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Removed old matlab files

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1 parent bd16ba8 commit d31e02d35fdb9e33b90355c635e2bcca32dd39fa @miscco committed Mar 21, 2016
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Showing with 0 additions and 341 deletions.
  1. +0 −174 Test_Parameters.m
  2. +0 −167 Test_Stimulation.m
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174 Test_Parameters.m
@@ -1,174 +0,0 @@
-% mex command is given by:
-
-function Test_Parameters(type)
-if nargin == 0
- type = 2;
-end
-
-
-mex CXXFLAGS="\$CXXFLAGS -std=c++11 -O3" TC_mex.cpp Cortical_Column.cpp Thalamic_Column.cpp
-
-% Path to fieltrip preprocessing function
-if(isempty(strfind(path, '/nfshome/schellen/Documents/MATLAB/Tools/fieldtrip/preproc')))
- addpath('~/Documents/MATLAB/Tools/fieldtrip/preproc');
-end
-
-% Path to helper function
-if(isempty(strfind(path, '/nfshome/schellen/Documents/MATLAB/Tools/boundedline')))
- addpath('~/Documents/MATLAB/Tools/boundedline');
-end
-
-if type == 1
- Param_Cortex = [4.7; % sigma_e
- 1.33; % g_KNa
- 120E-3]; % dphi
-
- Param_Thalamus = [0.051; % g_h
- 0.024]; % g_LK
-
- fn_data = '/nfshome/schellen/Documents/MATLAB/TC_model/Data/SO_Average_N3';
- Model_Range_ERP = [-75, -45];
- Model_Range_FSP = [-0.25, 1.25];
- Data_Range_ERP = [-75, 35];
- Data_Range_FSP = [2, 5];
- xRange = -0.5:0.25:1.5;
-else
- Param_Cortex = [6; % sigma_e
- 2.0; % g_KNa
- 120E-3]; % dphi
-
- Param_Thalamus = [0.051; % g_h
- 0.0205]; % g_LK
-
- fn_data = '/nfshome/schellen/Documents/MATLAB/TC_model/Data/SO_Average_N3';
- Model_Range_ERP = [-75, -45];
- Model_Range_FSP = [-0.25, 1.25];
- Data_Range_ERP = [-75, 35];
- Data_Range_FSP = [2, 5];
- xRange = -0.5:0.25:1.5;
-end
-
-Connectivity = [ 2.5; % N_et
- 2.5; % N_er
- 15; % N_te
- 15]; % N_ti
-
-load(fn_data);
-
-% stimulation parameters
-% first number is the mode of stimulation
-% 0 == none
-% 1 == semi-periodic
-% 2 == phase dependend
-
-var_stim = [ 0; % mode of stimulation
- 40; % strength of the stimulus in Hz (spikes per second)
- 100; % duration of the stimulus in ms
- 5; % time between stimulation events in s (ISI)
- 0; % range of ISI in s [ISI-range,ISI+range]
- 3; % Number of stimuli per event
- 1050; % time between stimuli within a event in ms
- 450]; % time until stimuli after minimum in ms
-
-T = 300; % duration of the simulation
-
-[Ve, Vi, Vt, Vr] = TC_mex(T, Param_Cortex, Param_Thalamus, Connectivity, var_stim);
-Fs = length(Ve)/T;
-
-
-Ve_low = ft_preproc_bandpassfilter(Ve, Fs, [0.25,4], 513, 'fir') + mean(Ve);
-Ve_FSP = ft_preproc_hilbert(ft_preproc_bandpassfilter(Ve, Fs, [12, 15], 513, 'fir'), 'abs').^2;
-
