diff options
Diffstat (limited to 'astro/fitting/fit_betasbp_cut.py')
| -rwxr-xr-x | astro/fitting/fit_betasbp_cut.py | 161 |
1 files changed, 130 insertions, 31 deletions
diff --git a/astro/fitting/fit_betasbp_cut.py b/astro/fitting/fit_betasbp_cut.py index 4e37f91..9dcf730 100755 --- a/astro/fitting/fit_betasbp_cut.py +++ b/astro/fitting/fit_betasbp_cut.py @@ -11,6 +11,11 @@ # 2015/05/29 # # Changelogs: +# v0.5.0, 2015/06/07, Aaron LI +# * added 'fit_model_bounds' using 'scipy.optimize.minimize' to +# perform function minimization with bounds +# * split the data cut section to function 'cut_data' +# * added argument 'options' to 'fit_model_bounds' # v0.4.0, 2015/06/06, Aaron LI # * replace getopt with 'argparse' # * added 'get_parameter' to process model parameter initial value and bounds @@ -28,14 +33,17 @@ # * Support read model initial parameter values from input file. # # TODO: -# * to allow restrict the range of parameters / just fix it +# * calculate fitting parameter's standard deviation +# * to normalize fitting paramters to be the same order of magnitude +# for better minimization +# Ref: http://stackoverflow.com/questions/21369139/normalization-for-optimization-in-python # from __future__ import print_function, division -__version__ = "0.4.0" -__date__ = "2015/06/06" +__version__ = "0.5.0" +__date__ = "2015/06/07" import numpy as np from scipy.optimize import curve_fit, minimize @@ -49,6 +57,8 @@ import argparse # modes of to cut data CUT_POINT = 'CUT_POINT' CUT_RADIUS = 'CUT_RADIUS' +# default minimize method +MINIMIZE_METHOD = 'L-BFGS-B' def beta_model(r, s0, rc, beta, c): @@ -62,6 +72,15 @@ def calc_chisq(func, xdata, ydata, yerrdata, *args): """ Calculate the chi-squared values for the given function according to the provided data points. + + Arguments: + xdata: x values of data points + ydata: y values of data points + yerrdata: y standard deviation values + args: additional arguments for 'func' + + Return: + chi-squared value """ xdata = np.array(xdata) ydata = np.array(ydata) @@ -69,46 +88,29 @@ def calc_chisq(func, xdata, ydata, yerrdata, *args): return np.sum(((ydata - func(xdata, *args)) / yerrdata) ** 2) -def fit_model(func, xdata, ydata, yerrdata, p0, - cutmode=CUT_POINT, cutvalue=0): +def fit_model(func, xdata, ydata, yerrdata, p0): """ Fit the provided data with the beta model. Arguments: p0: initial values for the parameters of beta model - cutmode: 'point' / 'radius'; ignore data by number of data points, - or by radius value less than the given value - cutvalue: the cut limit Return: - (popt, pcov, infodict) + (popt, infodict) popt: optimal values for the parameters - pcov: 2d array; the estimated covariance of popt infodict: * fvec: the function evaluated at the output parameters * dof: degree of freedom * chisq: chi-squared * perr: one standard deviation errors on the parameters """ - if cutmode == CUT_POINT: - xdata2 = xdata[cutvalue:] - ydata2 = ydata[cutvalue:] - yerrdata2 = yerrdata[cutvalue:] - elif cutmode == CUT_RADIUS: - ii = xdata >= cutvalue - xdata2 = xdata[ii] - ydata2 = ydata[ii] - yerrdata2 = yerrdata[ii] - else: - raise ValueError('Unknown cut mode: %s' % cutmode) - popt, pcov = curve_fit(func, xdata2, ydata2, - p0=p0, sigma=yerrdata2) + popt, pcov = curve_fit(func, xdata, ydata, p0=p0, sigma=yerrdata) # the function evaluated at the output parameters fvec = lambda x: func(x, *popt) # degree of freedom - dof = len(xdata2) - len(popt) + dof = len(xdata) - len(popt) # chi squared - chisq = np.sum(((ydata2 - fvec(xdata2)) / yerrdata2) ** 2) + chisq = np.sum(((ydata - fvec(xdata)) / yerrdata) ** 2) # one standard deviation errors on the parameters perr = np.sqrt(np.diag(pcov)) infodict = { @@ -117,7 +119,84 @@ def fit_model(func, xdata, ydata, yerrdata, p0, 'chisq': chisq, 'perr': perr } - return (popt, pcov, infodict) + return (popt, infodict) + + +def fit_model_bounds(func, xdata, ydata, yerrdata, p0=None, bounds=None, + options=None): + """ + Fit the provided data with the beta model. + + Arguments: + p0: initial values for the parameters of beta model + bounds: (min, max) pairs for each parameter bound + options: a dict of solver options (=> minimize: options) + + Return: + (popt, infodict) + popt: optimal values for the parameters + infodict: + * fvec: the function evaluated at the output parameters + * dof: degree