[ { "path": ".github/workflows/github-repo-stats.yml", "content": "name: github-repo-stats\n\non:\n schedule:\n # Run this once per day, towards the end of the day for keeping the most\n # recent data point most meaningful (hours are interpreted in UTC).\n - cron: \"0 23 * * *\"\n workflow_dispatch: # Allow for running this manually.\n\njobs:\n j1:\n name: github-repo-stats\n runs-on: ubuntu-latest\n steps:\n - name: run-ghrs\n # Use latest release.\n uses: jgehrcke/github-repo-stats@RELEASE\n with:\n ghtoken: ${{ secrets.ghrs_github_api_token }}\n\n\n" }, { "path": ".gitignore", "content": "*.prj\n*.mexmaci64\n*.mexmaca64\n*.mat\n*.m~\n.DS_Store\n*.vscode\n*.code-workspace\n*/run_features\n" }, { "path": "C/.gitignore", "content": "codegen\n*.prj\n*.mexmaci64\n*.o\nC_polished\ngenerated\nheader\ntimeSeries*\nfeatureOutputs" }, { "path": "C/CO_AutoCorr.c", "content": "#if __cplusplus\n# include \ntypedef std::complex< double > cplx;\n#else\n# include \n#if defined(__GNUC__) || defined(__GNUG__)\ntypedef double complex cplx;\n#elif defined(_MSC_VER)\ntypedef _Dcomplex cplx;\n#endif\n#endif\n\n#include \n#include \n#include \n#include \n\n#include \"stats.h\"\n#include \"fft.h\"\n#include \"histcounts.h\"\n\n#include \"helper_functions.h\"\n\n#ifndef CMPLX\n#define CMPLX(x, y) ((cplx)((double)(x) + _Imaginary_I * (double)(y)))\n#endif\n#define pow2(x) (1 << x)\n\nint nextpow2(int n)\n{\n n--;\n n |= n >> 1;\n n |= n >> 2;\n n |= n >> 4;\n n |= n >> 8;\n n |= n >> 16;\n n++;\n return n;\n}\n\n/*\nstatic void apply_conj(cplx a[], int size, int normalize)\n{ \n switch(normalize) {\n case(1):\n for (int i = 0; i < size; i++) {\n a[i] = conj(a[i]) / size;\n }\n break;\n default:\n for (int i = 0; i < size; i++) {\n a[i] = conj(a[i]);\n }\n break;\n }\n}\n */\n\nvoid dot_multiply(cplx a[], cplx b[], int size)\n{\n for (int i = 0; i < size; i++) {\n a[i] = _Cmulcc(a[i], conj(b[i]));\n }\n}\n\ndouble * CO_AutoCorr(const double y[], const int size, const int tau[], const int tau_size)\n{\n double m, nFFT;\n m = mean(y, size);\n nFFT = nextpow2(size) << 1;\n\n cplx * F = malloc(nFFT * sizeof *F);\n cplx * tw = malloc(nFFT * sizeof *tw);\n for (int i = 0; i < size; i++) {\n \n #if defined(__GNUC__) || defined(__GNUG__)\n F[i] = CMPLX(y[i] - m, 0.0);\n #elif defined(_MSC_VER)\n cplx tmp = { y[i] - m, 0.0 };\n F[i] = tmp;\n #endif\n \n }\n for (int i = size; i < nFFT; i++) {\n #if defined(__GNUC__) || defined(__GNUG__)\n F[i] = CMPLX(0.0, 0.0);\n #elif defined(_MSC_VER)\n cplx tmp = { 0.0, 0.0 };\n F[i] = tmp; // CMPLX(0.0, 0.0);\n #endif\n \n }\n // size = nFFT;\n\n twiddles(tw, nFFT);\n fft(F, nFFT, tw);\n dot_multiply(F, F, nFFT);\n fft(F, nFFT, tw);\n cplx divisor = F[0];\n for (int i = 0; i < nFFT; i++) {\n //F[i] = F[i] / divisor;\n F[i] = _Cdivcc(F[i], divisor);\n }\n \n double * out = malloc(tau_size * sizeof(out));\n for (int i = 0; i < tau_size; i++) {\n out[i] = creal(F[tau[i]]);\n }\n free(F);\n free(tw);\n return out;\n}\n\ndouble * co_autocorrs(const double y[], const int size)\n{\n double m, nFFT;\n m = mean(y, size);\n nFFT = nextpow2(size) << 1;\n \n cplx * F = malloc(nFFT * 2 * sizeof *F);\n cplx * tw = malloc(nFFT * 2 * sizeof *tw);\n for (int i = 0; i < size; i++) {\n \n #if defined(__GNUC__) || defined(__GNUG__)\n F[i] = CMPLX(y[i] - m, 0.0);\n #elif defined(_MSC_VER)\n cplx tmp = { y[i] - m, 0.0 };\n F[i] = tmp;\n #endif\n }\n for (int i = size; i < nFFT; i++) {\n \n #if defined(__GNUC__) || defined(__GNUG__)\n F[i] = CMPLX(0.0, 0.0);\n #elif defined(_MSC_VER)\n cplx tmp = { 0.0, 0.0 };\n F[i] = tmp;\n #endif\n }\n //size = nFFT;\n \n twiddles(tw, nFFT);\n fft(F, nFFT, tw);\n dot_multiply(F, F, nFFT);\n fft(F, nFFT, tw);\n cplx divisor = F[0];\n for (int i = 0; i < nFFT; i++) {\n F[i] = _Cdivcc(F[i], divisor); // F[i] / divisor;\n }\n \n double * out = malloc(nFFT * 2 * sizeof(out));\n for (int i = 0; i < nFFT; i++) {\n out[i] = creal(F[i]);\n }\n free(F);\n free(tw);\n return out;\n}\n\nint co_firstzero(const double y[], const int size, const int maxtau)\n{\n \n //double * autocorrs = malloc(size * sizeof * autocorrs);\n //autocorrs = co_autocorrs(y, size);\n \n double * autocorrs = co_autocorrs(y, size);\n \n int zerocrossind = 0;\n while(autocorrs[zerocrossind] > 0 && zerocrossind < maxtau)\n {\n zerocrossind += 1;\n }\n \n free(autocorrs);\n return zerocrossind;\n \n}\n\ndouble CO_f1ecac(const double y[], const int size)\n{\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return 0;\n }\n }\n \n // compute autocorrelations\n double * autocorrs = co_autocorrs(y, size);\n\n // threshold to cross\n double thresh = 1.0/exp(1);\n \n double out = (double)size;\n for(int i = 0; i < size-2; i++){\n // printf(\"i=%d autocorrs_i=%1.3f\\n\", i, autocorrs[i]);\n if ( autocorrs[i+1] < thresh ){\n double m = autocorrs[i+1] - autocorrs[i];\n double dy = thresh - autocorrs[i];\n double dx = dy/m;\n out = ((double)i) + dx;\n // printf(\"thresh=%1.3f AC(i)=%1.3f AC(i-1)=%1.3f m=%1.3f dy=%1.3f dx=%1.3f out=%1.3f\\n\", thresh, autocorrs[i], autocorrs[i-1], m, dy, dx, out);\n free(autocorrs);\n return out;\n }\n }\n \n free(autocorrs);\n \n return out;\n \n}\n\ndouble CO_Embed2_Basic_tau_incircle(const double y[], const int size, const double radius, const int tau)\n{\n int tauIntern = 0;\n \n if(tau < 0)\n {\n tauIntern = co_firstzero(y, size, size);\n }\n else{\n tauIntern = tau;\n }\n \n double insidecount = 0;\n for(int i = 0; i < size-tauIntern; i++)\n {\n if(y[i]*y[i] + y[i+tauIntern]*y[i+tauIntern] < radius)\n {\n insidecount += 1;\n }\n }\n \n return insidecount/(size-tauIntern);\n}\n\ndouble CO_Embed2_Dist_tau_d_expfit_meandiff(const double y[], const int size)\n{\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n int tau = co_firstzero(y, size, size);\n \n //printf(\"co_firstzero ran\\n\");\n \n if (tau > (double)size/10){\n tau = floor((double)size/10);\n }\n //printf(\"tau = %i\\n\", tau);\n \n double * d = malloc((size-tau) * sizeof(double));\n for(int i = 0; i < size-tau-1; i++)\n {\n \n d[i] = sqrt((y[i+1]-y[i])*(y[i+1]-y[i]) + (y[i+tau]-y[i+tau+1])*(y[i+tau]-y[i+tau+1]));\n \n //printf(\"d[%i]: %1.3f\\n\", i, d[i]);\n if (isnan(d[i])){\n free(d);\n return NAN;\n }\n \n /*\n if(i<100)\n printf(\"%i, y[i]=%1.3f, y[i+1]=%1.3f, y[i+tau]=%1.3f, y[i+tau+1]=%1.3f, d[i]: %1.3f\\n\", i, y[i], y[i+1], y[i+tau], y[i+tau+1], d[i]);\n */\n }\n \n //printf(\"embedding finished\\n\");\n \n // mean for exponential fit\n double l = mean(d, size-tau-1);\n \n // count histogram bin contents\n /*\n int * histCounts;\n double * binEdges;\n int nBins = histcounts(d, size-tau-1, -1, &histCounts, &binEdges);\n */\n \n int nBins = num_bins_auto(d, size-tau-1);\n if (nBins == 0){\n return 0;\n }\n int * histCounts = malloc(nBins * sizeof(double));\n double * binEdges = malloc((nBins + 1) * sizeof(double));\n histcounts_preallocated(d, size-tau-1, nBins, histCounts, binEdges);\n \n //printf(\"histcount ran\\n\");\n \n // normalise to probability\n double * histCountsNorm = malloc(nBins * sizeof(double));\n for(int i = 0; i < nBins; i++){\n //printf(\"histCounts %i: %i\\n\", i, histCounts[i]);\n histCountsNorm[i] = (double)histCounts[i]/(double)(size-tau-1);\n //printf(\"histCounts norm %i: %1.3f\\n\", i, histCountsNorm[i]);\n }\n \n /*\n for(int i = 0; i < nBins; i++){\n printf(\"histCounts[%i] = %i\\n\", i, histCounts[i]);\n }\n for(int i = 0; i < nBins; i++){\n printf(\"histCountsNorm[%i] = %1.3f\\n\", i, histCountsNorm[i]);\n }\n for(int i = 0; i < nBins+1; i++){\n printf(\"binEdges[%i] = %1.3f\\n\", i, binEdges[i]);\n }\n */\n \n \n //printf(\"histcounts normed\\n\");\n \n double * d_expfit_diff = malloc(nBins * sizeof(double));\n for(int i = 0; i < nBins; i++){\n double expf = exp(-(binEdges[i] + binEdges[i+1])*0.5/l)/l;\n if (expf < 0){\n expf = 0;\n }\n d_expfit_diff[i] = fabs(histCountsNorm[i]-expf);\n //printf(\"d_expfit_diff %i: %1.3f\\n\", i, d_expfit_diff[i]);\n }\n \n double out = mean(d_expfit_diff, nBins);\n \n //printf(\"out = %1.6f\\n\", out);\n //printf(\"reached free statements\\n\");\n \n // arrays created dynamically in function histcounts\n free(d);\n free(d_expfit_diff);\n free(binEdges);\n free(histCountsNorm);\n free(histCounts);\n \n return out;\n \n}\n\nint CO_FirstMin_ac(const double y[], const int size)\n{\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return 0;\n }\n }\n \n double * autocorrs = co_autocorrs(y, size);\n \n int minInd = size;\n for(int i = 1; i < size-1; i++)\n {\n if(autocorrs[i] < autocorrs[i-1] && autocorrs[i] < autocorrs[i+1])\n {\n minInd = i;\n break;\n }\n }\n \n free(autocorrs);\n \n return minInd;\n \n}\n\ndouble CO_trev_1_num(const double y[], const int size)\n{\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n int tau = 1;\n \n double * diffTemp = malloc((size-1) * sizeof * diffTemp);\n \n for(int i = 0; i < size-tau; i++)\n {\n diffTemp[i] = pow(y[i+1] - y[i],3);\n }\n \n double out;\n \n out = mean(diffTemp, size-tau);\n \n free(diffTemp);\n \n return out;\n}\n\n#define tau 2\n#define numBins 5\n\ndouble CO_HistogramAMI_even_2_5(const double y[], const int size)\n{\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n //const int tau = 2;\n //const int numBins = 5;\n \n double * y1 = malloc((size-tau) * sizeof(double));\n double * y2 = malloc((size-tau) * sizeof(double));\n \n for(int i = 0; i < size-tau; i++){\n y1[i] = y[i];\n y2[i] = y[i+tau];\n }\n \n // set bin edges\n const double maxValue = max_(y, size);\n const double minValue = min_(y, size);\n \n double binStep = (maxValue - minValue + 0.2)/5;\n //double binEdges[numBins+1] = {0};\n\tdouble binEdges[5+1] = {0};\n for(int i = 0; i < numBins+1; i++){\n binEdges[i] = minValue + binStep*i - 0.1;\n // printf(\"binEdges[%i] = %1.3f\\n\", i, binEdges[i]);\n }\n \n \n // count histogram bin contents\n int * bins1;\n bins1 = histbinassign(y1, size-tau, binEdges, numBins+1);\n \n int * bins2;\n bins2 = histbinassign(y2, size-tau, binEdges, numBins+1);\n \n /*\n // debug\n for(int i = 0; i < size-tau; i++){\n printf(\"bins1[%i] = %i, bins2[%i] = %i\\n\", i, bins1[i], i, bins2[i]);\n }\n */\n \n // joint\n double * bins12 = malloc((size-tau) * sizeof(double));\n //double binEdges12[(numBins + 1) * (numBins + 1)] = {0};\n\tdouble binEdges12[(5 + 1) * (5 + 1)] = {0}; \n\n for(int i = 0; i < size-tau; i++){\n bins12[i] = (bins1[i]-1)*(numBins+1) + bins2[i];\n // printf(\"bins12[%i] = %1.3f\\n\", i, bins12[i]);\n }\n \n for(int i = 0; i < (numBins+1)*(numBins+1); i++){\n binEdges12[i] = i+1;\n // printf(\"binEdges12[%i] = %1.3f\\n\", i, binEdges12[i]);\n }\n \n // fancy solution for joint histogram here\n int * jointHistLinear;\n jointHistLinear = histcount_edges(bins12, size-tau, binEdges12, (numBins + 1) * (numBins + 1));\n \n /*\n // debug\n for(int i = 0; i < (numBins+1)*(numBins+1); i++){\n printf(\"jointHistLinear[%i] = %i\\n\", i, jointHistLinear[i]);\n }\n */\n \n // transfer to 2D histogram (no last bin, as in original implementation)\n double pij[numBins][numBins];\n int sumBins = 0;\n for(int i = 0; i < numBins; i++){\n for(int j = 0; j < numBins; j++){\n pij[j][i] = jointHistLinear[i*(numBins+1)+j];\n \n // printf(\"pij[%i][%i]=%1.3f\\n\", i, j, pij[i][j]);\n \n sumBins += pij[j][i];\n }\n }\n \n // normalise\n for(int i = 0; i < numBins; i++){\n for(int j = 0; j < numBins; j++){\n pij[j][i] /= sumBins;\n }\n }\n\n // marginals\n //double pi[numBins] = {0};\n\tdouble pi[5] = {0};\n //double pj[numBins] = {0};\n\tdouble pj[5] = {0};\n for(int i = 0; i < numBins; i++){\n for(int j = 0; j < numBins; j++){\n pi[i] += pij[i][j];\n pj[j] += pij[i][j];\n // printf(\"pij[%i][%i]=%1.3f, pi[%i]=%1.3f, pj[%i]=%1.3f\\n\", i, j, pij[i][j], i, pi[i], j, pj[j]);\n }\n }\n \n /*\n // debug\n for(int i = 0; i < numBins; i++){\n printf(\"pi[%i]=%1.3f, pj[%i]=%1.3f\\n\", i, pi[i], i, pj[i]);\n }\n */\n \n // mutual information\n double ami = 0;\n for(int i = 0; i < numBins; i++){\n for(int j = 0; j < numBins; j++){\n if(pij[i][j] > 0){\n //printf(\"pij[%i][%i]=%1.3f, pi[%i]=%1.3f, pj[%i]=%1.3f, logarg=, %1.3f, log(...)=%1.3f\\n\",\n // i, j, pij[i][j], i, pi[i], j, pj[j], pij[i][j]/(pi[i]*pj[j]), log(pij[i][j]/(pi[i]*pj[j])));\n ami += pij[i][j] * log(pij[i][j]/(pj[j]*pi[i]));\n }\n }\n }\n \n free(bins1);\n free(bins2);\n free(jointHistLinear);\n \n free(y1);\n free(y2);\n free(bins12);\n \n return ami;\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "C/CO_AutoCorr.h", "content": "#ifndef CO_AUTOCORR_H\n#define CO_AUTOCORR_H\n\n#if __cplusplus\n# include \ntypedef std::complex< double > cplx;\n#else\n# include \n#if defined(__GNUC__) || defined(__GNUG__)\ntypedef double complex cplx;\n#elif defined(_MSC_VER)\ntypedef _Dcomplex cplx;\n#endif\n#endif\n\n#include \n#include \n#include \n#include \n#include \"stats.h\"\n#include \"fft.h\"\n\nextern int nextpow2(int n);\nextern void dot_multiply(cplx a[], cplx b[], int size);\nextern double * CO_AutoCorr(const double y[], const int size, const int tau[], const int tau_size);\nextern double * co_autocorrs(const double y[], const int size);\nextern int co_firstzero(const double y[], const int size, const int maxtau);\nextern double CO_Embed2_Basic_tau_incircle(const double y[], const int size, const double radius, const int tau);\nextern double CO_Embed2_Dist_tau_d_expfit_meandiff(const double y[], const int size);\nextern int CO_FirstMin_ac(const double y[], const int size);\nextern double CO_trev_1_num(const double y[], const int size);\nextern double CO_f1ecac(const double y[], const int size);\nextern double CO_HistogramAMI_even_2_5(const double y[], const int size);\n\n#endif\n" }, { "path": "C/DN_HistogramMode_10.c", "content": "#include \n#include \n#include \n#include \n\n#include \"stats.h\"\n#include \"histcounts.h\"\n\ndouble DN_HistogramMode_10(const double y[], const int size)\n{\n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n const int nBins = 10;\n \n int * histCounts;\n double * binEdges;\n \n histcounts(y, size, nBins, &histCounts, &binEdges);\n \n double maxCount = 0;\n int numMaxs = 1;\n double out = 0;;\n for(int i = 0; i < nBins; i++)\n {\n // printf(\"binInd=%i, binCount=%i, binEdge=%1.3f \\n\", i, histCounts[i], binEdges[i]);\n \n if (histCounts[i] > maxCount)\n {\n maxCount = histCounts[i];\n numMaxs = 1;\n out = (binEdges[i] + binEdges[i+1])*0.5;\n }\n else if (histCounts[i] == maxCount){\n \n numMaxs += 1;\n out += (binEdges[i] + binEdges[i+1])*0.5;\n }\n }\n out = out/numMaxs;\n \n // arrays created dynamically in function histcounts\n free(histCounts);\n free(binEdges);\n \n return out;\n}\n\n/*\n double DN_HistogramMode_10(double y[], int size)\n {\n \n double min = DBL_MAX, max=-DBL_MAX;\n for(int i = 0; i < size; i++)\n {\n if (y[i] < min)\n {\n min = y[i];\n }\n if (y[i] > max)\n {\n max = y[i];\n }\n }\n \n double binStep = (max - min)/10;\n \n // fprintf(stdout, \"min=%f, max=%f, binStep=%f \\n\", min, max, binStep);\n \n int histCounts[10] = {0};\n for(int i = 0; i < size; i++)\n {\n int binsLeft = 10;\n int lowerInd = 0, upperInd = 10;\n while(binsLeft > 1)\n {\n int limitInd = (upperInd - lowerInd)/2 + lowerInd;\n double limit = limitInd * binStep + min;\n \n if (y[i] < limit)\n {\n upperInd = limitInd;\n }\n else\n {\n lowerInd = limitInd;\n }\n binsLeft = upperInd - lowerInd;\n }\n histCounts[lowerInd] += 1;\n }\n \n double maxCount = 0;\n int maxCountInd = 0;\n for(int i = 0; i < 10; i++)\n {\n // fprintf(stdout, \"binInd=%i, binCount=%i \\n\", i, histCounts[i]);\n \n if (histCounts[i] > maxCount)\n {\n maxCountInd = i;\n maxCount = histCounts[i];\n }\n }\n return binStep*(maxCountInd+0.5) + min;\n }\n */\n" }, { "path": "C/DN_HistogramMode_10.h", "content": "#ifndef DN_HISTOGRAMMODE_10\n#define DN_HISTOGRAMMODE_10\n#include \n#include \n#include \"stats.h\"\n\nextern double DN_HistogramMode_10(const double y[], const int size);\n\n#endif\n" }, { "path": "C/DN_HistogramMode_5.c", "content": "#include \n#include \n#include \n#include \n#include \"stats.h\"\n#include \"histcounts.h\"\n\ndouble DN_HistogramMode_5(const double y[], const int size)\n{\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n const int nBins = 5;\n \n int * histCounts;\n double * binEdges;\n \n histcounts(y, size, nBins, &histCounts, &binEdges);\n \n /*\n for(int i = 0; i < nBins; i++){\n printf(\"histCounts[%i] = %i\\n\", i, histCounts[i]);\n }\n for(int i = 0; i < nBins+1; i++){\n printf(\"binEdges[%i] = %1.3f\\n\", i, binEdges[i]);\n }\n */\n \n double maxCount = 0;\n int numMaxs = 1;\n double out = 0;;\n for(int i = 0; i < nBins; i++)\n {\n // printf(\"binInd=%i, binCount=%i, binEdge=%1.3f \\n\", i, histCounts[i], binEdges[i]);\n \n if (histCounts[i] > maxCount)\n {\n maxCount = histCounts[i];\n numMaxs = 1;\n out = (binEdges[i] + binEdges[i+1])*0.5;\n }\n else if (histCounts[i] == maxCount){\n \n numMaxs += 1;\n out += (binEdges[i] + binEdges[i+1])*0.5;\n }\n }\n out = out/numMaxs;\n \n // arrays created dynamically in function histcounts\n free(histCounts);\n free(binEdges);\n \n return out;\n}\n\n/*\ndouble DN_HistogramMode_5(double y[], int size)\n{\n \n double min = DBL_MAX, max=-DBL_MAX;\n for(int i = 0; i < size; i++)\n {\n if (y[i] < min)\n {\n min = y[i];\n }\n if (y[i] > max)\n {\n max = y[i];\n }\n }\n \n double binStep = (max - min)/5;\n \n // fprintf(stdout, \"min=%f, max=%f, binStep=%f \\n\", min, max, binStep);\n \n int histCounts[5] = {0};\n for(int i = 0; i < size; i++)\n {\n int binsLeft = 5;\n int lowerInd = 0, upperInd = 10;\n while(binsLeft > 1)\n {\n int limitInd = (upperInd - lowerInd)/2 + lowerInd;\n double limit = limitInd * binStep + min;\n \n if (y[i] < limit)\n {\n upperInd = limitInd;\n }\n else\n {\n lowerInd = limitInd;\n }\n binsLeft = upperInd - lowerInd;\n }\n histCounts[lowerInd] += 1;\n }\n \n double maxCount = 0;\n int maxCountInd = 0;\n for(int i = 0; i < 5; i++)\n {\n // fprintf(stdout, \"binInd=%i, binCount=%i \\n\", i, histCounts[i]);\n \n if (histCounts[i] > maxCount)\n {\n maxCountInd = i;\n maxCount = histCounts[i];\n }\n }\n return binStep*(maxCountInd+0.5) + min;\n}\n \n */\n" }, { "path": "C/DN_HistogramMode_5.h", "content": "#ifndef DN_HISTOGRAMMODE_5\n#define DN_HISTOGRAMMODE_5\n#include \n#include \n#include \"stats.h\"\n\nextern double DN_HistogramMode_5(const double y[], const int size);\n\n#endif\n" }, { "path": "C/DN_Mean.c", "content": "#include \n\ndouble DN_Mean(const double a[], const int size)\n{\n double m = 0.0;\n for (int i = 0; i < size; i++) {\n m += a[i];\n }\n m /= size;\n return m;\n}\n" }, { "path": "C/DN_Mean.h", "content": "//\n// Created by Trent Henderson 27 September 2021\n//\n\n#ifndef DN_MEAN\n#define DN_MEAN\n\n#include \n\nextern double DN_Mean(const double a[], const int size);\n\n#endif /* DN_MEAN */" }, { "path": "C/DN_OutlierInclude.c", "content": "#include \n#include \n#include \n#include \n#include \n#include \n#include \"stats.h\"\n\ndouble DN_OutlierInclude_np_001_mdrmd(const double y[], const int size, const int sign)\n{\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n double inc = 0.01;\n int tot = 0;\n double * yWork = malloc(size * sizeof(double));\n \n // apply sign and check constant time series\n int constantFlag = 1;\n for(int i = 0; i < size; i++)\n {\n if(y[i] != y[0])\n {\n constantFlag = 0;\n }\n \n // apply sign, save in new variable\n yWork[i] = sign*y[i];\n \n // count pos/ negs\n if(yWork[i] >= 0){\n tot += 1;\n }\n \n }\n if(constantFlag){\n free(yWork);\n return 0; // if constant, return 0\n }\n \n // find maximum (or minimum, depending on sign)\n double maxVal = max_(yWork, size);\n \n // maximum value too small? return 0\n if(maxVal < inc){\n free(yWork);\n return 0;\n }\n \n int nThresh = maxVal/inc + 1;\n \n // save the indices where y > threshold\n double * r = malloc(size * sizeof * r);\n \n // save the median over indices with absolute value > threshold\n double * msDti1 = malloc(nThresh * sizeof(double));\n double * msDti3 = malloc(nThresh * sizeof(double));\n double * msDti4 = malloc(nThresh * sizeof(double));\n \n for(int j = 0; j < nThresh; j++)\n {\n //printf(\"j=%i, thr=%1.3f\\n\", j, j*inc);\n \n int highSize = 0;\n \n for(int i = 0; i < size; i++)\n {\n if(yWork[i] >= j*inc)\n {\n r[highSize] = i+1;\n //printf(\"r[%i]=%1.f \\n\", highSize, r[highSize]);\n highSize += 1;\n }\n }\n \n // intervals between high-values\n double * Dt_exc = malloc(highSize * sizeof(double));\n \n for(int i = 0; i < highSize-1; i++)\n {\n //printf(\"i=%i, r[i+1]=%1.f, r[i]=%1.f \\n\", i, r[i+1], r[i]);\n Dt_exc[i] = r[i+1] - r[i];\n }\n\n /*\n // median\n double medianOut;\n medianOut = median(r, highSize);\n */\n \n msDti1[j] = mean(Dt_exc, highSize-1);\n msDti3[j] = (highSize-1)*100.0/tot;\n msDti4[j] = median(r, highSize) / ((double)size/2) - 1;\n \n //printf(\"msDti1[%i] = %1.3f, msDti13[%i] = %1.3f, msDti4[%i] = %1.3f\\n\",\n // j, msDti1[j], j, msDti3[j], j, msDti4[j]);\n \n free(Dt_exc);\n \n }\n \n int trimthr = 2;\n int mj = 0;\n int fbi = nThresh-1;\n for(int i = 0; i < nThresh; i ++)\n {\n if (msDti3[i] > trimthr)\n {\n mj = i;\n }\n if (isnan(msDti1[nThresh-1-i]))\n {\n fbi = nThresh-1-i;\n }\n }\n \n double outputScalar;\n int trimLimit = mj < fbi ? mj : fbi;\n outputScalar = median(msDti4, trimLimit+1);\n \n free(r);\n free(yWork);\n free(msDti1);\n free(msDti3);\n free(msDti4);\n \n return outputScalar;\n}\n\ndouble DN_OutlierInclude_p_001_mdrmd(const double y[], const int size)\n{\n return DN_OutlierInclude_np_001_mdrmd(y, size, 1.0);\n}\n\ndouble DN_OutlierInclude_n_001_mdrmd(const double y[], const int size)\n{\n return DN_OutlierInclude_np_001_mdrmd(y, size, -1.0);\n}\n\ndouble DN_OutlierInclude_abs_001(const double y[], const int size)\n{\n double inc = 0.01;\n double maxAbs = 0;\n double * yAbs = malloc(size * sizeof * yAbs);\n \n for(int i = 0; i < size; i++)\n {\n // yAbs[i] = (y[i] > 0) ? y[i] : -y[i];\n yAbs[i] = (y[i] > 0) ? y[i] : -y[i];\n \n if(yAbs[i] > maxAbs)\n {\n maxAbs = yAbs[i];\n }\n }\n \n int nThresh = maxAbs/inc + 1;\n \n printf(\"nThresh = %i\\n\", nThresh);\n \n // save the indices where y > threshold\n double * highInds = malloc(size * sizeof * highInds);\n \n // save the median over indices with absolute value > threshold\n double * msDti3 = malloc(nThresh * sizeof * msDti3);\n double * msDti4 = malloc(nThresh * sizeof * msDti4);\n\n for(int j = 0; j < nThresh; j++)\n {\n int highSize = 0;\n \n for(int i = 0; i < size; i++)\n {\n if(yAbs[i] >= j*inc)\n {\n // fprintf(stdout, \"%i, \", i);\n \n highInds[highSize] = i;\n highSize += 1;\n }\n }\n \n // median\n double medianOut;\n medianOut = median(highInds, highSize);\n \n msDti3[j] = (highSize-1)*100.0/size;\n msDti4[j] = medianOut / (size/2) - 1;\n \n }\n \n int trimthr = 2;\n int mj = 0;\n for(int i = 0; i < nThresh; i ++)\n {\n if (msDti3[i] > trimthr)\n {\n mj = i;\n }\n }\n \n double outputScalar;\n outputScalar = median(msDti4, mj);\n\n free(highInds);\n free(yAbs);\n free(msDti4);\n \n return outputScalar;\n}\n" }, { "path": "C/DN_OutlierInclude.h", "content": "#ifndef DN_OUTLIERINCLUDE_ABS_001\n#define DN_OUTLIERINCLUDE_ABS_001\n#include \n#include \n#include \n#include \n#include \"stats.h\"\n\nextern double DN_OutlierInclude_abs_001(const double y[], const int size);\nextern double DN_OutlierInclude_np_001_mdrmd(const double y[], const int size, const int sign);\nextern double DN_OutlierInclude_p_001_mdrmd(const double y[], const int size);\nextern double DN_OutlierInclude_n_001_mdrmd(const double y[], const int size);\n\n#endif\n" }, { "path": "C/DN_Spread_Std.c", "content": "#include \n#include \"stats.h\"\n\ndouble DN_Spread_Std(const double a[], const int size)\n{\n double m = mean(a, size);\n double sd = 0.0;\n for (int i = 0; i < size; i++) {\n sd += pow(a[i] - m, 2);\n }\n sd = sqrt(sd / (size - 1));\n return sd;\n}\n" }, { "path": "C/DN_Spread_Std.h", "content": "//\n// Created by Trent Henderson 27 September 2021\n//\n\n#ifndef DN_SPREADSTD\n#define DN_SPREADSTD\n\n#include \n\nextern double DN_Spread_Std(const double a[], const int size);\n\n#endif /* DN_SPREADSTD */" }, { "path": "C/FC_LocalSimple.c", "content": "#include \n#include \n#include \"stats.h\"\n#include \"CO_AutoCorr.h\"\n\nstatic void abs_diff(const double a[], const int size, double b[])\n{\n for (int i = 1; i < size; i++) {\n b[i - 1] = fabs(a[i] - a[i - 1]);\n }\n}\n\ndouble fc_local_simple(const double y[], const int size, const int train_length)\n{\n double * y1 = malloc((size - 1) * sizeof *y1);\n abs_diff(y, size, y1);\n double m = mean(y1, size - 1);\n free(y1);\n return m;\n}\n\ndouble FC_LocalSimple_mean_tauresrat(const double y[], const int size, const int train_length)\n{\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n double * res = malloc((size - train_length) * sizeof *res);\n \n for (int i = 0; i < size - train_length; i++)\n {\n double yest = 0;\n for (int j = 0; j < train_length; j++)\n {\n yest += y[i+j];\n \n }\n yest /= train_length;\n \n res[i] = y[i+train_length] - yest;\n }\n \n double resAC1stZ = co_firstzero(res, size - train_length, size - train_length);\n double yAC1stZ = co_firstzero(y, size, size);\n double output = resAC1stZ/yAC1stZ;\n \n free(res);\n return output;\n \n}\n\ndouble FC_LocalSimple_mean_stderr(const double y[], const int size, const int train_length)\n{\n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n double * res = malloc((size - train_length) * sizeof *res);\n \n for (int i = 0; i < size - train_length; i++)\n {\n double yest = 0;\n for (int j = 0; j < train_length; j++)\n {\n yest += y[i+j];\n \n }\n yest /= train_length;\n \n res[i] = y[i+train_length] - yest;\n }\n \n double output = stddev(res, size - train_length);\n \n free(res);\n return output;\n \n}\n\ndouble FC_LocalSimple_mean3_stderr(const double y[], const int size)\n{\n return FC_LocalSimple_mean_stderr(y, size, 3);\n}\n\ndouble FC_LocalSimple_mean1_tauresrat(const double y[], const int size){\n return FC_LocalSimple_mean_tauresrat(y, size, 1);\n}\n\ndouble FC_LocalSimple_mean_taures(const double y[], const int size, const int train_length)\n{\n double * res = malloc((size - train_length) * sizeof *res);\n \n // first z-score\n // no, assume ts is z-scored!!\n //zscore_norm(y, size);\n \n for (int i = 0; i < size - train_length; i++)\n {\n double yest = 0;\n for (int j = 0; j < train_length; j++)\n {\n yest += y[i+j];\n \n }\n yest /= train_length;\n \n res[i] = y[i+train_length] - yest;\n }\n \n int output = co_firstzero(res, size - train_length, size - train_length);\n \n free(res);\n return output;\n \n}\n\ndouble FC_LocalSimple_lfit_taures(const double y[], const int size)\n{\n // set tau from first AC zero crossing\n int train_length = co_firstzero(y, size, size);\n \n double * xReg = malloc(train_length * sizeof * xReg);\n // double * yReg = malloc(train_length * sizeof * yReg);\n for(int i = 1; i < train_length+1; i++)\n {\n xReg[i-1] = i;\n }\n \n double * res = malloc((size - train_length) * sizeof *res);\n \n double m = 0.0, b = 0.0;\n \n for (int i = 0; i < size - train_length; i++)\n {\n linreg(train_length, xReg, y+i, &m, &b);\n \n // fprintf(stdout, \"i=%i, m=%f, b=%f\\n\", i, m, b);\n \n res[i] = y[i+train_length] - (m * (train_length+1) + b);\n }\n \n int output = co_firstzero(res, size - train_length, size - train_length);\n \n free(res);\n free(xReg);\n // free(yReg);\n \n return output;\n \n}\n\n\n" }, { "path": "C/FC_LocalSimple.h", "content": "#ifndef FC_LOCALSIMPLE_H\n#define FC_LOCALSIMPLE_H\n#include \n#include \n#include \"stats.h\"\n#include \"CO_AutoCorr.h\"\n\nextern double fc_local_simple(const double y[], const int size, const int train_length);\nextern double FC_LocalSimple_mean_taures(const double y[], const int size, const int train_length);\nextern double FC_LocalSimple_lfit_taures(const double y[], const int size);\nextern double FC_LocalSimple_mean_tauresrat(const double y[], const int size, const int train_length);\nextern double FC_LocalSimple_mean1_tauresrat(const double y[], const int size);\nextern double FC_LocalSimple_mean_stderr(const double y[], const int size, const int train_length);\nextern double FC_LocalSimple_mean3_stderr(const double y[], const int size);\n\n#endif\n" }, { "path": "C/IN_AutoMutualInfoStats.c", "content": "//\n// IN_AutoMutualInfoStats.c\n// C_polished\n//\n// Created by Carl Henning Lubba on 22/09/2018.