def fit_report(group, show_correl=True, min_correl=0.1, _larch=None, **kws):
"""print report of fit statistics given 'fit parameter group'
"""
if not _larch.symtable.isgroup(group):
print 'must pass Group to fit_report()'
return
topline = '===================== FIT RESULTS ====================='
header = '[[%s]]'
varformat = ' %12s = % f +/- %f (init= % f)'
exprformat = ' %12s = % f = \'%s\''
out = [topline, header % 'Statistics']
npts = len(group.residual)
out.append(' npts, nvarys = %i, %i' % (npts, group.nvarys))
out.append(' nfree, nfcn_calls = %i, %i' % (group.nfree, group.nfcn_calls))
out.append(' chi_square = %f' % (group.chi_square))
out.append(' reduced chi_square = %f' % (group.chi_reduced))
out.append(' ')
out.append(header % 'Variables')
exprs = []
for name in dir(group):
var = getattr(group, name)
if len(name) < 14:
name = (name + ' '*14)[:14]
if isParameter(var):
if var.vary:
out.append(varformat % (name, var.value,
var.stderr, var._initval))
elif var.expr is not None:
exprs.append(exprformat % (name, var.value, var.expr))
if len(exprs) > 0:
out.append(header % 'Constraint Expressions')
out.extend(exprs)
covar_vars = getattr(group, 'covar_vars', [])
if show_correl and len(covar_vars) > 0:
out.append(' ')
out.append(header % 'Correlations' +
' (unreported correlations are < % .3f)' % min_correl)
correls = {}
for i, name in enumerate(covar_vars):
par = getattr(group, name)
if not par.vary:
continue
if hasattr(par, 'correl') and par.correl is not None:
for name2 in covar_vars[i+1:]:
if name != name2 and name2 in par.correl:
correls["%s, %s" % (name, name2)] = par.correl[name2]
sort_correl = sorted(correls.items(), key=lambda it: abs(it[1]))
sort_correl.reverse()
for name, val in sort_correl:
if abs(val) < min_correl:
break
if len(name) < 20:
name = (name + ' '*20)[:20]
out.append(' %s = % .3f ' % (name, val))
out.append('='*len(topline))
return '\n'.join(out)
def feffit(params, datasets, _larch=None, rmax_out=10,
path_outputs=True, **kws):
def _resid(params, datasets=None, _larch=None, **kws):
""" this is the residua function """
# print '---feffit residual '
#for i in dir(params):
# print i, getattr(params, i)
return concatenate([d.residual() for d in datasets])
if isinstance(datasets, FeffitDataSet):
datasets = [datasets]
for ds in datasets:
if not isinstance(ds, FeffitDataSet):
print "feffit needs a list of FeffitDataSets"
return
fitkws = dict(datasets=datasets)
fit = Minimizer(_resid, params, fcn_kws=fitkws, _larch=_larch)
fit.leastsq()
# scale uncertainties to sqrt(n_idp - n_varys)
n_idp = 0
for ds in datasets:
n_idp += ds.transform.n_idp
err_scale = sqrt(n_idp - params.nvarys)
for name in dir(params):
p = getattr(params, name)
if isParameter(p) and p.vary:
p.stderr *= err_scale
# here we create outputs:
for ds in datasets:
ds.save_ffts(rmax_out=rmax_out, path_outputs=path_outputs)
out = larch.Group(name='feffit fit results',
fit = fit,
params = params,
datasets = datasets)
return out
def prepare_fit(self):
"""prepare parameters for fit
determine which parameters are actually variables
and which are defined expressions.
"""
if self.__prepared:
return
# set larch's paramGroup to this group of parameters
if self._larch.symtable.isgroup(self.paramgroup):
self._larch.symtable._sys.paramGroup = self.paramgroup
else:
self._larch.write.write('Minimize Error: invalid parameter group!')
return
self.nfev_calls = 0
self.var_names = []
self.defvars = []
self.vars = []
self.nvarys = 0
for name in dir(self.paramgroup):
par = getattr(self.paramgroup, name)
if isParameter(par):
if par.expr is not None:
par._getval()
elif par.vary:
self.var_names.append(name)
self.vars.append(par.value)
if not hasattr(par, 'name') or par.name is None:
par.name = name
self.nvarys = len(self.vars)
