├── ClusterPhase ├── CLUSTER PHASE Readme.pdf ├── ClusterPhase_do.m ├── G201EC1.txt ├── G201EO1.txt ├── G201EO2.txt ├── G202EC1.txt ├── G202EO1.txt └── G202EO2.txt ├── Fractal_Analysis_Toolbox ├── AutoCorrel.m ├── DFA.m ├── ExampleFractalDataFiles │ ├── fBm1.txt │ ├── fBm2.txt │ ├── fBm3.txt │ ├── fBm4.txt │ ├── fBm5.txt │ ├── fGn1.txt │ ├── fGn2.txt │ ├── fGn3.txt │ ├── fGn4.txt │ └── fGn5.txt ├── Fractal Analysis ToolBox Readme.pdf ├── PSD.m ├── SDA.m ├── simulateFGn.fig └── simulateFGn.m ├── LICENSE ├── README.md ├── RQAToolbox ├── Elvis.txt ├── PostureData.txt ├── RQA ToolBox Readme.pdf ├── RockingChairData.txt ├── WhiteNoiseData.txt ├── aRQA.m ├── aRQACat.m ├── aRQACat_batch.m ├── aRQA_batch.m ├── ami.m ├── fnn.m ├── xRQA.m ├── xRQACat.m ├── xRQACat_batch.m └── xRQA_batch.m └── Synchro_Toolbox ├── ExData_Antiphase.csv ├── ExData_Inphase.csv ├── ExData_Intermit.csv ├── ExData_ThreeToTwo.csv ├── ExData_TwoToOne.csv ├── Synchro ToolBox Readme.pdf ├── amplitude.m ├── coherence.m ├── discretephase.m ├── hilbertphase.m ├── period.m ├── returnPlot.m ├── synchroBatch.m ├── synchroDRP.m └── synchroIRP.m /ClusterPhase/CLUSTER PHASE Readme.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xkiwilabs/MATLAB-Toolboxes/e6af49c2460e8c25dedf27ede3161d8ec465091c/ClusterPhase/CLUSTER PHASE Readme.pdf -------------------------------------------------------------------------------- /ClusterPhase/ClusterPhase_do.m: -------------------------------------------------------------------------------- 1 | function [GRPrhoM INDrhoM INDrpM TSrhoGRP TSrpIND] = ClusterPhase_do(TSfilename, TSnumber, TSfsamp, TSlsamp, TSsamplerate, plotflag) 2 | %-------------------------------------------------------------------------- 3 | %-------------------------------------------------------------------------- 4 | % ClusterPhase_do.m 5 | % 6 | % [GRPrhoM INDrhoM INDrpM TSrhoGRP TSrpIND] = ClusterPhase_do(TSfilename, TSnumber, TSfsamp, TSlsamp, TSsamplerate, plotflag) 7 | % 8 | % Input: 9 | % TSfilename : data file 10 | % TSnumber : number of time series 11 | % TSsamplerate : sample rate of the time series 12 | % TSfsamp : first data point in time series used 13 | % TSlsamp : last data point in time series used 14 | % plotflag : do plots (0=no, 1=yes) 15 | % 16 | % Output: 17 | % GRPrhoM : mean group rho (0 to 1; 1 = perfect sync) 18 | % INDrhoM : mean rho for each TS to group (0 to 1; 1 = perfect sync) 19 | % INDrpM : mean Relative Phase for each TS to group cluster phase 20 | % TSrhoGRP : group rho time-series 21 | % TSrpIND : relative phase time-series for each individual TS to cluster phase 22 | % 23 | % Example: 24 | % [GRPrhoM INDrhoM INDrpM TSrhoGRP TSrpIND] = ClusterPhase_do('G201EO1.txt', 6, 1, 7200, 120, 1); 25 | % 26 | % BY (2008): 27 | % Michael J Richardson (Univeristy of Cincinnati) & Till D. Frank (UCONN) 28 | % 29 | % UPDATED (2011): 30 | % Michael J Richardson (Univeristy of Cincinnati) 31 | % 32 | % References: 33 | % [1] Frank, T. D., & Richardson, M. J. (2010). On a test statistic for 34 | % the Kuramoto order parameter of synchronization: with an illustration 35 | % for group synchronization during rocking chairs. 36 | % 37 | % [2] Richardson,M.J., Garcia, R., Frank, T. D., Gregor, M., & 38 | % Marsh,K. L. (2010). Measuring Group Synchrony: A Cluster-Phase Method 39 | % for Analyzing Multivariate Movement Time-Series 40 | % 41 | % Code Contact & References: 42 | % michael.richardson@uc.edu 43 | % http://homepages.uc.edu/~richamo/ 44 | %-------------------------------------------------------------------------- 45 | %-------------------------------------------------------------------------- 46 | 47 | %% Set fixed parameters 48 | filterfreq = 10; 49 | close all; 50 | 51 | 52 | %% load time-series (TS) 53 | %************************************************************************** 54 | data = load(TSfilename); 55 | 56 | for nts=1:TSnumber 57 | ts_data(:,nts) = data(TSfsamp:TSlsamp,nts); 58 | end 59 | 60 | TSlength = length(ts_data(:,1)); 61 | delta_t = 1/TSsamplerate; 62 | t = (1:TSlength)*delta_t; 63 | 64 | 65 | %% Downsample, Filter and normalize data 66 | %************************************************************************** 67 | %linear detrend data to remove drift (chiar moving slightly during trial) 68 | for nts=1:TSnumber 69 | ts_data(:,nts) = detrend(ts_data(:,nts)); 70 | end 71 | 72 | %normlaize data 73 | for nts=1:TSnumber 74 | ts_data(:,nts) = zscore(ts_data(:,nts)); 75 | end 76 | 77 | %filter data 78 | for nts=1:TSnumber 79 | [weight_b,weight_a] = butter(2,filterfreq/(TSsamplerate/2)); 80 | ts_data(:,nts) = filtfilt(weight_b,weight_a,ts_data(:,nts)); 81 | end 82 | 83 | 84 | %% Compute phase for each TS using Hilbert transform 85 | %************************************************************************** 86 | TSphase = zeros(TSlength-1,TSnumber); 87 | for k=1:TSnumber 88 | hrp = hilbert(ts_data(:,k)); 89 | for n=1:TSlength-1 90 | TSphase(n,k)=atan2(real(hrp(n)),imag(hrp(n))); 91 | end 92 | TSphase(:,k)=unwrap(TSphase(:,k)); 93 | end 94 | 95 | 96 | %% Compute mean running (Cluster) phase 97 | %************************************************************************** 98 | clusterphase = zeros(1,TSlength-1); 99 | for n=1:TSlength-1 100 | ztot=complex(0,0); 101 | for k=1:TSnumber 102 | z=exp(1i*TSphase(n,k)); 103 | ztot=ztot+z; 104 | end 105 | ztot=ztot/TSnumber; 106 | clusterphase(n)=angle(ztot); 107 | end 108 | clusterphase = unwrap(clusterphase); 109 | 110 | 111 | %% Compute relative phases between phase of TS and cluster phase 112 | %************************************************************************** 113 | TSrpIND=zeros(TSlength-1,TSnumber); 114 | INDrpM = zeros(TSnumber,1); 115 | INDrhoM = zeros(TSnumber,1); 116 | for k=1:TSnumber 117 | ztot=complex(0,0); 118 | for n=1:TSlength-1 119 | z=exp(1i*(TSphase(n,k)-clusterphase(n))); 120 | TSrpIND(n,k) = z; 121 | ztot=ztot+z; 122 | end 123 | TSrpIND(:,k) = angle(TSrpIND(:,k))*360/(2*pi); % convert radian to degrees 124 | ztot=ztot/(TSlength-1); 125 | INDrpM(k) = angle(ztot); 126 | INDrhoM(k) = abs(ztot); 127 | end 128 | TSRPM = INDrpM; 129 | INDrpM = (INDrpM(:,1)./(2*pi)*360); % convert radian to degrees 130 | disp(' '); 131 | disp('Mean relative phases of individuals to cluster phase') 132 | disp(INDrpM(:,1).'); 133 | disp('Averaged degree of synchronization of individuals (Rho = 1-circular variance)') 134 | disp(INDrhoM(:,1).'); 135 | 136 | 137 | %% Compute cluster amplitude rhotot in rotation frame 138 | %************************************************************************** 139 | TSrhoGRP=zeros(TSlength-1,1); 140 | for n=1:TSlength-1 141 | ztot=complex(0,0); 142 | for k=1:TSnumber 143 | z=exp(1i*(TSphase(n,k)-clusterphase(n)-TSRPM(k))); 144 | ztot=ztot+z; 145 | end 146 | ztot=ztot/TSnumber; 147 | TSrhoGRP(n)=abs(ztot); 148 | end 149 | 150 | GRPrhoM = mean(TSrhoGRP); 151 | disp('Averaged degree of synchronization of the group') 152 | disp(GRPrhoM); 153 | 154 | 155 | %% Do Plot 156 | %************************************************************************** 157 | % plot data for time-series (separeted on graph for display purposes) 158 | scrsz = get(0,'ScreenSize'); 159 | h = figure('Position',[scrsz(3)/3 scrsz(4)/3 scrsz(3)/2 scrsz(4)/2]); 160 | 161 | if plotflag == 1 162 | subplot(3,1,1); 163 | hold on; 164 | tmpdata = zeros(TSlength,TSnumber); 165 | for nts=1:TSnumber 166 | tmpdata(:,nts) = (ts_data(:,nts)+(nts*4)); 167 | end 168 | plot(t(1:TSlength), tmpdata); 169 | xlabel('Time','fontsize',10) 170 | ylabel('RAW Data','fontsize',10) 171 | hold off; 172 | end 173 | 174 | % plot individ-cluster relative phase 175 | if plotflag == 1 176 | subplot(3,1,2); 177 | set(gca,'fontsize',10) 178 | plot(t(1:length(t)-1), TSrpIND) 179 | xlabel('Time','fontsize',10) 180 | ylabel('IND-Clust Relative Phase','fontsize',10) 181 | xlim([0 max(t)]); 182 | ylim([-185 185]); 183 | end 184 | get(0, 'CurrentFigure'); 185 | str(1) = {['Mean RPs: ', sprintf('%.2f ', INDrpM(:,1))]}; 186 | text(0, 220, 0, str, 'FontSize', 10, 'Color', 'k'); 187 | 188 | 189 | % plot group-cluster amplitiude (rho) timeseries 190 | if plotflag == 1 191 | subplot(3,1,3); 192 | set(gca,'fontsize',10) 193 | plot(t(1:length(t)-1), TSrhoGRP) 194 | xlabel('Time','fontsize',10) 195 | ylabel('GRP-Clust Amplitude','fontsize',10) 196 | ylim([0 1]); 197 | xlim([0 max(t)]); 198 | end 199 | get(0, 'CurrentFigure'); 200 | str(1) = {['Mean GRP Rho: ', sprintf('%.3f ', GRPrhoM), ' Mean IND Rhos: ', sprintf('%.3f ', INDrhoM(:,1))]}; 201 | text(0, -.4, 0, str, 'FontSize', 10, 'Color', 'k'); 202 | 203 | 204 | %% 205 | return; 206 | %************************************************************************** 207 | %************************************************************************** 208 | %************************************************************************** 209 | -------------------------------------------------------------------------------- /Fractal_Analysis_Toolbox/AutoCorrel.m: -------------------------------------------------------------------------------- 1 | function AutoCorrel(filename, lag) 2 | % 3 | % AutoCorrel - Calculates Auto Correlation Function 4 | % 5 | % Examples: 6 | % AutoCorrel('fGn1.txt', 128); 7 | % AutoCorrel('fGn2.txt', 128); 8 | % AutoCorrel('fGn3.txt', 128); 9 | %-------------------------------------------------------------------------- 10 | 11 | %% Load Data 12 | dataRaw = load(filename); 13 | data = dataRaw(:,1); 14 | 15 | %% Z-score transform 16 | data_z = zscore(data); 17 | 18 | %% ACF calculation 19 | c = zeros(lag,1); 20 | c(1) = sum((data_z-mean(data_z)).*(data_z-mean(data_z)))./length(data_z); 21 | for k = 1:lag 22 | c(k+1) = sum((data_z(1:end-k)-mean(data_z)).*(data_z(1+k:end)-mean(data_z)))./length(data_z); 23 | end 24 | r=c./repmat(c(1),length(c),1); 25 | 26 | %% Plot the result 27 | figure; 28 | subplot(1,2,1); 29 | plot(data,'k.-','linewidth',1); 30 | xlabel('Time'); 31 | ylabel('Amplitude, A(t)'); 32 | xlim([0 length(data)]); 33 | 34 | subplot(1,2,2); 35 | stem(0:lag,r,'k','linewidth',1); 36 | xlabel('Lag'); 37 | ylabel('Autocorrelation'); 38 | xlim([0 lag]); 39 | 40 | 41 | 42 | 43 | 44 | 45 | -------------------------------------------------------------------------------- /Fractal_Analysis_Toolbox/DFA.m: -------------------------------------------------------------------------------- 1 | function DFA(filename, output) 2 | % 3 | % DFA - Detrended Fluctuation Analysis 4 | % 5 | % NOTES: DFA can be sued for both fGn and fBm time-seres data. 6 | % 7 | % DFA works very well for fGn-like time series data even when the 8 | % data are relatively short (256 points). 9 | % 10 | % DFA may be problematic for classifying low values of H when 11 | % the process is fBm (under-diffusive fBm with H from 0 to about 0.2) because 12 | % they produce negative H estimates! One soluation is to 13 | % differentiate your fBm data prior to analysis (i.e., convert it to fGn). 