\n// Copyright © 2018 Carl Henning Lubba. All rights reserved.\n//\n#include \n\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"CO_AutoCorr.h\"\n#include \"stats.h\"\n\ndouble IN_AutoMutualInfoStats_40_gaussian_fmmi(const double y[], const int size)\n{\n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n // maximum time delay\n int tau = 40;\n \n // don't go above half the signal length\n if(tau > ceil((double)size/2)){\n tau = ceil((double)size/2);\n }\n \n // compute autocorrelations and compute automutual information\n double * ami = malloc(size * sizeof(double));\n for(int i = 0; i < tau; i++){\n double ac = autocorr_lag(y,size, i+1);\n ami[i] = -0.5 * log(1 - ac*ac);\n // printf(\"ami[%i]=%1.7f\\n\", i, ami[i]);\n }\n \n // find first minimum of automutual information\n double fmmi = tau;\n for(int i = 1; i < tau-1; i++){\n if(ami[i] < ami[i-1] & ami[i] < ami[i+1]){\n fmmi = i;\n // printf(\"found minimum at %i\\n\", i);\n break;\n }\n }\n \n free(ami);\n \n return fmmi;\n}\n" }, { "path": "C/IN_AutoMutualInfoStats.h", "content": "//\n// IN_AutoMutualInfoStats.h\n// C_polished\n//\n// Created by Carl Henning Lubba on 22/09/2018.\n// Copyright © 2018 Carl Henning Lubba. All rights reserved.\n//\n\n#ifndef IN_AutoMutualInfoStats_h\n#define IN_AutoMutualInfoStats_h\n\n#include \n\nextern double IN_AutoMutualInfoStats_40_gaussian_fmmi(const double y[], const int size);\n\n#endif /* IN_AutoMutualInfoStats_h */\n" }, { "path": "C/MD_hrv.c", "content": "//\n// MD_hrv.c\n// C_polished\n//\n// Created by Carl Henning Lubba on 22/09/2018.\n// Copyright © 2018 Carl Henning Lubba. All rights reserved.\n//\n\n#include \"MD_hrv.h\"\n#include \"stats.h\"\n\ndouble MD_hrv_classic_pnn40(const double y[], const int size){\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n const int pNNx = 40;\n \n // compute diff\n double * Dy = malloc((size-1) * sizeof(double));\n diff(y, size, Dy);\n \n double pnn40 = 0;\n for(int i = 0; i < size-1; i++){\n if(fabs(Dy[i])*1000 > pNNx){\n pnn40 += 1;\n }\n }\n \n free(Dy);\n \n return pnn40/(size-1);\n}\n\n" }, { "path": "C/MD_hrv.h", "content": "//\n// MD_hrv.h\n// C_polished\n//\n// Created by Carl Henning Lubba on 22/09/2018.\n// Copyright © 2018 Carl Henning Lubba. All rights reserved.\n//\n\n#ifndef MD_hrv_h\n#define MD_hrv_h\n\n#include \n\nextern double MD_hrv_classic_pnn40(const double y[], const int size);\n\n#endif /* MD_hrv_h */\n" }, { "path": "C/PD_PeriodicityWang.c", "content": "//\n// PD_PeriodicityWang.c\n// C_polished\n//\n// Created by Carl Henning Lubba on 28/09/2018.\n// Copyright © 2018 Carl Henning Lubba. All rights reserved.\n//\n\n#include \n#include \n\n#include \"PD_PeriodicityWang.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nint PD_PeriodicityWang_th0_01(const double * y, const int size){\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return 0;\n }\n }\n \n const double th = 0.01;\n \n double * ySpline = malloc(size * sizeof(double));\n \n // fit a spline with 3 nodes to the data\n splinefit(y, size, ySpline);\n \n //printf(\"spline fit complete.\\n\");\n \n // subtract spline from data to remove trend\n double * ySub = malloc(size * sizeof(double));\n for(int i = 0; i < size; i++){\n ySub[i] = y[i] - ySpline[i];\n //printf(\"ySub[%i] = %1.5f\\n\", i, ySub[i]);\n }\n \n // compute autocorrelations up to 1/3 of the length of the time series\n int acmax = (int)ceil((double)size/3);\n \n double * acf = malloc(acmax*sizeof(double));\n for(int tau = 1; tau <= acmax; tau++){\n // correlation/ covariance the same, don't care for scaling (cov would be more efficient)\n acf[tau-1] = autocov_lag(ySub, size, tau);\n //printf(\"acf[%i] = %1.9f\\n\", tau-1, acf[tau-1]);\n }\n \n //printf(\"ACF computed.\\n\");\n \n // find troughts and peaks\n double * troughs = malloc(acmax * sizeof(double));\n double * peaks = malloc(acmax * sizeof(double));\n int nTroughs = 0;\n int nPeaks = 0;\n double slopeIn = 0;\n double slopeOut = 0;\n for(int i = 1; i < acmax-1; i ++){\n slopeIn = acf[i] - acf[i-1];\n slopeOut = acf[i+1] - acf[i];\n \n if(slopeIn < 0 & slopeOut > 0)\n {\n // printf(\"trough at %i\\n\", i);\n troughs[nTroughs] = i;\n nTroughs += 1;\n }\n else if(slopeIn > 0 & slopeOut < 0)\n {\n // printf(\"peak at %i\\n\", i);\n peaks[nPeaks] = i;\n nPeaks += 1;\n }\n }\n \n //printf(\"%i troughs and %i peaks found.\\n\", nTroughs, nPeaks);\n \n \n // search through all peaks for one that meets the conditions:\n // (a) a trough before it\n // (b) difference between peak and trough is at least 0.01\n // (c) peak corresponds to positive correlation\n int iPeak = 0;\n double thePeak = 0;\n int iTrough = 0;\n double theTrough = 0;\n \n int out = 0;\n \n for(int i = 0; i < nPeaks; i++){\n iPeak = peaks[i];\n thePeak = acf[iPeak];\n \n //printf(\"i=%i/%i, iPeak=%i, thePeak=%1.3f\\n\", i, nPeaks-1, iPeak, thePeak);\n \n // find trough before this peak\n int j = -1;\n while(troughs[j+1] < iPeak && j+1 < nTroughs){\n // printf(\"j=%i/%i, iTrough=%i, theTrough=%1.3f\\n\", j+1, nTroughs-1, (int)troughs[j+1], acf[(int)troughs[j+1]]);\n j++;\n }\n if(j == -1)\n continue;\n \n iTrough = troughs[j];\n theTrough = acf[iTrough];\n \n // (a) should be implicit\n \n // (b) different between peak and trough it as least 0.01\n if(thePeak - theTrough < th)\n continue;\n \n // (c) peak corresponds to positive correlation\n if(thePeak < 0)\n continue;\n \n // use this frequency that first fulfils all conditions.\n out = iPeak;\n break;\n }\n \n //printf(\"Before freeing stuff.\\n\");\n \n free(ySpline);\n free(ySub);\n free(acf);\n free(troughs);\n free(peaks);\n \n return out;\n \n}\n" }, { "path": "C/PD_PeriodicityWang.h", "content": "//\n// PD_PeriodicityWang.h\n// C_polished\n//\n// Created by Carl Henning Lubba on 28/09/2018.\n// Copyright © 2018 Carl Henning Lubba. All rights reserved.\n//\n\n#ifndef PD_PeriodicityWang_h\n#define PD_PeriodicityWang_h\n\n#include \n\nextern int PD_PeriodicityWang_th0_01(const double * y, const int size);\n\n#endif /* PD_PeriodicityWang_h */\n" }, { "path": "C/SB_BinaryStats.c", "content": "//\n// SB_BinaryStats.c\n// C_polished\n//\n// Created by Carl Henning Lubba on 22/09/2018.\n// Copyright © 2018 Carl Henning Lubba. All rights reserved.\n//\n\n#include \"SB_BinaryStats.h\"\n#include \"stats.h\"\n\ndouble SB_BinaryStats_diff_longstretch0(const double y[], const int size){\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n // binarize\n int * yBin = malloc((size-1) * sizeof(int));\n for(int i = 0; i < size-1; i++){\n \n double diffTemp = y[i+1] - y[i];\n yBin[i] = diffTemp < 0 ? 0 : 1;\n \n /*\n if( i < 300)\n printf(\"%i, y[i+1]=%1.3f, y[i]=%1.3f, yBin[i]=%i\\n\", i, y[i+1], y[i], yBin[i]);\n */\n \n }\n \n int maxstretch0 = 0;\n int last1 = 0;\n for(int i = 0; i < size-1; i++){\n if(yBin[i] == 1 | i == size-2){\n double stretch0 = i - last1;\n if(stretch0 > maxstretch0){\n maxstretch0 = stretch0;\n }\n last1 = i;\n }\n }\n \n free(yBin);\n \n return maxstretch0;\n}\n\ndouble SB_BinaryStats_mean_longstretch1(const double y[], const int size){\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n // binarize\n int * yBin = malloc((size-1) * sizeof(int));\n double yMean = mean(y, size);\n for(int i = 0; i < size-1; i++){\n \n yBin[i] = (y[i] - yMean <= 0) ? 0 : 1;\n //printf(\"yBin[%i]=%i\\n\", i, yBin[i]);\n \n }\n \n int maxstretch1 = 0;\n int last1 = 0;\n for(int i = 0; i < size-1; i++){\n if(yBin[i] == 0 | i == size-2){\n double stretch1 = i - last1;\n if(stretch1 > maxstretch1){\n maxstretch1 = stretch1;\n }\n last1 = i;\n }\n \n }\n \n free(yBin);\n \n return maxstretch1;\n}\n" }, { "path": "C/SB_BinaryStats.h", "content": "//\n// SB_BinaryStats.h\n// C_polished\n//\n// Created by Carl Henning Lubba on 22/09/2018.\n// Copyright © 2018 Carl Henning Lubba. All rights reserved.\n//\n\n#ifndef SB_BinaryStats_h\n#define SB_BinaryStats_h\n\n#include \n\nextern double SB_BinaryStats_diff_longstretch0(const double y[], const int size);\nextern double SB_BinaryStats_mean_longstretch1(const double y[], const int size);\n\n#endif /* SB_BinaryStats_h */\n" }, { "path": "C/SB_CoarseGrain.c", "content": "#include \n#include \n#include \n#include \n#include \"stats.h\"\n#include \"helper_functions.h\"\n\nvoid sb_coarsegrain(const double y[], const int size, const char how[], const int num_groups, int labels[])\n{\n int i, j;\n if (strcmp(how, \"quantile\") == 1) {\n fprintf(stdout, \"ERROR in sb_coarsegrain: unknown coarse-graining method\\n\");\n exit(1);\n }\n \n /*\n for(int i = 0; i < size; i++){\n printf(\"yin coarsegrain[%i]=%1.4f\\n\", i, y[i]);\n }\n */\n \n double * th = malloc((num_groups + 1) * 2 * sizeof(th));\n double * ls = malloc((num_groups + 1) * 2 * sizeof(th));\n linspace(0, 1, num_groups + 1, ls);\n for (i = 0; i < num_groups + 1; i++) {\n //double quant = quantile(y, size, ls[i]);\n th[i] = quantile(y, size, ls[i]);\n }\n th[0] -= 1;\n for (i = 0; i < num_groups; i++) {\n for (j = 0; j < size; j++) {\n if (y[j] > th[i] && y[j] <= th[i + 1]) {\n labels[j] = i + 1;\n }\n }\n }\n \n free(th);\n free(ls);\n}\n" }, { "path": "C/SB_CoarseGrain.h", "content": "#ifndef SB_COARSEGRAIN_H\n#define SB_COARSEGRAIN_H\n#include \n#include \n#include \n#include \n#include \"stats.h\"\n#include \"helper_functions.h\"\n\nextern void sb_coarsegrain(const double y[], const int size, const char how[], const int num_groups, int labels[]);\n\n#endif\n" }, { "path": "C/SB_MotifThree.c", "content": "#include \n#include \n#include \n#include \n#include \"SB_CoarseGrain.h\"\n#include \"helper_functions.h\"\n\ndouble SB_MotifThree_quantile_hh(const double y[], const int size)\n{\n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n int tmp_idx, r_idx;\n int dynamic_idx;\n int alphabet_size = 3;\n int array_size;\n int * yt = malloc(size * sizeof(yt)); // alphabetized array\n double hh; // output\n double * out = malloc(124 * sizeof(out)); // output array\n \n // transfer to alphabet\n sb_coarsegrain(y, size, \"quantile\", 3, yt);\n \n // words of length 1\n array_size = alphabet_size;\n int ** r1 = malloc(array_size * sizeof(*r1));\n int * sizes_r1 = malloc(array_size * sizeof(sizes_r1));\n double * out1 = malloc(array_size * sizeof(out1));\n for (int i = 0; i < alphabet_size; i++) {\n r1[i] = malloc(size * sizeof(r1[i])); // probably can be rewritten\n // using selfresizing array for memory efficiency. Time complexity\n // should be comparable due to ammotization.\n r_idx = 0;\n sizes_r1[i] = 0;\n for (int j = 0; j < size; j++) {\n if (yt[j] == i + 1) {\n r1[i][r_idx++] = j;\n sizes_r1[i]++;\n }\n }\n }\n \n // words of length 2\n array_size *= alphabet_size;\n // removing last item if it is == max possible idx since later we are taking idx + 1\n // from yt\n for (int i = 0; i < alphabet_size; i++) {\n if (sizes_r1[i] != 0 && r1[i][sizes_r1[i] - 1] == size - 1) {\n //int * tmp_ar = malloc((sizes_r1[i] - 1) * sizeof(tmp_ar));\n int* tmp_ar = malloc(sizes_r1[i] * sizeof(tmp_ar));\n subset(r1[i], tmp_ar, 0, sizes_r1[i]);\n memcpy(r1[i], tmp_ar, (sizes_r1[i] - 1) * sizeof(tmp_ar));\n sizes_r1[i]--;\n free(tmp_ar);\n }\n }\n \n /*\n int *** r2 = malloc(array_size * sizeof(**r2));\n int ** sizes_r2 = malloc(array_size * sizeof(*sizes_r2));\n double ** out2 = malloc(array_size * sizeof(*out2));\n */\n int*** r2 = malloc(alphabet_size * sizeof(**r2));\n int** sizes_r2 = malloc(alphabet_size * sizeof(*sizes_r2));\n double** out2 = malloc(alphabet_size * sizeof(*out2));\n \n\n // allocate separately\n for (int i = 0; i < alphabet_size; i++) {\n r2[i] = malloc(alphabet_size * sizeof(*r2[i]));\n sizes_r2[i] = malloc(alphabet_size * sizeof(*sizes_r2[i]));\n //out2[i] = malloc(alphabet_size * sizeof(out2[i]));\n out2[i] = malloc(alphabet_size * sizeof(**out2));\n for (int j = 0; j < alphabet_size; j++) {\n r2[i][j] = malloc(size * sizeof(*r2[i][j]));\n }\n }\n\n // fill separately\n for (int i = 0; i < alphabet_size; i++) {\n // for (int i = 0; i < array_size; i++) {\n //r2[i] = malloc(alphabet_size * sizeof(r2[i]));\n //sizes_r2[i] = malloc(alphabet_size * sizeof(sizes_r2[i]));\n //out2[i] = malloc(alphabet_size * sizeof(out2[i]));\n for (int j = 0; j < alphabet_size; j++) {\n //r2[i][j] = malloc(size * sizeof(r2[i][j]));\n sizes_r2[i][j] = 0;\n dynamic_idx = 0; //workaround as you can't just add elements to array\n // like in python (list.append()) for example, so since for some k there will be no adding,\n // you need to keep track of the idx at which elements will be inserted\n for (int k = 0; k < sizes_r1[i]; k++) {\n tmp_idx = yt[r1[i][k] + 1];\n if (tmp_idx == (j + 1)) {\n r2[i][j][dynamic_idx++] = r1[i][k];\n sizes_r2[i][j]++;\n // printf(\"dynamic_idx=%i, size = %i\\n\", dynamic_idx, size);\n }\n }\n double tmp = (double)sizes_r2[i][j] / ((double)(size) - (double)(1.0));\n out2[i][j] = tmp;\n }\n }\n\n hh = 0.0;\n for (int i = 0; i < alphabet_size; i++) {\n hh += f_entropy(out2[i], alphabet_size);\n }\n\n free(yt);\n free(out);\n free(out1);\n\n free(sizes_r1);\n\n // free nested array\n for (int i = 0; i < alphabet_size; i++) {\n free(r1[i]);\n }\n free(r1);\n // free(sizes_r1);\n \n for (int i = 0; i < alphabet_size; i++) {\n //for (int i = alphabet_size - 1; i >= 0; i--) {\n\n free(sizes_r2[i]);\n free(out2[i]);\n }\n\n //for (int i = alphabet_size-1; i >= 0 ; i--) {\n for(int i = 0; i < alphabet_size; i++) {\n for (int j = 0; j < alphabet_size; j++) {\n free(r2[i][j]);\n }\n free(r2[i]);\n }\n \n free(r2);\n free(sizes_r2);\n free(out2);\n \n \n return hh;\n \n}\n\ndouble * sb_motifthree(const double y[], int size, const char how[])\n{\n int tmp_idx, r_idx, i, j, k, l, m, array_size;\n int dynamic_idx;\n int * tmp_ar;\n int alphabet_size = 3;\n int out_idx = 0;\n int * yt = malloc(size * sizeof(yt));\n double tmp;\n double * out = malloc(124 * sizeof(out)); // output array\n if (strcmp(how, \"quantile\") == 0) {\n sb_coarsegrain(y, size, how, alphabet_size, yt);\n } else if (strcmp(how, \"diffquant\") == 0) {\n double * diff_y = malloc((size - 1) * sizeof(diff_y));\n diff(y, size, diff_y);\n sb_coarsegrain(diff_y, size, how, alphabet_size, yt);\n size--;\n } else {\n fprintf(stdout, \"ERROR in sb_motifthree: Unknown how method\");\n exit(1);\n }\n\n // words of length 1\n array_size = alphabet_size;\n int ** r1 = malloc(array_size * sizeof(*r1));\n int * sizes_r1 = malloc(array_size * sizeof(sizes_r1));\n double * out1 = malloc(array_size * sizeof(out1));\n for (i = 0; i < array_size; i++) {\n r1[i] = malloc(size * sizeof(r1[i])); // probably can be rewritten\n // using selfresizing array for memory efficiency. Time complexity\n // should be comparable due to ammotization.\n r_idx = 0;\n sizes_r1[i] = 0;\n for (j = 0; j < size; j++) {\n if (yt[j] == i + 1) {\n r1[i][r_idx++] = j;\n sizes_r1[i]++;\n }\n }\n tmp = (double)sizes_r1[i] / size;\n\n out1[i] = tmp;\n out[out_idx++] = tmp;\n }\n out[out_idx++] = f_entropy(out1, array_size);\n\n // words of length 2\n array_size *= alphabet_size;\n // removing last item if it is == max possible idx since later we are taking idx + 1\n // from yt\n for (i = 0; i < alphabet_size; i++) {\n if (sizes_r1[i] != 0 && r1[i][sizes_r1[i] - 1] == size - 1) {\n tmp_ar = malloc((sizes_r1[i] - 1) * sizeof(tmp_ar));\n subset(r1[i], tmp_ar, 0, sizes_r1[i]);\n memcpy(r1[i], tmp_ar, (sizes_r1[i] - 1) * sizeof(tmp_ar));\n sizes_r1[i]--;\n }\n }\n\n int *** r2 = malloc(array_size * sizeof(**r2));\n int ** sizes_r2 = malloc(array_size * sizeof(*sizes_r2));\n double ** out2 = malloc(array_size * sizeof(*out2));\n for (i = 0; i < alphabet_size; i++) {\n r2[i] = malloc(alphabet_size * sizeof(r2[i]));\n sizes_r2[i] = malloc(alphabet_size * sizeof(sizes_r2[i]));\n out2[i] = malloc(alphabet_size * sizeof(out2[i]));\n for (j = 0; j < alphabet_size; j++) {\n r2[i][j] = malloc(size * sizeof(r2[i][j]));\n sizes_r2[i][j] = 0;\n dynamic_idx = 0; //workaround as you can't just add elements to array\n // like in python (list.append()) for example, so since for some k there will be no adding,\n // you need to keep track of the idx at which elements will be inserted\n for (k = 0; k < sizes_r1[i]; k++) {\n tmp_idx = yt[r1[i][k] + 1];\n if (tmp_idx == (j + 1)) {\n r2[i][j][dynamic_idx++] = r1[i][k];\n sizes_r2[i][j]++;\n }\n }\n tmp = (double)sizes_r2[i][j] / (size - 1);\n out2[i][j] = tmp;\n out[out_idx++] = tmp;\n }\n }\n tmp = 0.0;\n for (i = 0; i < alphabet_size; i++) {\n tmp += f_entropy(out2[i], alphabet_size);\n }\n out[out_idx++] = tmp;\n\n // words of length 3\n array_size *= alphabet_size;\n for (i = 0; i < alphabet_size; i++) {\n for (j = 0; j < alphabet_size; j++) {\n if (sizes_r2[i][j] != 0 && r2[i][j][sizes_r2[i][j] - 1] == size - 2) {\n subset(r2[i][j], tmp_ar, 0, sizes_r2[i][j]);\n memcpy(r2[i][j], tmp_ar, (sizes_r2[i][j] - 1) * sizeof(tmp_ar));\n sizes_r2[i][j]--;\n }\n }\n }\n\n int **** r3 = malloc(array_size * sizeof(***r3));\n int *** sizes_r3 = malloc(array_size * sizeof(**sizes_r3));\n double *** out3 = malloc(array_size * sizeof(**out3));\n for (i = 0; i < alphabet_size; i++) {\n r3[i] = malloc(alphabet_size * sizeof(r3[i]));\n sizes_r3[i] = malloc(alphabet_size * sizeof(sizes_r3[i]));\n out3[i] = malloc(alphabet_size * sizeof(out3[i]));\n for (j = 0; j < alphabet_size; j++) {\n r3[i][j] = malloc(alphabet_size * sizeof(r3[i][j]));\n sizes_r3[i][j] = malloc(alphabet_size * sizeof(sizes_r3[i][j]));\n out3[i][j] = malloc(alphabet_size * sizeof(out3[i][j]));\n for (k = 0; k < alphabet_size; k++) {\n r3[i][j][k] = malloc(size * sizeof(r3[i][j][k]));\n sizes_r3[i][j][k] = 0;\n dynamic_idx = 0;\n for (l = 0; l < sizes_r2[i][j]; l++) {\n tmp_idx = yt[r2[i][j][l] + 2];\n if (tmp_idx == (k + 1)) {\n r3[i][j][k][dynamic_idx++] = r2[i][j][l];\n sizes_r3[i][j][k]++;\n }\n }\n tmp = (double)sizes_r3[i][j][k] / (size - 2);\n out3[i][j][k] = tmp;\n out[out_idx++] = tmp;\n }\n }\n }\n tmp = 0.0;\n for (i = 0; i < alphabet_size; i++) {\n for (j = 0; j < alphabet_size; j++) {\n tmp += f_entropy(out3[i][j], alphabet_size);\n }\n }\n out[out_idx++] = tmp;\n\n // words of length 4\n array_size *= alphabet_size;\n for (i = 0; i < alphabet_size; i++) {\n for (j = 0; j < alphabet_size; j++) {\n for (k = 0; k < alphabet_size; k++) {\n if (sizes_r3[i][j][k] != 0 && r3[i][j][k][sizes_r3[i][j][k] - 1] == size - 3) {\n subset(r3[i][j][k], tmp_ar, 0, sizes_r3[i][j][k]);\n memcpy(r3[i][j][k], tmp_ar, (sizes_r3[i][j][k] - 1) * sizeof(tmp_ar));\n sizes_r3[i][j][k]--;\n }\n }\n }\n }\n\n int ***** r4 = malloc(array_size * sizeof(****r4));\n // just an array of pointers of array of pointers of array of pointers\n // of array of pointers of array of ints... We need to go deeper (c)\n int **** sizes_r4 = malloc(array_size * sizeof(***sizes_r3));\n double **** out4 = malloc(array_size * sizeof(***out4));\n for (i = 0; i < alphabet_size; i++) {\n r4[i] = malloc(alphabet_size * sizeof(r4[i]));\n sizes_r4[i] = malloc(alphabet_size * sizeof(sizes_r4[i]));\n out4[i] = malloc(alphabet_size * sizeof(out4[i]));\n for (j = 0; j < alphabet_size; j++) {\n r4[i][j] = malloc(alphabet_size * sizeof(r4[i][j]));\n sizes_r4[i][j] = malloc(alphabet_size * sizeof(sizes_r4[i][j]));\n out4[i][j] = malloc(alphabet_size * sizeof(out4[i][j]));\n for (k = 0; k < alphabet_size; k++) {\n r4[i][j][k] = malloc(alphabet_size * sizeof(r4[i][j][k]));\n sizes_r4[i][j][k] = malloc(alphabet_size * sizeof(sizes_r4[i][j][k]));\n out4[i][j][k] = malloc(alphabet_size * sizeof(out4[i][j][k]));\n for (l = 0; l < alphabet_size; l++) {\n r4[i][j][k][l] = malloc(size * sizeof(r4[i][j][k][l]));\n sizes_r4[i][j][k][l] = 0;\n dynamic_idx = 0;\n for (m = 0; m < sizes_r3[i][j][k]; m++) {\n tmp_idx = yt[r3[i][j][k][m] + 3];\n if (tmp_idx == l + 1) {\n r4[i][j][k][l][dynamic_idx++] = r3[i][j][k][m];\n sizes_r4[i][j][k][l]++;\n }\n }\n tmp = (double)sizes_r4[i][j][k][l] / (size - 3);\n out4[i][j][k][l] = tmp;\n out[out_idx++] = tmp;\n }\n }\n }\n }\n tmp = 0.0;\n for (i = 0; i < alphabet_size; i++) {\n for (j = 0; j < alphabet_size; j++) {\n for (k = 0; k < alphabet_size; k++) {\n tmp += f_entropy(out4[i][j][k], alphabet_size);\n }\n }\n }\n out[out_idx++] = tmp;\n\n return out;\n}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "C/SB_MotifThree.h", "content": "#ifndef SB_MOTIFTHREE_H\n#define SB_MOTIFTHREE_H\n#include \n#include \n#include \n#include \"SB_CoarseGrain.h\"\n#include \"helper_functions.h\"\n\nextern double SB_MotifThree_quantile_hh(const double y[], const int size);\nextern double * sb_motifthree(const double y[], int size, const char how[]);\n\n#endif\n" }, { "path": "C/SB_TransitionMatrix.c", "content": "//\n// SB_TransitionMatrix.c\n// \n//\n// Created by Carl Henning Lubba on 23/09/2018.\n//\n\n#include \"SB_TransitionMatrix.h\"\n#include \"butterworth.h\"\n#include \"CO_AutoCorr.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"stats.h\"\n\ndouble SB_TransitionMatrix_3ac_sumdiagcov(const double y[], const int size)\n{\n \n // NaN and const check\n int constant = 1;\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n if(y[i] != y[0]){\n constant = 0;\n }\n }\n if (constant){\n return NAN;\n }\n \n const int numGroups = 3;\n \n int tau = co_firstzero(y, size, size);\n \n double * yFilt = malloc(size * sizeof(double));\n \n // sometimes causes problems in filt!!! needs fixing.\n /*\n if(tau > 1){\n butterworthFilter(y, size, 4, 0.8/tau, yFilt);\n }\n */\n \n for(int i = 0; i < size; i++){\n yFilt[i] = y[i];\n }\n \n /*\n for(int i = 0; i < size; i++){\n printf(\"yFilt[%i]=%1.4f\\n\", i, yFilt[i]);\n }\n */\n \n int nDown = (size-1)/tau+1;\n double * yDown = malloc(nDown * sizeof(double));\n \n for(int i = 0; i < nDown; i++){\n yDown[i] = yFilt[i*tau];\n }\n \n /*\n for(int i = 0; i < nDown; i++){\n printf(\"yDown[%i]=%1.4f\\n\", i, yDown[i]);\n }\n */\n \n \n // transfer to alphabet\n int * yCG = malloc(nDown * sizeof(double));\n sb_coarsegrain(yDown, nDown, \"quantile\", numGroups, yCG);\n \n /*\n for(int i = 0; i < nDown; i++){\n printf(\"yCG[%i]=%i\\n\", i, yCG[i]);\n }\n */\n \n \n double T[3][3];\n for(int i = 0; i < numGroups; i++){\n for(int j = 0; j < numGroups; j++){\n T[i][j] = 0;\n }\n }\n \n // more efficient way of doing the below \n for(int j = 0; j < nDown-1; j++){\n T[yCG[j]-1][yCG[j+1]-1] += 1;\n }\n \n /*\n for(int i = 0; i < numGroups; i++){\n for(int j = 0; j < numGroups; j++){\n printf(\"%1.f, \", T[i][j]);\n }\n printf(\"\\n\");\n }\n */\n \n /*\n for(int i = 0; i < numGroups; i++){\n for(int j = 0; j < nDown-1; j++){\n if(yCG[j] == i+1){\n T[i][yCG[j+1]-1] += 1;\n }\n }\n }\n */\n \n for(int i = 0; i < numGroups; i++){\n for(int j = 0; j < numGroups; j++){\n T[i][j] /= (nDown-1);\n // printf(\"T(%i, %i) = %1.3f\\n\", i, j, T[i][j]);\n \n }\n }\n \n double column1[3] = {0};\n double column2[3] = {0};\n double column3[3] = {0};\n \n for(int i = 0; i < numGroups; i++){\n column1[i] = T[i][0];\n column2[i] = T[i][1];\n column3[i] = T[i][2];\n // printf(\"column3(%i) = %1.3f\\n\", i, column3[i]);\n }\n \n double *columns[3];\n columns[0] = &(column1[0]);\n columns[1] = &(column2[0]);\n columns[2] = &(column3[0]);\n \n \n double COV[3][3];\n double covTemp = 0;\n for(int i = 0; i < numGroups; i++){\n for(int j = i; j < numGroups; j++){\n \n covTemp = cov(columns[i], columns[j], 3);\n \n COV[i][j] = covTemp;\n COV[j][i] = covTemp;\n \n // printf(\"COV(%i , %i) = %1.3f\\n\", i, j, COV[i][j]);\n }\n }\n \n double sumdiagcov = 0;\n for(int i = 0; i < numGroups; i++){\n sumdiagcov += COV[i][i];\n }\n \n free(yFilt);\n free(yDown);\n free(yCG);\n \n return sumdiagcov;\n \n \n}\n" }, { "path": "C/SB_TransitionMatrix.h", "content": "//\n// SB_TransitionMatrix.h\n// \n//\n// Created by Carl Henning Lubba on 23/09/2018.\n//\n\n#ifndef SB_TransitionMatrix_h\n#define SB_TransitionMatrix_h\n\n#include \n\nextern double SB_TransitionMatrix_3ac_sumdiagcov(const double y[], const int size);\n\n#endif /* SB_TransitionMatrix_h */\n" }, { "path": "C/SC_FluctAnal.c", "content": "#include \n#include \n#include \n#include \n#include \n#include \"stats.h\"\n#include \"CO_AutoCorr.h\"\n\ndouble SC_FluctAnal_2_50_1_logi_prop_r1(const double y[], const int size, const int lag, const char how[])\n{\n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n // generate log spaced tau vector\n double linLow = log(5);\n double linHigh = log(size/2);\n \n int nTauSteps = 50;\n double tauStep = (linHigh - linLow) / (nTauSteps-1);\n \n int tau[50];\n for(int i = 0; i < nTauSteps; i++)\n {\n tau[i] = round(exp(linLow + i*tauStep));\n }\n \n // check for uniqueness, use ascending order\n int nTau = nTauSteps;\n for(int i = 0; i < nTauSteps-1; i++)\n {\n \n while (tau[i] == tau[i+1] && i < nTau-1)\n {\n for(int j = i+1; j < nTauSteps-1; j++)\n {\n tau[j] = tau[j+1];\n }\n // lost one\n nTau -= 1;\n }\n }\n \n // fewer than 12 points -> leave.\n if(nTau < 12){\n return 0;\n }\n \n int sizeCS = size/lag;\n double * yCS = malloc(sizeCS * sizeof(double));\n \n /*\n for(int i = 0; i < 50; i++)\n {\n printf(\"y[%i]=%1.3f\\n\", i, y[i]);\n }\n */\n \n // transform input vector to cumsum\n yCS[0] = y[0];\n for(int i = 0; i < sizeCS-1; i++)\n {\n yCS[i+1] = yCS[i] + y[(i+1)*lag];\n \n /*\n if(i<300)\n printf(\"yCS[%i]=%1.3f\\n\", i, yCS[i]);\n */\n }\n \n //for each value of tau, cut signal into snippets of length tau, detrend and\n \n // first generate a support for regression (detrending)\n double * xReg = malloc(tau[nTau-1] * sizeof * xReg);\n for(int i = 0; i < tau[nTau-1]; i++)\n {\n xReg[i] = i+1;\n }\n \n // iterate over taus, cut signal, detrend and save amplitude of remaining signal\n double * F = malloc(nTau * sizeof * F);\n for(int i = 0; i < nTau; i++)\n {\n int nBuffer = sizeCS/tau[i];\n double * buffer = malloc(tau[i] * sizeof * buffer);\n double m = 0.0, b = 0.0;\n \n //printf(\"tau[%i]=%i\\n\", i, tau[i]);\n \n F[i] = 0;\n for(int j = 0; j < nBuffer; j++)\n {\n \n //printf(\"%i th buffer\\n\", j);\n \n linreg(tau[i], xReg, yCS+j*tau[i], &m, &b);\n \n \n for(int k = 0; k < tau[i]; k++)\n {\n buffer[k] = yCS[j*tau[i]+k] - (m * (k+1) + b);\n //printf(\"buffer[%i]=%1.3f\\n\", k, buffer[k]);\n }\n \n if (strcmp(how, \"rsrangefit\") == 0) {\n F[i] += pow(max_(buffer, tau[i]) - min_(buffer, tau[i]), 2);\n }\n else if (strcmp(how, \"dfa\") == 0) {\n for(int k = 0; k leave.