diff --git a/src/backend/nsl/nsl_fit.c b/src/backend/nsl/nsl_fit.c index 713517dfe..e400b404f 100644 --- a/src/backend/nsl/nsl_fit.c +++ b/src/backend/nsl/nsl_fit.c @@ -1,646 +1,647 @@ /*************************************************************************** File : nsl_fit.c Project : LabPlot Description : NSL (non)linear fit functions -------------------------------------------------------------------- Copyright : (C) 2016-2017 by Stefan Gerlach (stefan.gerlach@uni.kn) ***************************************************************************/ /*************************************************************************** * * * This program is free software; you can redistribute it and/or modify * * it under the terms of the GNU General Public License as published by * * the Free Software Foundation; either version 2 of the License, or * * (at your option) any later version. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ #include "nsl_fit.h" #include "nsl_common.h" #include #include #include #include #include const char* nsl_fit_model_category_name[] = {i18n("Basic functions"), i18n("Peak functions"), i18n("Growth (sigmoidal)"), i18n("Statistics (distributions)"), i18n("Custom")}; const char* nsl_fit_model_basic_name[] = {i18n("Polynomial"), i18n("Power"), i18n("Exponential"), i18n("Inverse exponential"), i18n("Fourier")}; const char* nsl_fit_model_basic_equation[] = {"c0 + c1*x", "a*x^b", "a*exp(b*x)", "a*(1-exp(b*x)) + c", "a0 + (a1*cos(w*x) + b1*sin(w*x))"}; const char* nsl_fit_model_basic_pic_name[] = {"polynom", "power", "exponential", "inv_exponential", "fourier"}; const char* nsl_fit_model_peak_name[] = {i18n("Gaussian (normal)"), i18n("Cauchy-Lorentz"), i18n("Hyperbolic secant (sech)"), i18n("Logistic (sech squared)")}; const char* nsl_fit_model_peak_equation[] = {"a/sqrt(2*pi)/s * exp(-((x-mu)/s)^2/2)", "a/pi * g/(g^2+(x-mu)^2)", "a/pi/s * sech((x-mu)/s)", "a/4/s * sech((x-mu)/2/s)**2"}; const char* nsl_fit_model_peak_pic_name[] = {"gaussian", "cauchy_lorentz", "sech", "logistic"}; const char* nsl_fit_model_growth_name[] = {i18n("Inverse tangent"), i18n("Hyperbolic tangent"), i18n("Algebraic sigmoid"), i18n("Logistic function"), i18n("Error function (erf)"), i18n("Hill"), i18n("Gompertz"), i18n("Gudermann (gd)")}; const char* nsl_fit_model_growth_equation[] = {"a * atan((x-mu)/s)", "a * tanh((x-mu)/s)", "a * (x-mu)/s/sqrt(1+((x-mu)/s)^2)", "a/(1+exp(-k*(x-mu)))", "a/2 * erf((x-mu)/s/sqrt(2))", "a * x^n/(s^n + x^n)", "a*exp(-b*exp(-c*x))", "a * asin(tanh((x-mu)/s))"}; const char* nsl_fit_model_growth_pic_name[] = {"atan", "tanh", "alg_sigmoid", "logistic_function", "erf", "hill", "gompertz", "gd"}; +const char* nsl_fit_error_type_name[] = {"No", "Direct (col)", "Inverse (1/col)"}; const char* nsl_fit_weight_type_name[] = {"No", "Instrumental (1/col^2)", "Direct (col)", "Inverse (1/col)", "Statistical (1/data)", "Statistical (Fit)", "Relative (1/data^2)", "Relative (Fit)"}; /* see http://seal.web.cern.ch/seal/documents/minuit/mnusersguide.pdf and https://lmfit.github.io/lmfit-py/bounds.html */ double nsl_fit_map_bound(double x, double min, double max) { if (max <= min) { printf("given bounds must fulfill max > min (min = %g, max = %g)! Giving up.\n", min, max); return DBL_MAX; } /* not bounded */ if (min == -DBL_MAX && max == DBL_MAX) return x; /* open bounds */ if (min == -DBL_MAX) return max + 1. - sqrt(x*x + 1.); if (max == DBL_MAX) return min - 1. + sqrt(x*x + 1.); return min + sin(x + 1.) * (max - min)/2.; /* alternative transformation for closed bounds return min + (max - min)/(1. + exp(-x)); */ } /* see http://seal.web.cern.ch/seal/documents/minuit/mnusersguide.pdf and https://lmfit.github.io/lmfit-py/bounds.html */ double nsl_fit_map_unbound(double x, double min, double max) { if (max <= min) { printf("given bounds must fulfill max > min (min = %g, max = %g)! Giving up.\n", min, max); return DBL_MAX; } if (x < min || x > max) { printf("given value must be within bounds! Giving up.\n"); return -DBL_MAX; } /* not bounded */ if (min == -DBL_MAX && max == DBL_MAX) return x; /* open bounds */ if (min == -DBL_MAX) return sqrt(gsl_pow_2(max - x + 1.) - 1.); if (max == DBL_MAX) return sqrt(gsl_pow_2(x - min + 1.) - 1.); return asin(2. * (x - min)/(max - min) - 1.); /* alternative transformation for closed bounds return -log((max - x)/(x - min)); */ } /********************** parameter derivatives ******************/ /* basic */ double nsl_fit_model_polynomial_param_deriv(double x, int j, double weight) { return weight*pow(x, j); } double nsl_fit_model_power1_param_deriv(int param, double x, double a, double b, double weight) { if (param == 0) return weight*pow(x, b); if (param == 1) return weight*a*pow(x, b)*log(x); return 0; } double nsl_fit_model_power2_param_deriv(int param, double x, double b, double c, double weight) { if (param == 0) return weight; if (param == 1) return weight*pow(x, c); if (param == 2) return weight*b*pow(x, c)*log(x); return 0; } double nsl_fit_model_exponentialn_param_deriv(int param, double x, double *p, double weight) { if (param % 2 == 0) return weight*exp(p[param+1]*x); else return weight*p[param-1]*x*exp(p[param]*x); } double nsl_fit_model_inverse_exponential_param_deriv(int param, double x, double a, double b, double weight) { if (param == 0) return weight*(1. - exp(b*x)); if (param == 1) return -weight*a*x*exp(b*x); if (param == 2) return weight; return 0; } double nsl_fit_model_fourier_param_deriv(int param, int degree, double x, double w, double weight) { if (param == 0) return weight*cos(degree*w*x); if (param == 1) return weight*sin(degree*w*x); return 0; } /* peak */ double nsl_fit_model_gaussian_param_deriv(int param, double x, double s, double mu, double A, double weight) { double s2 = s*s, norm = weight/sqrt(2.*M_PI)/s, efactor = exp(-(x-mu)*(x-mu)/(2.*s2)); if (param == 0) return A * norm/(s*s2) * ((x-mu)*(x-mu) - s2) * efactor; if (param == 1) return A * norm/s2 * (x-mu) * efactor; if (param == 2) return norm * efactor; return 0; } double nsl_fit_model_lorentz_param_deriv(int param, double x, double s, double t, double A, double weight) { double norm = weight/M_PI, denom = s*s+(x-t)*(x-t); if (param == 0) return A * norm * ((x-t)*(x-t) - s*s)/(denom*denom); if (param == 1) return A * norm * 2.*s*(x-t)/(denom*denom); if (param == 2) return norm * s/denom; return 0; } double nsl_fit_model_sech_param_deriv(int param, double x, double s, double mu, double A, double weight) { double y = (x-mu)/s, norm = weight/M_PI/s; if (param == 0) return A/s * norm * (y*tanh(y)-1.)/cosh(y); if (param == 1) return A/s * norm * tanh(y)/cosh(y); if (param == 2) return norm/cosh(y); return 0; } double nsl_fit_model_logistic_param_deriv(int param, double x, double s, double mu, double A, double weight) { double y = (x-mu)/2./s, norm = weight/4./s; if (param == 0) return A/s * norm * (2.*y*tanh(y)-1.)/cosh(y); if (param == 1) return A/s * norm * tanh(y)/cosh(y)/cosh(y); if (param == 2) return norm/cosh(y)/cosh(y); return 0; } /* growth */ double nsl_fit_model_atan_param_deriv(int param, double x, double s, double mu, double A, double weight) { double norm = weight, y = (x-mu)/s; if (param == 0) return -A/s * norm * y/(1.+y*y); if (param == 1) return -A/s * norm * 1./(1+y*y); if (param == 2) return norm * atan(y); return 0; } double nsl_fit_model_tanh_param_deriv(int param, double x, double s, double mu, double A, double weight) { double norm = weight, y = (x-mu)/s; if (param == 0) return -A/s * norm * y/cosh(y)/cosh(y); if (param == 1) return -A/s * norm * 1./cosh(y)/cosh(y); if (param == 2) return norm * tanh(y); return 0; } double nsl_fit_model_algebraic_sigmoid_param_deriv(int param, double x, double s, double mu, double A, double weight) { double norm = weight, y = (x-mu)/s, y2 = y*y; if (param == 0) return -A/s * norm * y/pow(1.+y2, 1.5); if (param == 1) return -A/s * norm * 1./pow(1.+y2, 1.5); if (param == 2) return norm * y/sqrt(1.+y2); return 0; } double nsl_fit_model_sigmoid_param_deriv(int param, double x, double k, double mu, double A, double weight) { double norm = weight, y = k*(x-mu); if (param == 0) return A/k * norm * y*exp(-y)/gsl_pow_2(1. + exp(-y)); if (param == 1) return -A*k * norm * exp(-y)/gsl_pow_2(1. + exp(-y)); if (param == 2) return norm/(1. + exp(-y)); return 0; } double nsl_fit_model_erf_param_deriv(int param, double x, double s, double mu, double A, double weight) { double norm = weight, y = (x-mu)/(sqrt(2.)*s); if (param == 0) return -A/sqrt(M_PI)/s * norm * y*exp(-y*y); if (param == 1) return -A/sqrt(2.*M_PI)/s * norm * exp(-y*y); if (param == 2) return norm/2. * gsl_sf_erf(y); return 0; } double nsl_fit_model_hill_param_deriv(int param, double x, double s, double n, double A, double weight) { double norm = weight, y = x/s; if (param == 0) return -A*n/s * norm * pow(y, n)/gsl_pow_2(1.+pow(y, n)); if (param == 1) return A * norm * log(y)*pow(y, n)/gsl_pow_2(1.+pow(y, n)); if (param == 2) return norm * pow(y, n)/(1.+pow(y, n)); return 0; } double nsl_fit_model_gompertz_param_deriv(int param, double x, double a, double b, double c, double weight) { if (param == 0) return weight*exp(-b*exp(-c*x)); if (param == 1) return -weight*a*exp(-c*x-b*exp(-c*x)); if (param == 2) return weight*a*b*x*exp(-c*x-b*exp(-c*x)); return 0; } double nsl_fit_model_gudermann_param_deriv(int param, double x, double s, double mu, double A, double weight) { double norm = weight, y = (x-mu)/s; if (param == 0) return -A/s * norm * y/cosh(y); if (param == 1) return -A/s * norm * 1./cosh(y); if (param == 2) return -asin(tanh(y)); return 0; } /* distributions */ double nsl_fit_model_gaussian_tail_param_deriv(int param, double x, double s, double mu, double A, double a, double weight) { if (x < a) return 0; double s2 = s*s, N = erfc(a/s/M_SQRT2)/2., norm = weight/sqrt(2.*M_PI)/s/N, efactor = exp(-(x-mu)*(x-mu)/(2.*s2)); if (param == 0) return A * norm/(s*s2) * ((x-mu)*(x-mu) - s2) * efactor; if (param == 1) return A * norm/s2 * (x-mu) * efactor; if (param == 2) return norm * efactor; if (param == 3) return A/norm/norm * efactor * exp(-a*a/(2.*s2)); return 0; } double nsl_fit_model_exponential_param_deriv(int param, double x, double l, double mu, double A, double weight) { if (x < mu) return 0; double y = l*(x-mu), efactor = exp(-y); if (param == 0) return weight * A * (1. - y) * efactor; if (param == 1) return weight * A * gsl_pow_2(l) * efactor; if (param == 2) return weight * l * efactor; return 0; } double nsl_fit_model_laplace_param_deriv(int param, double x, double s, double mu, double A, double weight) { double norm = weight/(2.*s), y = fabs((x-mu)/s), efactor = exp(-y); if (param == 0) return A/s*norm * (y-1.) * efactor; if (param == 1) return A/(s*s)*norm * (x-mu)/y * efactor; if (param == 2) return norm * efactor; return 0; } double nsl_fit_model_exp_pow_param_deriv(int param, double x, double s, double mu, double b, double a, double weight) { double norm = weight/2./s/gsl_sf_gamma(1.+1./b), y = (x-mu)/s, efactor = exp(-pow(fabs(y), b)); if (param == 0) return norm * a/s * efactor * (b * y * pow(fabs(1./y), 1.-b) * GSL_SIGN(y) - 1.); if (param == 1) return norm * a*b/s * efactor * pow(fabs(y), b-1.) * GSL_SIGN(y); if (param == 2) return norm * a/b * gsl_sf_gamma(1.+1./b)/gsl_sf_gamma(1./b) * efactor * (gsl_sf_psi(1.+1./b) - gsl_pow_2(b) * pow(fabs(y), b) * log(fabs(y))); if (param == 3) return norm * efactor; return 0; } double nsl_fit_model_maxwell_param_deriv(int param, double x, double a, double c, double weight) { double a2 = a*a, a3 = a*a2, norm = weight*sqrt(2./M_PI)/a3, x2 = x*x, efactor = exp(-x2/2./a2); if (param == 0) return c * norm * x2*(x2-3.*a2)/a3 * efactor; if (param == 1) return norm * x2 * efactor; return 0; } double nsl_fit_model_poisson_param_deriv(int param, double x, double l, double A, double weight) { double norm = weight*pow(l, x)/gsl_sf_gamma(x+1.); if (param == 0) return A/l * norm *(x-l)*exp(-l); if (param == 1) return norm * exp(-l); return 0; } double nsl_fit_model_lognormal_param_deriv(int param, double x, double s, double mu, double A, double weight) { double norm = weight/sqrt(2.*M_PI)/(x*s), y = log(x)-mu, efactor = exp(-(y/s)*(y/s)/2.); if (param == 0) return A * norm * (y*y - s*s) * efactor; if (param == 1) return A * norm * y/(s*s) * efactor; if (param == 2) return norm * efactor; return 0; } double nsl_fit_model_gamma_param_deriv(int param, double x, double t, double k, double A, double weight) { double factor = weight*pow(x, k-1.)/pow(t, k)/gsl_sf_gamma(k), efactor = exp(-x/t); if (param == 0) return A * factor/t * (x/t-k) * efactor; if (param == 1) return A * factor * (log(x/t) - gsl_sf_psi(k)) * efactor; if (param == 2) return factor * efactor; return 0; } double nsl_fit_model_flat_param_deriv(int param, double x, double a, double b, double A, double weight) { if (x < a || x > b) return 0; if (param == 0) return weight * A/gsl_pow_2(a-b); if (param == 1) return - weight * A/gsl_pow_2(a-b); if (param == 2) return weight/(b-a); return 0; } double nsl_fit_model_rayleigh_param_deriv(int param, double x, double s, double A, double weight) { double y=x/s, norm = weight*y/s, efactor = exp(-y*y/2.); if (param == 0) return A*y/(s*s) * (y*y-2.)*efactor; if (param == 1) return norm * efactor; return 0; } double nsl_fit_model_rayleigh_tail_param_deriv(int param, double x, double s, double mu, double A, double weight) { double norm = weight*x/(s*s), y = (mu*mu - x*x)/2./(s*s); if (param == 0) return -2. * A * norm/s * (1. + y) * exp(y); if (param == 1) return A * mu * norm/(s*s) * exp(y); if (param == 2) return norm * exp(y); return 0; } double nsl_fit_model_levy_param_deriv(int param, double x, double g, double mu, double A, double weight) { double y=x-mu, norm = weight*sqrt(g/(2.*M_PI))/pow(y, 1.5), efactor = exp(-g/2./y); if (param == 0) return A/2.*norm/g/y * (y - g) * efactor; if (param == 1) return A/2.*norm/y/y * (3.*y - g) * efactor; if (param == 2) return norm * efactor; return 0; } double nsl_fit_model_landau_param_deriv(int param, double x, double weight) { if (param == 0) return weight * gsl_ran_landau_pdf(x); return 0; } double nsl_fit_model_chi_square_param_deriv(int param, double x, double n, double A, double weight) { double y=n/2., norm = weight*pow(x, y-1.)/pow(2., y)/gsl_sf_gamma(y), efactor = exp(-x/2.); if (param == 0) return A/2. * norm * (log(x/2.) - gsl_sf_psi(y)) * efactor; if (param == 1) return norm * efactor; return 0; } double nsl_fit_model_students_t_param_deriv(int param, double x, double n, double A, double weight) { if (param == 0) return weight * A * gsl_sf_gamma((n+1.)/2.)/2./pow(n, 1.5)/sqrt(M_PI)/gsl_sf_gamma(n/2.) * pow(1.+x*x/n, - (n+3.)/2.) * (x*x - 1. - (n+x*x)*log(1.+x*x/n) + (n+x*x)*(gsl_sf_psi((n+1.)/2.) - gsl_sf_psi(n/2.)) ) ; if (param == 1) return weight * gsl_ran_tdist_pdf(x, n); return 0; } double nsl_fit_model_fdist_param_deriv(int param, double x, double n1, double n2, double A, double weight) { double norm = weight * gsl_sf_gamma((n1+n2)/2.)/gsl_sf_gamma(n1/2.)/gsl_sf_gamma(n2/2.) * pow(n1, n1/2.) * pow(n2, n2/2.) * pow(x, n1/2.-1.); double y = n2+n1*x; if (param == 0) return A/2. * norm * pow(y, -(n1+n2+2.)/2.) * (n2*(1.-x) + y*(log(n1) + log(x) - log(y) + gsl_sf_psi((n1+n2)/2.) - gsl_sf_psi(n1/2.))); if (param == 1) return A/2. * norm * pow(y, -(n1+n2+2.)/2.) * (n1*(x-1.) + y*(log(n2) - log(y) + gsl_sf_psi((n1+n2)/2.) - gsl_sf_psi(n2/2.))); if (param == 2) return weight * gsl_ran_fdist_pdf(x, n1, n2); return 0; } double nsl_fit_model_beta_param_deriv(int param, double x, double a, double b, double A, double weight) { double norm = weight * A * gsl_sf_gamma(a+b)/gsl_sf_gamma(a)/gsl_sf_gamma(b) * pow(x, a-1.) * pow(1.-x, b-1.); if (param == 0) return norm * (log(x) - gsl_sf_psi(a) + gsl_sf_psi(a+b)); if (param == 1) return norm * (log(1.-x) - gsl_sf_psi(b) + gsl_sf_psi(a+b)); if (param == 2) return weight * gsl_ran_beta_pdf(x, a, b); return 0; } double nsl_fit_model_pareto_param_deriv(int param, double x, double a, double b, double A, double weight) { if (x < b) return 0; double norm = weight * A; if (param == 0) return norm * pow(b/x, a) * (1. + a * log(b/x))/x; if (param == 1) return norm * a*a * pow(b/x, a-1.)/x/x; if (param == 2) return weight * gsl_ran_pareto_pdf(x, a, b); return 0; } double nsl_fit_model_weibull_param_deriv(int param, double x, double k, double l, double mu, double A, double weight) { double y = (x-mu)/l, z = pow(y, k), efactor = exp(-z); if (param == 0) return weight * A/l * z/y*(k*log(y)*(1.-z) + 1.) * efactor; if (param == 1) return weight * A*k*k/l/l * z/y*(z-1.) * efactor; if (param == 2) return weight * A*k/l/l * z/y/y*(k*z + 1. - k) * efactor; if (param == 3) return weight * k/l * z/y * efactor; return 0; } double nsl_fit_model_frechet_param_deriv(int param, double x, double g, double mu, double s, double A, double weight) { double y = (x-mu)/s, efactor = exp(-pow(y, -g)); if (param == 0) return weight * A/s * pow(y, -2.*g-1.) * (g*log(y)*(1.-pow(y, g))+pow(y, g)) * efactor; if (param == 1) return A * weight * g/(s*s)*pow(y, -g-2.) * (g+1.