/usr/lib/python3/dist-packages/lmfit/models.py is in python3-lmfit 0.8.0+dfsg.1-1.
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from .model import Model
from .lineshapes import (gaussian, lorentzian, voigt, pvoigt, pearson7,
step, rectangle, breit_wigner, logistic,
students_t, lognormal, damped_oscillator,
expgaussian, skewed_gaussian, donaich,
skewed_voigt, exponential, powerlaw, linear,
parabolic)
class DimensionalError(Exception):
pass
def _validate_1d(independent_vars):
if len(independent_vars) != 1:
raise DimensionalError(
"This model requires exactly one independent variable.")
def index_of(arr, val):
"""return index of array nearest to a value
"""
if val < min(arr):
return 0
return np.abs(arr-val).argmin()
def fwhm_expr(model):
"return constraint expression for fwhm"
return "%.7f*%ssigma" % (model.fwhm_factor, model.prefix)
def guess_from_peak(model, y, x, negative, ampscale=1.0, sigscale=1.0):
"estimate amp, cen, sigma for a peak, create params"
if x is None:
return 1.0, 0.0, 1.0
maxy, miny = max(y), min(y)
maxx, minx = max(x), min(x)
imaxy = index_of(y, maxy)
cen = x[imaxy]
amp = (maxy - miny)*2.0
sig = (maxx-minx)/6.0
halfmax_vals = np.where(y > (maxy+miny)/2.0)[0]
if negative:
imaxy = index_of(y, miny)
amp = -(maxy - miny)*2.0
halfmax_vals = np.where(y < (maxy+miny)/2.0)[0]
if len(halfmax_vals) > 2:
sig = (x[halfmax_vals[-1]] - x[halfmax_vals[0]])/2.0
cen = x[halfmax_vals].mean()
amp = amp*sig*ampscale
sig = sig*sigscale
pars = model.make_params(amplitude=amp, center=cen, sigma=sig)
pars['%ssigma' % model.prefix].set(min=0.0)
return pars
def update_param_vals(pars, prefix, **kwargs):
"""convenience function to update parameter values
with keyword arguments"""
for key, val in kwargs.items():
pname = "%s%s" % (prefix, key)
if pname in pars:
pars[pname].value = val
return pars
COMMON_DOC = """
Parameters
----------
independent_vars: list of strings to be set as variable names
missing: None, 'drop', or 'raise'
None: Do not check for null or missing values.
'drop': Drop null or missing observations in data.
Use pandas.isnull if pandas is available; otherwise,
silently fall back to numpy.isnan.
'raise': Raise a (more helpful) exception when data contains null
or missing values.
suffix: string to append to paramter names, needed to add two Models that
have parameter names in common. None by default.
"""
class ConstantModel(Model):
__doc__ = "x -> c" + COMMON_DOC
def __init__(self, *args, **kwargs):
def constant(x, c):
return c
super(ConstantModel, self).__init__(constant, *args, **kwargs)
def guess(self, data, **kwargs):
pars = self.make_params()
pars['%sc' % self.prefix].set(value=data.mean())
return update_param_vals(pars, self.prefix, **kwargs)
class LinearModel(Model):
__doc__ = linear.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(LinearModel, self).__init__(linear, *args, **kwargs)
def guess(self, data, x=None, **kwargs):
sval, oval = 0., 0.
if x is not None:
sval, oval = np.polyfit(x, data, 1)
pars = self.make_params(intercept=oval, slope=sval)
return update_param_vals(pars, self.prefix, **kwargs)
class QuadraticModel(Model):
__doc__ = parabolic.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(QuadraticModel, self).__init__(parabolic, *args, **kwargs)
def guess(self, data, x=None, **kwargs):
a, b, c = 0., 0., 0.
if x is not None:
a, b, c = np.polyfit(x, data, 2)
pars = self.make_params(a=a, b=b, c=c)
return update_param_vals(pars, self.prefix, **kwargs)
ParabolicModel = QuadraticModel
class PolynomialModel(Model):
__doc__ = "x -> c0 + c1 * x + c2 * x**2 + ... c7 * x**7" + COMMON_DOC
MAX_DEGREE=7
DEGREE_ERR = "degree must be an integer less than %d."