\n if(nTau < 8){\n return 0;\n }\n \n // transform input vector to cumsum\n for(int i = 0; i < size-1; i++)\n {\n y[i+1] = y[i] + y[i+1];\n }\n \n //for each value of tau, cut signal into snippets of length tau, detrend and\n \n // first generate a support for regression (detrending)\n double * xReg = malloc(tau[nTau-1] * sizeof * xReg);\n for(int i = 0; i < tau[nTau-1]; i++)\n {\n xReg[i] = i+1;\n }\n \n // iterate over taus, cut signal, detrend and save amplitude of remaining signal\n double * F = malloc(nTau * sizeof * F);\n for(int i = 0; i < nTau; i++)\n {\n int nBuffer = size/tau[i];\n double * buffer = malloc(tau[i] * sizeof * buffer);\n double m = 0.0, b = 0.0;\n \n F[i] = 0;\n for(int j = 0; j < nBuffer; j++)\n {\n \n linreg(tau[i], xReg, y+j*tau[i], &m, &b);\n \n for(int k = 0; k < tau[i]; k++)\n {\n buffer[k] = y[j*tau[i]+k] - (m * (k+1) + b);\n }\n \n F[i] += pow(max(buffer, tau[i]) - min(buffer, tau[i]), 2);\n }\n \n F[i] = sqrt(F[i]/nBuffer);\n \n free(buffer);\n \n }\n \n double * logtt = malloc(nTau * sizeof * logtt);\n double * logFF = malloc(nTau * sizeof * logFF);\n int ntt = nTau;\n \n for (int i = 0; i < nTau; i++)\n {\n logtt[i] = log(tau[i]);\n logFF[i] = log(F[i]);\n }\n \n int minPoints = 6;\n int nsserr = (ntt - 2*minPoints + 1);\n double * sserr = malloc(nsserr * sizeof * sserr);\n double * buffer = malloc((ntt - minPoints + 1) * sizeof * buffer);\n for (int i = minPoints; i < ntt - minPoints + 1; i++)\n {\n // this could be done with less variables of course\n double m1 = 0.0, b1 = 0.0;\n double m2 = 0.0, b2 = 0.0;\n \n sserr[i - minPoints] = 0.0;\n \n linreg(i, logtt, logFF, &m1, &b1);\n linreg(ntt-i+1, logtt+i-1, logFF+i-1, &m2, &b2);\n \n for(int j = 0; j < i; j ++)\n {\n buffer[j] = logtt[j] * m1 + b1 - logFF[j];\n }\n \n sserr[i - minPoints] += norm(buffer, i);\n \n for(int j = 0; j < ntt-i+1; j++)\n {\n buffer[j] = logtt[j+i-1] * m2 + b2 - logFF[j+i-1];\n }\n \n sserr[i - minPoints] += norm(buffer, ntt-i+1);\n \n }\n \n double firstMinInd = 0.0;\n double minimum = min(sserr, nsserr);\n for(int i = 0; i < nsserr; i++)\n {\n if(sserr[i] == minimum)\n {\n firstMinInd = i + minPoints - 1;\n break;\n }\n }\n \n free(xReg);\n free(F);\n free(logtt);\n free(logFF);\n free(sserr);\n free(buffer);\n \n return (firstMinInd+1)/ntt;\n \n}\n */\n" }, { "path": "C/SC_FluctAnal.h", "content": "#ifndef SC_FLUCTANAL\n#define SC_FLUCTANAL\n#include \n#include \n#include \"stats.h\"\n#include \"CO_AutoCorr.h\"\n\nextern double SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1(const double y[], const int size);\nextern double SC_FluctAnal_2_50_1_logi_prop_r1(const double y[], const int size, const char how[]);\nextern double SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1(const double y[], const int size);\n#endif\n" }, { "path": "C/SP_Summaries.c", "content": "//\n// SP_Summaries.c\n// \n//\n// Created by Carl Henning Lubba on 23/09/2018.\n//\n\n#include \"SP_Summaries.h\"\n#include \"CO_AutoCorr.h\"\n\nint welch(const double y[], const int size, const int NFFT, const double Fs, const double window[], const int windowWidth, double ** Pxx, double ** f){\n \n double dt = 1.0/Fs;\n double df = 1.0/(nextpow2(windowWidth))/dt;\n double m = mean(y, size);\n \n // number of windows, should be 1\n int k = floor((double)size/((double)windowWidth/2.0))-1;\n \n // normalising scale factor\n double KMU = k * pow(norm_(window, windowWidth),2);\n \n double * P = malloc(NFFT * sizeof(double));\n for(int i = 0; i < NFFT; i++){\n P[i] = 0;\n }\n \n // fft variables\n cplx * F = malloc(NFFT * sizeof *F);\n cplx * tw = malloc(NFFT * sizeof *tw);\n twiddles(tw, NFFT);\n \n double * xw = malloc(windowWidth * sizeof(double));\n for(int i = 0; i0 & i < Nout-1){\n (*Pxx)[i] *= 2;\n }\n }\n /*\n for(int i = 0; i < Nout; i++){\n printf(\"Pxx[%i]: %1.3f\\n\", i, Pxx[i]);\n }\n */\n \n *f = malloc(Nout * sizeof(double));\n for(int i = 0; i < Nout; i++){\n (*f)[i] = (double)i*df;\n }\n /*\n for(int i = 0; i < Nout; i++){\n printf(\"f[%i]: %1.3f\\n\", i, (*f)[i]);\n }\n */\n \n free(P);\n free(F);\n free(tw);\n free(xw);\n \n return Nout;\n}\n\ndouble SP_Summaries_welch_rect(const double y[], const int size, const char what[])\n{\n \n // NaN check\n for(int i = 0; i < size; i++)\n {\n if(isnan(y[i]))\n {\n return NAN;\n }\n }\n \n // rectangular window for Welch-spectrum\n double * window = malloc(size * sizeof(double));\n for(int i = 0; i < size; i++){\n window[i] = 1;\n }\n \n double Fs = 1.0; // sampling frequency\n int N = nextpow2(size);\n \n double * S;\n double * f;\n \n // compute Welch-power\n int nWelch = welch(y, size, N, Fs, window, size, &S, &f);\n free(window);\n \n // angualr frequency and spectrum on that\n double * w = malloc(nWelch * sizeof(double));\n double * Sw = malloc(nWelch * sizeof(double));\n \n double PI = 3.14159265359;\n for(int i = 0; i < nWelch; i++){\n w[i] = 2*PI*f[i];\n Sw[i] = S[i]/(2*PI);\n //printf(\"w[%i]=%1.3f, Sw[%i]=%1.3f\\n\", i, w[i], i, Sw[i]);\n if(isinf(Sw[i]) | isinf(-Sw[i])){\n return 0;\n }\n }\n \n double dw = w[1] - w[0];\n \n double * csS = malloc(nWelch * sizeof(double));\n cumsum(Sw, nWelch, csS);\n /*\n for(int i=0; i csSThres){\n centroid = w[i];\n break;\n }\n }\n \n output = centroid;\n \n }\n else if(strcmp(what, \"area_5_1\") == 0){\n double area_5_1 = 0;;\n for(int i=0; i\n\nextern double SP_Summaries_welch_rect(const double y[], const int size, const char what[]);\nextern double SP_Summaries_welch_rect_area_5_1(const double y[], const int size);\nextern double SP_Summaries_welch_rect_centroid(const double y[], const int size);\n\n#endif /* SP_Summaries_h */\n" }, { "path": "C/butterworth.c", "content": "//\n// butterworth.c\n// \n//\n// Created by Carl Henning Lubba on 23/09/2018.\n//\n\n#include \n#include \n\n#if __cplusplus\n# include \ntypedef std::complex< double > cplx;\n#else\n# include \n#if defined(__GNUC__) || defined(__GNUG__)\n typedef double complex cplx;\n#elif defined(_MSC_VER)\n typedef _Dcomplex cplx;\n#endif\n#endif\n\n#include \"helper_functions.h\"\n#include \"butterworth.h\"\n\n#ifndef CMPLX\n#define CMPLX(x, y) ((cplx)((double)(x) + _Imaginary_I * (double)(y)))\n#endif\n\nvoid poly(cplx x[], int size, cplx out[])\n{\n /* Convert roots x to polynomial coefficients */\n \n // initialise\n #if defined(__GNUC__) || defined(__GNUG__)\n out[0] = 1;\n for(int i=1; i= 0)\n {\n out[i] += b[j]*(y[i-j]-offset);\n out[i] -= a[j]*out[i-j];\n }\n else{\n out[i] += 0; //b[j]*offset; // 'padding'\n out[i] -= 0; //a[j]*offset;\n }\n }\n }\n \n for(int i = 0; i < size; i++){\n out[i] += offset;\n }\n}\n\nvoid reverse_array(double a[], int size){\n \n /* Reverse the order of the elements in an array. Write back into the input array.*/\n \n double temp;\n for(int i = 0; i < size/2; i++){\n temp = a[i];\n a[i] = a[size-i-1];\n a[size-1-i] = temp;\n /*\n printf(\"indFrom = %i, indTo = %i\\n\", i, size-1-i);\n for(int i=0; i < size; i++){\n printf(\"reversed[%i]=%1.3f\\n\", i, a[i]);\n }\n */\n }\n}\n\nvoid filt_reverse(double y[], int size, double a[], double b[], int nCoeffs, double out[]){\n \n /* Filter a signal y with the filter coefficients a and b _in reverse order_, output to array out.*/\n \n double * yTemp = malloc(size * sizeof(double));\n for(int i = 0; i < size; i++){\n yTemp[i] = y[i];\n }\n \n /*\n for(int i=0; i < size; i++){\n printf(\"yTemp[%i]=%1.3f\\n\", i, yTemp[i]);\n }\n */\n \n reverse_array(yTemp, size);\n \n /*\n for(int i=0; i < size; i++){\n printf(\"reversed[%i]=%1.3f\\n\", i, yTemp[i]);\n }\n */\n \n double offset = yTemp[0];\n \n for(int i = 0; i < size; i++){\n out[i] = 0;\n for(int j = 0; j < nCoeffs; j++){\n if(i - j >= 0)\n {\n out[i] += b[j]*(yTemp[i-j]-offset);\n out[i] -= a[j]*out[i-j];\n }\n else{\n out[i] += 0; //b[j]*offset; // 'padding'\n out[i] -= 0; //a[j]*offset;\n }\n }\n }\n \n for(int i = 0; i < size; i++){\n out[i] += offset;\n }\n \n reverse_array(out, size);\n \n free(yTemp);\n \n}\n\n/*\nvoid butterworthFilter(const double y[], int size, const int nPoles, const double W, double out[]){\n \n double PI = 3.14159265359;\n\n double V = tan(W * PI/2);\n cplx * Q = malloc(nPoles * sizeof(cplx));\n \n for(int i = 0; i 0){\n // prod1mSp *= (1 - Sp[i]);\n //}\n \n }\n\n double G = creal(Sg / prod1mSp);\n \n cplx * Zpoly = malloc((nPoles+1) * sizeof(cplx));\n cplx * Ppoly = malloc((nPoles+1) * sizeof(cplx));\n \n // polynomial coefficients from poles and zeros for filtering\n poly(Z, nPoles, Zpoly);\n \n //for(int i = 0; i < nPoles+1; i++){\n // printf(\"Zpoly[%i]= %1.3f + %1.3f i\\n\", i, creal(Zpoly[i]), cimag(Zpoly[i]));\n //}\n \n poly(P, nPoles, Ppoly);\n \n //for(int i = 0; i < nPoles+1; i++){\n // printf(\"Ppoly[%i]= %1.3f + %1.3f i\\n\", i, creal(Ppoly[i]), cimag(Ppoly[i]));\n //}\n \n \n // coeffs for filtering\n double * b = malloc((nPoles+1) * sizeof(double)); // zeros\n double * a = malloc((nPoles+1) * sizeof(double)); // poles\n \n for(int i = 0; i\n\nextern void butterworthFilter(const double y[], const int size, const int nPoles, const double W, double out[]);\n\n#endif /* butterworth_h */\n" }, { "path": "C/fft.c", "content": "#include \n#include \n#include \n\n#if __cplusplus\n# include \ntypedef std::complex< double > cplx;\n#else\n# include \n#if defined(__GNUC__) || defined(__GNUG__)\ntypedef double complex cplx;\n#elif defined(_MSC_VER)\ntypedef _Dcomplex cplx;\n#endif\n#endif\n\n#ifndef CMPLX\n#define CMPLX(x, y) ((cplx)((double)(x) + _Imaginary_I * (double)(y)))\n#endif\n\n#include \"helper_functions.h\"\n\nvoid twiddles(cplx a[], int size)\n{\n\n double PI = 3.14159265359;\n\n for (int i = 0; i < size; i++) {\n // cplx tmp = { 0, -PI * i / size };\n #if defined(__GNUC__) || defined(__GNUG__)\n\t cplx tmp = 0.0 - PI * i / size * I;\n #elif defined(_MSC_VER)\n\t cplx tmp = {0.0, -PI * i / size };\n #endif\n a[i] = cexp(tmp);\n //a[i] = cexp(-I * M_PI * i / size);\n }\n}\n\nstatic void _fft(cplx a[], cplx out[], int size, int step, cplx tw[])\n{ \n if (step < size) {\n _fft(out, a, size, step * 2, tw);\n _fft(out + step, a + step, size, step * 2, tw);\n\n for (int i = 0; i < size; i += 2 * step) {\n //cplx t = tw[i] * out[i + step];\n cplx t = _Cmulcc(tw[i], out[i + step]);\n a[i / 2] = _Caddcc(out[i], t);\n a[(i + size) / 2] = _Cminuscc(out[i], t);\n }\n }\n}\n\nvoid fft(cplx a[], int size, cplx tw[])\n{\n cplx * out = malloc(size * sizeof(cplx));\n memcpy(out, a, size * sizeof(cplx));\n _fft(a, out, size, 1, tw);\n free(out);\n}\n" }, { "path": "C/fft.h", "content": "#ifndef FFT_H\n#define FFT_H\n//#include \n\n#if __cplusplus\n# include \n typedef std::complex< double > cplx;\n#else\n# include \n#if defined(__GNUC__) || defined(__GNUG__)\ntypedef double complex cplx;\n#elif defined(_MSC_VER)\ntypedef _Dcomplex cplx;\n#endif\n#endif\n\n#include \n#include \n#ifndef CMPLX\n#define CMPLX(x, y) ((cplx)((double)(x) + _Complex_I * (double)(y)))\n#endif\nextern void twiddles(cplx a[], int size);\n// extern void _fft(cplx a[], cplx out[], int size, int step, cplx tw[]);\nextern void fft(cplx a[], int size, cplx tw[]);\nextern void ifft(cplx a[], int size, cplx tw[]);\n#endif\n" }, { "path": "C/helper_functions.c", "content": "#if __cplusplus\n# include \ntypedef std::complex< double > cplx;\n#else\n# include \n#if defined(__GNUC__) || defined(__GNUG__)\ntypedef double complex cplx;\n#elif defined(_MSC_VER)\ntypedef _Dcomplex cplx;\n#endif\n#endif\n\n#include \n#include \n#include \n#include \n#include \"stats.h\"\n\n// compare function for qsort, for array of doubles\nstatic int compare (const void * a, const void * b)\n{\n if (*(double*)a < *(double*)b) {\n return -1;\n } else if (*(double*)a > *(double*)b) {\n return 1;\n } else {\n return 0;\n }\n}\n\n// wrapper for qsort for array of doubles. Sorts in-place\nvoid sort(double y[], int size)\n{\n qsort(y, size, sizeof(*y), compare);\n}\n\n// linearly spaced vector\nvoid linspace(double start, double end, int num_groups, double out[])\n{\n double step_size = (end - start) / (num_groups - 1);\n for (int i = 0; i < num_groups; i++) {\n out[i] = start;\n start += step_size;\n }\n return;\n}\n\ndouble quantile(const double y[], const int size, const double quant)\n{ \n double quant_idx, q, value;\n int idx_left, idx_right;\n double * tmp = malloc(size * sizeof(*y));\n memcpy(tmp, y, size * sizeof(*y));\n sort(tmp, size);\n \n /*\n for(int i=0; i < size; i++){\n printf(\"y[%i]=%1.4f\\n\", i, y[i]);\n }\n for(int i=0; i < size; i++){\n printf(\"sorted[%i]=%1.4f\\n\", i, tmp[i]);\n }\n */\n \n // out of range limit?\n q = 0.5 / size;\n if (quant < q) {\n value = tmp[0]; // min value\n free(tmp);\n return value; \n } else if (quant > (1 - q)) {\n value = tmp[size - 1]; // max value\n free(tmp);\n return value; \n }\n \n quant_idx = size * quant - 0.5;\n idx_left = (int)floor(quant_idx);\n idx_right = (int)ceil(quant_idx);\n value = tmp[idx_left] + (quant_idx - idx_left) * (tmp[idx_right] - tmp[idx_left]) / (idx_right - idx_left);\n free(tmp);\n return value;\n}\n\nvoid binarize(const double a[], const int size, int b[], const char how[])\n{ \n double m = 0.0;\n if (strcmp(how, \"mean\") == 0) {\n m = mean(a, size);\n } else if (strcmp(how, \"median\") == 0) {\n m = median(a, size);\n }\n for (int i = 0; i < size; i++) {\n b[i] = (a[i] > m) ? 1 : 0;\n } \n return;\n}\n\ndouble f_entropy(const double a[], const int size)\n{\n double f = 0.0;\n for (int i = 0; i < size; i++) {\n if (a[i] > 0) {\n f += a[i] * log(a[i]);\n }\n }\n return -1 * f;\n}\n\nvoid subset(const int a[], int b[], const int start, const int end)\n{\n int j = 0;\n for (int i = start; i < end; i++) {\n b[j++] = a[i];\n }\n return;\n}\n\n#if defined(__GNUC__) || defined(__GNUG__)\n cplx _Cmulcc(const cplx x, const cplx y) {\n /*double a = x._Val[0];\n double b = x._Val[1];\n\n double c = y._Val[0];\n double d = y._Val[1];\n\n cplx result = { (a * c - b * d), (a * d + c * b) };\n */\n return x*y;\n }\n\n cplx _Cminuscc(const cplx x, const cplx y) {\n //cplx result = { x._Val[0] - y._Val[0], x._Val[1] - y._Val[1] };\n return x - y;\n }\n\n cplx _Caddcc(const cplx x, const cplx y) {\n // cplx result = { x._Val[0] + y._Val[0], x._Val[1] + y._Val[1] };\n return x + y;\n }\n\n cplx _Cdivcc(const cplx x, const cplx y) {\n \n double a = creal(x);\n double b = cimag(x);\n\n double c = creal(y);\n double d = cimag(y);\n\n cplx result = (a*c + b*d) / (c*c + d*d) + (b*c - a*d)/(c*c + d*d) * I;\n \n return result;\n \n // return x / y;\n }\n\n#elif defined(_MSC_VER)\n cplx _Cminuscc(const cplx x, const cplx y) {\n cplx result = { x._Val[0] - y._Val[0], x._Val[1] - y._Val[1] };\n return result;\n }\n\n cplx _Caddcc(const cplx x, const cplx y) {\n cplx result = { x._Val[0] + y._Val[0], x._Val[1] + y._Val[1] };\n return result;\n }\n\n cplx _Cdivcc(const cplx x, const cplx y) {\n double a = x._Val[0];\n double b = x._Val[1];\n\n double c = y._Val[0];\n double d = y._Val[1];\n\n cplx result = { (a*c + b*d) / (c*c + d*d), (b*c - a*d)/(c*c + d*d)};\n\n return result;\n }\n#endif\n" }, { "path": "C/helper_functions.h", "content": "#ifndef HELPER_FUNCTIONS_H\n#define HELPER_FUNCTIONS_H\n#include \n#include \n#include \n#include \"stats.h\"\n\n#if __cplusplus\n# include \ntypedef std::complex< double > cplx;\n#else\n# include \n#if defined(__GNUC__) || defined(__GNUG__)\ntypedef double complex cplx;\n#elif defined(_MSC_VER)\ntypedef _Dcomplex cplx;\n#endif\n#endif\n\nextern void linspace(double start, double end, int num_groups, double out[]);\nextern double quantile(const double y[], const int size, const double quant);\nextern void sort(double y[], int size);\nextern void binarize(const double a[], const int size, int b[], const char how[]);\nextern double f_entropy(const double a[], const int size);\nextern void subset(const int a[], int b[], const int start, const int end);\n\nextern cplx _Cminuscc(const cplx x, const cplx y);\nextern cplx _Caddcc(const cplx x, const cplx y);\nextern cplx _Cdivcc(const cplx x, const cplx y);\n#if defined(__GNUC__) || defined(__GNUG__)\nextern cplx _Cmulcc(const cplx x, const cplx y);\n#endif\n\n#endif\n" }, { "path": "C/histcounts.c", "content": "//\n// histcounts.c\n// C_polished\n//\n// Created by Carl Henning Lubba on 19/09/2018.\n// Copyright © 2018 Carl Henning Lubba. All rights reserved.\n//\n\n#include \n#include \n\n#include \"stats.h\"\n#include \"histcounts.h\"\n\nint num_bins_auto(const double y[], const int size){\n \n double maxVal = max_(y, size);\n double minVal = min_(y, size);\n \n if (stddev(y, size) < 0.001){\n return 0;\n }\n \n return ceil((maxVal-minVal)/(3.5*stddev(y, size)/pow(size, 1/3.)));\n \n}\n\nint histcounts_preallocated(const double y[], const int size, int nBins, int * binCounts, double * binEdges)\n{\n \n int i = 0;\n \n // check min and max of input array\n double minVal = DBL_MAX, maxVal=-DBL_MAX;\n for(int i = 0; i < size; i++)\n {\n // printf(\"histcountInput %i: %1.3f\\n\", i, y[i]);\n \n if (y[i] < minVal)\n {\n minVal = y[i];\n }\n if (y[i] > maxVal)\n {\n maxVal = y[i];\n }\n }\n \n // and derive bin width from it\n double binStep = (maxVal - minVal)/nBins;\n \n // variable to store counted occurances in\n for(i = 0; i < nBins; i++)\n {\n binCounts[i] = 0;\n }\n \n for(i = 0; i < size; i++)\n {\n \n int binInd = (y[i]-minVal)/binStep;\n if(binInd < 0)\n binInd = 0;\n if(binInd >= nBins)\n binInd = nBins-1;\n //printf(\"histcounts, i=%i, binInd=%i, nBins=%i\\n\", i, binInd, nBins);\n binCounts[binInd] += 1;\n \n }\n \n for(i = 0; i < nBins+1; i++)\n {\n binEdges[i] = i * binStep + minVal;\n }\n \n /*\n // debug\n for(i=0;i maxVal)\n {\n maxVal = y[i];\n }\n }\n \n // if no number of bins given, choose spaces automatically\n if (nBins <= 0){\n nBins = ceil((maxVal-minVal)/(3.5*stddev(y, size)/pow(size, 1/3.)));\n }\n \n // and derive bin width from it\n double binStep = (maxVal - minVal)/nBins;\n \n // variable to store counted occurances in\n *binCounts = malloc(nBins * sizeof(int));\n for(i = 0; i < nBins; i++)\n {\n (*binCounts)[i] = 0;\n }\n \n for(i = 0; i < size; i++)\n {\n \n int binInd = (y[i]-minVal)/binStep;\n if(binInd < 0)\n binInd = 0;\n if(binInd >= nBins)\n binInd = nBins-1;\n (*binCounts)[binInd] += 1;\n \n }\n \n *binEdges = malloc((nBins+1) * sizeof(double));\n for(i = 0; i < nBins+1; i++)\n {\n (*binEdges)[i] = i * binStep + minVal;\n }\n \n /*\n // debug\n for(i=0;i 0\n binIdentity[i] = 0;\n \n // go through bin edges\n for(int j = 0; j < nEdges; j++){\n if(y[i] < binEdges[j]){\n binIdentity[i] = j;\n break;\n }\n }\n }\n \n return binIdentity;\n \n}\n\nint * histcount_edges(const double y[], const int size, const double binEdges[], const int nEdges)\n{\n \n \n int * histcounts = malloc(nEdges * sizeof(int));\n for(int i = 0; i < nEdges; i++){\n histcounts[i] = 0;\n }\n \n for(int i = 0; i < size; i++)\n {\n // go through bin edges\n for(int j = 0; j < nEdges; j++){\n if(y[i] <= binEdges[j]){\n histcounts[j] += 1;\n break;\n }\n }\n }\n \n return histcounts;\n \n}\n" }, { "path": "C/histcounts.h", "content": "//\n// histcounts.h\n// C_polished\n//\n// Created by Carl Henning Lubba on 19/09/2018.\n// Copyright © 2018 Carl Henning Lubba. All rights reserved.\n//\n\n#ifndef histcounts_h\n#define histcounts_h\n\n#include \n#include \n#include \n\nextern int num_bins_auto(const double y[], const int size);\nextern int histcounts(const double y[], const int size, int nBins, int ** binCounts, double ** binEdges);\nextern int histcounts_preallocated(const double y[], const int size, int nBins, int * binCounts, double * binEdges);\nextern int * histcount_edges(const double y[], const int size, const double binEdges[], const int nEdges);\nextern int * histbinassign(const double y[], const int size, const double binEdges[], const int nEdges);\n\n#endif /* histcounts_h */\n" }, { "path": "C/main.c", "content": "/* Include files */\n#include \"main.h\"\n#include \n#include \n#include \n#include \n//#include \n\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"CO_AutoCorr.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_MotifThree.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"PD_PeriodicityWang.h\"\n\n#include \"stats.h\"\n\n// check if data qualifies to be caught22\nint quality_check(const double y[], const int size)\n{\n int minSize = 10;\n\n if(size < minSize)\n {\n return 1;\n }\n for(int i = 0; i < size; i++)\n {\n double val = y[i];\n if(val == INFINITY || -val == INFINITY)\n {\n return 2;\n }\n if(isnan(val))\n {\n return 3;\n }\n }\n return 0;\n}\n\nvoid run_features(double y[], int size, FILE * outfile, bool catch24)\n{\n int quality = quality_check(y, size);\n if(quality != 0)\n {\n fprintf(stdout, \"Time series quality test not passed (code %i).\\n\", quality);\n return;\n }\n\n double * y_zscored = malloc(size * sizeof * y_zscored);\n\n // variables to keep time\n clock_t begin;\n double timeTaken;\n\n // output\n double result;\n\n // z-score first for all.\n zscore_norm2(y, size, y_zscored);\n\n // GOOD\n begin = clock();\n result = DN_OutlierInclude_n_001_mdrmd(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"DN_OutlierInclude_n_001_mdrmd\", timeTaken);\n\n // GOOD\n begin = clock();\n result = DN_OutlierInclude_p_001_mdrmd(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"DN_OutlierInclude_p_001_mdrmd\", timeTaken);\n \n // GOOD\n begin = clock();\n result = DN_HistogramMode_5(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"DN_HistogramMode_5\", timeTaken);\n\n // GOOD\n begin = clock();\n result = DN_HistogramMode_10(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"DN_HistogramMode_10\", timeTaken);\n\n //GOOD\n begin = clock();\n result = CO_Embed2_Dist_tau_d_expfit_meandiff(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"CO_Embed2_Dist_tau_d_expfit_meandiff\", timeTaken);\n\n //GOOD (memory leak?)\n begin = clock();\n result = CO_f1ecac(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"CO_f1ecac\", timeTaken);\n\n //GOOD\n begin = clock();\n result = CO_FirstMin_ac(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"CO_FirstMin_ac\", timeTaken);\n\n // GOOD (memory leak?)\n begin = clock();\n result = CO_HistogramAMI_even_2_5(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"CO_HistogramAMI_even_2_5\", timeTaken);\n\n // GOOD\n begin = clock();\n result = CO_trev_1_num(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"CO_trev_1_num\", timeTaken);\n\n //GOOD\n begin = clock();\n result = FC_LocalSimple_mean1_tauresrat(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"FC_LocalSimple_mean1_tauresrat\", timeTaken);\n\n //GOOD\n begin = clock();\n result = FC_LocalSimple_mean3_stderr(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"FC_LocalSimple_mean3_stderr\", timeTaken);\n\n //GOOD (memory leak?)\n begin = clock();\n result = IN_AutoMutualInfoStats_40_gaussian_fmmi(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"IN_AutoMutualInfoStats_40_gaussian_fmmi\", timeTaken);\n\n //GOOD\n begin = clock();\n result = MD_hrv_classic_pnn40(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"MD_hrv_classic_pnn40\", timeTaken);\n\n //GOOD\n begin = clock();\n result = SB_BinaryStats_diff_longstretch0(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"SB_BinaryStats_diff_longstretch0\", timeTaken);\n\n //GOOD\n begin = clock();\n result = SB_BinaryStats_mean_longstretch1(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"SB_BinaryStats_mean_longstretch1\", timeTaken);\n\n //GOOD (memory leak?)\n begin = clock();\n result = SB_MotifThree_quantile_hh(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"SB_MotifThree_quantile_hh\", timeTaken);\n\n //GOOD (memory leak?)\n begin = clock();\n result = SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1\", timeTaken);\n\n //GOOD\n begin = clock();\n result = SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1\", timeTaken);\n\n //GOOD\n begin = clock();\n result = SP_Summaries_welch_rect_area_5_1(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"SP_Summaries_welch_rect_area_5_1\", timeTaken);\n\n //GOOD\n begin = clock();\n result = SP_Summaries_welch_rect_centroid(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"SP_Summaries_welch_rect_centroid\", timeTaken);\n\n //OK, BUT filt in Butterworth sometimes diverges, now removed alltogether, let's see results.\n begin = clock();\n result = SB_TransitionMatrix_3ac_sumdiagcov(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"SB_TransitionMatrix_3ac_sumdiagcov\", timeTaken);\n\n // GOOD\n begin = clock();\n result = PD_PeriodicityWang_th0_01(y_zscored, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"PD_PeriodicityWang_th0_01\", timeTaken);\n\n if (catch24) {\n \n // GOOD\n begin = clock();\n result = DN_Mean(y, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"DN_Mean\", timeTaken);\n\n // GOOD\n begin = clock();\n result = DN_Spread_Std(y, size);\n timeTaken = (double)(clock()-begin)*1000/CLOCKS_PER_SEC;\n fprintf(outfile, \"%.14f, %s, %f\\n\", result, \"DN_Spread_Std\", timeTaken);\n } else {\n\n }\n \n fprintf(outfile, \"\\n\");\n\n free(y_zscored);\n}\n\nvoid print_help(char *argv[], char msg[])\n{\n if (strlen(msg) > 0) {\n fprintf(stdout, \"ERROR: %s\\n\", msg);\n }\n fprintf(stdout, \"Usage is %s \\n\", argv[0]);\n fprintf(stdout, \"\\n\\tSpecifying outfile is optional, by default it is stdout\\n\");\n // fprintf(stdout, \"\\tOutput order is:\\n%s\\n\", HEADER);\n exit(1);\n}\n\n// memory leak check; use with valgrind.\n#if 0\nint main(int argc, char * argv[])\n{\n double * y = malloc(1000 * sizeof(double));\n\n srand(42);\n for (int i = 0; i < 1000; ++i) {\n y[i] = rand() % RAND_MAX;\n }\n run_features(y, 1000, stdout);\n free(y);\n}\n#endif\n\n#if 1\nint main(int argc, char * argv[])\n{\n FILE * infile, * outfile;\n int array_size;\n double * y;\n int size;\n double value;\n // DIR *d;\n struct dirent *dir;\n\n\n switch (argc) {\n case 1:\n print_help(argv, \"\");\n break;\n case 2:\n if ((infile = fopen(argv[1], \"r\")) == NULL) {\n print_help(argv, \"Can't open input file\\n\");\n }\n outfile = stdout;\n break;\n case 3:\n if ((infile = fopen(argv[1], \"r\")) == NULL) {\n print_help(argv, \"Can't open input file\\n\");\n }\n if ((outfile = fopen(argv[2], \"w\")) == NULL) {\n print_help(argv, \"Can't open output file\\n\");\n }\n break;\n }\n\n /*\n // debug: fix these.\n infile = fopen(\"/Users/carl/PycharmProjects/catch22/C/timeSeries/tsid0244.txt\", \"r\");\n outfile = stdout;\n */\n\n // fprintf(outfile, \"%s\", HEADER);\n array_size = 50;\n size = 0;\n y = malloc(array_size * sizeof *y);\n\n while (fscanf(infile, \"%lf\", &value) != EOF) {\n if (size == array_size) {\n y = realloc(y, 2 * array_size * sizeof *y);\n array_size *= 2;\n }\n y[size++] = value;\n }\n fclose(infile);\n y = realloc(y, size * sizeof *y);\n //printf(\"size=%i\\n\", size);\n\n // catch24 specification\n\n int catch24;\n printf(\"Do you want to run catch24? Enter 0 for catch22 or 1 for catch24.\");\n scanf(\"%d\", &catch24);\n\n if (catch24 == 1) {\n run_features(y, size, outfile, true);\n } else {\n run_features(y, size, outfile, false);\n }\n\n fclose(outfile);\n free(y);\n\n return 0;\n}\n#endif\n\n#if 0\nint main(int argc, char * argv[])\n{\n (void)argc;\n (void)argv;\n\n /*\n // generate some data\n const int size = 31; // 211;\n\n double y[size];\n int i;\n double sinIn=0;\n for(i=0; i\n#include \n#include \n#include \n\n/* Function Declarations */\n//extern int main(int argc, const char * const argv[]);\nextern int main(int argc, char * argv[]);\n\n#endif\n\n/* End of code generation (main.h) */\n" }, { "path": "C/runAllTS.sh", "content": "#!