb6fc6f5746f1ff011fca5a0b09c7de35476445c4a1ac2036 | MATLAB | 3,262 | 105 | % Classifier unit test
%
% Classifier: kernel_fda
rng(42) %% do not change - might affect the results
%%% Create Gaussian data
nsamples = 80;
nfeatures = 10;
nclasses = 2;
prop = [];
scale = 0.0001;
do_plot = 0;
[X_gauss,clabel_gauss] = simulate_gaussian_data(nsamples, nfeatures, nclasses, prop, scale, do_plot);
... |
3488e437949c8c78a76fe5b17f6675b96117b61dac72d95baf1f0d8bad709467 | MATLAB | 3,263 | 76 | function [accumulatedField,accumulatedCertainty] = ...
add_update_field_to_acc_field_with_cert(...
updateField,accumulatedField,updateCertainty,accumulatedCertainty,...
accumulationMethod,interpolation)
% ADD_UPDATE_FIELD_TO_ACC_FIELD_WITH_CERT Accumulates the update field to the accumulated field
%
% [accu... |
62c6ac227a84af743e9f15362f62d710b89f820acb883a8d533835809088c799 | MATLAB | 3,265 | 90 | %ACTIGRAPHY
%
% Descriptions tba
%
% $Author: Peng Li, Ph.D.
% $Date: Dec 23, 2019
%
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
% (C) Peng Li 2019 -
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
%
classdef actigraphy < handle
... |
cef12150cd3016bd21ba56630575dbb9e7e383f88d17122101defa774a6efa25 | MATLAB | 3,266 | 97 | function make_video_sch1000(enstrophy1,outname)
% script to render surfaces for video
addpath(genpath('C:\Users\yonis\Documents\Laburo\PostDoc\Turbulence\RenderSurface\render_utils\'))
make_it_tight = true;
subplot = @(m,n,p)subtightplot(m, n, p, [0.01 0.05], [0.1 0.01], [0.1 0.01]);
if ~make_it_tight, clear subplot... |
606511450f2aeed6f7210552ec7f667ea2230efb10af92588cd5cc8e4ac42576 | MATLAB | 3,267 | 113 | % @title Test script for 3 RS neurons
% @file RS_x3.m
% @author Romain Beaubois
% @date 18 Feb 2020
% @copyright
% SPDX-FileCopyrightText: © 2020 Romain Beaubois <refbeaubois@yahoo.com>
% SPDX-License-Identifier: GPL-3.0-or-later
%
% @brief Test script for 3 RS neurons
%
% @details
% > **18 Feb 20... |
bb211062d49fbc95bb4afeb6fe2254fb90acf5472bea516785819c6cbb23dfb5 | MATLAB | 3,270 | 113 | % @title Test script for 3 FS neurons
% @file FS_x3.m
% @author Romain Beaubois
% @date 18 Feb 2020
% @copyright
% SPDX-FileCopyrightText: © 2020 Romain Beaubois <refbeaubois@yahoo.com>
% SPDX-License-Identifier: GPL-3.0-or-later
%
% @brief Test script for 3 FS neurons
%
% @details
% > **18 Feb 20... |
1dd60a038c32ce5e9c2bc4b4ea018d7ad3c5d6d548d1be3975b54c30ea69d96b | MATLAB | 3,275 | 84 | function [dHandle, sHandle] = plotFreq(frequencies, gains, phases,...
linespec,color,fillcolor, sHandle, newplot)
% Make bode plots with data and model output
% plotFreq(RL_data.freqs, RL_data.gains1, RL_data.phases1+180,
% linespec, I.learn_color(2,:), I.learn_color(2,:));
lw = 1.2;
msize = 3;
phases = unwrap(wrapTo1... |
523368897583ad7a4c5e5b772d85a2aa2c79e58ca4fb32e46a0ead46ea95a23b | MATLAB | 3,279 | 78 | % function decoding_out = get_decoding_out_from_passed_data(cfg,labels_test,passed_data,i_decoding,curr_mask_index,i_step)
%
% Helper function used to get data from passed_data if only result
% transformations (e.g. AUC) should be calculated using previously stored
% results.
