https://github.com/lmfit/lmfit-py
Tip revision: c2b7ea15507baf2032f497441de917e505c6b784 authored by Matthew Newville on 31 May 2017, 19:11:22 UTC
correctly create non-constraint parameter values and bounds for least_squares()
correctly create non-constraint parameter values and bounds for least_squares()
Tip revision: c2b7ea1
printfuncs.py
"""Functions to display fitting results and confidence intervals."""
from __future__ import print_function
import re
from .parameter import Parameters
def alphanumeric_sort(s, _nsre=re.compile('([0-9]+)')):
"""Sort alphanumeric string."""
return [int(text) if text.isdigit() else text.lower()
for text in re.split(_nsre, s)]
def getfloat_attr(obj, attr, fmt='%.3f'):
"""Format an attribute of an object for printing."""
val = getattr(obj, attr, None)
if val is None:
return 'unknown'
if isinstance(val, int):
return '%d' % val
if isinstance(val, float):
return fmt % val
else:
return repr(val)
def gformat(val, length=11):
"""Format a number with '%g'-like format.
The return will be length ``length`` (default is 12) and have at
least length-6 significant digits.
"""
length = max(length, 7)
fmt = '{0: .%ig}' % (length-6)
if isinstance(val, int):
out = ('{0: .%ig}' % (length-2)).format(val)
if len(out) > length:
out = fmt.format(val)
else:
out = fmt.format(val)
if len(out) < length:
if 'e' in out:
ie = out.find('e')
if '.' not in out[:ie]:
out = out[:ie] + '.' + out[ie:]
out = out.replace('e', '0'*(length-len(out))+'e')
else:
fmt = '{0: .%ig}' % (length-1)
out = fmt.format(val)[:length]
if len(out) < length:
pad = '0' if '.' in out else ' '
out += pad*(length-len(out))
return out
CORREL_HEAD = '[[Correlations]] (unreported correlations are < % .3f)'
def fit_report(inpars, modelpars=None, show_correl=True, min_correl=0.1,
sort_pars=False):
"""Generate a report of the fitting results.
The report contains the best-fit values for the parameters and their
uncertainties and correlations.
Parameters
----------
inpars : Parameters
Input Parameters from fit or MinimizerResult returned from a fit.
modelpars : Parameters, optional
Known Model Parameters.
show_correl : bool, optional
Whether to show list of sorted correlations (default is True).
min_correl : float, optional
Smallest correlation in absolute value to show (default is 0.1).
sort_pars : bool or callable, optional
Whether to show parameter names sorted in alphanumerical order. If
False (default), then the parameters will be listed in the order they
were added to the Parameters dictionary. If callable, then this (one
argument) function is used to extract a comparison key from each
list element.
Returns
-------
string
Multi-line text of fit report.
"""
if isinstance(inpars, Parameters):
result, params = None, inpars
if hasattr(inpars, 'params'):
result = inpars
params = inpars.params
if sort_pars:
if callable(sort_pars):
key = sort_pars
else:
key = alphanumeric_sort
parnames = sorted(params, key=key)
else:
# dict.keys() returns a KeysView in py3, and they're indexed
# further down
parnames = list(params.keys())
buff = []
add = buff.append
if result is not None:
add("[[Fit Statistics]]")
add(" # function evals = %s" % getfloat_attr(result, 'nfev'))
add(" # data points = %s" % getfloat_attr(result, 'ndata'))
add(" # variables = %s" % getfloat_attr(result, 'nvarys'))
add(" chi-square = %s" % getfloat_attr(result, 'chisqr'))
add(" reduced chi-square = %s" % getfloat_attr(result, 'redchi'))
add(" Akaike info crit = %s" % getfloat_attr(result, 'aic'))
add(" Bayesian info crit = %s" % getfloat_attr(result, 'bic'))
namelen = max([len(n) for n in parnames])
add("[[Variables]]")
for name in parnames:
par = params[name]
space = ' '*(namelen+1-len(name))
nout = "%s:%s" % (name, space)
inval = '(init= ?)'
if par.init_value is not None:
inval = '(init=% .7g)' % par.init_value
if modelpars is not None and name in modelpars:
inval = '%s, model_value =% .7g' % (inval, modelpars[name].value)
try:
sval = gformat(par.value)
except (TypeError, ValueError):
sval = 'Non Numeric Value?'
if par.stderr is not None:
serr = gformat(par.stderr, length=9)
try:
spercent = '({0:.2%})'.format(abs(par.stderr/par.value))
except ZeroDivisionError:
spercent = ''
sval = '%s +/-%s %s' % (sval, serr, spercent)
if par.vary:
add(" %s %s %s" % (nout, sval, inval))
elif par.expr is not None:
add(" %s %s == '%s'" % (nout, sval, par.expr))
else:
add(" %s % .7g (fixed)" % (nout, par.value))
if show_correl:
correls = {}
for i, name in enumerate(parnames):
par = params[name]
if not par.vary:
continue
if hasattr(par, 'correl') and par.correl is not None:
for name2 in parnames[i+1:]:
if (name != name2 and name2 in par.correl and
abs(par.correl[name2]) > min_correl):
correls["%s, %s" % (name, name2)] = par.correl[name2]
sort_correl = sorted(correls.items(), key=lambda it: abs(it[1]))
sort_correl.reverse()
if len(sort_correl) > 0:
add(CORREL_HEAD % min_correl)
for name, val in sort_correl:
lspace = max(1, 25 - len(name))
add(' C(%s)%s = % .3f ' % (name, (' '*30)[:lspace], val))
return '\n'.join(buff)
def report_errors(params, **kws):
"""Print a report for fitted params: see error_report()."""
print(fit_report(params, **kws))
def report_fit(params, **kws):
"""Print a report for fitted params: see error_report()."""
print(fit_report(params, **kws))
def ci_report(ci, with_offset=True, ndigits=5):
"""Return text of a report for confidence intervals.
Parameters
----------
with_offset : bool, optional
Whether to subtract best value from all other values (default is True).
ndigits : int, optional
Number of significant digits to show (default is 5).
Returns
-------
str
Text of formatted report on confidence intervals.
"""
maxlen = max([len(i) for i in ci])
buff = []
add = buff.append
def convp(x):
"""TODO: function docstring."""
if abs(x[0]) < 1.e-2:
return "_BEST_"
return "%.2f%%" % (x[0]*100)
title_shown = False
fmt_best = fmt_diff = "{0:.%if}" % ndigits
if with_offset:
fmt_diff = "{0:+.%if}" % ndigits
for name, row in ci.items():
if not title_shown:
add("".join([''.rjust(maxlen+1)] + [i.rjust(ndigits+5)
for i in map(convp, row)]))
title_shown = True
thisrow = [" %s:" % name.ljust(maxlen)]
offset = 0.0
if with_offset:
for cval, val in row:
if abs(cval) < 1.e-2:
offset = val
for cval, val in row:
if cval < 1.e-2:
sval = fmt_best.format(val)
else:
sval = fmt_diff.format(val-offset)
thisrow.append(sval.rjust(ndigits+5))
add("".join(thisrow))
return '\n'.join(buff)
def report_ci(ci):
"""Print a report for confidence intervals."""
print(ci_report(ci))