-% Search for peaks
-[~, x_SO] = findpeaks(-Ve_low, 'MINPEAKHEIGHT', 68, 'MINPEAKDISTANCE', 0.2*Fs);
-
-% Remove those events, that are too close to begin/end
-x_SO = x_SO(x_SO<(T-2)*Fs);
-x_SO = x_SO(x_SO> 2*Fs);
-x_SO = x_SO-3; % fix a different min position wrt data
-
-% Set the variables
-N_Stim = length(x_SO);
-Range_ERP = [-0.5, 1.5];
-time_event = linspace(Range_ERP(1), Range_ERP(2), (Range_ERP(2)-Range_ERP(1))*Fs);
-Events = zeros(length(time_event), N_Stim);
-Events_FSP = zeros(length(time_event), N_Stim);
-
-% Segmentation
-for i=1:N_Stim
- Events(:,i) = Ve ((x_SO(i)+Range_ERP(1)*Fs)+1:(x_SO(i)+Range_ERP(2)*Fs));
- Events_FSP(:,i) = Ve_FSP((x_SO(i)+Range_ERP(1)*Fs)+1:(x_SO(i)+Range_ERP(2)*Fs));
-end
-
-mean_ERP_model= mean(Events, 2); %#ok<*NASGU>
-mean_FSP_model= mean(Events_FSP,2);
-sd_ERP_model = std (Events, 0, 2);
-sd_FSP_model = std (Events_FSP,0, 2);
-
-% Define handle for plotting
-BL_model =@(y,x) boundedline(y,x(:,1), x(:,2), 'alpha', 'transparency', 0.1, 'r');
-BL_data =@(y,x) boundedline(y,x(:,1), x(:,2), 'alpha', 'transparency', 0.1, 'black');
-
-% Option array for set
-Option_Name = { 'ylim';
- 'ytick';
- 'yticklabel';
- 'ycolor';
- 'xtick'}';
-
-Option_Model_ERP = {Model_Range_ERP;
- linspace(Model_Range_ERP(1), Model_Range_ERP(2), 5);
- linspace(Model_Range_ERP(1), Model_Range_ERP(2), 5);
- 'black';
- xRange}'; %#ok<*NBRAK>
-
-Option_Data_ERP = {Data_Range_ERP;
- linspace(Data_Range_ERP(1), Data_Range_ERP(2), 5);
- linspace(Data_Range_ERP(1), Data_Range_ERP(2), 5);
- 'black';
- xRange}';
-
-Option_Model_FSP = {Model_Range_FSP;
- linspace(Model_Range_FSP(1), Model_Range_FSP(2), 5);
- linspace(Model_Range_FSP(1), Model_Range_FSP(2), 5);
- 'black';
- xRange}';
-
-Option_Data_FSP = {Data_Range_FSP;
- linspace(Data_Range_FSP(1), Data_Range_FSP(2), 5);
- linspace(Data_Range_FSP(1), Data_Range_FSP(2), 5);
- 'black';
- xRange}';
-
-figure(1)
-subplot(411)
-plot(linspace(0,30,3000),Ve(101:3100));
-title(['Ve with a mean of :',num2str(mean(Ve))]);
-subplot(412)
-plot(linspace(0,30,3000),Vi(101:3100));
-title(['Vi with a mean of :',num2str(mean(Vi))]);
-subplot(413)
-plot(linspace(0,30,3000),Vt(101:3100));
-title(['Vt with a mean of :',num2str(mean(Vt))]);
-subplot(414)
-plot(linspace(0,30,3000),Vr(101:3100));
-title(['Vr with a mean of :',num2str(mean(Vr))]);
-
-% Create figure
-figure(2)
-clf
-subplot(211)
-[AX1, ~, ~] = plotyy(time_events,[mean_SO_data, sem_SO_data],time_events,[mean_ERP_model, sd_ERP_model], BL_data, BL_model);
-set(AX1(1), Option_Name, Option_Data_ERP);
-set(AX1(2), Option_Name, Option_Model_ERP);
-ylabel(AX1(1),'EEG [$\mu$V]');
-ylabel(AX1(2),'$V_{e}$ [mV]');
-title([num2str(N_Stim), ' Events'])
-subplot(212)