-g*pow(y, -g)) * efactor; if (param == 2) return A * weight * gsl_pow_2(g/s)*pow(y, -2.*g-1.) * (pow(y, g)-1.) * efactor; if (param == 3) return g * weight/s * pow(y, -g-1.) * efactor; return 0; } double nsl_fit_model_gumbel1_param_deriv(int param, double x, double s, double b, double mu, double A, double weight) { double norm = weight/s, y = (x-mu)/s, efactor = exp(-y - b*exp(-y)); if (param == 0) return A/s * norm * (y - 1. - b*exp(-y)) * efactor; if (param == 1) return -A * norm * exp(-y) * efactor; if (param == 2) return A/s * norm * (1. - b*exp(-y)) * efactor; if (param == 3) return norm * efactor; return 0; } double nsl_fit_model_gumbel2_param_deriv(int param, double x, double a, double b, double mu, double A, double weight) { double y = x - mu, norm = A * weight * exp(-b * pow(y, -a)); if (param == 0) return norm * b * pow(y, -1. -2.*a) * (pow(y, a) -a*(pow(y, a)-b)*log(y)); if (param == 1) return norm * a * pow(y, -1. -2.*a) * (pow(y, a) - b); if (param == 2) return norm * a * b * pow(y, -2.*(a + 1.)) * ((1. + a)*pow(y, a) - a*b); if (param == 3) return weight * gsl_ran_gumbel2_pdf(y, a, b); return 0; } double nsl_fit_model_binomial_param_deriv(int param, double k, double p, double n, double A, double weight) { if (k < 0 || k > n || n < 0 || p < 0 || p > 1.) return 0; k = round(k); n = round(n); double norm = weight * gsl_sf_fact(n)/gsl_sf_fact(n-k)/gsl_sf_fact(k); if (param == 0) return A * norm * pow(p, k-1.) * pow(1.-p, n-k-1.) * (k-n*p); if (param == 1) return A * norm * pow(p, k) * pow(1.-p, n-k) * (log(1.-p) + gsl_sf_psi(n+1.) - gsl_sf_psi(n-k+1.)); if (param == 2) return weight * gsl_ran_binomial_pdf(k, p, n); return 0; } double nsl_fit_model_negative_binomial_param_deriv(int param, double k, double p, double n, double A, double weight) { if (k < 0 || k > n || n < 0 || p < 0 || p > 1.) return 0; double norm = A * weight * gsl_sf_gamma(n+k)/gsl_sf_gamma(k+1.)/gsl_sf_gamma(n); if (param == 0) return - norm * pow(p, n-1.) * pow(1.-p, k-1.) * (n*(p-1.) + k*p); if (param == 1) return norm * pow(p, n) * pow(1.-p, k) * (log(p) - gsl_sf_psi(n) + gsl_sf_psi(n+k)); if (param == 2) return weight * gsl_ran_negative_binomial_pdf(k, p, n); return 0; } double nsl_fit_model_pascal_param_deriv(int param, double k, double p, double n, double A, double weight) { return nsl_fit_model_negative_binomial_param_deriv(param, k, p, round(n), A, weight); } double nsl_fit_model_geometric_param_deriv(int param, double k, double p, double A, double weight) { if (param == 0) return A * weight * pow(1.-p, k-2.) * (1.-k*p); if (param == 1) return weight * gsl_ran_geometric_pdf(k, p); return 0; } double nsl_fit_model_hypergeometric_param_deriv(int param, double k, double n1, double n2, double t, double A, double weight) { if (t > n1 + n2) return 0; double norm = weight * gsl_ran_hypergeometric_pdf(k, n1, n2, t); if (param == 0) return A * norm * (gsl_sf_psi(n1+1.) - gsl_sf_psi(n1-k+1.) - gsl_sf_psi(n1+n2+1.) + gsl_sf_psi(n1+n2-t+1.)); if (param == 1) return A * norm * (gsl_sf_psi(n2+1.) - gsl_sf_psi(n2+k-t+1.) - gsl_sf_psi(n1+n2+1.) + gsl_sf_psi(n1+n2-t+1.)); if (param == 2) return A * norm * (gsl_sf_psi(n2+k-t+1.) - gsl_sf_psi(n1+n2-t+1.) - gsl_sf_psi(t-k+1.) + gsl_sf_psi(t+1.)); if (param == 3) return norm; return 0; } double nsl_fit_model_logarithmic_param_deriv(int param, double k, double p, double A, double weight) { if (param == 0) return A * weight * pow(1.-p, k-2.) * (1.-k*p); if (param == 1) return weight * gsl_ran_logarithmic_pdf(k, p); return 0; } double nsl_fit_model_sech_dist_param_deriv(int param, double x, double s, double mu, double A, double weight) { double norm = weight/2./s, y = M_PI/2.*(x-mu)/s; if (param == 0) return -A/s * norm * (y*tanh(y)+1.)/cosh(y); if (param == 1) return A*M_PI/2./s * norm * tanh(y)/cosh(y); if (param == 2) return norm * 1./cosh(y); return 0; } diff --git a/src/backend/nsl/nsl_fit.h b/src/backend/nsl/nsl_fit.h index 7b0bc8950..47e58b7bf 100644 --- a/src/backend/nsl/nsl_fit.h +++ b/src/backend/nsl/nsl_fit.h @@ -1,126 +1,132 @@ /*************************************************************************** File : nsl_fit.h Project : LabPlot Description : NSL (non)linear fitting functions -------------------------------------------------------------------- Copyright : (C) 2016 by Stefan Gerlach (stefan.gerlach@uni.kn) ***************************************************************************/ /*************************************************************************** * * * This program is free software; you can redistribute it and/or modify * * it under the terms of the GNU General Public License as published by * * the Free Software Foundation; either version 2 of the License, or * * (at your option) any later version. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ #ifndef NSL_FIT_H #define NSL_FIT_H #define NSL_FIT_MODEL_CATEGORY_COUNT 5 typedef enum {nsl_fit_model_basic, nsl_fit_model_peak, nsl_fit_model_growth, nsl_fit_model_distribution, nsl_fit_model_custom=99} nsl_fit_model_category; #define NSL_FIT_MODEL_BASIC_COUNT 5 typedef enum {nsl_fit_model_polynomial, nsl_fit_model_power, nsl_fit_model_exponential, nsl_fit_model_inverse_exponential, nsl_fit_model_fourier} nsl_fit_model_type_basic; extern const char* nsl_fit_model_basic_pic_name[]; #define NSL_FIT_MODEL_PEAK_COUNT 4 typedef enum {nsl_fit_model_gaussian, nsl_fit_model_lorentz, nsl_fit_model_sech, nsl_fit_model_logistic} nsl_fit_model_type_peak; extern const char* nsl_fit_model_peak_pic_name[]; #define NSL_FIT_MODEL_GROWTH_COUNT 8 typedef enum {nsl_fit_model_atan, nsl_fit_model_tanh, nsl_fit_model_algebraic_sigmoid, nsl_fit_model_sigmoid, nsl_fit_model_erf, nsl_fit_model_hill, nsl_fit_model_gompertz, nsl_fit_model_gudermann} nsl_fit_model_type_growth; extern const char* nsl_fit_model_growth_pic_name[]; extern const char* nsl_fit_model_category_name[]; extern const char* nsl_fit_model_basic_name[]; extern const char* nsl_fit_model_peak_name[]; extern const char* nsl_fit_model_growth_name[]; extern const char* nsl_fit_model_basic_equation[]; extern const char* nsl_fit_model_peak_equation[]; extern const char* nsl_fit_model_growth_equation[]; +#define NSL_FIT_ERROR_TYPE_COUNT 3 +typedef enum {nsl_fit_error_no, /* e = 1 */ + nsl_fit_error_direct, /* e = c: default */ + nsl_fit_error_inverse, /* e = 1/c */ +} nsl_fit_error_type; +extern const char* nsl_fit_error_type_name[]; #define NSL_FIT_WEIGHT_TYPE_COUNT 8 typedef enum {nsl_fit_weight_no, /* w = 1 */ nsl_fit_weight_instrumental, /* w = 1/c^2 (Gaussian, Given errors): default */ nsl_fit_weight_direct, /* w = c */ nsl_fit_weight_inverse, /* w = 1/c */ nsl_fit_weight_statistical, /* w = 1/y (Poisson) */ nsl_fit_weight_statistical_fit, /* w = 1/Y (Poisson) */ nsl_fit_weight_relative, /* w = 1/y^2 (Variance) */ nsl_fit_weight_relative_fit, /* w = 1/Y^2 (Variance) */ } nsl_fit_weight_type; extern const char* nsl_fit_weight_type_name[]; /* convert unbounded variable x to bounded variable where bounds are [min, max] */ double nsl_fit_map_bound(double x, double min, double max); /* convert bounded variable x to unbounded variable where bounds are [min, max] */ double nsl_fit_map_unbound(double x, double min, double max); /* model parameter derivatives */ /* basic */ double nsl_fit_model_polynomial_param_deriv(double x, int j, double weight); double nsl_fit_model_power1_param_deriv(int param, double x, double a, double b, double weight); double nsl_fit_model_power2_param_deriv(int param, double x, double b, double c, double weight); double nsl_fit_model_exponentialn_param_deriv(int param, double x, double *p, double weight); double nsl_fit_model_inverse_exponential_param_deriv(int param, double x, double a, double b, double weight); double nsl_fit_model_fourier_param_deriv(int param, int degree, double x, double w, double weight); /* peak */ double nsl_fit_model_gaussian_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_lorentz_param_deriv(int param, double x, double s, double t, double a, double weight); double nsl_fit_model_sech_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_logistic_param_deriv(int param, double x, double s, double mu, double a, double weight); /* growth */ double nsl_fit_model_atan_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_tanh_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_algebraic_sigmoid_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_sigmoid_param_deriv(int param, double x, double k, double mu, double a, double weight); double nsl_fit_model_erf_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_hill_param_deriv(int param, double x, double s, double n, double a, double weight); double nsl_fit_model_gompertz_param_deriv(int param, double x, double a, double b, double c, double weight); double nsl_fit_model_gudermann_param_deriv(int param, double x, double s, double mu, double a, double weight); /* distributions */ double nsl_fit_model_gaussian_tail_param_deriv(int param, double x, double s, double mu, double A, double a, double weight); double nsl_fit_model_exponential_param_deriv(int param, double x, double l, double mu, double a, double weight); double nsl_fit_model_laplace_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_exp_pow_param_deriv(int param, double x, double s, double mu, double b, double a, double weight); double nsl_fit_model_poisson_param_deriv(int param, double x, double l, double a, double weight); double nsl_fit_model_lognormal_param_deriv(int param, double x, double b, double mu, double a, double weight); double nsl_fit_model_gamma_param_deriv(int param, double x, double t, double k, double a, double weight); double nsl_fit_model_flat_param_deriv(int param, double x, double a, double b, double A, double weight); double nsl_fit_model_rayleigh_param_deriv(int param, double x, double s, double a, double weight); double nsl_fit_model_rayleigh_tail_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_landau_param_deriv(int param, double x, double weight); double nsl_fit_model_chi_square_param_deriv(int param, double x, double n, double a, double weight); double nsl_fit_model_students_t_param_deriv(int param, double x, double n, double a, double weight); double nsl_fit_model_fdist_param_deriv(int param, double x, double n1, double n2, double a, double weight); double nsl_fit_model_beta_param_deriv(int param, double x, double a, double b, double A, double weight); double nsl_fit_model_pareto_param_deriv(int param, double x, double a, double b, double A, double weight); double nsl_fit_model_weibull_param_deriv(int param, double x, double k, double l, double mu, double a, double weight); double nsl_fit_model_gumbel1_param_deriv(int param, double x, double s, double b, double mu, double a, double weight); double nsl_fit_model_gumbel2_param_deriv(int param, double x, double a, double b, double mu, double A, double weight); double nsl_fit_model_binomial_param_deriv(int param, double x, double p, double n, double A, double weight); double nsl_fit_model_negative_binomial_param_deriv(int param, double k, double p, double n, double A, double weight); double nsl_fit_model_pascal_param_deriv(int param, double k, double p, double n, double A, double weight); double nsl_fit_model_geometric_param_deriv(int param, double k, double p, double A, double weight); double nsl_fit_model_hypergeometric_param_deriv(int param, double k, double n1, double n2, double t, double A, double weight); double nsl_fit_model_logarithmic_param_deriv(int param, double k, double p, double A, double weight); double nsl_fit_model_maxwell_param_deriv(int param, double x, double a, double c, double weight); double nsl_fit_model_sech_dist_param_deriv(int param, double x, double s, double mu, double a, double weight); double nsl_fit_model_levy_param_deriv(int param, double x, double g, double mu, double a, double weight); double nsl_fit_model_frechet_param_deriv(int param, double x, double a, double mu, double s, double c, double weight); #endif /* NSL_FIT_H */ diff --git a/src/backend/worksheet/plots/cartesian/XYFitCurve.cpp b/src/backend/worksheet/plots/cartesian/XYFitCurve.cpp index 850c295d2..b982ee2c9 100644 --- a/src/backend/worksheet/plots/cartesian/XYFitCurve.cpp +++ b/src/backend/worksheet/plots/cartesian/XYFitCurve.cpp @@ -1,2171 +1,2157 @@ /*************************************************************************** File : XYFitCurve.cpp Project : LabPlot Description : A xy-curve defined by a fit model -------------------------------------------------------------------- Copyright : (C) 2014-2017 Alexander Semke (alexander.semke@web.de) Copyright : (C) 2016-2017 Stefan Gerlach (stefan.gerlach@uni.kn) ***************************************************************************/ /*************************************************************************** * * * This program is free software; you can redistribute it and/or modify * * it under the terms of the GNU General Public License as published by * * the Free Software Foundation; either version 2 of the License, or * * (at your option) any later version. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ /*! \class XYFitCurve \brief A xy-curve defined by a fit model \ingroup worksheet */ #include "XYFitCurve.h" #include "XYFitCurvePrivate.h" #include "backend/core/AbstractColumn.h" #include "backend/core/column/Column.h" #include "backend/lib/commandtemplates.h" #include "backend/lib/macros.h" #include "backend/gsl/errors.h" #include "backend/gsl/ExpressionParser.h" extern "C" { #include #include #include #include #include #include #include "backend/gsl/parser.h" #include "backend/nsl/nsl_sf_stats.h" #include "backend/nsl/nsl_stats.h" } #include #include #include XYFitCurve::XYFitCurve(const QString& name) : XYCurve(name, new XYFitCurvePrivate(this)) { init(); } XYFitCurve::XYFitCurve(const QString& name, XYFitCurvePrivate* dd) : XYCurve(name, dd) { init(); } XYFitCurve::~XYFitCurve() { //no need to delete the d-pointer here - it inherits from QGraphicsItem //and is deleted during the cleanup in QGraphicsScene } void XYFitCurve::init() { Q_D(XYFitCurve); //TODO: read from the saved settings for XYFitCurve? d->lineType = XYCurve::Line; d->symbolsStyle = Symbol::NoSymbols; } void XYFitCurve::recalculate() { Q_D(XYFitCurve); d->recalculate(); } void XYFitCurve::initFitData(PlotDataDialog::AnalysisAction action) { if (!action) return; Q_D(XYFitCurve); XYFitCurve::FitData& fitData = d->fitData; if (action == PlotDataDialog::FitLinear) { //Linear fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_polynomial; fitData.degree = 1; } else if (action == PlotDataDialog::FitPower) { //Power fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_power; fitData.degree = 1; } else if (action == PlotDataDialog::FitExp1) { //Exponential (degree 1) fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_exponential; fitData.degree = 1; } else if (action == PlotDataDialog::FitExp2) { //Exponential (degree 2) fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_exponential; fitData.degree = 2; } else if (action == PlotDataDialog::FitInvExp) { //Inverse exponential fitData.modelCategory = nsl_fit_model_basic; fitData.modelType = nsl_fit_model_inverse_exponential; } else if (action == PlotDataDialog::FitGauss) { //Gauss fitData.modelCategory = nsl_fit_model_peak; fitData.modelType = nsl_fit_model_gaussian; fitData.degree = 1; } else if (action == PlotDataDialog::FitCauchyLorentz) { //Cauchy-Lorentz fitData.modelCategory = nsl_fit_model_peak; fitData.modelType = nsl_fit_model_lorentz; fitData.degree = 1; } else if (action == PlotDataDialog::FitTan) { //Arc tangent fitData.modelCategory = nsl_fit_model_growth; fitData.modelType = nsl_fit_model_atan; } else if (action == PlotDataDialog::FitTanh) { //Hyperbolic tangent fitData.modelCategory = nsl_fit_model_growth; fitData.modelType = nsl_fit_model_tanh; } else if (action == PlotDataDialog::FitErrFunc) { //Error function fitData.modelCategory = nsl_fit_model_growth; fitData.modelType = nsl_fit_model_erf; } else { //Custom fitData.modelCategory = nsl_fit_model_custom; fitData.modelType = 0; } XYFitCurve::initFitData(fitData); } /*! * sets the model expression and the parameter names for given model category, model type and degree in \c fitData */ void XYFitCurve::initFitData(XYFitCurve::FitData& fitData) { nsl_fit_model_category modelCategory = fitData.modelCategory; unsigned int modelType = fitData.modelType; QString& model = fitData.model; QStringList& paramNames = fitData.paramNames; QStringList& paramNamesUtf8 = fitData.paramNamesUtf8; int degree = fitData.degree; QVector& paramStartValues = fitData.paramStartValues; QVector& paramLowerLimits = fitData.paramLowerLimits; QVector& paramUpperLimits = fitData.paramUpperLimits; QVector& paramFixed = fitData.paramFixed; DEBUG("XYFitCurve::initFitData() for model category = " << modelCategory << ", model type = " << modelType << ", degree = " << degree); if (modelCategory != nsl_fit_model_custom) paramNames.clear(); paramNamesUtf8.clear(); // 10 indices used in multi