def fit_peak(x, y, model, dy=None, background=None, step=None,
negative=False, use_gamma=False, _larch=None):
"""fit peak to one a selection of simple 1d models
out = fit_peak(x, y, model, dy=None,
background='linear', step='linear')
arguments:
---------
x array of values at which to calculate model
y array of values for model to try to match
dy array of values for uncertainty in y data to be matched.
model name of model to use. One of (case insensitive)
'linear', 'quadratic', 'step', 'rectangle',
'gaussian', 'lorentzian', 'voigt', 'exponential'
background name of background model to use. One of (case insensitive)
None, 'constant', 'linear', or 'quadratic'
this is ignored when model is 'linear' or 'quadratic'
step name of step model to use for 'step' and 'rectangle' models.
One of (case insensitive):
'linear', 'erf', or 'atan'
negative True/False for whether peak or steps are expected to go down.
use_gamma True/False for whether to use separate gamma parameter for
voigt model.
output:
-------
Group with fit parameters, and more...
"""
out = Group(x=x*1.0, y=y*1.0, dy=1.0, model=model,
background=background, step=step)
if dy is not None:
out.dy = 1.0*dy
if model.lower() not in MODELS:
_larch.writer.write('Unknown fit model: %s ' % model)
return None
kwargs = dict(negative=negative, background=background,
step=step, _larch=_larch)
fitclass = MODELS[model.lower()]
if fitclass == VoigtModel:
kwargs['use_gamma'] = use_gamma
mod = fitclass(**kwargs)
mod.guess_starting_values(out.y, out.x)
out.fit_init = mod.model(x=out.x)
if background is not None:
out.bkg_init = mod.calc_background(out.x)
out.fit_init += out.bkg_init
mod.fit(out.y, x=out.x, dy=out.dy, _larch=_larch)
out.fit = mod.model(x=out.x)
if background is not None:
out.bkg = mod.calc_background(out.x)
out.fit += out.bkg
out.params = mod.params
return out
def fit_peak(x, y, model, dy=None, background=None, form=None, step=None,
negative=False, use_gamma=False, _larch=None):
"""fit peak to one a selection of simple 1d models
out = fit_peak(x, y, model, dy=None,
background='linear', form='linear')
arguments:
---------
x array of values at which to calculate model
y array of values for model to try to match
dy array of values for uncertainty in y data to be matched.
model name of model to use. One of (case insensitive)
'linear', 'quadratic', 'step', 'rectangle',
'gaussian', 'lorentzian', 'voigt', 'exponential'
background name of background model to use. One of (case insensitive)
None, 'constant', 'linear', or 'quadratic'
this is ignored when model is 'linear' or 'quadratic'
form name of form to use for 'step' and 'rectangle' models.
One of (case insensitive):
'linear', 'erf', or 'atan'
negative True/False for whether peak or steps are expected to go down.
use_gamma True/False for whether to use separate gamma parameter for
voigt model.
output:
-------
Group with fit parameters, and more...
"""
if form is None and step is not None:
form = step
out = Group(name='fit_peak result', x=x*1.0, y=y*1.0, dy=1.0,
model=model, background=background, form=form)
weight = None
if dy is not None:
out.dy = 1.0*dy
weight = 1.0/max(1.e-16, abs(dy))
if model.lower() not in MODELS:
_larch.writer.write('Unknown fit model: %s ' % model)
return None
kwargs = dict(negative=negative, background=background,
form=form, weight=weight, _larch=_larch)
fitclass = MODELS[model.lower()]
if fitclass == VoigtModel:
kwargs['use_gamma'] = use_gamma
mod = fitclass(**kwargs)
pars = mod.guess(out.y, out.x)
if background is not None:
bkg = MODELS[background.lower()](prefix='bkg_')
bpars = bkg.guess(out.y, x=out.x)
for p, par in bpars.items():
par.value = 0.
par.vary = True
pars += bpars
mod += bkg
out.init_params = pars
result = mod.fit(out.y, params=pars, x=out.x) # , dy=out.dy)
out.fit = mod.eval(result.params, x=out.x)
out.fit_init = mod.eval(pars, x=out.x)
out.fit_details = result
out.chi_square = result.chisqr
out.chi_reduced = result.redchi
for attr in ('aic', 'bic', 'covar', 'rfactor', 'params', 'nvarys',
'nfree', 'ndata', 'var_names', 'nfev', 'success',
'errorbars', 'message', 'lmdif_message', 'residual'):
setattr(out, attr, getattr(result, attr, None))
if background is not None:
comps = mod.eval_components(x=out.x)
out.bkg = comps['bkg_']
return out
def fit_peak(x,
y,
model,
dy=None,
background=None,
form=None,
step=None,
negative=False,
use_gamma=False,
_larch=None):
"""fit peak to one a selection of simple 1d models
out = fit_peak(x, y, model, dy=None,
background='linear', form='linear')
arguments:
---------
x array of values at which to calculate model
y array of values for model to try to match
dy array of values for uncertainty in y data to be matched.
model name of model to use. One of (case insensitive)
'linear', 'quadratic', 'step', 'rectangle',
'gaussian', 'lorentzian', 'voigt', 'exponential'
background name of background model to use. One of (case insensitive)
None, 'constant', 'linear', or 'quadratic'
this is ignored when model is 'linear' or 'quadratic'
form name of form to use for 'step' and 'rectangle' models.
One of (case insensitive):
'linear', 'erf', or 'atan'
negative True/False for whether peak or steps are expected to go down.
use_gamma True/False for whether to use separate gamma parameter for
voigt model.
output:
-------
Group with fit parameters, and more...
"""
if form is None and step is not None:
form = step
out = Group(name='fit_peak result',
x=x * 1.0,
y=y * 1.0,
dy=1.0,
model=model,
background=background,
form=form)
weight = None
if dy is not None:
out.dy = 1.0 * dy
weight = 1.0 / max(1.e-16, abs(dy))
if model.lower() not in MODELS:
_larch.writer.write('Unknown fit model: %s ' % model)
return None
kwargs = dict(negative=negative,
background=background,
form=form,
weight=weight,
_larch=_larch)
fitclass = MODELS[model.lower()]
if fitclass == VoigtModel:
kwargs['use_gamma'] = use_gamma
mod = fitclass(**kwargs)
pars = mod.guess(out.y, out.x)
if background is not None:
bkg = MODELS[background.lower()](prefix='bkg_')
bpars = bkg.guess(out.y, x=out.x)
for p, par in bpars.items():
par.value = 0.
par.vary = True
pars += bpars
mod += bkg
out.init_params = pars
result = mod.fit(out.y, params=pars, x=out.x) # , dy=out.dy)
out.fit = mod.eval(result.params, x=out.x)
out.fit_init = mod.eval(pars, x=out.x)
out.fit_details = result
out.chi_square = result.chisqr
out.chi_reduced = result.redchi
for attr in ('aic', 'bic', 'covar', 'rfactor', 'params', 'nvarys', 'nfree',
'ndata', 'var_names', 'nfev', 'success', 'errorbars',
'message', 'lmdif_message', 'residual'):
setattr(out, attr, getattr(result, attr, None))
if background is not None:
comps = mod.eval_components(x=out.x)
out.bkg = comps['bkg_']
return out