def report(self):
"return text report of parameters"
tmpvals = self.__path_params()
pathpars = {}
for pname in ('degen', 's02', 'e0', 'deltar', 'sigma2', 'third',
'fourth', 'ei'):
parname = fix_varname(PATHPAR_FMT % (pname, self.label))
if parname in self.params:
pathpars[pname] = (self.params[parname].value,
self.params[parname].stderr)
geomlabel = ' atom x y z ipot'
geomformat = ' %4s % .4f, % .4f, % .4f %i'
out = [' Path %s, Feff.dat file = %s' % (self.label, self.filename)]
out.append(geomlabel)
for atsym, iz, ipot, amass, x, y, z in self.geom:
s = geomformat % (atsym, x, y, z, ipot)
if ipot == 0: s = "%s (absorber)" % s
out.append(s)
stderrs = {}
out.append(' {:7s}= {:s}'.format('reff',
gformat(self._feffdat.reff)))
for pname in ('degen', 's02', 'e0', 'r', 'deltar', 'sigma2', 'third',
'fourth', 'ei'):
val = strval = getattr(self, pname, 0)
parname = fix_varname(PATHPAR_FMT % (pname, self.label))
std = None
if pname == 'r':
parname = fix_varname(PATHPAR_FMT % ('deltar', self.label))
par = self.params.get(parname, None)
val = par.value + self._feffdat.reff
strval = 'reff + ' + getattr(self, 'deltar', 0)
std = par.stderr
else:
if pname in pathpars:
val, std = pathpars[pname]
else:
par = self.params.get(parname, None)
if par is not None:
val = par.value
std = par.stderr
if std is None or std <= 0:
svalue = gformat(val)
else:
svalue = "{:s} +/-{:s}".format(gformat(val), gformat(std))
if pname == 's02':
pname = 'n*s02'
svalue = " {:7s}= {:s}".format(pname, svalue)
if isinstance(strval, str):
svalue = "{:s} '{:s}'".format(svalue, strval)
if val == 0 and pname in ('third', 'fourth', 'ei'):
continue
out.append(svalue)
return '\n'.join(out)
def show_fitresult(self, nfit=0, datagroup=None):
if datagroup is not None:
self.datagroup = datagroup
result = self.get_fitresult(nfit=nfit)
wids = self.wids
wids['data_title'].SetLabel(self.datagroup.filename)
wids['hist_info'].SetLabel("Fit #%2.2d of %d" % (nfit+1, len(self.peakfit_history)))
parts = []
model_repr = result.model._reprstring(long=True)
for word in model_repr.split('Model('):
if ',' in word:
pref, suff = word.split(', ')
parts.append( ("%sModel(%s" % (pref.title(), suff) ))
else:
parts.append(word)
desc = ''.join(parts)
parts = []
tlen = 90
while len(desc) >= tlen:
i = desc[tlen-1:].find('+')
if i < 0:
break
parts.append(desc[:tlen+i])
desc = desc[tlen+i:]
parts.append(desc)
wids['model_desc'].SetLabel('\n'.join(parts))
wids['params'].DeleteAllItems()
wids['paramsdata'] = []
for param in reversed(result.params.values()):
pname = param.name
try:
val = gformat(param.value)
except (TypeError, ValueError):
val = ' ??? '
serr = ' N/A '
if param.stderr is not None:
serr = gformat(param.stderr, 10)
extra = ' '
if param.expr is not None:
extra = ' = %s ' % param.expr
elif not param.vary:
extra = ' (fixed)'
elif param.init_value is not None:
extra = ' (init=%s)' % gformat(param.init_value, 11)
wids['params'].AppendItem((pname, val, serr, extra))
wids['paramsdata'].append(pname)
self.Refresh()
def show_fitresult(self, nfit=0, datagroup=None):
if datagroup is not None:
self.datagroup = datagroup
result = self.get_fitresult(nfit=nfit)
wids = self.wids
wids['data_title'].SetLabel(self.datagroup.filename)
wids['hist_info'].SetLabel("Fit #%2.2d of %d" % (nfit+1, len(self.peakfit_history)))
parts = []
model_repr = result.model._reprstring(long=True)
for word in model_repr.split('Model('):
if ',' in word:
pref, suff = word.split(', ', 1)
parts.append( ("%sModel(%s" % (pref.title(), suff) ))
else:
parts.append(word)
desc = ''.join(parts)
parts = []
tlen = 90
while len(desc) >= tlen:
i = desc[tlen-1:].find('+')
if i < 0:
break
parts.append(desc[:tlen+i])
desc = desc[tlen+i:]
parts.append(desc)
wids['model_desc'].SetLabel('\n'.join(parts))
wids['params'].DeleteAllItems()
wids['paramsdata'] = []
for param in reversed(result.params.values()):
pname = param.name
try:
val = gformat(param.value)
except (TypeError, ValueError):
val = ' ??? '
serr = ' N/A '
if param.stderr is not None:
serr = gformat(param.stderr, 10)
extra = ' '
if param.expr is not None:
extra = ' = %s ' % param.expr
elif not param.vary:
extra = ' (fixed)'
elif param.init_value is not None:
extra = ' (init=%s)' % gformat(param.init_value, 11)
wids['params'].AppendItem((pname, val, serr, extra))
wids['paramsdata'].append(pname)
self.Refresh()
def show_fitresult(self, n):
fit_result = self.datagroup.lcf_result[n]
self.current_fit = n
wids = self.wids
wids['nfits_title'].SetLabel('Showing Fit # %2.2d' % (n+1))
wids['paramstitle'].SetLabel('[[Parameters for Fit # %2.2d]]' % (n+1))
wids['params'].DeleteAllItems()
for pname, par in fit_result.params.items():
args = [pname, gformat(par.value, 12), '--']
if par.stderr is not None:
args[2] = gformat(par.stderr, 12)
self.wids['params'].AppendItem(tuple(args))
def show_fitresult(self, n):
fit_result = self.datagroup.lcf_result[n]
self.current_fit = n
wids = self.wids
wids['nfits_title'].SetLabel('Showing Fit # %2.2d of %i Best Fits' % (n+1, self.nresults))
wids['paramstitle'].SetLabel('[[Parameters for Fit # %2.2d]]' % (n+1))
wids['params'].DeleteAllItems()
for pname, par in fit_result.params.items():
args = [pname, gformat(par.value, 11), '--']
if par.stderr is not None:
args[2] = gformat(par.stderr, 11)
self.wids['params'].AppendItem(tuple(args))
def onBuildPCAModel(self, event=None):
self.wids['status'].SetLabel(" training model...")
