pyerrors/pyerrors/fits.py

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import gc
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from collections.abc import Sequence
import warnings
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import numpy as np
import autograd.numpy as anp
import scipy.optimize
import scipy.stats
import matplotlib.pyplot as plt
from matplotlib import gridspec
from scipy.odr import ODR, Model, RealData
import iminuit
from autograd import jacobian
from autograd import elementwise_grad as egrad
from .obs import Obs, derived_observable, covariance, cov_Obs
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class Fit_result(Sequence):
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"""Represents fit results.
Attributes
----------
fit_parameters : list
results for the individual fit parameters,
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also accessible via indices.
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"""
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def __init__(self):
self.fit_parameters = None
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def __getitem__(self, idx):
return self.fit_parameters[idx]
def __len__(self):
return len(self.fit_parameters)
def gamma_method(self, **kwargs):
"""Apply the gamma method to all fit parameters"""
[o.gamma_method(**kwargs) for o in self.fit_parameters]
def __str__(self):
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my_str = 'Goodness of fit:\n'
if hasattr(self, 'chisquare_by_dof'):
my_str += '\u03C7\u00b2/d.o.f. = ' + f'{self.chisquare_by_dof:2.6f}' + '\n'
elif hasattr(self, 'residual_variance'):
my_str += 'residual variance = ' + f'{self.residual_variance:2.6f}' + '\n'
if hasattr(self, 'chisquare_by_expected_chisquare'):
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my_str += '\u03C7\u00b2/\u03C7\u00b2exp = ' + f'{self.chisquare_by_expected_chisquare:2.6f}' + '\n'
if hasattr(self, 'p_value'):
my_str += 'p-value = ' + f'{self.p_value:2.4f}' + '\n'
my_str += 'Fit parameters:\n'
for i_par, par in enumerate(self.fit_parameters):
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my_str += str(i_par) + '\t' + ' ' * int(par >= 0) + str(par).rjust(int(par < 0.0)) + '\n'
return my_str
def __repr__(self):
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m = max(map(len, list(self.__dict__.keys()))) + 1
return '\n'.join([key.rjust(m) + ': ' + repr(value) for key, value in sorted(self.__dict__.items())])
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def least_squares(x, y, func, priors=None, silent=False, **kwargs):
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r'''Performs a non-linear fit to y = func(x).
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Parameters
----------
x : list
list of floats.
y : list
list of Obs.
func : object
fit function, has to be of the form
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```python
import autograd.numpy as anp
def func(a, x):
return a[0] + a[1] * x + a[2] * anp.sinh(x)
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```
For multiple x values func can be of the form
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```python
def func(a, x):
(x1, x2) = x
return a[0] * x1 ** 2 + a[1] * x2
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```
It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
will not work.
priors : list, optional
priors has to be a list with an entry for every parameter in the fit. The entries can either be
Obs (e.g. results from a previous fit) or strings containing a value and an error formatted like
0.548(23), 500(40) or 0.5(0.4)
silent : bool, optional
If true all output to the console is omitted (default False).
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initial_guess : list
can provide an initial guess for the input parameters. Relevant for
non-linear fits with many parameters. In case of correlated fits the guess is used to perform
an uncorrelated fit which then serves as guess for the correlated fit.
method : str, optional
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can be used to choose an alternative method for the minimization of chisquare.
The possible methods are the ones which can be used for scipy.optimize.minimize and
migrad of iminuit. If no method is specified, Levenberg-Marquard is used.
Reliable alternatives are migrad, Powell and Nelder-Mead.
correlated_fit : bool
If True, use the full inverse covariance matrix in the definition of the chisquare cost function.
For details about how the covariance matrix is estimated see `pyerrors.obs.covariance`.
In practice the correlation matrix is Cholesky decomposed and inverted (instead of the covariance matrix).
This procedure should be numerically more stable as the correlation matrix is typically better conditioned (Jacobi preconditioning).
At the moment this option only works for `prior==None` and when no `method` is given.
expected_chisquare : bool
If True estimates the expected chisquare which is
corrected by effects caused by correlated input data (default False).