14 | % 15 | % Examples: 16 | % DFA('fGn1.txt', 1); 17 | % DFA('fBm1.txt', 1); 18 | % 19 | % Adpated and updated code from an uncountable number of authors over the years: 20 | % Jay Holden, Michael J. Richardson, R. C. Schmidt, Charles Coey, Nikita Kuznetsov 21 | % Jing Hu, Jianbo Gao 22 | %-------------------------------------------------------------------------- 23 | 24 | %% Load Data 25 | dataRaw = load(filename); 26 | dataRaw = dataRaw(:,1)'; 27 | 28 | %% Setup Analysis Parameters and Data 29 | dataLength = length(dataRaw); 30 | minscale = 2; 31 | maxscale = floor(log2(dataLength)); 32 | step = 1; 33 | 34 | %% Center and Integrate Data 35 | dataINT = dataRaw - mean(dataRaw); 36 | dataINT = cumsum(dataINT-mean(dataINT)); 37 | 38 | 39 | %% Divide the integrated time series into boxes of equal length for analysis 40 | % This is where the DFA action is. 41 | k = 1; 42 | for i = minscale : step : maxscale 43 | 44 | l = floor(2^i); 45 | 46 | b_block = floor(dataLength/l); 47 | clear z; 48 | 49 | for j = 1 : b_block 50 | zx = 1 : l; 51 | zy = dataINT((j - 1)*l + 1 : j*l); 52 | sx = sum(zx); 53 | sy = sum(zy); 54 | sxy = sum(zx.*zy); 55 | sx2 = sum(zx.^2); 56 | x_bar = sx/l; 57 | y_bar = sy/l; 58 | slope = (l*sxy-sx*sy)/(l*sx2-sx*sx); 59 | zz = slope*(zx - x_bar) + y_bar; 60 | z((j - 1)*l + 1 : j*l) = (dataINT((j - 1)*l + 1 : j*l) - zz).^2; 61 | end 62 | 63 | dfa_data(k) = (sum(z)/length(z))^(1/2); 64 | k = k + 1; 65 | end 66 | 67 | %% Organize DFA results 68 | DFAresult(1, :) = minscale : step : maxscale; 69 | DFAresult(2, :) = log2(dfa_data); 70 | 71 | %% Plot Results 72 | figure; 73 | subplot(1,3,1:2); % Plot data 74 | plot(dataRaw, '-b'); 75 | xlim([1 length(dataRaw)]); 76 | 77 | subplot(1,3,3); 78 | plot(DFAresult(1,:),DFAresult(2,:),'.-'); hold on; 79 | ylabel('Log_2(F(n))') 80 | xlabel('Log_2(Window size, n)') 81 | p = polyfit(DFAresult(1,:),DFAresult(2,:),1); 82 | plot(DFAresult(1,:),polyval(p,DFAresult(1,:)),'r','linewidth',2); hold off; 83 | 84 | %% Get Scaling 85 | alpha_r = round(p(1)*100)/100; 86 | if alpha_r > 1; 87 | hurst = alpha_r-1; 88 | fprintf('%s Hurst (fBm): %.2f\n\n', filename, alpha_r-1); 89 | else 90 | hurst = alpha_r; 91 | fprintf('%s Hurst (fGn): %.2f\n\n', filename, alpha_r); 92 | end 93 | 94 | %% Output to File 95 | if output == 1; 96 | fid=fopen('DFA_Stats.csv','a'); 97 | fprintf(fid,'%s,%.4f,%.4f\n',filename, alpha_r, hurst); 98 | fclose(fid); 99 | end 100 | 101 | 102 | 103 | 104 | 105 | -------------------------------------------------------------------------------- /Fractal_Analysis_Toolbox/ExampleFractalDataFiles/fBm1.txt: -------------------------------------------------------------------------------- 1 | 0.1599 2 | -0.0827 3 | -0.6896 4 | 1.5308 5 | 0.7384 6 | -0.5123 7 | 0.8709 8 | -0.4106 9 | -0.2998 10 | 0.1444 11 | -0.2718 12 | 0.1422 13 | 0.7230 14 | 1.7957 15 | 2.0482 16 | 1.8131 17 | 0.1732 18 | 1.1154 19 | 1.3093 20 | 0.6348 21 | 2.2586 22 | 1.4113 23 | 1.1520 24 | 1.2264 25 | 0.6823 26 | 1.1060 27 | 1.0819 28 | -0.1133 29 | 1.1989 30 | -1.0017 31 | 0.3592 32 | 0.9130 33 | 0.1890 34 | 0.8867 35 | 1.0873 36 | 1.3002 37 | 0.6219 38 | 0.8689 39 | 0.4720 40 | -0.0677 41 | 0.3793 42 | 2.0347 43 | 0.0226 44 | -0.7792 45 | 0.7021 46 | -0.9404 47 | 2.2735 48 | 0.7395 49 | -0.7384 50 | 1.5513 51 | 0.1489 52 | 0.2349 53 | 0.0147 54 | 1.1070 55 | 1.0965 56 | 2.0144 57 | 0.8601 58 | 1.7055 59 | 1.7429 60 | 0.9658 61 | 0.2248 62 | 1.4025 63 | 1.6402 64 | 1.8557 65 | 1.8261 66 | 1.3506 67 | 1.5315 68 | 1.5314 69 | 2.2406 70 | 2.2792 71 | 1.2812 72 | 1.7858 73 | 1.1073 74 | 1.7590 75 | 2.0601 76 | 2.2958 77 | 0.5633 78 | 2.2272 79 | 1.0794 80 | 2.4249 81 | 2.8639 82 | 3.2837 83 | 3.1763 84 | 2.2221 85 | 1.4603 86 | 2.3570 87 | 2.5381 88 | 1.1097 89 | -0.7154 90 | 0.6891 91 | 3.2808 92 | 0.2992 93 | 1.4002 94 | 2.0197 95 | -0.2785 96 | 0.7537 97 | 2.0447 98 | 1.8211 99 | 2.5261 100 | 2.3560 101 | 2.6439 102 | 1.2618 103 | -0.3861 104 | 1.9122 105 | 2.2245 106 | 0.5259 107 | 2.2293 108 | 2.0204 109 | 0.7362 110 | 0.0630 111 | 1.6652 112 | 3.4796 113 | 1.3651 114 | 1.7022 115 | 2.1100 116 | 1.3515 117 | 1.4504 118 | 2.6369 119 | 0.6740 120 | 0.6816 121 | 0.7463 122 | -0.5310 123 | 1.1172 124 | 0.6190 125 | 1.9485 126 | 1.6131 127 | 1.3469 128 | 1.9497 129 | 0.4375 130 | 2.3640 131 | 0.6186 132 | 0.2619 133 | 1.0589 134 | 0.5742 135 | 1.9017 136 | 0.7832 137 | 0.3776 138 | -0.3699 139 | 1.3840 140 | 0.1250 141 | -0.2681 142 | 0.9688 143 | 0.4637 144 | -0.2228 145 | -0.1198 146 | 0.3220 147 | 0.5356 148 | 1.1326 149 | -0.7229 150 | 1.1926 151 | 0.9202 152 | 1.5547 153 | 2.3215 154 | 1.3420 155 | 2.6144 156 | -0.3108 157 | 1.3783 158 | -0.4278 159 | -0.1172 160 | 1.4384 161 | 0.0318 162 | 0.7974 163 | 1.4814 164 | -0.4974 165 | 1.8142 166 | 0.8376 167 | 0.3660 168 | -0.2564 169 | 1.0391 170 | 1.0352 171 | 0.4374 172 | 0.2611 173 | 1.6872 174 | 0.7151 175 | 1.9236 176 | 0.2613 177 | 0.4687 178 | 0.6273 179 | 0.9922 180 | 0.1963 181 | 0.1434 182 | 1.0842 183 | -0.0119 184 | 0.8699 185 | 0.4831 186 | 0.4371 187 | 0.1852 188 | 1.7490 189 | 1.1513 190 | 0.7058 191 | 0.5903 192 | 0.5067 193 | 0.6191 194 | -0.0572 195 | 0.6359 196 | 0.8002 197 | 2.2880 198 | 0.6824 199 | 0.4792 200 | 0.4585 201 | 0.8861 202 | 0.2054 203 | 2.2855 204 | 1.2820 205 | 0.3512 206 | 1.5160 207 | 2.0637 208 | 0.3188 209 | 0.6762 210 | 0.9897 211 | 1.7440 212 | 3.1119 213 | 0.0298 214 | 1.3835 215 | 1.6533 216 | -0.1859 217 | -0.6326 218 | -0.1384 219 | 0.0633 220 | 1.1231 221 | 0.4529 222 | -0.6620 223 | -0.7704 224 | 0.2030 225 | 0.3034 226 | 2.3746 227 | -0.9130 228 | 1.4160 229 | 2.2294 230 | 0.8316 231 | 0.1917 232 | 0.8942 233 | 1.1256 234 | 1.3679 235 | 0.5873 236 | 0.0491 237 | 0.1809 238 | 0.2244 239 | 0.4596 240 | 1.0015 241 | 0.4877 242 | -0.1875 243 | -0.7191 244 | 0.7532 245 | -0.4092 246 | 0.0961 247 | 0.5482 248 | 1.4923 249 | 1.3167 250 | 1.4694 251 | -0.1199 252 | 0.0733 253 | 0.0076 254 | 1.7224 255 | -1.2514 256 | 0.1531 257 | 0.4179 258 | 0.9567 259 | -1.2734 260 | -0.2317 261 | 0.5856 262 | 1.1040 263 | -0.4011 264 | -0.9436 265 | -0.6918 266 | -1.3119 267 | -0.6624 268 | 1.1945 269 | -0.7376 270 | -1.4642 271 | -2.6931 272 | -0.8181 273 | -3.2530 274 | -3.7104 275 | -2.7124 276 | -2.8712 277 | -3.4050 278 | -2.3269 279 | -1.7951 280 | -2.0249 281 | -2.2887 282 | -0.6931 283 | -0.1498 284 | -0.9822 285 | -0.8516 286 | -1.1304 287 | -1.5953 288 | -1.1877 289 | -2.1900 290 | -0.9677 291 | -0.8754 292 | -0.9043 293 | -0.4952 294 | 0.2639 295 | 0.4258 296 | 0.7877 297 | 0.1679 298 | -0.2004 299 | 0.3467 300 | -0.8906 301 | -0.6710 302 | -0.0692 303 | 1.3599 304 | 1.1250 305 | -0.0130 306 | -0.8392 307 | -0.5810 308 | 0.9659 309 | -0.2567 310 | -0.1954 311 | -0.2233 312 | -0.3189 313 | -1.6719 314 | -0.1633 315 | -0.0784 316 | -0.5159 317 | -0.1984 318 | -0.9242 319 | 0.5288 320 | -0.3100 321 | -0.1441 322 | -1.5114 323 | -1.7942 324 | -0.4991 325 | 0.2777 326 | 0.1116 327 | -1.1273 328 | 0.4837 329 | 0.2181 330 | -0.7168 331 | -0.4694 332 | 0.0225 333 | -0.6999 334 | -0.5887 335 | 0.9192 336 | 0.0616 337 | 1.5192 338 | 1.1475 339 | 0.8008 340 | 0.3580 341 | -1.2296 342 | 1.2702 343 | -0.3007 344 | 0.5358 345 | 1.1911 346 | 1.8936 347 | 1.7666 348 | 0.8259 349 | 1.0081 350 | 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1021 | 4.0761 1022 | 3.0706 1023 | 2.8755 1024 | 1.6569 1025 | -------------------------------------------------------------------------------- /Fractal_Analysis_Toolbox/ExampleFractalDataFiles/fBm2.txt: -------------------------------------------------------------------------------- 1 | 0.8894 2 | 1.1209 3 | 1.3943 4 | 1.4222 5 | 2.6590 6 | 3.2126 7 | 1.4628 8 | 0.6629 9 | 0.2284 10 | -0.3551 11 | 1.0609 12 | 2.8480 13 | 2.1257 14 | 2.4977 15 | 3.5488 16 | 2.7817 17 | 3.9093 18 | 4.3316 19 | 5.7671 20 | 5.1202 21 | 4.9610 22 | 2.9023 23 | 2.8278 24 | 3.8083 25 | 4.3050 26 | 3.0141 27 | 5.2174 28 | 6.1811 29 | 4.4224 30 | 5.6116 31 | 6.3605 32 | 5.8213 33 | 6.0418 34 | 4.0896 35 | 4.8858 36 | 5.4038 37 | 6.0324 38 | 7.5385 39 | 6.0952 40 | 6.6777 41 | 4.9825 42 | 4.1498 43 | 3.9552 44 | 3.7267 45 | 4.4608 46 | 3.7669 47 | 5.4970 48 | 5.1315 49 | 4.8729 50 | 3.6720 51 | 4.1046 52 | 2.6618 53 | 4.2955 54 | 3.7952 55 | 3.5069 56 | 3.2938 57 | 3.6433 58 | 4.2237 59 | 2.6514 60 | 5.1421 61 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1017 | -0.1393 1018 | -0.9615 1019 | 1.6939 1020 | -0.5256 1021 | 0.3995 1022 | 0.3532 1023 | -0.3204 1024 | 1.7581 1025 | -------------------------------------------------------------------------------- /Fractal_Analysis_Toolbox/ExampleFractalDataFiles/fGn5.txt: -------------------------------------------------------------------------------- 1 | 0.1599 2 | -0.2426 3 | -0.6068 4 | 2.2204 5 | -0.7924 6 | -1.2507 7 | 1.3832 8 | -1.2815 9 | 0.1108 10 | 0.4442 11 | -0.4161 12 | 0.4140 13 | 0.5808 14 | 1.0727 15 | 0.2525 16 | -0.2351 17 | -1.6399 18 | 0.9422 19 | 0.1939 20 | -0.6745 21 | 1.6238 22 | -0.8472 23 | -0.2593 24 | 0.0743 25 | -0.5441 26 | 0.4237 27 | -0.0241 28 | -1.1952 29 | 1.3121 30 | -2.2006 31 | 1.3609 32 | 0.5539 33 | -0.7240 34 | 0.6976 35 | 0.2006 36 | 0.2129 37 | -0.6783 38 | 0.2470 39 | -0.3969 40 | -0.5397 41 | 0.4470 42 | 1.6554 43 | -2.0121 44 | -0.8019 45 | 1.4813 46 | -1.6425 47 | 3.2139 48 | -1.5340 49 | -1.4779 50 | 2.2898 51 | -1.4024 52 | 0.0859 53 | -0.2202 54 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1019 | -0.3978 1020 | 0.4359 1021 | 1.3498 1022 | -1.0055 1023 | -0.1951 1024 | -1.2186 1025 | -------------------------------------------------------------------------------- /Fractal_Analysis_Toolbox/Fractal Analysis ToolBox Readme.