# now evaluate make sure initial values are set
# are used to set values of the defined expressions.
# this also acts as a check of expression syntax.
self.__prepared = True
def json_encode(name, symtable=None):
"""
return json encoded object from larch symbol table
"""
if symtable is None:
return None
if not symtable.has_symbol(name):
return None
obj = symtable.get_symbol(name)
if isinstance(obj, np.ndarray):
out = {'__class__': 'Array', '__shape__': obj.shape,
'__dtype__': obj.dtype.name}
out['value'] = json.dumps(obj.tolist())
elif symtable.isgroup(obj):
out = {'__class__': 'Group'}
for member in dir(obj):
try:
out[member] = json_encode(getattr(obj, member))
except:
pass
return out
elif isParameter(obj):
out = {'__class__': 'Parameter'}
for attr in ('value', 'name', 'vary', 'min', 'max', 'expr'):
val = getattr(obj, attr, None)
if val is not None:
out[attr] = val
else:
try:
out = json.dumps(obj)
except:
try:
out = json.dumps(repr(obj))
except:
out = None
return out
def feffit_report(result, min_correl=0.1, with_paths=True,
_larch=None, **kws):
"""print report of fit for feffit"""
good_to_go = False
try:
fit = result.fit
params = result.params
datasets = result.datasets
good_to_go = True
except:
pass
if not good_to_go:
print 'must pass output of feffit()!'
return
topline = '=================== FEFFIT RESULTS ===================='
header = '[[%s]]'
varformat = ' %12s = % f +/- %f (init= % f)'
exprformat = ' %12s = % f = \'%s\''
out = [topline, header % 'Statistics']
# print 'Params ', dir(params)
# print 'Fit ', dir(fit)
# for ds in datasets:
# print dir(ds)
# print dir(ds.transform)
npts = len(params.residual)
out.append(' npts, nvarys = %i, %i' % (npts, params.nvarys))
out.append(' nfree, nfcn_calls = %i, %i' % (params.nfree, params.nfcn_calls))
out.append(' chi_square = %f' % (params.chi_square))
out.append(' reduced chi_square = %f' % (params.chi_reduced))
out.append(' ')
if len(datasets) == 1:
out.append(header % 'Data')
else:
out.append(header % 'Datasets (%i)' % len(datasets))
for i, ds in enumerate(datasets):
trans = ds.transform
if len(datasets) > 1:
out.append(' dataset %i:' % (i+1))
out.append(' n_independent = %.3f ' % (trans.n_idp))
out.append(' eps_k, eps_r = %f, %f' % (ds.transform.epsilon_k, ds.transform.epsilon_r))
out.append(' fit space = %s ' % (trans.fitspace))
out.append(' r-range = %.3f, %.3f' % (trans.rmin, trans.rmax))
out.append(' k-range = %.3f, %.3f' % (trans.kmin, trans.kmax))
kwin = ' k window, dk = %s, %.3f' % (trans.window, trans.dk)
if trans.dk2 is not None:
kwin = "%s, %.3f" % (kwin, trans.dk2)
out.append(kwin)
out.append(' k-weight = %s' % (repr(trans.kweight)))
out.append(' paths used in fit = %s' % (repr([p.filename for p in ds.pathlist])))
out.append(' ')
out.append(header % 'Variables')
exprs = []
for name in dir(params):
var = getattr(params, name)
if len(name) < 14:
name = (name + ' '*14)[:14]
if isParameter(var):
if var.vary:
out.append(varformat % (name, var.value,
var.stderr, var._initval))
elif var.expr is not None:
exprs.append(exprformat % (name, var.value, var.expr))
if len(exprs) > 0:
out.append(header % 'Constraint Expressions')
out.extend(exprs)
covar_vars = getattr(params, 'covar_vars', [])
if len(covar_vars) > 0:
out.append(' ')
out.append(header % 'Correlations' +
' (unreported correlations are < % .3f)' % min_correl)
correls = {}
for i, name in enumerate(covar_vars):
par = getattr(params, name)
if not par.vary:
continue
if hasattr(par, 'correl') and par.correl is not None:
for name2 in covar_vars[i+1:]:
if name != name2 and name2 in par.correl:
correls["%s, %s" % (name, name2)] = par.correl[name2]
sort_correl = sorted(correls.items(), key=lambda it: abs(it[1]))
sort_correl.reverse()
for name, val in sort_correl:
if abs(val) < min_correl:
break
if len(name) < 20:
name = (name + ' '*20)[:20]
out.append(' %s = % .3f ' % (name, val))
if with_paths:
out.append(' ')
out.append(header % 'Paths')
for ds in datasets:
for p in ds.pathlist:
out.append('%s\n' % p.report())
out.append('='*len(topline))
return '\n'.join(out)