form = self.read_form()
selected_groups = self.controller.filelist.GetCheckedStrings()
groups = [self.controller.file_groups[cn] for cn in selected_groups]
for gname in groups:
grp = self.controller.get_group(gname)
if not hasattr(grp, 'norm'):
self.parent.nb_panels[0].process(grp)
groups = ', '.join(groups)
opts = dict(groups=groups, arr='norm', xmin=form['xmin'], xmax=form['xmax'])
cmd = "pca_result = pca_train([{groups}], arrayname='{arr}', xmin={xmin:.2f}, xmax={xmax:.2f})"
self.larch_eval(cmd.format(**opts))
r = self.result = self.larch_get('pca_result')
ncomps = len(r.components)
wmin = form['weight_min']
nsig = len(np.where(r.variances > wmin)[0])
status = " PCA model built, %d components, %d with weight > %.3f"
self.wids['status'].SetLabel(status % (ncomps, nsig, wmin))
self.wids['max_components'].SetValue(min(ncomps, 1+nsig))
for b in ('fit_group',): # , 'save_model'):
self.wids[b].Enable()
self.wids['stats'].DeleteAllItems()
for i, val in enumerate(r.variances):
sig = {True: 'Yes', False: 'No'}[val > wmin]
self.wids['stats'].AppendItem((' #%d' % (i+1), gformat(val), sig))
self.plot_pca_components()
self.plot_pca_weights()
def onSaveAllStats(self, evt=None):
"Save All Statistics and Weights "
deffile = "LinearFitStats.csv"
wcards = 'CVS Files (*.csv)|*.csv|All files (*.*)|*.*'
path = FileSave(self,
'Save Statistics Report',
default_file=deffile,
wildcard=wcards)
if path is None:
return
form = self.form
out = [
'# Larch Linear Fit Statistics Report (best results) %s' %
time.ctime(),
'# Array name: %s' % form['arrayname'],
'# Energy fit range: [%f, %f]' % (form['elo'], form['ehi'])
]
label = [('Data Set' + ' ' * 25)[:25], 'n_varys', 'chi-square',
'chi-square_red', 'akaike_info', 'bayesian_info']
label.extend(form['comp_names'])
label.append('Total')
for i in range(len(label)):
if len(label[i]) < 12:
label[i] = (" %s " % label[i])[:12]
label = ', '.join(label)
out.append('# %s' % label)
for name, dgroup in self.datasets.items():
res = dgroup.lcf_result[0]
label = dgroup.filename
if len(label) < 25:
label = (label + ' ' * 25)[:25]
dat = [label]
for attr in ('nvarys', 'chisqr', 'redchi', 'aic', 'bic'):
dat.append(gformat(getattr(res.result, attr), 12))
for cname in form['comp_names'] + ['total']:
val = 0
if cname in res.params:
val = res.params[cname].value
dat.append(gformat(val, 12))
out.append(', '.join(dat))
out.append('')
with open(path, 'w') as fh:
fh.write('\n'.join(out))
def report(self):
"return text report of parameters"
(deg, s02, e0, ei, delr, ss2, c3, c4) = self.__path_params()
geomlabel = ' atom x y z ipot'
geomformat = ' %4s % .4f, % .4f, % .4f %i'
out = [' Path %s, Feff.dat file = %s' % (self.label, self.filename)]
out.append(geomlabel)
for atsym, iz, ipot, amass, x, y, z in self.geom:
s = geomformat % (atsym, x, y, z, ipot)
if ipot == 0: s = "%s (absorber)" % s
out.append(s)
stderrs = {}
out.append(' {:7s}= {:s}'.format('reff',
gformat(self._feffdat.reff)))
for pname in ('degen', 's02', 'e0', 'r',
'deltar', 'sigma2', 'third', 'fourth', 'ei'):
val = strval = getattr(self, pname, 0)
parname = fix_varname(PATHPAR_FMT % (pname, self.label))
std = None
if pname == 'r':
parname = fix_varname(PATHPAR_FMT % ('deltar', self.label))
par = self.params.get(parname, None)
val = par.value + self._feffdat.reff
strval = 'reff + ' + getattr(self, 'deltar', 0)
std = par.stderr
else:
par = self.params.get(parname, None)
if par is not None:
val = par.value
std = par.stderr
if std is None or std <= 0:
svalue = gformat(val)
else:
svalue = "{:s} +/-{:s}".format(gformat(val), gformat(std))
if pname == 's02': pname = 'n*s02'
svalue = " {:7s}= {:s}".format(pname, svalue)
if isinstance(strval, six.string_types):
svalue = "{:s} '{:s}'".format(svalue, strval)
if val == 0 and pname in ('third', 'fourth', 'ei'):
continue
out.append(svalue)
return '\n'.join(out)
def onSaveAllStats(self, evt=None):
"Save All Statistics and Weights "
deffile = "LinearFitStats.csv"
wcards = 'CVS Files (*.csv)|*.csv|All files (*.*)|*.*'
path = FileSave(self, 'Save Statistics Report',
default_file=deffile, wildcard=wcards)
if path is None:
return
form = self.form
out = ['# Larch Linear Fit Statistics Report (best results) %s' % time.ctime(),
'# Array name: %s' % form['arrayname'],
'# Energy fit range: [%f, %f]' % (form['elo'], form['ehi'])]
label = [('Data Set' + ' '*25)[:25],
'n_varys', 'chi-square',
'chi-square_red', 'akaike_info', 'bayesian_info']
label.extend(form['comp_names'])
label.append('Total')
for i in range(len(label)):
if len(label[i]) < 12:
label[i] = (" %s " % label[i])[:12]
label = ', '.join(label)
out.append('# %s' % label)
for name, dgroup in self.datasets.items():
res = dgroup.lcf_result[0]
label = dgroup.filename
if len(label) < 25:
label = (label + ' '*25)[:25]
dat = [label]
for attr in ('nvarys', 'chisqr', 'redchi', 'aic', 'bic'):
dat.append(gformat(getattr(res.result, attr), 12))
for cname in form['comp_names'] + ['total']:
val = 0
if cname in res.params:
val = res.params[cname].value
dat.append(gformat(val, 12))
out.append(', '.join(dat))
out.append('')
with open(path, 'w') as fh:
fh.write('\n'.join(out))
def onFitGroup(self, event=None):
form = self.read_form()
if self.result is None:
print("need result first!")
ncomps = int(form['max_components'])
gname = form['groupname']
cmd = "pca_fit(%s, pca_result, ncomps=%d)" % (gname, ncomps)
self.larch_eval(cmd)
dgroup = self.controller.get_group()
pca_chisquare = dgroup.pca_result.chi_square
self.wids['fit_chi2'].SetLabel(gformat(pca_chisquare))
self.plot_pca_fit()
def onSaveCSV(self, event=None):
dlg = wx.FileDialog(self, message="Save CSV Data File",
defaultDir=os.getcwd(),
defaultFile=self.save_csvfile,
wildcard=FILE_WILDCARDS,
style=wx.FD_SAVE)
fname = None
if dlg.ShowModal() == wx.ID_OK:
fname = dlg.GetPath()
dlg.Destroy()
if fname is None:
return
self.save_csvfile = os.path.split(fname)[1]
buff = []
for row in self.wids['table'].table.data:
buff.append("%s, %s, %s" % (row[0], gformat(row[1]), gformat(row[2])))
buff.append('')
with open(fname, 'w') as fh:
fh.write('\n'.join(buff))
self.write_message('Wrote CSV File %s ' % fname)
def onFitGroup(self, event=None):
form = self.read_form()
if self.result is None:
print("need result first!")