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xkiwilabs/MATLAB-Toolboxes/e6af49c2460e8c25dedf27ede3161d8ec465091c/Fractal_Analysis_Toolbox/Fractal Analysis ToolBox Readme.pdf -------------------------------------------------------------------------------- /Fractal_Analysis_Toolbox/PSD.m: -------------------------------------------------------------------------------- 1 | function PSD(filename, diffTS, nfreq, percent_spec, output) 2 | % 3 | % PSD - Power Spectral Density Analysis 4 | % Compute power spectrum using Welch's method from a single time-series 5 | % of a length of a power of two. 6 | % 7 | % Examples: 8 | % PSD('fGn1.txt', 0, 128, 75, 0); 9 | % PSD('fGn2.txt', 0, 128, 75, 0); 10 | % PSD('fGn3.txt', 0, 128, 75, 0); 11 | % PSD('fBm1.txt', 0, 128, 75, 1); 12 | % PSD('fBm2.txt', 0, 128, 75, 1); 13 | % 14 | % Adpated and updated code from an uncountable number of authors over the years: 15 | % Jay Holden, Michael J. Richardson, R. C. Schmidt, Charles Coey, Nikita Kuznetsov 16 | %-------------------------------------------------------------------------- 17 | 18 | %% Load and Check Data 19 | x_data = load(filename); 20 | x = x_data(:,1); 21 | 22 | if length(x) < nfreq*4 23 | disp(' ERROR: data to short for nFreq...try again'); 24 | return; 25 | end 26 | 27 | %% Diff TS 28 | if diffTS == 1 29 | x = diff(x); 30 | end 31 | 32 | 33 | %% Z-Score Normalization 34 | if abs(mean(x))>=1e-6 || std(x,1)~=1; 35 | mu=mean(x); sigma=std(x,1); 36 | x = (x - mu)./ sigma; 37 | end 38 | 39 | 40 | %% Power Spectrum Density 41 | [P,F]=pwelch(x,triang(2*nfreq),nfreq,2*nfreq,1,'onesided'); 42 | F(1)=[]; 43 | F(nfreq)=[]; 44 | P(1,:)=[]; 45 | P(nfreq,:)=[]; 46 | Plog=log10(P); 47 | Flog=log10(F); 48 | 49 | spec_len = (length(P)+1); 50 | quart_spec = spec_len/4; 51 | 52 | switch (percent_spec) 53 | case 75 54 | pSCL = spec_len - quart_spec; 55 | powers = Plog(1:pSCL); 56 | freqs = Flog(1:pSCL); 57 | case 50 58 | pSCL = spec_len - (2*quart_spec); 59 | powers = Plog(1:pSCL); 60 | freqs = Flog(1:pSCL); 61 | case 25 62 | pSCL = spec_len - (3*quart_spec); 63 | powers = Plog(1:pSCL); 64 | freqs = Flog(1:pSCL); 65 | otherwise 66 | powers = Plog; 67 | freqs = Flog; 68 | end 69 | 70 | p = polyfit(freqs,powers,1); 71 | slope = p(:,1); 72 | alpha = -1*(slope); 73 | 74 | fprintf('%s Slope: %.2f Alpha: %.2f\n\n', filename, slope, alpha) 75 | 76 | %% Plot Results 77 | figure; 78 | subplot(2,2,1:2); % Plot data 79 | plot(x, '-b'); 80 | xlim([1 length(x)]); 81 | 82 | subplot(2,2,3); 83 | plot(F,P,'.-'); 84 | ylabel('Power'); 85 | xlabel('Frequency'); 86 | 87 | subplot(2,2,4); 88 | plot(Flog,Plog,'.-'); hold on; 89 | ylabel('Log_1_0(Power)'); 90 | xlabel('Log_1_0(Frequency)'); 91 | plot(freqs,polyval(p,freqs),'r','linewidth',2); hold off; 92 | 93 | %% Output to File 94 | if output == 1; 95 | fid=fopen('PSD_Stats.csv','a'); 96 | fprintf(fid,'%s,',filename); 97 | fprintf(fid,'%d,%d,%1.4f\r\n',nfreq,percent_spec,alpha); 98 | fclose(fid); 99 | end 100 | 101 | 102 | 103 | 104 | 105 | -------------------------------------------------------------------------------- /Fractal_Analysis_Toolbox/SDA.m: -------------------------------------------------------------------------------- 1 | function SDAstats = SDA(filename, diffTS, nfreq, percent_spec, output) 2 | % 3 | % SDA - Standardized Dispersion Analysis 4 | % 5 | % Examples: 6 | % SDA('fGn1.txt', 0, 128, 50, 1); 7 | % SDA('fGn2.txt', 0, 128, 50, 1); 8 | % SDA('fGn3.txt', 0, 128, 50, 1); 9 | % 10 | % Adpated and updated code from an uncountable number of authors over the years: 11 | % Jay Holden, Michael J. Richardson, R. C. Schmidt, Charles Coey, Nikita Kuznetsov 12 | %-------------------------------------------------------------------------- 13 | 14 | %% Load and Check Data 15 | x_data = load(filename); 16 | x = x_data(:,1); 17 | len_p2 = log2(length(x)); 18 | 19 | if length(x) < nfreq*4 20 | disp(' ERROR: data to short for nFreq...try again'); 21 | return; 22 | end 23 | 24 | %% Diff TS 25 | if diffTS == 1 26 | x = diff(x); 27 | end 28 | 29 | 30 | %% Z-Score Normalization 31 | if abs(mean(x))>=1e-6 || std(x,1)~=1; 32 | mu=mean(x); sigma=std(x,1); 33 | x = (x - mu)./ sigma; 34 | end 35 | 36 | 37 | %% Begin Standardized Dispersion 38 | dispersion=zeros(len_p2,1); % This initalizes the dispersion vector 39 | 40 | for j=1:(len_p2-1) 41 | % computes bin size for each scale 42 | bin_size=length(x)/pow2(j); 43 | k = fix(length(x)/bin_size); 44 | start=1; 45 | segment_means=zeros(k,1); 46 | 47 | for i=1:k %Compute mean for each bin 48 | segment_means(i,1)=mean(x(start:start+bin_size-1,1)); 49 | start=start+bin_size; 50 | end 51 | 52 | %Compute std (population formula) across the bins 53 | dispersion(j,1)=std(segment_means,1); 54 | end 55 | 56 | %Get the standard deviation of all points, (bins of 1 data 57 | %point). Should equal 1, so round to emiminate e-15 output 58 | dispersion(len_p2,1)=round(std(x,1)); 59 | 60 | 61 | %% Finish up 62 | %create the bin size vector 63 | bin_sz=(0:len_p2-1)'; 64 | 65 | % flip vector since it was done from large to small samples and 66 | % is ploted from small to large samples. 67 | disper=flipud(log2(dispersion)); 68 | sdaoutput=[bin_sz disper]; 69 | srlength = round(length(sdaoutput)*(percent_spec/100)); 70 | 71 | 72 | %% Plot Results 73 | figure; 74 | subplot(1,2,1); % Plot data 75 | plot(x, '-b'); 76 | xlim([1 length(x)]); 77 | 78 | subplot(1,2,2); 79 | plot(sdaoutput(:,1),sdaoutput(:,2),'bo-','linewidth',2) 80 | hold on; 81 | xlabel('Log_2(Window Size)') 82 | ylabel('Log_2(SD Dispersion)') 83 | p = polyfit(sdaoutput(1:srlength,1),sdaoutput(1:srlength,2),1); 84 | plot(sdaoutput(1:srlength,1),polyval(p,sdaoutput(1:srlength,1),1),'r','linewidth',2); 85 | hold off; 86 | 87 | %% Get final SDA statistics 88 | pfit=round(p(1)*100)/100; 89 | SDAstats = [round(pfit*100)/100, round((pfit+1)*100)/100, round((1-pfit)*100)/100]; 90 | fprintf('%s Slope: %.3f Hurst: %.3f FD: %.3f\n\n', filename, SDAstats(1), SDAstats(2), SDAstats(3)) 91 | 92 | 93 | %% Output to File 94 | if output == 1; 95 | fid=fopen('SDA_Stats.csv','a'); 96 | fprintf(fid,'%s,',filename); 97 | fprintf(fid,'%.4f,%.4f,%.4f\r\n',SDAstats(1), SDAstats(2), SDAstats(3)); 98 | fclose(fid); 99 | end 100 | 101 | 102 | 103 | 104 | 105 | -------------------------------------------------------------------------------- /Fractal_Analysis_Toolbox/simulateFGn.fig: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xkiwilabs/MATLAB-Toolboxes/e6af49c2460e8c25dedf27ede3161d8ec465091c/Fractal_Analysis_Toolbox/simulateFGn.fig -------------------------------------------------------------------------------- /Fractal_Analysis_Toolbox/simulateFGn.m: -------------------------------------------------------------------------------- 1 | function varargout = simulateFGn(varargin) 2 | % SIMULATEFGN MATLAB code for simulateFGn.fig 3 | % SIMULATEFGN, by itself, creates a new SIMULATEFGN or raises the existing 4 | % singleton*. 5 | % 6 | % H = SIMULATEFGN returns the handle to a new SIMULATEFGN or the handle to 7 | % the existing singleton*. 8 | % 9 | % SIMULATEFGN('CALLBACK',hObject,eventData,handles,...) calls the local 10 | % function named CALLBACK in SIMULATEFGN.M with the given input arguments. 11 | % 12 | % SIMULATEFGN('Property','Value',...) creates a new SIMULATEFGN or raises the 13 | % existing singleton*. Starting from the left, property value pairs are 14 | % applied to the GUI before simulateFGn_OpeningFcn gets called. An 15 | % unrecognized property name or invalid value makes property application 16 | % stop. All inputs are passed to simulateFGn_OpeningFcn via varargin. 17 | % 18 | % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one 19 | % instance to run (singleton)". 20 | % 21 | % Last Modified by Michael J. Richardson v2.5 29-May-2015 14:42:03 22 | 23 | % Begin initialization code - DO NOT EDIT 24 | gui_Singleton = 1; 25 | gui_State = struct('gui_Name', mfilename, ... 26 | 'gui_Singleton', gui_Singleton, ... 27 | 'gui_OpeningFcn', @simulateFGn_OpeningFcn, ... 28 | 'gui_OutputFcn', @simulateFGn_OutputFcn, ... 29 | 'gui_LayoutFcn', [] , ... 30 | 'gui_Callback', []); 31 | if nargin && ischar(varargin{1}) 32 | gui_State.gui_Callback = str2func(varargin{1}); 33 | end 34 | 35 | if nargout 36 | [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); 37 | else 38 | gui_mainfcn(gui_State, varargin{:}); 39 | end 40 | % End initialization code - DO NOT EDIT 41 | 42 | % --- Executes just before simulateFGn is made visible. 43 | function simulateFGn_OpeningFcn(hObject, eventdata, handles, varargin) %#ok<*INUSL> 44 | % This function has no output args, see OutputFcn. 45 | % hObject handle to figure 46 | % eventdata reserved - to be defined in a future version of MATLAB 47 | % handles structure with handles and user data (see GUIDATA) 48 | % varargin command line arguments to simulateFGn (see VARARGIN) 49 | 50 | % Choose default command line output for simulateFGn 51 | handles.output = hObject; 52 | handles.fdata = 0; 53 | handles.FBnDo = 0; 54 | 55 | % Update handles structure 56 | guidata(hObject, handles); 57 | 58 | 59 | % --- Outputs from this function are returned to the command line. 60 | function varargout = simulateFGn_OutputFcn(hObject, eventdata, handles) 61 | % varargout cell array for returning output args (see VARARGOUT); 62 | % hObject handle to figure 63 | % eventdata reserved - to be defined in a future version of MATLAB 64 | % handles structure with handles and user data (see GUIDATA) 65 | 66 | % Get default command line output from handles structure 67 | varargout{1} = handles.output; 68 | 69 | % --- Executes on button press in pbSimulate. 70 | function pbSimulate_Callback(hObject, eventdata, handles) %#ok<*DEFNU> 71 | % hObject handle to pbSimulate (see GCBO) 72 | % eventdata reserved - to be defined in a future version of MATLAB 73 | % handles structure with handles and user data (see GUIDATA) 74 | H = str2double(get(handles.edParamH,'String')); 75 | datalength = str2double(get(handles.edParamLength,'String')); 76 | 77 | if handles.FBnDo == 1 78 | fdata = simulate_fGn(H, datalength); 79 | fdata = cumsum(fdata); 80 | else 81 | fdata = simulate_fGn(H, datalength); 82 | end 83 | 84 | handles.fdata = fdata; 85 | guidata(hObject, handles); 86 | 87 | set(handles.pbOutput,'Enable','On'); 88 | 89 | %% plot simulated data 90 | axes(handles.axTS); 91 | plot(1:length(fdata), fdata,'b-'); 92 | xlabel('Time, t'); 93 | ylabel('Amplitude, Y(t)'); 94 | xlim([1 length(fdata)]); 95 | 96 | 97 | % -------------------------------------------------------------------- 98 | function PrintMenuItem_Callback(hObject, eventdata, handles) 99 | % hObject handle to PrintMenuItem (see GCBO) 100 | % eventdata reserved - to be defined in a future version of MATLAB 101 | % handles structure with handles and user data (see GUIDATA) 102 | printdlg(handles.figure1) 103 | 104 | % -------------------------------------------------------------------- 105 | function CloseMenuItem_Callback(hObject, eventdata, handles) 106 | % hObject handle to CloseMenuItem (see GCBO) 107 | % eventdata reserved - to be defined in a future version of MATLAB 108 | % handles structure with handles and user data (see GUIDATA) 109 | selection = questdlg(['Close ' get(handles.figure1,'Name') '?'],... 110 | ['Close ' get(handles.figure1,'Name') '...'],... 111 | 'Yes','No','Yes'); 112 | if strcmp(selection,'No') 113 | return; 114 | end 115 | 116 | delete(handles.figure1) 117 | 118 | 119 | % --- Executes on button press in pbOutput. 