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resplot : bool
If True, a plot which displays fit, data and residuals is generated (default False).
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qqplot : bool
If True, a quantile-quantile plot of the fit result is generated (default False).
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'''
if priors is not None:
return _prior_fit(x, y, func, priors, silent=silent, **kwargs)
else:
return _standard_fit(x, y, func, silent=silent, **kwargs)
def total_least_squares(x, y, func, silent=False, **kwargs):
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r'''Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
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Parameters
----------
x : list
list of Obs, or a tuple of lists of Obs
y : list
list of Obs. The dvalues of the Obs are used as x- and yerror for the fit.
func : object
func has to be of the form
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```python
import autograd.numpy as anp
def func(a, x):
return a[0] + a[1] * x + a[2] * anp.sinh(x)
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```
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For multiple x values func can be of the form
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```python
def func(a, x):
(x1, x2) = x
return a[0] * x1 ** 2 + a[1] * x2
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```
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It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
will not work.
silent : bool, optional
If true all output to the console is omitted (default False).
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initial_guess : list
can provide an initial guess for the input parameters. Relevant for non-linear
fits with many parameters.
expected_chisquare : bool
If true prints the expected chisquare which is
corrected by effects caused by correlated input data.
This can take a while as the full correlation matrix
has to be calculated (default False).
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Notes
-----
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Based on the orthogonal distance regression module of scipy
'''
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output = Fit_result()
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output.fit_function = func
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x = np.array(x)
x_shape = x.shape
if not callable(func):
raise TypeError('func has to be a function.')
for i in range(42):
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try:
func(np.arange(i), x.T[0])
except (IndexError, TypeError) as e:
continue
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else:
break
else:
raise RuntimeError("Fit function is not valid.")
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n_parms = i
if not silent:
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print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
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x_f = np.vectorize(lambda o: o.value)(x)
dx_f = np.vectorize(lambda o: o.dvalue)(x)
y_f = np.array([o.value for o in y])
dy_f = np.array([o.dvalue for o in y])
if np.any(np.asarray(dx_f) <= 0.0):
raise Exception('No x errors available, run the gamma method first.')
if np.any(np.asarray(dy_f) <= 0.0):
raise Exception('No y errors available, run the gamma method first.')
if 'initial_guess' in kwargs:
x0 = kwargs.get('initial_guess')
if len(x0) != n_parms:
raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
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else:
x0 = [1] * n_parms
data = RealData(x_f, y_f, sx=dx_f, sy=dy_f)
model = Model(func)
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odr = ODR(data, model, x0, partol=np.finfo(np.float64).eps)
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odr.set_job(fit_type=0, deriv=1)
out = odr.run()
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output.residual_variance = out.res_var
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output.method = 'ODR'
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output.message = out.stopreason
output.xplus = out.xplus
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if not silent:
print('Method: ODR')
print(*out.stopreason)
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print('Residual variance:', output.residual_variance)
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if out.info > 3:
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raise Exception('The minimization procedure did not converge.')