/bin/bash\n\nhelp()\n{\n echo \"\"\n echo \"Usage: $0 -i indir -o outdir -a append_string -s\"\n echo -e \"\\t-h Show this help message\"\n echo -e \"\\t-i Path to a directory containing input time-series files (.txt with one time series value per line). Default: './timeSeries'\"\n echo -e \"\\t-o Path to a directory in which to save output feature values. Default: './featureOutput'\"\n echo -e \"\\t-a A string (minus extension) appended to the input file names to create the output file names. Default: 'output'\"\n echo -e \"\\t-s A switch to evaluate catch22 (0) or catch24 (1). Default: 0\"\n exit 1\n}\n\nwhile getopts \"i:o:a:s:h\" opt\ndo\n case \"$opt\" in\n i) indir=\"$OPTARG\" ;;\n o) outdir=\"$OPTARG\" ;;\n a) append=\"$OPTARG\" ;;\n s) catch24=\"$OPTARG\" ;;\n h) help ;;\n esac\ndone\n\nsrcdir=$(dirname \"$0}\")\n\nif [ -z \"$indir\" ]\nthen\n indir=\"./timeSeries\"\nfi\n\nif [ -z \"$outdir\" ]\nthen\n outdir=\"./featureOutput\"\nfi\n\nif [ -z \"$append\" ]\nthen\n append=\"output\"\nfi\n\nif [ -z \"$catch24\" ]\nthen\n catch24=0\nfi\n\nindir=\"$(dirname $indir)/$(basename $indir)\"\noutdir=\"$(dirname $outdir)/$(basename $outdir)\"\nmkdir -p $outdir\n\n# Loop through each file in indir and save the feature outputs\nfor entry in \"${indir}\"/*.txt\ndo\n filename=$(basename \"$entry\")\n extension=\"${filename##*.}\"\n filename=\"${filename%.*}\"\n fullfile=\"${outdir}/${filename}${append}.${extension}\"\n if [ \"${filename: -${#append}}\" != \"${append}\" ]\n then\n yes $catch24 | \"${srcdir}/run_features\" $entry $fullfile > /dev/null\n\n if [ -s $fullfile ] # Remove file if catch22 errors\n then\n echo \"Output written to ${fullfile}\"\n else\n rm $fullfile\n fi\n\n fi\ndone\n" }, { "path": "C/splinefit.c", "content": "// Created by Carl Henning Lubba on 27/09/2018.\n// Copyright © 2018 Carl Henning Lubba. All rights reserved.\n//\n// Based on the work of Jonas Lundgren in his Matlab Central contribution 'SPLINEFIT'.\n//\n#include \n#include \n#include \n\n#include \"splinefit.h\"\n#include \"stats.h\"\n\n#define nCoeffs 3\n#define nPoints 4\n\n#define pieces 2\n#define nBreaks 3\n#define deg 3\n#define nSpline 4\n#define piecesExt 8 //3 * deg - 1\n\n\nvoid matrix_multiply(const int sizeA1, const int sizeA2, const double *A, const int sizeB1, const int sizeB2, const double *B, double *C){\n//void matrix_multiply(int sizeA1, int sizeA2, double **A, int sizeB1, int sizeB2, double **B, double C[sizeA1][sizeB2]){\n \n if(sizeA2 != sizeB1){\n return;\n }\n \n /*\n // show input\n for(int i = 0; i < sizeA1; i++){\n for(int j = 0; j < sizeA2; j++){\n printf(\"A[%i][%i] = %1.3f\\n\", i, j, A[i*sizeA2 + j]);\n }\n }\n */\n \n for(int i = 0; i < sizeA1; i++){\n for(int j = 0; j < sizeB2; j++){\n \n //C[i][j] = 0;\n C[i*sizeB2 + j] = 0;\n for(int k = 0; k < sizeB1; k++){\n // C[i][j] += A[i][k]*B[k][j];\n C[i*sizeB2 + j] += A[i * sizeA2 + k]*B[k * sizeB2 + j];\n //printf(\"C[%i][%i] (k=%i) = %1.3f\\n\", i, j, k, C[i * sizeB2 + j]);\n }\n \n }\n }\n \n}\n\nvoid matrix_times_vector(const int sizeA1, const int sizeA2, const double *A, const int sizeb, const double *b, double *c){ //c[sizeb]\n \n if(sizeA2 != sizeb){\n return;\n }\n \n // row\n for(int i = 0; i < sizeA1; i++){\n \n // column\n c[i] = 0;\n for(int k = 0; k < sizeb; k++){\n c[i] += A[i * sizeA2 + k]*b[k];\n }\n \n }\n \n}\n\nvoid gauss_elimination(int size, double *A, double *b, double *x){\n// void gauss_elimination(int size, double A[size][size], double b[size], double x[size]){\n \n double factor;\n \n // create temp matrix and vector\n // double *AElim[size];\n double* AElim[nSpline + 1];\n for (int i = 0; i < size; i++)\n AElim[i] = (double *)malloc(size * sizeof(double));\n double * bElim = malloc(size * sizeof(double));\n \n // -- create triangular matrix\n \n // initialise to A and b\n for(int i = 0; i < size; i++){\n for(int j = 0; j < size; j++){\n AElim[i][j] = A[i*size + j];\n }\n bElim[i] = b[i];\n }\n \n /*\n printf(\"AElim\\n\");\n for(int i = 0; i < size; i++){\n for(int j = 0; j < size; j++){\n printf(\"%1.3f, \", AElim[i][j]);\n }\n printf(\"\\n\");\n }\n */\n \n // go through columns in outer loop\n for(int i = 0; i < size; i++){\n \n // go through rows to eliminate\n for(int j = i+1; j < size; j++){\n \n factor = AElim[j][i]/AElim[i][i];\n \n // subtract in vector\n bElim[j] = bElim[j] - factor*bElim[i];\n \n // go through entries of this row\n for(int k = i; k < size; k++){\n AElim[j][k] = AElim[j][k] - factor*AElim[i][k];\n }\n \n /*\n printf(\"AElim i=%i, j=%i\\n\", i, j);\n for(int i = 0; i < size; i++){\n for(int j = 0; j < size; j++){\n printf(\"%1.3f, \", AElim[i][j]);\n }\n printf(\"\\n\");\n }\n */\n \n }\n \n }\n \n /*\n for(int i = 0; i < size; i++){\n for(int j = 0; j < size; j++){\n printf(\"AElim[%i][%i] = %1.3f\\n\", i, j, AElim[i][j]);\n }\n }\n for(int i = 0; i < size; i++){\n printf(\"bElim[%i] = %1.3f\\n\", i, bElim[i]);\n }\n */\n \n \n // -- go backwards through triangular matrix and solve for x\n \n // row\n double bMinusATemp;\n for(int i = size-1; i >= 0; i--){\n \n bMinusATemp = bElim[i];\n for(int j = i+1; j < size; j++){\n bMinusATemp -= x[j]*AElim[i][j];\n }\n \n x[i] = bMinusATemp/AElim[i][i];\n }\n /*\n for(int j = 0; j < size; j++){\n printf(\"x[%i] = %1.3f\\n\", j, x[j]);\n }\n */\n \n for (int i = 0; i < size; i++)\n free(AElim[i]);\n free(bElim);\n}\n\nvoid lsqsolve_sub(const int sizeA1, const int sizeA2, const double *A, const int sizeb, const double *b, double *x)\n//void lsqsolve_sub(int sizeA1, int sizeA2, double A[sizeA1][sizeA2], int sizeb, double b[sizeb], double x[sizeA1])\n{\n // create temp matrix and vector\n /*\n double *AT[sizeA1*sizeA2];\n for (int i = 0; i < sizeA2; i++)\n AT[i] = (double *)malloc(sizeA1 * sizeof(double));\n double *ATA[sizeA2];\n for (int i = 0; i < sizeA2; i++)\n ATA[i] = (double *)malloc(sizeA2 * sizeof(double));\n double * ATb = malloc(sizeA1 * sizeof(double));\n */\n \n double * AT = malloc(sizeA2 * sizeA1 * sizeof(double));\n double * ATA = malloc(sizeA2 * sizeA2 * sizeof(double));\n double * ATb = malloc(sizeA2 * sizeof(double));\n \n \n for(int i = 0; i < sizeA1; i++){\n for(int j = 0; j < sizeA2; j++){\n //AT[i,j] = A[j,i]\n AT[j * sizeA1 + i] = A[i * sizeA2 + j];\n }\n }\n \n /*\n printf(\"\\n b \\n\");\n for(int i = 0; i < sizeA1; i++){\n printf(\"%i, %1.3f\\n\", i, b[i]);\n }\n */\n \n /*\n printf(\"\\nA\\n\");\n for(int i = 0; i < sizeA2; i++){\n for(int j = 0; j < sizeA1; j++){\n printf(\"%1.3f, \", AT[i * sizeA1 + j]);\n }\n printf(\"\\n\");\n }\n */\n \n \n matrix_multiply(sizeA2, sizeA1, AT, sizeA1, sizeA2, A, ATA);\n \n /*\n printf(\"ATA\\n\");\n for(int i = 0; i < sizeA2; i++){\n for(int j = 0; j < sizeA2; j++){\n printf(\"%1.3f, \", ATA[i * sizeA2 + j]);\n }\n printf(\"\\n\");\n }\n */\n \n \n \n matrix_times_vector(sizeA2, sizeA1, AT, sizeA1, b, ATb);\n \n /*\n for(int i = 0; i < sizeA2; i++){\n ATb[i] = 0;\n for(int j = 0; j < sizeA1; j++){\n ATb[i] += AT[i*sizeA1 + j]*b[j];\n //printf(\"%i, ATb[%i]=%1.3f, AT[i*sizeA1 + j]=%1.3f, b[j]=%1.3f\\n\", i, i, ATb[i], AT[i*sizeA1 + j],b[j]);\n }\n }\n */\n \n /*\n for(int i = 0; i < nCoeffs; i++){\n printf(\"b[%i] = %1.3f\\n\", i, b[i]);\n }\n */\n \n /*\n for(int i = 0; i < sizeA2; i++){\n printf(\"ATb[%i] = %1.3f\\n\", i, ATb[i]);\n }\n */\n \n \n gauss_elimination(sizeA2, ATA, ATb, x);\n \n free(AT);\n free(ATA);\n free(ATb);\n \n}\n\n/*\nint lsqsolve()\n{\n //const int nPoints = 4;\n //const int nCoeffs = 3;\n \n //double A[nPoints][nCoeffs] = {};\n\tdouble A[4][3];\n A[0][0] = 1;\n A[1][0] = 3;\n A[2][0] = 6;\n A[3][0] = 8;\n A[0][1] = 4;\n A[1][1] = 5;\n A[2][1] = 3;\n A[3][1] = 12;\n A[0][2] = 4;\n A[1][2] = 1;\n A[2][2] = 0;\n A[3][2] = 7;\n //double b[nPoints] = {};\n\tdouble b[4];\n b[0] = 2;\n b[1] = 8;\n b[2] = 3;\n b[3] = 1;\n \n double x[4];\n \n double * Alin = malloc(nPoints * nCoeffs * sizeof(double));\n \n for(int i = 0; i < nPoints; i++){\n for(int j = 0; j < nCoeffs; j++){\n //AT[i,j] = A[j,i]\n Alin[i * nCoeffs + j] = A[i][j];\n }\n }\n \n lsqsolve_sub(nPoints, nCoeffs, Alin, nPoints, b, x);\n \n free(Alin);\n \n return 0;\n \n \n}\n*/\n\nint iLimit(int x, int lim){\n return x= breaks[breakInd] & breakInd= breaks[1])\n breakInd = 1;\n A[(i%nSpline)+breakInd + (i/nSpline)*(nSpline+1)] = vB[i];\n }\n \n /*\n printf(\"\\nA:\\n\");\n for(int i = 0; i < size; i++){\n for(int j = 0; j < n+1; j++){\n printf(\"%1.5f, \", A[i * (n+1) + j]);\n }\n printf(\"\\n\");\n }\n */\n \n \n \n double * x = malloc((nSpline+1)*sizeof(double));\n // lsqsolve_sub(int sizeA1, int sizeA2, double *A, int sizeb, double *b, double *x)\n lsqsolve_sub(size, nSpline+1, A, size, y, x);\n \n /*\n printf(\"\\nsolved x\\n\");\n for(int i = 0; i < n+1; i++){\n printf(\"%i, %1.4f\\n\", i, x[i]);\n }\n */\n \n // coeffs of B-splines to combine by optimised weighting in x\n double C[pieces+nSpline-1][nSpline*pieces];\n // initialise to 0\n for(int i = 0; i < nSpline+1; i++){\n for(int j = 0; j < nSpline*pieces; j++){\n C[i][j] = 0;\n }\n }\n \n int CRow, CCol, coefRow, coefCol;\n for(int i = 0; i < nSpline*nSpline*pieces; i++){\n \n CRow = i%nSpline + (i/nSpline)%2;\n CCol = i/nSpline;\n \n coefRow = i%(nSpline*2);\n coefCol =i/(nSpline*2);\n \n C[CRow][CCol] = coefsOut[coefRow][coefCol];\n \n }\n \n /*\n printf(\"\\nC:\\n\");\n for(int i = 0; i < n+1; i++){\n for(int j = 0; j < n*pieces; j++){\n printf(\"%1.5f, \", C[i][j]);\n }\n printf(\"\\n\");\n }\n */\n \n // final coefficients\n double coefsSpline[pieces][nSpline];\n for(int i = 0; i < pieces; i++){\n for(int j = 0; j < nSpline; j++){\n coefsSpline[i][j] = 0;\n }\n }\n \n //multiply with x\n for(int j = 0; j < nSpline*pieces; j++){\n coefCol = j/pieces;\n coefRow = j%pieces;\n \n for(int i = 0; i < nSpline+1; i++){\n \n coefsSpline[coefRow][coefCol] += C[i][j]*x[i];\n \n }\n }\n \n /*\n printf(\"\\ncoefsSpline:\\n\");\n for(int i = 0; i < pieces; i++){\n for(int j = 0; j < n; j++){\n printf(\"%1.5f, \", coefsSpline[i][j]);\n }\n printf(\"\\n\");\n }\n */\n \n \n // compute piecewise polynomial\n \n int secondHalf = 0;\n for(int i = 0; i < size; i++){\n secondHalf = i < breaks[1] ? 0 : 1;\n yOut[i] = coefsSpline[secondHalf][0];\n }\n \n /*\n printf(\"\\nvSpline first iter\\n\");\n for(int i = 0; i < size; i++){\n printf(\"%i, %1.5f\\n\", i, vSpline[i]);\n }\n */\n \n for(int i = 1; i < nSpline; i ++){\n for(int j = 0; j < size; j++){\n secondHalf = j < breaks[1] ? 0 : 1;\n yOut[j] = yOut[j]*(j - breaks[1]*secondHalf) + coefsSpline[secondHalf][i];\n }\n \n /*\n printf(\"\\nvSpline %i th iter\\n\", i);\n for(int i = 0; i < size; i++){\n printf(\"%i, %1.4f\\n\", i, vSpline[i]);\n }\n */\n }\n \n /*\n printf(\"\\nvSpline\\n\");\n for(int i = 0; i < size; i++){\n printf(\"%i, %1.4f\\n\", i, yOut[i]);\n }\n */\n \n free(xsB);\n free(indexB);\n free(vB);\n free(A);\n free(x);\n \n return 0;\n \n}\n\n\n" }, { "path": "C/splinefit.h", "content": "//\n// splinefit.h\n// C_polished\n//\n// Created by Carl Henning Lubba on 27/09/2018.\n// Copyright © 2018 Carl Henning Lubba. All rights reserved.\n//\n\n#ifndef splinefit_h\n#define splinefit_h\n\n#include \n\nextern int splinefit(const double *y, const int size, double *yOut);\n\n#endif /* splinefit_h */\n" }, { "path": "C/stats.c", "content": "#include \n#include \n#include \n#include \n#include \"helper_functions.h\"\n\ndouble min_(const double a[], const int size)\n{\n double m = a[0];\n for (int i = 1; i < size; i++) {\n if (a[i] < m) {\n m = a[i];\n }\n }\n return m;\n}\n\ndouble max_(const double a[], const int size)\n{\n double m = a[0];\n for (int i = 1; i < size; i++) {\n if (a[i] > m) {\n m = a[i];\n }\n }\n return m;\n}\n\ndouble mean(const double a[], const int size)\n{\n double m = 0.0;\n for (int i = 0; i < size; i++) {\n m += a[i];\n }\n m /= size;\n return m;\n}\n\ndouble sum(const double a[], const int size)\n{\n double m = 0.0;\n for (int i = 0; i < size; i++) {\n m += a[i];\n }\n return m;\n}\n\nvoid cumsum(const double a[], const int size, double b[])\n{\n b[0] = a[0];\n for (int i = 1; i < size; i++) {\n b[i] = a[i] + b[i-1];\n //printf(\"b[%i]%1.3f = a[%i]%1.3f + b[%i-1]%1.3f\\n\", i, b[i], i, a[i], i, a[i-1]);\n }\n \n}\n\nvoid icumsum(const int a[], const int size, int b[])\n{\n b[0] = a[0];\n for (int i = 1; i < size; i++) {\n b[i] = a[i] + b[i-1];\n //printf(\"b[%i]%1.3f = a[%i]%1.3f + b[%i-1]%1.3f\\n\", i, b[i], i, a[i], i, a[i-1]);\n }\n \n}\n\ndouble isum(const int a[], const int size)\n{\n double m = 0.0;\n for (int i = 0; i < size; i++) {\n m += a[i];\n }\n return m;\n}\n\ndouble median(const double a[], const int size)\n{\n double m;\n double * b = malloc(size * sizeof *b);\n memcpy(b, a, size * sizeof *b);\n sort(b, size);\n if (size % 2 == 1) {\n m = b[size / 2];\n } else {\n int m1 = size / 2;\n int m2 = m1 - 1;\n m = (b[m1] + b[m2]) / (double)2.0;\n }\n free(b);\n return m;\n}\n\ndouble stddev(const double a[], const int size)\n{\n double m = mean(a, size);\n double sd = 0.0;\n for (int i = 0; i < size; i++) {\n sd += pow(a[i] - m, 2);\n }\n sd = sqrt(sd / (size - 1));\n return sd;\n}\n\ndouble cov(const double x[], const double y[], const int size){\n \n double covariance = 0;\n \n double meanX = mean(x, size);\n double meanY = mean(y, size);\n \n for(int i = 0; i < size; i++){\n // double xi =x[i];\n // double yi =y[i];\n covariance += (x[i] - meanX) * (y[i] - meanY);\n \n }\n \n return covariance/(size-1);\n \n}\n\ndouble cov_mean(const double x[], const double y[], const int size){\n \n double covariance = 0;\n \n for(int i = 0; i < size; i++){\n // double xi =x[i];\n // double yi =y[i];\n covariance += x[i] * y[i];\n \n }\n \n return covariance/size;\n \n}\n\ndouble corr(const double x[], const double y[], const int size){\n \n double nom = 0;\n double denomX = 0;\n double denomY = 0;\n \n double meanX = mean(x, size);\n double meanY = mean(y, size);\n \n for(int i = 0; i < size; i++){\n nom += (x[i] - meanX) * (y[i] - meanY);\n denomX += (x[i] - meanX) * (x[i] - meanX);\n denomY += (y[i] - meanY) * (y[i] - meanY);\n \n //printf(\"x[%i]=%1.3f, y[%i]=%1.3f, nom[%i]=%1.3f, denomX[%i]=%1.3f, denomY[%i]=%1.3f\\n\", i, x[i], i, y[i], i, nom, i, denomX, i, denomY);\n }\n \n return nom/sqrt(denomX * denomY);\n \n}\n\ndouble autocorr_lag(const double x[], const int size, const int lag){\n \n return corr(x, &(x[lag]), size-lag);\n \n}\n\ndouble autocov_lag(const double x[], const int size, const int lag){\n \n return cov_mean(x, &(x[lag]), size-lag);\n \n}\n\nvoid zscore_norm(double a[], int size)\n{\n double m = mean(a, size);\n double sd = stddev(a, size);\n for (int i = 0; i < size; i++) {\n a[i] = (a[i] - m) / sd;\n }\n return;\n}\n\nvoid zscore_norm2(const double a[], const int size, double b[])\n{\n double m = mean(a, size);\n double sd = stddev(a, size);\n for (int i = 0; i < size; i++) {\n b[i] = (a[i] - m) / sd;\n }\n return;\n}\n\ndouble moment(const double a[], const int size, const int start, const int end, const int r)\n{\n int win_size = end - start + 1;\n a += start;\n double m = mean(a, win_size);\n double mr = 0.0;\n for (int i = 0; i < win_size; i++) {\n mr += pow(a[i] - m, r);\n }\n mr /= win_size;\n mr /= stddev(a, win_size); //normalize\n return mr;\n}\n\nvoid diff(const double a[], const int size, double b[])\n{\n for (int i = 1; i < size; i++) {\n b[i - 1] = a[i] - a[i - 1];\n }\n}\n\nint linreg(const int n, const double x[], const double y[], double* m, double* b) //, double* r)\n{\n double sumx = 0.0; /* sum of x */\n double sumx2 = 0.0; /* sum of x**2 */\n double sumxy = 0.0; /* sum of x * y */\n double sumy = 0.0; /* sum of y */\n double sumy2 = 0.0; /* sum of y**2 */\n \n /*\n for (int i = 0; i < n; i++)\n {\n fprintf(stdout, \"x[%i] = %f, y[%i] = %f\\n\", i, x[i], i, y[i]);\n }\n */\n \n for (int i=0;i\n#include \n#include \n\nextern double max_(const double a[], const int size);\nextern double min_(const double a[], const int size);\nextern double mean(const double a[], const int size);\nextern double sum(const double a[], const int size);\nextern void cumsum(const double a[], const int size, double b[]);\nextern void icumsum(const int a[], const int size, int b[]);\nextern double isum(const int a[], const int size);\nextern double median(const double a[], const int size);\nextern double stddev(const double a[], const int size);\nextern double corr(const double x[], const double y[], const int size);\nextern double cov(const double x[], const double y[], const int size);\nextern double cov_mean(const double x[], const double y[], const int size);\nextern double autocorr_lag(const double x[], const int size, const int lag);\nextern double autocov_lag(const double x[], const int size, const int lag);\nextern void zscore_norm(double a[], int size);\nextern void zscore_norm2(const double a[], const int size, double b[]);\nextern double moment(const double a[], const int size, const int start, const int end, const int r);\nextern void diff(const double a[], const int size, double b[]);\nextern int linreg(const int n, const double x[], const double y[], double* m, double* b); //, double* r);\nextern double norm_(const double a[], const int size);\n\n#endif\n" }, { "path": "LICENSE", "content": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free Software Foundation, Inc. \n Everyone is permitted to copy and distribute verbatim copies\n of this license document, but changing it is not allowed.\n\n Preamble\n\n The GNU General Public License is a free, copyleft license for\nsoftware and other kinds of works.\n\n The licenses for most software and other practical works are designed\nto take away your freedom to share and change the works. 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No Surrender of Others' Freedom.\n\n If conditions are imposed on you (whether by court order, agreement or\notherwise) that contradict the conditions of this License, they do not\nexcuse you from the conditions of this License. If you cannot convey a\ncovered work so as to satisfy simultaneously your obligations under this\nLicense and any other pertinent obligations, then as a consequence you may\nnot convey it at all. For example, if you agree to terms that obligate you\nto collect a royalty for further conveying from those to whom you convey\nthe Program, the only way you could satisfy both those terms and this\nLicense would be to refrain entirely from conveying the Program.\n\n 13. Use with the GNU Affero General Public License.\n\n Notwithstanding any other provision of this License, you have\npermission to link or combine any covered work with a work licensed\nunder version 3 of the GNU Affero General Public License into a single\ncombined work, and to convey the resulting work. The terms of this\nLicense will continue to apply to the part which is the covered work,\nbut the special requirements of the GNU Affero General Public License,\nsection 13, concerning interaction through a network will apply to the\ncombination as such.\n\n 14. Revised Versions of this License.\n\n The Free Software Foundation may publish revised and/or new versions of\nthe GNU General Public License from time to time. Such new versions will\nbe similar in spirit to the present version, but may differ in detail to\naddress new problems or concerns.\n\n Each version is given a distinguishing version number. If the\nProgram specifies that a certain numbered version of the GNU General\nPublic License \"or any later version\" applies to it, you have the\noption of following the terms and conditions either of that numbered\nversion or of any later version published by the Free Software\nFoundation. If the Program does not specify a version number of the\nGNU General Public License, you may choose any version ever published\nby the Free Software Foundation.\n\n If the Program specifies that a proxy can decide which future\nversions of the GNU General Public License can be used, that proxy's\npublic statement of acceptance of a version permanently authorizes you\nto choose that version for the Program.\n\n Later license versions may give you additional or different\npermissions. However, no additional obligations are imposed on any\nauthor or copyright holder as a result of your choosing to follow a\nlater version.\n\n 15. Disclaimer of Warranty.\n\n THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY\nAPPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT\nHOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM \"AS IS\" WITHOUT WARRANTY\nOF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,\nTHE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR\nPURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM\nIS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF\nALL NECESSARY SERVICING, REPAIR OR CORRECTION.\n\n 16. Limitation of Liability.\n\n IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING\nWILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS\nTHE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY\nGENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE\nUSE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF\nDATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD\nPARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),\nEVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF\nSUCH DAMAGES.\n\n 17. Interpretation of Sections 15 and 16.\n\n If the disclaimer of warranty and limitation of liability provided\nabove cannot be given local legal effect according to their terms,\nreviewing courts shall apply local law that most closely approximates\nan absolute waiver of all civil liability in connection with the\nProgram, unless a warranty or assumption of liability accompanies a\ncopy of the Program in return for a fee.\n\n END OF TERMS AND CONDITIONS\n\n How to Apply These Terms to Your New Programs\n\n If you develop a new program, and you want it to be of the greatest\npossible use to the public, the best way to achieve this is to make it\nfree software which everyone can redistribute and change under these terms.\n\n To do so, attach the following notices to the program. It is safest\nto attach them to the start of each source file to most effectively\nstate the exclusion of warranty; and each file should have at least\nthe \"copyright\" line and a pointer to where the full notice is found.\n\n \n Copyright (C) \n\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program. If not, see .\n\nAlso add information on how to contact you by electronic and paper mail.\n\n If the program does terminal interaction, make it output a short\nnotice like this when it starts in an interactive mode:\n\n Copyright (C) \n This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.\n This is free software, and you are welcome to redistribute it\n under certain conditions; type `show c' for details.\n\nThe hypothetical commands `show w' and `show c' should show the appropriate\nparts of the General Public License. Of course, your program's commands\nmight be different; for a GUI interface, you would use an \"about box\".\n\n You should also get your employer (if you work as a programmer) or school,\nif any, to sign a \"copyright disclaimer\" for the program, if necessary.\nFor more information on this, and how to apply and follow the GNU GPL, see\n.\n\n The GNU General Public License does not permit incorporating your program\ninto proprietary programs. If your program is a subroutine library, you\nmay consider it more useful to permit linking proprietary applications with\nthe library. If this is what you want to do, use the GNU Lesser General\nPublic License instead of this License. But first, please read\n.\n" }, { "path": "Matlab/BasisOperations/BF_Binarize.m", "content": "function yBin = BF_Binarize(y,binarizeHow)\n% BF_Binarize Converts an input vector into a binarized version\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n%-------------------------------------------------------------------------------\n% Check inputs, set defaults:\n%-------------------------------------------------------------------------------\nif nargin < 2 || isempty(binarizeHow)\n binarizeHow = 'diff';\nend\n\n%-------------------------------------------------------------------------------\n% function to transform real values to 0 if <=0 and 1 if >0:\n%-------------------------------------------------------------------------------\n\nfunction Y = stepBinary(X)\n\n Y = zeros(size(X),'like',X);\n Y(X > 0) = 1;\n \nend\n\n%-------------------------------------------------------------------------------\n% Do the binary transformation:\n%-------------------------------------------------------------------------------\n\nswitch binarizeHow\n case 'diff'\n % Binary signal: 1 for stepwise increases, 0 for stepwise decreases\n yBin = stepBinary(diff(y));\n\n case 'mean'\n % Binary signal: 1 for above mean, 0 for below mean\n yBin = stepBinary(y - mean(y));\n\n case 'median'\n % Binary signal: 1 for above median, 0 for below median\n yBin = stepBinary(y - median(y));\n\n case 'iqr'\n % Binary signal: 1 if inside interquartile range, 0 otherwise\n iqr = quantile(y,[0.25, 0.75]);\n iniqr = (y > iqr(1) & y <= iqr(2));\n yBin = zeros(length(y),1);\n yBin(iniqr) = 1;\n\n otherwise\n error('Unknown binary transformation setting ''%s''',binarizeHow)\nend\n\nend\n" }, { "path": "Matlab/BasisOperations/CO_AutoCorr.m", "content": "function out = CO_AutoCorr(y,tau,whatMethod)\n% CO_AutoCorr Compute the autocorrelation of an input time series\n%\n%---INPUTS:\n% y, a scalar time series column vector.\n%\n% tau, the time-delay. If tau is a scalar, returns autocorrelation for y at that\n% lag. If tau is a vector, returns autocorrelations for y at that set of\n% lags. Can set tau empty, [], to return the full function for the\n% 'Fourier' estimation method.\n%\n% whatMethod, the method of computing the autocorrelation: 'Fourier',\n% 'TimeDomainStat', or 'TimeDomain'.\n%\n%---OUTPUT: the autocorrelation at the given time-lag.\n%\n%---NOTES:\n% Specifying whatMethod = 'TimeDomain' can tolerate NaN values in the time\n% series.\n%\n% Computing mean/std across the full time series makes a significant difference\n% for short time series, but can produce values outside [-1,+1]. The\n% filtering-based method used by Matlab's autocorr, is probably the best for\n% short time series, and is implemented here by specifying: whatMethod =\n% 'Fourier'.\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% ------------------------------------------------------------------------------\n% Check inputs:\n% ------------------------------------------------------------------------------\nif nargin < 2\n tau = 1; % Use a lag of 1 by default\nend\n\nif nargin < 3 || isempty(whatMethod)\n whatMethod = 'Fourier';\nend\n\n% ------------------------------------------------------------------------------\n% Evaluate the time-series autocorrelation\n% ------------------------------------------------------------------------------\n\nswitch whatMethod\ncase 'Fourier'\n % ------------------------------------------------------------------------------\n % Estimation based on Matlab function autocorr, based on method of:\n % [1] Box, G. E. P., G. M. Jenkins, and G. C. Reinsel. Time Series\n % Analysis: Forecasting and Control. 