import enum
import numpy as np
import uncertainties.unumpy as unp
from smpl import debug
from smpl import functions
from smpl import stat
from smpl import util
from smpl import wrap
from smpl import doc
from smpl import data
from numpy.linalg import LinAlgError
from tqdm import tqdm
from .minuit import _fit_minuit_leastsquares
from .scipy import _fit_curvefit, _fit_odr
unv = unp.nominal_values
usd = unp.std_devs
[docs]class Fitter(enum.Enum):
"""
Different implementations to perform a fit.
"""
AUTO = 0
SCIPY_CURVEFIT = 1
SCIPY_ODR = 2
MINUIT_LEASTSQUARES = 3
default = {
'params': [None, "Initial fit parameters", ],
# 'frange': [None, "Limit the fit to given range. First integer is the lowest and second the highest index.", ],
# 'fselector': [None, "Function that takes ``x`` and ``y`` as parameters and returns an array mask in order to limit the data points for fitting. Alternatively a mask for selecting elements from datax and datay.", ],
'fixed_params': [True, "Enable fixing parameters by choosing the same-named variables from ``kwargs``.", ],
# 'sortbyx': [True, "Enable sorting the x and y data so that x is sorted.", ],
'maxfev': [10000, "Maximum function evaluations during fitting.", ],
'epsfcn': [0.0001, "Suitable step length for jacobian approximation.", ],
'xvar': [None, "Variable in fit function parameters that corresponds to the x axis. If it is None the last of the alphabetical sorted parameters is used.", ],
# 'bins': [0, "Number of bins for histogram", ],
# 'binunc': [stat.poisson_dist, "Number of bins for histogram", ],
'autotqdm': [True, "Auto fitting display tqdm", ],
# 'xerror': [True, "enable xerrors"],
# 'yerror': [True, "enable yerrors"],
'fitter': [Fitter.AUTO, "Choose from :class:`Fitter`s."]
}
# @doc.insert_str("\tDefault kwargs\n\n\t")
[docs]@doc.append_doc(data.data_kwargs)
@doc.append_str("\t")
@doc.append_str(doc.table(default, init=False))
@doc.append_str(doc.table({"fit_kwargs": ["default", "description"]}, bottom=False))
def fit_kwargs(kwargs):
"""Set default fit_kwargs if not set.
"""
kwargs = data.data_kwargs(kwargs)
for k, v in default.items():
if not k in kwargs:
kwargs[k] = v[0]
return kwargs
# @append_doc(default_kwargs)
[docs]def auto(datax, datay, funcs=None, **kwargs):
"""
Automatically loop over functions and fit the best one.
Parameters
----------
funcs : function array
functions to consider as fit. Default all ``smpl.functions``.
**kwargs : optional
see :func:`fit_kwargs`.
Returns
-------
The best fit function and it's parameters and a ``lambda`` where the parameters are already applied to the function.
"""
kwargs = fit_kwargs(kwargs)
min_sq = None
best_f = None
best_ff = None
if funcs is None:
funcs = functions.__dict__.values()
for f in tqdm(funcs, disable=not kwargs['autotqdm']):
if callable(f):
try:
ff = fit(datax, datay, f, **kwargs)
fy = f(datax, *ff)
except (ValueError, LinAlgError) as ve:
debug.msg(ve)
continue
sum_sq = np.sum((fy - datay)**2) + np.sum((fy + usd(fy) -
datay)**2) + np.sum((fy - usd(fy) - datay)**2)
if min_sq is None or sum_sq < min_sq:
min_sq = sum_sq
best_f = f
best_ff = ff
# if not best_f is None:
# fit(datax,datay,best_f,**kwargs)
return best_f, best_ff, lambda x: best_f(x, *best_ff)
[docs]def fit(datax, datay, function, **kwargs):
"""
Returns a fit of ``function`` to ``datax`` and ``datay``.
Parameters
----------
datax : array_like
X data either as ``unp.uarray`` or ``np.array`` or ``list``
datay : array_like
Y data either as ``unp.uarray`` or ``np.array`` or ``list``
function : func
Fit function with parameters: ``x``, ``params``
**kwargs : optional
see :func:`fit_kwargs`.
"""
kwargs = fit_kwargs(kwargs)
x, y, xerr, yerr = fit_split(datax, datay, **kwargs)
params = None
if util.has('params', kwargs):
params = kwargs['params']
fixed = {}
vnames = wrap.get_varnames(function, kwargs['xvar'])
Ntot = len(vnames)-1
if util.has("fixed_params", kwargs) and kwargs['fixed_params']:
for i in range(1, len(vnames)):
if util.has(vnames[i], kwargs):
fixed[i] = kwargs[vnames[i]]
# Count parameters for function
if params is None:
N = len(vnames)
params = [1 for i in range(N-1)]
tmp_params = []
for i, pi in enumerate(params):
if not util.has(i+1, fixed):
tmp_params += [pi]
params = tmp_params
N = len(params)
def tmp(*x):
tmp_x = []
j = 1
# print(x)
for i in range(1, Ntot+1):
# print(i," ",j)
if not util.has(i, fixed):
tmp_x += [x[j]]
# print(x[j])
j = j+1
else:
tmp_x += [fixed[i]]
# print(Ntot)
# print(tmp_x)
return unv(wrap.get_lambda(function, kwargs['xvar'])(x[0], *tmp_x))
fitter = kwargs["fitter"]
if fitter is Fitter.AUTO:
if xerr is not None:
fitter = Fitter.SCIPY_ODR
else:
fitter = Fitter.SCIPY_CURVEFIT
if fitter is Fitter.MINUIT_LEASTSQUARES:
fit = _fit_minuit_leastsquares(x, y, tmp, params=params, yerr=yerr)
elif fitter is Fitter.SCIPY_CURVEFIT:
fit = _fit_curvefit(x, y, tmp, params=params, yerr=yerr)
elif fitter is Fitter.SCIPY_ODR:
fit = _fit_odr(x, y, tmp, params=params, xerr=xerr, yerr=yerr)
rfit = []
j = 0
for i in range(1, Ntot+1):
if not util.has(i, fixed):
rfit += [fit[j]]
j = j+1
else:
rfit += [fixed[i]]
return rfit
[docs]@doc.insert_doc(stat.Chi2)
def Chi2(datax, datay, function, ff, **kwargs):
kwargs = fit_kwargs(kwargs)
x, y, _, yerr = fit_split(datax, datay, **kwargs)
sigmas = yerr
return stat.Chi2(y, unv(function(x, *ff)), sigmas)
[docs]@doc.insert_doc(stat.R2)
def R2(datax, datay, function, ff, **kwargs):
kwargs = fit_kwargs(kwargs)
x, y, _, _= fit_split(datax, datay, **kwargs)
return stat.R2(y, unv(function(x, *ff)))
[docs]def data_split(datax, datay, **kwargs):
"""
Split data + errors
"""
return data.__data_split(datax, datay, **kwargs)
[docs]def fit_split(datax, datay, **kwargs):
"""
Splits datax and datay into (x,y,xerr,yerr).
Parameters
----------
**kwargs : optional
see :func:`fit_kwargs`.
"""
kwargs = fit_kwargs(kwargs)
return data.filtered_data_split(datax, datay, **kwargs)
if __name__ == "__main__":
import doctest
doctest.testmod()