ncomps = int(form['max_components'])
gname = form['groupname']
cmd = "pca_fit(%s, pca_result, ncomps=%d)" % (gname, ncomps)
self.larch_eval(cmd)
dgroup = self.controller.get_group()
pca_chisquare = dgroup.pca_result.chi_square
self.wids['fit_chi2'].SetLabel(gformat(dgroup.pca_result.chi_square))
self.wids['fit_dscale'].SetLabel(gformat(dgroup.pca_result.data_scale))
grid_data = self.wids['table'].table.data
for g in grid_data: g[3] = '-'
for i, wt in enumerate(dgroup.pca_result.weights):
grid_data[i][3] = gformat(wt)
self.wids['table'].table.data = grid_data
self.wids['table'].table.View.Refresh()
self.plot_pca_fit()
def onSaveCSV(self, event=None):
dlg = wx.FileDialog(self,
message="Save CSV Data File",
defaultDir=os.getcwd(),
defaultFile=self.save_csvfile,
wildcard=FILE_WILDCARDS,
style=wx.FD_SAVE)
fname = None
if dlg.ShowModal() == wx.ID_OK:
fname = dlg.GetPath()
dlg.Destroy()
if fname is None:
return
self.save_csvfile = os.path.split(fname)[1]
buff = []
for row in self.wids['table'].table.data:
buff.append("%s, %s, %s" %
(row[0], gformat(row[1]), gformat(row[2])))
buff.append('')
with open(fname, 'w') as fh:
fh.write('\n'.join(buff))
self.write_message('Wrote CSV File %s ' % fname)
def onFitGroup(self, event=None):
form = self.read_form()
if self.result is None:
print("need result first!")
ncomps = int(form['max_components'])
gname = form['groupname']
cmd = "pca_fit(%s, pca_result, ncomps=%d)" % (gname, ncomps)
self.larch_eval(cmd)
dgroup = self.controller.get_group()
pca_chisquare = dgroup.pca_result.chi_square
self.wids['fit_chi2'].SetLabel(gformat(dgroup.pca_result.chi_square))
self.wids['fit_dscale'].SetLabel(gformat(dgroup.pca_result.data_scale))
grid_data = self.wids['table'].table.data
for g in grid_data:
g[3] = '-'
for i, wt in enumerate(dgroup.pca_result.weights):
grid_data[i][3] = gformat(wt)
self.wids['table'].table.data = grid_data
self.wids['table'].table.View.Refresh()
self.plot_pca_fit()
def show_results(self):
cur = self.get_fitresult()
wids = self.wids
wids['stats'].DeleteAllItems()
for i, res in enumerate(self.peakfit_history):
args = ['%2.2d' % (i+1)]
for attr in ('ndata', 'nvarys', 'nfev', 'chisqr', 'redchi', 'aic', 'bic'):
val = getattr(res.result, attr)
if isinstance(val, int):
val = '%d' % val
else:
val = gformat(val, 11)
args.append(val)
wids['stats'].AppendItem(tuple(args))
wids['data_title'].SetLabel(self.datagroup.filename)
self.show_fitresult(nfit=0)
def show_results(self):
cur = self.get_fitresult()
wids = self.wids
wids['stats'].DeleteAllItems()
for i, res in enumerate(self.peakfit_history):
args = ['%2.2d' % (i+1)]
for attr in ('ndata', 'nvarys', 'nfev', 'chisqr', 'redchi', 'aic', 'bic'):
val = getattr(res.result, attr)
if isinstance(val, int):
val = '%d' % val
else:
val = gformat(val, 11)
args.append(val)
wids['stats'].AppendItem(tuple(args))
wids['data_title'].SetLabel(self.datagroup.filename)
self.show_fitresult(nfit=0)
def export(self, filename):
"""save result to text file"""
buff = [
'# XRF Fit %s: %s' % (self.mca.label, self.label),
'#### Fit Script:'
]
for a in self.script.split('\n'):
buff.append('# %s' % a)
buff.append('#' * 60)
buff.append('#### Fit Report:')
for a in self.fit_report.split('\n'):
buff.append('# %s' % a)
buff.append('#' * 60)
labels = [
'energy', 'counts', 'best_fit', 'best_energy', 'fit_window',
'fit_weight', 'attenuation'
]
labels.extend(list(self.comps.keys()))
buff.append('# %s' % (' '.join(labels)))
npts = len(self.mca.energy)
for i in range(npts):
dline = [
gformat(self.mca.energy[i]),
gformat(self.mca.counts[i]),
gformat(self.best_fit[i]),
gformat(self.best_en[i]),
gformat(self.fit_window[i]),
gformat(self.fit_weight[i]),
gformat(self.atten[i])
]
for c in self.comps.values():
dline.append(gformat(c[i]))
buff.append(' '.join(dline))
buff.append('\n')
with open(filename, 'w') as fh:
fh.write('\n'.join(buff))
def export_modelresult(result, filename='fitresult.xdi',
datafile=None, ydata=None, yerr=None,
_larch=None, **kwargs):
"""
export an lmfit ModelResult to an XDI data file
Arguments
---------
result ModelResult, required
filename name of output file ['fitresult.xdi']
datafile name of data file [`None`]
ydata data array used for fit [`None`]
yerr data error array used for fit [`None`]
Notes
-----
keyword arguments should include independent variables
Example
-------
result = model.fit(ydata, params, x=x)
export_modelresult(result, 'fitresult_1.xdi', x=x,
datafile='XYData.txt')
"""
if not isinstance(result, ModelResult):
raise ValueError("export_fit needs a lmfit ModelReult")
header = ["XDI/1.1 Lmfit Result File"]
hadd = header.append
if datafile is not None:
hadd(" Datafile.name: %s " % datafile)
else:
hadd(" Datafile.name: <unknnown>")
ndata = len(result.best_fit)
columns = OrderedDict()
for aname in result.model.independent_vars:
val = kwargs.get(aname, None)
if val is not None and len(val) == ndata:
columns[aname] = val
if ydata is not None:
columns['ydata'] = ydata
if yerr is not None:
columns['yerr'] = yerr
columns['best_fit'] = result.best_fit
columns['init_fit'] = result.init_fit
delta_fit = result.eval_uncertainty(result.params, **kwargs)
columns['delta_fit'] = delta_fit
if len(result.model.components) > 1:
comps = result.eval_components(result.params, **kwargs)
for name, val in comps.items():
columns[name] = val
clabel = []
for i, cname in enumerate(columns):
hadd(" Column.%i: %s" % (i+1, cname))
clabel.append('%15s ' % cname)
hadd("Fit.Statistics: Start here")
hadd(" Fit.model_name: %s" % result.model.name)
hadd(" Fit.method: %s" % result.method)
hadd(" Fit.n_function_evals: %s" % getfloat_attr(result, 'nfev'))
hadd(" Fit.n_data_points: %s" % getfloat_attr(result, 'ndata'))
hadd(" Fit.n_variables: %s" % getfloat_attr(result, 'nvarys'))
hadd(" Fit.chi_square: %s" % getfloat_attr(result, 'chisqr', length=11))
hadd(" Fit.reduced_chi_square: %s" % getfloat_attr(result, 'redchi', length=11))
hadd(" Fit.akaike_info_crit: %s" % getfloat_attr(result, 'aic', length=11))
hadd(" Fit.bayesian_info_crit: %s" % getfloat_attr(result, 'bic', length=11))
hadd("Param.Statistics: Start here")
namelen = max([len(p) for p in result.params])
for name, par in result.params.items():
space = ' '*(namelen+1-len(name))
nout = "Param.%s:%s" % (name, space)
inval = '(init= ?)'
