diff options
Diffstat (limited to 'python')
| -rw-r--r-- | python/sbp_fit.py | 110 |
1 files changed, 66 insertions, 44 deletions
diff --git a/python/sbp_fit.py b/python/sbp_fit.py index 0b7d29a..0ce8d4d 100644 --- a/python/sbp_fit.py +++ b/python/sbp_fit.py @@ -3,9 +3,13 @@ # # Aaron LI # Created: 2016-03-13 -# Updated: 2016-03-14 +# Updated: 2016-03-31 # # Changelogs: +# 2016-03-31: +# * Remove `ci_report()' +# * Add `make_results()' to orgnize all results as s Python dictionary +# * Report results as json string # 2016-03-28: # * Add `main()', `make_model()' # * Use `configobj' to handle configurations @@ -25,6 +29,7 @@ or the double-beta model: s(r) = s01 * [1.0 + (r/rc1)^2] ^ (0.5-3*beta1) + s02 * [1.0 + (r/rc2)^2] ^ (0.5-3*beta2) + bkg + Sample config file: ------------------------------------------------- name = <NAME> @@ -61,8 +66,8 @@ bkg = 1.0e-9, 0.0, 1.0e-7 ------------------------------------------------- """ -__version__ = "0.4.0" -__date__ = "2016-03-28" +__version__ = "0.5.0" +__date__ = "2016-03-31" import numpy as np import lmfit @@ -76,6 +81,8 @@ import os import sys import re import argparse +import json +from collections import OrderedDict plt.style.use("ggplot") @@ -456,54 +463,69 @@ class SbpFit: if not hasattr(self, "f_residual") or self.f_residual is None: self.set_residual() self.fitter = lmfit.Minimizer(self.f_residual, self.model.params) - self.results = self.fitter.minimize(method=method) - self.fitted_model = lambda x: self.model.func(x, self.results.params) + self.fitted = self.fitter.minimize(method=method) + self.fitted_model = lambda x: self.model.func(x, self.fitted.params) def calc_ci(self, sigmas=[0.68, 0.90]): # `conf_interval' requires the fitted results have valid `stderr', # so we need to re-fit the model with method `leastsq'. - results = self.fitter.minimize(method="leastsq", - params=self.results.params) - self.ci, self.trace = lmfit.conf_interval(self.fitter, results, + fitted = self.fitter.minimize(method="leastsq", + params=self.fitted.params) + self.ci, self.trace = lmfit.conf_interval(self.fitter, fitted, sigmas=sigmas, trace=True) - @staticmethod - def ci_report(ci, with_offset=True): + def make_results(self): """ - Format and return the report for confidence intervals. - This function is intended to replace the `lmfit.ci_report()` - which do not use scitenfic notation for small values. + Make the `self.results' dictionary which contains all the fitting + results as well as the confidence intervals. """ - pnames = list(ci.keys()) - plen = max(map(len, pnames)) - report = [] - # header - sigmas = [ v[0] for v in ci.get(pnames[0]) if v[0] > 0.0 ] - fmt = "{:<%d}" % (plen+1) + \ - " {:11.2%} {:11.2%} _BEST_ {:11.2%} {:11.2%}" - report.append(fmt.format(" ", *sigmas)) - # parameters ci - for par in pnames: - values = [ v[1] for v in ci.get(par) ] - if with_offset: - N = len(values) - offsets = [ values[N//2] for i in range(N) ] - offsets[N//2] = 0.0 - values = list(map(lambda a,b: a-b, values, offsets)) - fmt = "{:<%d}" % (plen+1) + \ - " {:+11.5g} {:+11.5g} {:11.5g} {:+11.5g} {:+11.5g}" - report.append(fmt.format(par+":", *values)) - return "\n".join(report) + fitted = self.fitted + self.results = OrderedDict() + ## fitting results + self.results.update( + nfev = fitted.nfev, + ndata = fitted.ndata, + nvarys = fitted.nvarys, # number of varible paramters + nfree = fitted.nfree, # degree of freem + chisqr = fitted.chisqr, + redchi = fitted.redchi, + aic = fitted.aic, + bic = fitted.bic) + params = fitted.params + pnames = list(params.keys()) + pvalues = OrderedDict() + for pn in pnames: + par = params.get(pn) + pvalues[pn] = [par.value, par.min, par.max, par.vary] + self.results["params"] = pvalues + ## confidence intervals + if hasattr(self, "ci") and self.ci is not None: + ci = self.ci + ci_values = OrderedDict() + ci_sigmas = [ "ci%02d" % (v[0]*100) for v in ci.get(pnames[0]) ] + ci_names = sorted(list(set(ci_sigmas))) + ci_idx = { k: [] for k in ci_names } + for cn, idx in zip(ci_sigmas, range(len(ci_sigmas))): + ci_idx[cn].append(idx) + # parameters ci + for pn in pnames: + ci_pv = OrderedDict() + pv = [ v[1] for v in ci.get(pn) ] + # best + pv_best = pv[ ci_idx["ci00"][0] ] + ci_pv["best"] = pv_best + # ci of each sigma + pv2 = [ v-pv_best for v in pv ] + for cn in ci_names[1:]: + ci_pv[cn] = [ pv2[idx] for idx in ci_idx[cn] ] + ci_values[pn] = ci_pv + self.results["ci"] = ci_values def report(self, outfile=sys.stdout): - if hasattr(self, "results") and self.results is not None: - p = lmfit.fit_report(self.results) - print(p, file=outfile) - if hasattr(self, "ci") and self.ci is not None: - print("-----------------------------------------------", - file=outfile) - p = self.ci_report(self.ci) - print(p, file=outfile) + if not hasattr(self, "results") or self.results is None: + self.make_results() + jd = json.dumps(self.results, indent=2) + print(jd, file=outfile) def plot(self, ax=None, fig=None): if ax is None: @@ -525,7 +547,7 @@ class SbpFit: ypred = self.fitted_model(xpred) ymin = min(min(self.ydata), min(ypred)) ymax = max(max(self.ydata), max(ypred)) - self.model.plot(params=self.results.params, xdata=xpred, ax=ax) + self.model.plot(params=self.fitted.params, xdata=xpred, ax=ax) ax.set_xscale("log") ax.set_yscale("log") ax.set_xlim(1.0, xmax) @@ -537,8 +559,8 @@ class SbpFit: ax.set_xlabel("Radius (pixel)") ax.set_ylabel(r"Surface Brightness (photons/cm$^2$/pixel$^2$/s)") ax.text(x=xmax, y=ymax, - s="redchi: %.2f / %.2f = %.2f" % (self.results.chisqr, - self.results.nfree, self.results.chisqr/self.results.nfree), + s="redchi: %.2f / %.2f = %.2f" % (self.fitted.chisqr, + self.fitted.nfree, self.fitted.chisqr/self.fitted.nfree), horizontalalignment="right", verticalalignment="top") return (fig, ax) |