def __init__(self, degree, *args, **kwargs):
if not isinstance(degree, int) or degree > self.MAX_DEGREE:
raise TypeError(self.DEGREE_ERR % self.MAX_DEGREE)
self.poly_degree = degree
pnames = ['c%i' % (i) for i in range(degree + 1)]
kwargs['param_names'] = pnames
def polynomial(x, c0=0, c1=0, c2=0, c3=0, c4=0, c5=0, c6=0, c7=0):
return np.polyval([c7, c6, c5, c4, c3, c2, c1, c0], x)
super(PolynomialModel, self).__init__(polynomial, *args, **kwargs)
def guess(self, data, x=None, **kwargs):
pars = self.make_params()
if x is not None:
out = np.polyfit(x, data, self.poly_degree)
for i, coef in enumerate(out[::-1]):
pars['%sc%i'% (self.prefix, i)].set(value=coef)
return update_param_vals(pars, self.prefix, **kwargs)
class GaussianModel(Model):
__doc__ = gaussian.__doc__ + COMMON_DOC
fwhm_factor = 2.354820
def __init__(self, *args, **kwargs):
super(GaussianModel, self).__init__(gaussian, *args, **kwargs)
self.set_param_hint('sigma', min=0)
self.set_param_hint('fwhm', expr=fwhm_expr(self))
def guess(self, data, x=None, negative=False, **kwargs):
pars = guess_from_peak(self, data, x, negative)
return update_param_vals(pars, self.prefix, **kwargs)
class LorentzianModel(Model):
__doc__ = lorentzian.__doc__ + COMMON_DOC
fwhm_factor = 2.0
def __init__(self, *args, **kwargs):
super(LorentzianModel, self).__init__(lorentzian, *args, **kwargs)
self.set_param_hint('sigma', min=0)
self.set_param_hint('fwhm', expr=fwhm_expr(self))
def guess(self, data, x=None, negative=False, **kwargs):
pars = guess_from_peak(self, data, x, negative, ampscale=1.25)
return update_param_vals(pars, self.prefix, **kwargs)
class VoigtModel(Model):
__doc__ = voigt.__doc__ + COMMON_DOC
fwhm_factor = 3.60131
def __init__(self, *args, **kwargs):
super(VoigtModel, self).__init__(voigt, *args, **kwargs)
self.set_param_hint('sigma', min=0)
self.set_param_hint('gamma', expr='%ssigma' % self.prefix)
self.set_param_hint('fwhm', expr=fwhm_expr(self))
def guess(self, data, x=None, negative=False, **kwargs):
pars = guess_from_peak(self, data, x, negative,
ampscale=1.5, sigscale=0.65)
return update_param_vals(pars, self.prefix, **kwargs)
class PseudoVoigtModel(Model):
__doc__ = pvoigt.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(PseudoVoigtModel, self).__init__(pvoigt, *args, **kwargs)
self.set_param_hint('fraction', value=0.5)
def guess(self, data, x=None, negative=False, **kwargs):
pars = guess_from_peak(self, data, x, negative, ampscale=1.25)
pars['%sfraction' % self.prefix].set(value=0.5)
return update_param_vals(pars, self.prefix, **kwargs)
class Pearson7Model(Model):
__doc__ = pearson7.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(Pearson7Model, self).__init__(pearson7, *args, **kwargs)
self.set_param_hint('expon', value=1.5)
def guess(self, data, x=None, negative=False, **kwargs):
pars = guess_from_peak(self, data, x, negative)
pars['%sexpon' % self.prefix].set(value=1.5)
return update_param_vals(pars, self.prefix, **kwargs)
class StudentsTModel(Model):
__doc__ = students_t.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(StudentsTModel, self).__init__(students_t, *args, **kwargs)
def guess(self, data, x=None, negative=False, **kwargs):
pars = guess_from_peak(self, data, x, negative)
return update_param_vals(pars, self.prefix, **kwargs)
class BreitWignerModel(Model):
__doc__ = breit_wigner.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(BreitWignerModel, self).__init__(breit_wigner, *args, **kwargs)
def guess(self, data, x=None, negative=False, **kwargs):
pars = guess_from_peak(self, data, x, negative)
pars['%sq' % self.prefix].set(value=1.0)
return update_param_vals(pars, self.prefix, **kwargs)
class LognormalModel(Model):
__doc__ = lognormal.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(LognormalModel, self).__init__(lognormal, *args, **kwargs)
def guess(self, data, x=None, negative=False, **kwargs):