120 | function pbOutput_Callback(hObject, eventdata, handles) 121 | % hObject handle to pbOutput (see GCBO) 122 | % eventdata reserved - to be defined in a future version of MATLAB 123 | % handles structure with handles and user data (see GUIDATA) 124 | 125 | %% File Saving prompts here: 126 | [OutFileName,OutPathName] = uiputfile('*.txt','Specify an output file name'); 127 | if isequal(OutFileName,0) || isequal(OutPathName,0) 128 | %quit if no outfile is specified 129 | h=warndlg('No file created; Unable to save data', 'File Error!!'); uiwait(h); 130 | return; 131 | end 132 | 133 | %% Save data 134 | fid=fopen([OutPathName OutFileName],'wt'); 135 | fprintf(fid,'%1.4f\r\n',handles.fdata); 136 | fclose(fid); 137 | 138 | 139 | 140 | function edParamH_Callback(hObject, eventdata, handles) %#ok<*INUSD> 141 | % hObject handle to edParamH (see GCBO) 142 | % eventdata reserved - to be defined in a future version of MATLAB 143 | % handles structure with handles and user data (see GUIDATA) 144 | temp = str2double(get(handles.edParamH,'String')); 145 | if temp < .01 146 | temp = .01; 147 | set(handles.edParamH,'String', num2str(temp)); 148 | end 149 | 150 | if temp > 0.99 151 | temp = 0.99; 152 | set(handles.edParamH,'String', num2str(temp)); 153 | end 154 | 155 | % --- Executes during object creation, after setting all properties. 156 | function edParamH_CreateFcn(hObject, eventdata, handles) 157 | % hObject handle to edParamH (see GCBO) 158 | % eventdata reserved - to be defined in a future version of MATLAB 159 | % handles empty - handles not created until after all CreateFcns called 160 | if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) 161 | set(hObject,'BackgroundColor','white'); 162 | end 163 | 164 | 165 | 166 | function edParamLength_Callback(hObject, eventdata, handles) 167 | % hObject handle to edParamLength (see GCBO) 168 | % eventdata reserved - to be defined in a future version of MATLAB 169 | % handles structure with handles and user data (see GUIDATA) 170 | temp = str2double(get(handles.edParamLength,'String')); 171 | temp = round(temp); 172 | set(handles.edParamLength,'String', num2str(temp)); 173 | if temp < 32 174 | temp = 32; 175 | set(handles.edParamLength,'String', num2str(temp)); 176 | end 177 | 178 | if mod(log2(temp),1)~=0 179 | set(handles.edParamLength,'String', '1024'); 180 | end 181 | 182 | % --- Executes during object creation, after setting all properties. 183 | function edParamLength_CreateFcn(hObject, eventdata, handles) 184 | % hObject handle to edParamLength (see GCBO) 185 | % eventdata reserved - to be defined in a future version of MATLAB 186 | % handles empty - handles not created until after all CreateFcns called 187 | if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) 188 | set(hObject,'BackgroundColor','white'); 189 | end 190 | 191 | % --- Executes when selected object is changed in uipanel8. 192 | function uipanel8_SelectionChangeFcn(hObject, eventdata, handles) 193 | % hObject handle to the selected object in uipanel8 194 | % eventdata structure with the following fields (see UIBUTTONGROUP) 195 | % EventName: string 'SelectionChanged' (read only) 196 | % OldValue: handle of the previously selected object or empty if none was selected 197 | % NewValue: handle of the currently selected object 198 | % handles structure with handles and user data (see GUIDATA) 199 | switch get(eventdata.NewValue,'Tag') % Get Tag of selected object. 200 | case 'rbFGn' 201 | handles.FBnDo = 0; 202 | if (length(handles.fdata) > 2) 203 | fdata = diff(handles.fdata); 204 | handles.fdata = fdata; 205 | %% plot simulated data 206 | axes(handles.axTS); 207 | plot(1:length(fdata), fdata,'b-'); 208 | xlabel('Time, t'); 209 | ylabel('Amplitude, Y(t)'); 210 | xlim([1 length(fdata)]); 211 | end 212 | case 'rbFBn' 213 | handles.FBnDo = 1; 214 | if (length(handles.fdata) > 2) 215 | fdata = cumsum(handles.fdata); 216 | handles.fdata = fdata; 217 | %% plot simulated data 218 | axes(handles.axTS); 219 | plot(1:length(fdata), fdata,'b-'); 220 | xlabel('Time, t'); 221 | ylabel('Amplitude, Y(t)'); 222 | xlim([1 length(fdata)]); 223 | end 224 | end 225 | guidata(hObject, handles); 226 | 227 | 228 | function fgn = simulate_fGn(H, lng) 229 | % This function simulates fGn time series using Davies and Harte's (1997) algorithm. Steps 230 | % in the algorighm were presented in Caccia et al. (1996). The 231 | % implementation of steps 3 and 4 is borrowed from the ffgn function by 232 | % Yingchun Zhou (Jasmine), zhouyc@math.bu.edu and Stilian Stoev, sstoev@umich.edu) 233 | 234 | N=lng; 235 | if mod(log2(N),1)~=0 %Integer test, make sure no decimal remainder after dividing by 1 236 | %disp('Series must be an integer power of 2 in length.') 237 | N=2^nextpow2(N); 238 | %disp(['N set to ' num2str(N) ]) 239 | end 240 | 241 | % N = 2^13; 242 | M = 2*N; 243 | n = 1; 244 | 245 | sigma = 1; 246 | tau = 0:M/2; 247 | % H = .15; 248 | 249 | %% Specify desired autocorrelation 250 | gamma = (sigma^2/2).*(abs(tau+1).^(2*H)-2.*abs(tau).^(2*H)+abs(tau-1).^(2*H)); 251 | g = [gamma(1:end) fliplr(gamma(2:end-1))]; 252 | 253 | %% Step 1 (Calculate exact spectral power expected for the theoretical autocorrelation function) 254 | S = real(fft(g)); 255 | 256 | %% Step 2 (Check if all spectral coefficients are greater than 0) 257 | if min(S)<0, 258 | h = warndlg(' Some of the Sj are negative!', 'Analaysis Error'); uiwait(h); 259 | return; 260 | end 261 | S = abs(S); 262 | 263 | %% Step 3 (Calculate randomized spectral amplitudes) 264 | z(:,1)=sqrt(2)*randn(n,1); 265 | y(:,1)=z(:,1); 266 | 267 | z(:,N+1)=sqrt(2)*randn(n,1); 268 | y(:,N+1)=z(:,N+1); 269 | 270 | a=randn(n,N-1); 271 | b=randn(n,N-1); 272 | 273 | z1=a+b*1i; 274 | z(:,2:N)=z1; %#ok 275 | y1=z1; 276 | 277 | y(:,2:N)=y1; 278 | y(:,N+2:2*N)=conj(y(:,N:-1:2)); 279 | 280 | y = y.*(ones(n,1)*sqrt(S)); 281 | 282 | %% Step 4 (use 283 | fgn = real(fft(y')')/sqrt(4*N); 284 | fgn = fgn(:,1:N); 285 | 286 | % Random number generator for H = 0.5 287 | if H == .5 288 | fgn = randn(N,1); 289 | end 290 | 291 | return 292 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MATLAB-Toolboxes 2 | The repository includes various time-series analysis toolboxes for MATLAB. 3 | 4 | All of the methods have been employed across numerous published research papers over the years. 5 | 6 | A description of each toolbox is provided as a pdf file. 7 | Each toolbox includes example data for testing. 8 | 9 | The following toolboxes are current included: 10 | 11 | Synchro Toolbox - discrete and continuous relative-phase analysis and cross-spectral coherence analysis 12 | 13 | Cluster Phase Analysis - for multivariate relative phase analysis 14 | 15 | RQA Toolbox - auto and cross-recurrence quantification analysis (continuous and categorical data) 16 | 17 | Fractal Toolbox - a range of (mono) fractal times series methods, as well as artificial data generation. 18 | -------------------------------------------------------------------------------- /RQAToolbox/Elvis.txt: -------------------------------------------------------------------------------- 1 | 19 1 2 | 2 2 3 | 7 3 4 | 16 4 5 | 15 5 6 | 5 6 7 | 5 7 8 | 9 8 9 | 6 5 10 | 15 1 11 | 5 2 12 | 2 9 13 | 14 9 14 | 14 5 15 | 5 10 16 | 2 11 17 | 14 12 18 | 5 13 19 | 10 3 20 | 15 5 21 | 5 5 22 | 12 13 23 | 19 14 24 | 2 5 25 | 16 15 26 | 12 2 27 | 5 11 28 | 5 5 29 | 8 9 30 | 2 13 31 | 7 4 32 | 18 16 33 | 17 5 34 | 5 13 35 | 15 17 36 | 2 5 37 | 11 5 38 | 5 13 39 | 12 14 40 | 17 5 41 | 16 15 42 | 20 2 43 | 5 11 44 | 2 5 45 | 7 14 46 | 5 16 47 | 10 16 48 | 15 9 49 | 5 5 50 | 21 13 51 | 9 17 52 | 11 5 53 | 16 5 54 | 5 15 55 | 12 2 56 | 11 11 57 | 16 5 58 | 8 3 59 | 16 6 60 | 5 7 61 | 12 18 62 | 19 17 63 | 2 5 64 | 16 19 65 | 12 16 66 | 5 9 67 | 5 20 68 | 15 5 69 | 2 21 70 | 11 11 71 | 5 17 72 | 3 5 73 | 6 10 74 | 7 2 75 | 5 22 76 | 8 16 77 | 2 5 78 | 5 17 79 | 6 2 80 | 7 5 81 | 15 13 82 | 17 17 83 | 19 5 84 | 13 5 85 | 7 17 86 | 24 19 87 | 5 6 88 | 21 17 89 | 11 18 90 | 17 12 91 | 5 5 92 | 9 23 93 | 6 19 94 | 15 6 95 | 5 17 96 | 2 5 97 | 14 19 98 | 14 6 99 | 5 20 100 | 2 20 101 | 14 16 102 | 5 7 103 | 10 12 104 | 15 5 105 | 5 17 106 | 21 2 107 | 9 5 108 | 11 10 109 | 16 16 110 | 5 5 111 | 12 5 112 | 11 13 113 | 16 5 114 | 8 3 115 | 16 6 116 | 5 7 117 | 12 18 118 | 19 17 119 | 2 5 120 | 16 19 121 | 12 16 122 | -------------------------------------------------------------------------------- /RQAToolbox/RQA ToolBox Readme.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xkiwilabs/MATLAB-Toolboxes/e6af49c2460e8c25dedf27ede3161d8ec465091c/RQAToolbox/RQA ToolBox Readme.pdf -------------------------------------------------------------------------------- /RQAToolbox/aRQACat_batch.m: -------------------------------------------------------------------------------- 1 | function aRQACat_batch() 2 | % ********************************************************************************************** 3 | % function aRQACat_batch() 4 | % 5 | % Performs a batch Categorical Auto Recurrence Quantification Analysis 6 | % 7 | % M.J. Richardson 2009, updated 2015 8 | % 9 | %------------------------------------------------------------------------------------- 10 | fprintf('Processes xRqaCat on txt or csv files\n'); 11 | 12 | dirtxt_prim = input('Enter primary .txt file pattern (wildcards, but NO EXTENSION): ','s'); 13 | ext1 = input('Enter file extension (e.g. .txt or .csv): ','s'); 14 | doStatsFile = input('Output stats to file (1=yes, 0=no):' ); 15 | 16 | if isempty(findstr(dirtxt_prim,'.')) 17 | dirtxt_prim = strcat(dirtxt_prim,ext1); 18 | end 19 | dir_in_prim=dir(dirtxt_prim); 20 | nfiles_prim = length(dir_in_prim); 21 | 22 | if nfiles_prim==0 23 | fprintf('No %s files found\n',ext1); 24 | return 25 | end 26 | 27 | fns_prim = sortfiles(dir_in_prim); 28 | 29 | fprintf('\n\n%d file(s) to process... ... ...\n',nfiles_prim); 30 | 31 | for i = 1:nfiles_prim 32 | file_name = fns_prim(i,:); 33 | file_name = deblank(file_name); 34 | aRQACat(file_name, 0, doStatsFile); 35 | end 36 | 37 | fprintf('\nDone\n'); 38 | return; 39 | 40 | %************************************************************************** 41 | function sorted_names = sortfiles(direc); 42 | %function sorted_names = sortfiles(direc); 43 | %Sort a list of file names 44 | %M.J. Richardson 2004 45 | ld = length(direc); 46 | [fn{1:ld,1}] = deal(direc.name); 47 | sorted_names = char(fn); 48 | sorted_names = sortrows(sorted_names); 49 | return -------------------------------------------------------------------------------- /RQAToolbox/aRQA_batch.m: -------------------------------------------------------------------------------- 1 | function aRQA_batch() 2 | % ********************************************************************************************** 3 | % function aRQA_batch() 4 | % 5 | % Performs a batch Auto Recurrence Quantification Analysis 6 | % 7 | % M.J. Richardson 7/2004, updated 2009, 2015 8 | % 9 | %------------------------------------------------------------------------------------- 10 | fprintf('Processes xRqa on txt files\n'); 11 | 12 | % Get User Input for Parameters and File Settings 13 | dirtxt_prim = input('Enter primary .txt or .csv file pattern (wildcards, but NO EXTENSION): ','s'); 14 | ext1 = input('Enter file extension (e.g. .txt or .csv): ','s'); 15 | norm = input('Normalize data (0=none, 1=unit interval, 2=zscore, 3=center): '); 16 | Edim = input('Embedding dimen: '); 17 | Tlag = input('Time lag: '); 18 | rescale = input('Rescale option (1=mean 2=max): '); 19 | radius = input('Radius: '); 20 | doStatsFile = input('Output stats to file (1=yes, 0=no):' ); 21 | 22 | if isempty(strfind(dirtxt_prim,'.')) 