degree models QStringList indices = {QString::fromUtf8("\u2081"), QString::fromUtf8("\u2082"), QString::fromUtf8("\u2083"), QString::fromUtf8("\u2084"), QString::fromUtf8("\u2085"), QString::fromUtf8("\u2086"), QString::fromUtf8("\u2087"), QString::fromUtf8("\u2088"), QString::fromUtf8("\u2089"), QString::fromUtf8("\u2081") + QString::fromUtf8("\u2080")}; switch (modelCategory) { case nsl_fit_model_basic: model = nsl_fit_model_basic_equation[fitData.modelType]; switch (modelType) { case nsl_fit_model_polynomial: paramNames << "c0" << "c1"; paramNamesUtf8 << QString::fromUtf8("c\u2080") << QString::fromUtf8("c\u2081"); if (degree == 2) { model += " + c2*x^2"; paramNames << "c2"; paramNamesUtf8 << QString::fromUtf8("c\u2082"); } else if (degree > 2) { for (int i = 2; i <= degree; ++i) { QString numStr = QString::number(i); model += "+c" + numStr + "*x^" + numStr; paramNames << "c" + numStr; paramNamesUtf8 << "c" + indices[i-1]; } } break; case nsl_fit_model_power: if (degree == 1) { paramNames << "a" << "b"; } else { paramNames << "a" << "b" << "c"; model = "a + b*x^c"; } break; case nsl_fit_model_exponential: if (degree == 1) { paramNames << "a" << "b"; } else { for (int i = 1; i <= degree; i++) { QString numStr = QString::number(i); if (i == 1) model = "a1*exp(b1*x)"; else model += " + a" + numStr + "*exp(b" + numStr + "*x)"; paramNames << "a" + numStr << "b" + numStr; paramNamesUtf8 << "a" + indices[i-1] << "b" + indices[i-1]; } } break; case nsl_fit_model_inverse_exponential: degree = 1; paramNames << "a" << "b" << "c"; break; case nsl_fit_model_fourier: paramNames << "w" << "a0" << "a1" << "b1"; paramNamesUtf8 << QString::fromUtf8("\u03c9") << QString::fromUtf8("a\u2080") << QString::fromUtf8("a\u2081") << QString::fromUtf8("b\u2081"); if (degree > 1) { for (int i = 1; i <= degree; ++i) { QString numStr = QString::number(i); model += "+ (a" + numStr + "*cos(" + numStr + "*w*x) + b" + numStr + "*sin(" + numStr + "*w*x))"; paramNames << "a" + numStr << "b" + numStr; paramNamesUtf8 << "a" + indices[i-1] << "b" + indices[i-1]; } } break; } break; case nsl_fit_model_peak: model = nsl_fit_model_peak_equation[fitData.modelType]; switch (modelType) { case nsl_fit_model_gaussian: switch (degree) { case 1: paramNames << "s" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "A"; break; case 2: model = "1./sqrt(2*pi) * (a1/s1 * exp(-((x-mu1)/s1)^2/2) + a2/s2 * exp(-((x-mu2)/s2)^2/2))"; paramNames << "s1" << "mu1" << "a1" << "s2" << "mu2" << "a2"; paramNamesUtf8 << QString::fromUtf8("\u03c3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03c3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082"); break; case 3: model = "1./sqrt(2*pi) * (a1/s1 * exp(-((x-mu1)/s1)^2/2) + a2/s2 * exp(-((x-mu2)/s2)^2/2) + a3/s3 * exp(-((x-mu3)/s3)^2/2))"; paramNames << "s1" << "mu1" << "a1" << "s2" << "mu2" << "a2" << "s3" << "mu3" << "a3"; paramNamesUtf8 << QString::fromUtf8("\u03c3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03c3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082") << QString::fromUtf8("\u03c3\u2083") << QString::fromUtf8("\u03bc\u2083") << QString::fromUtf8("A\u2083"); break; default: model = "1./sqrt(2*pi) * ("; for (int i = 1; i <= degree; ++i) { QString numStr = QString::number(i); if (i > 1) model += " + "; model += "a" + numStr + "/s" + numStr + "* exp(-((x-mu" + numStr + ")/s" + numStr + ")^2/2)"; paramNames << "s" + numStr << "mu" + numStr << "a" + numStr; paramNamesUtf8 << QString::fromUtf8("\u03c3") + indices[i-1] << QString::fromUtf8("\u03bc") + indices[i-1] << QString::fromUtf8("A") + indices[i-1]; } model += ")"; } break; case nsl_fit_model_lorentz: switch (degree) { case 1: paramNames << "g" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03b3") << QString::fromUtf8("\u03bc") << "A"; break; case 2: model = "1./pi * (a1 * g1/(g1^2+(x-mu1)^2) + a2 * g2/(g2^2+(x-mu2)^2))"; paramNames << "g1" << "mu1" << "a1" << "g2" << "mu2" << "a2"; paramNamesUtf8 << QString::fromUtf8("\u03b3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03b3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082"); break; case 3: model = "1./pi * (a1 * g1/(g1^2+(x-mu1)^2) + a2 * g2/(g2^2+(x-mu2)^2) + a3 * g3/(g3^2+(x-mu3)^2))"; paramNames << "g1" << "mu1" << "a1" << "g2" << "mu2" << "a2" << "g3" << "mu3" << "a3"; paramNamesUtf8 << QString::fromUtf8("\u03b3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03b3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082") << QString::fromUtf8("\u03b3\u2083") << QString::fromUtf8("\u03bc\u2083") << QString::fromUtf8("A\u2083"); break; default: model = "1./pi * ("; for (int i = 1; i <= degree; ++i) { QString numStr = QString::number(i); if (i > 1) model += " + "; model += "a" + numStr + " * g" + numStr + "/(g" + numStr + "^2+(x-mu" + numStr + ")^2)"; paramNames << "g" + numStr << "mu" + numStr << "a" + numStr; paramNamesUtf8 << QString::fromUtf8("\u03b3") + indices[i-1] << QString::fromUtf8("\u03bc") + indices[i-1] << QString::fromUtf8("A") + indices[i-1]; } model += ")"; } break; case nsl_fit_model_sech: switch (degree) { case 1: paramNames << "s" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "A"; break; case 2: model = "1/pi * (a1/s1 * sech((x-mu1)/s1) + a2/s2 * sech((x-mu2)/s2))"; paramNames << "s1" << "mu1" << "a1" << "s2" << "mu2" << "a2"; paramNamesUtf8 << QString::fromUtf8("\u03c3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03c3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082"); break; case 3: model = "1/pi * (a1/s1 * sech((x-mu1)/s1) + a2/s2 * sech((x-mu2)/s2) + a3/s3 * sech((x-mu3)/s3))"; paramNames << "s1" << "mu1" << "a1" << "s2" << "mu2" << "a2" << "s3" << "mu3" << "a3"; paramNamesUtf8 << QString::fromUtf8("\u03c3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03c3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082") << QString::fromUtf8("\u03c3\u2083") << QString::fromUtf8("\u03bc\u2083") << QString::fromUtf8("A\u2083"); break; default: model = "1/pi * ("; for (int i = 1; i <= degree; ++i) { QString numStr = QString::number(i); if (i > 1) model += " + "; model += "a" + numStr + "/s" + numStr + "* sech((x-mu" + numStr + ")/s" + numStr + ")"; paramNames << "s" + numStr << "mu" + numStr << "a" + numStr; paramNamesUtf8 << QString::fromUtf8("\u03c3") + indices[i-1] << QString::fromUtf8("\u03bc") + indices[i-1] << QString::fromUtf8("A") + indices[i-1]; } model += ")"; } break; case nsl_fit_model_logistic: switch (degree) { case 1: paramNames << "s" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "A"; break; case 2: model = "1/4 * (a1/s1 * sech((x-mu1)/2/s1)**2 + a2/s2 * sech((x-mu2)/2/s2)**2)"; paramNames << "s1" << "mu1" << "a1" << "s2" << "mu2" << "a2"; paramNamesUtf8 << QString::fromUtf8("\u03c3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03c3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082"); break; case 3: model = "1/4 * (a1/s1 * sech((x-mu1)/2/s1)**2 + a2/s2 * sech((x-mu2)/2/s2)**2 + a3/s3 * sech((x-mu3)/2/s3)**2)"; paramNames << "s1" << "mu1" << "a1" << "s2" << "mu2" << "a2" << "s3" << "mu3" << "a3"; paramNamesUtf8 << QString::fromUtf8("\u03c3\u2081") << QString::fromUtf8("\u03bc\u2081") << QString::fromUtf8("A\u2081") << QString::fromUtf8("\u03c3\u2082") << QString::fromUtf8("\u03bc\u2082") << QString::fromUtf8("A\u2082") << QString::fromUtf8("\u03c3\u2083") << QString::fromUtf8("\u03bc\u2083") << QString::fromUtf8("A\u2083"); break; default: model = "1/4 * ("; for (int i = 1; i <= degree; ++i) { QString numStr = QString::number(i); if (i > 1) model += " + "; model += "a" + numStr + "/s" + numStr + "* sech((x-mu" + numStr + ")/2/s" + numStr + ")**2"; paramNames << "s" + numStr << "mu" + numStr << "a" + numStr; paramNamesUtf8 << QString::fromUtf8("\u03c3") + indices[i-1] << QString::fromUtf8("\u03bc") + indices[i-1] << QString::fromUtf8("A") + indices[i-1]; } model += ")"; } break; } break; case nsl_fit_model_growth: model = nsl_fit_model_growth_equation[fitData.modelType]; switch (modelType) { case nsl_fit_model_atan: case nsl_fit_model_tanh: case nsl_fit_model_algebraic_sigmoid: case nsl_fit_model_erf: case nsl_fit_model_gudermann: paramNames << "s" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "A"; break; case nsl_fit_model_sigmoid: paramNames << "k" << "mu" << "a"; paramNamesUtf8 << "k" << QString::fromUtf8("\u03bc") << "A"; break; case nsl_fit_model_hill: paramNames << "s" << "n" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << "n" << "A"; break; case nsl_fit_model_gompertz: paramNames << "a" << "b" << "c"; break; } break; case nsl_fit_model_distribution: model = nsl_sf_stats_distribution_equation[fitData.modelType]; switch (modelType) { case nsl_sf_stats_gaussian: case nsl_sf_stats_laplace: case nsl_sf_stats_rayleigh_tail: case nsl_sf_stats_lognormal: case nsl_sf_stats_logistic: case nsl_sf_stats_sech: paramNames << "s" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "A"; break; case nsl_sf_stats_gaussian_tail: paramNames << "s" << "mu" << "A" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "A" << "a"; break; case nsl_sf_stats_exponential: paramNames << "l" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03bb") << QString::fromUtf8("\u03bc") << "A"; break; case nsl_sf_stats_exponential_power: paramNames << "s" << "mu" << "b" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03bc") << "b" << "A"; break; case nsl_sf_stats_cauchy_lorentz: case nsl_sf_stats_levy: paramNames << "g" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03b3") << QString::fromUtf8("\u03bc") << "A"; break; case nsl_sf_stats_rayleigh: paramNames << "s" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << "A"; break; case nsl_sf_stats_landau: paramNames << "a"; paramNamesUtf8 << "A"; break; case nsl_sf_stats_levy_alpha_stable: // unused distributions case nsl_sf_stats_levy_skew_alpha_stable: case nsl_sf_stats_bernoulli: break; case nsl_sf_stats_gamma: paramNames << "t" << "k" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03b8") << "k" << "A"; break; case nsl_sf_stats_flat: paramNames << "a" << "b" << "A"; break; case nsl_sf_stats_chi_squared: paramNames << "n" << "a"; paramNamesUtf8 << "n" << "A"; break; case nsl_sf_stats_fdist: paramNames << "n1" << "n2" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03bd") + QString::fromUtf8("\u2081") << QString::fromUtf8("\u03bd") + QString::fromUtf8("\u2082") << "A"; break; case nsl_sf_stats_tdist: paramNames << "n" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03bd") << "A"; break; case nsl_sf_stats_beta: case nsl_sf_stats_pareto: paramNames << "a" << "b" << "A"; break; case nsl_sf_stats_weibull: paramNames << "k" << "l" << "mu" << "a"; paramNamesUtf8 << "k" << QString::fromUtf8("\u03bb") << QString::fromUtf8("\u03bc") << "A"; break; case nsl_sf_stats_gumbel1: paramNames << "s" << "b" << "mu" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << QString::fromUtf8("\u03b2") << QString::fromUtf8("\u03bc") << "A"; break; case nsl_sf_stats_gumbel2: paramNames << "a" << "b" << "mu" << "A"; paramNamesUtf8 << "a" << "b" << QString::fromUtf8("\u03bc") << "A"; break; case nsl_sf_stats_poisson: paramNames << "l" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03bb") << "A"; break; case nsl_sf_stats_binomial: case nsl_sf_stats_negative_binomial: case nsl_sf_stats_pascal: paramNames << "p" << "n" << "a"; paramNamesUtf8 << "p" << "n" << "A"; break; case nsl_sf_stats_geometric: case nsl_sf_stats_logarithmic: paramNames << "p" << "a"; paramNamesUtf8 << "p" << "A"; break; case nsl_sf_stats_hypergeometric: paramNames << "n1" << "n2" << "t" << "a"; paramNamesUtf8 << "n" + QString::fromUtf8("\u2081") << "n" + QString::fromUtf8("\u2082") << "t" << "A"; break; case nsl_sf_stats_maxwell_boltzmann: paramNames << "s" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03c3") << "A"; break; case nsl_sf_stats_frechet: paramNames << "g" << "mu" << "s" << "a"; paramNamesUtf8 << QString::fromUtf8("\u03b3") << QString::fromUtf8("\u03bc") << QString::fromUtf8("\u03c3") << "A"; break; } break; case nsl_fit_model_custom: break; } if (paramNamesUtf8.isEmpty()) paramNamesUtf8 << paramNames; //resize the vector for the start values and set the elements to 1.0 //in case a custom model is used, do nothing, we take over the previous values if (modelCategory != nsl_fit_model_custom) { const int np = paramNames.size(); paramStartValues.resize(np); paramFixed.resize(np); paramLowerLimits.resize(np); paramUpperLimits.resize(np); for (int i = 0; i < np; ++i) { paramStartValues[i] = 1.0; paramFixed[i] = false; paramLowerLimits[i] = -std::numeric_limits::max(); paramUpperLimits[i] = std::numeric_limits::max(); } // set some model-dependent start values if (modelCategory == nsl_fit_model_distribution) { nsl_sf_stats_distribution type = (nsl_sf_stats_distribution)modelType; if (type == nsl_sf_stats_flat) paramStartValues[0] = -1.0; else if (type == nsl_sf_stats_frechet || type == nsl_sf_stats_levy || type == nsl_sf_stats_exponential_power) paramStartValues[1] = 0.0; else if (type == nsl_sf_stats_weibull || type == nsl_sf_stats_gumbel2) paramStartValues[2] = 0.0; else if (type == nsl_sf_stats_binomial || type == nsl_sf_stats_negative_binomial || type == nsl_sf_stats_pascal || type == nsl_sf_stats_geometric || type == nsl_sf_stats_logarithmic) paramStartValues[0] = 0.5; } } } /*! Returns an icon to be used in the project explorer. */ QIcon XYFitCurve::icon() const { return QIcon::fromTheme("labplot-xy-fit-curve"); } //############################################################################## //########################## getter methods ################################## //############################################################################## BASIC_SHARED_D_READER_IMPL(XYFitCurve, const AbstractColumn*, xDataColumn, xDataColumn) BASIC_SHARED_D_READER_IMPL(XYFitCurve, const AbstractColumn*, yDataColumn, yDataColumn) BASIC_SHARED_D_READER_IMPL(XYFitCurve, const AbstractColumn*, xErrorColumn, xErrorColumn) BASIC_SHARED_D_READER_IMPL(XYFitCurve, const AbstractColumn*, yErrorColumn, yErrorColumn) const QString& XYFitCurve::xDataColumnPath() const { Q_D(const XYFitCurve); return d->xDataColumnPath; } const QString& XYFitCurve::yDataColumnPath() const { Q_D(const XYFitCurve); return d->yDataColumnPath; } const QString& XYFitCurve::xErrorColumnPath() const { Q_D(const XYFitCurve);return d->xErrorColumnPath; } const QString& XYFitCurve::yErrorColumnPath() const { Q_D(const XYFitCurve);return d->yErrorColumnPath; } BASIC_SHARED_D_READER_IMPL(XYFitCurve, XYFitCurve::FitData, fitData, fitData) const XYFitCurve::FitResult& XYFitCurve::fitResult() const { Q_D(const XYFitCurve); return d->fitResult; } //############################################################################## //################# setter methods and undo commands ########################## //############################################################################## STD_SETTER_CMD_IMPL_S(XYFitCurve, SetXDataColumn, const AbstractColumn*, xDataColumn) void XYFitCurve::setXDataColumn(const AbstractColumn* column) { Q_D(XYFitCurve); if (column != d->xDataColumn) { exec(new XYFitCurveSetXDataColumnCmd(d, column, i18n("%1: assign x-data"))); handleSourceDataChanged(); if (column) { connect(column, SIGNAL(dataChanged(const AbstractColumn*)), this, SLOT(handleSourceDataChanged())); //TODO disconnect on undo } } } STD_SETTER_CMD_IMPL_S(XYFitCurve, SetYDataColumn, const AbstractColumn*, yDataColumn) void XYFitCurve::setYDataColumn(const AbstractColumn* column) { Q_D(XYFitCurve); if (column != d->yDataColumn) { exec(new XYFitCurveSetYDataColumnCmd(d, column, i18n("%1: assign y-data"))); handleSourceDataChanged(); if (column) { connect(column, SIGNAL(dataChanged(const AbstractColumn*)), this, SLOT(handleSourceDataChanged())); //TODO disconnect on undo } } } STD_SETTER_CMD_IMPL_S(XYFitCurve, SetXErrorColumn, const AbstractColumn*, xErrorColumn) void XYFitCurve::setXErrorColumn(const AbstractColumn* column) { Q_D(XYFitCurve); if (column != d->xErrorColumn) { exec(new XYFitCurveSetXErrorColumnCmd(d, column, i18n("%1: assign x-error"))); handleSourceDataChanged(); if (column) { connect(column, SIGNAL(dataChanged(const AbstractColumn*)), this, SLOT(handleSourceDataChanged())); //TODO disconnect on undo } } } STD_SETTER_CMD_IMPL_S(XYFitCurve, SetYErrorColumn, const AbstractColumn*, yErrorColumn) void XYFitCurve::setYErrorColumn(const AbstractColumn* column) { Q_D(XYFitCurve); if (column != d->yErrorColumn) { exec(new XYFitCurveSetYErrorColumnCmd(d, column, i18n("%1: assign y-error"))); handleSourceDataChanged(); if (column) { connect(column, SIGNAL(dataChanged(const AbstractColumn*)), this, SLOT(handleSourceDataChanged())); //TODO disconnect on undo } } } STD_SETTER_CMD_IMPL_F_S(XYFitCurve, SetFitData, XYFitCurve::FitData, fitData, recalculate); void XYFitCurve::setFitData(const XYFitCurve::FitData& fitData) { Q_D(XYFitCurve); exec(new XYFitCurveSetFitDataCmd(d, fitData, i18n("%1: set fit options and perform the fit"))); } //############################################################################## //######################### Private