%
% Kai, 2014-07-31
function decoding_ou... |
e57763fc2fe4dfbf99e994b19f43dace2f668ff30debb219c6cd4e3fd839423a | MATLAB | 3,280 | 115 | data = importdata('C:\Users\Noga\Documents\Vermis Data\saccade_8_dir_75and25\\4243 PC ss.mat')
data = importdata('C:\Users\Noga\Documents\Vermis Data\saccade_8_dir_75and25\\4814 CRB.mat')
figure;
raster_params.time_before = -399;
raster_params.time_after = 1200;
raster_params.smoothing_margins = 100;
raster_params.SD... |
8c34e22371880466ed561157009f44aac4a913bb385d4b653555dbabc0d49c95 | MATLAB | 3,282 | 119 | %% RT XOR vs Tests
clear all;
clc;
mdir=pwd;
cd('../Data')
load XORrespTimes;
cd(mdir);
%%
XORs=[5 15];
RTMat=[];
for ex=1:length(XORrespTimes)
for st=1:length(XORs)
RTxst=XORrespTimes{ex}; RTxst=RTxst(RTxst(:,2)==XORs(st),:);
for sb=unique(RTxst(:,1))'
RTsb=R... |
fdef2fa63e57c99a6b2ac440f8620ecc60aaf6f3760ab01b561523d70843a42f | MATLAB | 3,286 | 99 | close all; clc;
Fz = 52;
SelectedClip = 1:11;
%% Simple testing Copy "a" from excel sheets
if exist('a')
Frms = a(:, 1);
MarkX = a(:, 3);
PhysX = [a(1, 5) a(end, 5)];
PhysX = MarkX*(PhysX(end)-PhysX(1))/(MarkX(end)-MarkX(1));
iFrms = Frms(1):Frms(end);
iPhysX = interp1(Frms, PhysX, iF... |
0d69dd8d114de789439ed3e44f511eaed7d20996895353daec152ee8184a6d2f | MATLAB | 3,287 | 72 |
function [taskStruct, dispStruct] = runPractice(taskStruct, dispStruct)
fireEvent(taskStruct.LPT, taskStruct.ePRACTICE_START);
% a few practice trials
Screen('TextSize', dispStruct.wPtr, 30);
Screen('TextColor', dispStruct.wPtr, dispStruct.textColor);
text1 = 'Before we start, let''s try 5 pra... |
b155ecf168de7e4632afaf6df67939b572d556901087069a36165904e5b3b3e9 | MATLAB | 3,288 | 65 | % function check_datatrans(caller_function_name, cfg)
%
% Check that no transformation (e.g. scaling, feature_transformation) of
% the initial data occured, or if transformations occur that the user
% knows that the data transformations will change the output of this
% function.
% These checks are here to ensure that... |
36e74c2474787cf324178a0df35d6ca3c4a820a0c8beb74889798dd650349fcd | MATLAB | 3,291 | 104 | function [subj] = set_objfield(subj,objtype,objname,fieldname,newval,varargin)
% Replace or add a field to an object
%
% [SUBJ] = SET_OBJFIELD(SUBJ,OBJTYPE,OBJNAME,FIELDNAME,NEWVAL,...)
%
% Replaces/adds the FIELDNAME field in OBJNAME object of type OBJTYPE
% with NEWVAL. Will create the field if it doesn't exist, but... |
d0bc832249b3c882c75df4c9a37c76d86ebeae3c246ae38db24e108114454a70 | MATLAB | 3,292 | 95 | %% Loop through all the clip and generate the traces and evaluation (correlation)
radius_c = 50; % in um
radius_s = 300;
pixel2um = 2;
FolderPath = '\\storage1.ris.wustl.edu\kerschensteinerd\Active\Emily\NaturalSceneVideo\Preprocessed\Blue\';
nClip = 11;
MovieSize = nan(nClip, 3);
SharedVar = nan(nClip, 1);
cl... |
988d19c6c578277f62224af807addeff6e8396f81714a5335425844ab6234845 | MATLAB | 3,298 | 112 | function [hit_rate,false_alarm_rate,p_val] = ...
plot_psychometrics_boxplot( sessions, beh_dat)
% Initialize variables
% cTab = cell(1,length(sessions));
% p_val = zeros(length(sessions_looped),num_of_iters);
% hit_rate = cell(1,2);
% false_alarm_rate = cell(1,2);
monkeys = {'Mo... |
87a006a5d65aa4456fef3ead8458abbf23b124cd76b8c049c775544ac4e82edd | MATLAB | 3,299 | 84 | function info_v = minc_hdr2info(hdr)
% Convert a MINC header into a simplified structure
% This is called internally by MINC_READ and not meant to be used by itself.