23 | dirtxt_prim = strcat(dirtxt_prim,ext1); 24 | end 25 | dir_in_prim=dir(dirtxt_prim); 26 | nfiles_prim = length(dir_in_prim); 27 | 28 | if nfiles_prim==0 29 | fprintf('No %s files found\n',ext1); 30 | return 31 | end 32 | 33 | fns_prim = sortfiles(dir_in_prim); 34 | 35 | fprintf('\n\n%d file(s) to process... ... ...\n',nfiles_prim); 36 | 37 | for i = 1:nfiles_prim 38 | file_name = fns_prim(i,:); 39 | file_name = deblank(file_name); 40 | 41 | aRQA(file_name, norm, Edim, Tlag, rescale, radius, 0, doStatsFile); 42 | 43 | end 44 | 45 | fprintf('\nDone!\n'); 46 | return; 47 | 48 | 49 | %************************************************************************** 50 | function sorted_names = sortfiles(direc); 51 | %function sorted_names = sortfiles(direc); 52 | %Sort a list of file names 53 | %M.J. Richardson 2004 54 | ld = length(direc); 55 | [fn{1:ld,1}] = deal(direc.name); 56 | sorted_names = char(fn); 57 | sorted_names = sortrows(sorted_names); 58 | return -------------------------------------------------------------------------------- /RQAToolbox/ami.m: -------------------------------------------------------------------------------- 1 | function amiF = ami(filename,min_lag,max_lag) 2 | 3 | %% *************************************************************************************% 4 | % ami_do: 5 | % 6 | % Syntax: 7 | % amiF = ami(file_name,min_lag,max_lag) 8 | % 9 | % Example: 10 | % amiF = ami('datafile.txt',1,100); 11 | % 12 | % BY: C. A. Coey and Michael Richardson (03/11/2013) 13 | % 14 | % Based on example code by Alexandros Leontitsis 15 | %***************************************************************************************% 16 | 17 | %% Load Data 18 | x_data = load(filename); 19 | x = x_data(:,1); 20 | len = length(x); 21 | 22 | 23 | %% Create Lag Vector 24 | if max_lag <= (len/2 - 1) 25 | if min_lag < max_lag 26 | i = min_lag; 27 | j = 1; 28 | while i <= max_lag 29 | lag(j) = i; 30 | j = j+1; 31 | i = i+1; 32 | end; 33 | else 34 | fprintf('error - maximum lag not greater than minimum lag\n'); 35 | fprintf('default lag vector used (0 - 50)\n'); 36 | lag = (0:50); 37 | end; 38 | 39 | else 40 | fprintf('error - maximum lag exceeds recommendation\n'); 41 | fprintf('maximum lag set to n/2-1\n'); 42 | lag = (0:(len/2 - 1)); 43 | end; 44 | 45 | %% Compute Average Mutual Information 46 | x=x-min(x); 47 | x=x/max(x); 48 | h = waitbar(0, 'Processing AMI...'); 49 | for i=1:length(lag) 50 | % Define the number of bins 51 | k=floor(1+log2(len-lag(i))+0.5); 52 | 53 | % If the time series has no variance then the MAI is 0 54 | if var(x,1)==0 55 | v(i)=0; 56 | else 57 | v(i)=0; 58 | for k1=1:k 59 | for k2=1:k 60 | 61 | ppp=find((k1-1)/k0 67 | ppp=ppp/(len-lag(i)); 68 | px1=length(px1)/(len-lag(i)); 69 | px2=length(px2)/(len-lag(i)); 70 | v(i)=v(i)+ppp*log2(ppp/px1/px2); 71 | end 72 | end 73 | end 74 | end 75 | waitbar(i/length(lag), h); 76 | end 77 | delete(h); 78 | amiF = [lag' v']; 79 | 80 | %% Plot AMI Function 81 | figure; 82 | plot(amiF(:,1), amiF(:,2)); 83 | xlabel('TLag'); 84 | ylabel('AMI'); 85 | 86 | return; 87 | -------------------------------------------------------------------------------- /RQAToolbox/fnn.m: -------------------------------------------------------------------------------- 1 | function fnnF = fnn(file_name,tlag,min_dimension,max_dimension) 2 | %***************************************************************************************** 3 | %Performs Global False Nearest Nieghbor analysis 4 | % 5 | %syntax: 6 | % fnnF = fnn(filename,tlag,min_dimension,max_dimension) 7 | % 8 | %example: 9 | % fnnF = fnn('datafile.txt',15,1,15); 10 | % 11 | %input: 12 | % min_dimension(scalar):the smallest number of dimensions to check 13 | % max_dimension(scalar):the max number of dimensions to check. 14 | % lag(scalar):the lag (in samples) to use between dimensions in the embedding 15 | % 16 | %returns: 17 | % percents(1D):percent(i) is the percent of points in a de(i)embedding who's nearest nieghbor 18 | % seems to be false. percents is max_dimension in length. A neighbor is called false if the change 19 | % in distance between the point and the nearest neighbor that occurs when another dimension is added 20 | % to the embedding in greater than 15 time the distance in the current embedding. See Abarbanel's book, 21 | % Analysis of Observed Chaotic Data, pages 40-50 for more details. 22 | % de(1D):the tested embedding dimensions, such that percent(i) is the percent of false nearest nieghbors 23 | % in a de(i) embedding 24 | % 25 | % All based on code borrowed from Steve Boker and Bruce Kay 26 | 27 | %% Load Data 28 | x_data = load(file_name); 29 | fprintf('processing... %s\n',file_name); 30 | time_series = x_data(:,1); 31 | 32 | 33 | %% Borrowed Code 34 | percent=ones(max_dimension-min_dimension+1,1); 35 | 36 | %in order to compensate for "short" (not infinite!) time series, it may be necessary to 37 | %rule points out based on the attractor width. To estimate this we need the mean and the 38 | %square difference between the mean and each point 39 | mean_x=mean(time_series); 40 | Ra=sqrt((1/length(time_series))*sum((time_series-repmat(mean_x,size(time_series))).^2)); 41 | 42 | %the main loop indexes through the desired embedding dimensions 43 | de=min_dimension:max_dimension; 44 | h = waitbar(0, 'Processing FNN...'); 45 | for c=min_dimension:max_dimension 46 | %some initialization 47 | number_false=0; 48 | max_l=length(time_series)-(c)*tlag; 49 | 50 | %do the embedding 51 | curr_embedding=embed_time_series(time_series,c,tlag); 52 | 53 | %build a kd tree to find neighborhoods. There are currently 100 54 | %points set as the limit for leaf creation. If you wish to 55 | %modify this number, change the last argument in the call below 56 | %to the number of points that you want. 57 | 58 | %I believe it is possible to optimize this routine quite a bit by 59 | %making better use of the kd-tree. I just have not done it yet. wjd 60 | tree=make_kd_tree(curr_embedding,1:length(curr_embedding),100); 61 | %index through the current embedding 62 | for c2=1:max_l 63 | %search the tree for the nearest neighbor 64 | [NN,nearest_d]=find_the_nearest_neighbor(tree,curr_embedding(c2,:),c2); 65 | 66 | if NN > max_l 67 | %we cannot find this point in the next embedding because we are at the end of the data. 68 | %so we assume it is NOT false 69 | test_stat1=0; 70 | test_stat2=0; 71 | else 72 | %we can find the NN in the next embedding, so test the distance change 73 | if nearest_d==0 74 | %the (hopefully) unusual case in which two points happen to exactly occupy the 75 | %same point in embedding space. This is likely caused by insufficient sampling 76 | %resolution 77 | test_stat1=1; 78 | else 79 | %in the most common case we will use this line to calculate the 80 | %ratio between the change in distance between points c2 and NN that 81 | %occurs when we add a dimension to the distance between then in the 82 | %current embedding. 83 | test_stat1=abs(time_series(c2+c*tlag)-time_series(NN+c*tlag))/nearest_d; 84 | end 85 | %To test the case of a "distant" nearest neighbor we compare the distance at the next higher 86 | %embedding d to Ra from above 87 | test_stat2=abs(time_series(c2+c*tlag)-time_series(NN+c*tlag))/Ra; 88 | 89 | end 90 | 91 | %Abarbanel suggests that we use 15 (or so as a cut off value) for stat1, and 2 for stat2, so we will. 92 | %a nearest neighbor counts as false if either test_stat1 is >=15 or test_stat2 is >=2 93 | number_false=number_false+((test_stat1>=15)|(test_stat2>=2)); 94 | end 95 | percent(c-min_dimension+1)=number_false/max_l; 96 | %fprintf('\tresult-> %5.2f percent\n',percent(c-min_dimension+1)*100); 97 | 98 | waitbar(c/length(de), h); 99 | end 100 | delete(h); 101 | 102 | %% Do Plots 103 | figure; 104 | plot(de, percent*100, 'k-o'); 105 | xlim([min_dimension max_dimension]); 106 | xlabel('# Embedding Dimensions'); 107 | ylabel('% FNN'); 108 | 109 | fnnF = [de' percent*100]; 110 | return; 111 | 112 | %% - Embed Time-Series Code - 113 | function embedded=embed_time_series(data,embedding_dim,lag) 114 | 115 | %usage: 116 | % embedded=embed_time_series(data,embedding_dim,lag) 117 | % 118 | %input: 119 | % data(1D):vector time series, may be a row or column. 120 | % embedding_dim(scalar):the number of dimensions to use in the embedding. 121 | % lag(scalar):the lag (in samples) to use between dimensions in the embedding. 122 | % 123 | %returns: 124 | % embedded:(2D) array NxM where M=embedding_dim 125 | % and N=length(data)-(embedding_dim-1)*lag, such the each row is a point in the 126 | % embedding space. 127 | % embedded(i,:)=[data(i),data(i+lag),data(i+2lag),...,data(i+(embedding_dim-1)lag] 128 | 129 | %check the dim of data 130 | s=size(data); 131 | if s(1)==1 132 | %a row 133 | data=data'; 134 | elseif s(2)~=1 135 | %not a vector 136 | error(sprintf('time series (data) must be a vector. you used a %dX%d matrix',s(1),s(2))); 137 | else 138 | %it is already a column vector. do nothing. 139 | end 140 | 141 | %how long should it be? 142 | l=length(data); 143 | data_length=l-(embedding_dim-1)*lag; 144 | embedded=zeros(data_length,embedding_dim); 145 | for c=1:embedding_dim 146 | embedded(:,c)=data((1:data_length)+lag*(c-1)); 147 | end; 148 | 149 | %% - Find Neighborhood KD Search Code - 150 | function [neighborhood,indexes,encounters,moves]=find_neighborhood_kd_search(tree,kd_point) 151 | 152 | %this is the kd tree search for neighborhoods. 153 | 154 | if ~isempty(tree.values) 155 | %we are at a leaf 156 | neighborhood=tree.values; 157 | indexes=tree.indices; 158 | moves=''; 159 | encounters=[]; 160 | else 161 | %still climbing down a branch 162 | 163 | %each node breaks left and right based on one of the 164 | %k dimensions. which one? it is in tree.decission_dimension 165 | %so we look at the value of kd_point in that dim. to decide 166 | %which way to go. 167 | switch_value=kd_point(tree.decission_dimension); 168 | 169 | miss_dist=abs(switch_value-tree.decission_value); 170 | 171 | if switch_value=search_tree.decission_value); 288 | left_data=data(left_points,:); 289 | right_data=data(right_points,:); 290 | left_indices=indices(left_points); 291 | right_indices=indices(right_points); 292 | 293 | search_tree.left=make_kd_tree(left_data,left_indices,threshold); 294 | search_tree.right=make_kd_tree(right_data,right_indices,threshold); 295 | search_tree.values=[]; 296 | search_tree.indices=[]; 297 | end 298 | -------------------------------------------------------------------------------- /RQAToolbox/xRQACat_batch.m: -------------------------------------------------------------------------------- 1 | function xRQACat_batch() 2 | % ********************************************************************************************** 3 | % function xRQACat_batch() 4 | % 5 | % Performs a batch Categorical Cross Recurrence Quantification Analysis 6 | % 7 | % M.J. Richardson 2009, updated 2015 8 | % 9 | %------------------------------------------------------------------------------------- 10 | fprintf('Processes xRqaCat on txt or csv files\n'); 11 | 12 | dirtxt_prim = input('Enter primary .txt file pattern (wildcards, but NO EXTENSION): ','s'); 13 | ext1 = input('Enter file extension (e.g. .txt or .csv): ','s'); 14 | doStatsFile = input('Output stats to file (1=yes, 0=no):' ); 15 | 16 | if isempty(findstr(dirtxt_prim,'.')) 