m = x_f.size
def odr_chisquare(p):
model = func(p[:n_parms], p[n_parms:].reshape(x_shape))
chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((x_f - p[n_parms:].reshape(x_shape)) / dx_f) ** 2)
return chisq
if kwargs.get('expected_chisquare') is True:
W = np.diag(1 / np.asarray(np.concatenate((dy_f.ravel(), dx_f.ravel()))))
if kwargs.get('covariance') is not None:
cov = kwargs.get('covariance')
else:
cov = covariance(np.concatenate((y, x.ravel())))
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number_of_x_parameters = int(m / x_f.shape[-1])
old_jac = jacobian(func)(out.beta, out.xplus)
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fused_row1 = np.concatenate((old_jac, np.concatenate((number_of_x_parameters * [np.zeros(old_jac.shape)]), axis=0)))
fused_row2 = np.concatenate((jacobian(lambda x, y: func(y, x))(out.xplus, out.beta).reshape(x_f.shape[-1], x_f.shape[-1] * number_of_x_parameters), np.identity(number_of_x_parameters * old_jac.shape[0])))
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new_jac = np.concatenate((fused_row1, fused_row2), axis=1)
A = W @ new_jac
P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
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expected_chisquare = np.trace((np.identity(P_phi.shape[0]) - P_phi) @ W @ cov @ W)
if expected_chisquare <= 0.0:
warnings.warn("Negative expected_chisquare.", RuntimeWarning)
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expected_chisquare = np.abs(expected_chisquare)
output.chisquare_by_expected_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel()))) / expected_chisquare
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if not silent:
print('chisquare/expected_chisquare:',
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output.chisquare_by_expected_chisquare)
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fitp = out.beta
try:
hess = jacobian(jacobian(odr_chisquare))(np.concatenate((fitp, out.xplus.ravel())))
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
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def odr_chisquare_compact_x(d):
model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((d[n_parms + m:].reshape(x_shape) - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
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return chisq
jac_jac_x = jacobian(jacobian(odr_chisquare_compact_x))(np.concatenate((fitp, out.xplus.ravel(), x_f.ravel())))
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# Compute hess^{-1} @ jac_jac_x[:n_parms + m, n_parms + m:] using LAPACK dgesv
try:
deriv_x = -scipy.linalg.solve(hess, jac_jac_x[:n_parms + m, n_parms + m:])
except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.")
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def odr_chisquare_compact_y(d):
model = func(d[:n_parms], d[n_parms:n_parms + m].reshape(x_shape))
chisq = anp.sum(((d[n_parms + m:] - model) / dy_f) ** 2) + anp.sum(((x_f - d[n_parms:n_parms + m].reshape(x_shape)) / dx_f) ** 2)
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return chisq
jac_jac_y = jacobian(jacobian(odr_chisquare_compact_y))(np.concatenate((fitp, out.xplus.ravel(), y_f)))
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# Compute hess^{-1} @ jac_jac_y[:n_parms + m, n_parms + m:] using LAPACK dgesv
try:
deriv_y = -scipy.linalg.solve(hess, jac_jac_y[:n_parms + m, n_parms + m:])
except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.")
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result = []
for i in range(n_parms):
result.append(derived_observable(lambda my_var, **kwargs: (my_var[0] + np.finfo(np.float64).eps) / (x.ravel()[0].value + np.finfo(np.float64).eps) * out.beta[i], list(x.ravel()) + list(y), man_grad=list(deriv_x[i]) + list(deriv_y[i])))
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output.fit_parameters = result
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output.odr_chisquare = odr_chisquare(np.concatenate((out.beta, out.xplus.ravel())))
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output.dof = x.shape[-1] - n_parms
output.p_value = 1 - scipy.stats.chi2.cdf(output.odr_chisquare, output.dof)
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return output
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def _prior_fit(x, y, func, priors, silent=False, **kwargs):
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output = Fit_result()
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output.fit_function = func
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x = np.asarray(x)
if not callable(func):
raise TypeError('func has to be a function.')
for i in range(100):
try:
func(np.arange(i), 0)
except (IndexError, TypeError) as e:
continue
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else:
break
else:
raise RuntimeError("Fit function is not valid.")
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n_parms = i
if n_parms != len(priors):
raise Exception('Priors does not have the correct length.')
def extract_val_and_dval(string):
split_string = string.split('(')
if '.' in split_string[0] and '.' not in split_string[1][:-1]:
factor = 10 ** -len(split_string[0].partition('.')[2])
else:
factor = 1
return float(split_string[0]), float(split_string[1][:-1]) * factor
loc_priors = []
for i_n, i_prior in enumerate(priors):
if isinstance(i_prior, Obs):
loc_priors.append(i_prior)
else:
loc_val, loc_dval = extract_val_and_dval(i_prior)
loc_priors.append(cov_Obs(loc_val, loc_dval ** 2, '#prior' + str(i_n) + f"_{np.random.randint(2147483647):010d}"))
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output.priors = loc_priors
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if not silent:
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print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
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y_f = [o.value for o in y]
dy_f = [o.dvalue for o in y]
if np.any(np.asarray(dy_f) <= 0.0):
raise Exception('No y errors available, run the gamma method first.')