if par.init_value is not None:
inval = '(init=% .7g)' % par.init_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)
sval = '%s +/-%s' % (sval, serr)
if par.vary:
bounds = "[%s: %s]" % (gformat(par.min), gformat(par.max))
hadd(" %s %s %s %s" % (nout, sval, bounds, inval))
elif par.expr is not None:
hadd(" %s %s == '%s'" % (nout, sval, par.expr))
else:
hadd(" %s % .7g (fixed)" % (nout, par.value))
hadd("//////// Fit Report ////////")
for r in result.fit_report().split('\n'):
hadd(" %s" % r)
hadd("-" * 77)
hadd("".join(clabel)[1:])
header[0] = "XDI/1.1 Lmfit Result File %i header lines" % (len(header))
dtable = []
for key, dat in columns.items():
dtable.append(dat)
dtable = np.array(dtable).transpose()
datatable = []
for i in range(ndata):
col = dtable[i, :]*1.0
row = []
for cval in col:
try:
val = gformat(cval, length=15)
except:
val = repr(cval)
row.append(val)
datatable.append(" ".join(row))
datatable.append('')
with open(filename, 'w') as fh:
fh.write("\n".join(['#%s' % s for s in header]))
fh.write("\n")
fh.write("\n".join(datatable))
def test_gformat(test_input, expected):
"""Test gformat function."""
output = gformat(test_input)
assert output == expected
def feffit_report(result, min_correl=0.1, with_paths=True, _larch=None):
"""return a printable report of fit for feffit
Parameters:
------------
result: Feffit result, output group from feffit()
min_correl: minimum correlation to report [0.1]
wit_paths: boolean (True/False) for whether to list all paths [True]
Returns:
---------
printable string of report.
"""
input_ok = False
try:
params = result.params
datasets = result.datasets
input_ok = True
except:
pass
if not input_ok:
print( 'must pass output of feffit()!')
return
topline = '=================== FEFFIT RESULTS ===================='
header = '[[%s]]'
varformat = ' %12s = %s +/-%s (init= %s)'
fixformat = ' %12s = %s (fixed)'
exprformat = ' %12s = %s +/-%s = \'%s\''
out = [topline, header % 'Statistics']
out.append(' nvarys, npts = %i, %i' % (result.nvarys,
result.ndata))
out.append(' n_independent = %.3f' % (result.n_independent))
out.append(' chi_square = %s' % gformat(result.chi_square))
out.append(' reduced chi_square = %s' % gformat(result.chi_reduced))
out.append(' r-factor = %s' % gformat(result.rfactor))
out.append(' Akaike info crit = %s' % gformat(result.aic))
out.append(' Bayesian info crit = %s' % gformat(result.bic))
out.append(' ')
if len(datasets) == 1:
out.append(header % 'Data')
else:
out.append(header % 'Datasets (%i)' % len(datasets))
for i, ds in enumerate(datasets):
tr = ds.transform
if len(datasets) > 1:
out.append(' dataset %i:' % (i+1))
if isinstance(tr.kweight, Iterable):
if isinstance(ds.epsilon_k[0], np.ndarray):
msg = []
for eps in ds.epsilon_k:
msg.append('Array(mean=%s, std=%s)' % (gformat(eps.mean()).strip(),
gformat(eps.std()).strip()))
eps_k = ', '.join(msg)
else:
eps_k = ', '.join([gformat(eps).strip() for eps in ds.epsilon_k])
eps_r = ', '.join([gformat(eps).strip() for eps in ds.epsilon_r])
kweigh = ', '.join(['%i' % kwe for kwe in tr.kweight])
else:
if isinstance(ds.epsilon_k, np.ndarray):
eps_k = 'Array(mean=%s, std=%s)' % (gformat(ds.epsilon_k.mean()).strip(),
gformat(ds.epsilon_k.std()).strip())
else:
eps_k = gformat(ds.epsilon_k)
eps_r = gformat(ds.epsilon_r).strip()
kweigh = '%i' % tr.kweight
out.append(' fit space = \'%s\'' % (tr.fitspace))
out.append(' r-range = %.3f, %.3f' % (tr.rmin, tr.rmax))
out.append(' k-range = %.3f, %.3f' % (tr.kmin, tr.kmax))
kwin = ' k window, dk = \'%s\', %.3f' % (tr.window, tr.dk)
if tr.dk2 is not None:
kwin = "%s, %.3f" % (kwin, tr.dk2)
out.append(kwin)
pathfiles = [p.filename for p in ds.pathlist]
out.append(' paths used in fit = %s' % (repr(pathfiles)))
out.append(' k-weight = %s' % kweigh)
out.append(' epsilon_k = %s' % eps_k)
out.append(' epsilon_r = %s' % eps_r)
out.append(' n_independent = %.3f' % (ds.n_idp))
#
out.append(' ')
out.append(header % 'Variables')
# exprs = []
for name, par in params.items():
# var = getattr(params, name)
# print(name, par, dir(par))
if len(name) < 14:
name = (name + ' '*14)[:14]
if isParameter(par):
if par.vary:
stderr = 'unknown'
if par.stderr is not None:
stderr = gformat(par.stderr)
out.append(varformat % (name, gformat(par.value),
stderr, gformat(par.init_value)))
elif par.expr is not None:
stderr = 'unknown'
if par.stderr is not None:
stderr = gformat(par.stderr)
out.append(exprformat % (name, gformat(par.value),
stderr, par.expr))
else:
out.append(fixformat % (name, gformaat(par.value)))
# if len(exprs) > 0:
# out.append(header % 'Constraint Expressions')
# out.extend(exprs)
covar_vars = result.var_names
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 = 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 ids, ds in enumerate(datasets):
if len(datasets) > 1:
out.append(' dataset %i:' % (ids+1))
for p in ds.pathlist:
out.append('%s\n' % p.report())
out.append('='*len(topline))
return '\n'.join(out)
def feffit_report(result, min_correl=0.1, with_paths=True, _larch=None):
"""return a printable report of fit for feffit
Parameters:
------------
result: Feffit result, output group from feffit()
min_correl: minimum correlation to report [0.1]
wit_paths: boolean (True/False) for whether to list all paths [True]
Returns:
---------
printable string of report.