17 | dirtxt_prim = strcat(dirtxt_prim,ext1); 18 | end 19 | dir_in_prim=dir(dirtxt_prim); 20 | nfiles_prim = length(dir_in_prim); 21 | 22 | if nfiles_prim==0 23 | fprintf('No %s files found\n',ext1); 24 | return 25 | end 26 | 27 | fns_prim = sortfiles(dir_in_prim); 28 | 29 | fprintf('\n\n%d file(s) to process... ... ...\n',nfiles_prim); 30 | 31 | for i = 1:nfiles_prim 32 | file_name = fns_prim(i,:); 33 | file_name = deblank(file_name); 34 | xRQACat(file_name, 0, doStatsFile); 35 | end 36 | 37 | fprintf('\nDone\n'); 38 | return; 39 | 40 | %************************************************************************** 41 | function sorted_names = sortfiles(direc); 42 | %function sorted_names = sortfiles(direc); 43 | %Sort a list of file names 44 | %M.J. Richardson 2004 45 | ld = length(direc); 46 | [fn{1:ld,1}] = deal(direc.name); 47 | sorted_names = char(fn); 48 | sorted_names = sortrows(sorted_names); 49 | return -------------------------------------------------------------------------------- /RQAToolbox/xRQA_batch.m: -------------------------------------------------------------------------------- 1 | function xRQA_batch() 2 | % ********************************************************************************************** 3 | % function xRQA_batch() 4 | % 5 | % Performs a batch Cross Recurrence Quantification Analysis 6 | % 7 | % M.J. Richardson 7/2004, updated 2009, 2015 8 | % 9 | %------------------------------------------------------------------------------------- 10 | fprintf('Processes xRqa on txt files\n'); 11 | 12 | % Get User Input for Parameters and File Settings 13 | dirtxt_prim = input('Enter primary .txt or .csv file pattern (wildcards, but NO EXTENSION): ','s'); 14 | ext1 = input('Enter file extension (e.g. .txt or .csv): ','s'); 15 | norm = input('Normalize data (0=none, 1=unit interval, 2=zscore, 3=center): '); 16 | Edim = input('Embedding dimen: '); 17 | Tlag = input('Time lag: '); 18 | rescale = input('Rescale option (1=mean 2=max): '); 19 | radius = input('Radius: '); 20 | doStatsFile = input('Output stats to file (1=yes, 0=no):' ); 21 | 22 | if isempty(strfind(dirtxt_prim,'.')) 23 | dirtxt_prim = strcat(dirtxt_prim,ext1); 24 | end 25 | dir_in_prim=dir(dirtxt_prim); 26 | nfiles_prim = length(dir_in_prim); 27 | 28 | if nfiles_prim==0 29 | fprintf('No %s files found\n',ext1); 30 | return 31 | end 32 | 33 | fns_prim = sortfiles(dir_in_prim); 34 | 35 | fprintf('\n\n%d file(s) to process... ... ...\n',nfiles_prim); 36 | 37 | for i = 1:nfiles_prim 38 | file_name = fns_prim(i,:); 39 | file_name = deblank(file_name); 40 | 41 | xRQA(file_name, norm, Edim, Tlag, rescale, radius, 0, doStatsFile); 42 | 43 | end 44 | 45 | fprintf('\nDone!\n'); 46 | return; 47 | 48 | 49 | %************************************************************************** 50 | function sorted_names = sortfiles(direc); 51 | %function sorted_names = sortfiles(direc); 52 | %Sort a list of file names 53 | %M.J. Richardson 2004 54 | ld = length(direc); 55 | [fn{1:ld,1}] = deal(direc.name); 56 | sorted_names = char(fn); 57 | sorted_names = sortrows(sorted_names); 58 | return -------------------------------------------------------------------------------- /Synchro_Toolbox/Synchro ToolBox Readme.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/xkiwilabs/MATLAB-Toolboxes/e6af49c2460e8c25dedf27ede3161d8ec465091c/Synchro_Toolbox/Synchro ToolBox Readme.pdf -------------------------------------------------------------------------------- /Synchro_Toolbox/amplitude.m: -------------------------------------------------------------------------------- 1 | function [meanAmp, sdAmp, peaks, pkLocs, valleys, vLocs] = amplitude(data, samplerate, DistFLT, AmpFLT) 2 | %************************************************************************************ 3 | % PERIOD Calculates the amplitude for rhythmic time-series data. 4 | % 5 | % Returns the peaks and valleys and peaks and valley locations for a 6 | % rhythmic positional time-series, as well as the mean and SD amplitude. 7 | % 8 | % Syntax: 9 | % [meanAmp, sdAmp, peaks, pkLocs, valleys, vLocs] = amplitude(data, 100, .5, .3) 10 | % 11 | % BY: Michael J. Richardson (michael.richardson@uc.edu), 2004, updated 2009 12 | % 13 | %------------------------------------------------------------------------------------- 14 | 15 | %% Center Data 16 | x=data(:,1)-mean(data(:,1)); 17 | 18 | %% Determine Peaks and Peak Locations 19 | [peaks, pkLocs] = findpeaks(x,'MinPeakDistance',floor(samplerate*DistFLT),'MinPeakHeight',(max(x))*AmpFLT); 20 | 21 | %% Determine Valleys and Valley Locations 22 | [valleys, vLocs] = findpeaks(x.*-1,'MinPeakDistance',floor(samplerate*DistFLT),'MinPeakHeight',(max(x))*AmpFLT); 23 | valleys = valleys.*-1; 24 | 25 | %% Determine appropriate data length for Amp calculations 26 | pvLength = length(peaks); 27 | if pvLength > length(valleys) 28 | pvLength = length(valleys); 29 | end 30 | 31 | %% Calculate mean and SD 32 | meanAmp = mean(peaks(1:pvLength)-valleys(1:pvLength)); 33 | sdAmp = std(peaks(1:pvLength)-valleys(1:pvLength)); 34 | 35 | %% End of function 36 | return 37 | %--------------------------------------------------------------------------------------- -------------------------------------------------------------------------------- /Synchro_Toolbox/coherence.m: -------------------------------------------------------------------------------- 1 | function [cohereStats, FP1, FP2, cohereFP] = coherence(x, y, samplerate, windowSize, windowOverlap) % COHERENCE calculates the average cross-spectral coherence at the % fundamental frequencies of the two time series. Outputs mean coherence % and the coherence at each fundamental frequency (in cohereStats), as % well as the power/frequency functions for each TS and the cross-spectral % coherence analysis. % % Example Syntax: % [cohereStats, FP1, FP2, cohereFP] = coherence(x, y, 100, 256, .5) % % M. J. Richardson and R. C. Schmidt 2005 % Revision 2009 (Michael J Richardson) %------------------------------------------------------------------------------------- %% Linear Detrend x = detrend(x); y = detrend(y); %% Normalize as zscore x = x-mean(x)./std(x); y = y-mean(y)./std(y); %% Determine FFT Parameters window = hanning(windowSize); % window function n_overlap = windowSize*windowOverlap; % number of samples overlap %% Calculate Spectrum for each TS spec [power1,freq1] = pwelch(x,window,n_overlap,windowSize,samplerate); [power2,freq2] = pwelch(y,window,n_overlap,windowSize,samplerate); FP1 = [power1,freq1]; FP2 = [power2,freq2]; %% Calculate Coherence [Cxy,f3] = mscohere(x,y,window,n_overlap,windowSize,samplerate); cohereFP = [Cxy,f3]; %% Calculate Coherence Stats [~,x_i] = max(power1(2:end)); [~,y_i] = max(power2(2:end)); cohereStats = [(Cxy(x_i+1) + Cxy(y_i+1))/2, Cxy(x_i+1),Cxy(y_i+1)]; %% End of function return; %************************************************************************** %************************************************************************** -------------------------------------------------------------------------------- /Synchro_Toolbox/discretephase.m: -------------------------------------------------------------------------------- 1 | function [meanRP, sdRP, rvRP, radians] = discretephase(x1, x2, samplerate, DistFLT, AmpFLT) 2 | %************************************************************************************ 3 | % DISCRETEPHASE calculates discrete instantaneous relative phase angle 4 | % between two time-series (using Hilbert transform), with time-series 1 as 5 | % the referent. Returns the discrete relative phase time-series in radians 6 | % as well as the circular mean and SD of the discrete relative phase in 7 | % radians and the mean resultant vector (rvRP = 0 to 1) 8 | % 9 | % Syntax: 10 | % [meanRP, sdRP, rvRP, radians] = discretephase(x1, x2) 11 | % 12 | % BY: Michael J. Richardson (michael.richardson@uc.edu), 2007 13 | % Updated to include circular stats in 2011 14 | % 15 | %------------------------------------------------------------------------------------- 16 | 17 | %% Center Data 18 | x1=x1-mean(x1); 19 | x2=x2-mean(x2); 20 | 21 | %% Get Peak locations for first time-Series (i.e., x1); 22 | [~, locs] = findpeaks(x1,'MinPeakDistance',floor(samplerate*DistFLT),'MinPeakHeight',(max(x1))*AmpFLT); 23 | 24 | %% Using Hilbert Transform to Calculate Phase Angles for Second time-series 25 | hbt = hilbert(x2); 26 | ihbt = imag(hbt); 27 | radians = atan2(ihbt,x2); 28 | 29 | %% Calculate Phase Angle of second time-series at peak locations of first time-series. 30 | radians = radians(locs); 31 | 32 | %% Get Circular Stats 33 | wgh = ones(size(radians)); 34 | wr = wgh'*exp(1i*radians); 35 | meanRP = angle(wr); 36 | rvRP = abs(wr)/sum(wgh); 37 | sdRP = sqrt(2*(1-rvRP)); 38 | 39 | %% End of function 40 | return 41 | %--------------------------------------------------------------------------------------- 42 | 43 | 44 | -------------------------------------------------------------------------------- /Synchro_Toolbox/hilbertphase.m: -------------------------------------------------------------------------------- 1 | function [meanRP, sdRP, rvRP, radians] = hilbertphase(x1, x2) 2 | %************************************************************************************ 3 | % HILBERTPHASE calculates instantaneous relative phase angle between 4 | % two time-series. Returns the instantaneous relative phase time-series in 5 | % radians (using Hilbert transform), the circular mean and SD of the 6 | % relative phase in radians, and the mean resultant vector (rvRP = 0 to 1) 7 | % 8 | % 9 | % Syntax: 10 | % [meanRP, sdRP, rvRP, radians] = hilbertphase(x1, x2) 11 | % 12 | % BY: Michael J. Richardson (michael.richardson@uc.edu), 2004 13 | % Updated to include circular stats in 2009. 14 | % 15 | %------------------------------------------------------------------------------------- 16 | 17 | %% Center Data 18 | x1=x1-mean(x1); 19 | x2=x2-mean(x2); 20 | 21 | %% Using Hilbert Transform to Calculate Relative Phase 22 | h1 = hilbert(x1); 23 | h2 = hilbert(x2); 24 | h1 = imag(h1); 25 | h2 = imag(h2); 26 | num = h2.