%
% SYNTAX:
% INFO_V = MINC_HDR2INFO(HDR)
%
% Copyright (c) Pierre Bellec, Centre de recherche de l'institut de
% gériatrie de Montréal, Département d'inf... |
07967adf23053ad9679e932be06fb874a6265bd29ce4e7b1108d2ac50be0ba1a | MATLAB | 3,300 | 115 | function this = fit(this)
% FIT Fit a cosinor object based on LSE
%
% This can be done through minimizing the residual squared error
% to solve normal equations -- RSS's first order derivatives w.r.t. each
% parameter are zero
% notation in Ref. [1] (check COSINOR)
% y = M + Acos(w... |
4c137a986860e83e943789c335775711abba076848471becfea0242f79b2edec | MATLAB | 3,302 | 70 | % GROUP LEVEL ANALYSIS TO SEE WHICH BRAIN AREAS SURVIVE DURING NAV > MATH TASK (Nav-Math Experiment)
%% Set up the environment
clc
clear
restoredefaultpath
SPMPath = '/Applications/spm12';
addpath(SPMPath);
spm('defaults', 'fMRI');
spm_jobman('initcfg');
pmDataPath='/Users/federica/data/GridMagination/3Iso/Script/0... |
a19e9abcff7ff6b33adb1a6dbf3f1ea2c1edce9fc716b3f89eb0520d7ef184d4 | MATLAB | 3,303 | 121 | function [burstCell,burstLim,Burst] = BurstSpikes(S)
dispFig=0;
Burst = cell(length(S),1);
burstCell = zeros(length(S),1);
burstLim = zeros(length(S),1);
disp('Cell #:')
for c=1:length(S)
fprintf([num2str(c) ' ']);
if ~mod(c,10)
fprintf(... |
d4cf5d7657ca9d0fa8e19564a522626d56f4da6c6958b7ac6cfe57f9f1bd3f98 | MATLAB | 3,303 | 75 | % This script is a template for people who are interested in carrying out
% between-subject decoding of brain imaging data. This is the case when you
% have multiple conditions *within* each subject that you want to
% decode across subjects. If you have multiple different groups of
% subjects, see decoding_template_bet... |
f13fbd9e3e510535c9d54593783337d3a9aec3b7300ee68f1da17365e66fb300 | MATLAB | 3,303 | 90 | function output = betaBurstFeatures(cfg, input)
%if ~isfield(cfg,'channel'); cfg.channel = input.label(1) ; end
if isfield(cfg,'iTrial');
trials = cfg.iTrial;
else trials = 1: size(input.trial,2);
end
fs = input.fsample;
idxChan=find(strcmp(input.label, 'LFP_cl'));
thresh = cfg.thresh;
for iTrial=trials
dat... |
12c304afb6b21e4f23013d459beabe88eed518651ded78ff9892b569b8df3d62 | MATLAB | 3,305 | 85 | function [update] = optical_flow_registration(...
moving, fixed, transformationModel, multiModal,varargin)
% OPTICAL_FLOW_REGISTRATION Estimates a displacement field using optical flow
%
% [update] = optical_flow_registration(moving,fixed,transformationModel,multiModal)
%
% INPUT ARGUMENTS
% moving ... |
40504371e11538d0850ea3d19332647c1ab871ea05a94bee42a0385ca9c9aa0b | MATLAB | 3,305 | 126 | function val = RasterPlot(S, varargin)
% RasterImagePlot2(S, height, bar, tstart, tend)
%
% Plot a continuous function in a spike raster like form.
% RasterImagePlot2 plots all waveforms in a matrix form.
% INPUT:
% S: tsdArray of ts objects, spike trains
% OPTIONS:
% Height: the height allocated to each cell (incl... |
bd2b154adaf2d2c9ae31d2056424b0d6172a2796e3b5cdfd186c17d52582b415 | MATLAB | 3,306 | 114 | function create_scatterhist(data1, data2, X1, Y1, figure_handle, meth, stim, coh, save_flag)
% _
% Function to create the figure of a scatterplot with histograms on each
% axis
%
% function CREATEFIGURE(DATA1, DATA2, X1, Y1)
% IN:
% DATA1: histogram data
% DATA2: histogram data
% X1: vector of x data
% ... |
4fae6f38e9a8386b21e893b6b73522287523ab47868f12948298afffa96120cc | MATLAB | 3,310 | 116 | function plot_cue_opp_moving(cueMI, oppMI, iWindow, iMonkey, yl, window, trial_type, sum_metric, slide)
%% Plots V4 f-MI cued vs opp moving window plot (onset)
%% Code
cueMI_med = []; oppMI_med = [];
for iBin = 1:length(cueMI{1}.(trial_type){iWindow})
if(iMonkey==1)
cueMI_med = [cueMI_med;(... |
56c9cfb4d41a865b74643d6f047e638905daae19c16c5aa4ec717672547f46a6 | MATLAB | 3,310 | 98 | function KLd = KLdivergence(x, y, method)
% Compute the Kullback-Leibler divergence between two multivariate samples.
% Parameters
% ----------
% x : 2D array (n,d)
% Samples from distribution P, which typically represents the true
% distribution.
% y : 2D array (m,d)
% Samples from distribu... |
5b3d26a30d4ff0119c4d0b453c492fe978e04dadd86f4fa5620092ef2a6f263e | MATLAB | 3,312 | 106 | function [h] = crosshair(pos, varargin)
% CROSSHAIR adds a crosshair at position (x,y[,z]) to the current axes
% additional options are passed to the builtin line function
% the handles of the lines are returned
%
% h = crosshair([x,y]) or crosshair([x,y,z])
%
% You can specify the handle to the axes in which to pl... |
cb63d164773a7a139056f5587a98c9c014729628446b3986c7e7e4e2a23f46b8 | MATLAB | 3,314 | 109 | function [t, outind] = tensorprod(varargin)
% TENSORPROD - Multiply two or more arrays while summing over repeated
% indices. The indices for each array are indicated by strings.