"""
input_ok = False
try:
params = result.params
datasets = result.datasets
input_ok = True
except:
pass
if not input_ok:
print( 'must pass output of feffit()!')
return
topline = '=================== FEFFIT RESULTS ===================='
header = '[[%s]]'
varformat = ' %12s = %s +/-%s (init= %s)'
fixformat = ' %12s = %s (fixed)'
exprformat = ' %12s = %s +/-%s = \'%s\''
out = [topline, header % 'Statistics']
out.append(' nvarys, npts = %i, %i' % (result.nvarys,
result.ndata))
out.append(' n_independent = %.3f' % (result.n_independent))
out.append(' chi_square = %s' % gformat(result.chi_square))
out.append(' reduced chi_square = %s' % gformat(result.chi_reduced))
out.append(' r-factor = %s' % gformat(result.rfactor))
out.append(' Akaike info crit = %s' % gformat(result.aic))
out.append(' Bayesian info crit = %s' % gformat(result.bic))
out.append(' ')
if len(datasets) == 1:
out.append(header % 'Data')
else:
out.append(header % 'Datasets (%i)' % len(datasets))
for i, ds in enumerate(datasets):
tr = ds.transform
if len(datasets) > 1:
out.append(' dataset %i:' % (i+1))
if isinstance(tr.kweight, Iterable):
if isinstance(ds.epsilon_k[0], np.ndarray):
msg = []
for eps in ds.epsilon_k:
msg.append('Array(mean=%s, std=%s)' % (gformat(eps.mean()).strip(),
gformat(eps.std()).strip()))
eps_k = ', '.join(msg)
else:
eps_k = ', '.join([gformat(eps).strip() for eps in ds.epsilon_k])
eps_r = ', '.join([gformat(eps).strip() for eps in ds.epsilon_r])
kweigh = ', '.join(['%i' % kwe for kwe in tr.kweight])
else:
if isinstance(ds.epsilon_k, np.ndarray):
eps_k = 'Array(mean=%s, std=%s)' % (gformat(ds.epsilon_k.mean()).strip(),
gformat(ds.epsilon_k.std()).strip())
else:
eps_k = gformat(ds.epsilon_k)
eps_r = gformat(ds.epsilon_r).strip()
kweigh = '%i' % tr.kweight
out.append(' fit space = \'%s\'' % (tr.fitspace))
out.append(' r-range = %.3f, %.3f' % (tr.rmin, tr.rmax))
out.append(' k-range = %.3f, %.3f' % (tr.kmin, tr.kmax))
kwin = ' k window, dk = \'%s\', %.3f' % (tr.window, tr.dk)
if tr.dk2 is not None:
kwin = "%s, %.3f" % (kwin, tr.dk2)
out.append(kwin)
pathfiles = [p.filename for p in ds.pathlist]
out.append(' paths used in fit = %s' % (repr(pathfiles)))
out.append(' k-weight = %s' % kweigh)
out.append(' epsilon_k = %s' % eps_k)
out.append(' epsilon_r = %s' % eps_r)
out.append(' n_independent = %.3f' % (ds.n_idp))
#
out.append(' ')
out.append(header % 'Variables')
# exprs = []
for name, par in params.items():
# var = getattr(params, name)
# print(name, par, dir(par))
if len(name) < 14:
name = (name + ' '*14)[:14]
if isParameter(par):
if par.vary:
stderr = 'unknown'
if par.stderr is not None:
stderr = gformat(par.stderr)
out.append(varformat % (name, gformat(par.value),
stderr, gformat(par.init_value)))
elif par.expr is not None:
stderr = 'unknown'
if par.stderr is not None:
stderr = gformat(par.stderr)
out.append(exprformat % (name, gformat(par.value),
stderr, par.expr))
else:
out.append(fixformat % (name, gformat(par.value)))
# if len(exprs) > 0:
# out.append(header % 'Constraint Expressions')
# out.extend(exprs)
covar_vars = result.var_names
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 = 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 ids, ds in enumerate(datasets):
if len(datasets) > 1:
out.append(' dataset %i:' % (ids+1))
for p in ds.pathlist:
out.append('%s\n' % p.report())
out.append('='*len(topline))
return '\n'.join(out)
def upload2xdi(opts, upload_folder):
"""upload result of upload form to XDI file"""
fname = opts['filename']
filename = fname.replace('.', '_')
filename = secure_filename('%s_%s.xdi' % (random_string(4), filename))
filename = path.abspath(pathjoin(upload_folder, filename))
buff = ['#XDI/1.0 XASDataLibrary/1.0']
arrays = opts['data']
dcolumns = [('energy', arrays['energy'])]
if arrays['i0'] is not None:
dcolumns.append(('i0', arrays['i0']))
if opts['mode'] == 'transmission':
if arrays['itrans'] is not None:
dcolumns.append(('itrans', arrays['itrans']))
elif arrays['mu'] is not None:
dcolumns.append(('mutrans', arrays['mu']))
else:
if arrays['ifluor'] is not None:
dcolumns.append(('ifluor', arrays['ifluor']))
elif arrays['mu'] is not None:
dcolumns.append(('mufluor', arrays['mu']))
if opts['has_reference'] and arrays.get('irefer', None) is not None:
dcolumns.append(('irefer', arrays['irefer']))
icol = 0
array_labels = []
for name, darray in dcolumns:
array_labels.append((" %s " % name)[:13])
if name == 'energy':
name = 'energy eV'
icol += 1
buff.append('# Column.%d: %s' % (icol, name))
buff.append('# Mono.d_spacing: %.6f' % float(opts['d_spacing']))
buff.append('# Mono.name: %s' % opts['mono_name'])
for tag, attr in (('Beamline.name', 'beamline'),
('Element.symbol', 'elem_sym'), ('Element.edge', 'edge'),
('Scan.start_time',
'collection_date'), ('Data.upload_date', 'upload_date'),
('Data.submitted_by', 'person_name'),
('Sample.name', 'sample_name'), ('Sample.formula',
'sample_formula'),
('Sample.preparation', 'sample_prep'), ('Sample.notes',
'sample_notes')):
attr = opts.get(attr, '')
if len(attr) > 0:
buff.append('# %s: %s' % (tag, attr))
if opts['has_reference'] and len(opts.get('ref_mode', '')) > 0:
buff.append('# Reference.mode: %s' % opts['ref_mode'])
buff.append('# ///')
comments = opts['comments'].split('\n')
for c in comments:
buff.append('# %s' % c)
buff.append('#-----------------------')
buff.append('# %s' % (' '.join(array_labels)))
for i in range(opts['npts']):
row = []
for name, darray in dcolumns:
row.append(gformat(darray[i], 15))