*x1 - x2.*h1; 27 | denom = x2.*x1 + h2.*h1; 28 | radians = atan2(num,denom); 29 | 30 | %% Get Circular Stats 31 | wgh = ones(size(radians)); 32 | wr = wgh'*exp(1i*radians); 33 | meanRP = angle(wr); 34 | rvRP = abs(wr)/sum(wgh); 35 | sdRP = sqrt(2*(1-rvRP)); 36 | 37 | %% End of function 38 | return 39 | %--------------------------------------------------------------------------------------- 40 | -------------------------------------------------------------------------------- /Synchro_Toolbox/period.m: -------------------------------------------------------------------------------- 1 | function [meanPeriod, sdPeriod, peaks, pkLocs] = period(data, samplerate, DistFLT, AmpFLT) 2 | %************************************************************************************ 3 | % PERIOD calculates the periods for rhythmic time-series data. 4 | % 5 | % Returns the peaks and peaks locations for rhythmic time-series data, 6 | % as well as the mean period and SD period in seconds. 7 | % 8 | % Syntax: 9 | % [meanPeriod, sdPeriod, peaks, pkLocs] = period(data, 100, .5, .3) 10 | % 11 | % BY: Michael J. Richardson (michael.richardson@uc.edu), 2004, updated 2009 12 | % 13 | %------------------------------------------------------------------------------------- 14 | 15 | %% Center Data 16 | x=data(:,1)-mean(data(:,1)); 17 | 18 | %% Determine Peaks and Peak Locations 19 | [peaks, pkLocs] = findpeaks(x,'MinPeakDistance',floor(samplerate*DistFLT),'MinPeakHeight',(max(x))*AmpFLT); 20 | 21 | %% Get Period Stats 22 | meanPeriod = mean(diff(pkLocs))/samplerate; 23 | sdPeriod = std(diff(pkLocs))/samplerate; 24 | 25 | %% End of function 26 | return 27 | %--------------------------------------------------------------------------------------- -------------------------------------------------------------------------------- /Synchro_Toolbox/returnPlot.m: -------------------------------------------------------------------------------- 1 | function returnPlot (filename, samplerate) 2 | %************************************************************************** 3 | % RETURNPLOT calculates the discrete relative phase between two times 4 | % series and plots the n:m relative phase on return plot. 5 | % Great for determining if coordination is polyrhythmic (i.e., 1:2, 2:3, etc). 6 | % 7 | % User needs to specify: 8 | % filename : data file to open; should be 2-column txt or csv file 9 | % samplerate : sample rate of the time series 10 | % 11 | % Syntax: 12 | % returnPlot(filename, samplerate) 13 | % 14 | % Examples: 15 | % >> returnPlot('ExData_TwoToOne.csv', 100); 16 | % >> returnPlot('ExData_ThreeToTwo.csv', 100); 17 | % 18 | % Michael J. Richardson (2005) 19 | % Last Updated 2009, 2013. 20 | % 21 | %************************************************************************** 22 | 23 | %% Define Fixed Parameters 24 | linearDetrend = 1; % 0=no; 1= perform linear detrend (good idea if drift in data) 25 | peakDistance = 0.4; % 0.5 second minimum period 26 | peakAmp = .3; % 20% of max amplitude 27 | filterCutoff = 20; % cutoff frequency for filter (Hz) 28 | rad2deg = 360/(2*pi); % for converting radians to degrees 29 | 30 | 31 | %% Load Data from file 32 | x_data = load(filename); % should be a 2-column txt or csv file 33 | x1 = x_data(:,1); % 1 indicates the first columns of data 34 | x2 = x_data(:,2); % 2 indicates the second column of data 35 | 36 | 37 | %% Filter Data using 2nd Order Low-Pass Butterworth Filter 38 | [weight_b,weight_a] = butter(2,filterCutoff/(samplerate/2)); 39 | x1 = filtfilt(weight_b,weight_a,x1); 40 | x2 = filtfilt(weight_b,weight_a,x2); 41 | 42 | 43 | %% Linear detrend data 44 | if linearDetrend == 1 45 | x1 = detrend(x1); 46 | x2 = detrend(x2); 47 | end 48 | 49 | %% Normalize Data 50 | x1 = x1-mean(x1); 51 | x2 = x2-mean(x2); 52 | 53 | 54 | %% Get Peaks 55 | [~, ~, ~, pLocs1] = period(x1, samplerate, peakDistance, peakAmp); 56 | [~, ~, ~, pLocs2] = period(x2, samplerate, peakDistance, peakAmp); 57 | 58 | 59 | %% Get Relative Phase Time Series x1:x2 and x2:x1 60 | [~, ~, ~, radians] = discretephase(x1, x2, samplerate, peakDistance, peakAmp); 61 | angles1 = radians*rad2deg; 62 | [~, ~, ~, radians] = discretephase(x2, x1, samplerate, peakDistance, peakAmp); 63 | angles2 = radians*rad2deg; 64 | 65 | 66 | %% Define time vector for plotting 67 | delta_t = 1/samplerate; % for time array for plotting 68 | data_len = length(x1); 69 | t = 1:data_len; 70 | t = t*delta_t; 71 | 72 | 73 | %% Plot Data 74 | scrsz = get(0,'ScreenSize'); 75 | figure('Position',[scrsz(3)/4 scrsz(4)/4 scrsz(3)/2 scrsz(4)/3]); 76 | h = axes('Position', [0 0 1 1], 'Visible', 'off'); 77 | 78 | % Plot time-series with peaks 79 | axes('Position',[.07 .575 .4 .35]); 80 | hold on; 81 | plot(t, x1,'r-'); 82 | plot(pLocs1*delta_t,x1(pLocs1),'r+', 'MarkerSize', 6); 83 | plot(t, x2,'b-'); 84 | plot(pLocs2*delta_t,x2(pLocs2),'b+', 'MarkerSize', 6); 85 | xlabel('time (s)'); 86 | ylabel('Data'); 87 | hold off; 88 | 89 | % Plot discrete relative phase angles 90 | axes('Position',[.07 .1 .4 .35]); 91 | hold on; 92 | plot(pLocs1*delta_t, angles1,'or'); 93 | plot(pLocs2*delta_t, angles2,'ob'); 94 | ylim([-200 200]); 95 | set(gca,'YTick',[-180 0 180]) 96 | xlabel('time (s)'); 97 | ylabel('DRP'); 98 | hold off; 99 | 100 | %Plot relative phase return plot 101 | axes('Position',[.55 .1 .4 .825]); 102 | hold on; 103 | plot(angles1(1:end-1), angles1(2:end), 'or', 'MarkerSize', 4); 104 | plot(angles2(1:end-1), angles2(2:end), 'ob', 'MarkerSize', 4); 105 | xlim([-200 200]); 106 | set(gca,'XTick',[-180 -90 0 90 180]) 107 | ylim([-200 200]); 108 | set(gca,'YTick',[-180 -90 0 90 180]) 109 | xlabel('RPt'); 110 | ylabel('RPt+1'); 111 | hold off; 112 | 113 | %% end of function 114 | return 115 | 116 | 117 | 118 | -------------------------------------------------------------------------------- /Synchro_Toolbox/synchroBatch.m: -------------------------------------------------------------------------------- 1 | function synchroBatch() 2 | %************************************************************************** 3 | % SYNCHROBATCH calculates and outputs all of the above IRP or DRP measures 4 | % on a batch of selected continuous two column .txt or .csv data files in 5 | % the current directory. 6 | % 7 | % Syntax: 8 | % synchroBatch(); 9 | % 10 | % Michael J. Richardson 2009 11 | % Last Updated 2013. 12 | %-------------------------------------------------------------------------- 13 | 14 | %% Get User Input for Parameter settings 15 | dirtxt = input('Enter file pattern (wildcards (i.e., myFiles*), but NO EXTENSION): ','s'); 16 | ext = input('Enter file extension (e.g. .txt or .csv): ','s'); 17 | samplerate = input('Sample rate (Hz): '); 18 | phaseType = input('relative phase type (1 = IRP, 2 = DRP): '); 19 | phaseMode = input('relative phase mode (1 = inphase, 2 = antiphase): '); 20 | plotFlag = input('display plots (0 = no, 1 = yes): '); 21 | outFlag = input('output stats file (0 = none, 1 = stats + 0-180 dist, 2 = stats + 0-360 dist): '); 22 | 23 | %% Find and sort files defined by dirtxt.ext in current directory 24 | if isempty(findstr(dirtxt,'.')) 25 | dirtxt = strcat(dirtxt,ext); 26 | end 27 | dir_in=dir(dirtxt); 28 | nfiles = length(dir_in); 29 | if nfiles==0 30 | fprintf('No %s files found\n',ext); 31 | return 32 | end; 33 | ld = length(dir_in); 34 | [fn{1:ld,1}] = deal(dir_in.name); 35 | fns = char(fn); 36 | fns = sortrows(fns); 37 | 38 | fprintf('\n\n%d file(s) to process... ... ...\n',nfiles); 39 | 40 | %% Process loop for all files defined by dirtxt.ext in current directory 41 | for i = 1:nfiles 42 | filename = fns(i,:); 43 | filename = deblank(filename); 44 | fprintf('processing... %s\n',filename); 45 | if phaseType == 2 46 | synchroDRP(filename, samplerate, phaseMode, plotFlag, outFlag); 47 | else 48 | synchroIRP(filename, samplerate, phaseMode, plotFlag, outFlag); 49 | end 50 | fprintf('\n'); 51 | end 52 | 53 | %% End of Function 54 | return; -------------------------------------------------------------------------------- /Synchro_Toolbox/synchroDRP.m: -------------------------------------------------------------------------------- 1 | function synchroDRP(filename, samplerate, phaseMode, doPlot, printStats) % SYNCHRODRP Calculates and plots synchrony stats between two time-series % % This function calculates the periods, amplitudes, discrete relative phase % and average coherence between two times series (and annotates their peaks), % plots the data and RP time series or distribution of relative phase and % outputs the stats to a file. % % User needs to specify: % filename : data file to open; should be 2-column txt or csv file % samplerate : sample rate of the time series % phaseMode : 1 = inphase, 2 = antiphase % doPlot : 1 = yes, 0 = no % printStats : output file type (0 = none, 1 = stats + 0-180 distribution, 2 = stats + 0-360 distribution). % % Syntax: % synchroDRP(filename, samplerate, phaseMode, doPlot, printStats) % % Examples: % >> synchroDRP('ExData_Inphase.csv', 120, 1, 1, 2); % >> synchroDRP('ExData_Antiphase.csv', 120, 2, 1, 2); % >> synchroDRP('ExData_Intermit.csv', 120, 1, 1, 2); % % Michael J. Richardson & R. C. Schmidt (2005) % Last Updated 2009, 2013. % %************************************************************************** %% Define Fixed Parameters linearDetrend = 1; % 0=no; 1= perform linear detrend (good idea if drift in data) peakDistance = 0.5; % 0.5 second minimum period peakAmp = .3; % 20% of max amplitude filterCutoff = 20; % cutoff frequency for filter (Hz) rad2deg = 360/(2*pi); % for converting radians to degrees plot_freq = 10; % for spectral and coherence plots %% Load Data from file x_data = load(filename); % should be a 2-column txt or csv file x1 = x_data(:,1); % 1 indicates the first columns of data x2 = x_data(:,2); % 2 indicates the second column of data %% Filter Data using 2nd Order Low-Pass Butterworth Filter [weight_b,weight_a] = butter(2,filterCutoff/(samplerate/2)); x1 = filtfilt(weight_b,weight_a,x1); x2 = filtfilt(weight_b,weight_a,x2); %% Linear detrend data if linearDetrend == 1 x1 = detrend(x1); x2 = detrend(x2); end %% Normalize Data x1 = x1-mean(x1); x2 = x2-mean(x2); %% Get Peaks and Calculate Period Stats& Data [meanPeriod1, sdPeriod1, ~, pLocs1] = period(x1, samplerate, peakDistance, peakAmp); [meanPeriod2, sdPeriod2, ~, pLocs2] = period(x2, samplerate, peakDistance, peakAmp); %% Get Amplitude Stats & Data [meanAmp1, sdAmp1, ~, ~, ~, vLocs1] = amplitude(x1, samplerate, peakDistance, peakAmp); [meanAmp2, sdAmp2, ~, ~, ~, vLocs2] = amplitude(x2, samplerate, peakDistance, peakAmp); %% Get Coherence Stats & Data [cohereStats, FP1, FP2, cohereFP] = coherence(x1, x2, 100, 256, .5); %% Get Relative Phase Stats and Time Series [meanRP, sdRP, rvRP, radians] = discretephase(x1, x2, samplerate, peakDistance, peakAmp); meanRP = meanRP*rad2deg; sdRP = sdRP*rad2deg; %% Define time vector for plotting delta_t = 1/samplerate; % for time array for plotting data_len = length(x1); t = 1:data_len; t = t*delta_t; %% Do PLots if doPlot ~= 0 %% Create figure as a function a screen size scrsz = get(0,'ScreenSize'); figure('Position',[scrsz(3)/4 scrsz(4)/4 scrsz(3)/2 scrsz(4)/2]); h = axes('Position', [0 0 1 1], 'Visible', 'off'); %% annotate figure with stats set(gcf, 'CurrentAxes', h); str = 'Time-Series 1:'; text(.075, .12, str, 'FontSize', 10, 'Color', 'b', 'FontWeight', 'bold'); str = sprintf('Period (SD) = %3.3f (%3.3f)',meanPeriod1, sdPeriod1); text(.075, .08, str, 'FontSize', 10, 'Color', 'k'); str = sprintf('Amplitude (SD) = %3.3f (%3.3f)',meanAmp1, sdAmp1); text(.075, .04, str, 'FontSize', 10, 'Color', 'k'); str = 'Time-Series 2:'; text(.375, .12, str, 'FontSize', 10, 'Color', 'b', 'FontWeight', 'bold'); str = sprintf('Period (SD) = %3.3f (%3.3f)',meanPeriod2, sdPeriod2); text(.375, .08, str, 'FontSize', 10, 'Color', 'k'); str = sprintf('Amplitude (SD) = %3.3f (%3.3f)',meanAmp2, sdAmp2); text(.375, .04, str, 'FontSize', 10, 'Color', 'k'); str = 'Coordination Measures:'; text(.675, .12, str, 'FontSize', 10, 'Color', 'b', 'FontWeight', 'bold'); str = sprintf('Relative Phase (SD, r) = %3.3f (%3.3f, %3.3f)',meanRP, sdRP, rvRP); text(.675, .08, str, 'FontSize', 10, 'Color', 'k'); str = sprintf('Average Coherence = %3.3f',cohereStats(1)); text(.675, .04, str, 'FontSize', 10, 'Color', 'k'); str = 'File Analyzed:'; text(.42, .97, str, 'FontSize', 10, 'Color', 'b'); str = sprintf('%s',filename); text(.51, .97, str, 'FontSize', 10, 'Color', 'b', 'FontWeight', 'bold'); %% Plot x1 and x2 time series and mark peaks axes('Position',[.07 .625 .4 .3]) hold on plot(t, x1,'r-'); plot(pLocs1*delta_t,x1(pLocs1),'r+', 'MarkerSize', 6) plot(vLocs1*delta_t,x1(vLocs1),'ro', 'MarkerSize', 4) plot(t, x2, 'b-') plot(pLocs2*delta_t,x2(pLocs2),'b+', 'MarkerSize', 6) plot(vLocs2*delta_t,x2(vLocs2),'bo', 'MarkerSize', 4) hold off ylabel('Time-Series'); xlabel('Time'); %% plot x1 and x2 spectrum in log-log axes('Position',[.56 .625 .17 .3]) f1 = FP1(:,2); p1 = FP1(:,1); f2 = FP2(:,2); p2 = FP2(:,1); hold on; plot(f1(f1 180 hRP(i) = 180 - (absRP(i) - 180); else hRP(i) = absRP(i); end end centers = [10; 30; 50; 70; 90; 110; 130; 150; 170]; axes('Position',[.81 .225 .17 .3]) hist(hRP,centers, 'b'); h = findobj(gca,'Type','patch'); set(h,'FaceColor', [.5 .5 .5],'EdgeColor','k'); xlim([0 180]); set(gca,'XTick',[0 90 180]); ylabel('Freq Occurence'); xlabel('Relative Phase Angle'); end %% Print Collected Stats to output file if printStats == 1 absRP = abs(radians.*rad2deg); for i = 1:length(absRP) if absRP(i) > 180 hRP(i) = 180 - (absRP(i) - 180); else hRP(i) = absRP(i); end end centers = [10; 30; 50; 70; 90; 110; 130; 150; 170]; counts = hist(hRP,centers); fid = fopen('synchroDRP_Stats.csv','a'); fprintf(fid,'%s,',filename); fprintf(fid,'%.4f,%.4f,%.4f,%.4f,',meanPeriod1, sdPeriod1, meanAmp1, sdAmp1); fprintf(fid,'%.4f,%.4f,%.4f,%.4f,',meanPeriod2, sdPeriod2, meanAmp2, sdAmp2); fprintf(fid,'%.4f,%.4f,%.4f,%.4f,',cohereStats(1), meanRP, sdRP, rvRP); fprintf(fid,'%.4f,',counts/length(absRP)*100); fprintf(fid,'\n'); fclose(fid); elseif printStats == 2 RP = radians.*rad2deg; centers = [-170; -150; -130; -110; -90; -70; -50; -30; -10;... 10; 30; 50; 70; 90; 110; 130; 150; 170]; counts = hist(RP,centers); fid = fopen('synchroDRP_Stats.csv','a'); fprintf(fid,'%s,',filename); fprintf(fid,'%.4f,%.4f,%.4f,%.4f,',meanPeriod1, sdPeriod1, meanAmp1, sdAmp1); fprintf(fid,'%.4f,%.4f,%.4f,%.4f,',meanPeriod2, sdPeriod2, meanAmp2, sdAmp2); fprintf(fid,'%.4f,%.4f,%.4f,%.4f,',cohereStats(1), meanRP, sdRP, rvRP); fprintf(fid,'%.4f,',counts/length(RP)*100); fprintf(fid,'\n'); fclose(fid); end %% end of function %------------------------------------------------------------------------------------------------------------------ -------------------------------------------------------------------------------- /Synchro_Toolbox/synchroIRP.m: -------------------------------------------------------------------------------- 1 | function synchroIRP(filename, samplerate, phaseMode, doPlot, printStats) % SYNCHROIRP Calculates and plots synchrony stats between two time-series % % This function calculates the periods, amplitudes, instantaneous relative phase % and average coherence between two times series (and annotates their peaks), % plots the data and RP time series or distribution of relative phase and % outputs the stats to a file. % % User needs to specify: % filename : data file to open; should be 2-column txt or csv file % samplerate : sample rate of the time series % phaseMode : 1 = inphase, 2 = antiphase % doPlot : 1 = yes, 0 = no % printStats : output file type (0 = none, 1 = stats + 0-180 distribution, 2 = stats + 0-360 distribution). % % Syntax: % synchroIRP(filename, samplerate, phaseMode, doPlot, printStats) % % Examples: % >> synchroIRP('ExData_Inphase.csv', 120, 1, 1, 2); % >> synchroIRP('ExData_Antiphase.csv', 120, 2, 1, 2); % >> synchroIRP('ExData_Intermit.csv', 120, 1, 1, 2); % % Michael J. Richardson & R. C. Schmidt (2004) % Last Updated 2009, 2013. % %************************************************************************** %% Define Fixed Parameters linearDetrend = 1; % 0=no; 1= perform linear detrend (good idea if drift in data) peakDistance = 0.5; % 0.5 second minimum period peakAmp = .3; % 20% of max amplitude filterCutoff = 20; % cutoff frequency for filter (Hz) rad2deg = 360/(2*pi); % for converting radians to degrees plot_freq = 10; % for spectral and coherence plots %% Load Data from file x_data = load(filename); % should be a 2-column txt or csv file x1 = x_data(:,1); % 1 indicates the first columns of data x2 = x_data(:,2); % 2 indicates the second column of data %% Filter Data using 2nd Order Low-Pass Butterworth Filter [weight_b,weight_a] = butter(2,filterCutoff/(samplerate/2)); x1 = filtfilt(weight_b,weight_a,x1); x2 = filtfilt(weight_b,weight_a,x2); %% Linear detrend data if linearDetrend == 1 x1 = detrend(x1); x2 = detrend(x2); end %% Normalize Data x1 = x1-mean(x1); x2 = x2-mean(x2); %% Get Peaks and Calculate Period Stats& Data [meanPeriod1, sdPeriod1, ~, pLocs1] = period(x1, samplerate, peakDistance, peakAmp); [meanPeriod2, sdPeriod2, ~, pLocs2] = period(x2, samplerate, peakDistance, peakAmp); %% Get Amplitude Stats & Data [meanAmp1, sdAmp1, ~, ~, ~, vLocs1] = amplitude(x1, samplerate, peakDistance, peakAmp); [meanAmp2, sdAmp2, ~, ~, ~, vLocs2] = amplitude(x2, samplerate, peakDistance, peakAmp); %% Get Coherence Stats & Data [cohereStats, FP1, FP2, cohereFP] = coherence(x1, x2, 100, 256, .5); %% Get Instant Realtive Phase Stats and Time Series [meanRP, sdRP, rvRP, radians] = hilbertphase(x1, x2); meanRP = meanRP*rad2deg; sdRP = sdRP*rad2deg; %% Define time vector for plotting delta_t = 1/samplerate; % for time array for plotting data_len = length(x1); t = 1:data_len; t = t*delta_t; %% Do PLots if doPlot > 0 %% Create figure as a function a screen size scrsz = get(0,'ScreenSize'); figure('Position',[scrsz(3)/4 scrsz(4)/4 scrsz(3)/2 scrsz(4)/2]); h = axes('Position', [0 0 1 1], 'Visible', 'off'); %% annotate figure with stats set(gcf, 'CurrentAxes', h); str = 'Time-Series 1:'; text(.075, .12, str, 'FontSize', 10, 'Color', 'b', 'FontWeight', 'bold'); str = sprintf('Period (SD) = %3.3f (%3.3f)',meanPeriod1, sdPeriod1); text(.075, .08, str, 'FontSize', 10, 'Color', 'k'); str = sprintf('Amplitude (SD) = %3.3f (%3.3f)',meanAmp1, sdAmp1); text(.075, .04, str, 'FontSize', 10, 'Color', 'k'); str = 'Time-Series 2:'; text(.375, .12, str, 'FontSize', 10, 'Color', 'b', 'FontWeight', 'bold'); str = sprintf('Period (SD) = %3.3f (%3.3f)',meanPeriod2, sdPeriod2); text(.375, .08, str, 'FontSize', 10, 'Color', 'k'); str = sprintf('Amplitude (SD) = %3.3f (%3.3f)',meanAmp2, sdAmp2); text(.375, .04, str, 'FontSize', 10, 'Color', 'k'); str = 'Coordination Measures:'; text(.675, .12, str, 'FontSize', 10, 'Color', 'b', 'FontWeight', 'bold'); str = sprintf('Relative Phase (SD, r) = %3.3f (%3.3f, %3.3f)',meanRP, sdRP, rvRP); text(.675, .08, str, 'FontSize', 10, 'Color', 'k'); str = sprintf('Average Coherence = %3.3f',cohereStats(1)); text(.675, .04, str, 'FontSize', 10, 'Color', 'k'); str = 'File Analyzed:'; text(.42, .97, str, 'FontSize', 10, 'Color', 'b'); str = sprintf('%s',filename); text(.51, .97, str, 'FontSize', 10, 'Color', 'b', 'FontWeight', 'bold'); %% Plot x1 and x2 time series and mark peaks axes('Position',[.07 .625 .4 .3]) hold on plot(t, x1,'r-'); plot(pLocs1*delta_t,x1(pLocs1),'r+', 'MarkerSize', 6) plot(vLocs1*delta_t,x1(vLocs1),'ro', 'MarkerSize', 4) plot(t, x2, 'b-') plot(pLocs2*delta_t,x2(pLocs2),'b+', 'MarkerSize', 6) plot(vLocs2*delta_t,x2(vLocs2),'bo', 'MarkerSize', 4) hold off ylabel('Time-Series'); xlabel('Time'); %% plot x1 and x2 spectrum in log-log axes('Position',[.56 .625 .17 .3]) f1 = FP1(:,2); p1 = FP1(:,1); f2 = FP2(:,2); p2 = FP2(:,1); hold on; plot(f1(f1 180 hRP(i) = 180 - (absRP(i) - 180); else hRP(i) = absRP(i); end end centers = [10; 30; 50; 70; 90; 110; 130; 150; 170]; axes('Position',[.81 .225 .17 .3]) hist(hRP,centers, 'b'); h = findobj(gca,'Type','patch'); set(h,'FaceColor', [.5 .5 .5],'EdgeColor','k'); xlim([0 180]); set(gca,'XTick',[0 90 180]); ylabel('Freq Occurence'); xlabel('Relative Phase Angle'); end %% Print Collected Stats to output file if printStats == 1 absRP = abs(radians.*rad2deg); for i = 1:length(absRP) if absRP(i) > 180 hRP(i) = 180 - (absRP(i) - 180); else hRP(i) = absRP(i); end end centers = [10; 30; 50; 70; 90; 110; 130; 150; 170]; counts = hist(hRP,centers); fid = fopen('synchroIRP_Stats.csv','a'); fprintf(fid,'%s,',filename); fprintf(fid,'%.4f,%.4f,%.4f,%.4f,',meanPeriod1, sdPeriod1, meanAmp1, sdAmp1); fprintf(fid,'%.4f,%.4f,%.4f,%.4f,',meanPeriod2, sdPeriod2, meanAmp2, sdAmp2); fprintf(fid,'%.4f,%.4f,%.4f,%.4f,',cohereStats(1), meanRP, sdRP, rvRP); fprintf(fid,'%.4f,',counts/length(absRP)*100); fprintf(fid,'\n'); fclose(fid); elseif printStats == 2 RP = radians.*rad2deg; centers = [-170; -150; -130; -110; -90; -70; -50; -30; -10;... 10; 30; 50; 70; 90; 110; 130; 150; 170]; counts = hist(RP,centers); fid = fopen('synchroIRP_Stats.csv','a'); fprintf(fid,'%s,',filename); fprintf(fid,'%.4f,%.4f,%.4f,%.4f,',meanPeriod1, sdPeriod1, meanAmp1, sdAmp1); fprintf(fid,'%.4f,%.4f,%.4f,%.4f,',meanPeriod2, sdPeriod2, meanAmp2, sdAmp2); fprintf(fid,'%.4f,%.4f,%.4f,%.4f,',cohereStats(1), meanRP, sdRP, rvRP); fprintf(fid,'%.4f,',counts/length(RP)*100); fprintf(fid,'\n'); fclose(fid); end %% end of function %------------------------------------------------------------------------------------------------------------------ --------------------------------------------------------------------------------