implementation ############################# //############################################################################## XYFitCurvePrivate::XYFitCurvePrivate(XYFitCurve* owner) : XYCurvePrivate(owner), xDataColumn(0), yDataColumn(0), xErrorColumn(0), yErrorColumn(0), xColumn(0), yColumn(0), residualsColumn(0), xVector(0), yVector(0), residualsVector(0), q(owner) { } XYFitCurvePrivate::~XYFitCurvePrivate() { //no need to delete xColumn and yColumn, they are deleted //when the parent aspect is removed } // data structure to pass parameter to fit functions struct data { size_t n; //number of data points double* x; //pointer to the vector with x-data values double* y; //pointer to the vector with y-data values double* weight; //pointer to the vector with weight values nsl_fit_model_category modelCategory; unsigned int modelType; int degree; QString* func; // string containing the definition of the model/function QStringList* paramNames; double* paramMin; // lower parameter limits double* paramMax; // upper parameter limits bool* paramFixed; // parameter fixed? }; /*! * \param paramValues vector containing current values of the fit parameters * \param params * \param f vector with the weighted residuals weight[i]*(Yi - y[i]) */ int func_f(const gsl_vector* paramValues, void* params, gsl_vector* f) { size_t n = ((struct data*)params)->n; double* x = ((struct data*)params)->x; double* y = ((struct data*)params)->y; double* weight = ((struct data*)params)->weight; nsl_fit_model_category modelCategory = ((struct data*)params)->modelCategory; unsigned int modelType = ((struct data*)params)->modelType; QByteArray funcba = ((struct data*)params)->func->toLatin1(); // a local byte array is needed! const char *func = funcba.constData(); // function to evaluate QStringList* paramNames = ((struct data*)params)->paramNames; double *min = ((struct data*)params)->paramMin; double *max = ((struct data*)params)->paramMax; // set current values of the parameters for (int i = 0; i < paramNames->size(); i++) { double x = gsl_vector_get(paramValues, i); // bound values if limits are set QByteArray paramnameba = paramNames->at(i).toLatin1(); assign_variable(paramnameba.constData(), nsl_fit_map_bound(x, min[i], max[i])); QDEBUG("Parameter"<at(k).toLatin1(); value = nsl_fit_map_bound(gsl_vector_get(paramValues, k), min[k], max[k]); assign_variable(nameba.data(), value); } } nameba = paramNames->at(j).toLatin1(); const char *name = nameba.data(); value = nsl_fit_map_bound(gsl_vector_get(paramValues, j), min[j], max[j]); assign_variable(name, value); const double f_p = parse(func); const double eps = 1.e-9 * std::abs(f_p); // adapt step size to value value += eps; assign_variable(name, value); const double f_pdp = parse(func); // qDebug()<<"evaluate deriv"<* >(xColumn->data()); yVector = static_cast* >(yColumn->data()); residualsVector = static_cast* >(residualsColumn->data()); xColumn->setHidden(true); q->addChild(xColumn); yColumn->setHidden(true); q->addChild(yColumn); q->addChild(residualsColumn); q->setUndoAware(false); q->setXColumn(xColumn); q->setYColumn(yColumn); q->setUndoAware(true); } else { xVector->clear(); yVector->clear(); residualsVector->clear(); } // clear the previous result fitResult = XYFitCurve::FitResult(); //fit settings const unsigned int maxIters = fitData.maxIterations; //maximal number of iterations const double delta = fitData.eps; //fit tolerance const unsigned int np = fitData.paramNames.size(); //number of fit parameters if (np == 0) { fitResult.available = true; fitResult.valid = false; fitResult.status = i18n("Model has no parameters."); emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; return; } //determine the data source columns const AbstractColumn* tmpXDataColumn = 0; const AbstractColumn* tmpYDataColumn = 0; if (dataSourceType == XYCurve::DataSourceSpreadsheet) { //spreadsheet columns as data source tmpXDataColumn = xDataColumn; tmpYDataColumn = yDataColumn; } else { //curve columns as data source tmpXDataColumn = dataSourceCurve->xColumn(); tmpYDataColumn = dataSourceCurve->yColumn(); } if (!tmpXDataColumn || !tmpYDataColumn) { emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; return; } //check column sizes if (tmpXDataColumn->rowCount() != tmpYDataColumn->rowCount()) { fitResult.available = true; fitResult.valid = false; fitResult.status = i18n("Number of x and y data points must be equal."); emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; return; } if (yErrorColumn) { if (yErrorColumn->rowCount() < xDataColumn->rowCount()) { fitResult.available = true; fitResult.valid = false; fitResult.status = i18n("Not sufficient weight data points provided."); emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; return; } } //copy all valid data point for the fit to temporary vectors QVector xdataVector; QVector ydataVector; QVector xerrorVector; QVector yerrorVector; double xmin, xmax; if (fitData.autoRange) { xmin = tmpXDataColumn->minimum(); xmax = tmpXDataColumn->maximum(); } else { xmin = fitData.xRange.first(); xmax = fitData.xRange.last(); } for (int row = 0; row < tmpXDataColumn->rowCount(); ++row) { //only copy those data where _all_ values (for x and y and errors, if given) are valid if (!std::isnan(tmpXDataColumn->valueAt(row)) && !std::isnan(tmpYDataColumn->valueAt(row)) && !tmpXDataColumn->isMasked(row) && !tmpYDataColumn->isMasked(row)) { // only when inside given range if (tmpXDataColumn->valueAt(row) >= xmin && tmpXDataColumn->valueAt(row) <= xmax) { if (dataSourceType == XYCurve::DataSourceCurve || (!xErrorColumn && !yErrorColumn) || !fitData.useDataErrors) { // x-y xdataVector.append(tmpXDataColumn->valueAt(row)); ydataVector.append(tmpYDataColumn->valueAt(row)); } else if (!xErrorColumn) { // x-y-dy if (!std::isnan(yErrorColumn->valueAt(row))) { xdataVector.append(tmpXDataColumn->valueAt(row)); ydataVector.append(tmpYDataColumn->valueAt(row)); yerrorVector.append(yErrorColumn->valueAt(row)); } } else { // x-y-dx-dy if (!std::isnan(xErrorColumn->valueAt(row)) && !std::isnan(yErrorColumn->valueAt(row))) { xdataVector.append(tmpXDataColumn->valueAt(row)); ydataVector.append(tmpYDataColumn->valueAt(row)); xerrorVector.append(xErrorColumn->valueAt(row)); yerrorVector.append(yErrorColumn->valueAt(row)); } } } } } //number of data points to fit const size_t n = xdataVector.size(); DEBUG("number of data points: " << n); if (n == 0) { fitResult.available = true; fitResult.valid = false; fitResult.status = i18n("No data points available."); emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; return; } if (n < np) { fitResult.available = true; fitResult.valid = false; fitResult.status = i18n("The number of data points (%1) must be greater than or equal to the number of parameters (%2).", n, np); emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; return; } double* xdata = xdataVector.data(); double* ydata = ydataVector.data(); double* xerror = xerrorVector.data(); // size may be 0 double* yerror = yerrorVector.data(); // size may be 0 DEBUG("x errors: " << xerrorVector.size()); DEBUG("y errors: " << yerrorVector.size()); double* weight = new double[n]; - switch(fitData.xWeightsType) { - case nsl_fit_weight_no: - case nsl_fit_weight_statistical_fit: - case nsl_fit_weight_relative_fit: - case nsl_fit_weight_direct: - break; - case nsl_fit_weight_instrumental: - for(int i = 0; i < xerrorVector.size(); i++) - xerror[i] = 1./gsl_pow_2(xerror[i]); + switch(fitData.xErrorsType) { + case nsl_fit_error_no: + case nsl_fit_error_direct: break; - case nsl_fit_weight_inverse: + case nsl_fit_error_inverse: for(int i = 0; i < xerrorVector.size(); i++) xerror[i] = 1./xerror[i]; break; - case nsl_fit_weight_statistical: - for(int i = 0; i < xerrorVector.size(); i++) - xerror[i] = 1./xdata[i]; - break; - case nsl_fit_weight_relative: - for(int i = 0; i < xerrorVector.size(); i++) - xerror[i] = 1./gsl_pow_2(xdata[i]); - break; } for (size_t i = 0; i < n; i++) weight[i] = 1.; switch (fitData.yWeightsType) { case nsl_fit_weight_no: case nsl_fit_weight_statistical_fit: case nsl_fit_weight_relative_fit: break; case nsl_fit_weight_instrumental: for(int i = 0; i < (int)n; i++) if (i < yerrorVector.size()) weight[i] = 1./gsl_pow_2(yerror[i]); break; case nsl_fit_weight_direct: for(int i = 0; i < (int)n; i++) if (i < yerrorVector.size()) weight[i] = yerror[i]; break; case nsl_fit_weight_inverse: for(int i = 0; i < (int)n; i++) if (i < yerrorVector.size()) weight[i] = 1./yerror[i]; break; case nsl_fit_weight_statistical: for (int i = 0; i < (int)n; i++) weight[i] = 1./ydata[i]; break; case nsl_fit_weight_relative: for (int i = 0; i < (int)n; i++) weight[i] = 1./gsl_pow_2(ydata[i]); break; } /////////////////////// GSL >= 2 has a complete new interface! But the old one is still supported. /////////////////////////// // GSL >= 2 : "the 'fdf' field of gsl_multifit_function_fdf is now deprecated and does not need to be specified for nonlinear least squares problems" for (unsigned int i = 0; i < np; i++) DEBUG("parameter " << i << " fixed: " << fitData.paramFixed.data()[i]); //function to fit gsl_multifit_function_fdf f; DEBUG("model = " << fitData.model.toStdString()); struct data params = {n, xdata, ydata, weight, fitData.modelCategory, fitData.modelType, fitData.degree, &fitData.model, &fitData.paramNames, fitData.paramLowerLimits.data(), fitData.paramUpperLimits.data(), fitData.paramFixed.data()}; f.f = &func_f; f.df = &func_df; f.fdf = &func_fdf; f.n = n; f.p = np; f.params = ¶ms; // initialize the derivative solver (using Levenberg-Marquardt robust solver) const gsl_multifit_fdfsolver_type* T = gsl_multifit_fdfsolver_lmsder; gsl_multifit_fdfsolver* s = gsl_multifit_fdfsolver_alloc(T, n, np); // set start values double* x_init = fitData.paramStartValues.data(); double* x_min = fitData.paramLowerLimits.data(); double* x_max = fitData.paramUpperLimits.data(); // scale start values if limits are set for (unsigned int i = 0; i < np; i++) x_init[i] = nsl_fit_map_unbound(x_init[i], x_min[i], x_max[i]); gsl_vector_view x = gsl_vector_view_array(x_init, np); // initialize solver with function f and initial guess x gsl_multifit_fdfsolver_set(s, &f, &x.vector); // iterate int status; unsigned int iter = 0; fitResult.solverOutput.clear(); writeSolverState(s); do { iter++; // update weights for Y-depending weights if (fitData.yWeightsType == nsl_fit_weight_statistical_fit) { for (size_t i = 0; i < n; i++) weight[i] = 1./(gsl_vector_get(s->f, i) + ydata[i]); // 1/Y_i } else if (fitData.yWeightsType == nsl_fit_weight_relative_fit) { for (size_t i = 0; i < n; i++) weight[i] = 1./gsl_pow_2(gsl_vector_get(s->f, i) + ydata[i]); // 1/Y_i^2 } status = gsl_multifit_fdfsolver_iterate(s); writeSolverState(s); if (status) { DEBUG("iter " << iter << ", status = " << gsl_strerror(status)); break; } status = gsl_multifit_test_delta(s->dx, s->x, delta, delta); } while (status == GSL_CONTINUE && iter < maxIters); // second run for x-error fitting if (xerrorVector.size() > 0) { DEBUG("Rerun fit with x errors"); // y'(x) double *yd = new double[n]; for (size_t i = 0; i < n; i++) { size_t index = i; if (index == n-1) index = n-2; yd[i] = gsl_vector_get(s->f, index+1) + ydata[index+1] - gsl_vector_get(s->f, index) - ydata[index]; yd[i] /= (xdata[index+1] - xdata[index]); } switch (fitData.yWeightsType) { case nsl_fit_weight_no: break; case nsl_fit_weight_instrumental: for (size_t i = 0; i < n; i++) { double sigma; if (yerrorVector.size() > 0) // x- and y-error // sigma = sqrt(sigma_y^2 + (y'(x)*sigma_x)^2) sigma = sqrt(gsl_pow_2(yerror[i]) + gsl_pow_2(yd[i] * xerror[i])); else // only x-error sigma = yd[i] * xerror[i]; weight[i] = 1./gsl_pow_2(sigma); } break; // other weight types: y'(x) considered correctly? case nsl_fit_weight_direct: for (size_t i = 0; i < n; i++) { weight[i] = xerror[i]/yd[i]; if (yerrorVector.size() > 0) weight[i] += yerror[i]; } break; case nsl_fit_weight_inverse: for (size_t i = 0; i < n; i++) { weight[i] = yd[i]/xerror[i]; if (yerrorVector.size() > 0) weight[i] += 1./yerror[i]; } break; case nsl_fit_weight_statistical: case nsl_fit_weight_relative: break; case nsl_fit_weight_statistical_fit: for (size_t i = 0; i < n; i++) weight[i] = 1./(gsl_vector_get(s->f, i) + ydata[i]); // 1/Y_i break; case nsl_fit_weight_relative_fit: for (size_t i = 0; i < n; i++) weight[i] = 1./gsl_pow_2(gsl_vector_get(s->f, i) + ydata[i]); // 1/Y_i^2 break; } delete[] yd; do { iter++; status = gsl_multifit_fdfsolver_iterate(s); writeSolverState(s); if (status) break; status = gsl_multifit_test_delta(s->dx, s->x, delta, delta); } while (status == GSL_CONTINUE && iter < maxIters); } delete[] weight; // unscale start values for (unsigned int i = 0; i < np; i++) x_init[i] = nsl_fit_map_bound(x_init[i], x_min[i], x_max[i]); //get the covariance matrix //TODO: scale the Jacobian when limits are used before constructing the covar matrix? gsl_matrix* covar = gsl_matrix_alloc(np, np); #if GSL_MAJOR_VERSION >= 2 // the Jacobian is not part of the solver anymore gsl_matrix *J = gsl_matrix_alloc(s->fdf->n, s->fdf->p); gsl_multifit_fdfsolver_jac(s, J); gsl_multifit_covar(J, 0.0, covar); #else gsl_multifit_covar(s->J, 0.0, covar); #endif //write the result fitResult.available = true; fitResult.valid = true; fitResult.status = gslErrorToString(status); fitResult.iterations = iter; fitResult.dof = n - np; //gsl_blas_dnrm2() - computes the Euclidian norm (||r||_2 = \sqrt {\sum r_i^2}) of the vector with the elements weight[i]*(Yi - y[i]) //gsl_blas_dasum() - computes the absolute sum \sum |r_i| of the elements of the vector with the elements weight[i]*(Yi - y[i]) fitResult.sse = gsl_pow_2(gsl_blas_dnrm2(s->f)); if (fitResult.dof != 0) { fitResult.rms = fitResult.sse/fitResult.dof; fitResult.rsd = sqrt(fitResult.rms); } fitResult.mse = fitResult.sse/n; fitResult.rmse = sqrt(fitResult.mse); fitResult.mae = gsl_blas_dasum(s->f)/n; //needed for coefficient of determination, R-squared fitResult.sst = gsl_stats_tss(ydata, 1, n); fitResult.rsquare = nsl_stats_rsquare(fitResult.sse, fitResult.sst); fitResult.rsquareAdj = nsl_stats_rsquareAdj(fitResult.rsquare, np, fitResult.dof); fitResult.chisq_p = nsl_stats_chisq_p(fitResult.sse, fitResult.dof); fitResult.fdist_F = nsl_stats_fdist_F(fitResult.sst, fitResult.rms); fitResult.fdist_p = nsl_stats_fdist_p(fitResult.fdist_F, np, fitResult.dof); fitResult.aic = nsl_stats_aic(fitResult.sse, n, np); fitResult.bic = nsl_stats_bic(fitResult.sse, n, np); //parameter values const double c = GSL_MIN_DBL(1., sqrt(fitResult.rms)); //limit error for poor fit fitResult.paramValues.resize(np); fitResult.errorValues.resize(np); fitResult.tdist_tValues.resize(np); fitResult.tdist_pValues.resize(np); fitResult.tdist_marginValues.resize(np); for (unsigned int i = 0; i < np; i++) { // scale resulting values if they are bounded fitResult.paramValues[i] = nsl_fit_map_bound(gsl_vector_get(s->x, i), x_min[i], x_max[i]); // use results as start values if desired if (fitData.useResults) { fitData.paramStartValues.data()[i] = fitResult.paramValues[i]; DEBUG("saving parameter " << i << ": " << fitResult.paramValues[i] << ' ' << fitData.paramStartValues.data()[i]); } fitResult.errorValues[i] = c*sqrt(gsl_matrix_get(covar, i, i)); fitResult.tdist_tValues[i] = nsl_stats_tdist_t(fitResult.paramValues.at(i), fitResult.errorValues.at(i)); fitResult.tdist_pValues[i] = nsl_stats_tdist_p(fitResult.tdist_tValues.at(i), fitResult.dof); fitResult.tdist_marginValues[i] = nsl_stats_tdist_margin(0.05, fitResult.dof, fitResult.errorValues.at(i)); } // fill residuals vector. To get residuals on the correct x values, fill the rest with zeros. residualsVector->resize(tmpXDataColumn->rowCount()); if (fitData.evaluateFullRange) { // evaluate full range of residuals xVector->resize(tmpXDataColumn->rowCount()); for (int i = 0; i < tmpXDataColumn->rowCount(); i++) (*xVector)[i] = tmpXDataColumn->valueAt(i); ExpressionParser* parser = ExpressionParser::getInstance(); bool rc = parser->evaluateCartesian(fitData.model, xVector, residualsVector, fitData.paramNames, fitResult.paramValues); for (int i = 0; i < tmpXDataColumn->rowCount(); i++) (*residualsVector)[i] = tmpYDataColumn->valueAt(i) - (*residualsVector)[i]; if (!rc) residualsVector->clear(); } else { // only selected range size_t j = 0; for (int i = 0; i < tmpXDataColumn->rowCount(); i++) { if (tmpXDataColumn->valueAt(i) >= xmin && tmpXDataColumn->valueAt(i) <= xmax) residualsVector->data()[i] = - gsl_vector_get(s->f, j++); else // outside range residualsVector->data()[i] = 0; } } residualsColumn->setChanged(); //free resources gsl_multifit_fdfsolver_free(s); gsl_matrix_free(covar); //calculate the fit function (vectors) ExpressionParser* parser = ExpressionParser::getInstance(); if (fitData.evaluateFullRange) { // evaluate fit on full data range if selected xmin = tmpXDataColumn->minimum(); xmax = tmpXDataColumn->maximum(); } xVector->resize(fitData.evaluatedPoints); yVector->resize(fitData.evaluatedPoints); bool rc = parser->evaluateCartesian(fitData.model, QString::number(xmin), QString::number(xmax), fitData.evaluatedPoints, xVector, yVector, fitData.paramNames, fitResult.paramValues); if (!rc) { xVector->clear(); yVector->clear(); } fitResult.elapsedTime = timer.elapsed(); //redraw the curve emit (q->dataChanged()); sourceDataChangedSinceLastRecalc = false; } /*! * writes out the current state of the solver \c s */ void XYFitCurvePrivate::writeSolverState(gsl_multifit_fdfsolver* s) { QString state; //current parameter values, semicolon separated double* min = fitData.paramLowerLimits.data(); double* max = fitData.paramUpperLimits.data(); for (int i = 0; i < fitData.paramNames.size(); ++i) { const double x = gsl_vector_get(s->x, i); // map parameter if bounded state += QString::number(nsl_fit_map_bound(x, min[i], max[i])) + '\t'; } //current value of the chi2-function state += QString::number(gsl_pow_2(gsl_blas_dnrm2(s->f))); state += ';'; fitResult.solverOutput += state; } //############################################################################## //################## Serialization/Deserialization ########################### //############################################################################## //! Save as XML void XYFitCurve::save(QXmlStreamWriter* writer) const { Q_D(const XYFitCurve); writer->writeStartElement("xyFitCurve"); //write xy-curve information XYCurve::save(writer); //write xy-fit-curve specific information //fit data - only save model expression and parameter names for custom model, otherwise they are set in XYFitCurve::initFitData() writer->writeStartElement("fitData"); WRITE_COLUMN(d->xDataColumn, xDataColumn); WRITE_COLUMN(d->yDataColumn, yDataColumn); WRITE_COLUMN(d->xErrorColumn, xErrorColumn); WRITE_COLUMN(d->yErrorColumn, yErrorColumn); writer->writeAttribute("autoRange", QString::number(d->fitData.autoRange)); writer->writeAttribute("xRangeMin", QString::number(d->fitData.xRange.first(), 'g', 15)); writer->writeAttribute("xRangeMax", QString::number(d->fitData.xRange.last(), 'g', 15)); writer->writeAttribute("modelCategory", QString::number(d->fitData.modelCategory)); writer->writeAttribute("modelType", QString::number(d->fitData.modelType)); - writer->writeAttribute("xWeightsType", QString::number(d->fitData.xWeightsType)); + writer->writeAttribute("xErrorsType", QString::number(d->fitData.xErrorsType)); writer->writeAttribute("weightsType", QString::number(d->fitData.yWeightsType)); writer->writeAttribute("degree", QString::number(d->fitData.degree)); if (d->fitData.modelCategory == nsl_fit_model_custom) writer->writeAttribute("model", d->fitData.model); writer->writeAttribute("maxIterations", QString::number(d->fitData.maxIterations)); writer->writeAttribute("eps", QString::number(d->fitData.eps, 'g', 15)); writer->writeAttribute("evaluatedPoints", QString::number(d->fitData.evaluatedPoints)); writer->writeAttribute("evaluateFullRange", QString::number(d->fitData.evaluateFullRange)); writer->writeAttribute("useDataErrors", QString::number(d->fitData.useDataErrors)); writer->writeAttribute("useResults", QString::number(d->fitData.useResults)); if (d->fitData.modelCategory == nsl_fit_model_custom) { writer->writeStartElement("paramNames"); foreach (const QString &name, d->fitData.paramNames) writer->writeTextElement("name", name); writer->writeEndElement(); } writer->writeStartElement("paramStartValues"); foreach (const double &value, d->fitData.paramStartValues) writer->writeTextElement("startValue", QString::number(value, 'g', 15)); writer->writeEndElement(); // use 16 digits to handle -DBL_MAX writer->writeStartElement("paramLowerLimits"); foreach (const double &limit, d->fitData.paramLowerLimits) writer->writeTextElement("lowerLimit", QString::number(limit, 'g', 16)); writer->writeEndElement(); // use 16 digits to handle DBL_MAX writer->writeStartElement("paramUpperLimits"); foreach (const double &limit, d->fitData.paramUpperLimits) writer->writeTextElement("upperLimit", QString::number(limit, 'g', 16)); writer->writeEndElement(); writer->writeStartElement("paramFixed"); foreach (const double &fixed, d->fitData.paramFixed) writer->writeTextElement("fixed", QString::number(fixed)); writer->writeEndElement(); writer->writeEndElement(); //"fitData" //fit results (generated columns and goodness of the fit) writer->writeStartElement("fitResult"); writer->writeAttribute("available", QString::number(d->fitResult.available)); writer->writeAttribute("valid", QString::number(d->fitResult.valid)); writer->writeAttribute("status", d->fitResult.status); writer->writeAttribute("iterations", QString::number(d->fitResult.iterations)); writer->writeAttribute("time", QString::number(d->fitResult.elapsedTime)); writer->writeAttribute("dof", QString::number(d->fitResult.dof)); writer->writeAttribute("sse", QString::number(d->fitResult.sse, 'g', 15)); writer->writeAttribute("sst", QString::number(d->fitResult.sst, 'g', 15)); writer->writeAttribute("rms", QString::number(d->fitResult.rms, 'g', 15)); writer->writeAttribute("rsd", QString::number(d->fitResult.rsd, 'g', 15)); writer->writeAttribute("mse", QString::number(d->fitResult.mse, 'g', 15)); writer->writeAttribute("rmse", QString::number(d->fitResult.rmse, 'g', 15)); writer->writeAttribute("mae", QString::number(d->fitResult.mae, 'g', 15)); writer->writeAttribute("rsquare", QString::number(d->fitResult.rsquare, 'g', 15)); writer->writeAttribute("rsquareAdj", QString::number(d->fitResult.rsquareAdj, 'g', 15)); writer->writeAttribute("chisq_p", QString::number(d->fitResult.chisq_p, 'g', 15)); writer->writeAttribute("fdist_F", QString::number(d->fitResult.fdist_F, 'g', 15)); writer->writeAttribute("fdist_p", QString::number(d->fitResult.fdist_p, 'g', 15)); writer->writeAttribute("aic", QString::number(d->fitResult.aic, 'g', 15)); writer->writeAttribute("bic", QString::number(d->fitResult.bic, 'g', 15)); writer->writeAttribute("solverOutput", d->fitResult.solverOutput); writer->writeStartElement("paramValues"); foreach (const double &value, d->fitResult.paramValues) writer->writeTextElement("value", QString::number(value, 'g', 15)); writer->writeEndElement(); writer->writeStartElement("errorValues"); foreach (const double &value, d->fitResult.errorValues) writer->writeTextElement("error", QString::number(value, 'g', 15)); writer->writeEndElement(); writer->writeStartElement("tdist_tValues"); foreach (const double &value, d->fitResult.tdist_tValues) writer->writeTextElement("tdist_t", QString::number(value, 'g', 15)); writer->writeEndElement(); writer->writeStartElement("tdist_pValues"); foreach (const double &value, d->fitResult.tdist_pValues) writer->writeTextElement("tdist_p", QString::number(value, 'g', 15)); writer->writeEndElement(); writer->writeStartElement("tdist_marginValues"); foreach (const double &value, d->fitResult.tdist_marginValues) writer->writeTextElement("tdist_margin", QString::number(value, 'g', 15)); writer->writeEndElement(); //save calculated columns if available if (d->xColumn && d->yColumn && d->residualsColumn) { d->xColumn->save(writer); d->yColumn->save(writer); d->residualsColumn->save(writer); } writer->writeEndElement(); //"fitResult" writer->writeEndElement(); //"xyFitCurve" } //! Load from XML bool XYFitCurve::load(XmlStreamReader* reader, bool preview) { Q_D(XYFitCurve); if (!reader->isStartElement() || reader->name() != "xyFitCurve") { reader->raiseError(i18n("no xy fit curve element found")); return false; } QString attributeWarning = i18n("Attribute '%1' missing or empty, default value is used"); QXmlStreamAttributes attribs; QString str; while (!reader->atEnd()) { reader->readNext(); if (reader->isEndElement() && reader->name() == "xyFitCurve") break; if (!reader->isStartElement()) continue; if (reader->name() == "xyCurve") { if ( !XYCurve::load(reader, preview) ) return false; } else if (!preview && reader->name() == "fitData") { attribs = reader->attributes(); READ_COLUMN(xDataColumn); READ_COLUMN(yDataColumn); READ_COLUMN(xErrorColumn); READ_COLUMN(yErrorColumn); READ_INT_VALUE("autoRange", fitData.autoRange, bool); READ_DOUBLE_VALUE("xRangeMin", fitData.xRange.first()); READ_DOUBLE_VALUE("xRangeMax", fitData.xRange.last()); READ_INT_VALUE("modelCategory", fitData.modelCategory, nsl_fit_model_category); READ_INT_VALUE("modelType", fitData.modelType, unsigned int); - READ_INT_VALUE("xWeightsType", fitData.xWeightsType, nsl_fit_weight_type); + READ_INT_VALUE("xErrorsType", fitData.xErrorsType, nsl_fit_error_type); READ_INT_VALUE("weightsType", fitData.yWeightsType, nsl_fit_weight_type); READ_INT_VALUE("degree", fitData.degree, int); if (d->fitData.modelCategory == nsl_fit_model_custom) { READ_STRING_VALUE("model", fitData.model); DEBUG("read model = " << d->fitData.model.toStdString()); } READ_INT_VALUE("maxIterations", fitData.maxIterations, int); READ_DOUBLE_VALUE("eps", fitData.eps); READ_INT_VALUE("fittedPoints", fitData.evaluatedPoints, size_t); // old name READ_INT_VALUE("evaluatedPoints", fitData.evaluatedPoints, size_t); READ_INT_VALUE("evaluateFullRange", fitData.evaluateFullRange, bool); READ_INT_VALUE("useDataErrors", fitData.useDataErrors, bool); READ_INT_VALUE("useResults", fitData.useResults, bool); //set the model expression and the parameter names (can be derived from the saved values for category, type and degree) XYFitCurve::initFitData(d->fitData); } else if (!preview && reader->name() == "name") { // needed for custom model d->fitData.paramNames << reader->readElementText(); } else if (!preview && reader->name() == "startValue") { d->fitData.paramStartValues << reader->readElementText().toDouble(); } else if (!preview && reader->name() == "fixed") { d->fitData.paramFixed << (bool)reader->readElementText().toInt(); } else if (!preview && reader->name() == "lowerLimit") { bool ok; double x = reader->readElementText().toDouble(&ok); if (ok) // -DBL_MAX results in conversion error d->fitData.paramLowerLimits << x; else d->fitData.paramLowerLimits << -std::numeric_limits::max(); } else if (!preview && reader->name() == "upperLimit") { bool ok; double x = reader->readElementText().toDouble(&ok); if (ok) // DBL_MAX results in conversion error d->fitData.paramUpperLimits << x; else d->fitData.paramUpperLimits << std::numeric_limits::max(); } else if (!preview && reader->name() == "value") { d->fitResult.paramValues << reader->readElementText().toDouble(); } else if (!preview && reader->name() == "error") { d->fitResult.errorValues << reader->readElementText().toDouble(); } else if (!preview && reader->name() == "tdist_t") { d->fitResult.tdist_tValues << reader->readElementText().toDouble(); } else if (!preview && reader->name() == "tdist_p") { d->fitResult.tdist_pValues << reader->readElementText().toDouble(); } else if (!preview && reader->name() == "tdist_margin") { d->fitResult.tdist_marginValues << reader->readElementText().toDouble(); } else if (!preview && reader->name() == "fitResult") { attribs = reader->attributes(); READ_INT_VALUE("available", fitResult.available, int); READ_INT_VALUE("valid", fitResult.valid, int); READ_STRING_VALUE("status", fitResult.status); READ_INT_VALUE("iterations", fitResult.iterations, int); READ_INT_VALUE("time", fitResult.elapsedTime, int); READ_DOUBLE_VALUE("dof", fitResult.dof); READ_DOUBLE_VALUE("sse", fitResult.sse); READ_DOUBLE_VALUE("sst", fitResult.sst); READ_DOUBLE_VALUE("rms", fitResult.rms); READ_DOUBLE_VALUE("rsd", fitResult.rsd); READ_DOUBLE_VALUE("mse", fitResult.mse); READ_DOUBLE_VALUE("rmse", fitResult.rmse); READ_DOUBLE_VALUE("mae", fitResult.mae); READ_DOUBLE_VALUE("rsquare", fitResult.rsquare); READ_DOUBLE_VALUE("rsquareAdj", fitResult.rsquareAdj); READ_DOUBLE_VALUE("chisq_p", fitResult.chisq_p); READ_DOUBLE_VALUE("fdist_F", fitResult.fdist_F); READ_DOUBLE_VALUE("fdist_p", fitResult.fdist_p); READ_DOUBLE_VALUE("aic", fitResult.aic); READ_DOUBLE_VALUE("bic", fitResult.bic); READ_STRING_VALUE("solverOutput", fitResult.solverOutput); } else if (reader->name() == "column") { Column* column = new Column("", AbstractColumn::Numeric); if (!column->load(reader, preview)) { delete column; return false; } if (column->name() == "x") d->xColumn = column; else if (column->name() == "y") d->yColumn = column; else if (column->name() == "residuals") d->residualsColumn = column; } } if (preview) return true; // new fit model style (reset model type of old projects) if (d->fitData.modelCategory == nsl_fit_model_basic && d->fitData.modelType >= NSL_FIT_MODEL_BASIC_COUNT) { DEBUG("reset old fit model"); d->fitData.modelType = 0; d->fitData.degree = 1; // reset size of fields not touched by initFitData() d->fitData.paramStartValues.resize(2); d->fitData.paramFixed.resize(2); d->fitResult.paramValues.resize(2); d->fitResult.errorValues.resize(2); d->fitResult.tdist_tValues.resize(2); d->fitResult.tdist_pValues.resize(2); d->fitResult.tdist_marginValues.resize(2); } // not present in old projects if (d->fitResult.tdist_tValues.size() == 0) d->fitResult.tdist_tValues.resize(d->fitResult.paramValues.size()); if (d->fitResult.tdist_pValues.size() == 0) d->fitResult.tdist_pValues.resize(d->fitResult.paramValues.size()); if (d->fitResult.tdist_marginValues.size() == 0) d->fitResult.tdist_marginValues.resize(d->fitResult.paramValues.size()); // wait for data to be read before using the pointers QThreadPool::globalInstance()->waitForDone(); if (d->xColumn && d->yColumn && d->residualsColumn) { d->xColumn->setHidden(true); addChild(d->xColumn); d->yColumn->setHidden(true); addChild(d->yColumn); addChild(d->residualsColumn); d->xVector = static_cast* >(d->xColumn->data()); d->yVector = static_cast* >(d->yColumn->data()); d->residualsVector = static_cast* >(d->residualsColumn->data()); XYCurve::d_ptr->xColumn = d->xColumn; XYCurve::d_ptr->yColumn = d->yColumn; } return true; } diff --git a/src/backend/worksheet/plots/cartesian/XYFitCurve.h b/src/backend/worksheet/plots/cartesian/XYFitCurve.h index 89606386d..e526280b9 100644 --- a/src/backend/worksheet/plots/cartesian/XYFitCurve.h +++ b/src/backend/worksheet/plots/cartesian/XYFitCurve.h @@ -1,164 +1,164 @@ /*************************************************************************** File : XYFitCurve.h Project : LabPlot Description : A xy-curve defined by a fit model -------------------------------------------------------------------- Copyright : (C) 2014-2016 Alexander Semke (alexander.semke@web.de) Copyright : (C) 2016 Stefan Gerlach (stefan.gerlach@uni.kn) ***************************************************************************/ /*************************************************************************** * * * This program is free software; you can redistribute it and/or modify * * it under the terms of the GNU General Public License as published by * * the Free Software Foundation; either version 2 of the License, or * * (at your option) any later version. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ #ifndef XYFITCURVE_H #define XYFITCURVE_H #include "backend/worksheet/plots/cartesian/XYCurve.h" #include "kdefrontend/spreadsheet/PlotDataDialog.h" //for PlotDataDialog::AnalysisAction. TODO: find a better place for this enum. extern "C" { #include "backend/nsl/nsl_fit.h" } class XYFitCurvePrivate; class XYFitCurve : public XYCurve { Q_OBJECT public: struct FitData { FitData() : modelCategory(nsl_fit_model_basic), modelType(0), - xWeightsType(nsl_fit_weight_no), + xErrorsType(nsl_fit_error_no), yWeightsType(nsl_fit_weight_no), degree(1), maxIterations(500), eps(1e-4), evaluatedPoints(100), evaluateFullRange(true), useDataErrors(true), useResults(true), autoRange(true), xRange(2) {}; nsl_fit_model_category modelCategory; unsigned int modelType; - nsl_fit_weight_type xWeightsType; + nsl_fit_error_type xErrorsType; nsl_fit_weight_type yWeightsType; int degree; QString model; QStringList paramNames; QStringList paramNamesUtf8; // Utf8 version of paramNames QVector paramStartValues; QVector paramLowerLimits; QVector paramUpperLimits; QVector paramFixed; int maxIterations; double eps; size_t evaluatedPoints; bool evaluateFullRange; // evaluate fit function on full data range (default) bool useDataErrors; // use given data errors when fitting (default) bool useResults; // use results as new start values (default) bool autoRange; // use all data? QVector xRange; // x range for integration }; struct FitResult { FitResult() : available(false), valid(false), iterations(0), elapsedTime(0), dof(0), sse(0), sst(0), rms(0), rsd(0), mse(0), rmse(0), mae(0), rsquare(0), rsquareAdj(0), chisq_p(0), fdist_F(0), fdist_p(0), aic(0), bic(0) {}; bool available; bool valid; QString status; int iterations; qint64 elapsedTime; double dof; //degrees of freedom // residuals: r_i = y_i - Y_i double sse; // sum of squared errors (SSE) / residual sum of squares (RSS) / sum of sq. residuals (SSR) / S = chi^2 = \sum_i^n r_i^2 double sst; // total sum of squares (SST) = \sum_i^n (y_i - )^2 double rms; // residual mean square / reduced chi^2 = SSE/dof double rsd; // residual standard deviation = sqrt(SSE/dof) double mse; // mean squared error = SSE/n double rmse; // root-mean squared error = \sqrt(mse) double mae; // mean absolute error = \sum_i^n |r_i| double rsquare; double rsquareAdj; double chisq_p; // chi^2 distribution p-value double fdist_F; // F distribution F-value double fdist_p; // F distribution p-value double aic; // Akaike information criterion double bic; // Schwarz Bayesian information criterion // see also http://www.originlab.com/doc/Origin-Help/NLFit-Algorithm QVector paramValues; QVector errorValues; QVector tdist_tValues; QVector tdist_pValues; QVector tdist_marginValues; QString solverOutput; }; explicit XYFitCurve(const QString& name); virtual ~XYFitCurve(); void recalculate(); void initFitData(PlotDataDialog::AnalysisAction); static void initFitData(XYFitCurve::FitData&); virtual QIcon icon() const override; virtual void save(QXmlStreamWriter*) const override; virtual bool load(XmlStreamReader*, bool preview) override; POINTER_D_ACCESSOR_DECL(const AbstractColumn, xDataColumn, XDataColumn) POINTER_D_ACCESSOR_DECL(const AbstractColumn, yDataColumn, YDataColumn) POINTER_D_ACCESSOR_DECL(const AbstractColumn, xErrorColumn, XErrorColumn) POINTER_D_ACCESSOR_DECL(const AbstractColumn, yErrorColumn, YErrorColumn) const QString& xDataColumnPath() const; const QString& yDataColumnPath() const; const QString& xErrorColumnPath() const; const QString& yErrorColumnPath() const; CLASS_D_ACCESSOR_DECL(FitData, fitData, FitData) const FitResult& fitResult() const; typedef XYFitCurvePrivate Private; protected: XYFitCurve(const QString& name, XYFitCurvePrivate* dd); private: Q_DECLARE_PRIVATE(XYFitCurve) void init(); signals: friend class XYFitCurveSetXDataColumnCmd; friend class XYFitCurveSetYDataColumnCmd; friend class XYFitCurveSetXErrorColumnCmd; friend class XYFitCurveSetYErrorColumnCmd; void xDataColumnChanged(const AbstractColumn*); void yDataColumnChanged(const AbstractColumn*); void xErrorColumnChanged(const AbstractColumn*); void yErrorColumnChanged(const AbstractColumn*); friend class XYFitCurveSetFitDataCmd; void fitDataChanged(const XYFitCurve::FitData&); }; #endif diff --git a/src/kdefrontend/dockwidgets/XYFitCurveDock.cpp b/src/kdefrontend/dockwidgets/XYFitCurveDock.cpp index 09e530f75..368faab4f 100644 --- a/src/kdefrontend/dockwidgets/XYFitCurveDock.cpp +++ b/src/kdefrontend/dockwidgets/XYFitCurveDock.cpp @@ -1,1140 +1,1128 @@ /*************************************************************************** File : XYFitCurveDock.cpp Project : LabPlot -------------------------------------------------------------------- Copyright : (C) 2014-2017 Alexander Semke (alexander.semke@web.de) Copyright : (C) 2016-2017 Stefan Gerlach (stefan.gerlach@uni.kn) Description : widget for editing properties of fit curves ***************************************************************************/ /*************************************************************************** * * * This program is free software; you can redistribute it and/or modify * * it under the terms of the GNU General Public License as published by * * the Free Software Foundation; either version 2 of the License, or * * (at your option) any later version. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ #include "XYFitCurveDock.h" #include "backend/core/AspectTreeModel.h" #include "backend/core/Project.h" #include "backend/lib/macros.h" #include "backend/gsl/ExpressionParser.h" #include "commonfrontend/widgets/TreeViewComboBox.h" #include "kdefrontend/widgets/ConstantsWidget.h" #include "kdefrontend/widgets/FunctionsWidget.h" #include "kdefrontend/widgets/FitOptionsWidget.h" #include "kdefrontend/widgets/FitParametersWidget.h" #include #include #include #include #include extern "C" { #include "backend/nsl/nsl_sf_stats.h" } /*! \class XYFitCurveDock \brief Provides a widget for editing the properties of the XYFitCurves (2D-curves defined by a fit model) currently selected in the project explorer. If more then one curves are set, the properties of the first column are shown. The changes of the properties are applied to all curves. The exclusions are the name, the comment and the datasets (columns) of the curves - these properties can only be changed if there is only one single curve. \ingroup kdefrontend */ XYFitCurveDock::XYFitCurveDock(QWidget* parent) : XYCurveDock(parent), cbDataSourceCurve(0), cbXDataColumn(0), cbYDataColumn(0), cbXErrorColumn(0), cbYErrorColumn(0), m_fitCurve(0) { //remove the tab "Error bars" ui.tabWidget->removeTab(5); } /*! * // Tab "General" */ void XYFitCurveDock::setupGeneral() { QWidget* generalTab = new QWidget(ui.tabGeneral); uiGeneralTab.setupUi(generalTab); QGridLayout* gridLayout = qobject_cast(generalTab->layout()); if (gridLayout) { gridLayout->setContentsMargins(2, 2, 2, 2); gridLayout->setHorizontalSpacing(2); gridLayout->setVerticalSpacing(2); } uiGeneralTab.cbDataSourceType->addItem(i18n("Spreadsheet")); uiGeneralTab.cbDataSourceType->addItem(i18n("XY-Curve")); cbDataSourceCurve = new TreeViewComboBox(generalTab); gridLayout->addWidget(cbDataSourceCurve, 6, 4, 1, 4); cbXDataColumn = new TreeViewComboBox(generalTab); gridLayout->addWidget(cbXDataColumn, 7, 4, 1, 4); cbXErrorColumn = new TreeViewComboBox(generalTab); cbXErrorColumn->setEnabled(false); uiGeneralTab.hlXError->addWidget(cbXErrorColumn); cbYDataColumn = new TreeViewComboBox(generalTab); gridLayout->addWidget(cbYDataColumn, 8, 4, 1, 4); cbYErrorColumn = new TreeViewComboBox(generalTab); cbYErrorColumn->setEnabled(false); uiGeneralTab.hlYWeight->addWidget(cbYErrorColumn); - //Weights - for(int i = 0; i < NSL_FIT_WEIGHT_TYPE_COUNT; i++) - uiGeneralTab.cbXWeight->addItem(nsl_fit_weight_type_name[i]); + //X-Error/Y-Weight + for(int i = 0; i < NSL_FIT_ERROR_TYPE_COUNT; i++) + uiGeneralTab.cbXError->addItem(nsl_fit_error_type_name[i]); + uiGeneralTab.cbXError->setCurrentIndex(nsl_fit_error_no); for(int i = 0; i < NSL_FIT_WEIGHT_TYPE_COUNT; i++) uiGeneralTab.cbYWeight->addItem(nsl_fit_weight_type_name[i]); - uiGeneralTab.cbXWeight->setCurrentIndex(nsl_fit_weight_no); uiGeneralTab.cbYWeight->setCurrentIndex(nsl_fit_weight_no); - // disable nsl_fit_weight_statistical_fit and nsl_fit_weight_relative_fit for xWeight - const QStandardItemModel* model = qobject_cast(uiGeneralTab.cbXWeight->model()); - QStandardItem* item = model->item(nsl_fit_weight_statistical_fit); - item->setFlags(item->flags() & ~(Qt::ItemIsSelectable|Qt::ItemIsEnabled)); - item = model->item(nsl_fit_weight_relative_fit); - item->setFlags(item->flags() & ~(Qt::ItemIsSelectable|Qt::ItemIsEnabled)); - for(int i = 0; i < NSL_FIT_MODEL_CATEGORY_COUNT; i++) uiGeneralTab.cbCategory->addItem(nsl_fit_model_category_name[i]); //show the fit-model category for the currently selected default (first) fit-model category categoryChanged(uiGeneralTab.cbCategory->currentIndex()); uiGeneralTab.teEquation->setMaximumHeight(uiGeneralTab.leName->sizeHint().height() * 2); //use white background in the preview label QPalette p; p.setColor(QPalette::Window, Qt::white); uiGeneralTab.lFuncPic->setAutoFillBackground(true); uiGeneralTab.lFuncPic->setPalette(p); uiGeneralTab.tbConstants->setIcon(QIcon::fromTheme("labplot-format-text-symbol")); uiGeneralTab.tbFunctions->setIcon(QIcon::fromTheme("preferences-desktop-font")); uiGeneralTab.pbRecalculate->setIcon(QIcon::fromTheme("run-build")); uiGeneralTab.twLog->setEditTriggers(QAbstractItemView::NoEditTriggers); uiGeneralTab.twParameters->setEditTriggers(QAbstractItemView::NoEditTriggers); uiGeneralTab.twGoodness->setEditTriggers(QAbstractItemView::NoEditTriggers); // context menus uiGeneralTab.twParameters->setContextMenuPolicy(Qt::CustomContextMenu); uiGeneralTab.twGoodness->setContextMenuPolicy(Qt::CustomContextMenu); uiGeneralTab.twLog->setContextMenuPolicy(Qt::CustomContextMenu); connect(uiGeneralTab.twParameters, SIGNAL(customContextMenuRequested(const QPoint &)), this, SLOT(resultParametersContextMenuRequest(const QPoint &)) ); connect(uiGeneralTab.twGoodness, SIGNAL(customContextMenuRequested(const QPoint &)), this, SLOT(resultGoodnessContextMenuRequest(const QPoint &)) ); connect(uiGeneralTab.twLog, SIGNAL(customContextMenuRequested(const QPoint &)), this, SLOT(resultLogContextMenuRequest(const QPoint &)) ); uiGeneralTab.twLog->horizontalHeader()->resizeSections(QHeaderView::ResizeToContents); uiGeneralTab.twGoodness->horizontalHeader()->resizeSections(QHeaderView::ResizeToContents); uiGeneralTab.twGoodness->item(0, 1)->setText(QString::fromUtf8("\u03c7") + QString::fromUtf8("\u00b2")); uiGeneralTab.twGoodness->item(1, 1)->setText(i18n("reduced") + " " + QString::fromUtf8("\u03c7") + QString::fromUtf8("\u00b2") + " (" + QString::fromUtf8("\u03c7") + QString::fromUtf8("\u00b2") + "/dof)"); uiGeneralTab.twGoodness->item(3, 1)->setText("R" + QString::fromUtf8("\u00b2")); uiGeneralTab.twGoodness->item(4, 1)->setText("R" + QString::fromUtf8("\u0304") + QString::fromUtf8("\u00b2")); uiGeneralTab.twGoodness->item(5, 0)->setText(QString::fromUtf8("\u03c7") + QString::fromUtf8("\u00b2") + ' ' + i18n("test")); uiGeneralTab.twGoodness->item(5, 1)->setText("P > " + QString::fromUtf8("\u03c7") + QString::fromUtf8("\u00b2")); QHBoxLayout* layout = new QHBoxLayout(ui.tabGeneral); layout->setMargin(0); layout->addWidget(generalTab); //Slots connect(uiGeneralTab.leName, SIGNAL(returnPressed()), this, SLOT(nameChanged())); connect(uiGeneralTab.leComment, SIGNAL(returnPressed()), this, SLOT(commentChanged())); connect(uiGeneralTab.chkVisible, SIGNAL(clicked(bool)), this, SLOT(visibilityChanged(bool))); connect(uiGeneralTab.cbDataSourceType, SIGNAL(currentIndexChanged(int)), this, SLOT(dataSourceTypeChanged(int))); - connect(uiGeneralTab.cbXWeight, SIGNAL(currentIndexChanged(int)), this, SLOT(xWeightChanged(int))); + connect(uiGeneralTab.cbXError, SIGNAL(currentIndexChanged(int)), this, SLOT(xErrorChanged(int))); connect(uiGeneralTab.cbYWeight, SIGNAL(currentIndexChanged(int)), this, SLOT(yWeightChanged(int))); connect(uiGeneralTab.cbCategory, SIGNAL(currentIndexChanged(int)), this, SLOT(categoryChanged(int))); connect(uiGeneralTab.cbModel, SIGNAL(currentIndexChanged(int)), this, SLOT(modelTypeChanged(int))); connect(uiGeneralTab.sbDegree, SIGNAL(valueChanged(int)), this, SLOT(updateModelEquation())); connect(uiGeneralTab.teEquation, SIGNAL(expressionChanged()), this, SLOT(enableRecalculate())); connect(uiGeneralTab.tbConstants, SIGNAL(clicked()), this, SLOT(showConstants())); connect(uiGeneralTab.tbFunctions, SIGNAL(clicked()), this, SLOT(showFunctions())); connect(uiGeneralTab.pbParameters, SIGNAL(clicked()), this, SLOT(showParameters())); connect(uiGeneralTab.pbOptions, SIGNAL(clicked()), this, SLOT(showOptions())); connect(uiGeneralTab.pbRecalculate, SIGNAL(clicked()), this, SLOT(recalculateClicked())); connect(cbDataSourceCurve, SIGNAL(currentModelIndexChanged(QModelIndex)), this, SLOT(dataSourceCurveChanged(QModelIndex))); connect(cbXDataColumn, SIGNAL(currentModelIndexChanged(QModelIndex)), this, SLOT(xDataColumnChanged(QModelIndex))); connect(cbYDataColumn, SIGNAL(currentModelIndexChanged(QModelIndex)), this, SLOT(yDataColumnChanged(QModelIndex))); connect(cbXErrorColumn, SIGNAL(currentModelIndexChanged(QModelIndex)), this, SLOT(xErrorColumnChanged(QModelIndex))); connect(cbYErrorColumn, SIGNAL(currentModelIndexChanged(QModelIndex)), this, SLOT(yErrorColumnChanged(QModelIndex))); } void XYFitCurveDock::initGeneralTab() { //if there are more then one curve in the list, disable the tab "general" if (m_curvesList.size() == 1) { uiGeneralTab.lName->setEnabled(true); uiGeneralTab.leName->setEnabled(true); uiGeneralTab.lComment->setEnabled(true); uiGeneralTab.leComment->setEnabled(true); uiGeneralTab.leName->setText(m_curve->name()); uiGeneralTab.leComment->setText(m_curve->comment()); } else { uiGeneralTab.lName->setEnabled(false); uiGeneralTab.leName->setEnabled(false); uiGeneralTab.lComment->setEnabled(false); uiGeneralTab.leComment->setEnabled(false); uiGeneralTab.leName->setText(""); uiGeneralTab.leComment->setText(""); } //show the properties of the first curve m_fitCurve = dynamic_cast(m_curve); Q_ASSERT(m_fitCurve); uiGeneralTab.cbDataSourceType->setCurrentIndex(m_fitCurve->dataSourceType()); this->dataSourceTypeChanged(uiGeneralTab.cbDataSourceType->currentIndex()); XYCurveDock::setModelIndexFromAspect(cbDataSourceCurve, m_fitCurve->dataSourceCurve()); XYCurveDock::setModelIndexFromAspect(cbXDataColumn, m_fitCurve->xDataColumn()); XYCurveDock::setModelIndexFromAspect(cbYDataColumn, m_fitCurve->yDataColumn()); XYCurveDock::setModelIndexFromAspect(cbXErrorColumn, m_fitCurve->xErrorColumn()); XYCurveDock::setModelIndexFromAspect(cbYErrorColumn, m_fitCurve->yErrorColumn()); unsigned int tmpModelType = m_fitData.modelType; // save type because it's reset when category changes if (m_fitData.modelCategory == nsl_fit_model_custom) uiGeneralTab.cbCategory->setCurrentIndex(uiGeneralTab.cbCategory->count() - 1); else uiGeneralTab.cbCategory->setCurrentIndex(m_fitData.modelCategory); m_fitData.modelType = tmpModelType; if (m_fitData.modelCategory != nsl_fit_model_custom) uiGeneralTab.cbModel->setCurrentIndex(m_fitData.modelType); - uiGeneralTab.cbXWeight->setCurrentIndex(m_fitData.xWeightsType); + uiGeneralTab.cbXError->setCurrentIndex(m_fitData.xErrorsType); uiGeneralTab.cbYWeight->setCurrentIndex(m_fitData.yWeightsType); uiGeneralTab.sbDegree->setValue(m_fitData.degree); updateModelEquation(); this->showFitResult(); uiGeneralTab.chkVisible->setChecked(m_curve->isVisible()); //Slots connect(m_fitCurve, SIGNAL(aspectDescriptionChanged(const AbstractAspect*)), this, SLOT(curveDescriptionChanged(const AbstractAspect*))); connect(m_fitCurve, SIGNAL(dataSourceTypeChanged(XYCurve::DataSourceType)), this, SLOT(curveDataSourceTypeChanged(XYCurve::DataSourceType))); connect(m_fitCurve, SIGNAL(dataSourceCurveChanged(const XYCurve*)), this, SLOT(curveDataSourceCurveChanged(const XYCurve*))); connect(m_fitCurve, SIGNAL(xDataColumnChanged(const AbstractColumn*)), this, SLOT(curveXDataColumnChanged(const AbstractColumn*))); connect(m_fitCurve, SIGNAL(yDataColumnChanged(const AbstractColumn*)), this, SLOT(curveYDataColumnChanged(const AbstractColumn*))); connect(m_fitCurve, SIGNAL(xErrorColumnChanged(const AbstractColumn*)), this, SLOT(curveXErrorColumnChanged(const AbstractColumn*))); connect(m_fitCurve, SIGNAL(yErrorColumnChanged(const AbstractColumn*)), this, SLOT(curveYErrorColumnChanged(const AbstractColumn*))); connect(m_fitCurve, SIGNAL(fitDataChanged(XYFitCurve::FitData)), this, SLOT(curveFitDataChanged(XYFitCurve::FitData))); connect(m_fitCurve, SIGNAL(sourceDataChanged()), this, SLOT(enableRecalculate())); } void XYFitCurveDock::setModel() { QList list; list << "Folder" << "Datapicker" << "Worksheet" << "CartesianPlot" << "XYCurve"; cbDataSourceCurve->setTopLevelClasses(list); QList hiddenAspects; for (auto* curve: m_curvesList) hiddenAspects << curve; cbDataSourceCurve->setHiddenAspects(hiddenAspects); list.clear(); list << "Folder" << "Workbook" << "Spreadsheet" << "FileDataSource" << "Column" << "CantorWorksheet" << "Datapicker"; cbXDataColumn->setTopLevelClasses(list); cbYDataColumn->setTopLevelClasses(list); cbXErrorColumn->setTopLevelClasses(list); cbYErrorColumn->setTopLevelClasses(list); cbDataSourceCurve->setModel(m_aspectTreeModel); cbXDataColumn->setModel(m_aspectTreeModel); cbYDataColumn->setModel(m_aspectTreeModel); cbXErrorColumn->setModel(m_aspectTreeModel); cbYErrorColumn->setModel(m_aspectTreeModel); XYCurveDock::setModel(); } /*! sets the curves. The properties of the curves in the list \c list can be edited in this widget. */ void XYFitCurveDock::setCurves(QList list) { m_initializing = true; m_curvesList = list; m_curve = list.first(); m_fitCurve = dynamic_cast(m_curve); Q_ASSERT(m_fitCurve); m_aspectTreeModel = new AspectTreeModel(m_curve->project()); this->setModel(); m_fitData = m_fitCurve->fitData(); initGeneralTab(); initTabs(); m_initializing = false; } //************************************************************* //**** SLOTs for changes triggered in XYFitCurveDock ***** //************************************************************* void XYFitCurveDock::nameChanged() { if (m_initializing) return; m_curve->setName(uiGeneralTab.leName->text()); } void XYFitCurveDock::commentChanged() { if (m_initializing) return; m_curve->setComment(uiGeneralTab.leComment->text()); } void XYFitCurveDock::dataSourceTypeChanged(int index) { const XYCurve::DataSourceType type = (XYCurve::DataSourceType)index; if (type == XYCurve::DataSourceSpreadsheet) { uiGeneralTab.lDataSourceCurve->hide(); cbDataSourceCurve->hide(); uiGeneralTab.lXColumn->show(); cbXDataColumn->show(); uiGeneralTab.lYColumn->show(); cbYDataColumn->show(); } else { uiGeneralTab.lDataSourceCurve->show(); cbDataSourceCurve->show(); uiGeneralTab.lXColumn->hide(); cbXDataColumn->hide(); uiGeneralTab.lYColumn->hide(); cbYDataColumn->hide(); } if (m_initializing) return; for (auto* curve: m_curvesList) dynamic_cast(curve)->setDataSourceType(type); } void XYFitCurveDock::dataSourceCurveChanged(const