-[AX1, ~, ~] = plotyy(time_events,[mean_FSP_data, sem_FSP_data],time_events,[mean_FSP_model, sd_FSP_model], BL_data, BL_model);
-set(AX1(1), Option_Name, Option_Data_FSP);
-set(AX1(2), Option_Name, Option_Model_FSP);
-ylabel(AX1(1),'FSP data [a.u.]');
-ylabel(AX1(2),'$FSP model$ [a.u.]');
-title([num2str(N_Stim), ' Events'])
-end
View
167 Test_Stimulation.m
@@ -1,167 +0,0 @@
-% mex command is given by:
-
-function Test_Stimulation(type)
-if nargin == 0
- type = 4;
-end
-
-
-mex CXXFLAGS="\$CXXFLAGS -std=c++11 -O3" TC_mex.cpp Cortical_Column.cpp Thalamic_Column.cpp
-
-% Path to fieltrip preprocessing function
-if(isempty(strfind(path, '/nfshome/schellen/Documents/MATLAB/Tools/fieldtrip/preproc')))
- addpath('~/Documents/MATLAB/Tools/fieldtrip/preproc');
-end
-
-% Path to helper function
-if(isempty(strfind(path, '/nfshome/schellen/Documents/MATLAB/Tools/boundedline')))
- addpath('~/Documents/MATLAB/Tools/boundedline');
-end
-
-Param_Cortex = [6; % sigma_e
- 2.05; % g_KNa
- 120E-3]; % dphi
-
-Param_Thalamus = [0.052; % g_h
- 0.02]; % g_LK
-
-Connectivity = [ 2.6; % N_et
- 2.6; % N_er
- 5; % N_te
- 10]; % N_ti
-
-% stimulation parameters
-% first number is the mode of stimulation
-% 0 == none
-% 1 == semi-periodic
-% 2 == phase dependend
-
-var_stim = [ 2; % mode of stimulation
- 70; % strength of the stimulus in Hz (spikes per second)
- 80; % duration of the stimulus in ms
- 5; % time between stimulation events in s (ISI)
- 0; % range of ISI in s [ISI-range,ISI+range]
- 2; % Number of stimuli per event
- 1075; % time between stimuli within a event in ms
- 450]; % time until stimuli after minimum in ms
-
-T = 3600; % duration of the simulation
-
-load('/nfshome/schellen/Documents/MATLAB/TC_model/Data/ERP_Average_data');
-
-Model_Range_ERP = [-75, -45];
-Data_Range_ERP = [-80, 50];
-Model_Range_FSP = [-0.25, 1.25];
-Data_Range_FSP = [2, 8];
-xRange = -1:0.5:3;
-
-% Option array for set
-Option_Name = { 'ylim';
- 'ytick';
- 'yticklabel';
- 'ycolor';
- 'xtick';
- 'xlim'}';
-
-Option_Model_ERP = {Model_Range_ERP;
- -75:10:-40;
- -75:10:-40;
- 'black';
- xRange;
- [xRange(1),xRange(end)]}'; %#ok<*NBRAK>
-
-Option_Data_ERP = {Data_Range_ERP;
- -80:40:40;
- -80:40:40;
- 'black';
- xRange;
- [xRange(1),xRange(end)]}';
-
-Option_Model_FSP = {Model_Range_FSP;
- linspace(Model_Range_FSP(1), Model_Range_FSP(2), 4);
- linspace(Model_Range_FSP(1), Model_Range_FSP(2), 4);
- 'black';
- xRange;
- [xRange(1),xRange(end)]}';
-
-Option_Data_FSP = {Data_Range_FSP;
- linspace(Data_Range_FSP(1), Data_Range_FSP(2), 4);
- linspace(Data_Range_FSP(1), Data_Range_FSP(2), 4);
- 'black';
- xRange;
- [xRange(1),xRange(end)]}';
-
-[Ve, Vt, Ca, ah, Marker_Stim] = TC_mex(T, Param_Cortex, Param_Thalamus, Connectivity, var_stim);