p_f = [o.value for o in loc_priors]
dp_f = [o.dvalue for o in loc_priors]
if np.any(np.asarray(dp_f) <= 0.0):
raise Exception('No prior errors available, run the gamma method first.')
if 'initial_guess' in kwargs:
x0 = kwargs.get('initial_guess')
if len(x0) != n_parms:
raise Exception('Initial guess does not have the correct length.')
else:
x0 = p_f
def chisqfunc(p):
model = func(p, x)
chisq = anp.sum(((y_f - model) / dy_f) ** 2) + anp.sum(((p_f - p) / dp_f) ** 2)
return chisq
if not silent:
print('Method: migrad')
m = iminuit.Minuit(chisqfunc, x0)
m.errordef = 1
m.print_level = 0
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if 'tol' in kwargs:
m.tol = kwargs.get('tol')
else:
m.tol = 1e-4
m.migrad()
params = np.asarray(m.values)
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output.chisquare_by_dof = m.fval / len(x)
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output.method = 'migrad'
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if not silent:
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print('chisquare/d.o.f.:', output.chisquare_by_dof)
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if not m.fmin.is_valid:
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raise Exception('The minimization procedure did not converge.')
hess_inv = np.linalg.pinv(jacobian(jacobian(chisqfunc))(params))
def chisqfunc_compact(d):
model = func(d[:n_parms], x)
chisq = anp.sum(((d[n_parms: n_parms + len(x)] - model) / dy_f) ** 2) + anp.sum(((d[n_parms + len(x):] - d[:n_parms]) / dp_f) ** 2)
return chisq
jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((params, y_f, p_f)))
deriv = -hess_inv @ jac_jac[:n_parms, n_parms:]
result = []
for i in range(n_parms):
result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * params[i], list(y) + list(loc_priors), man_grad=list(deriv[i])))
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output.fit_parameters = result
output.chisquare = chisqfunc(np.asarray(params))
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if kwargs.get('resplot') is True:
residual_plot(x, y, func, result)
if kwargs.get('qqplot') is True:
qqplot(x, y, func, result)
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return output
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def _standard_fit(x, y, func, silent=False, **kwargs):
output = Fit_result()
output.fit_function = func
x = np.asarray(x)
if x.shape[-1] != len(y):
raise Exception('x and y input have to have the same length')
if len(x.shape) > 2:
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raise Exception('Unknown format for x values')
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if not callable(func):
raise TypeError('func has to be a function.')
for i in range(42):
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try:
func(np.arange(i), x.T[0])
except (IndexError, TypeError) as e:
continue
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else:
break
else:
raise RuntimeError("Fit function is not valid.")
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n_parms = i
if not silent:
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print('Fit with', n_parms, 'parameter' + 's' * (n_parms > 1))
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y_f = [o.value for o in y]
dy_f = [o.dvalue for o in y]
if np.any(np.asarray(dy_f) <= 0.0):
raise Exception('No y errors available, run the gamma method first.')