QModelIndex& index) { AbstractAspect* aspect = static_cast(index.internalPointer()); XYCurve* dataSourceCurve = 0; if (aspect) { dataSourceCurve = dynamic_cast(aspect); Q_ASSERT(dataSourceCurve); } if (m_initializing) return; for (auto* curve: m_curvesList) dynamic_cast(curve)->setDataSourceCurve(dataSourceCurve); } void XYFitCurveDock::xDataColumnChanged(const QModelIndex& index) { if (m_initializing) return; AbstractAspect* aspect = static_cast(index.internalPointer()); AbstractColumn* column = 0; if (aspect) { column = dynamic_cast(aspect); Q_ASSERT(column); } for (auto* curve: m_curvesList) dynamic_cast(curve)->setXDataColumn(column); } void XYFitCurveDock::yDataColumnChanged(const QModelIndex& index) { if (m_initializing) return; AbstractAspect* aspect = static_cast(index.internalPointer()); AbstractColumn* column = 0; if (aspect) { column = dynamic_cast(aspect); Q_ASSERT(column); } for (auto* curve: m_curvesList) dynamic_cast(curve)->setYDataColumn(column); } void XYFitCurveDock::xErrorColumnChanged(const QModelIndex& index) { if (m_initializing) return; AbstractAspect* aspect = static_cast(index.internalPointer()); AbstractColumn* column = 0; if (aspect) { column = dynamic_cast(aspect); Q_ASSERT(column); } for (auto* curve: m_curvesList) dynamic_cast(curve)->setXErrorColumn(column); } void XYFitCurveDock::yErrorColumnChanged(const QModelIndex& index) { if (m_initializing) return; AbstractAspect* aspect = static_cast(index.internalPointer()); AbstractColumn* column = 0; if (aspect) { column = dynamic_cast(aspect); Q_ASSERT(column); } for (auto* curve: m_curvesList) dynamic_cast(curve)->setYErrorColumn(column); } -void XYFitCurveDock::xWeightChanged(int index) { - DEBUG("xWeightChanged() weight = " << nsl_fit_weight_type_name[index]); +void XYFitCurveDock::xErrorChanged(int index) { + DEBUG("xErrorChanged() error = " << nsl_fit_error_type_name[index]); - m_fitData.xWeightsType = (nsl_fit_weight_type)index; + m_fitData.xErrorsType = (nsl_fit_error_type)index; // enable/disable weight column - switch ((nsl_fit_weight_type)index) { - case nsl_fit_weight_no: - case nsl_fit_weight_statistical: - case nsl_fit_weight_statistical_fit: - case nsl_fit_weight_relative: - case nsl_fit_weight_relative_fit: + switch ((nsl_fit_error_type)index) { + case nsl_fit_error_no: cbXErrorColumn->setEnabled(false); uiGeneralTab.lXErrorCol->setEnabled(false); break; - case nsl_fit_weight_instrumental: - case nsl_fit_weight_direct: - case nsl_fit_weight_inverse: + case nsl_fit_error_direct: + case nsl_fit_error_inverse: cbXErrorColumn->setEnabled(true); uiGeneralTab.lXErrorCol->setEnabled(true); break; } enableRecalculate(); } void XYFitCurveDock::yWeightChanged(int index) { DEBUG("yWeightChanged() weight = " << nsl_fit_weight_type_name[index]); m_fitData.yWeightsType = (nsl_fit_weight_type)index; // enable/disable weight column switch ((nsl_fit_weight_type)index) { case nsl_fit_weight_no: case nsl_fit_weight_statistical: case nsl_fit_weight_statistical_fit: case nsl_fit_weight_relative: case nsl_fit_weight_relative_fit: cbYErrorColumn->setEnabled(false); uiGeneralTab.lYErrorCol->setEnabled(false); break; case nsl_fit_weight_instrumental: case nsl_fit_weight_direct: case nsl_fit_weight_inverse: cbYErrorColumn->setEnabled(true); uiGeneralTab.lYErrorCol->setEnabled(true); break; } enableRecalculate(); } /*! * called when the fit model category (basic functions, peak functions etc.) was changed. * In the combobox for the model type shows the model types for the current category \index and calls \c modelTypeChanged() * to update the model type dependent widgets in the general-tab. */ void XYFitCurveDock::categoryChanged(int index) { DEBUG("categoryChanged() category = \"" << nsl_fit_model_category_name[index] << "\""); if (uiGeneralTab.cbCategory->currentIndex() == uiGeneralTab.cbCategory->count() - 1) m_fitData.modelCategory = nsl_fit_model_custom; else m_fitData.modelCategory = (nsl_fit_model_category)index; m_initializing = true; uiGeneralTab.cbModel->clear(); uiGeneralTab.cbModel->show(); uiGeneralTab.lModel->show(); switch (m_fitData.modelCategory) { case nsl_fit_model_basic: for(int i = 0; i < NSL_FIT_MODEL_BASIC_COUNT; i++) uiGeneralTab.cbModel->addItem(nsl_fit_model_basic_name[i]); break; case nsl_fit_model_peak: for(int i = 0; i < NSL_FIT_MODEL_PEAK_COUNT; i++) uiGeneralTab.cbModel->addItem(nsl_fit_model_peak_name[i]); break; case nsl_fit_model_growth: for(int i = 0; i < NSL_FIT_MODEL_GROWTH_COUNT; i++) uiGeneralTab.cbModel->addItem(nsl_fit_model_growth_name[i]); break; case nsl_fit_model_distribution: { for(int i = 0; i < NSL_SF_STATS_DISTRIBUTION_COUNT; i++) uiGeneralTab.cbModel->addItem(nsl_sf_stats_distribution_name[i]); // not-used items are disabled here const QStandardItemModel* model = qobject_cast(uiGeneralTab.cbModel->model()); for(int i = 1; i < NSL_SF_STATS_DISTRIBUTION_COUNT; i++) { // unused distributions if (i == nsl_sf_stats_levy_alpha_stable || i == nsl_sf_stats_levy_skew_alpha_stable || i == nsl_sf_stats_bernoulli) { QStandardItem* item = model->item(i); item->setFlags(item->flags() & ~(Qt::ItemIsSelectable|Qt::ItemIsEnabled)); } } break; } case nsl_fit_model_custom: uiGeneralTab.cbModel->addItem(i18n("Custom")); uiGeneralTab.cbModel->hide(); uiGeneralTab.lModel->hide(); } //show the fit-model for the currently selected default (first) fit-model m_fitData.modelType = 0; uiGeneralTab.cbModel->setCurrentIndex(m_fitData.modelType); modelTypeChanged(m_fitData.modelType); m_initializing = false; enableRecalculate(); } /*! * called when the fit model type (polynomial, power, etc.) was changed. * Updates the model type dependent widgets in the general-tab and calls \c updateModelEquation() to update the preview pixmap. */ void XYFitCurveDock::modelTypeChanged(int index) { DEBUG("modelTypeChanged() type = " << index << ", initializing = " << m_initializing); // leave if there is no selection if(index == -1) return; unsigned int type = 0; bool custom = false; if (m_fitData.modelCategory == nsl_fit_model_custom) custom = true; else type = (unsigned int)index; m_fitData.modelType = type; uiGeneralTab.teEquation->setReadOnly(!custom); uiGeneralTab.tbFunctions->setVisible(custom); uiGeneralTab.tbConstants->setVisible(custom); // default settings uiGeneralTab.lDegree->setText(i18n("Degree")); switch (m_fitData.modelCategory) { case nsl_fit_model_basic: switch (type) { case nsl_fit_model_polynomial: case nsl_fit_model_fourier: uiGeneralTab.lDegree->setVisible(true); uiGeneralTab.sbDegree->setVisible(true); uiGeneralTab.sbDegree->setMaximum(10); uiGeneralTab.sbDegree->setValue(1); break; case nsl_fit_model_power: uiGeneralTab.lDegree->setVisible(true); uiGeneralTab.sbDegree->setVisible(true); uiGeneralTab.sbDegree->setMaximum(2); uiGeneralTab.sbDegree->setValue(1); break; case nsl_fit_model_exponential: uiGeneralTab.lDegree->setVisible(true); uiGeneralTab.sbDegree->setVisible(true); uiGeneralTab.sbDegree->setMaximum(10); uiGeneralTab.sbDegree->setValue(1); break; default: uiGeneralTab.lDegree->setVisible(false); uiGeneralTab.sbDegree->setVisible(false); } break; case nsl_fit_model_peak: // all models support multiple peaks uiGeneralTab.lDegree->setText(i18n("Number of peaks")); uiGeneralTab.lDegree->setVisible(true); uiGeneralTab.sbDegree->setVisible(true); uiGeneralTab.sbDegree->setMaximum(9); uiGeneralTab.sbDegree->setValue(1); break; case nsl_fit_model_growth: case nsl_fit_model_distribution: case nsl_fit_model_custom: uiGeneralTab.lDegree->setVisible(false); uiGeneralTab.sbDegree->setVisible(false); } this->updateModelEquation(); } /*! * Show the preview pixmap of the fit model expression for the current model category and type. * Called when the model type or the degree of the model were changed. */ void XYFitCurveDock::updateModelEquation() { DEBUG("updateModelEquation() category = " << m_fitData.modelCategory << ", type = " << m_fitData.modelType); //this function can also be called when the value for the degree was changed -> update the fit data structure int degree = uiGeneralTab.sbDegree->value(); m_fitData.degree = degree; XYFitCurve::initFitData(m_fitData); // variables/parameter that are known QStringList vars = {"x"}; vars << m_fitData.paramNames; uiGeneralTab.teEquation->setVariables(vars); // set formula picture uiGeneralTab.lEquation->setText(QLatin1String("f(x) =")); QString file; switch (m_fitData.modelCategory) { case nsl_fit_model_basic: { // formula pic depends on degree QString numSuffix = QString::number(degree); if (degree > 4) numSuffix = "4"; if ((nsl_fit_model_type_basic)m_fitData.modelType == nsl_fit_model_power && degree > 2) numSuffix = "2"; file = QStandardPaths::locate(QStandardPaths::AppDataLocation, "pics/fit_models/" + QString(nsl_fit_model_basic_pic_name[m_fitData.modelType]) + numSuffix + ".jpg"); break; } case nsl_fit_model_peak: { // formula pic depends on number of peaks QString numSuffix = QString::number(degree); if (degree > 4) numSuffix = "4"; file = QStandardPaths::locate(QStandardPaths::AppDataLocation, "pics/fit_models/" + QString(nsl_fit_model_peak_pic_name[m_fitData.modelType]) + numSuffix + ".jpg"); break; } case nsl_fit_model_growth: file = QStandardPaths::locate(QStandardPaths::AppDataLocation, "pics/fit_models/" + QString(nsl_fit_model_growth_pic_name[m_fitData.modelType]) + ".jpg"); break; case nsl_fit_model_distribution: file = QStandardPaths::locate(QStandardPaths::AppDataLocation, "pics/gsl_distributions/" + QString(nsl_sf_stats_distribution_pic_name[m_fitData.modelType]) + ".jpg"); // change label if (m_fitData.modelType == nsl_sf_stats_poisson) uiGeneralTab.lEquation->setText(QLatin1String("f(k)/A =")); else uiGeneralTab.lEquation->setText(QLatin1String("f(x)/A =")); break; case nsl_fit_model_custom: uiGeneralTab.teEquation->show(); uiGeneralTab.teEquation->clear(); uiGeneralTab.teEquation->insertPlainText(m_fitData.model); uiGeneralTab.lFuncPic->hide(); } if (m_fitData.modelCategory != nsl_fit_model_custom) { uiGeneralTab.lFuncPic->setPixmap(file); uiGeneralTab.lFuncPic->show(); uiGeneralTab.teEquation->hide(); } } void XYFitCurveDock::showConstants() { QMenu menu; ConstantsWidget constants(&menu); connect(&constants, SIGNAL(constantSelected(QString)), this, SLOT(insertConstant(QString))); connect(&constants, SIGNAL(constantSelected(QString)), &menu, SLOT(close())); connect(&constants, SIGNAL(canceled()), &menu, SLOT(close())); QWidgetAction* widgetAction = new QWidgetAction(this); widgetAction->setDefaultWidget(&constants); menu.addAction(widgetAction); QPoint pos(-menu.sizeHint().width() + uiGeneralTab.tbConstants->width(), -menu.sizeHint().height()); menu.exec(uiGeneralTab.tbConstants->mapToGlobal(pos)); } void XYFitCurveDock::showFunctions() { QMenu menu; FunctionsWidget functions(&menu); connect(&functions, SIGNAL(functionSelected(QString)), this, SLOT(insertFunction(QString))); connect(&functions, SIGNAL(functionSelected(QString)), &menu, SLOT(close())); connect(&functions, SIGNAL(canceled()), &menu, SLOT(close())); QWidgetAction* widgetAction = new QWidgetAction(this); widgetAction->setDefaultWidget(&functions); menu.addAction(widgetAction); QPoint pos(-menu.sizeHint().width() + uiGeneralTab.tbFunctions->width(), -menu.sizeHint().height()); menu.exec(uiGeneralTab.tbFunctions->mapToGlobal(pos)); } void XYFitCurveDock::updateParameterList() { // use current model function m_fitData.model = uiGeneralTab.teEquation->toPlainText(); ExpressionParser* parser = ExpressionParser::getInstance(); QStringList vars; // variables that are known vars << "x"; //TODO: others? m_fitData.paramNames = m_fitData.paramNamesUtf8 = parser->getParameter(m_fitData.model, vars); // if number of parameter changed bool moreParameter = false; if (m_fitData.paramNames.size() > m_fitData.paramStartValues.size()) moreParameter = true; if (m_fitData.paramNames.size() != m_fitData.paramStartValues.size()) { m_fitData.paramStartValues.resize(m_fitData.paramNames.size()); m_fitData.paramFixed.resize(m_fitData.paramNames.size()); m_fitData.paramLowerLimits.resize(m_fitData.paramNames.size()); m_fitData.paramUpperLimits.resize(m_fitData.paramNames.size()); } if (moreParameter) { for (int i = m_fitData.paramStartValues.size() - 1; i < m_fitData.paramNames.size(); ++i) { m_fitData.paramStartValues[i] = 1.0; m_fitData.paramFixed[i] = false; m_fitData.paramLowerLimits[i] = -std::numeric_limits::max(); m_fitData.paramUpperLimits[i] = std::numeric_limits::max(); } } parametersChanged(); } void XYFitCurveDock::showParameters() { if (m_fitData.modelCategory == nsl_fit_model_custom) updateParameterList(); QMenu menu; FitParametersWidget w(&menu, &m_fitData); connect(&w, SIGNAL(finished()), &menu, SLOT(close())); connect(&w, SIGNAL(parametersChanged()), this, SLOT(parametersChanged())); QWidgetAction* widgetAction = new QWidgetAction(this); widgetAction->setDefaultWidget(&w); menu.addAction(widgetAction); menu.setMinimumWidth(w.width()); QPoint pos(-menu.sizeHint().width() + uiGeneralTab.pbParameters->width(), -menu.sizeHint().height()); menu.exec(uiGeneralTab.pbParameters->mapToGlobal(pos)); } /*! * called when parameter names and/or start values for the custom model were changed */ void XYFitCurveDock::parametersChanged() { //parameter names were (probably) changed -> set the new names in EquationTextEdit uiGeneralTab.teEquation->setVariables(m_fitData.paramNames); enableRecalculate(); } void XYFitCurveDock::showOptions() { QMenu menu; FitOptionsWidget w(&menu, &m_fitData, m_fitCurve); connect(&w, SIGNAL(finished()), &menu, SLOT(close())); connect(&w, SIGNAL(optionsChanged()), this, SLOT(enableRecalculate())); QWidgetAction* widgetAction = new QWidgetAction(this); widgetAction->setDefaultWidget(&w); menu.addAction(widgetAction); QPoint pos(-menu.sizeHint().width() + uiGeneralTab.pbParameters->width(), -menu.sizeHint().height()); menu.exec(uiGeneralTab.pbOptions->mapToGlobal(pos)); } void XYFitCurveDock::insertFunction(const QString& str) const { uiGeneralTab.teEquation->insertPlainText(str + "(x)"); } void XYFitCurveDock::insertConstant(const QString& str) const { uiGeneralTab.teEquation->insertPlainText(str); } void XYFitCurveDock::recalculateClicked() { QApplication::setOverrideCursor(QCursor(Qt::WaitCursor)); m_fitData.degree = uiGeneralTab.sbDegree->value(); if (m_fitData.modelCategory == nsl_fit_model_custom) updateParameterList(); for (XYCurve* curve: m_curvesList) dynamic_cast(curve)->setFitData(m_fitData); this->showFitResult(); uiGeneralTab.pbRecalculate->setEnabled(false); emit info(i18n("Fit status: ") + m_fitCurve->fitResult().status); QApplication::restoreOverrideCursor(); } void XYFitCurveDock::enableRecalculate() const { if (m_initializing || m_fitCurve == nullptr) return; //no fitting possible without the x- and y-data bool hasSourceData = false; if (m_fitCurve->dataSourceType() == XYCurve::DataSourceSpreadsheet) { AbstractAspect* aspectX = static_cast(cbXDataColumn->currentModelIndex().internalPointer()); AbstractAspect* aspectY = static_cast(cbYDataColumn->currentModelIndex().internalPointer()); hasSourceData = (aspectX != 0 && aspectY != 0); } else { hasSourceData = (m_fitCurve->dataSourceCurve() != nullptr); } uiGeneralTab.pbRecalculate->setEnabled(hasSourceData); } void XYFitCurveDock::resultCopySelection() { QTableWidget* tw = nullptr; int currentTab = uiGeneralTab.twResults->currentIndex(); DEBUG("current tab = " << currentTab); if (currentTab == 0) tw = uiGeneralTab.twParameters; else if (currentTab == 1) tw = uiGeneralTab.twGoodness; else if (currentTab == 2) tw = uiGeneralTab.twLog; else return; QTableWidgetSelectionRange range = tw->selectedRanges().first(); QString str; for (int i = 0; i < range.rowCount(); ++i) { if (i > 0) str += "\n"; for (int j = 0; j < range.columnCount(); ++j) { if (j > 0) str += "\t"; str += tw->item(range.topRow() + i, range.leftColumn() + j)->text(); } } str += "\n"; QApplication::clipboard()->setText(str); DEBUG(QApplication::clipboard()->text().toStdString()); } void XYFitCurveDock::resultCopyAll() { const XYFitCurve::FitResult& fitResult = m_fitCurve->fitResult(); int currentTab = uiGeneralTab.twResults->currentIndex(); QString str; if (currentTab == 0) { str = i18n("Parameters:") + "\n"; const int np = fitResult.paramValues.size(); for (int i = 0; i < np; i++) { if (m_fitData.paramFixed.at(i)) str += m_fitData.paramNamesUtf8.at(i) + QString(" = ") + QString::number(fitResult.paramValues.at(i)) + "\n"; else { str += m_fitData.paramNamesUtf8.at(i) + QString(" = ") + QString::number(fitResult.paramValues.at(i)) + QString::fromUtf8("\u00b1") + QString::number(fitResult.errorValues.at(i)) + " (" + QString::number(100.