%
% For example,
%
% C = tensorprod(A, 'ij', B, 'jk');
%
% computes a normal matrix product. The names of the indices are limite... |
e7e517f01936ec0ad98fc59d19c580c6ddc01e9106cfef01d6bc171cb59b9c5e | MATLAB | 3,315 | 91 | % function sim = pattern_similarity(x,y,method,param)
%
% This function calculates pattern similarity for the desired similarity
% metric (e.g. 'pearson' for pearson correlation or 'cov' for covariance).
% Importantly, it returns a similarity metric or a dissimilarity metric,
% depending on the type of output (a c... |
8a1c5c2828135c71d981a1b0e9c2de2bcc6e18b8880afa5ffe7c16069e56a7b2 | MATLAB | 3,324 | 132 |
clear
[task_info,supPath,~,task_DB_path] = loadDBAndSpecifyDataPaths('Vermis');
EPOCH = 'targetMovementOnset';
DIRECIONS = 0:45:315;
PROBABILITIES = [25,75];
PLOT_CELL = false;
TASK = "pursuit";
req_params = reqParamsEffectSize(TASK);
%req_params.cell_type = {'BG msn'};
lines = findLinesInDB (task_info, req_params)... |
4f3d00ae6ff8955e782b7b62ba7ab194ab3b87e7ea908445d3c6aba1714bd689 | MATLAB | 3,327 | 72 | % This script is a template that can be used for a decoding analysis on
% brain image data. It is for people who don't have betas available to do
% their classification and who need to enter their image names, labels and
% decoding chunks (e.g. run numbers) separately.
% Make sure the decoding toolbox and your f... |
20d6881cd57665e3f0a77b74b4ab5d95d052ae73fd2f4c2becedcb4283fc6a1d | MATLAB | 3,328 | 113 | %
% [Sout,SNout] = Sout_vsh_fast(r_sphere,R,EX,EY,EZ,ch_types,Lout)
%
% Calculate the outer SSS basis Sout using
% vector spherical harmonics
%
function [Sout,SNout] = Sout_vsh_fast(r_sphere,R,EX,EY,EZ,ch_types,Lout)
% Copyright (c) 2016, Elekta Oy
% ---------------------------------------
%
% Redistribution and use ... |
ea8e778c8a3902e8fa3d9c133d62dd03a56cb8f84f58e4955e7a02b22b32af1a | MATLAB | 3,330 | 76 |
function trialStruct = runSummary(taskStruct, dispStruct, trialStruct)
% clear the screen for 1 second
tSummaryStart = Screen(dispStruct.wPtr, 'Flip');
Screen('TextSize', dispStruct.wPtr, 40);
Screen('TextColor', dispStruct.wPtr, dispStruct.textColor);
[~, ny] = DrawFormattedText(dis... |
8951b55e46409d51b5fc8092ec5de94c4019f5288c48a5b73a9db3dd1eb0d287 | MATLAB | 3,331 | 94 | % script to render surfaces for video
addpath(genpath('C:\Users\yonis\Documents\Laburo\PostDoc\Turbulence\RenderSurface\render_utils\'))
make_it_tight = true;
subplot = @(m,n,p)subtightplot(m, n, p, [0.01 0.05], [0.1 0.01], [0.1 0.01]);
if ~make_it_tight, clear subplot; end
% load surfaces for rendering on
... |
2365dc5784ca859ad9f3d6f63f276e9bdebae22eb7ac9e1d797b57484263b4d3 | MATLAB | 3,335 | 101 | function [scratch] = train_lda(trainpats,traintargs,in_args,cv_args)
% USAGE :
% function [scratch] = train_lda(trainpats,traintargs,in_args,cv_args)
%
% This is a support vector machine training function. It train the
% classifier and makes is ready for testing.
% NOTE : This classfier currently does only two catego... |
5457db47c977b65aea2c809a739f217f33253781825d5cbedd714e883b62fb09 | MATLAB | 3,336 | 51 | %Evaluate flicker LFP phase locking value significance and amplitude.
%2024/02/25
function evaluate_ssep_plv(fnames)
%create parent output dir if does not exist:
if ~exist([fnames.analysis_folder,'/LFP/static_ent'],'dir')
mkdir([fnames.analysis_folder,'/LFP/static_ent']);
end
for ref_... |
3b43a6639d47ce0ba89f9fb54048f9c564706e2de101c5aaa0a0a700487f12aa | MATLAB | 3,339 | 105 | % This script generates images for understanding the pathology utility and
% predictive power
%% Inputs
modelName = 'paper v2'; % Part of the model name
isCorrectAspectRatio2To1 = true;
outputFolder = [pwd '\tmp\'];
scaleBar = 100; % Plot scale bar [um]
% Notice that total number of images to be rated b... |
ae6d8457d4e767fc2ba19a8a669b7e17302a5c878decf364c23abcbef6f479aa | MATLAB | 3,339 | 90 | % beta is (#coefficients x HbX x #Channels x #conditions)
% bvar is #coefficients x # Channels x #HbX
% bvar is square of standard error, not SD
% sigAct is HbX x #Channels x #conditions
% sigDiff is HbX x #Channels x #pairs
% For sbj 12, # of coefficients is 16.