if 'initial_guess' in kwargs:
x0 = kwargs.get('initial_guess')
if len(x0) != n_parms:
raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
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else:
x0 = [0.1] * n_parms
if kwargs.get('correlated_fit') is True:
corr = covariance(y, correlation=True, **kwargs)
covdiag = np.diag(1 / np.asarray(dy_f))
condn = np.linalg.cond(corr)
if condn > 0.1 / np.finfo(float).eps:
raise Exception(f"Cannot invert correlation matrix as its condition number exceeds machine precision ({condn:1.2e})")
if condn > 1 / np.sqrt(np.finfo(float).eps):
warnings.warn("Correlation matrix may be ill-conditioned, condition number: {%1.2e}" % (condn), RuntimeWarning)
chol = np.linalg.cholesky(corr)
chol_inv = scipy.linalg.solve_triangular(chol, covdiag, lower=True)
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def chisqfunc_corr(p):
model = func(p, x)
chisq = anp.sum(anp.dot(chol_inv, (y_f - model)) ** 2)
return chisq
def chisqfunc(p):
model = func(p, x)
chisq = anp.sum(((y_f - model) / dy_f) ** 2)
return chisq
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output.method = kwargs.get('method', 'Levenberg-Marquardt')
if not silent:
print('Method:', output.method)
if output.method != 'Levenberg-Marquardt':
if output.method == 'migrad':
fit_result = iminuit.minimize(chisqfunc, x0, tol=1e-4) # Stopping criterion 0.002 * tol * errordef
if kwargs.get('correlated_fit') is True:
fit_result = iminuit.minimize(chisqfunc_corr, fit_result.x, tol=1e-4) # Stopping criterion 0.002 * tol * errordef
output.iterations = fit_result.nfev
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else:
fit_result = scipy.optimize.minimize(chisqfunc, x0, method=kwargs.get('method'), tol=1e-12)
if kwargs.get('correlated_fit') is True:
fit_result = scipy.optimize.minimize(chisqfunc_corr, fit_result.x, method=kwargs.get('method'), tol=1e-12)
output.iterations = fit_result.nit
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chisquare = fit_result.fun
else:
if kwargs.get('correlated_fit') is True:
def chisqfunc_residuals_corr(p):
model = func(p, x)
chisq = anp.dot(chol_inv, (y_f - model))
return chisq
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def chisqfunc_residuals(p):
model = func(p, x)
chisq = ((y_f - model) / dy_f)
return chisq
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fit_result = scipy.optimize.least_squares(chisqfunc_residuals, x0, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
if kwargs.get('correlated_fit') is True:
fit_result = scipy.optimize.least_squares(chisqfunc_residuals_corr, fit_result.x, method='lm', ftol=1e-15, gtol=1e-15, xtol=1e-15)
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chisquare = np.sum(fit_result.fun ** 2)
if kwargs.get('correlated_fit') is True:
assert np.isclose(chisquare, chisqfunc_corr(fit_result.x), atol=1e-14)
else:
assert np.isclose(chisquare, chisqfunc(fit_result.x), atol=1e-14)
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output.iterations = fit_result.nfev
if not fit_result.success:
raise Exception('The minimization procedure did not converge.')
if x.shape[-1] - n_parms > 0:
output.chisquare_by_dof = chisquare / (x.shape[-1] - n_parms)
else:
output.chisquare_by_dof = float('nan')
output.message = fit_result.message
if not silent:
print(fit_result.message)
print('chisquare/d.o.f.:', output.chisquare_by_dof)
if kwargs.get('expected_chisquare') is True:
if kwargs.get('correlated_fit') is not True:
W = np.diag(1 / np.asarray(dy_f))
cov = covariance(y)
A = W @ jacobian(func)(fit_result.x, x)
P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
expected_chisquare = np.trace((np.identity(x.shape[-1]) - P_phi) @ W @ cov @ W)
output.chisquare_by_expected_chisquare = chisquare / expected_chisquare
if not silent:
print('chisquare/expected_chisquare:',
output.chisquare_by_expected_chisquare)
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fitp = fit_result.x
try:
if kwargs.get('correlated_fit') is True:
hess = jacobian(jacobian(chisqfunc_corr))(fitp)
else:
hess = jacobian(jacobian(chisqfunc))(fitp)
except TypeError:
raise Exception("It is required to use autograd.numpy instead of numpy within fit functions, see the documentation for details.") from None
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if kwargs.get('correlated_fit') is True:
def chisqfunc_compact(d):
model = func(d[:n_parms], x)
chisq = anp.sum(anp.dot(chol_inv, (d[n_parms:] - model)) ** 2)
return chisq
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else:
def chisqfunc_compact(d):
model = func(d[:n_parms], x)
chisq = anp.sum(((d[n_parms:] - model) / dy_f) ** 2)
return chisq
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jac_jac = jacobian(jacobian(chisqfunc_compact))(np.concatenate((fitp, y_f)))
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# Compute hess^{-1} @ jac_jac[:n_parms, n_parms:] using LAPACK dgesv
try:
deriv = -scipy.linalg.solve(hess, jac_jac[:n_parms, n_parms:])
except np.linalg.LinAlgError:
raise Exception("Cannot invert hessian matrix.")