buff.append(' '.join(row))
buff.append('')
with open(filename, 'w') as fh:
fh.write('\n'.join(buff))
return filename
def export_modelresult(result, filename='fitresult.xdi',
datafile=None, ydata=None, yerr=None,
_larch=None, **kwargs):
"""
export an lmfit ModelResult to an XDI data file
Arguments
---------
result ModelResult, required
filename name of output file ['fitresult.xdi']
datafile name of data file [`None`]
ydata data array used for fit [`None`]
yerr data error array used for fit [`None`]
Notes
-----
keyword arguments should include independent variables
Example
-------
result = model.fit(ydata, params, x=x)
export_modelresult(result, 'fitresult_1.xdi', x=x,
datafile='XYData.txt')
"""
if not isinstance(result, ModelResult):
raise ValueError("export_fit needs a lmfit ModelReult")
header = ["XDI/1.1 Lmfit Result File"]
hadd = header.append
if datafile is not None:
hadd(" Datafile.name: %s " % datafile)
else:
hadd(" Datafile.name: <unknnown>")
ndata = len(result.best_fit)
columns = OrderedDict()
for aname in result.model.independent_vars:
val = kwargs.get(aname, None)
if val is not None and len(val) == ndata:
columns[aname] = val
if ydata is not None:
columns['ydata'] = ydata
if yerr is not None:
columns['yerr'] = yerr
columns['best_fit'] = result.best_fit
columns['init_fit'] = result.init_fit
delta_fit = 0.0*result.best_fit
if not any([p.stderr is None for p in result.params.values()]):
delta_fit = result.eval_uncertainty(result.params, **kwargs)
columns['delta_fit'] = delta_fit
if len(result.model.components) > 1:
comps = result.eval_components(result.params, **kwargs)
for name, val in comps.items():
columns[name] = val
clabel = []
for i, cname in enumerate(columns):
hadd(" Column.%i: %s" % (i+1, cname))
clabel.append('%15s ' % cname)
hadd("Fit.Statistics: Start here")
hadd(" Fit.model_name: %s" % result.model.name)
hadd(" Fit.method: %s" % result.method)
hadd(" Fit.n_function_evals: %s" % getfloat_attr(result, 'nfev'))
hadd(" Fit.n_data_points: %s" % getfloat_attr(result, 'ndata'))
hadd(" Fit.n_variables: %s" % getfloat_attr(result, 'nvarys'))
hadd(" Fit.chi_square: %s" % getfloat_attr(result, 'chisqr', length=11))
hadd(" Fit.reduced_chi_square: %s" % getfloat_attr(result, 'redchi', length=11))
hadd(" Fit.akaike_info_crit: %s" % getfloat_attr(result, 'aic', length=11))
hadd(" Fit.bayesian_info_crit: %s" % getfloat_attr(result, 'bic', length=11))
hadd("Param.Statistics: Start here")
namelen = max([len(p) for p in result.params])
for name, par in result.params.items():
space = ' '*(namelen+1-len(name))
nout = "Param.%s:%s" % (name, space)
inval = '(init= ?)'
if par.init_value is not None:
inval = '(init=% .7g)' % par.init_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)
sval = '%s +/-%s' % (sval, serr)
if par.vary:
bounds = "[%s: %s]" % (gformat(par.min), gformat(par.max))
hadd(" %s %s %s %s" % (nout, sval, bounds, inval))
elif par.expr is not None:
hadd(" %s %s == '%s'" % (nout, sval, par.expr))
else:
hadd(" %s % .7g (fixed)" % (nout, par.value))
hadd("//////// Fit Report ////////")
for r in result.fit_report().split('\n'):
hadd(" %s" % r)
hadd("-" * 77)
hadd("".join(clabel)[1:])
header[0] = "XDI/1.1 Lmfit Result File %i header lines" % (len(header))
dtable = []
for key, dat in columns.items():
dtable.append(dat)
dtable = np.array(dtable).transpose()
datatable = []
for i in range(ndata):
col = dtable[i, :]*1.0
row = []
for cval in col:
try:
val = gformat(cval, length=15)
except:
val = repr(cval)
row.append(val)
datatable.append(" ".join(row))
datatable.append('')
with open(filename, 'w') as fh:
fh.write("\n".join(['#%s' % s for s in header]))
fh.write("\n")
fh.write("\n".join(datatable))
def process(self, dgroup=None, force_mback=False, noskip=False, **kws):
""" handle process (pre-edge/normalize) of XAS data from XAS form
"""
if self.skip_process and not noskip:
return
if dgroup is None:
dgroup = self.controller.get_group()
if dgroup is None:
return
self.skip_process = True
conf = self.get_config(dgroup)
dgroup.custom_plotopts = {}
form = self.read_form()
form['group'] = dgroup.groupname
if dgroup.datatype != 'xas':
self.skip_process = False
dgroup.mu = dgroup.ydat * 1.0
opts = {'group': dgroup.groupname, 'scale': conf.get('scale', 1.0)}
self.larch_eval("{group:s}.scale = {scale:.8f}".format(**opts))
self.larch_eval(
"{group:s}.norm = {scale:.8f}*{group:s}.ydat".format(**opts))
return
en_units = getattr(dgroup, 'energy_units', None)
if en_units is None:
en_units = guess_energy_units(dgroup.energy)
if en_units != 'eV':
mono_dspace = getattr(dgroup, 'mono_dspace', 1)
dlg = EnergyUnitsDialog(self.parent,
dgroup.energy,
unitname=en_units,
dspace=mono_dspace)
res = dlg.GetResponse()
dlg.Destroy()
if res.ok:
en_units = res.units
dgroup.mono_dspace = res.dspace
dgroup.xdat = dgroup.energy = res.energy
dgroup.energy_units = en_units
e0 = form['e0']
edge_step = form['edge_step']
copts = [dgroup.groupname]
if not form['auto_e0']:
if e0 < max(dgroup.energy) and e0 > min(dgroup.energy):
copts.append("e0=%.4f" % float(e0))
if not form['auto_step']:
copts.append("step=%s" % gformat(float(edge_step)))
for attr in ('pre1', 'pre2', 'nvict', 'nnorm', 'norm1', 'norm2'):
if form[attr] is None:
copts.append("%s=None" % attr)
else:
copts.append("%s=%.2f" % (attr, form[attr]))
self.larch_eval("pre_edge(%s)" % (', '.join(copts)))
self.larch_eval(
"{group:s}.norm_poly = 1.0*{group:s}.norm".format(**form))
norm_method = form['norm_method'].lower()
form['normmeth'] = 'poly'