*fitResult.errorValues.at(i)/std::abs(fitResult.paramValues.at(i)), 'g', 3) + " %)\n"; const double margin = fitResult.tdist_marginValues.at(i); QString tdistValueString; if (fitResult.tdist_tValues.at(i) < std::numeric_limits::max()) tdistValueString = QString::number(fitResult.tdist_tValues.at(i), 'g', 3); else tdistValueString = QString::fromUtf8("\u221e"); str += " (" + i18n("t statistic:") + ' ' + tdistValueString + ", " + i18n("p value:") + ' ' + QString::number(fitResult.tdist_pValues.at(i), 'g', 3) + ", " + i18n("conf. interval:") + ' '; if (std::abs(fitResult.tdist_tValues.at(i)) < 1.e6) { str += QString::number(fitResult.paramValues.at(i) - margin) + " .. " + QString::number(fitResult.paramValues.at(i) + margin) + ")\n"; } else { str += i18n("too small"); } } } } else if (currentTab == 1) { str = i18n("Goodness of fit:") + "\n"; str += i18n("sum of squared residuals") + " (" + QString::fromUtf8("\u03c7") + QString::fromUtf8("\u00b2") + "): " + QString::number(fitResult.sse) + "\n"; if (fitResult.dof != 0) { str += i18n("reduced") + ' ' + QString::fromUtf8("\u03c7") + QString::fromUtf8("\u00b2") + ": " + QString::number(fitResult.rms) + '\n'; str += i18n("root mean square error") + " (RMSE): " + QString::number(fitResult.rsd) + "\n"; str += i18n("coefficient of determination") + " (R" + QString::fromUtf8("\u00b2") + "): " + QString::number(fitResult.rsquare, 'g', 15) + '\n'; str += i18n("adj. coefficient of determination")+ " (R" + QString::fromUtf8("\u0304") + QString::fromUtf8("\u00b2") + "): " + QString::number(fitResult.rsquareAdj, 'g', 15) + "\n\n"; str += i18n("P > ") + QString::fromUtf8("\u03c7") + QString::fromUtf8("\u00b2") + ": " + QString::number(fitResult.chisq_p, 'g', 3) + '\n'; str += i18n("F statistic") + ": " + QString::number(fitResult.fdist_F, 'g', 3) + '\n'; str += i18n("P > F") + ": " + QString::number(fitResult.fdist_p, 'g', 3) + '\n'; } str += i18n("mean absolute error:") + ' ' + QString::number(fitResult.mae) + '\n'; str += i18n("Akaike information criterion:") + ' ' + QString::number(fitResult.aic) + '\n'; str += i18n("Bayesian information criterion:") + ' ' + QString::number(fitResult.bic) + '\n'; } else if (currentTab == 2) { str = i18n("status:") + ' ' + fitResult.status + '\n'; str += i18n("iterations:") + ' ' + QString::number(fitResult.iterations) + '\n'; str += i18n("tolerance:") + ' ' + QString::number(m_fitData.eps) + '\n'; if (fitResult.elapsedTime > 1000) str += i18n("calculation time: %1 s", fitResult.elapsedTime/1000) + '\n'; else str += i18n("calculation time: %1 ms", fitResult.elapsedTime) + '\n'; str += i18n("degrees of freedom:") + ' ' + QString::number(fitResult.dof) + '\n'; str += i18n("number of parameters:") + ' ' + QString::number(fitResult.paramValues.size()) + '\n'; str += i18n("X range:") + ' ' + QString::number(m_fitData.xRange.first()) + " .. " + QString::number(m_fitData.xRange.last()) + '\n'; str += i18n("Iterations:") + '\n'; for (const auto &s: m_fitData.paramNamesUtf8) str += s + '\t'; str += QString::fromUtf8("\u03c7") + QString::fromUtf8("\u00b2"); const QStringList iterations = fitResult.solverOutput.split(';'); for (const auto &s: iterations) if (!s.isEmpty()) str += '\n' + s; } QApplication::clipboard()->setText(str); DEBUG(QApplication::clipboard()->text().toStdString()); } void XYFitCurveDock::resultParametersContextMenuRequest(const QPoint &pos) { QMenu *contextMenu = new QMenu; contextMenu->addAction("Copy selection", this, SLOT(resultCopySelection())); contextMenu->addAction("Copy all", this, SLOT(resultCopyAll())); contextMenu->exec(uiGeneralTab.twParameters->mapToGlobal(pos)); } void XYFitCurveDock::resultGoodnessContextMenuRequest(const QPoint &pos) { QMenu *contextMenu = new QMenu; contextMenu->addAction("Copy selection", this, SLOT(resultCopySelection())); contextMenu->addAction("Copy all", this, SLOT(resultCopyAll())); contextMenu->exec(uiGeneralTab.twGoodness->mapToGlobal(pos)); } void XYFitCurveDock::resultLogContextMenuRequest(const QPoint &pos) { QMenu *contextMenu = new QMenu; contextMenu->addAction("Copy selection", this, SLOT(resultCopySelection())); contextMenu->addAction("Copy all", this, SLOT(resultCopyAll())); contextMenu->exec(uiGeneralTab.twLog->mapToGlobal(pos)); } /*! * show the result and details of fit */ void XYFitCurveDock::showFitResult() { DEBUG("XYFitCurveDock::showFitResult()"); //clear the previous result uiGeneralTab.twParameters->setRowCount(0); for (int row = 0; row < uiGeneralTab.twGoodness->rowCount(); ++row) uiGeneralTab.twGoodness->item(row, 2)->setText(""); for (int row = 0; row < uiGeneralTab.twLog->rowCount(); ++row) uiGeneralTab.twLog->item(row, 1)->setText(""); const XYFitCurve::FitResult& fitResult = m_fitCurve->fitResult(); if (!fitResult.available) { DEBUG("fit result not available"); return; } // General uiGeneralTab.twLog->item(0, 1)->setText(fitResult.status); if (!fitResult.valid) { DEBUG("fit result not valid"); return; } uiGeneralTab.twLog->item(1, 1)->setText(QString::number(fitResult.iterations)); uiGeneralTab.twLog->item(2, 1)->setText(QString::number(m_fitData.eps)); if (fitResult.elapsedTime > 1000) uiGeneralTab.twLog->item(3, 1)->setText(QString::number(fitResult.elapsedTime/1000) + " s"); else uiGeneralTab.twLog->item(3, 1)->setText(QString::number(fitResult.elapsedTime) + " ms"); uiGeneralTab.twLog->item(4, 1)->setText(QString::number(fitResult.dof)); uiGeneralTab.twLog->item(5, 1)->setText(QString::number(fitResult.paramValues.size())); uiGeneralTab.twLog->item(6, 1)->setText(QString::number(m_fitData.xRange.first()) + " .. " + QString::number(m_fitData.xRange.last()) ); // show all iterations QString str; for (const auto &s: m_fitData.paramNamesUtf8) str += s + '\t'; str += QString::fromUtf8("\u03c7") + QString::fromUtf8("\u00b2"); const QStringList iterations = fitResult.solverOutput.split(';'); for (const auto &s: iterations) if (!s.isEmpty()) str += '\n' + s; uiGeneralTab.twLog->item(7, 1)->setText(str); uiGeneralTab.twLog->resizeRowsToContents(); // Parameters const int np = m_fitData.paramNames.size(); uiGeneralTab.twParameters->setRowCount(np); QStringList headerLabels; headerLabels << i18n("Name") << i18n("Value") << i18n("Error") << i18n("Error, %") << i18n("t statistic") << QLatin1String("P > |t|") << i18n("Conf. Interval"); uiGeneralTab.twParameters->setHorizontalHeaderLabels(headerLabels); for (int i = 0; i < np; i++) { const double paramValue = fitResult.paramValues.at(i); const double errorValue = fitResult.errorValues.at(i); QTableWidgetItem* item = new QTableWidgetItem(m_fitData.paramNamesUtf8.at(i)); uiGeneralTab.twParameters->setItem(i, 0, item); item = new QTableWidgetItem(QString::number(paramValue)); uiGeneralTab.twParameters->setItem(i, 1, item); if (!m_fitData.paramFixed.at(i)) { item = new QTableWidgetItem(QString::number(errorValue, 'g', 6)); uiGeneralTab.twParameters->setItem(i, 2, item); item = new QTableWidgetItem(QString::number(100.*errorValue/std::abs(paramValue), 'g', 3)); uiGeneralTab.twParameters->setItem(i, 3, item); // t values QString tdistValueString; if (fitResult.tdist_tValues.at(i) < std::numeric_limits::max()) tdistValueString = QString::number(fitResult.tdist_tValues.at(i), 'g', 3); else tdistValueString = QString::fromUtf8("\u221e"); item = new QTableWidgetItem(tdistValueString); uiGeneralTab.twParameters->setItem(i, 4, item); // p values const double p = fitResult.tdist_pValues.at(i); item = new QTableWidgetItem(QString::number(p, 'g', 3)); // color p values depending on value if (p > 0.05) item->setTextColor(QApplication::palette().color(QPalette::LinkVisited)); else if (p > 0.01) item->setTextColor(Qt::darkGreen); else if (p > 0.001) item->setTextColor(Qt::darkCyan); else if (p > 0.0001) item->setTextColor(QApplication::palette().color(QPalette::Link)); else item->setTextColor(QApplication::palette().color(QPalette::Highlight)); uiGeneralTab.twParameters->setItem(i, 5, item); // Conf. interval const double margin = fitResult.tdist_marginValues.at(i); if (fitResult.tdist_tValues.at(i) < 1.e6) item = new QTableWidgetItem(QString::number(paramValue - margin) + QLatin1String(" .. ") + QString::number(paramValue + margin)); else item = new QTableWidgetItem(i18n("too small")); uiGeneralTab.twParameters->setItem(i, 6, item); } } // Goodness of fit uiGeneralTab.twGoodness->horizontalHeader()->setSectionResizeMode(QHeaderView::Stretch); uiGeneralTab.twGoodness->item(0, 2)->setText(QString::number(fitResult.sse)); if (fitResult.dof != 0) { uiGeneralTab.twGoodness->item(1, 2)->setText(QString::number(fitResult.rms)); uiGeneralTab.twGoodness->item(2, 2)->setText(QString::number(fitResult.rsd)); uiGeneralTab.twGoodness->item(3, 2)->setText(QString::number(fitResult.rsquare, 'g', 15)); uiGeneralTab.twGoodness->item(4, 2)->setText(QString::number(fitResult.rsquareAdj, 'g', 15)); // chi^2 and F test p-values uiGeneralTab.twGoodness->item(5, 2)->setText(QString::number(fitResult.chisq_p, 'g', 3)); uiGeneralTab.twGoodness->item(6, 2)->setText(QString::number(fitResult.fdist_F, 'g', 3)); uiGeneralTab.twGoodness->item(7, 2)->setText(QString::number(fitResult.fdist_p, 'g', 3)); uiGeneralTab.twGoodness->item(9, 2)->setText(QString::number(fitResult.aic, 'g', 3)); uiGeneralTab.twGoodness->item(10, 2)->setText(QString::number(fitResult.bic, 'g', 3)); } uiGeneralTab.twGoodness->item(8, 2)->setText(QString::number(fitResult.mae)); //resize the table headers to fit the new content uiGeneralTab.twLog->resizeColumnsToContents(); uiGeneralTab.twParameters->resizeColumnsToContents(); //twGoodness doesn't have any header -> resize sections uiGeneralTab.twGoodness->resizeColumnToContents(0); uiGeneralTab.twGoodness->resizeColumnToContents(1); uiGeneralTab.twGoodness->resizeColumnToContents(2); //enable the "recalculate"-button if the source data was changed since the last fit uiGeneralTab.pbRecalculate->setEnabled(m_fitCurve->isSourceDataChangedSinceLastRecalc()); } //************************************************************* //*********** SLOTs for changes triggered in XYCurve ********** //************************************************************* //General-Tab void XYFitCurveDock::curveDescriptionChanged(const AbstractAspect* aspect) { if (m_curve != aspect) return; m_initializing = true; if (aspect->name() != uiGeneralTab.leName->text()) { uiGeneralTab.leName->setText(aspect->name()); } else if (aspect->comment() != uiGeneralTab.leComment->text()) { uiGeneralTab.leComment->setText(aspect->comment()); } m_initializing = false; } void XYFitCurveDock::curveDataSourceTypeChanged(XYCurve::DataSourceType type) { m_initializing = true; uiGeneralTab.cbDataSourceType->setCurrentIndex(type); m_initializing = false; } void XYFitCurveDock::curveDataSourceCurveChanged(const XYCurve* curve) { m_initializing = true; XYCurveDock::setModelIndexFromAspect(cbDataSourceCurve, curve); m_initializing = false; } void XYFitCurveDock::curveXDataColumnChanged(const AbstractColumn* column) { m_initializing = true; XYCurveDock::setModelIndexFromAspect(cbXDataColumn, column); m_initializing = false; } void XYFitCurveDock::curveYDataColumnChanged(const AbstractColumn* column) { m_initializing = true; XYCurveDock::setModelIndexFromAspect(cbYDataColumn, column); m_initializing = false; } void XYFitCurveDock::curveXErrorColumnChanged(const AbstractColumn* column) { m_initializing = true; XYCurveDock::setModelIndexFromAspect(cbXErrorColumn, column); m_initializing = false; } void XYFitCurveDock::curveYErrorColumnChanged(const AbstractColumn* column) { m_initializing = true; XYCurveDock::setModelIndexFromAspect(cbYErrorColumn, column); m_initializing = false; } void XYFitCurveDock::curveFitDataChanged(const XYFitCurve::FitData& data) { m_initializing = true; m_fitData = data; if (m_fitData.modelCategory == nsl_fit_model_custom) uiGeneralTab.teEquation->setPlainText(m_fitData.model); else uiGeneralTab.cbModel->setCurrentIndex(m_fitData.modelType); uiGeneralTab.sbDegree->setValue(m_fitData.degree); this->showFitResult(); m_initializing = false; } void XYFitCurveDock::dataChanged() { this->enableRecalculate(); } diff --git a/src/kdefrontend/dockwidgets/XYFitCurveDock.h b/src/kdefrontend/dockwidgets/XYFitCurveDock.h index 80c56d1dc..5c0ab5b49 100644 --- a/src/kdefrontend/dockwidgets/XYFitCurveDock.h +++ b/src/kdefrontend/dockwidgets/XYFitCurveDock.h @@ -1,113 +1,113 @@ /*************************************************************************** File : XYFitCurveDock.h Project : LabPlot -------------------------------------------------------------------- Copyright : (C) 2014 Alexander Semke (alexander.semke@web.de) Copyright : (C) 2017 Stefan Gerlach (stefan.gerlach@uni.kn) Description : widget for editing properties of equation curves ***************************************************************************/ /*************************************************************************** * * * This program is free software; you can redistribute it and/or modify * * it under the terms of the GNU General Public License as published by * * the Free Software Foundation; either version 2 of the License, or * * (at your option) any later version. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ #ifndef XYFITCURVEDOCK_H #define XYFITCURVEDOCK_H #include "kdefrontend/dockwidgets/XYCurveDock.h" #include "backend/worksheet/plots/cartesian/XYFitCurve.h" #include "ui_xyfitcurvedockgeneraltab.h" class TreeViewComboBox; class XYFitCurveDock: public XYCurveDock { Q_OBJECT public: explicit XYFitCurveDock(QWidget* parent); void setCurves(QList); virtual void setupGeneral(); private: virtual void initGeneralTab(); void showFitResult(); void updateSettings(const AbstractColumn*); Ui::XYFitCurveDockGeneralTab uiGeneralTab; TreeViewComboBox* cbDataSourceCurve; TreeViewComboBox* cbXDataColumn; TreeViewComboBox* cbYDataColumn; TreeViewComboBox* cbXErrorColumn; TreeViewComboBox* cbYErrorColumn; XYFitCurve* m_fitCurve; XYFitCurve::FitData m_fitData; QList parameters; QList parameterValues; protected: virtual void setModel(); private slots: //SLOTs for changes triggered in XYFitCurveDock //general tab void nameChanged(); void commentChanged(); void dataSourceTypeChanged(int); void dataSourceCurveChanged(const QModelIndex&); - void xWeightChanged(int); + void xErrorChanged(int); void yWeightChanged(int); void categoryChanged(int); void modelTypeChanged(int); void xDataColumnChanged(const QModelIndex&); void yDataColumnChanged(const QModelIndex&); void xErrorColumnChanged(const QModelIndex&); void yErrorColumnChanged(const QModelIndex&); void showConstants(); void showFunctions(); void updateParameterList(); void showParameters(); void parametersChanged(); void showOptions(); void insertFunction(const QString&) const; void insertConstant(const QString&) const; void recalculateClicked(); void updateModelEquation(); void enableRecalculate() const; void resultParametersContextMenuRequest(const QPoint &); void resultGoodnessContextMenuRequest(const QPoint &); void resultLogContextMenuRequest(const QPoint &); void resultCopySelection(); void resultCopyAll(); //SLOTs for changes triggered in XYCurve //General-Tab void curveDescriptionChanged(const AbstractAspect*); void curveDataSourceTypeChanged(XYCurve::DataSourceType); void curveDataSourceCurveChanged(const XYCurve*); void curveXDataColumnChanged(const AbstractColumn*); void curveYDataColumnChanged(const AbstractColumn*); void curveXErrorColumnChanged(const AbstractColumn*); void curveYErrorColumnChanged(const AbstractColumn*); void curveFitDataChanged(const XYFitCurve::FitData&); void dataChanged(); }; #endif diff --git a/src/kdefrontend/ui/dockwidgets/xyfitcurvedockgeneraltab.ui b/src/kdefrontend/ui/dockwidgets/xyfitcurvedockgeneraltab.ui index e2d945caa..d6fc6065a 100644 --- a/src/kdefrontend/ui/dockwidgets/xyfitcurvedockgeneraltab.ui +++ b/src/kdefrontend/ui/dockwidgets/xyfitcurvedockgeneraltab.ui @@ -1,922 +1,922 @@ XYFitCurveDockGeneralTab 0 0 919 1026 true 0 0 Category 0 0 Curve Comment Recalculate Qt::Vertical QSizePolicy::Fixed 24 20 y-Weight Qt::Horizontal Qt::Horizontal QFrame::NoFrame QFrame::Raised 0 Qt::Horizontal 41 20 75 true Errors: true 0 Parameters true 7 false 75 15 false false Goodness of fit true 11 3 false true 200 50 true false Sum of squared residuals Mean square error Root mean square error RMSE, SD Coefficient of determination Adj. coefficient of determ. F test F P > F Mean absolute error MAE Akaike information criterion AIC Bayesian information criterion BIC Log true 8 2 false true 150 true false false Status Iterations Tolerance Calculation time Degrees of freedom Number of parameter X range Iterations Name 75 true Results: Qt::AlignLeading|Qt::AlignLeft|Qt::AlignTop Qt::Vertical QSizePolicy::Fixed 20 10 Advanced fit options Options x-Error Qt::Vertical QSizePolicy::Fixed 20 10 Model Source f(x) = Qt::Horizontal QSizePolicy::Fixed 10 23 Specify parameters and their properties Parameters Degree 75 true Fit: Qt::Horizontal 97 20 y-Data 1 50 1 x-Data visible Qt::Vertical QSizePolicy::Fixed 20 10 0 0 16777215 16777215 QFrame::NoFrame QFrame::Raised 0 0 0 0 16777215 16777215 Functions Constants 75 true Data: Qt::Vertical QSizePolicy::Fixed 20 10 false 0 0 col = Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter - + false 0 0 col = Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter KComboBox QComboBox
kcombobox.h
 ExpressionTextEdit QTextEdit
kdefrontend/widgets/ExpressionTextEdit.h