% use welch t-test
% Average beta
% Use Wald's test for... |
05c88076469e74481c8d1bb6595d26485e0b3f519f60ad9792062814632c1f3e | MATLAB | 3,340 | 102 | function vidObjMoving = msAlignBetweenSessions(vidObjFixed, vidObjMoving)
%MSALIGNBETWEENSESSIONS Summary of this function goes here
% Detailed explanation goes here
hLarge = fspecial('average', 80);
hSmall = fspecial('average', 5);
refFrame = vidObjFixed.meanFrame{vidObjFixed.selectedAlignment};
... |
1ff0a611c45d841b9160f14989b479853fea5dba25412f935bc478dce1b9f029 | MATLAB | 3,340 | 94 | function coord_v = minc_world2voxel(coord_w,mat,opt);
% Convert coordinates in the world space into coordinates in the voxel
% space.
%
% SYNTAX:
% COORD_V = MINC_WORLD2VOXEL(COORD_W,MAT,OPT)
%
% _________________________________________________________________________
% INPUTS:
%
% COORD_W
% (matrix N*3) each r... |
6d522d36cd291fa7af9b7b583373ef59485fd0247b956387e5b6cc12bb50e1d7 | MATLAB | 3,340 | 161 | function status = keep_file_for_query(file_struct, options)
%
% USAGE::
%
% status = keep_file_for_query(file_struct, options)
%
% returns ``false`` if the file is to be kept when running ``bids.query``
%
% (C) Copyright 2021 BIDS-MATLAB developers
status = true;
% prefix, suffix, extensions ... |
f42f973a76099e34c91d82a034f3f10d6b3d4ae021062b2a875d870dbe93c5e7 | MATLAB | 3,340 | 91 | function violinplot_STATS(DIST_STRUCT, NORMBY, TITLE, YLABEL,...
cmap, cmap_num, STATS)
% violinplot_STATS plots a violin plot for each field of DIST_STRUCT and
% carries out a one-way ANOVA and multiple comparisons analysis to test
% differences between groups (fields in DIST_STRUCT) or a rank sum test, writin... |
9d8e61b0ca03fe832724584897caa27bf6e368da8fd3487bd3f68c414fde856d | MATLAB | 3,341 | 94 | function mix = gmminit(mix, x, options)
%GMMINIT Initialises Gaussian mixture model from data
%
% Description
% MIX = GMMINIT(MIX, X, OPTIONS) uses a dataset X to initialise the
% parameters of a Gaussian mixture model defined by the data structure
% MIX. The k-means algorithm is used to determine the centres. The
% p... |
06b05e3509c190862f2002222cd2da69b02993b04c7b0ee24bc8e672f0075ecc | MATLAB | 3,348 | 135 | function [alltests] = run_unit_tests(varargin)
% [ALLTESTS] = RUN_UNIT_TESTS(...)
%
% This is the main function for the test suite. It goes
% through the TESTSDIR, looking for test_*.m files. Then, it
% runs all those test functions and tells you how they
% did. Each one returns an 'errors' cell array of strings, whic... |
ddc54cf7b8ccbd6eb9c82a43544d4cc290f5ff2de51b115eb12e32ec6d5479fb | MATLAB | 3,351 | 92 | function net = mlp(nin, nhidden, nout, outfunc, prior, beta)
%MLP Create a 2-layer feedforward network.
%
% Description
% NET = MLP(NIN, NHIDDEN, NOUT, FUNC) takes the number of inputs,
% hidden units and output units for a 2-layer feed-forward network,
% together with a string FUNC which specifies the output unit
% ac... |
48659bdfd5d0340639c838cb0737a7b9f5295aa222b5008a8f18d013dd858313 | MATLAB | 3,352 | 112 | %% Add path
addpath(genpath('../../Ionnic Channel'),genpath('../../Tools'), genpath('../../Synapses'), genpath('../../Datas'));
%% Import data from neuron (FS)
dt = 0.01;
Vmem = -90:0.01:60;
SplineVmem = -90:1:60;
l_V = length(Vmem);
Spline_lV = length(SplineVmem);
... |
721dde09d78475d4f96512d7e523169c88b8d489c99aea40bb589c82d7df708d | MATLAB | 3,353 | 90 | % to be run after preprocessFNIRS06_FInteraction_MultiOnly.m
function plot_Subset_CumsumNDelta(s,numClasses,fefOnlyOp,saveOp)
fs = 50;
timeLen = [0.06 0.1 0.2 0.5 1 1.5 2 3 4 5].*fs;
deltaLen = [5 11 25 49];
nrept = 10;
kFold = 5;
sbjNum = s.name;