-Fs = length(Ve)/T;
-Ve_FSP = ft_preproc_hilbert(ft_preproc_bandpassfilter(Ve, Fs, [12, 15], 513, 'fir'), 'abs').^2;
-xRange = [-1, 3];
-
-% Search for peaks
-x_SO = Marker_Stim;
-
-% Remove those events, that are too close to begin/end
-x_SO = x_SO(x_SO<(T-xRange(end))*Fs);
-x_SO = x_SO(x_SO> -xRange(1)*Fs);
-
-% Set the variables
-N_Stim = length(x_SO);
-time_event = linspace(xRange(1), xRange(end), (xRange(end)-xRange(1))*Fs+1);
-Events = zeros(length(time_event), N_Stim);
-Events_FSP = zeros(length(time_event), N_Stim);
-
-% Segmentation
-for i=1:N_Stim
- Events(:,i) = Ve ((x_SO(i)+xRange(1)*Fs):(x_SO(i)+xRange(end)*Fs));
- Events_FSP(:,i) = Ve_FSP((x_SO(i)+xRange(1)*Fs):(x_SO(i)+xRange(end)*Fs));
-end
-
-mean_ERP_model= mean(Events, 2); %#ok<*NASGU>
-mean_FSP_model= mean(Events_FSP,2);
-sd_ERP_model = std (Events, 0, 2);
-sd_FSP_model = std (Events_FSP,0, 2);
-
-% Define handle for plotting
-BL_model =@(y,x) boundedline(y,x(:,1), x(:,2), 'alpha', 'transparency', 0.1, 'r');
-BL_data =@(y,x) boundedline(y,x(:,1), x(:,2), 'alpha', 'transparency', 0.1, 'black');
-
-figure(1)
-subplot(411)
-plot(linspace(0,30,3000),Ve(101:3100));
-title(['Ve with a mean of :',num2str(mean(Ve))]);
-subplot(412)
-plot(linspace(0,30,3000),Vt(101:3100));
-title(['Vt with a mean of :',num2str(mean(Vt))]);
-subplot(413)
-plot(linspace(0,30,3000),Ca(101:3100));
-title(['Ca with a mean of :',num2str(mean(Ca))]);
-subplot(414)
-plot(linspace(0,30,3000),ah(101:3100));
-title(['ah with a mean of :',num2str(mean(ah))]);
-
-% Create figure
-figure(2)
-clf
-subplot(211)
-[AX1, ~, ~] = plotyy(time_events,[mean_ERP, sem_FSP],time_events,[mean_ERP_model, sd_ERP_model], BL_data, BL_model);
-set(AX1(1),Option_Name, Option_Data_ERP, 'box', 'off');
-set(AX1(2),Option_Name, Option_Model_ERP);
-ylabel(AX1(1),'EEG [$\mu$V]');
-ylabel(AX1(2),'V$_{p}$ [mV]');
-
-subplot(212)
-[AX2, ~, ~] = plotyy(time_events,[mean_FSP, sem_FSP],time_events,[mean_FSP_model, sd_FSP_model], BL_data, BL_model);
-set(AX2(1),Option_Name, Option_Data_FSP);
-set(AX2(2),Option_Name, Option_Model_FSP);
-ylabel(AX2(1),'Spindle Power [$\mu$V$^{2}$]');
-ylabel(AX2(2),'Spindle Power [mV$^{2}$]');
-title([num2str(N_Stim), ' Events'])
-
-% Marker for stimulation
-for i=1:2
- hx1 = graph2d.constantline((i-1)*1.05+0.125*(i-1)*(i-2)/2,'ydata', get(AX1(1),'ylim'), 'parent', AX1(1), 'color', 'black', 'LineStyle', ':');
- hx2 = graph2d.constantline((i-1)*1.05+0.125*(i-1)*(i-2)/2,'ydata', get(AX2(1),'ylim'), 'parent', AX2(1), 'color', 'black', 'LineStyle', ':');
- changedependvar(hx1,'x');
- changedependvar(hx2,'x');
-end
-
-end

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