def save_fit(self):
"""Saves fit results to an NXentry"""
self.read_parameters()
group = NXprocess()
group['data'] = self.data
for f in self.functions:
group[f.name] = self.get_model(f)
parameters = NXparameters()
for p in f.parameters:
parameters[p.name] = NXfield(p.value,
error=p.stderr,
initial_value=p.init_value,
min=str(p.min),
max=str(p.max))
group[f.name].insert(parameters)
if self.fit is not None:
group['program'] = 'lmfit'
group['version'] = lmfit.__version__
group['title'] = 'Fit Results'
group['fit'] = self.get_model()
fit = NXparameters()
fit.nfev = self.fit.result.nfev
fit.ier = self.fit.result.ier
fit.chisq = self.fit.result.chisqr
fit.redchi = self.fit.result.redchi
fit.message = self.fit.result.message
fit.lmdif_message = self.fit.result.lmdif_message
group['statistics'] = fit
group.note = NXnote(
self.fit.result.message,
('%s\n' % self.fit.result.lmdif_message +
'scipy.optimize.leastsq error value = %s\n' %
self.fit.result.ier +
'Chi^2 = %s\n' % self.fit.result.chisqr +
'Reduced Chi^2 = %s\n' % self.fit.result.redchi +
'No. of Function Evaluations = %s\n' % self.fit.result.nfev +
'No. of Variables = %s\n' % self.fit.result.nvarys +
'No. of Data Points = %s\n' % self.fit.result.ndata +
'No. of Degrees of Freedom = %s\n' % self.fit.result.nfree +
'%s' % self.fit.fit_report()))
else:
group['title'] = 'Fit Model'
group['model'] = self.get_model()
if 'w0' not in self.tree:
self.tree['w0'] = nxload(self.mainwindow.scratch_file, 'rw')
ind = []
for key in self.tree['w0']:
try:
if key.startswith('f'):
ind.append(int(key[1:]))
except ValueError:
pass
if not ind:
ind = [0]
name = 'f' + str(sorted(ind)[-1] + 1)
self.tree['w0'][name] = group
def save_fit(self):
"""Saves fit results to an NXentry"""
self.read_parameters()
group = NXprocess()
group['data'] = self.data
for f in self.functions:
group[f.name] = self.get_model(f)
parameters = NXparameters()
for p in f.parameters:
parameters[p.name] = NXfield(p.value, error=p.stderr,
initial_value=p.init_value,
min=str(p.min), max=str(p.max))
group[f.name].insert(parameters)
if self.fit is not None:
group['program'] = 'lmfit'
group['version'] = lmfit.__version__
group['title'] = 'Fit Results'
group['fit'] = self.get_model()
fit = NXparameters()
fit.nfev = self.fit.result.nfev
fit.chisq = self.fit.result.chisqr
fit.redchi = self.fit.result.redchi
fit.message = self.fit.result.message
group['statistics'] = fit
group.note = NXnote(self.fit.result.message,
('Chi^2 = %s\n' % self.fit.result.chisqr +
'Reduced Chi^2 = %s\n' % self.fit.result.redchi +
'No. of Function Evaluations = %s\n' % self.fit.result.nfev +
'No. of Variables = %s\n' % self.fit.result.nvarys +
'No. of Data Points = %s\n' % self.fit.result.ndata +
'No. of Degrees of Freedom = %s\n' % self.fit.result.nfree +
'%s' % self.fit.fit_report()))
else:
group['title'] = 'Fit Model'
group['model'] = self.get_model()
if 'w0' not in self.tree:
self.tree['w0'] = nxload(self.mainwindow.scratch_file, 'rw')
ind = []
for key in self.tree['w0']:
try:
if key.startswith('f'):
ind.append(int(key[1:]))
except ValueError:
pass
if not ind:
ind = [0]
name = 'f'+str(sorted(ind)[-1]+1)
self.tree['w0'][name] = group
def setup_groups(self):
entry = self.configuration_file['entry']
entry['nxreduce'] = NXparameters()
entry['instrument'] = NXinstrument()
entry['instrument/monochromator'] = NXmonochromator()
entry['instrument/goniometer'] = NXgoniometer()
entry['instrument/detector'] = NXdetector()
def write_parameters(self):
if 'nxreduce' not in self.root['entry']:
self.root['entry/nxreduce'] = NXparameters()
self.root['entry/nxreduce/threshold'] = self.threshold
self.root['entry/nxreduce/first_frame'] = self.first
self.root['entry/nxreduce/last_frame'] = self.last
self.root['entry/nxreduce/monitor'] = self.monitor
