diff --git a/src/backend/nsl/nsl_fit.c b/src/backend/nsl/nsl_fit.c index 3509c7d66..0831b09c7 100644 --- a/src/backend/nsl/nsl_fit.c +++ b/src/backend/nsl/nsl_fit.c @@ -1,646 +1,646 @@ /*************************************************************************** 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_weight_type_name[] = {"No", "Instrumental", "Direct", "Inverse", "Statistical", "Statistical (Fit)", "Relative", "Relative (Fit)"}; +const char* nsl_fit_weight_type_name[] = {"No", "Instrumental (1/s^2)", "Direct (s)", "Inverse (1/s)", "Statistical (1/y)", "Statistical (Fit)", "Relative (1/y^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/kdefrontend/dockwidgets/XYFitCurveDock.cpp b/src/kdefrontend/dockwidgets/XYFitCurveDock.cpp index 14c8ec403..f95d8b946 100644 --- a/src/kdefrontend/dockwidgets/XYFitCurveDock.cpp +++ b/src/kdefrontend/dockwidgets/XYFitCurveDock.cpp @@ -1,1083 +1,1103 @@ /*************************************************************************** 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 #include // DBL_MAX #include // fabs() 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, 1); + gridLayout->addWidget(cbXDataColumn, 7, 4, 1, 4); cbXErrorColumn = new TreeViewComboBox(generalTab); - gridLayout->addWidget(cbXErrorColumn, 7, 5, 1, 4); + gridLayout->addWidget(cbXErrorColumn, 10, 5, 1, 4); cbYDataColumn = new TreeViewComboBox(generalTab); - gridLayout->addWidget(cbYDataColumn, 8, 4, 1, 1); + gridLayout->addWidget(cbYDataColumn, 8, 4, 1, 4); cbYErrorColumn = new TreeViewComboBox(generalTab); - gridLayout->addWidget(cbYErrorColumn, 8, 5, 1, 4); + gridLayout->addWidget(cbYErrorColumn, 11, 5, 1, 4); - //Weight + //Weights + //TODO: XWeight for(int i = 0; i < NSL_FIT_WEIGHT_TYPE_COUNT; i++) - uiGeneralTab.cbWeight->addItem(nsl_fit_weight_type_name[i]); - uiGeneralTab.cbWeight->setCurrentIndex(nsl_fit_weight_instrumental); + uiGeneralTab.cbYWeight->addItem(nsl_fit_weight_type_name[i]); + uiGeneralTab.cbYWeight->setCurrentIndex(nsl_fit_weight_instrumental); 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.cbWeight, SIGNAL(currentIndexChanged(int)), this, SLOT(weightChanged(int))); + //TODO: XWeight + 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.cbWeight->setCurrentIndex(m_fitData.weightsType); +// TODO: uiGeneralTab.cbXWeight->setCurrentIndex(m_fitData.weightsType); + uiGeneralTab.cbYWeight->setCurrentIndex(m_fitData.weightsType); 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(); cbXErrorColumn->show(); cbYErrorColumn->show(); } else { uiGeneralTab.lDataSourceCurve->show(); cbDataSourceCurve->show(); uiGeneralTab.lXColumn->hide(); cbXDataColumn->hide(); uiGeneralTab.lYColumn->hide(); cbYDataColumn->hide(); cbXErrorColumn->hide(); cbYErrorColumn->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); //y-error column was selected - in case no weighting is selected yet, automatically select instrumental weighting - if ( uiGeneralTab.cbWeight->currentIndex() == 0 ) - uiGeneralTab.cbWeight->setCurrentIndex((int)nsl_fit_weight_instrumental); + //TODO: XWeight + if (uiGeneralTab.cbYWeight->currentIndex() == 0) + uiGeneralTab.cbYWeight->setCurrentIndex((int)nsl_fit_weight_instrumental); } -void XYFitCurveDock::weightChanged(int index) { - DEBUG("weightChanged() weight = " << nsl_fit_weight_type_name[index]); +void XYFitCurveDock::yWeightChanged(int index) { + DEBUG("yWeightChanged() weight = " << nsl_fit_weight_type_name[index]); m_fitData.weightsType = (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); + break; + case nsl_fit_weight_instrumental: + case nsl_fit_weight_direct: + case nsl_fit_weight_inverse: + cbYErrorColumn->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; } /*! * 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] = -DBL_MAX; m_fitData.paramUpperLimits[i] = DBL_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) 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() != NULL); } 