if force_mback or norm_method.startswith('mback'):
form['normmeth'] = 'mback'
copts = [dgroup.groupname]
copts.append("z=%d" % atomic_number(form['atsym']))
copts.append("edge='%s'" % form['edge'])
for attr in ('pre1', 'pre2', 'nvict', 'nnorm', 'norm1', 'norm2'):
if form[attr] is None:
copts.append("%s=None" % attr)
else:
copts.append("%s=%.2f" % (attr, form[attr]))
self.larch_eval("mback_norm(%s)" % (', '.join(copts)))
if form['auto_step']:
norm_expr = """{group:s}.norm = 1.0*{group:s}.norm_{normmeth:s}
{group:s}.edge_step = 1.0*{group:s}.edge_step_{normmeth:s}"""
self.larch_eval(norm_expr.format(**form))
else:
norm_expr = """{group:s}.norm = 1.0*{group:s}.norm_{normmeth:s}
{group:s}.norm *= {group:s}.edge_step_{normmeth:s}/{edge_step:.8f}"""
self.larch_eval(norm_expr.format(**form))
if norm_method.startswith('area'):
form['normmeth'] = 'area'
expr = """{group:s}.norm = 1.0*{group:s}.norm_{normmeth:s}
{group:s}.edge_step = 1.0*{group:s}.edge_step_{normmeth:s}"""
self.larch_eval(expr.format(**form))
self.make_dnormde(dgroup)
if form['auto_e0']:
self.wids['e0'].SetValue(dgroup.e0)
if form['auto_step']:
self.wids['step'].SetValue(dgroup.edge_step)
autoset_fs_increment(self.wids['step'], dgroup.edge_step)
self.wids['atsym'].SetStringSelection(dgroup.atsym)
self.wids['edge'].SetStringSelection(dgroup.edge)
self.set_nnorm_widget(dgroup.pre_edge_details.nnorm)
for attr in ('e0', 'edge_step'):
conf[attr] = getattr(dgroup, attr)
for attr in ('pre1', 'pre2', 'norm1', 'norm2'):
conf[attr] = val = getattr(dgroup.pre_edge_details, attr, None)
if val is not None:
self.wids[attr].SetValue(val)
if hasattr(dgroup, 'mback_params'): # from mback
conf['atsym'] = getattr(dgroup.mback_params, 'atsym')
conf['edge'] = getattr(dgroup.mback_params, 'edge')
self.update_config(conf, dgroup=dgroup)
wx.CallAfter(self.unset_skip_process)
def show_results(self, datagroup=None, form=None, larch_eval=None):
if datagroup is not None:
self.datagroup = datagroup
if form is not None:
self.form = form
if larch_eval is not None:
self.larch_eval = larch_eval
lcf_history = getattr(self.datagroup, 'lcf_history', [])
wids = self.wids
wids['data_title'].SetLabel(self.datagroup.filename)
wids['show_e0'].SetValue(form['show_e0'])
wids['show_fitrange'].SetValue(form['show_fitrange'])
wids['stats'].DeleteAllItems()
results = self.datagroup.lcf_result[:20]
self.nresults = len(results)
wids['nfits_title'].SetLabel('showing %i best results' % self.nresults)
for i, res in enumerate(results):
args = ['%2.2d' % (i+1)]
for attr in ('nvarys', 'nfev', 'chisqr', 'redchi', 'aic', 'bic'):
val = getattr(res.result, attr)
if isinstance(val, int):
val = '%d' % val
else:
val = gformat(val, 11)
args.append(val)
wids['stats'].AppendItem(tuple(args))
wpan = self.wids['weightspanel']
wpan.DestroyChildren()
wview = self.wids['weights'] = dv.DataViewListCtrl(wpan, style=DVSTYLE)
wview.Bind(dv.EVT_DATAVIEW_SELECTION_CHANGED, self.onSelectFitParam)
wview.AppendTextColumn(' Fit #', width=50)
for i, cname in enumerate(form['comp_names']):
wview.AppendTextColumn(cname, width=100)
wview.AppendTextColumn('Total', width=100)
for col in range(len(form['comp_names'])+2):
this = wview.Columns[col]
isort, align = True, wx.ALIGN_RIGHT
if col == 0:
align = wx.ALIGN_CENTER
this.Sortable = isort
this.Alignment = this.Renderer.Alignment = align
for i, res in enumerate(results):
args = ['%2.2d' % (i+1)]
for cname in form['comp_names'] + ['total']:
val = '--'
if cname in res.params:
val = "%.4f" % res.params[cname].value
args.append(val)
wview.AppendItem(tuple(args))
os = wx.BoxSizer(wx.VERTICAL)
os.Add(wview, 1, wx.GROW|wx.ALL)
pack(wpan, os)
wview.SetMinSize((675, 200))
s1, s2 = self.GetSize()
if s2 % 2 == 0:
s2 = s2 + 1
else:
s2 = s2 - 1
self.SetSize((s1, s2))
self.show_fitresult(0)
self.Refresh()
def show_fitresult(self, datagroup=None, fit_number=None):
if datagroup is not None:
self.datagroup = datagroup
fit_history = getattr(self.datagroup, 'fit_history', [])
if len(fit_history) < 1:
print("No fit reults to show for datagroup ", self.datagroup)
if fit_number is None:
fit_number = len(fit_history)
result = fit_history[fit_number-1]
wids = self.wids
wids['method'].SetLabel(result.method)
wids['ndata'].SetLabel("%d" % result.ndata)
wids['nvarys'].SetLabel("%d" % result.nvarys)
wids['nfree'].SetLabel("%d" % result.nfree)
wids['nfev'].SetLabel("%d" % result.nfev)
if abs(result.redchi) < 1.e-3:
wids['redchi'].SetLabel("%.5g" % result.redchi)
else:
wids['redchi'].SetLabel("%f" % result.redchi)
if abs(result.chisqr) < 1.e-3:
wids['chisqr'].SetLabel("%.5g" % result.chisqr)
else:
wids['chisqr'].SetLabel("%f" % result.chisqr)
wids['aic'].SetLabel("%f" % result.aic)
wids['bic'].SetLabel("%f" % result.bic)
wids['hist_info'].SetLabel("Fit #%d of %d" % (fit_number, len(fit_history)))
wids['data_title'].SetLabel(self.datagroup.filename)
desc = result.model_repr
parts = []
tlen = 70
while len(desc) >= tlen:
i = desc[tlen-1:].find('+')
parts.append(desc[:tlen+i])
desc = desc[tlen+i:]
parts.append(desc)
wids['model_desc'].SetLabel('\n'.join(parts))
wids['params'].DeleteAllItems()
wids['paramsdata'] = []
for i, param in enumerate(result.params.values()):
pname = param.name
try:
val = gformat(param.value)
except (TypeError, ValueError):
val = ' ??? '
serr = ' N/A '
if param.stderr is not None:
serr = gformat(param.stderr, length=9)
extra = ' '
if param.expr is not None:
extra = ' = %s ' % param.expr
elif param.init_value is not None:
extra = ' (init=% .7g)' % param.init_value