3rd edition. Upper Saddle River,\n % NJ: Prentice-Hall, 1994.\n\n nFFT = 2^(nextpow2(length(y))+1);\n F = fft(y-mean(y),nFFT);\n F = F.*conj(F);\n acf = ifft(F);\n acf = acf./acf(1); % Normalize\n acf = real(acf);\n\n acf = acf(1:length(y));\n\n if isempty(tau) % return the full function\n out = acf;\n else % return a specific set of values\n out = zeros(length(tau),1);\n for i = 1:length(tau)\n if (tau(i) > length(acf)-1) || (tau(i) < 0)\n out(i) = NaN;\n else\n out(i) = acf(tau(i)+1);\n end\n end\n end\n\n\n\nend\n\nend\n" }, { "path": "Matlab/BasisOperations/CO_FirstZero.m", "content": "function out = CO_FirstZero(y,corrFun,maxTau)\n% CO_FirstZero The first zero-crossing of a given autocorrelation function\n%\n%---INPUTS:\n%\n% y, the input time series\n% corrFun, the self-correlation function to measure:\n% (i) 'ac': normal linear autocorrelation function. Uses CO_AutoCorr to\n% calculate autocorrelations.\n% maxTau, a maximum time-delay to search up to.\n%\n% In future, could add an option to return the point at which the function\n% crosses the axis, rather than the first integer lag at which it has already\n% crossed (what is currently implemented).\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% ------------------------------------------------------------------------------\n% Check inputs:\n% ------------------------------------------------------------------------------\n\nN = length(y); % the length of the time series\n\nif nargin < 2 || isempty(corrFun)\n corrFun = 'ac'; % autocorrelation by default\nend\nif nargin < 3 || isempty(maxTau)\n maxTau = N; % search up to a maximum of the length of the time series\n % maxTau = 400; % searches up to this maximum time lag\n % maxTau = min(maxTau,N); % searched up to the length of the time series if this is less than maxTau\nend\n\n% ------------------------------------------------------------------------------\n% Select the self-correlation function as an inline function\n% Eventually could add additional self-correlation functions\nswitch corrFun\ncase 'ac'\n % Autocorrelation at all time lags\n corrs = CO_AutoCorr(y,[],'Fourier');\n corrs = corrs(2:end); % remove the zero-lag result\notherwise\n error('Unknown correlation function ''%s''',corrFun);\nend\n\n% Calculate autocorrelation at increasing lags, until you find a negative one\nfor tau = 1:maxTau-1\n if corrs(tau) < 0 % we know it starts positive (1), so first negative will be the zero-crossing\n out = tau; return\n end\nend\n\n% If haven't left yet, set output to maxTau\nout = maxTau;\n\n% make sure this is a scalar output\ncoder.varsize('out', [1, 1], [0 0])\n\nend\n" }, { "path": "Matlab/BasisOperations/CO_trev.m", "content": "function out = CO_trev(y, tau)\n% CO_trev Normalized nonlinear autocorrelation, trev function of a time series\n%\n% Calculates the trev function, a normalized nonlinear autocorrelation,\n% mentioned in the documentation of the TSTOOL nonlinear time-series analysis\n% package (available here: http://www.physik3.gwdg.de/tstool/).\n%\n% The quantity is often used as a nonlinearity statistic in surrogate data\n% analysis, cf. \"Surrogate time series\", T. Schreiber and A. Schmitz, Physica D,\n% 142(3-4) 346 (2000).\n%\n%---INPUTS:\n%\n% y, time series\n%\n% tau, time lag (can be 'ac' or 'mi' to set as the first zero-crossing of the\n% autocorrelation function, or the first minimum of the automutual\n% information function, respectively)\n%\n%---OUTPUTS:\n% the trev numerator and the denominator.\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% ------------------------------------------------------------------------------\n%% Set defaults:\n% ------------------------------------------------------------------------------\nif nargin < 2 || isempty(tau)\n tau = 1;\nend\n\n% ------------------------------------------------------------------------------\n% Compute trev quantities\n% ------------------------------------------------------------------------------\n\nyn = y(1:end-tau);\nyn1 = y(1+tau:end); % yn, tau steps ahead\n\n% ------------------------------------------------------------------------------\n% Fill output struct\n% ------------------------------------------------------------------------------\n\n% % The numerator\nout.num = mean((yn1-yn).^3);\n\n% The denominator\nout.denom = (mean((yn1-yn).^2))^(3/2);\n\nend\n" }, { "path": "Matlab/BasisOperations/DN_HistogramMode.m", "content": "function out = DN_HistogramMode(y,numBins)\n% DN_HistogramMode Mode of a data vector.\n%\n% Measures the mode of the data vector using histograms with a given number\n% of bins.\n%\n%---INPUTS:\n%\n% y, the input data vector\n%\n% numBins, the number of bins to use in the histogram.\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\nif nargin < 2\n numBins = 'auto'; % ceil(sqrt(length(y)));\nend\n\n% Compute the histogram from the data:\n[N,binEdges] = histcounts_(y,numBins);\n\n% Compute bin centers from bin edges:\nbinCenters = mean([binEdges(1:end-1); binEdges(2:end)]);\n\n% Mean position of maximums (if multiple):\nout = mean(binCenters(N == max(N)));\n\nend\n" }, { "path": "Matlab/BasisOperations/DN_OutlierInclude_001_mdrmd.m", "content": "function out = DN_OutlierInclude_001_mdrmd(y,thresholdHow)\n% DN_OutlierInclude How statistics depend on distributional outliers.\n%\n% Measures a range of different statistics about the time series as more and\n% more outliers are included in the calculation according to a specified rule,\n% of outliers being furthest from the mean, greatest positive, or negative\n% deviations.\n%\n% The threshold for including time-series data points in the analysis increases\n% from zero to the maximum deviation, in increments of 0.01*sigma (by default),\n% where sigma is the standard deviation of the time series.\n%\n% At each threshold, the mean, standard error, proportion of time series points\n% included, median, and standard deviation are calculated, and outputs from the\n% algorithm measure how these statistical quantities change as more extreme\n% points are included in the calculation.\n%\n%---INPUTS:\n% y, the input time series (ideally z-scored)\n%\n% thresholdHow, the method of how to determine outliers:\n% (i) 'abs': outliers are furthest from the mean,\n% (ii) 'p': outliers are the greatest positive deviations from the mean, or\n% (iii) 'n': outliers are the greatest negative deviations from the mean.\n%\n% inc, the increment to move through (fraction of std if input time series is\n% z-scored)\n%\n% Most of the outputs measure either exponential, i.e., f(x) = Aexp(Bx)+C, or\n% linear, i.e., f(x) = Ax + B, fits to the sequence of statistics obtained in\n% this way.\n%\n% [future: could compare differences in outputs obtained with 'p', 'n', and\n% 'abs' -- could give an idea as to asymmetries/nonstationarities??]\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% ------------------------------------------------------------------------------\n%% Check Inputs\n% ------------------------------------------------------------------------------\n% If time series is all the same value -- ridiculous! ++BF 21/3/2010\nif all(y == y(1)) % the whole time series is just a single value\n% fprintf(1,'The time series is a constant!\\n');\n out = NaN; return % this method is not suitable for such time series: return a NaN\nend\n\nN = length(y); % length of the time series\n\ninc = 0.01;\n\n% ------------------------------------------------------------------------------\n%% Initialize thresholds\n% ------------------------------------------------------------------------------\nswitch thresholdHow\n\n case 'p' % analyze only positive deviations\n thr = (0:inc:max(y));\n tot = sum(y >= 0);\n\n otherwise % case 'n' % analyze only negative deviations\n thr = (0:inc:max(-y));\n tot = sum(y <= 0);\n \nend\n\nif isempty(thr)\n error('I suspect that this is a highly peculiar time series?!!!')\nend\n\n% msDt4 = zeros(length(thr),1); % median of index relative to middle\n% \n% for i = 1:length(thr)\n% th = thr(i); % the threshold\n% \n% % Construct a time series consisting of inter-event intervals for parts\n% % of the time serie exceeding the threshold, th\n% \n% if strcmp(thresholdHow,'n')% look at only positive deviations\n% r = find(y <= -th);\n% elseif strcmp(thresholdHow,'p')% look at only negative deviations\n% r = find(y >= th);\n% end\n% \n% msDt4 = median(r)/(N/2)-1;\n% \n% end\n\nmsDt = zeros(length(thr),6); % mean, std, proportion_of_time_series_included,\n % median of index relative to middle, mean,\n % error\nfor i = 1:length(thr)\n th = thr(i); % the threshold\n\n % Construct a time series consisting of inter-event intervals for parts\n % of the time serie exceeding the threshold, th\n\n if strcmp(thresholdHow,'n')% look at only positive deviations\n r = find(y <= -th);\n else % if strcmp(thresholdHow,'p')% look at only negative deviations\n r = find(y >= th);\n end\n \n Dt_exc = diff(r); % Delta t (interval) time series; exceeding threshold\n\n msDt(i,1) = mean(Dt_exc); % the mean value of this sequence\n msDt(i,3) = length(Dt_exc)/tot*100; % this is just really measuring the distribution\n % : the proportion of possible values\n % that are actually used in\n % calculation\n msDt(i,4) = median(r)/(N/2)-1;\n\n\nend\n\n% ------------------------------------------------------------------------------\n%% Trim\n% ------------------------------------------------------------------------------\n% Trim off where the number of events is only one; hence the differenced\n% series returns NaN\n%fbi = find(isnan(msDt(:,1)),1,'first'); % first bad index\nfbi = findFirstTrue(isnan(msDt(:,1)));\nif fbi ~= 0\n msDt = msDt(1:fbi-1,:);\n thr = thr(1:fbi-1);\nend\n\n% Trim off where the statistic power is lacking: less than 2% of data\n% included\ntrimthr = 2; % percent\n% mj = find(msDt(:,3) > trimthr,1,'last');\nmj = findLastTrue(msDt(:,3) > trimthr);\nif mj ~= 0\n msDt = msDt(1:mj,:);\n thr = thr(1:mj);\nend\n\n% ------------------------------------------------------------------------------\n%%% Generate output:\n% ------------------------------------------------------------------------------\n\n%% Stationarity assumption\n\n% mean, median and std of the median and mean of range indices\n% out.mdrm = mean(msDt(:,4));\n% out.mdrmd = median(msDt(:,4));\n% out.mdrstd = std(msDt(:,4));\n% \n% out.mrm = mean(msDt(:,5));\n% out.mrmd = median(msDt(:,5));\n% out.mrstd = std(msDt(:,5));\n\n% mdrmd\nout = median(msDt(:,4)); % median(msDt4(:));\n\n\nend\n\nfunction out = findFirstTrue(y)\n\n out = 0;\n \n coder.varsize('out', [1 1], [0 0]);\n\n for i = 1:length(y)\n if y(i)\n out = i;\n return\n end\n end\n\nend\n\nfunction out = findLastTrue(y)\n\n out = 0;\n \n coder.varsize('out', [1 1], [0 0]);\n\n for i = length(y):-1:1\n if y(i)\n out = i;\n return\n end\n end\n\nend\n" }, { "path": "Matlab/BasisOperations/NK_hist2.m", "content": "function [MN, xedges, yedges] = NK_hist2(x, y, xedges, yedges)\n% function MN = NK_hist2(x, y, xedges, yedges)\n%\n% 2D histogram: Extract the number of joint events - (x,y) data value pairs \n% that fall in each bin of the grid defined by xedges and yedges.\n%\n% ====================================================================\n% Uses:\n% [N,BIN] = histc(X,EDGES)\n% which returns\n% 1) N is a LENGTH(EDGES) vector, N(k) will count the value X(i) if EDGES(k) <= X(i) < EDGES(k+1). The\n% last bin will count any values of X that match EDGES(end).\n% 2) an index matrix BIN.\n% If X is a vector, N(K) = SUM(BIN==K).\n% BIN is zero for out of range values.\n% If X is an m-by-n matrix, then, for j=1:n, N(K,j) = SUM(BIN(:,j)==K); end\n% ====================================================================\n%\n% Please, see the notes to histc too.\n% N.B. It is always a better idea to use the\n% HISTC mex (a much faster compiled C code) if you have it\n% Then just replace the histc with HISTC in all calls\n% contained in the NK_hist2() .m function\n%\n% (c) Nedialko Krouchev 2006, Universite de Montreal, GRSNC\n\nif nargin ~= 4\n error('The four input arguments are required!');\nend\nif any(size(x) ~= size(y))\n error('The size of the two first input vectors should be same!');\nend\n\n[xn, xbin] = histc(x,xedges);\n[yn, ybin] = histc(y,yedges);\n\nxnbin = length(xedges);\nynbin = length(yedges);\n\n%% xbin, ybin are zero for out of range values\nkkL = find( xxedges(xnbin) );\nif ~isempty( kkR )\n xnbin = xnbin + 1; xedges(xnbin) = max(x);\nend\nxbin( kkR ) = xnbin;\n\nkkL = find( yyedges(ynbin) );\nif ~isempty( kkR )\n ynbin = ynbin + 1; \n yedges(ynbin) = max(y); \nend\nybin( kkR ) = ynbin;\n\nxyBinEdges = 1:xnbin*ynbin;\n\n% ====================================================================\n% A more Elegant end-spiel:\n%\n% If x belongs to jBin=xbin(x), and y belongs to iBin=ybin(y),\n% Then (x,y) pairs belong \"columnwise\" to:\n% ijBin = (jBin-1)*ynbin + iBin\n\nxyBin = (xbin-1)*ynbin + ybin;\n\n\n% ====================================================================\n\nMN = histc(xyBin, xyBinEdges);\nMN = reshape(MN, ynbin, xnbin);\nend" }, { "path": "Matlab/BasisOperations/SB_CoarseGrain_quantile.m", "content": "function yth = SB_CoarseGrain_quantile(y,numGroups)\n% SB_CoarseGrain Coarse-grains a continuous time series to a discrete alphabet.\n%\n%---INPUTS:\n% howtocg = 'quantile' puts an equal number into each bin, the method of coarse-graining\n%\n% numGroups, either specifies the size of the alphabet for 'quantile' and 'updown'\n% or sets the timedelay for the embedding subroutines\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\nN = length(y); % length of the input sequence\n\n% ------------------------------------------------------------------------------\n% Do the coarse graining\n% ------------------------------------------------------------------------------\n\n% quantile\nth = quantile(y,linspace(0,1,numGroups+1)); % thresholds for dividing the time series values\nth(1) = th(1)-1; % this ensures the first point is included\n% turn the time series into a set of numbers from 1:numGroups\nyth = zeros(N,1);\nfor i = 1:numGroups\n yth(y > th(i) & y <= th(i+1)) = i;\nend\n\nif any(yth == 0)\n error('All values in the sequence were not assigned to a group')\nend\n\nend\n" }, { "path": "Matlab/BasisOperations/SB_TransitionMatrix.m", "content": "function out = SB_TransitionMatrix(y, numGroups, tauIn)\n% SB_TransitionMatrix Transition probabilities between different time-series states.\n%\n% The time series is coarse-grained according to a given method.\n%\n% The input time series is transformed into a symbolic string using an\n% equiprobable alphabet of numGroups letters. The transition probabilities are\n% calculated at a lag tau.\n%\n%---INPUTS:\n% y, the input time series\n%\n%\n% numGroups: number of groups in the course-graining\n%\n% tau: analyze transition matricies corresponding to this lag. We\n% could either downsample the time series at this lag and then do the\n% discretization as normal, or do the discretization and then just\n% look at this dicrete lag. Here we do the former. Can also set tau to 'ac'\n% to set tau to the first zero-crossing of the autocorrelation function.\n%\n%---OUTPUTS: include the transition probabilities themselves, as well as the trace\n% of the transition matrix, measures of asymmetry, and eigenvalues of the\n% transition matrix.\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% % ------------------------------------------------------------------------------\n% % Check inputs:\n% % ------------------------------------------------------------------------------\nif nargin < 2 || isempty(numGroups)\n numGroups = 2;\nend\nif numGroups < 2\n error('Too few groups for coarse-graining')\nend\n\nif nargin < 3 || isempty(tauIn)\n tau = 1;\nelseif strcmp(tauIn,'ac') % determine tau from first zero of autocorrelation\n tau = CO_FirstZero(y,'ac');\nelseif ischar(tauIn) || isstring(tauIn)\n tau = str2double(tauIn); \nend\n\nif isnan(tau)\n error('Time series too short to estimate tau');\nend\n\nif tau > 1 % calculate transition matrix at non-unity lag\n % downsample at rate 1:tau\n yDown = downsample(y,tau);\n% yDown = decimate_(y,tau);\n% yDown = resample(y,1,tau);\nelse\n yDown = y;\nend\n\nN = length(yDown); % time-series length\n\n% ------------------------------------------------------------------------------\n%% (((1))) Discretize the time series\n% ------------------------------------------------------------------------------\nyth = SB_CoarseGrain_quantile(yDown,numGroups);\n\n% At this point we should have:\n% (*) yth: a thresholded y containing integers from 1 to numGroups\n\n% ------------------------------------------------------------------------------\n%% (((2))) find 1-time transition matrix\n% ------------------------------------------------------------------------------\n% Probably implemented already, but I'll do it myself\nT = zeros(numGroups); % probability of transition from state i -> state j\nfor i = 1:numGroups\n ri = (yth == i); % indices where the time series is in state i\n if sum(ri)==0 % is never in state i\n T(i,:) = 0; % all transition probabilities are zero (could be NaN)\n else\n % Indices of states immediately following a state i:\n ri_next = [logical(0);ri(1:end-1)];\n % Compute transitions from state i to each of the states j:\n for j = 1:numGroups\n T(i,j) = sum(yth(ri_next) == j); % the next element is of this class\n end\n end\nend\n\n% Normalize from counts to probabilities:\nT = T/(N-1); % N-1 is appropriate because it's a 1-time transition matrix\n\n% ------------------------------------------------------------------------------\n%% (((3))) output measures from the transition matrix\n% ------------------------------------------------------------------------------\n% % (i) Raw values of the transition matrix\n% % [this has to be done bulkily (only for numGroups = 2,3)]:\n% if numGroups == 2; % return all elements of T\n% for i = 1:4\n% out.(sprintf('T%u',i)) = T(i);\n% end\n% elseif numGroups == 3; % return all elements of T\n% for i = 1:9\n% out.(sprintf('T%u',i)) = T(i);\n% end\n% elseif numGroups > 3 % return just diagonal elements of T\n% for i = 1:numGroups\n% out.(sprintf('TD%u',i)) = T(i,i);\n% end\n% end\n\n% (ii) Measures on the diagonal\nout.ondiag = sum(diag(T)); % trace\n% out.stddiag = std(diag(T)); % std of diagonal elements\n% \n% % (iii) Measures of symmetry:\n% out.symdiff = sum(sum(abs((T-T')))); % sum of differences of individual elements\n% out.symsumdiff = sum(sum(tril(T,-1)))-sum(sum(triu(T,+1))); % difference in sums of upper and lower\n % triangular parts of T\n\n% (iv) Measures from covariance matrix:\ncovT = cov(T);\nout.sumdiagcov = sum(diag(covT)); % trace of covariance matrix\n\n% % % (v) Measures from eigenvalues of T\n% % eigT = eig(T);\n% % out.stdeig = std(eigT); % std of eigenvalues\n% % out.maxeig = max(real(eigT)); % maximum eigenvalue\n% % out.mineig = min(real(eigT)); % minimum eigenvalue\n% % % mean eigenvalue is equivalent to trace\n% % % (ought to be always zero? Not necessary to measure:)\n% % out.maximeig = max(imag(eigT)); % maximum imaginary part of eigenavlues\n% % \n% % (vi) Eigenvalues of covariance matrix:\n% % (mean eigenvalue of covariance matrix equivalent to trace of covariance matrix).\n% % (these measures don't make much sense in the case of 2 groups):\n% eigcovT = eig(covT);\n% % out.stdeigcov = std(eigcovT); % std of eigenvalues of covariance matrix\n% out.maxeigcov = real(max(eigcovT)); % (made real for C-implementation) max eigenvalue of covariance matrix\n% % out.mineigcov = min(eigcovT); % min eigenvalue of covariance matrix\n\nend\n" }, { "path": "Matlab/BasisOperations/SC_FluctAnal_q2_taustep50_k1_logi_prop_r1.m", "content": "function out = SC_FluctAnal_q2_taustep50_k1_logi_prop_r1(x,wtf,lag)\n% SC_FluctAnal Implements fluctuation analysis by a variety of methods.\n%\n% Much of our implementation is based on the well-explained discussion of\n% scaling methods in:\n% \"Power spectrum and detrended fluctuation analysis: Application to daily\n% temperatures\" P. Talkner and R. O. Weber, Phys. Rev. E 62(1) 150 (2000)\n%\n% The main difference between algorithms for estimating scaling exponents amount\n% to differences in how fluctuations, F, are quantified in time-series segments.\n% Many alternatives are implemented in this function.\n%\n%---INPUTS:\n% y, the input time series\n%\n% q=2, the parameter in the fluctuation function q = 2 (usual) gives RMS fluctuations.\n%\n% wtf, (what to fluctuate)\n% (i) 'dfa' removes a polynomial trend of order k in each segment,\n% \n% (ii) 'rsrangefit' fits a polynomial of order k and then returns the\n% range. The parameter q controls the order of fluctuations, for which\n% we mostly use the standard choice, q = 2, corresponding to root mean\n% square fluctuations.\n% An optional input parameter to this operation is a timelag for\n% computing the cumulative sum (or integrated profile), as suggested\n% by: \"Using detrended fluctuation analysis for lagged correlation\n% analysis of nonstationary signals\" J. Alvarez-Ramirez et al. Phys.\n% Rev. E 79(5) 057202 (2009)\n%\n% tauStep=50, increments in tau for linear range (i.e., if logInc = 0), or number of tau\n% steps in logarithmic range if login = 1\n% The spacing of time scales, tau, is commonly logarithmic through a range from\n% 5 samples to a quarter of the length of the time series, as suggested in\n% \"Statistical properties of DNA sequences\", C.-K. Peng et al. Physica A\n% 221(1-3) 180 (1995)\n%\n% Max A. Little's fractal paper used L = 4 to L = N/2:\n% \"Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection\"\n% M. A. Little et al. Biomed. Eng. Online 6(1) 23 (2007)\n%\n% k=1, polynomial order of detrending for 'dfa', 'rsrangefit'\n%\n% lag, optional time-lag, as in Alvarez-Ramirez (see (vii) above)\n%\n% logInc=1, whether to use logarithmic increments in tau (it should be logarithmic).\n%\n%---OUTPUTS: include statistics of fitting a linear function to a plot of log(F) as\n% a function of log(tau), and for fitting two straight lines to the same data,\n% choosing the split point at tau = tau_{split} as that which minimizes the\n% combined fitting errors.\n%\n% This function can also be applied to the absolute deviations of the time\n% series from its mean, and also for just the sign of deviations from the mean\n% (i.e., converting the time series into a series of +1, when the time series is\n% above its mean, and -1 when the time series is below its mean).\n%\n% All results are obtained with both linearly, and logarithmically-spaced time\n% scales tau.\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% ------------------------------------------------------------------------------\n% Check Inputs:\n% ------------------------------------------------------------------------------\n\nif nargin < 2 || isempty(wtf)\n wtf = 'rsrange'; % re-scaled range analysis by default\nend\n\nif nargin < 3 || isempty(lag)\n lag = '';\nend\n\nq = 2;\ntauStep = 50;\nk = 1;\n\n% ------------------------------------------------------------------------------\n% ------------------------------------------------------------------------------\n\nN = length(x); % length of the time series\ndoPlot = 0; % plot relevant outputs to figure\n\n% 1) Compute integrated sequence\n\nif isempty(lag)\n % didn't specify a lag, do a normal cumsum:\n y = cumsum(x);\nelse\n % specified a lag, do a decimation:\n y = cumsum(x(1:lag:end));\nend\n\n%-------------------------------------------------------------------------------\n% Perform scaling over a range of tau, up to a fifth the time-series length\n%-------------------------------------------------------------------------------\n% Peng (1995) suggests 5:N/4 for DFA\n% Caccia suggested from 10 to (N-1)/2...\n%-------------------------------------------------------------------------------\ntaur = unique(round(exp(linspace(log(5),log(floor(N/2)),tauStep))));\nntau = length(taur); % analyze the time series across this many timescales\n\nif ntau < 8 % fewer than 8 points\n% fprintf(1,'This time series (N = %u) is too short to analyze using this fluctuation analysis\\n',N);\n out = NaN;\n return\nend\n\n% 2) Compute the fluctuation function as follows\nF = zeros(1,ntau);\n% F is the fluctuation function\n% each entry correponds to a given scale tau, and contains\n% the fluctuation function at that scale\n\nfor i = 1:ntau\n % buffer the time series at the scale tau\n tau = taur(i); % the scale on which to compute fluctuations\n\n % y_buff = buffer(y,tau); % buff not supported by Coder\n y_buff = buffer_(y,tau);\n \n if size(y_buff,2) > floor(length(y)/tau) % zero-padded, remove trailing set of points...\n y_buff = y_buff(:,1:end-1);\n end\n\n % analyzed length of time series (with trailing end-points removed)\n nn = size(y_buff,2)*tau;\n\n switch wtf\n case 'dfa'\n tt = (1:tau)'; % faux time range\n for j = 1:size(y_buff,2)\n % fit a polynomial of order k in each subsegment\n p = polyfit(tt,y_buff(:,j),k);\n % remove the trend, store back in y_buff\n y_buff(:,j) = y_buff(:,j) - polyval(p,tt);\n end\n % reshape to a column vector, y_dt (detrended)\n y_dt = reshape(y_buff,nn,1);\n case 'rsrangefit' % polynomial fit (order k) rather than endpoints fit: (~DFA)\n tt = (1:tau)'; % faux time range\n for j = 1:size(y_buff,2)\n % fit a polynomial of order k in each subsegment\n p = polyfit(tt,y_buff(:,j),k);\n % remove the trend, store back in y_buff\n y_buff(:,j) = y_buff(:,j) - polyval(p,tt);\n end\n y_dt = max(y_buff) - min(y_buff); % range(y_buff);\n otherwise\n error('Unknown fluctuation analysis method ''%s''',wtf);\n end\n\n % Compute fluctuation function:\n F(i) = (mean(y_dt.^q)).^(1/q);\nend\n\n\n%-------------------------------------------------------------------------------\n% Smooth unevenly-distributed points in log space:\n%-------------------------------------------------------------------------------\nlogtt = log(taur);\nlogFF = log(F);\nntt = ntau;\n\n% ------------------------------------------------------------------------------\n%% Try assuming two components (2 distinct scaling regimes)\n% ------------------------------------------------------------------------------\n% Move through, and fit a straight line to loglog before and after each point.\n% Find point with the minimum sum of squared errors\n\n% First spline interpolate to get an even sampling of the interval\n% (currently, in the log scale, there are relatively more at large scales\n\n% Deterine the errors\nsserr = nan(ntt,1); % don't choose the end points\nminPoints = 6;\nfor i = minPoints:ntt-minPoints\n r1 = 1:i;\n p1 = polyfit(logtt(r1),logFF(r1),1);\n r2 = i:ntt;\n p2 = polyfit(logtt(r2),logFF(r2),1);\n % Sum of errors from fitting lines to both segments:\n sserr(i) = norm(polyval(p1,logtt(r1))-logFF(r1)) + norm(polyval(p2,logtt(r2))-logFF(r2));\nend\n\n% breakPt is the point where it's best to fit a line before and another line after\nbreakPt = find(sserr == min(sserr),1,'first');\nr1 = 1:breakPt;\nprop_r1 = length(r1)/ntt;\n\n% output\nout = prop_r1;\n\nend\n" }, { "path": "Matlab/BasisOperations/SP_Summaries_welch_rect.m", "content": "function out = SP_Summaries_welch_rect(y)\n% SP_Summaries Statistics of the power spectrum of a time series\n%\n% The estimation can be done using a periodogram, using the periodogram code in\n% Matlab's Signal Processing Toolbox, or a fast fourier transform, implemented\n% using Matlab's fft code.