processedDataDir = ['C:\Users\mn0mn\Documents\ResearchProjects\spat... |
f9059dbac946bde938752f1573c7b2b83d1cc201bbc84fb52ecca5b54522a705 | MATLAB | 3,354 | 62 | function PlotTSMean_PreVPost( TS_mean_preVpost, TS_idx, TITLE, YLABEL, YLIMS, stdOrsem )
% Michelle Cahill 20200601
%PlotTSMean_PreVPost plots the mean value of each t-series pre-stim and
% post-stim & the mean+-std or +- sem of all t-series. A paired t-test is carried
% out for identified pre/post pairs
%
%... |
cd728bfff167df7b21dce3a26313026ccb924de30585cd7ebed7be2996271a12 | MATLAB | 3,356 | 78 | % function [predicted_labels decision_values opt] = ens_bal_te(labels,data,model,cfg)
%
% Testing part of ensemble approach that is used for balancing data. All
% models that have been trained are combined and tested and decision values
% are averaged together for each sample to generate an overall vote (this
% is... |
f2818345b280f1c68e4f8fb5fa6ea4ffe21a10f1305a5662686c1f940351b679 | MATLAB | 3,356 | 116 | function Cr = CrossCorr(tsa, tsb, binsize, nbins, varargin)
% Computes cross-correlation between TSD objects
%
% USAGE:
% C = CrossCorr(tsa, tsb, binsize, nbins, options)
%
% INPUTS:
% tsa - the triggering time series
% tsb - the triggered time series
% binsize - the size of the bins
% nbins - the total... |
c53ead414e3f540c8e917c7ea88542fba27a147ccbed2e0507db662f91729752 | MATLAB | 3,357 | 107 | function ImportImage(importFolder, exportFolder, fileName)
info = imfinfo([importFolder, fileName]);
info = info(1).ImageDescription;
automateFlag = 1; %changed
if automateFlag == 1
%% Automated
% ChLoc = strfind(info, 'channels');
% FrLoc = strfind(info, 'frames');
% numChannels = str2double(info(ChLo... |
3c914f79f6bfad5576b38fc6befc3f37ef9046caced8a201e121b39b2e52d2f4 | MATLAB | 3,362 | 96 | %% Well processing
% Sam Lassers
% Processes and computes spiking dynamics of MCS MEA wells
%% Directory Setup
clear
clc
recorded_date=date;
parent_dir='D:\Brewer lab data\HFS';
%nostim
mcd_dir='D:\Brewer lab data\HFS\No Stim';
% well_dir='D:\Brewer lab data\HFS\No Stim\Wells_5SD_500maxSD';
well_dir='D:\Brewer lab d... |
b9530028cbe7d5803ed4ac18d3b2b51b8cf75642d09d133de1f22e3bc820c975 | MATLAB | 3,366 | 66 | %Plots PSDs for all flicker conditions (except random and occluded),
%all electrodes (1 depth electrode per figure), and saves the figure.
%The following colors correspond to:
%*orange: visual modality.
%*blue: auditory modality.
%*green: audio-visual modality.
%2024/02/25
function plot_depthelectrode_PSDs(PSD_result... |
a2106c1b75c071b6d761334a822f2951756719f067be785f7fd0ed432d15ce00 | MATLAB | 3,370 | 95 | %% Loop through all the clip and generate the traces and evaluation (correlation)
radius_c = 50; % in um
PassHz = 4;
pixel2um = 2;
FolderPath = '\\storage1.ris.wustl.edu\kerschensteinerd\Active\Emily\NaturalSceneVideo\Preprocessed\Blue\';
nClip = 11;
MovieSize = nan(nClip, 3);
SharedVar = nan(nClip, 1);
clea... |
6add85a18b47840039387807b7b178ec4f3440b3560b9c424e225a6cbf68c5dc | MATLAB | 3,371 | 104 | function [subj] = statmap_template(subj,data_patname,regsname,selname,new_map_patname,extra_arg)
%todo title
% This is the sample/template statmap generation function
% %todo synopsis
%
% [SUBJ] = STATMAP_TEMPLATE(SUBJ,DATA_PATNAME,REGSNAME,SELNAME,NEW_MAP_PATNAME,EXTRA_ARG)
% %todo func... |
751d28a331dade906c2f4e116d08a51a08d83a8ad793e06c26a99255552772f1 | MATLAB | 3,371 | 70 |
% Convert the frame and the mask to uint8 RGB.
frame = im2uint8(frame);
mask = uint8(repmat(mask, [1, 1, 3])) .* 255;
minVisibleCount = 8;
if ~isempty(tracks)
% Noisy detections tend to result in short-lived tracks.