pars = self.make_params(amplitude=1.0, center=0.0, sigma=0.25)
pars['%ssigma' % self.prefix].set(min=0.0)
return update_param_vals(pars, self.prefix, **kwargs)
class DampedOscillatorModel(Model):
__doc__ = damped_oscillator.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(DampedOscillatorModel, self).__init__(damped_oscillator, *args, **kwargs)
def guess(self, data, x=None, negative=False, **kwargs):
pars =guess_from_peak(self, data, x, negative,
ampscale=0.1, sigscale=0.1)
return update_param_vals(pars, self.prefix, **kwargs)
class ExponentialGaussianModel(Model):
__doc__ = expgaussian.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(ExponentialGaussianModel, self).__init__(expgaussian, *args, **kwargs)
def guess(self, data, x=None, negative=False, **kwargs):
pars = guess_from_peak(self, data, x, negative)
return update_param_vals(pars, self.prefix, **kwargs)
class SkewedGaussianModel(Model):
__doc__ = skewed_gaussian.__doc__ + COMMON_DOC
fwhm_factor = 2.354820
def __init__(self, *args, **kwargs):
super(SkewedGaussianModel, self).__init__(skewed_gaussian, *args, **kwargs)
self.set_param_hint('sigma', min=0)
def guess(self, data, x=None, negative=False, **kwargs):
pars = guess_from_peak(self, data, x, negative)
return update_param_vals(pars, self.prefix, **kwargs)
class DonaichModel(Model):
__doc__ = donaich.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(DonaichModel, self).__init__(donaich, *args, **kwargs)
def guess(self, data, x=None, negative=False, **kwargs):
pars = guess_from_peak(self, data, x, negative, ampscale=0.5)
return update_param_vals(pars, self.prefix, **kwargs)
class PowerLawModel(Model):
__doc__ = powerlaw.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(PowerLawModel, self).__init__(powerlaw, *args, **kwargs)
def guess(self, data, x=None, **kwargs):
try:
expon, amp = np.polyfit(np.log(x+1.e-14), np.log(data+1.e-14), 1)
except:
expon, amp = 1, np.log(abs(max(data)+1.e-9))
pars = self.make_params(amplitude=np.exp(amp), exponent=expon)
return update_param_vals(pars, self.prefix, **kwargs)
class ExponentialModel(Model):
__doc__ = exponential.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(ExponentialModel, self).__init__(exponential, *args, **kwargs)
def guess(self, data, x=None, **kwargs):
try:
sval, oval = np.polyfit(x, np.log(abs(data)+1.e-15), 1)
except:
sval, oval = 1., np.log(abs(max(data)+1.e-9))
pars = self.make_params(amplitude=np.exp(oval), decay=-1.0/sval)
return update_param_vals(pars, self.prefix, **kwargs)
class StepModel(Model):
__doc__ = step.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(StepModel, self).__init__(step, *args, **kwargs)
def guess(self, data, x=None, **kwargs):
if x is None:
return
ymin, ymax = min(data), max(data)
xmin, xmax = min(x), max(x)
pars = self.make_params(amplitude=(ymax-ymin),
center=(xmax+xmin)/2.0)
pars['%ssigma' % self.prefix].set(value=(xmax-xmin)/7.0, min=0.0)
return update_param_vals(pars, self.prefix, **kwargs)
class RectangleModel(Model):
__doc__ = rectangle.__doc__ + COMMON_DOC
def __init__(self, *args, **kwargs):
super(RectangleModel, self).__init__(rectangle, *args, **kwargs)
self.set_param_hint('midpoint',
expr='(%scenter1+%scenter2)/2.0' % (self.prefix,
self.prefix))
def guess(self, data, x=None, **kwargs):
if x is None:
return
ymin, ymax = min(data), max(data)
xmin, xmax = min(x), max(x)
pars = self.make_params(amplitude=(ymax-ymin),
center1=(xmax+xmin)/4.0,
center2=3*(xmax+xmin)/4.0)
pars['%ssigma1' % self.prefix].set(value=(xmax-xmin)/7.0, min=0.0)
pars['%ssigma2' % self.prefix].set(value=(xmax-xmin)/7.0, min=0.0)
return update_param_vals(pars, self.prefix, **kwargs)
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