\n%\n%---INPUTS:\n% y, the input time series\n%\n% psdMeth, the method of obtaining the spectrum from the signal:\n% (i) 'periodogram': periodogram\n% (ii) 'fft': fast fourier transform\n% (iii) 'welch': Welch's method\n%\n% windowType='rect', the window to use:\n% (i) 'boxcar'\n% (ii) 'rect'\n% (iii) 'bartlett'\n% (iv) 'hann'\n% (v) 'hamming'\n% (vi) 'none'\n%\n% nf, the number of frequency components to include, if\n% empty (default), it's approx length(y)\n%\n% dologabs, if 1, takes log amplitude of the signal before\n% transforming to the frequency domain.\n%\n% doPower, analyzes the power spectrum rather than amplitudes of a Fourier\n% transform\n%\n%---OUTPUTS:\n% Statistics summarizing various properties of the spectrum,\n% including its maximum, minimum, spread, correlation, centroid, area in certain\n% (normalized) frequency bands, moments of the spectrum, Shannon spectral\n% entropy, a spectral flatness measure, power-law fits, and the number of\n% crossings of the spectrum at various amplitude thresholds.\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% % ------------------------------------------------------------------------------\n% %% Check that a Curve-Fitting Toolbox license is available:\n% % ------------------------------------------------------------------------------\n% BF_CheckToolbox('curve_fitting_toolbox')\n\n% ------------------------------------------------------------------------------\n% Check inputs, set defaults:\n% ------------------------------------------------------------------------------\n% if size(y,2) > size(y,1)\n% y = y'; % Time series must be a column vector\n% end\n% if nargin < 2 || isempty(psdMeth)\n% psdMeth = 'fft'; % fft by default\n% end\n% if nargin < 3 || isempty(windowType)\n% windowType = 'hamming'; % Hamming window by default\n% end\n% if nargin < 4\n% nf = [];\n% end\n% if nargin < 5 || isempty(dologabs)\n% dologabs = false;\n% end\n% \n% if dologabs % a boolean\n% % Analyze the spectrum of logarithmic absolute deviations\n% y = log(abs(y));\n% end\n\ndoPlot = false; % plot outputs\nNy = length(y); % time-series length\n\n% default output\nout.centroid = NaN;\nout.area_5_1 = NaN;\n\n%-------------------------------------------------------------------------------\n% Set window (for periodogram and welch):\n%-------------------------------------------------------------------------------\n\nwindow = ones(Ny,1); % rectwin(Ny);\n\n% ------------------------------------------------------------------------------\n% Compute the Fourier Transform\n% ------------------------------------------------------------------------------\n\n% Welch power spectral density estimate:\nFs = 1; % sampling frequency\nN = 2^nextpow2(Ny);\n% [S, f] = pwelch(y,window,[],N,Fs);\n% substitute with own Welch spectrum\n[S_, f_] = welchy(y, N, Fs, window);\n\nw_ = 2*pi*f_'; % angular frequency\nS_ = S_/(2*pi); % adjust so that area remains normalized in angular frequency space\n\nif ~any(isfinite(S_)) % no finite values in the power spectrum\n % This time series must be really weird -- return NaN (unsuitable operation)...\n % warning('NaN in power spectrum? A weird time series.');\n return\nend\n\n% Ensure both w and S are row vectors:\nif size(S_,1) > size(S_,2)\n S = S_';\nelse\n S = S_;\nend\nif size(w_,1) > size(w_,2)\n w = w_';\nelse\n w = w_;\nend\n\n% if doPlot\n% figure('color','w')\n% plot(w,S,'.-k'); % plot the spectrum\n% % Area under S should sum to 1 if a power spectral density estimate:\n% title(sprintf('Area under psd curve = %.1f (= %.1f)',sum(S*(w(2)-w(1))),var(y)));\n% end\n\nN = length(S);\ndw = w(2) - w(1); % spacing increment in w\n\n% ------------------------------------------------------------------------------\n% Shape of cumulative sum curve\n% ------------------------------------------------------------------------------\ncsS = cumsum(S);\n\n% this original was not well received by coder.\n% f_frac_w_max = @(f) w(find(csS >= csS(end)*f,1,'first'));\n\n% At what frequency is csS a fraction 0.5 of its maximum?\ncsSThres = csS(end)*0.5;\nfor i = 1:length(csS)\n if csS(i) > csSThres\n out.centroid = w(i);\n break\n end\nend\n\n% % wInd = find((csS >= csS(end)*0.5),1, 'first');\n% % out = w(wInd); % f_frac_w_max(0.5);\n% out = NaN;\n\n% 5 bands\nsplit = buffer_(S,floor(N/5));\nif size(split,2) > 5, split = split(:,1:5); end\nout.area_5_1 = sum(split(:,1))*dw;\n\nend\n" }, { "path": "Matlab/BasisOperations/buffer_.m", "content": "function y_buff = buffer_(y, tau)\n\n% replacement for y_buff = buffer(y,tau)\n\nnCols = ceil(length(y)/tau);\nyPadded = zeros(nCols*tau,1);\nyPadded(1:length(y)) = y;\ny_buff = reshape(yPadded, tau, nCols); " }, { "path": "Matlab/BasisOperations/decimate_.m", "content": "function out = decimate_(y, q)\n\n nfilt = 4;\n \n nd = length(y);\n\n [b,a] = myButter(nfilt, 0.8/q);\n\n % be sure to filter in both directions to make sure the filtered data has zero phase\n % make a data vector properly pre- and ap- pended to filter forwards and back\n % so end effects can be obliterated.\n out_filt = filtfilt_(b,a,y);\n nbeg = 1;\n out = out_filt((nbeg:q:nd)');\n " }, { "path": "Matlab/BasisOperations/downsample_.m", "content": "%-------------------------------------------------------------------------\nfunction [y, h] = downsample_( x, q )\n\np = 1;\nbta = 5;\nN = 10;\n\nif (p==1) && (q==1)\n y = x; \n h = 1;\n return\nend\npqmax = max(p,q);\nif length(N)>1 % use input filter\n L = length(N);\n h = N;\nelse % design filter\n if( N>0 )\n fc = 1/2/pqmax;\n L = 2*N*pqmax + 1;\n h = firls( L-1, [0 2*fc 2*fc 1], [1 1 0 0]).*kaiser(L,bta)' ;\n h = p*h/sum(h);\n else\n L = p;\n h = ones(1,p);\n end\nend\n\nLhalf = (L-1)/2;\nisvect = any(size(x)==1);\nif isvect\n Lx = length(x);\nelse\n Lx = size(x, 1);\nend\n\n% Need to delay output so that downsampling by q hits center tap of filter.\nnz = floor(q-mod(Lhalf,q));\nz = zeros(1,nz);\nh = [z h(:).']; % ensure that h is a row vector.\nLhalf = Lhalf + nz;\n\n% Number of samples removed from beginning of output sequence \n% to compensate for delay of linear phase filter:\ndelay = floor(ceil(Lhalf)/q);\n\n% Need to zero-pad so output length is exactly ceil(Lx*p/q).\nnz1 = 0;\nwhile ceil( ((Lx-1)*p+length(h)+nz1 )/q ) - delay < ceil(Lx*p/q)\n nz1 = nz1+1;\nend\nh = [h zeros(1,nz1)];\n\n% ---- HERE'S THE CALL TO UPFIRDN ----------------------------\ny = upfirdn(x,h,p,q);\n\n% Get rid of trailing and leading data so input and output signals line up\n% temporally:\nLy = ceil(Lx*p/q); % output length\n% Ly = floor((Lx-1)*p/q+1); <-- alternately, to prevent \"running-off\" the\n% data (extrapolation)\nif isvect\n y(1:delay) = [];\n y(Ly+1:end) = [];\nelse\n y(1:delay,:) = [];\n y(Ly+1:end,:) = [];\nend\n\nh([1:nz (end-nz1+1):end]) = []; % get rid of leading and trailing zeros \n % in case filter is output" }, { "path": "Matlab/BasisOperations/filtfilt_.m", "content": "function y = filtfilt_(b,a,x)\n%FILTFILT Zero-phase forward and reverse digital IIR filtering.\n% Y = FILTFILT(B, A, X) filters the data in vector X with the filter\n% described by vectors A and B to create the filtered data Y. The filter\n% is described by the difference equation:\n%\n% a(1)*y(n) = b(1)*x(n) + b(2)*x(n-1) + ... + b(nb+1)*x(n-nb)\n% - a(2)*y(n-1) - ... - a(na+1)*y(n-na)\n%\n% The length of the input X must be more than three times the filter\n% order, defined as max(length(B)-1,length(A)-1).\n%\n% References:\n% [1] Sanjit K. Mitra, Digital Signal Processing, 2nd ed.,\n% McGraw-Hill, 2001\n% [2] Fredrik Gustafsson, Determining the initial states in forward-\n% backward filtering, IEEE Transactions on Signal Processing,\n% pp. 988-992, April 1996, Volume 44, Issue 4\n\n% Copyright 1988-2014 The MathWorks, Inc.\n\n\n%% Parse coefficient coefficients vectors and determine initial conditions\n\nna = numel(a);\n\nL = 1;\n% Check coefficients\nb = b(:);\na = a(:);\nnb = numel(b);\nnfilt = max(nb,na); \nnfact = max(1,3*(nfilt-1)); % length of edge transients\n\n% Zero pad shorter coefficient vector as needed\nif nb < nfilt\n b(nfilt,1)=0;\nelseif na < nfilt\n a(nfilt,1)=0;\nend\n\n% Compute initial conditions to remove DC offset\nif nfilt>1\n zi = ( eye(nfilt-1) - [-a(2:nfilt), [eye(nfilt-2); ...\n zeros(1,nfilt-2)]] ) \\ ...\n ( b(2:nfilt) - b(1)*a(2:nfilt) );\nelse\n zi = zeros(0,1);\nend\n\n%% Filter the data\nfor ii=1:L\n \n y = [2*x(1)-x(nfact+1:-1:2); x; 2*x(end)-x(end-1:-1:end-nfact)];\n \n % filter, reverse data, filter again, and reverse data again\n y = filter(b(:,ii),a(:,ii),y,zi(:,ii)*y(1));\n y = y(end:-1:1);\n y = filter(b(:,ii),a(:,ii),y,zi(:,ii)*y(1));\n \n % retain reversed central section of y\n y = y(end-nfact:-1:nfact+1);\nend\n" }, { "path": "Matlab/BasisOperations/histcounts_.m", "content": "function [N, binEdges] = histcounts_(d, nBinsIn, normalise)\n % own function for counting \n\n if nargin<2 || isempty(nBinsIn)\n nBinsIn = [];\n end\n \n if nargin<3 || isempty(normalise)\n normalise = false;\n end\n\n \n dMinMax = [min(d), max(d)];\n dRange = dMinMax(2) - dMinMax(1);\n \n % fixed or automatic number of bins\n if isempty(nBinsIn)\n nBins = ceil(dRange/(3.5*std(d)/(numel(d)^(1/3))));\n else\n nBins = nBinsIn;\n end\n \n % compute edges\n binWidth = dRange/nBins;\n binEdges = dMinMax(1) + (0:nBins)*binWidth;\n \n % count occurences in bins\n N = zeros(nBins, 1);\n d_ = d - dMinMax(1);\n for i = 1:length(d_)\n binInd = floor(d_(i)/binWidth)+1;\n binInd = max(1, binInd);\n binInd = min(nBins, binInd);\n N(binInd) = N(binInd) + 1;\n end\n \n % probability normalisation\n if normalise\n N = N./numel(d);\n end" }, { "path": "Matlab/BasisOperations/myButter.m", "content": "function [Z_out, P_out] = myButter(n, W)\n% Digital Butterworth filter, either 2 or 3 outputs\n% Jan Simon, 2014, BSD licence\n% See docs of BUTTER for input and output\n% Fast hack with limited accuracy: Handle with care!\n% Until n=15 the relative difference to Matlab's BUTTER is < 100*eps\nV = tan(W * 1.5707963267948966);\nQ = exp((1.5707963267948966i / n) * ((2 + n - 1):2:(3 * n - 1)));\nnQ = length(Q);\n\nSg = V ^ nQ;\nSp = V * Q;\nSz = [];\n\n% Bilinear transform:\nP = (1 + Sp) ./ (1 - Sp);\nZ = repmat(-1, size(P));\nif isempty(Sz)\n G = real(Sg / prod(1 - Sp));\nelse\n G = real(Sg * prod(1 - Sz) / prod(1 - Sp));\n Z(1:length(Sz)) = (1 + Sz) ./ (1 - Sz);\nend\n\n% From Zeros, Poles and Gain to B (numerator) and A (denominator):\nZ_out = G * real(poly(Z'));\nP_out = real(poly(P));\n" }, { "path": "Matlab/BasisOperations/poly_.m", "content": "function c = poly_(x)\n%POLY Convert roots to polynomial.\n% POLY(A), when A is an N by N matrix, is a row vector with\n% N+1 elements which are the coefficients of the\n% characteristic polynomial, det(lambda*eye(size(A)) - A).\n%\n% POLY(V), when V is a vector, is a vector whose elements are\n% the coefficients of the polynomial whose roots are the\n% elements of V. For vectors, ROOTS and POLY are inverse\n% functions of each other, up to ordering, scaling, and\n% roundoff error.\n%\n% Examples:\n%\n% roots(poly(1:20)) generates Wilkinson's famous example.\n%\n% Class support for inputs A,V:\n% float: double, single\n%\n% See also ROOTS, CONV, RESIDUE, POLYVAL.\n\n% Copyright 1984-2014 The MathWorks, Inc.\n\n[m,n] = size(x);\nif m == n\n % Characteristic polynomial (square x)\n e = eig(x);\nelseif (m==1) || (n==1)\n e = x;\nelse\n error(message('MATLAB:poly:InputSize'))\nend\n\n% Strip out infinities\ne = e( isfinite(e) );\n\n% Expand recursion formula\nn = length(e);\nc = [1 zeros(1,n,class(x))];\nfor j=1:n\n j\n c(2:(j+1))\n e(j)\n c(1:j)\n e(j).*c(1:j)\n \n c(2:(j+1)) = c(2:(j+1)) - e(j).*c(1:j);\nend\n\n% The result should be real if the roots are complex conjugates.\nif isequal(sort(e(imag(e)>0)),sort(conj(e(imag(e)<0))))\n c = real(c);\nend" }, { "path": "Matlab/BasisOperations/splinefit.m", "content": "function pp = splinefit(y)\n%SPLINEFIT Fit a spline to noisy data.\n% PP = SPLINEFIT(X,Y,BREAKS) fits a piecewise cubic spline with breaks\n% (knots) BREAKS to the noisy data (X,Y). X is a vector and Y is a vector\n% or an ND array. If Y is an ND array, then X(j) and Y(:,...,:,j) are\n% matched. Use PPVAL to evaluate PP.\n%\n% PP = SPLINEFIT(X,Y,P) where P is a positive integer interpolates the\n% breaks linearly from the sorted locations of X. P is the number of\n% spline pieces and P+1 is the number of breaks.\n%\n% OPTIONAL INPUT\n% Argument places 4 to 8 are reserved for optional input.\n% These optional arguments can be given in any order:\n%\n% PP = SPLINEFIT(...,'p') applies periodic boundary conditions to\n% the spline. The period length is MAX(BREAKS)-MIN(BREAKS).\n%\n% PP = SPLINEFIT(...,'r') uses robust fitting to reduce the influence\n% from outlying data points. Three iterations of weighted least squares\n% are performed. Weights are computed from previous residuals.\n%\n% PP = SPLINEFIT(...,BETA), where 0 < BETA < 1, sets the robust fitting\n% parameter BETA and activates robust fitting ('r' can be omitted).\n% Default is BETA = 1/2. BETA close to 0 gives all data equal weighting.\n% Increase BETA to reduce the influence from outlying data. BETA close\n% to 1 may cause instability or rank deficiency.\n%\n% PP = SPLINEFIT(...,N) sets the spline order to N. Default is a cubic\n% spline with order N = 4. A spline with P pieces has P+N-1 degrees of\n% freedom. With periodic boundary conditions the degrees of freedom are\n% reduced to P.\n%\n% PP = SPLINEFIT(...,CON) applies linear constraints to the spline.\n% CON is a structure with fields 'xc', 'yc' and 'cc':\n% 'xc', x-locations (vector)\n% 'yc', y-values (vector or ND array)\n% 'cc', coefficients (matrix).\n%\n% Constraints are linear combinations of derivatives of order 0 to N-2\n% according to\n%\n% cc(1,j)*y(x) + cc(2,j)*y'(x) + ... = yc(:,...,:,j), x = xc(j).\n%\n% The maximum number of rows for 'cc' is N-1. If omitted or empty 'cc'\n% defaults to a single row of ones. Default for 'yc' is a zero array.\n%\n% EXAMPLES\n%\n% % Noisy data\n% x = linspace(0,2*pi,100);\n% y = sin(x) + 0.1*randn(size(x));\n% % Breaks\n% breaks = [0:5,2*pi];\n%\n% % Fit a spline of order 5\n% pp = splinefit(x,y,breaks,5);\n%\n% % Fit a spline of order 3 with periodic boundary conditions\n% pp = splinefit(x,y,breaks,3,'p');\n%\n% % Constraints: y(0) = 0, y'(0) = 1 and y(3) + y\"(3) = 0\n% xc = [0 0 3];\n% yc = [0 1 0];\n% cc = [1 0 1; 0 1 0; 0 0 1];\n% con = struct('xc',xc,'yc',yc,'cc',cc);\n%\n% % Fit a cubic spline with 8 pieces and constraints\n% pp = splinefit(x,y,8,con);\n%\n% % Fit a spline of order 6 with constraints and periodicity\n% pp = splinefit(x,y,breaks,con,6,'p');\n%\n% See also SPLINE, PPVAL, PPDIFF, PPINT\n\n% Author: Jonas Lundgren 2010\n\n% 2009-05-06 Original SPLINEFIT.\n% 2010-06-23 New version of SPLINEFIT based on B-splines.\n% 2010-09-01 Robust fitting scheme added.\n% 2010-09-01 Support for data containing NaNs.\n% 2011-07-01 Robust fitting parameter added.\n\n%Copyright (c) 2017, Jonas Lundgren\n% All rights reserved.\n% \n% Redistribution and use in source and binary forms, with or without\n% modification, are permitted provided that the following conditions are met:\n% \n% * Redistributions of source code must retain the above copyright notice, this\n% list of conditions and the following disclaimer.\n% \n% * Redistributions in binary form must reproduce the above copyright notice,\n% this list of conditions and the following disclaimer in the documentation\n% and/or other materials provided with the distribution\n% THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n% AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n% IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n% DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE\n% FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n% DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n% SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n% CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n% OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n% OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n% inputs\nx = 1:length(y);\nbreaks = [1, floor(length(y)/2), length(y)];\nn = 4;\nbeta = 0;\ndim = 1;\n\n% Evaluate B-splines\n[breaksB,coefsB,nB] = splinebase(breaks,n);\n% % Make piecewise polynomial\n% base = mkpp(breaksB,coefsB,nB);\n\ncoder.varsize('coefsB',[8 4], [0 0]);\n\n% just don't use the struct...\nif nargin==2, nB = 1; else nB = nB(:).'; end\ndlk=numel(coefsB); l=length(breaksB)-1; dl=prod(nB)*l; k=fix(dlk/dl+100*eps);\n% if (k<=0)||(dl*k~=dlk)\n% error(message('MATLAB:mkpp:PPNumberMismatchCoeffs',...\n% int2str(l),int2str(d),int2str(dlk)))\n% end\n\nbreaksBase = reshape(breaksB,1,l+1);\ncoefsBase = coefsB; % reshape(coefsB,dl,k);\nlBase = l;\nkBase = k;\ndBase = nB;\n\npieces = lBase;\n\n% try this to get around unbounded-error in Coder.\ncoder.varsize('coefsBase',[8 4], [0 0]);\n\nbase = mkpp(breaksBase,coefsBase,dBase); % lBase,kBase,\nA_base = ppval(base,x);\n\n% Bin data\n[junk,ibin] = histc(x,[-inf,breaks(2:end-1),inf]); %#ok\n\n% Sparse system matrix\nmx = numel(x);\nii = [ibin; ones(n-1,mx)];\nii = cumsum(ii,1);\njj = repmat(1:mx,n,1);\n\nA = zeros(pieces+n-1,mx);\nlinIndices = sub2ind(size(A), ii, jj);\nA(linIndices)=A_base;\n\n% flipPoint = breaks(2)-1;\n% coder.varsize('flipPoint',[1 1], [0 0]);\n% A(1:end-1, 1:flipPoint) = A_base(:, 1:flipPoint);\n% A(2:end, (flipPoint+1):end) = A_base(:, (flipPoint+1):end);\n\n% Solve Min norm(u*A-y)\nu = lsqsolve(A,y,beta);\n\n% Compute polynomial coefficients\nii = [repmat(1:pieces,1,n); ones(n-1,n*pieces)];\nii = cumsum(ii,1);\njj = repmat(1:n*pieces,n,1);\n% C = sparse(ii,jj,base.coefs,pieces+n-1,n*pieces);\n\nC = zeros(pieces+n-1,n*pieces);\nlinIndices = sub2ind(size(C), ii, jj);\nC(linIndices)=base.coefs;\n\ncoefs = u*C;\ncoefs = reshape(coefs,[],n);\n\n% Make piecewise polynomial\npp = mkpp(breaks,coefs,dim);\n\n\n%--------------------------------------------------------------------------\nfunction [breaks0,coefsOut,n] = splinebase(breaks0,n)\n%SPLINEBASE Generate B-spline base PP of order N for breaks BREAKS\n\nassert (n == 4);\n\nbreaks0 = breaks0(:); % Breaks\n% breaks0 = breaks0'; % Initial breaks\nh0 = diff(breaks0); % Spacing\npieces0 = numel(h0); % Number of pieces\ndeg = n - 1; % Polynomial degree\n\n% distances between knots to replicate points to the sides\nhcopy = repmat(h0,ceil(deg/pieces0),1);\n\n% to the left\nhl = hcopy(end:-1:end-deg+1);\nbl = breaks0(1) - cumsum(hl);\n% and to the right\nhr = hcopy(1:deg);\nbr = breaks0(end) + cumsum(hr);\n\n% Add breaks\nbreaks = [bl(deg:-1:1); breaks0; br];\nh = diff(breaks);\npieces = numel(h);\n\nassert (pieces == 8);\n\n% Initiate polynomial coefficients\ncoefs = zeros(n*pieces,n);\ncoefs(1:n:end,1) = 1;\n\n% Expand h\nii = [1:pieces; ones(deg,pieces)];\nii = cumsum(ii,1);\nii = min(ii,pieces);\nH = h(ii(:));\n\n% Recursive generation of B-splines\nfor k = 2:n\n % Antiderivatives of splines\n for j = 1:k-1\n coefs(:,j) = coefs(:,j).*H/(k-j);\n end\n Q = sum(coefs,2);\n Q = reshape(Q,n,pieces);\n Q = cumsum(Q,1);\n c0 = [zeros(1,pieces); Q(1:deg,:)];\n coefs(:,k) = c0(:);\n % Normalize antiderivatives by max value\n fmax = repmat(Q(n,:),n,1);\n fmax = fmax(:);\n for j = 1:k\n coefs(:,j) = coefs(:,j)./fmax;\n end\n % Diff of adjacent antiderivatives\n coefs(1:end-deg,1:k) = coefs(1:end-deg,1:k) - coefs(n:end,1:k);\n coefs(1:n:end,k) = 0;\nend\n\n% Scale coefficients\nscale = ones(size(H));\nfor k = 1:n-1\n scale = scale./H;\n coefs(:,n-k) = scale.*coefs(:,n-k);\nend\n\n% Reduce number of pieces\npieces = pieces - 2*deg;\n\n% Sort coefficients by interval number\nii = [n*(1:pieces); deg*ones(deg,pieces)];\nii = cumsum(ii,1);\ncoefsOut = coefs(ii(:),:);\n\ncoder.varsize('coefsOut',[8 4], [0 0]);\n\n\n\n\n%--------------------------------------------------------------------------\nfunction u = lsqsolve(A,y,beta)\n%LSQSOLVE Solve Min norm(u*A-y)\n\n\n% Solution\nu = y/A;\n\n% % Robust fitting\n% if beta > 0\n% [m,n] = size(y);\n% alpha = 0.5*beta/(1-beta)/m;\n% for k = 1:3\n% % Residual\n% r = u*A - y;\n% rr = r.*conj(r);\n% rrmean = sum(rr,2)/n;\n% rrmean(~rrmean) = 1;\n% rrhat = (alpha./rrmean)'*rr;\n% % Weights\n% w = exp(-rrhat);\n% spw = spdiags(w',0,n,n);\n% % Solve weighted problem\n% u = (y*spw)/(A*spw);\n% end\n% end\n\n" }, { "path": "Matlab/BasisOperations/test_zp2ss.m", "content": "function out = test_zp2ss(z,p,k)\n\n out = zp2ss(z,p,k);" }, { "path": "Matlab/BasisOperations/testfilt.m", "content": "% testFilter\n\nfileID = fopen('/Users/carl/PycharmProjects/catch22/C/timeSeries/tsid0133.txt','r');\ntsData = fscanf(fileID,'%f');\nfclose(fileID);\n\n[b, a] = myButter(4, 0.8/24);\n\ntsDatafilt = filter(b,a,tsData)" }, { "path": "Matlab/BasisOperations/welchy.m", "content": "function [Pxx, F] = calc_psd(X, NFFT, Fs, WINDOW)\r\n% Calculate Power Spectral Density \r\n%\t[Pxx ,F]= PSD(X, NFFT, Fs, WINDOW) estimates the Power Spectral Density of \r\n%\tsignal vector X using Welch's averaged periodogram method. X is \r\n%\tdivided into sections, then windowed by the WINDOW parameter\r\n%\tThe magnitude squared of the length NFFT DFTs of the sections are \r\n%\taveraged to form Pxx. Pxx is length NFFT/2+1 for NFFT even. the default fot WINDOW is a rectangular window . \r\n% \tFs is the sampling frequency which is used for scaling of plots.\r\n%\r\n% \tAuthor(s): T. Krauss, 3-26-93\r\n%\tCopyright (c) 1984-94 by The MathWorks, Inc.\r\n% Revision: Roni P, 10.8.01\r\n%\t\t\tEU, dt used in computations\r\n%\t\t\tEU, df used to get frequency scale\r\n% X - The signal we want to estimate the psd for.\r\n% NFFT - number of data points of each window\r\n% Fs - Sampling frequency\r\n% Window - The type of window to use\r\n%\tExample of use: \r\n% dt = 0.01;\r\n% f = 10; % Frequency to find\r\n% t = 0:dt:10; % Create time vector\r\n% x = sin(2*pi*f*t); % create a signal\r\n% nfft = 64; % Window should be less than 256.\r\n% Fs = 1/dt; % Sampling frequency\r\n%\t\twindow = hanning(nfft); % Use hanning as window\r\n%\t\t[Pxx, F] = calc_psd(x, nfft, Fs, window);\r\n% plot(F,Pxx); xlabel('Frequency [Hz]'); ylabel('Amplitude');\r\n\r\nif (nargin <= 2)\r\n error('Not enough input parameters, exiting...');\r\nend\r\n\r\n% % make sure the windows width is a power of 2\r\n% NFFT = 2^(nextpow2(NFFT)-1); % this stinks.\r\n\r\nwindowWidth = numel(WINDOW);%2^(nextpow2(numel(WINDOW))-1); % \r\n\r\ndt = 1/Fs;\r\ndf = 1/(2^(nextpow2(numel(WINDOW))))/dt;\r\nif (nargin == 3)\r\n\t w = ones(windowWidth, 1);\r\nend\r\nif (nargin == 4)\r\n\tw = WINDOW;\r\n\tw = w(:); % Make sure w is a column vector\r\nend\r\n\t\r\nX = X(:); \t\t\t\t % Make sure X is a column vector\r\nk = floor(numel(X)/(windowWidth/2))-1; % Number of windows\r\nindex = 1:windowWidth;\r\nKMU = k*norm(w)^2; \t\t\t\t\t % Normalizing scale factor\r\nw = w(:);\r\nP = zeros(NFFT, 1);\r\n\r\nfor i = 1:k\r\n\txw = w.*X(index);\r\n\tindex = index + windowWidth/2; % Continue to next window\r\n\tP = P + abs(fft(xw, NFFT)).^2;\r\nend\r\n\r\nPxx = dt*(P(1:NFFT/2+1))/KMU;\r\n% strange correction here, don't know why it's neccesary!!\r\nPxx(2:end-1) = Pxx(2:end-1)*2;\r\n\r\nn = max(size(Pxx));\r\nF = (0:n-1)'*df;" }, { "path": "Matlab/CO_Embed2_Dist_tau_d_expfit_meandiff.m", "content": "function out = CO_Embed2_Dist_tau_d_expfit_meandiff(y)\n% CO_Embed2_Dist Analyzes distances in a 2-d embedding space of a time series.\n%\n% Returns statistics on the sequence of successive Euclidean distances between\n% points in a two-dimensional time-delay embedding space with a given\n% time-delay, tau.\n%\n% Outputs the the mean distance, the\n% spread of distances, and statistics from an exponential fit to the\n% distribution of distances.\n%\n%---INPUTS:\n% y, a z-scored column vector representing the input time series.\n% tau, the time delay.\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% no combination of single functions\ncoder.inline('never');\n\n% ------------------------------------------------------------------------------\n%% Check inputs:\n% ------------------------------------------------------------------------------\n\nN = length(y); % time-series length\n\ntau = CO_FirstZero(y,'ac');\nif tau > N/10\n tau = floor(N/10);\nend\n\n% ------------------------------------------------------------------------------\n% 2-dimensional time-delay embedding:\n% ------------------------------------------------------------------------------\n\nm = [y(1:end-tau), y(1+tau:end)];\n\n% ------------------------------------------------------------------------------\n% Calculate Euclidean distances between successive points in this space, d:\n% ------------------------------------------------------------------------------\n\nd = sqrt(diff(m(:,1)).^2 + diff(m(:,2)).^2); % Euclidean distance\n\n% % ------------------------------------------------------------------------------\n% % Empirical distance distribution often fits Exponential distribution quite well\n% % Fit to all values (often some extreme outliers, but oh well)\n% l = expfit(d);\nl = mean(d); % maybe this is stupid, but I seriously don't see the difference\n\n%% Sum of abs differences between exp fit and observed:\n\n[N, binEdges] = histcounts_(d, [], true);\n\n% from edges to centers\nbinCentres = mean([binEdges(1:end-1); binEdges(2:end)])';\n\n% exponential fit in each bin\nz = binCentres ./ l;\nexpf = exp(-z) ./ l;\nexpf(expf<0) = 0;\n\nd_expfit_meandiff = mean(abs(N - expf)); % mean absolute error of fit\n\nout = d_expfit_meandiff;\n\nend\n" }, { "path": "Matlab/CO_FirstMin_ac.m", "content": "function out = CO_FirstMin_ac(y)\n% CO_FirstMin Time of first minimum in a given correlation function\n%\n%---INPUTS:\n% y, the input time series\n%\n% Note that selecting 'ac' is unusual operation: standard operations are the\n% first zero-crossing of the autocorrelation (as in CO_FirstZero), or the first\n% minimum of the mutual information function ('mi').\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% no combination of single functions\ncoder.inline('never');\n\nN = length(y); % Time-series length\n\n% ------------------------------------------------------------------------------\n% Define the autocorrelation function\n% ------------------------------------------------------------------------------\n\n% Autocorrelation implemented as CO_AutoCorr\ncorrfn = @(x) CO_AutoCorr(y,x,'Fourier');\n\n% ------------------------------------------------------------------------------\n% Search for a minimum\n% ------------------------------------------------------------------------------\n% (Incrementally through time lags until a minimum is found)\n\nautoCorr = zeros(N-1,1); % preallocate autocorrelation vector\nfor i = 1:N-1\n % Calculate the auto-correlation at this lag:\n autoCorr(i) = corrfn(i);\n\n % Hit a NaN before got to a minimum -- there is no minimum\n if isnan(autoCorr(i))\n % warning('No minimum in %s [[time series too short to find it?]]',minWhat)\n out = NaN; return\n end\n\n % We're at a minimum:\n if i==2 && (autoCorr(2) > autoCorr(1))\n % already increases at lag of 2 from lag of 1: a minimum (since ac(0) is maximal)\n out = 1;\n return\n elseif (i > 2) && (autoCorr(i-2) > autoCorr(i-1)) && (autoCorr(i-1) < autoCorr(i)) % minimum at previous i\n out = i-1; % I found the first minimum!