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)/fabs(fitResult.paramValues.at(i)), 'g', 3) + " %)\n"; const double margin = fitResult.tdist_marginValues.at(i); str += " (" + i18n("t statistic:") + ' ' + QString::number(fitResult.tdist_tValues.at(i), 'g', 3) + ", " + i18n("p value:") + ' ' + QString::number(fitResult.tdist_pValues.at(i), 'g', 3) + ", " + i18n("conf. interval:") + ' '; if (fabs(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/fabs(paramValue), 'g', 3)); uiGeneralTab.twParameters->setItem(i, 3, item); // t values item = new QTableWidgetItem(QString::number(fitResult.tdist_tValues.at(i), 'g', 3)); 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 1a68a4c74..623a934e1 100644 --- a/src/kdefrontend/dockwidgets/XYFitCurveDock.h +++ b/src/kdefrontend/dockwidgets/XYFitCurveDock.h @@ -1,111 +1,112 @@ /*************************************************************************** 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 weightChanged(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 1aded715a..d53fe8989 100644 --- a/src/kdefrontend/ui/dockwidgets/xyfitcurvedockgeneraltab.ui +++ b/src/kdefrontend/ui/dockwidgets/xyfitcurvedockgeneraltab.ui @@ -1,837 +1,860 @@ XYFitCurveDockGeneralTab 0 0 919 1018 true - - - - Degree - - - - - - - - - - - 75 - true - - - - Results: - - - Qt::AlignLeading|Qt::AlignLeft|Qt::AlignTop - - - - - - - Name - - - - - - - - 0 - 0 - - - - - - - - Curve - - - - - - - Qt::Horizontal - - - - 97 - 20 - - - - - - - - - 0 - 0 - - - - - 16777215 - 16777215 - - - - QFrame::NoFrame - - - QFrame::Raised - - - - 0 - - - 0 - - - - - - 0 - 0 - - - - - 16777215 - 16777215 - - - - - - - - Functions - - - - - - - - - - Constants - - - - - - - - - - - - - - - - - - - - visible - - - - - - - Qt::Vertical - - - QSizePolicy::Fixed - - - - 20 - 13 - - - - - - - - Qt::Horizontal - - - - - - - - 75 - true - - - - Fit: - - - - - - - - 0 - 0 - - - - - - - - - 75 - true - - - - Data: - - + + Comment - - - - + Recalculate - + Qt::Vertical QSizePolicy::Fixed 24 20 - - - - - - - Specify parameters and their properties - + + - Parameters + f(x) = - - - - Qt::Vertical - - - QSizePolicy::Fixed - - - - 20 - 10 - - - - - - + + - Qt::Vertical + Qt::Horizontal QSizePolicy::Fixed - 20 - 13 + 10 + 23 - - - - Category - - - Source - + Model - - - - 1 - - - 50 - - - 1 + + + + x-Data - - - - QFrame::NoFrame - - - QFrame::Raised + + + + + + + Name - - - 0 - - - - - Qt::Horizontal - - - - 41 - 20 - - - - - - - + + - y-Data/Error + Category - + 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 - + + + + + 75 + true + + + + Data: + + + + + + + + + + + 0 + 0 + + + + + + + + + 75 + true + + + + Results: + + + Qt::AlignLeading|Qt::AlignLeft|Qt::AlignTop + + + + + + + Qt::Vertical + + + QSizePolicy::Fixed + + + + 20 + 13 + + + + + + + + + 0 + 0 + + + + + + + + + 0 + 0 + + + + + 16777215 + 16777215 + + + + QFrame::NoFrame + + + QFrame::Raised + + + + 0 + + + 0 + + + + + + 0 + 0 + + + + + 16777215 + 16777215 + + + + + + + + Functions + + + + + + + + + + Constants + + + + + + + + + + + + + + + + + + + + Qt::Horizontal + + + + + + + + 75 + true + + + + Fit: + + + + + + + Qt::Horizontal + + + + 97 + 20 + + + + + Qt::Horizontal - + - - + + - f(x) = + Curve - - + + + + visible + + + + + - Qt::Horizontal + Qt::Vertical QSizePolicy::Fixed - 10 - 23 + 20 + 13 - + + + + Degree + + + + Advanced fit options Options + + + + 1 + + + 50 + + + 1 + + + + + + + QFrame::NoFrame + + + QFrame::Raised + + + + 0 + + + + + Qt::Horizontal + + + + 41 + 20 + + + + + + + + + + + + 75 + true + + + + Weighting: + + + + + + - x-Data/Error + x-Data - + - Weight + y-Data + + + + + + + Specify parameters and their properties + + + Parameters + + + + + + + Qt::Vertical + + + QSizePolicy::Fixed + + + + 20 + 10 + + + + + + + + y-Data KComboBox QComboBox
kcombobox.h
 ExpressionTextEdit QTextEdit
kdefrontend/widgets/ExpressionTextEdit.h