% Only display tracks that have been ... |
4d530f784504159bc473c6a3e9f634ae35ac2f3a41b7b2e1fac02aa98ea52224 | MATLAB | 3,372 | 47 | function [z] = mtimes4x4(x, y)
% compute x*y
% and dimensionatity is 4x4xN, or Nx4x4
% if N==4 it is treated as Nx4x4
siz = size(x);
z = complex(zeros(siz));
if siz(2)==siz(3)
z(:,1,1)=x(:,1,1).*y(:,1,1)+x(:,1,2).*y(:,2,1)+x(:,1,3).*y(:,3,1)+x(:,1,4).*y(:,4,1);
z(:,2,1)=x(:,2,1).*y(:,1,1)+x(:,2,2).*y(:,2,1)+x(... |
640c98b77480688e43b349f9db9461fbaa36ef3a14cd8b808d94179f2df3e7fe | MATLAB | 3,373 | 69 | function [movingRegistered, transform] = register3D_intensity_multiscale_affine(im1,im2, initial_transform, initialise, downsample_factor, mode, iterations, type, min_I)
% iterate through the downsampling:
n_scales = length(downsample_factor);
% set up the basic registration settings
[optimizer, ... |
9c524ae42ff02ed1bd356ad8095234051cff01cd47003ba21f4483a5cd80047a | MATLAB | 3,374 | 147 | clearvars; clc;
close all;
datapaths
load([SumPath,filesep,'unitList_all.mat']);
ResPath = [SpkPath,'\'];
units = units(units(:,4)==1,:);
mice = unique(units(:,1));
nUnits = size(units,1);
nMice = length(mice);
SRate = nan(nUnits,13);
cnt = 1;
for k=1:nMice
load([ResPath 'M' num2str(mice(k)) '\M' num2str(mice(k))... |
19d206df3d64c8b2b90eb41d1e5025605b39a000f8f14c08cbe5a7c01191d2bd | MATLAB | 3,379 | 69 | function [movingRegistered, transform] = register2D_intensity_multiscale_affine(im1,im2, initial_transform, initialise, downsample_factor, mode, iterations, type, min_I)
% iterate through the downsampling:
n_scales = length(downsample_factor);
% set up the basic registration settings
[optimizer, ... |
86768151fc6a139ad0846fbb122bad979fd58885bf96525d6a1757ebe317bb79 | MATLAB | 3,380 | 84 |
function [conds, nConds_JR, tts, head, target, hevel, ...
PC, sines, light, dt, RR_data] = ...
loadJR_RR_combined(I, data_path)
% ------------ J. RAYMOND & LISBERGER DATA -----------------
[conds, tts, head, target, hevel, PC, sines, light, dt, cond_inds] = ...
loadJR(data_path, I.t_step_jr);
nConds_JR =... |
04472e32341d565856b8e99f1f95747a5c34ecc978e17ab432a7bc332b5e8214 | MATLAB | 3,385 | 74 | function AssertOSX
% AssertOSX
%
% OS X: ___________________________________________________________________
%
% Call AssertOSX at the head of scripts which use functions unique to the
% OS X Psychtoolbox. AssertOSX will exit with an error if called from
% MATLAB running on any OS other than OS X, providing an explan... |
ddd16fb1c75e95a44b2e5c08cd8a249bed33adecef6f3b39b4671963b1994a86 | MATLAB | 3,386 | 111 | %
% [Sin,SNin] = Sin_vsh_fast(r_sphere,R,EX,EY,EZ,ch_types,Lin)
%
% Calculate the internal SSS basis Sin using
% vector spherical harmonics
%
function [Sin,SNin] = Sin_vsh_fast(r_sphere,R,EX,EY,EZ,ch_types,Lin)
% Copyright (c) 2016, Elekta Oy
% ---------------------------------------
%
% Redistribution and use of the... |
f671f3ad346d13de0f7c0800ced3a2c72255b204e0e88417f1e2276557068495 | MATLAB | 3,387 | 92 | function [SC_recon_ratio, SC_recon_FC_r,...
SC_rewired_ratio,SC_rewired_FC_r,...
SC_moran_ratio,SC_moran_FC_r]= SC_recon_marmoset_activity(zX_RS,n_ROI,n_null)
SC = load('Data/marmoset_invivo_SC_Order.mat');
SC = SC.matrix_sc;
% only analysis 55 regions
SC = SC(1:n_ROI,1:n_ROI);
%% Threshold SC matrix... |
8e5656b0ff3b91800c859fc368f5f34ab1c603c0ab76f5eb0102a40ee5e34cb6 | MATLAB | 3,388 | 118 | clear all
[task_info,supPath,MaestroPath] = ...
loadDBAndSpecifyDataPaths('Vermis');
req_params.grade = 7;
req_params.cell_type = 'PC ss|CRB|SNR|BG';
req_params.task = 'choice';
req_params.ID = 4000:5845;
req_params.num_trials = 80;
req_params.remove_question_marks = 0;
req_params.remove_repeats = 0;
lines = find... |
a3a391b6dcee84a7ac8c02936a37263f42c709aa55d6fb5413c543dc4c69bc90 | MATLAB | 3,390 | 117 | function [perfmet] = perfmet_maxclass(acts,targs,scratchpad,varargin)
% This is the most basic performance metric.
%
% [PERFMET] = PERFMET_MAXCLASS(ACTS,TARGS,SCRATCHPAD,...)
%
% The classifier is considered to guess correctly if its maximally
% active row (the unit that it guessed) corresponds to the most active
% ro... |
d4453b25a350d098b7bbebb8e94970654aebd5c4e19609bca391d3eddd19dca0 | MATLAB | 3,392 | 111 | function [zval, p, w] = mv_stat_wilcoxon_signrank(X, X2)
% Two-tailed Wilcoxon signed rank test (one sample or two samples).