\n return\n end\nend\n\n% Still decreasing -- no minimum was found after searching all across the time series:\nout = N;\n\nend\n" }, { "path": "Matlab/CO_HistogramAMI_even_2_5.m", "content": "function out = CO_HistogramAMI_even_2_5(y)\n% CO_HistogramAMI The automutual information of the distribution using histograms.\n%\n% The approach used to bin the data is provided.\n%\n%---INPUTS:\n%\n% y, the input time series\n%\n% tau, 2: the time-lag\n%\n% meth, 'even': evenly-spaced bins through the range of the time series,\n%\n% numBins, 5: the number of bins\n%\n%---OUTPUT: the automutual information calculated in this way.\n\n% Uses the hist2 function (renamed NK_hist2.m here) by Nedialko Krouchev, obtained\n% from Matlab Central,\n% http://www.mathworks.com/matlabcentral/fileexchange/12346-hist2-for-the-people\n% [[hist2 for the people by Nedialko Krouchev, 20 Sep 2006 (Updated 21 Sep 2006)]]\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% no combination of single functions\ncoder.inline('never');\n\n% ------------------------------------------------------------------------------\n%% Check Inputs:\n% ------------------------------------------------------------------------------\n\n% Number of options:\n% remove outliers first?, number of bins, range of bins, bin sizes\n\ntau = 2;\n% meth = 'even';\nnumBins = 5;\n\n% ------------------------------------------------------------------------------\n%% Bins by standard deviation (=1)\n% ------------------------------------------------------------------------------\n\n% even\nb = linspace(min(y)-0.1,max(y)+0.1,numBins+1); % +0.1 to make sure all points included\n\nnb = length(b) - 1; % number of bins (-1 since b defines edges)\n\n% ------------------------------------------------------------------------------\n% Form the time-delay vectors y1 and y2\n% ------------------------------------------------------------------------------\n\ny1 = y(1:end-tau);\ny2 = y(1+tau:end);\n\n% (1) Joint distribution of y1 and y2\npij = NK_hist2(y1,y2,b,b);\npij = pij(1:nb,1:nb); % joint\npij = pij/sum(sum(pij)); % joint\npi = sum(pij,1); % marginal\npj = sum(pij,2); % other marginal\n\n% Old-fashioned method (should give same result):\n% pi = histc(y1,b); pi = pi(1:nb); pi = pi/sum(pi); % marginal\n% pj = histc(y2,b); pj= pj(1:nb); pj = pj/sum(pj); % other marginal\n\npii = ones(nb,1)*pi;\npjj = pj*ones(1,nb);\n\nr = (pij > 0); % Defining the range in this way, we set log(0) = 0\nami = sum(pij(r).*log(pij(r)./pii(r)./pjj(r)));\n\nout = ami;\n\nend\n" }, { "path": "Matlab/CO_f1ecac.m", "content": "function out = CO_f1ecac(y)\n% CO_f1ecac The 1/e correlation length\n% \n% Finds where autocorrelation function first crosses 1/e\n%\n%---INPUT:\n% y, the input time series.\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% no combination of single functions\ncoder.inline('never');\n\nN = length(y); % time-series length\nthresh = 1/exp(1); % 1/e threshold\n\na = zeros(N,1); % autocorrelations\na(1) = 1; % perfect autocorrelation when no lag\nfor i = 2:N\n a(i) = CO_AutoCorr(y,i-1,'Fourier');\n if ((a(i-1)-thresh)*(a(i)-thresh) < 0)\n % Crossed the 1/e line\n out = i-1; % -1 since i is tau+1\n return\n end\nend\n\n% If no minimum in entire spectrum return the maximum value\nout = N;\n\nend\n" }, { "path": "Matlab/CO_trev_1_num.m", "content": "function out = CO_trev_1_num(y)\n\n% no combination of single functions\ncoder.inline('never');\n\noutStruct = CO_trev(y, 1);\n\nout = outStruct.num;" }, { "path": "Matlab/DN_HistogramMode_10.m", "content": "function out = DN_HistogramMode_10(y)\n\n% no combination of single functions\ncoder.inline('never');\n\nout = DN_HistogramMode(y, 10);" }, { "path": "Matlab/DN_HistogramMode_5.m", "content": "function out = DN_HistogramMode_5(y)\n\n% no combination of single functions\ncoder.inline('never');\n\nout = DN_HistogramMode(y, 5);" }, { "path": "Matlab/DN_Mean.m", "content": "function out = DN_Mean(y)\n\n % no combination of single functions\n coder.inline('never');\n \n out = DN_Mean(y, 5);" }, { "path": "Matlab/DN_OutlierInclude_n_001_mdrmd.m", "content": "function out = DN_OutlierInclude_n_001_mdrmd(y)\n\n % no combination of single functions\n coder.inline('never');\n\n out = DN_OutlierInclude_001_mdrmd(y, 'n');\n" }, { "path": "Matlab/DN_OutlierInclude_p_001_mdrmd.m", "content": "function out = DN_OutlierInclude_p_001_mdrmd(y)\n\n % no combination of single functions\n coder.inline('never');\n\n out = DN_OutlierInclude_001_mdrmd(y, 'p');\n" }, { "path": "Matlab/DN_Spread_Std.m", "content": "function out = DN_Spread_Std(y)\n\n % no combination of single functions\n coder.inline('never');\n \n out = DN_Spread_Std(y, 5);" }, { "path": "Matlab/FC_LocalSimple_mean1_tauresrat.m", "content": "function out = FC_LocalSimple_mean1_tauresrat(y)\n% FC_LocalSimple Simple local time-series forecasting.\n%\n% Simple predictors using the past trainLength values of the time series to\n% predict its next value.\n%\n%---INPUTS:\n% y, the input time series\n%\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% no combination of single functions\ncoder.inline('never');\n\n% -------------------------------------------------------------------------\n%% Set defaults\n% -------------------------------------------------------------------------\n\ntrainLength = 1;\n\nN = length(y); % Time-series length\n\n% ------------------------------------------------------------------------------\n% Do the local prediction\n% ------------------------------------------------------------------------------\nif strcmp(trainLength,'ac')\n lp = CO_FirstZero(y,'ac'); % make it tau\nelse\n lp = trainLength; % the length of the subsegment preceeding to use to predict the subsequent value\nend\nevalr = lp+1:N; % range over which to evaluate the forecast\nif length(evalr)==0\n% warning('Time series too short for forecasting');\n out = NaN; return\nend\nres = zeros(length(evalr),1); % residuals\nfor i = 1:length(evalr)\n res(i) = mean(y(evalr(i)-lp:evalr(i)-1)) - y(evalr(i)); % prediction-value\nend\n\n% tauresrat\nout = CO_FirstZero(res,'ac')/CO_FirstZero(y,'ac');\n\nend\n" }, { "path": "Matlab/FC_LocalSimple_mean3_stderr.m", "content": "function out = FC_LocalSimple_mean3_stderr(y)\n% FC_LocalSimple Simple local time-series forecasting.\n%\n% Simple predictors using the past trainLength values of the time series to\n% predict its next value.\n%\n%---INPUTS:\n% y, the input time series\n%\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% no combination of single functions\ncoder.inline('never');\n\n%-------------------------------------------------------------------------------\n%% Set defaults:\n%-------------------------------------------------------------------------------\n\ntrainLength = 3;\n\nN = length(y); % Time-series length\n\n% ------------------------------------------------------------------------------\n% Do the local prediction\n% ------------------------------------------------------------------------------\n\nlp = trainLength; % the length of the subsegment preceeding to use to predict the subsequent value\n\nevalr = lp+1:N; % range over which to evaluate the forecast\nif length(evalr)==0\n% warning('Time series too short for forecasting');\n out = NaN; return\nend\nres = zeros(length(evalr),1); % residuals\nfor i = 1:length(evalr)\n res(i) = mean(y(evalr(i)-lp:evalr(i)-1)) - y(evalr(i)); % prediction-value\nend\n\n% Spread of residuals:\n% stderr\nout = std(res);\n\nend\n" }, { "path": "Matlab/IN_AutoMutualInfoStats_40_gaussian_fmmi.m", "content": "function out = IN_AutoMutualInfoStats_40_gaussian_fmmi(y)\n% IN_AutoMutualInfoStats Statistics on automutual information function for a time series.\n%\n%---INPUTS:\n% y, column vector of time series data\n%\n% maxTau, maximal time delay\n%\n% estMethod = 'gaussian'\n%\n%---OUTPUTS:\n% out = fmmi, statistic on the AMIs and their pattern across\n% the range of specified time delays.\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% no combination of single functions\ncoder.inline('never');\n\n% ------------------------------------------------------------------------------\n%% Preliminaries\n% ------------------------------------------------------------------------------\nN = length(y); % length of time series\n\n% ------------------------------------------------------------------------------\n%% Check Inputs\n% ------------------------------------------------------------------------------\n\n% maxTau: the maximum time delay to investigate\nmaxTau = 40;\n\n% Don't go above N/2\nmaxTau = min(maxTau,ceil(N/2));\n\n% ------------------------------------------------------------------------------\n%% Get the AMI data:\n% ------------------------------------------------------------------------------\n\n% do this manually, Gaussian really is the easiest MI\nautocorrs = autocorrelation(y, maxTau);\nami = -0.5*log(1 - autocorrs.^2);\n\n\n% ------------------------------------------------------------------------------\n% Output statistics:\n% ------------------------------------------------------------------------------\nlami = length(ami);\n\n% First minimum of mutual information across range\ndami = diff(ami);\nextremai = find(dami(1:end-1).*dami(2:end) < 0 & dami(1:end-1)<0);\nif isempty(extremai)\n % fmmi\n out = lami; % actually represents lag, because indexes don't but diff delays by 1\nelse\n % fmmi\n out = min(extremai);\nend\n\nend\n\nfunction out = autocorrelation(y, maxLag)\n\n N = length(y);\n\n if maxLag >= N\n maxLag = N-1;\n end\n \n out = zeros(1,maxLag);\n \n for lag = 1:maxLag\n corrs = corrcoef(y(1:end-lag), y(1+lag:end));\n out(lag) = corrs(2,1);\n end\n\nend\n" }, { "path": "Matlab/MD_hrv_classic_pnn40.m", "content": "function out = MD_hrv_classic_pnn40(y)\n% MD_hrv_classic Classic heart rate variability (HRV) statistics.\n%\n% Typically assumes an NN/RR time series in units of seconds.\n%\n%---INPUTS:\n% y, the input time series.\n%\n%---OUTPUTS:\n% pNN40\n% cf. \"The pNNx files: re-examining a widely used heart rate variability\n% measure\", J.E. Mietus et al., Heart 88(4) 378 (2002)\n%\n% Code is heavily derived from that provided by Max A. Little:\n% http://www.maxlittle.net/\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% no combination of single functions\ncoder.inline('never');\n\n% Standard defaults\ndiffy = diff(y);\nN = length(y); % time-series length\n\n% ------------------------------------------------------------------------------\n% Calculate pNNx percentage\n% ------------------------------------------------------------------------------\n% pNNx: recommendation as per Mietus et. al. 2002, \"The pNNx files: ...\", Heart\n% strange to do this for a z-scored time series...\n% pnntime = 20;\n\nDy = abs(diffy) * 1000;\n\n% Anonymous function to do the PNNx calcualtion:\nPNNxfn = @(x) sum(Dy > x)/(N-1);\n\n% % exceed = sum(Dy > pnntime);\n% out.pnn5 = PNNxfn(5); %sum(Dy > 5)/(N-1); % proportion of difference magnitudes greater than 0.005*sigma\n% out.pnn10 = PNNxfn(10); %sum(Dy > 10)/(N-1);\n% out.pnn20 = PNNxfn(20); %sum(Dy > 20)/(N-1);\n% out.pnn30 = PNNxfn(30); %sum(Dy > 30)/(N-1);\n% pnn40\nout = PNNxfn(40); %sum(Dy > 40)/(N-1);\n\n\nend\n" }, { "path": "Matlab/PD_PeriodicityWang_th0_01.m", "content": "function out = PD_PeriodicityWang_th0_01(y_in)\n% PD_PeriodicityWang Periodicity extraction measure of Wang et al.\n%\n% Implements an idea based on the periodicity extraction measure proposed in:\n%\n% \"Structure-based Statistical Features and Multivariate Time Series Clustering\"\n% Wang, X. and Wirth, A. and Wang, L.\n% Seventh IEEE International Conference on Data Mining, 351--360 (2007)\n% DOI: 10.1109/ICDM.2007.103\n%\n% Detrends the time series using a single-knot cubic regression spline\n% and then computes autocorrelations up to one third of the length of\n% the time series. The frequency is the first peak in the autocorrelation\n% function satisfying a set of conditions.\n% \n% This code uses the SPLINEFIT v1.13.0.0 function by Jonas Lundgren from\n% Matlab Central accessed on 25 Aug 2018.\n%\n%---INPUT:\n% y, the input time series.\n%\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% no combination of single functions\ncoder.inline('never');\n\n% ------------------------------------------------------------------------------\n%% Foreplay\n% ------------------------------------------------------------------------------\nN = length(y_in); % length of the time series\nth = 0.01; % [0, 0.01, 0.1, 0.2, 1/sqrt(N), 5/sqrt(N), 10/sqrt(N)]; % the thresholds with which to count a peak\n\n% ------------------------------------------------------------------------------\n%% 1: Detrend using a regression spline with 3 knots\n% ------------------------------------------------------------------------------\n\nppSpline = splinefit(y_in');\n\ny_spl = ppval(ppSpline,1:N);\n% x = (ppSpline.breaks(1):ppSpline.breaks(2))-1;\n% y_spl_1 = ppSpline.coefs(1,1)*x.^3 + ppSpline.coefs(1,2)*x.^2 + ppSpline.coefs(1,3)*x.^1 + ppSpline.coefs(1,4);\n% x = (ppSpline.breaks(2):ppSpline.breaks(3))-ppSpline.breaks(2)+1;\n% y_spl_2 = ppSpline.coefs(2,1)*x.^3 + ppSpline.coefs(2,2)*x.^2 + ppSpline.coefs(2,3)*x.^1 + ppSpline.coefs(2,4);\n% y_spl = [y_spl_1, y_spl_2];\n\ny = y_in - y_spl';\n\n%% 2. Compute autocorrelations up to 1/3 the length of the time series.\nacmax = ceil(N/3); % compute autocorrelations up to this lag\nacf = zeros(acmax,1); % the autocorrelation function\nfor i = 1:acmax % i is the \\tau, the AC lag\n acf(i) = mean(y(1:N-i).*y(i+1:N));\nend\n\n% ------------------------------------------------------------------------------\n%% 3. Frequency is the first peak satisfying the following conditions:\n% ------------------------------------------------------------------------------\n% (a) a trough before it\n% (b) difference between peak and trough is at least 0.01\n% (c) peak corresponds to positive correlation\n\n% (i) find peaks and troughs in ACF\ndiffac = diff(acf); % differenced time series\nsgndiffac = sign(diffac); % sign of differenced time series\nbath = diff(sgndiffac); % differenced, signed, differenced time series\ntroughs = find(bath == 2) + 1; % finds troughs\npeaks = find(bath == -2) + 1; % finds peaks\nnpeaks = length(peaks);\n\nout = 0;\n\nfor i = 1:npeaks % search through all peaks for one that meets the condition\n ipeak = peaks(i); % acf lag at which there is a peak\n thepeak = acf(ipeak); % acf at the peak\n ftrough = find(troughs < ipeak,1,'last');\n if isempty(ftrough); continue; end\n itrough = troughs(ftrough); % acf lag at which there is a trough (the first one preceeding the peak)\n thetrough = acf(itrough); % acf at the trough\n\n % (a) a trough before it: should be implicit in the ftrough bit above\n % if troughs(1)>ipeak % the first trough is after it\n % continue\n % end\n\n % (b) difference between peak and trough is at least 0.01\n if thepeak - thetrough < th\n continue\n end\n\n % (c) peak corresponds to positive correlation\n if thepeak < 0\n continue\n end\n\n % we made it! Use this frequency!\n out = ipeak; break\nend\n\n\nend\n" }, { "path": "Matlab/SB_BinaryStats_diff_longstretch0.m", "content": "function out = SB_BinaryStats_diff_longstretch0(y)\n% SB_BinaryStats Statistics on a binary symbolization of the time series\n%\n% Binary symbolization of the time series is a symbolic string of 0s and 1s.\n%\n% Provides information about the coarse-grained behavior of the time series\n%\n%---INPUTS:\n% y, the input time series\n%\n% binaryMethod, the symbolization rule:\n% 'diff': by whether incremental differences of the time series are\n% positive (1), or negative (0),\n%\n%---OUTPUTS:\n% Include the Shannon entropy of the string, the longest stretches of 0s\n% or 1s, the mean length of consecutive 0s or 1s, and the spread of consecutive\n% strings of 0s or 1s.\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% no combination of single functions\ncoder.inline('never');\n\n%-------------------------------------------------------------------------------\n% Set defaults:\n%-------------------------------------------------------------------------------\n\nbinaryMethod = 'diff';\n\n%-------------------------------------------------------------------------------\n% Binarize the time series:\n%-------------------------------------------------------------------------------\nyBin = BF_Binarize(y,binaryMethod);\n\n%-------------------------------------------------------------------------------\n% Consecutive string of ones / zeros (normalized by length)\n%-------------------------------------------------------------------------------\ndifffy = diff(find([1;yBin;1]));\nstretch0 = difffy(difffy ~= 1) - 1;\n\n%-------------------------------------------------------------------------------\n% pstretches\n%-------------------------------------------------------------------------------\n\nif isempty(stretch0) % all 1s (almost impossible to actually occur)\n longstretch0 = 0;\nelse\n longstretch0 = max(stretch0); % longest consecutive stretch of zeros\nend\n\nout = longstretch0;\n\nend\n" }, { "path": "Matlab/SB_BinaryStats_mean_longstretch1.m", "content": "function out = SB_BinaryStats_mean_longstretch1(y)\n% SB_BinaryStats Statistics on a binary symbolization of the time series\n%\n% Binary symbolization of the time series is a symbolic string of 0s and 1s.\n%\n% Provides information about the coarse-grained behavior of the time series\n%\n%---INPUTS:\n% y, the input time series\n%\n% binaryMethod, the symbolization rule:\n% 'diff': by whether incremental differences of the time series are\n% positive (1), or negative (0),\n%\n%---OUTPUTS:\n% Include the Shannon entropy of the string, the longest stretches of 0s\n% or 1s, the mean length of consecutive 0s or 1s, and the spread of consecutive\n% strings of 0s or 1s.\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% no combination of single functions\ncoder.inline('never');\n\n%-------------------------------------------------------------------------------\n% Set defaults:\n%-------------------------------------------------------------------------------\n\nbinaryMethod = 'mean';\n\n%-------------------------------------------------------------------------------\n% Binarize the time series:\n%-------------------------------------------------------------------------------\nyBin = BF_Binarize(y,binaryMethod);\n\n%-------------------------------------------------------------------------------\n% Consecutive string of ones (normalized by length)\n%-------------------------------------------------------------------------------\ndifffy = diff(find([0;yBin;0] == 0));\nstretch1 = difffy(difffy ~= 1) - 1;\n\n%-------------------------------------------------------------------------------\n% pstretches\n%-------------------------------------------------------------------------------\n\nif isempty(stretch1) % all 1s (almost impossible to actually occur)\n longstretch1 = 0;\nelse\n longstretch1 = max(stretch1); % longest consecutive stretch of zeros\nend\n\nout = longstretch1;\n\nend\n" }, { "path": "Matlab/SB_MotifThree_quantile_hh.m", "content": "function out = SB_MotifThree_quantile_hh(y)\n% SB_MotifThree Motifs in a coarse-graining of a time series to a 3-letter alphabet\n%\n% (As SB_MotifTwo but with a 3-letter alphabet)\n%\n%---INPUTS:\n% y, time series to analyze\n% cgHow, the coarse-graining method to use. 'quantile': equiprobable alphabet by time-series value\n%\n%---OUTPUTS:\n% Statistics on words of length 1, 2, 3, and 4.\n\n% ------------------------------------------------------------------------------\n% Copyright (C) 2017, Ben D. Fulcher ,\n% \n%\n% If you use this code for your research, please cite the following two papers:\n%\n% (1) B.D. Fulcher and N.S. Jones, \"hctsa: A Computational Framework for Automated\n% Time-Series Phenotyping Using Massive Feature Extraction, Cell Systems 5: 527 (2017).\n% DOI: 10.1016/j.cels.2017.10.001\n%\n% (2) B.D. Fulcher, M.A. Little, N.S. Jones, \"Highly comparative time-series\n% analysis: the empirical structure of time series and their methods\",\n% J. Roy. Soc. Interface 10(83) 20130048 (2013).\n% DOI: 10.1098/rsif.2013.0048\n%\n% This function is free software: you can redistribute it and/or modify it under\n% the terms of the GNU General Public License as published by the Free Software\n% Foundation, either version 3 of the License, or (at your option) any later\n% version.\n%\n% This program is distributed in the hope that it will be useful, but WITHOUT\n% ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n% FOR A PARTICULAR PURPOSE. See the GNU General Public License for more\n% details.\n%\n% You should have received a copy of the GNU General Public License along with\n% this program. If not, see .\n% ------------------------------------------------------------------------------\n\n% no combination of single functions\ncoder.inline('never');\n\n%-------------------------------------------------------------------------------\n%% Set defaults:\n%-------------------------------------------------------------------------------\n\n% quantile graining\nyt = SB_CoarseGrain_quantile(y,3);\n\nN = length(yt); % Length of the symbolized sequence derived from the time series\n\n% So we have a vector yt with entries \\in {1,2,3}\n\n% ------------------------------------------------------------------------------\n%% Words of length 1\n% ------------------------------------------------------------------------------\nr1 = cell(3,1); % stores ranges as vectors\n% out1 = zeros(3,1); % stores probabilities as doubles\n\nfor i = 1:3\n\tr1{i} = find(yt == i);\n% \tout1(i) = length(r1{i})/N;\nend\n\n\n% ------------------------------------------------------------------------------\n%% Words of length 2\n% ------------------------------------------------------------------------------\n% Make sure ranges are valid for looking at the next one\nfor i = 1:3\n\tif (~isempty(r1{i})) && (r1{i}(end) == N)\n r1{i} = r1{i}(1:end-1);\n end\nend\n\nr2 = cell(3,3);\nout2 = zeros(3,3);\nfor i = 1:3\n\tfor j = 1:3\n\t\tr2{i,j} = r1{i}(yt(r1{i}+1) == j);\n\t\tout2(i,j) = length(r2{i,j})/(N-1);\n\tend\nend\n\n% hh\nout = f_entropy(out2); % entropy of this result\n\n%-------------------------------------------------------------------------------\nfunction h = f_entropy(x)\n % entropy of a set of counts, log(0)=0\n h = -sum(x(x > 0).*log(x(x > 0)));\nend\n\nend\n" }, { "path": "Matlab/SB_TransitionMatrix_3ac_sumdiagcov.m", "content": "function out = SB_TransitionMatrix_3ac_sumdiagcov(y)\n\n% no combination of single functions\ncoder.inline('never');\n\noutStruct = SB_TransitionMatrix(y, 3, 'ac');\n\nout = outStruct.sumdiagcov;" }, { "path": "Matlab/SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1.m", "content": "function out = SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1(y)\n\n % no combination of single functions\n coder.inline('never');\n\n out = SC_FluctAnal_q2_taustep50_k1_logi_prop_r1(y, 'dfa', 2);" }, { "path": "Matlab/SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1.m", "content": "function out = SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1(y)\n\n % no combination of single functions\n coder.inline('never');\n\n out = SC_FluctAnal_q2_taustep50_k1_logi_prop_r1(y, 'rsrangefit');" }, { "path": "Matlab/SP_Summaries_welch_rect_area_5_1.m", "content": "function out = SP_Summaries_welch_rect_area_5_1(y)\n\n % no combination of single functions\n coder.inline('never');\n\n outStruct = SP_Summaries_welch_rect(y);\n out = outStruct.area_5_1;" }, { "path": "Matlab/SP_Summaries_welch_rect_centroid.m", "content": "function out = SP_Summaries_welch_rect_centroid(y)\n\n % no combination of single functions\n coder.inline('never');\n\n outStruct = SP_Summaries_welch_rect(y);\n out = outStruct.centroid;\n" }, { "path": "README.md", "content": "

\"catch22

\n\n

catch22: CAnonical Time-series CHaracteristics

\n\n

\n \t\n \n \t\n