elif not param.vary:
extra = ' (fixed)'
wids['params'].AppendItem((pname, val, serr, extra))
wids['paramsdata'].append(pname)
self.Refresh()
def show_results(self, datagroup=None, form=None, larch_eval=None):
if datagroup is not None:
self.datagroup = datagroup
if form is not None:
self.form = form
if larch_eval is not None:
self.larch_eval = larch_eval
form = self.form
datagroup = self.datagroup
lcf_history = getattr(self.datagroup, 'lcf_history', [])
wids = self.wids
wids['data_title'].SetLabel(self.datagroup.filename)
# wids['show_e0'].SetValue(form['show_e0'])
wids['show_fitrange'].SetValue(form['show_fitrange'])
wids['stats'].DeleteAllItems()
results = self.datagroup.lcf_result[:20]
self.nresults = len(results)
wids['nfits_title'].SetLabel('showing %i best results' % self.nresults)
for i, res in enumerate(results):
args = ['%2.2d' % (i + 1)]
for attr in ('nvarys', 'nfev', 'chisqr', 'redchi', 'aic', 'bic'):
val = getattr(res.result, attr)
if isinstance(val, int):
val = '%d' % val
else:
val = gformat(val, 12)
args.append(val)
wids['stats'].AppendItem(tuple(args))
wpan = self.wids['weightspanel']
wpan.DestroyChildren()
wview = self.wids['weights'] = dv.DataViewListCtrl(wpan, style=DVSTYLE)
wview.Bind(dv.EVT_DATAVIEW_SELECTION_CHANGED, self.onSelectFitParam)
wview.AppendTextColumn(' Fit #', width=50)
for i, cname in enumerate(form['comp_names']):
wview.AppendTextColumn(cname, width=100)
wview.AppendTextColumn('Total', width=100)
for col in range(len(form['comp_names']) + 2):
this = wview.Columns[col]
isort, align = True, wx.ALIGN_RIGHT
if col == 0:
align = wx.ALIGN_CENTER
this.Sortable = isort
this.Alignment = this.Renderer.Alignment = align
for i, res in enumerate(results):
args = ['%2.2d' % (i + 1)]
for cname in form['comp_names'] + ['total']:
val = '--'
if cname in res.params:
val = "%.4f" % res.params[cname].value
args.append(val)
wview.AppendItem(tuple(args))
os = wx.BoxSizer(wx.VERTICAL)
os.Add(wview, 1, wx.GROW | wx.ALL)
pack(wpan, os)
wview.SetMinSize((675, 200))
s1, s2 = self.GetSize()
if s2 % 2 == 0:
s2 = s2 + 1
else:
s2 = s2 - 1
self.SetSize((s1, s2))
self.show_fitresult(0)
self.Refresh()
def process(self, dgroup=None, force_mback=False, noskip=False, **kws):
""" handle process (pre-edge/normalize) of XAS data from XAS form
"""
if self.skip_process and not noskip:
return
if dgroup is None:
dgroup = self.controller.get_group()
if dgroup is None:
return
self.skip_process = True
__conf = self.get_config(dgroup)
dgroup.custom_plotopts = {}
if dgroup.datatype != 'xas':
self.skip_process = False
dgroup.mu = dgroup.ydat * 1.0
return
en_units = getattr(dgroup, 'energy_units', None)
if en_units is None:
en_units = 'eV'
units = guess_energy_units(dgroup.energy)
if units != 'eV':
dlg = EnergyUnitsDialog(self.parent, units, dgroup.energy)
res = dlg.GetResponse()
dlg.Destroy()
if res.ok:
en_units = res.units
dgroup.xdat = dgroup.energy = res.energy
dgroup.energy_units = en_units
form = self.read_form()
e0 = form['e0']
edge_step = form['edge_step']
form['group'] = dgroup.groupname
copts = [dgroup.groupname]
if not form['auto_e0']:
if e0 < max(dgroup.energy) and e0 > min(dgroup.energy):
copts.append("e0=%.4f" % float(e0))
if not form['auto_step']:
copts.append("step=%s" % gformat(float(edge_step)))
for attr in ('pre1', 'pre2', 'nvict', 'nnorm', 'norm1', 'norm2'):
copts.append("%s=%.2f" % (attr, form[attr]))
self.larch_eval("pre_edge(%s)" % (', '.join(copts)))
self.larch_eval("{group:s}.norm_poly = 1.0*{group:s}.norm".format(**form))
norm_method = form['norm_method'].lower()
form['normmeth'] = 'poly'
if force_mback or norm_method.startswith('mback'):
form['normmeth'] = 'mback'
copts = [dgroup.groupname]
copts.append("z=%d" % atomic_number(form['atsym']))
copts.append("edge='%s'" % form['edge'])
for attr in ('pre1', 'pre2', 'nvict', 'nnorm', 'norm1', 'norm2'):
copts.append("%s=%.2f" % (attr, form[attr]))
self.larch_eval("mback_norm(%s)" % (', '.join(copts)))
if form['auto_step']:
norm_expr = """{group:s}.norm = 1.0*{group:s}.norm_{normmeth:s}
{group:s}.edge_step = 1.0*{group:s}.edge_step_{normmeth:s}"""
self.larch_eval(norm_expr.format(**form))
else:
norm_expr = """{group:s}.norm = 1.0*{group:s}.norm_{normmeth:s}
{group:s}.norm *= {group:s}.edge_step_{normmeth:s}/{edge_step:.8f}"""
self.larch_eval(norm_expr.format(**form))
if norm_method.startswith('area'):
form['normmeth'] = 'area'
expr = """{group:s}.norm = 1.0*{group:s}.norm_{normmeth:s}
{group:s}.edge_step = 1.0*{group:s}.edge_step_{normmeth:s}"""
self.larch_eval(expr.format(**form))
self.make_dnormde(dgroup)
if form['auto_e0']:
self.wids['e0'].SetValue(dgroup.e0)
if form['auto_step']:
self.wids['step'].SetValue(dgroup.edge_step)
autoset_fs_increment(self.wids['step'], dgroup.edge_step)
self.wids['pre1'].SetValue(dgroup.pre_edge_details.pre1)
self.wids['pre2'].SetValue(dgroup.pre_edge_details.pre2)
self.wids['norm1'].SetValue(dgroup.pre_edge_details.norm1)
self.wids['norm2'].SetValue(dgroup.pre_edge_details.norm2)
self.wids['atsym'].SetStringSelection(dgroup.atsym)
self.wids['edge'].SetStringSelection(dgroup.edge)
conf = {}
for attr in ('e0', 'edge_step'):
conf[attr] = getattr(dgroup, attr)
for attr in ('pre1', 'pre2', 'nnorm', 'norm1', 'norm2'):
conf[attr] = getattr(dgroup.pre_edge_details, attr)
if hasattr(dgroup, 'mback_params'): # from mback
conf['atsym'] = getattr(dgroup.mback_params, 'atsym')
conf['edge'] = getattr(dgroup.mback_params, 'edge')
self.update_config(conf, dgroup=dgroup)
wx.CallAfter(self.unset_skip_process)