self.root['entry/nxreduce/norm'] = self.norm
self.root['entry/nxreduce/radius'] = self.radius
self.root['entry/nxreduce/qmax'] = self.qmax
def save_fit(self):
"""Saves fit results to an NXentry"""
self.read_parameters()
entry = NXentry()
entry['data'] = self.data
for f in self.functions:
entry[f.name] = self.get_model(f)
parameters = NXparameters()
for p in f.parameters:
parameters[p.name] = NXfield(p.value,
error=p.stderr,
initial_value=p.init_value,
min=str(p.min),
max=str(p.max))
entry[f.name].insert(parameters)
if self.fit is not None:
entry['title'] = 'Fit Results'
entry['fit'] = self.get_model()
fit = NXparameters()
fit.nfev = self.fit.result.nfev
fit.ier = self.fit.result.ier
fit.chisq = self.fit.result.chisqr
fit.redchi = self.fit.result.redchi
fit.message = self.fit.result.message
fit.lmdif_message = self.fit.result.lmdif_message
entry['statistics'] = fit
else:
entry['title'] = 'Fit Model'
entry['model'] = self.get_model()
if 'w0' not in self.tree.keys():
self.tree.add(NXroot(name='w0'))
ind = []
for key in self.tree['w0'].keys():
try:
if key.startswith('f'):
ind.append(int(key[1:]))
except ValueError:
pass
if not ind:
ind = [0]
name = 'f' + str(sorted(ind)[-1] + 1)
self.tree['w0'][name] = entry
def save(self, x=None):
"""Save the fit results in a NXprocess group.
Parameters
----------
x : ndarray, optional
x-values at which to calculate the model. Defaults to `self.x`
Returns
-------
group : NXprocess
NXprocess group that contains the data, models and parameters.
"""
group = NXprocess(program='lmfit', version=__version__)
group['data'] = self.data
for f in self.functions:
group[f.name] = NXdata(NXfield(self.get_model(x, f), name='model'),
NXfield(x, name=self.data.nxaxes[0].nxname),
title='Fit Results')
parameters = NXparameters()
for p in f.parameters:
parameters[p.name] = NXfield(p.value,
error=p.stderr,
initial_value=p.init_value,
min=str(p.min),
max=str(p.max))
group[f.name]['parameters'] = parameters
group['title'] = 'Fit Results'
group['fit'] = NXdata(NXfield(self.get_model(x), name='model'),
NXfield(x, name=self.data.nxaxes[0].nxname),
title='Fit Results')
if self.result is not None:
fit = NXparameters()
fit.nfev = self.result.nfev
fit.chisq = self.result.chisqr
fit.redchi = self.result.redchi
fit.message = self.result.message
group['statistics'] = fit
group.note = NXnote(
self.fit.result.message, f'Chi^2 = {self.fit.result.chisqr}\n'
f'Reduced Chi^2 = {self.fit.result.redchi}\n'
f'No. of Function Evaluations = {self.fit.result.nfev}\n'
f'No. of Variables = {self.fit.result.nvarys}\n'
f'No. of Data Points = {self.fit.result.ndata}\n'
f'No. of Degrees of Freedom = {self.fit.result.nfree}\n'
f'{self.fit.fit_report()}')
return group
def save_fit(self):
"""Saves fit results to an NXentry"""
self.read_parameters()
entry = NXentry()
entry['data'] = self.data
for f in self.functions:
entry[f.name] = self.get_model(f)
parameters = NXparameters()
for p in f.parameters:
parameters[p.name] = NXfield(p.value, error=p.stderr,
initial_value=p.init_value,
min=str(p.min), max=str(p.max))
entry[f.name].insert(parameters)
if self.fit is not None:
entry['title'] = 'Fit Results'
entry['fit'] = self.get_model()
fit = NXparameters()
fit.nfev = self.fit.result.nfev
fit.ier = self.fit.result.ier
fit.chisq = self.fit.result.chisqr
fit.redchi = self.fit.result.redchi
fit.message = self.fit.result.message
fit.lmdif_message = self.fit.result.lmdif_message
entry['statistics'] = fit
else:
entry['title'] = 'Fit Model'
entry['model'] = self.get_model()
if 'w0' not in self.tree:
self.tree.add(NXroot(name='w0'))
ind = []
for key in self.tree['w0']:
try:
if key.startswith('f'):
ind.append(int(key[1:]))
except ValueError:
pass
if not ind:
ind = [0]
name = 'f'+str(sorted(ind)[-1]+1)
self.tree['w0'][name] = entry