\n\n_catch22_ is a collection of 22 time-series features coded in C that can be run from Python, R, Matlab, and Julia, licensed under the [GNU GPL v3 license](http://www.gnu.org/licenses/gpl-3.0.html) (or later).\nThe _catch22_ features are a high-performing subset of the over 7000 features in [_hctsa_](https://github.com/benfulcher/hctsa).\n\nThe features were selected based on their classification performance across a collection of 93 real-world time-series classification problems, as described in our open-access paper, [📗 Lubba et al. (2019). _catch22_: CAnonical Time-series CHaracteristics](https://doi.org/10.1007/s10618-019-00647-x).\n\n## [📙📘📗___catch22_ documentation__](https://time-series-features.gitbook.io/catch22/)\n\nThere is [comprehensive documentation](https://time-series-features.gitbook.io/catch22/) for _catch22_, including:\n\n- Installation instructions (across C, python, R, Julia, and Matlab)\n- Information about the theory behind and behavior of each of the features,\n- A list of publications that have used or extended _catch22_\n- And more :yum:\n\n## Installation and Usage in Python, R, Matlab, Julia, and compiled C\n\nThere are also native versions of this code for other programming languages:\n\n- [Rcatch22](https://github.com/hendersontrent/Rcatch22) (R) `install.packages(\"Rcatch22\")`\n- [pycatch22](https://github.com/DynamicsAndNeuralSystems/pycatch22) (python) `pip install pycatch22`\n- [Catch22.jl](https://github.com/brendanjohnharris/Catch22.jl) (Julia) `Pkg.add(\"Catch22\")`\n\nYou can also use the C-compiled features directly, or in Matlab, following the [detailed installation instructions in the documentation](https://time-series-features.gitbook.io/catch22/).\n\n## Acknowledgement :+1:\n\nIf you use this software, please read and cite this open-access article:\n\n- 📗 Lubba et al. [_catch22_: CAnonical Time-series CHaracteristics](https://doi.org/10.1007/s10618-019-00647-x), _Data Min Knowl Disc_ __33__, 1821 (2019).\n\n## Performance Summary\n\nSummary of the performance of the _catch22_ feature set across 93 classification problems, and a comparison to the [_hctsa_ feature set](https://github.com/benfulcher/hctsa) (cf. Fig. 4 from [our paper](https://doi.org/10.1007/s10618-019-00647-x)):\n\n![](img/PerformanceComparisonFig4.png)\n\n## Notes\n\n- When presenting results using _catch22_, you must identify the version used to allow clear reproduction of your results. For example, `CO_f1ecac` was altered from an integer-valued output to a linearly interpolated real-valued output from v0.3.\n- _catch22_ features only evaluate _dynamical_ properties of time series and do not respond to basic differences in the location (e.g., mean) or spread (e.g., variance).\n- If the location and spread of the raw time-series distribution may be important for your application, you should apply the function argument `catch24 = true` (`TRUE` in R, `True` in Python) to your call to the _catch22_ function in the language of your choice. This will result in 24 features being calculated: the _catch22_ features in addition to mean and standard deviation.\n- Time series are _z_-scored internally (for features other than mean and standard deviation), which means that, e.g., constant time series will lead to `NaN` outputs.\n- Time-series data are taken as an ordered sequence of values (without time stamps). We assume an evenly sampled time series.\n- See language-specific usage information in the [docs](https://time-series-features.gitbook.io/catch22/).\n- The computational pipeline used to generate the _catch22_ feature set is in the [`op_importance`](https://github.com/chlubba/op_importance) repository.\n" }, { "path": "catch22.m", "content": "function out = catch22(y)\n% catch22 Compute all 22 catch22 features\n\n% No combination of single functions\ncoder.inline('never');\n\n% First z-score:\ny = (y - mean(y))/std(y);\n\nout = zeros(22,1);\n\nout(1) = DN_HistogramMode_5(y);\nout(2) = DN_HistogramMode_10(y);\nout(3) = CO_f1ecac(y);\nout(4) = CO_FirstMin_ac(y);\nout(5) = CO_HistogramAMI_even_2_5(y);\nout(6) = CO_trev_1_num(y);\nout(7) = MD_hrv_classic_pnn40(y);\nout(8) = SB_BinaryStats_mean_longstretch1(y);\nout(9) = SB_TransitionMatrix_3ac_sumdiagcov(y);\nout(10) = PD_PeriodicityWang_th0_01(y);\nout(11) = CO_Embed2_Dist_tau_d_expfit_meandiff(y);\nout(12) = IN_AutoMutualInfoStats_40_gaussian_fmmi(y);\nout(13) = FC_LocalSimple_mean1_tauresrat(y);\nout(14) = DN_OutlierInclude_p_001_mdrmd(y);\nout(15) = DN_OutlierInclude_n_001_mdrmd(y);\nout(16) = SP_Summaries_welch_rect_area_5_1(y);\nout(17) = SB_BinaryStats_diff_longstretch0(y);\nout(18) = SB_MotifThree_quantile_hh(y);\nout(19) = SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1(y);\nout(20) = SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1(y);\nout(21) = SP_Summaries_welch_rect_centroid(y);\nout(22) = FC_LocalSimple_mean3_stderr(y);\n\nend\n" }, { "path": "featureList.txt", "content": "# Full_feature_name_code short_feature_name\n# ---------------------------------------------------------------\nDN_HistogramMode_5 mode_5\nDN_HistogramMode_10 mode_10\nDN_OutlierInclude_p_001_mdrmd outlier_timing_pos\nDN_OutlierInclude_n_001_mdrmd outlier_timing_neg\nCO_f1ecac acf_timescale\nCO_FirstMin_ac acf_first_min\nSP_Summaries_welch_rect_area_5_1 low_freq_power\nSP_Summaries_welch_rect_centroid centroid_freq\nFC_LocalSimple_mean3_stderr forecast_error\nFC_LocalSimple_mean1_tauresrat whiten_timescale\nMD_hrv_classic_pnn40 high_fluctuation\nSB_BinaryStats_mean_longstretch1 stretch_high\nSB_BinaryStats_diff_longstretch0 stretch_decreasing\nSB_MotifThree_quantile_hh entropy_pairs\nCO_HistogramAMI_even_2_5 ami2\nCO_trev_1_num trev\nIN_AutoMutualInfoStats_40_gaussian_fmmi ami_timescale\nSB_TransitionMatrix_3ac_sumdiagcov transition_variance\nPD_PeriodicityWang_th0_01 periodicity\nCO_Embed2_Dist_tau_d_expfit_meandiff embedding_dist\nSC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1 rs_range\nSC_FluctAnal_2_dfa_50_1_2_logi_prop_r1 dfa\nDN_Mean mean\nDN_Spread_Std std\n" }, { "path": "testData/runtests.sh", "content": "#!/bin/bash\n\n\"$(dirname -- $0)/../C/runAllTS.sh\" -i \"$(dirname -- $0)\" -o \"$(dirname -- $0)\" -a \"_output\" -s 1 -i \"$(dirname -- $0)\" -o \"$(dirname -- $0)\" -a \"_output\" -s 1\n" }, { "path": "testData/test.txt", "content": "-0.89094\n-0.86099\n-0.82438\n-0.78214\n-0.73573\n-0.68691\n-0.63754\n-0.58937\n-0.54342\n-0.50044\n-0.46082\n-0.42469\n-0.3924\n-0.36389\n-0.33906\n-0.31795\n-0.30056\n-0.28692\n-0.27727\n-0.27105\n-0.26777\n-0.26678\n-0.26742\n-0.26927\n-0.27202\n-0.27529\n-0.27898\n-0.2832\n-0.28814\n-0.29431\n-0.30185\n-0.31086\n-0.32135\n-0.33326\n-0.3465\n-0.36103\n-0.37659\n-0.39297\n-0.40995\n-0.42731\n-0.44484\n-0.46226\n-0.47921\n-0.49534\n-0.51031\n-0.52382\n-0.53567\n-0.54568\n-0.55375\n-0.55984\n-0.56397\n-0.56617\n-0.56653\n-0.56511\n-0.56201\n-0.55736\n-0.55133\n-0.54419\n-0.53617\n-0.52758\n-0.51876\n-0.51004\n-0.50174\n-0.49418\n-0.48731\n-0.48128\n-0.47619\n-0.4721\n-0.46921\n-0.4674\n-0.46649\n-0.46659\n-0.46785\n-0.47045\n-0.4749\n-0.48081\n-0.48822\n-0.49726\n-0.50805\n-0.52086\n-0.53596\n-0.55266\n-0.57081\n-0.59021\n-0.61063\n-0.63238\n-0.65484\n-0.67722\n-0.69907\n-0.72004\n-0.73987\n-0.75965\n-0.77816\n-0.79527\n-0.8109\n-0.82509\n-0.83844\n-0.85159\n-0.86373\n-0.87474\n-0.88447\n-0.8929\n-0.90125\n-0.90911\n-0.91606\n-0.92197\n-0.92677\n-0.93071\n-0.93528\n-0.93931\n-0.9426\n-0.94494\n-0.94622\n-0.94735\n-0.94879\n-0.94965\n-0.94971\n-0.94878\n-0.94679\n-0.94571\n-0.94428\n-0.94176\n-0.93756\n-0.9311\n-0.92258\n-0.91342\n-0.90142\n-0.88573\n-0.86549\n-0.83998\n-0.81054\n-0.77682\n-0.73745\n-0.692\n-0.64033\n-0.58278\n-0.52295\n-0.45966\n-0.39357\n-0.32536\n-0.2557\n-0.18627\n-0.11888\n-0.052529\n0.012911\n0.077684\n0.14195\n0.2039\n0.26391\n0.3225\n0.37976\n0.43566\n0.48977\n0.53999\n0.58745\n0.63232\n0.6749\n0.71548\n0.75342\n0.78841\n0.82157\n0.85356\n0.88519\n0.91734\n0.94972\n0.98403\n1.0219\n1.0648\n1.1143\n1.1713\n1.2359\n1.3092\n1.3911\n1.4809\n1.5776\n1.6787\n1.7819\n1.8846\n1.9842\n2.0777\n2.1627\n2.2362\n2.2969\n2.3436\n2.3759\n2.3942\n2.3994\n2.3927\n2.3763\n2.3521\n2.3222\n2.2886\n2.2524\n2.2149\n2.1772\n2.1394\n2.1018\n2.0641\n2.0259\n1.9878\n1.9494\n1.9106\n1.8711\n1.8305\n1.7886\n1.7455\n1.7008\n1.6537\n1.6036\n1.5495\n1.492\n1.431\n1.3664\n1.2981\n1.2262\n1.1508\n1.0736\n0.99504\n0.91575\n0.83626\n0.75657\n0.67816\n0.60206\n0.52843\n0.45712\n0.38776\n0.31881\n0.2515\n0.18511\n0.11894\n0.052471\n-0.01492\n-0.082568\n-0.14848\n-0.21193\n-0.27229\n-0.32905\n-0.38303\n-0.43222\n-0.47628\n-0.51587\n-0.55188\n-0.58534\n-0.61763\n-0.64653\n-0.67227\n-0.69521\n-0.71576\n-0.73517\n-0.75286\n-0.76798\n-0.7812\n-0.79326\n-0.80474\n-0.81719\n-0.82831\n-0.83768\n-0.84538\n-0.85165\n-0.85731\n-0.86309\n-0.86791\n-0.87271\n-0.87846\n-0.88592\n-0.89619\n-0.90783\n-0.91942\n-0.93018\n-0.93939\n" }, { "path": "testData/test2.txt", "content": "-0.89094 -0.86099 -0.82438 -0.78214 -0.73573 -0.68691 -0.63754 -0.58937 -0.54342 -0.50044 -0.46082 -0.42469 -0.3924 -0.36389 -0.33906 -0.31795 -0.30056 -0.28692 -0.27727 -0.27105 -0.26777 -0.26678 -0.26742 -0.26927 -0.27202 -0.27529 -0.27898 -0.2832 -0.28814 -0.29431 -0.30185 -0.31086 -0.32135 -0.33326 -0.3465 -0.36103 -0.37659 -0.39297 -0.40995 -0.42731 -0.44484 -0.46226 -0.47921 -0.49534 -0.51031 -0.52382 -0.53567 -0.54568 -0.55375 -0.55984 -0.56397 -0.56617 -0.56653 -0.56511 -0.56201 -0.55736 -0.55133 -0.54419 -0.53617 -0.52758 -0.51876 -0.51004 -0.50174 -0.49418 -0.48731 -0.48128 -0.47619 -0.4721 -0.46921 -0.4674 -0.46649 -0.46659 -0.46785 -0.47045 -0.4749 -0.48081 -0.48822 -0.49726 -0.50805 -0.52086 -0.53596 -0.55266 -0.57081 -0.59021 -0.61063 -0.63238 -0.65484 -0.67722 -0.69907 -0.72004 -0.73987 -0.75965 -0.77816 -0.79527 -0.8109 -0.82509 -0.83844 -0.85159 -0.86373 -0.87474 -0.88447 -0.8929 -0.90125 -0.90911 -0.91606 -0.92197 -0.92677 -0.93071 -0.93528 -0.93931 -0.9426 -0.94494 -0.94622 -0.94735 -0.94879 -0.94965 -0.94971 -0.94878 -0.94679 -0.94571 -0.94428 -0.94176 -0.93756 -0.9311 -0.92258 -0.91342 -0.90142 -0.88573 -0.86549 -0.83998 -0.81054 -0.77682 -0.73745 -0.692 -0.64033 -0.58278 -0.52295 -0.45966 -0.39357 -0.32536 -0.2557 -0.18627 -0.11888 -0.052529 0.012911 0.077684 0.14195 0.2039 0.26391 0.3225 0.37976 0.43566 0.48977 0.53999 0.58745 0.63232 0.6749 0.71548 0.75342 0.78841 0.82157 0.85356 0.88519 0.91734 0.94972 0.98403 1.0219 1.0648 1.1143 1.1713 1.2359 1.3092 1.3911 1.4809 1.5776 1.6787 1.7819 1.8846 1.9842 2.0777 2.1627 2.2362 2.2969 2.3436 2.3759 2.3942 2.3994 2.3927 2.3763 2.3521 2.3222 2.2886 2.2524 2.2149 2.1772 2.1394 2.1018 2.0641 2.0259 1.9878 1.9494 1.9106 1.8711 1.8305 1.7886 1.7455 1.7008 1.6537 1.6036 1.5495 1.492 1.431 1.3664 1.2981 1.2262 1.1508 1.0736 0.99504 0.91575 0.83626 0.75657 0.67816 0.60206 0.52843 0.45712 0.38776 0.31881 0.2515 0.18511 0.11894 0.052471 -0.01492 -0.082568 -0.14848 -0.21193 -0.27229 -0.32905 -0.38303 -0.43222 -0.47628 -0.51587 -0.55188 -0.58534 -0.61763 -0.64653 -0.67227 -0.69521 -0.71576 -0.73517 -0.75286 -0.76798 -0.7812 -0.79326 -0.80474 -0.81719 -0.82831 -0.83768 -0.84538 -0.85165 -0.85731 -0.86309 -0.86791 -0.87271 -0.87846 -0.88592 -0.89619 -0.90783 -0.91942 -0.93018 -0.93939" }, { "path": "testData/test2_output.txt", "content": "-0.23703703703704, DN_OutlierInclude_n_001_mdrmd, 0.248000\n0.40740740740741, DN_OutlierInclude_p_001_mdrmd, 0.342000\n-0.61479911484527, DN_HistogramMode_5, 0.005000\n-0.78225446555221, DN_HistogramMode_10, 0.003000\n7.13507860878856, CO_Embed2_Dist_tau_d_expfit_meandiff, 0.283000\n32.50260547693646, CO_f1ecac, 0.197000\n77.00000000000000, CO_FirstMin_ac, 0.315000\n1.00638907799376, CO_HistogramAMI_even_2_5, 0.017000\n0.00001782472612, CO_trev_1_num, 0.008000\n0.84782608695652, FC_LocalSimple_mean1_tauresrat, 0.522000\n0.08029384289851, FC_LocalSimple_mean3_stderr, 0.010000\n40.00000000000000, IN_AutoMutualInfoStats_40_gaussian_fmmi, 0.114000\n0.31970260223048, MD_hrv_classic_pnn40, 0.002000\n83.00000000000000, SB_BinaryStats_diff_longstretch0, 0.003000\n88.00000000000000, SB_BinaryStats_mean_longstretch1, 0.003000\n1.21058781724385, SB_MotifThree_quantile_hh, 0.047000\n0.29545454545455, SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1, 0.193000\n0.75000000000000, SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1, 0.075000\n0.99313877887425, SP_Summaries_welch_rect_area_5_1, 0.077000\n0.03681553890926, SP_Summaries_welch_rect_centroid, 0.074000\n0.08000000000000, SB_TransitionMatrix_3ac_sumdiagcov, 0.309000\n0.00000000000000, PD_PeriodicityWang_th0_01, 0.135000\n\n" }, { "path": "testData/testInf.txt", "content": "inf\n-0.42469\n-0.3924\n-0.36389\n-0.33906\n-0.31795\n-0.30056\n-0.28692\n-0.27727\n-0.27105\n-0.26777\n-0.26678\n-0.26742\n-0.26927\n-0.27202\n-0.27529\n-0.27898\n-0.2832\n-0.28814\n-0.29431\n-0.30185\n-0.31086\n-0.32135\n-0.33326\n-0.3465\n-0.36103\n-0.37659\n-0.39297\n-0.40995\n-0.42731\n-0.44484\n-0.46226\n-0.47921\n-0.49534\n-0.51031\n-0.52382\n-0.53567\n-0.54568\n-0.55375\n-0.55984\n-0.56397\n-0.56617\n-0.56653\n-0.56511\n-0.56201\n-0.55736\n-0.55133\n-0.54419\n-0.53617\n-0.52758\n-0.51876\n-0.51004\n-0.50174\n-0.49418\n-0.48731\n-0.48128\n-0.47619\n-0.4721\n-0.46921\n-0.4674\n-0.46649\n-0.46659\n-0.46785\n-0.47045\n-0.4749\n-0.48081\n-0.48822\n-0.49726\n-0.50805\n-0.52086\n-0.53596\n-0.55266\n-0.57081\n-0.59021\n-0.61063\n-0.63238\n-0.65484\n-0.67722\n-0.69907\n-0.72004\n-0.73987\n-0.75965\n-0.77816\n-0.79527\n-0.8109\n-0.82509\n-0.83844\n-0.85159\n-0.86373\n-0.87474\n-0.88447\n-0.8929\n-0.90125\n-0.90911\n-0.91606\n-0.92197\n-0.92677\n-0.93071\n-0.93528\n-0.93931\n-0.9426\n-0.94494\n-0.94622\n-0.94735\n-0.94879\n-0.94965\n-0.94971\n-0.94878\n-0.94679\n-0.94571\n-0.94428\n-0.94176\n-0.93756\n-0.9311\n-0.92258\n-0.91342\n-0.90142\n-0.88573\n-0.86549\n-0.83998\n-0.81054\n-0.77682\n-0.73745\n-0.692\n-0.64033\n-0.58278\n-0.52295\n-0.45966\n-0.39357\n-0.32536\n-0.2557\n-0.18627\n-0.11888\n-0.052529\n0.012911\n0.077684\n0.14195\n0.2039\n0.26391\n0.3225\n0.37976\n0.43566\n0.48977\n0.53999\n0.58745\n0.63232\n0.6749\n0.71548\n0.75342\n0.78841\n0.82157\n0.85356\n0.88519\n0.91734\n0.94972\n0.98403\n1.0219\n1.0648\n1.1143\n1.1713\n1.2359\n1.3092\n1.3911\n1.4809\n1.5776\n1.6787\n1.7819\n1.8846\n1.9842\n2.0777\n2.1627\n2.2362\n2.2969\n2.3436\n2.3759\n2.3942\n2.3994\n2.3927\n2.3763\n2.3521\n2.3222\n2.2886\n2.2524\n2.2149\n2.1772\n2.1394\n2.1018\n2.0641\n2.0259\n1.9878\n1.9494\n1.9106\n1.8711\n1.8305\n1.7886\n1.7455\n1.7008\n1.6537\n1.6036\n1.5495\n1.492\n1.431\n1.3664\n1.2981\n1.2262\n1.1508\n1.0736\n0.99504\n0.91575\n0.83626\n0.75657\n0.67816\n0.60206\n0.52843\n0.45712\n0.38776\n0.31881\n0.2515\n0.18511\n0.11894\n0.052471\n-0.01492\n-0.082568\n-0.14848\n-0.21193\n-0.27229\n-0.32905\n-0.38303\n-0.43222\n-0.47628\n-0.51587\n-0.55188\n-0.58534\n-0.61763\n-0.64653\n-0.67227\n-0.69521\n-0.71576\n-0.73517\n-0.75286\n-0.76798\n-0.7812\n-0.79326\n-0.80474\n-0.81719\n-0.82831\n-0.83768\n-0.84538\n-0.85165\n-0.85731\n-0.86309\n-0.86791\n-0.87271\n-0.87846\n-0.88592\n-0.89619\n-0.90783\n-0.91942\n-0.93018\n-0.93939\n" }, { "path": "testData/testInfMinus.txt", "content": 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}, { "path": "testData/testNaN.txt", "content": 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}, { "path": "testData/testSinusoid_output.txt", "content": "0.13687262547490, DN_OutlierInclude_n_001_mdrmd, 8.703000\n-0.13777244551090, DN_OutlierInclude_p_001_mdrmd, 6.593000\n1.27292807660985, DN_HistogramMode_5, 0.041000\n-1.12758575737747, DN_HistogramMode_10, 0.039000\n1.64629633438112, CO_Embed2_Dist_tau_d_expfit_meandiff, 8.099000\n119.74079600337870, CO_f1ecac, 6.009000\n2.00000000000000, CO_FirstMin_ac, 4.579000\n1.07949995567938, CO_HistogramAMI_even_2_5, 0.320000\n-0.00017509308632, CO_trev_1_num, 0.110000\n0.00625000000000, FC_LocalSimple_mean1_tauresrat, 13.819000\n0.14051134209844, FC_LocalSimple_mean3_stderr, 0.190000\n1.00000000000000, IN_AutoMutualInfoStats_40_gaussian_fmmi, 1.845000\n0.82200000000000, MD_hrv_classic_pnn40, 0.041000\n6.00000000000000, SB_BinaryStats_diff_longstretch0, 0.051000\n301.00000000000000, SB_BinaryStats_mean_longstretch1, 0.046000\n1.38225822030023, SB_MotifThree_quantile_hh, 1.417000\n0.77551020408163, SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1, 3.221000\n0.69387755102041, SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1, 1.645000\n0.98817221358274, SP_Summaries_welch_rect_area_5_1, 2.410000\n0.00997087512126, SP_Summaries_welch_rect_centroid, 2.218000\n0.01005896635449, SB_TransitionMatrix_3ac_sumdiagcov, 6.992000\n628.00000000000000, PD_PeriodicityWang_th0_01, 17.879000\n\n" }, { "path": "testData/test_output.txt", "content": "-0.23703703703704, DN_OutlierInclude_n_001_mdrmd, 0.252000\n0.40740740740741, DN_OutlierInclude_p_001_mdrmd, 0.302000\n-0.61479911484527, DN_HistogramMode_5, 0.003000\n-0.78225446555221, DN_HistogramMode_10, 0.003000\n7.13507860878856, CO_Embed2_Dist_tau_d_expfit_meandiff, 0.234000\n32.50260547693646, CO_f1ecac, 0.192000\n77.00000000000000, CO_FirstMin_ac, 0.191000\n1.00638907799376, CO_HistogramAMI_even_2_5, 0.014000\n0.00001782472612, CO_trev_1_num, 0.006000\n0.84782608695652, FC_LocalSimple_mean1_tauresrat, 0.388000\n0.08029384289851, FC_LocalSimple_mean3_stderr, 0.007000\n40.00000000000000, IN_AutoMutualInfoStats_40_gaussian_fmmi, 0.086000\n0.31970260223048, MD_hrv_classic_pnn40, 0.002000\n83.00000000000000, SB_BinaryStats_diff_longstretch0, 0.002000\n88.00000000000000, SB_BinaryStats_mean_longstretch1, 0.003000\n1.21058781724385, SB_MotifThree_quantile_hh, 0.034000\n0.29545454545455, SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1, 0.169000\n0.75000000000000, SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1, 0.073000\n0.99313877887425, SP_Summaries_welch_rect_area_5_1, 0.075000\n0.03681553890926, SP_Summaries_welch_rect_centroid, 0.069000\n0.08000000000000, SB_TransitionMatrix_3ac_sumdiagcov, 0.198000\n0.00000000000000, PD_PeriodicityWang_th0_01, 0.100000\n\n" }, { "path": "wrap_Matlab/GetAllFeatureNames.m", "content": "function [featureNamesLong,featureNamesShort] = GetAllFeatureNames(doCatch24)\n% Retrieve all feature names from featureList.txt\n\nif nargin < 1 || isempty(doCatch24)\n doCatch24 = true;\nend\n%-------------------------------------------------------------------------------\n% Retrieve all (long) feature names from featureList.txt\nfid = fopen('../featureList.txt','r');\nformatSpec = '%s%s';\ndataIn = textscan(fid,formatSpec,'CommentStyle','#','EndOfLine','\\r\\n','CollectOutput',true);\nfclose(fid);\ndataIn = dataIn{1}; % Collect one big matrix of cells\nfeatureNamesLong = dataIn(:,1);\nfeatureNamesShort = dataIn(:,2);\n\n%-------------------------------------------------------------------------------\n% Filter out the two extra features: 'DN_Mean' and 'DN_Spread_Std'\nisMeanStd = strcmp(featureNamesLong,'DN_Mean') | strcmp(featureNamesLong,'DN_Spread_Std');\nassert(sum(isMeanStd) == 2)\nif ~doCatch24\n keepMe = ~isMeanStd;\n featureNamesLong = featureNamesLong(keepMe);\n featureNamesShort = featureNamesShort(keepMe);\n fprintf(1,'Using catch22.\\n');\nelse\n fprintf(1,'Using catch24.\\n');\nend\n\nend\n" }, { "path": "wrap_Matlab/M_wrapper.c", "content": "#include \"mex.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid M_wrapper_double( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[], \n double (*f) (const double*, const int), int normalize )\n \n{ \n double *inMatrix; /* 1xN input matrix */\n int ncols;\n double *outMatrix; /* output matrix */\n \n // check inputs\n if(nrhs != 1) {\n mexErrMsgIdAndTxt(\"catch22:nrhs\",\n \"One input required.\");\n }\n if(nlhs > 1) {\n mexErrMsgIdAndTxt(\"catch22:nlhs\",\n \"One output required.\");\n }\n if( !mxIsDouble(prhs[0]) || \n mxIsComplex(prhs[0])) {\n mexErrMsgIdAndTxt(\"catch22:notDouble\",\n \"Input vector must be type double.\");\n }\n if(mxGetM(prhs[0]) != 1) {\n mexErrMsgIdAndTxt(\"catch22:notRowVector\",\n \"Input must be a row vector.\");\n }\n \n // get input\n inMatrix = mxGetPr(prhs[0]);\n ncols = mxGetN(prhs[0]);\n \n // set output\n plhs[0] = mxCreateDoubleMatrix(1,1,mxREAL);\n outMatrix = mxGetPr(plhs[0]);\n \n // calculate result\n if (normalize){\n \n double * y_zscored = malloc(ncols * sizeof * y_zscored);\n zscore_norm2(inMatrix, ncols, y_zscored);\n\n outMatrix[0] = f(y_zscored, ncols);\n\n free(y_zscored);\n } \n else {\n outMatrix[0] = f(inMatrix, ncols);\n } \n \n return;\n \n}\n\nvoid M_wrapper_int( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[], \n int (*f) (const double*, const int), int normalize )\n \n{ \n double *inMatrix; /* 1xN input matrix */\n int ncols;\n int *outMatrix; /* output matrix */\n \n // check inputs\n if(nrhs != 1) {\n mexErrMsgIdAndTxt(\"catch22:nrhs\",\n \"One input required.\");\n }\n if(nlhs > 1) {\n mexErrMsgIdAndTxt(\"catch22:nlhs\",\n \"One output required.\");\n }\n if( !mxIsDouble(prhs[0]) || \n mxIsComplex(prhs[0])) {\n mexErrMsgIdAndTxt(\"catch22:notDouble\",\n \"Input vector must be type double.\");\n }\n if(mxGetM(prhs[0]) != 1) {\n mexErrMsgIdAndTxt(\"catch22:notRowVector\",\n \"Input must be a row vector.\");\n }\n \n // get input\n inMatrix = mxGetPr(prhs[0]);\n ncols = mxGetN(prhs[0]);\n \n // set output\n plhs[0] = mxCreateNumericMatrix(1,1, mxINT32_CLASS, mxREAL);\n outMatrix = (int*)mxGetData(plhs[0]);\n \n // calculate result\n if (normalize){\n \n double * y_zscored = malloc(ncols * sizeof * y_zscored);\n zscore_norm2(inMatrix, ncols, y_zscored);\n\n outMatrix[0] = f(y_zscored, ncols);\n\n free(y_zscored);\n } \n else {\n outMatrix[0] = f(inMatrix, ncols);\n } \n \n return;\n \n}" }, { "path": "wrap_Matlab/M_wrapper.h", "content": "#include \"mex.h\"\n\nextern void M_wrapper_double( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[], \n double (*f) (const double*, const int), int normalize );\n\nextern void M_wrapper_int( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[], \n int (*f) (const double*, const int), int normalize );" }, { "path": "wrap_Matlab/catch22_CO_Embed2_Dist_tau_d_expfit_meandiff.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &CO_Embed2_Dist_tau_d_expfit_meandiff, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_CO_FirstMin_ac.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[],\n int nrhs, const mxArray*prhs[] )\n\n{\n\n // check inputs and call feature C-function\nM_wrapper_int( nlhs, plhs, nrhs, prhs, &CO_FirstMin_ac, 1);\n\n return;\n\n}\n" }, { "path": "wrap_Matlab/catch22_CO_HistogramAMI_even_2_5.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &CO_HistogramAMI_even_2_5, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_CO_f1ecac.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[],\n int nrhs, const mxArray*prhs[] )\n\n{\n\n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &CO_f1ecac, 1);\n\n return;\n\n}\n" }, { "path": "wrap_Matlab/catch22_CO_trev_1_num.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &CO_trev_1_num, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_DN_HistogramMode_10.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &DN_HistogramMode_10, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_DN_HistogramMode_5.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &DN_HistogramMode_5, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_DN_Mean.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &DN_Mean, 0);\n \n return;\n \n}\n" }, { "path": "wrap_Matlab/catch22_DN_OutlierInclude_n_001_mdrmd.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &DN_OutlierInclude_n_001_mdrmd, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_DN_OutlierInclude_p_001_mdrmd.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &DN_OutlierInclude_p_001_mdrmd, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_DN_Spread_Std.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &DN_Spread_Std, 0);\n \n return;\n \n}\n" }, { "path": "wrap_Matlab/catch22_FC_LocalSimple_mean1_tauresrat.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &FC_LocalSimple_mean1_tauresrat, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_FC_LocalSimple_mean3_stderr.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &FC_LocalSimple_mean3_stderr, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_IN_AutoMutualInfoStats_40_gaussian_fmmi.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &IN_AutoMutualInfoStats_40_gaussian_fmmi, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_MD_hrv_classic_pnn40.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &MD_hrv_classic_pnn40, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_PD_PeriodicityWang_th0_01.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_int( nlhs, plhs, nrhs, prhs, &PD_PeriodicityWang_th0_01, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_SB_BinaryStats_diff_longstretch0.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &SB_BinaryStats_diff_longstretch0, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_SB_BinaryStats_mean_longstretch1.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &SB_BinaryStats_mean_longstretch1, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_SB_MotifThree_quantile_hh.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &SB_MotifThree_quantile_hh, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_SB_TransitionMatrix_3ac_sumdiagcov.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &SB_TransitionMatrix_3ac_sumdiagcov, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_SP_Summaries_welch_rect_area_5_1.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &SP_Summaries_welch_rect_area_5_1, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_SP_Summaries_welch_rect_centroid.c", "content": "/*=================================================================\n *\n *\n *=================================================================*/\n#include \n#include \"mex.h\"\n\n#include \"M_wrapper.h\"\n\n#include \"CO_AutoCorr.h\"\n#include \"DN_HistogramMode_10.h\"\n#include \"DN_HistogramMode_5.h\"\n#include \"DN_Mean.h\"\n#include \"DN_Spread_Std.h\"\n#include \"DN_OutlierInclude.h\"\n#include \"FC_LocalSimple.h\"\n#include \"IN_AutoMutualInfoStats.h\"\n#include \"MD_hrv.h\"\n#include \"PD_PeriodicityWang.h\"\n#include \"SB_BinaryStats.h\"\n#include \"SB_CoarseGrain.h\"\n#include \"SB_MotifThree.h\"\n#include \"SB_TransitionMatrix.h\"\n#include \"SC_FluctAnal.h\"\n#include \"SP_Summaries.h\"\n#include \"butterworth.h\"\n#include \"fft.h\"\n#include \"helper_functions.h\"\n#include \"histcounts.h\"\n#include \"splinefit.h\"\n#include \"stats.h\"\n\nvoid mexFunction( int nlhs, mxArray *plhs[], \n int nrhs, const mxArray*prhs[] )\n \n{ \n \n // check inputs and call feature C-function\nM_wrapper_double( nlhs, plhs, nrhs, prhs, &SP_Summaries_welch_rect_centroid, 1);\n \n return;\n \n}" }, { "path": "wrap_Matlab/catch22_all.m", "content": "function [featureValues, featureNamesLong, featureNamesShort] = catch22_all(data, doCatch24)\n% catch22_all Calculate all catch22 (or catch24) features from an input time series\n%\n% ---INPUTS\n% data: a univariate time series (row vector)\n% catch24: set to true to also include mean and standard deviation (a total of 24\n% features)\n% ---OUTPUTS:\n% featureValues: computed feature values for the input data\n\n%-------------------------------------------------------------------------------\n%% Check inputs and set defaults\n%-------------------------------------------------------------------------------\nif nargin < 2\n doCatch24 = true;\nend\n\n%-------------------------------------------------------------------------------\n% Define the features:\n%-------------------------------------------------------------------------------\n[featureNamesLong,featureNamesShort] = GetAllFeatureNames(doCatch24);\n\n%-------------------------------------------------------------------------------\n% Compute all features from their local compiled implementations\n%-------------------------------------------------------------------------------\nnumFeatures = length(featureNamesLong);\nfeatureValues = zeros(numFeatures,1);\n\nfor featureInd = 1:numFeatures\n featureName = featureNamesLong{featureInd};\n fh = str2func(['catch22_', featureName]);\n featureValues(featureInd) = fh(data');\nend\n\nend\n" }, { "path": "wrap_Matlab/mexAll.m", "content": "% mexAll Mex compile all .c files to be runnable from Matlab.\n\n% Path where the C implementation lives\nbasePath = '../C';\nipath = {['-I', basePath], '-I.'};\n\n% List all C files to include in the mex-call\nCFiles = dir(basePath);\nCFiles = CFiles(cellfun(@(x) contains(x, '.c'), {CFiles.name}));\nCFileNames = {CFiles.name};\n\n% Add path to the c file names\nincludeFiles = cellfun(@(x) fullfile(basePath, x), CFileNames, 'UniformOutput', false);\n\n% Get function names\nfeatureNames = GetAllFeatureNames(true);\n\n% mex all feature functions separately\nnumFeatures = length(featureNames);\nfor i = 1:numFeatures\n featureName = featureNames{i};\n\n fprintf('[%u/%u]: Compiling %s...\\n', i,numFeatures,featureName);\n mex(ipath{:}, ['catch22_', featureName,'.c'], 'M_wrapper.c', includeFiles{:})\n fprintf('\\n');\nend\n\nfprintf(1,'All done!\\n');\n" }, { "path": "wrap_Matlab/testing.m", "content": "%-------------------------------------------------------------------------------\n% Get the function names\n[featureNamesLong,featureNamesShort] = GetAllFeatureNames();\nnumFeatures = length(featureNamesLong);\n\n%-------------------------------------------------------------------------------\n% Get the data\ndataFileNames = {'../testData/test.txt', '../testData/test2.txt'};\nnumTestFiles = length(dataFileNames);\n\nfprintf(1,'Testing %u compiled features on %u data files.\\n',numFeatures,numTestFiles);\n\n%-------------------------------------------------------------------------------\n% Test all functions on all the data files\nfor j = 1:numTestFiles\n\n dataFileName = dataFileNames{j};\n fprintf('\\n%s\\n\\n', dataFileName)\n\n fileID = fopen(dataFileName,'r');\n data = fscanf(fileID,'%f');\n\n for featureInd = 1:numFeatures\n\n featureName = featureNamesLong{featureInd};\n fh = str2func(['catch22_', featureName]);\n out = fh(data');\n\n fprintf(\"%s (%s): %1.6f\\n\", featureNamesShort{featureInd}, featureName, out);\n end\n\n fclose(fileID);\nend\n" } ]