of freedom + * chisq: chi-squared + * perr: one standard deviation errors on the parameters + """ + # objective function to be minimized, required format of 'f(x, *args)' + f = lambda x: calc_chisq(func, xdata, ydata, yerrdata, *x) + # minimize the given function using 'scipy.optimize.minimize' with bounds + res = minimize(f, p0, method=MINIMIZE_METHOD, bounds=bounds, + options=options) + popt = res.x + print("DEBUG: minimization results:\n", res, file=sys.stderr) + + # check minimization results + if not res.success: + print("*** WARNING: minimization exited with error: ***\n" + \ + "*** %s ***" % res.message, file=sys.stderr) + + # the function evaluated at the output parameters + fvec = lambda x: func(x, *popt) + # degree of freedom + dof = len(xdata) - len(popt) + # chi squared + chisq = res.fun + # one standard deviation errors on the parameters + perr = popt * 0.0 # FIXME + infodict = { + 'fvec': fvec, + 'dof': dof, + 'chisq': chisq, + 'perr': perr + } + return (popt, infodict) + + +def cut_data(xdata, ydata, yerrdata, cutmode=CUT_POINT, cutvalue=0): + """ + Cut the given data with the provided cutmode and cutvalue, + return the cut data. + + Arguments: + xdata, ydata, yerrdata: input data (x, y, yerr) + cutmode: 'point' / 'radius'; ignore data by number of data points, + or by radius value less than the given value + cutvalue: the cut limit + + Return: + (xdata_cut, ydata_cut, yerrdata_cut) + """ + if cutmode == CUT_POINT: + xdata_cut = xdata[cutvalue:] + ydata_cut = ydata[cutvalue:] + yerrdata_cut = yerrdata[cutvalue:] + elif cutmode == CUT_RADIUS: + ii = xdata >= cutvalue + xdata_cut = xdata[ii] + ydata_cut = ydata[ii] + yerrdata_cut = yerrdata[ii] + else: + raise ValueError('Unknown cut mode: %s' % cutmode) + return (xdata_cut, ydata_cut, yerrdata_cut) def get_parameter(pstring): @@ -172,7 +251,7 @@ def main(): epilog="Version: %s (%s)" % (__version__, __date__)) parser.add_argument("-v", "--verbose", dest="verbose", action="store_true", default=False, - help="show verbose/debug information") + help="show verbose/debug information (False)") parser.add_argument("-V", "--version", action="version", version="%(prog)s " + "%s (%s)" % (__version__, __date__)) parser.add_argument("-i", "--infile", @@ -189,6 +268,9 @@ def main(): # c_0 = init_value, lower_limit, upper_limit""") parser.add_argument("-o", "--outfile", dest="outfilename", help="output file to store the fitted data") + parser.add_argument("-b", "--bounds", dest="bounds", + action="store_true", default=False, + help="whether apply paramenter bounds (False)") parser.add_argument("-c", "--cut-point", dest="cut_point", metavar="N", type=int, default=0, help="number of inner-most data points to be ignored") @@ -226,6 +308,8 @@ def main(): cutvalue = args.cut_radius if args.verbose: + print('DEBUG: apply parameter bounds: %s' % args.bounds, + file=sys.stderr) print("DEBUG: cutmode: %s, cutvalue: %s" % (cutmode, cutvalue)) # input data list @@ -315,17 +399,32 @@ def main(): rerr_data = np.array(rerr_data) s_data = np.array(s_data) serr_data = np.array(serr_data) + # cut data + r_data_cut, s_data_cut, serr_data_cut = cut_data(r_data, s_data, + serr_data, cutmode=cutmode, cutvalue=cutvalue) + # model parameters par_names = ["s0", "rc", "beta", "c"] # initial values - par_0 = [s0_0, rc_0, beta_0, c_0] + par_0 = np.array([s0_0, rc_0, beta_0, c_0]) # parameter bounds par_bounds = [(s0_lower, s0_upper), (rc_lower, rc_upper), (beta_lower, beta_upper), (c_lower, c_upper)] + # set eps for the parameters (required for the minimize method, + # otherwise error 'ABNORMAL_TERMINATION_IN_LNSRCH' occurs, which + # may due to the different order of magnitude of each parameters) + par_eps = par_0 * 1e-4 + + if args.bounds: + ## 'fit_model_bounds' to perform fitting with bounds + par_fit, infodict = fit_model_bounds(beta_model, r_data_cut, + s_data_cut, serr_data_cut, p0=par_0, bounds=par_bounds, + options={'eps': par_eps}) + else: + # 'fit_model' do not support parameter bounds + par_fit, infodict = fit_model(beta_model, r_data_cut, + s_data_cut, serr_data_cut, p0=par_0) - ## 'fit_beta_model' do not support parameter bounds - par_fit, cov_fit, infodict = fit_model(beta_model, r_data, s_data, - serr_data, p0=par_0, cutmode=cutmode, cutvalue=cutvalue) fvec = infodict['fvec'] dof = infodict['dof'] chisq = infodict['chisq'] |