% Unlike Matlab's signrank function, it also works for multi-dimensional
% arrays.
%
% Usage:
% [stat, p] = mv_stat_wilcoxon_signrank(X, <X2>)
%
%Parameters:
% X - [samples x ... x ... |
3166b4244c4af52ae99bf59d7290dac379c5322e794b6b5d633eb65554866a5c | MATLAB | 3,397 | 116 | function RasterPlot(PH, varargin)
% RasterPlot(S, height, bar, tstart, tend)
%
% Plot spike trains as rasters.
% INPUT:
% S: tsdArray of ts objects, spike trains
% OPTIONS:
% Height: the height allocated to each cell (includes spacing), default 1
% BarFraction: the fraction of height occupied by a cell, default 0.8
%... |
a2320dca31b8dc17e972be4cdef91564cb36a7134a476672e6d543d9b1dc459e | MATLAB | 3,397 | 114 | %DEMNS1 Demonstrate Neuroscale for visualisation.
%
% Description
% This script demonstrates the use of the Neuroscale algorithm for
% topographic projection and visualisation. A data sample is generated
% from a mixture of two Gaussians in 4d space, and an RBF is trained
% with the stress error function to project th... |
b797e1913baf93ba18c864b2c3a555bacf09f43a93d80fe3443a09227ba21acc | MATLAB | 3,397 | 106 | function [subj] = load_matrix_pattern(subj,new_patname,maskname,mat)
% Loads a 4D matrix into the SUBJ structure as a pattern
%
% [SUBJ] = LOAD_MATRIX_PATTERN(SUBJ,NEW_PATNAME,MASKNAME,MAT)
%
% Adds the following objects:
% - pattern object called NEW_PATNAME masked by MASKNAME
%
% Creates a pattern object in the subj... |
d4a1cf3e67af43672c899f2a20a9b863ea9f5ef4eacd477577ef2cbc51cc5e3d | MATLAB | 3,397 | 104 | % Extract the brain activity computed in the GLM2_Test Grid orientation in
% the EC
%% Set up the environment
clc
clear
restoredefaultpath
SPMPath = '/Applications/spm12';
addpath(SPMPath);
%% Enter
% Work Path
WorkingDirectory='/Users/federica/data/GridMagination/3Iso/CONT';
%SubjectList
pmDataPath='/Users/feder... |
0566ff66c20fa8c7ed4d8cc8593202fee31f7a50f00b5de097c3955af52a9ec0 | MATLAB | 3,399 | 90 | % STATUS: active
%
% SYNTAX:
% plot_Corr_CAvsBeh(numClasses)
%
% DESCRIPTION:
% Plot correlation between behavioral score and decoding performance at
% 2-3s window.
%
% RESTRICTION:
% None.
%
% INPUTS:
% numClasses - int: number of classes to classify
% 2 - classify between left & right
% 3 - classify... |
c7d38c7b1c60703bda1169e0903850ac4aa5861b5995adb9e087f5f0c493d43e | MATLAB | 3,399 | 91 | % [working, libsvmdir] = check_libsvm(cfg, display_path)
%
% Checks whether libsvm is working with the parameters in cfg
% An error will be thrown when libSVM is not working, and nargout == 0
%
% IN
% model_parameters: the model parameters to be used with libSVM
% display_path: if 1, the path to libSVM will be show... |
0965cf612aaebf1cd485a4c468b945c7597b7cb37cf4238c5346e6fbdc346752 | MATLAB | 3,400 | 100 | function [val scratch] = statmap_classify(pat,regs,scratch)
% Create a statmap based on cross-validation performance
%
% [VAL SCRATCH] = STATMAP_CLASSIFY(PAT,REGS,SCRATCH)
%
% This contains the logic for running classification on each
% voxel/sphere and creates a statmap of their
% results. Should work equally for a s... |
3b168084f1bb7950f4139f373c69a870fb387ca750f9761bd6f40b407d3358e0 | MATLAB | 3,404 | 118 | %This script incorporates deltaF/F calculations from Jenna Sternberg and
%members of the Wyart lab, and temperature measurements from Harry Bell in
%the Sengupta lab.
clear all; close all
disp ('Choose image acquisition file')
[image folder] = uigetfile('*.*');
imagefile = strcat(folder, image);
disp ('Choose image t... |
2489f29d0bbc47c334049c85e9dd6f1457d72ff4047888ae224ffcd3b728e7fb | MATLAB | 3,406 | 101 | function FlowClusterTracksData(fileParams, mainAnalysisParams)
%% File Parameters
exportFolder = fileParams.exportFolder;
pxPerMicron = fileParams.pxPerMicron;
secBtwFrames = fileParams.secBtwFrames;
thetaInDegrees = fileParams.thetaInDegrees;
thetaInRadians = fileParams.thetaInRadians;
minBtwFrame = secBtwFrames/60;
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.