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result = []
for i in range(n_parms):
result.append(derived_observable(lambda x, **kwargs: (x[0] + np.finfo(np.float64).eps) / (y[0].value + np.finfo(np.float64).eps) * fit_result.x[i], list(y), man_grad=list(deriv[i])))
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output.fit_parameters = result
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output.chisquare = chisquare
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output.dof = x.shape[-1] - n_parms
output.p_value = 1 - scipy.stats.chi2.cdf(output.chisquare, output.dof)
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if kwargs.get('resplot') is True:
residual_plot(x, y, func, result)
if kwargs.get('qqplot') is True:
qqplot(x, y, func, result)
return output
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def fit_lin(x, y, **kwargs):
"""Performs a linear fit to y = n + m * x and returns two Obs n, m.
Parameters
----------
x : list
Can either be a list of floats in which case no xerror is assumed, or
a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
y : list
List of Obs, the dvalues of the Obs are used as yerror for the fit.
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"""
def f(a, x):
y = a[0] + a[1] * x
return y
if all(isinstance(n, Obs) for n in x):
out = total_least_squares(x, y, f, **kwargs)
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return out.fit_parameters
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elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
out = least_squares(x, y, f, **kwargs)
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return out.fit_parameters
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else:
raise Exception('Unsupported types for x')
def qqplot(x, o_y, func, p):
"""Generates a quantile-quantile plot of the fit result which can be used to
check if the residuals of the fit are gaussian distributed.
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"""
residuals = []
for i_x, i_y in zip(x, o_y):
residuals.append((i_y - func(p, i_x)) / i_y.dvalue)
residuals = sorted(residuals)
my_y = [o.value for o in residuals]
probplot = scipy.stats.probplot(my_y)
my_x = probplot[0][0]
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plt.figure(figsize=(8, 8 / 1.618))
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plt.errorbar(my_x, my_y, fmt='o')
fit_start = my_x[0]
fit_stop = my_x[-1]
samples = np.arange(fit_start, fit_stop, 0.01)
plt.plot(samples, samples, 'k--', zorder=11, label='Standard normal distribution')
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plt.plot(samples, probplot[1][0] * samples + probplot[1][1], zorder=10, label='Least squares fit, r=' + str(np.around(probplot[1][2], 3)), marker='', ls='-')
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plt.xlabel('Theoretical quantiles')
plt.ylabel('Ordered Values')
plt.legend()
plt.draw()
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def residual_plot(x, y, func, fit_res):
""" Generates a plot which compares the fit to the data and displays the corresponding residuals"""
sorted_x = sorted(x)
xstart = sorted_x[0] - 0.5 * (sorted_x[1] - sorted_x[0])
xstop = sorted_x[-1] + 0.5 * (sorted_x[-1] - sorted_x[-2])
x_samples = np.arange(xstart, xstop + 0.01, 0.01)
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plt.figure(figsize=(8, 8 / 1.618))
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1], wspace=0.0, hspace=0.0)
ax0 = plt.subplot(gs[0])
ax0.errorbar(x, [o.value for o in y], yerr=[o.dvalue for o in y], ls='none', fmt='o', capsize=3, markersize=5, label='Data')
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ax0.plot(x_samples, func([o.value for o in fit_res], x_samples), label='Fit', zorder=10, ls='-', ms=0)
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ax0.set_xticklabels([])
ax0.set_xlim([xstart, xstop])
ax0.set_xticklabels([])
ax0.legend()
residuals = (np.asarray([o.value for o in y]) - func([o.value for o in fit_res], x)) / np.asarray([o.dvalue for o in y])
ax1 = plt.subplot(gs[1])
ax1.plot(x, residuals, 'ko', ls='none', markersize=5)
ax1.tick_params(direction='out')
ax1.tick_params(axis="x", bottom=True, top=True, labelbottom=True)
ax1.axhline(y=0.0, ls='--', color='k', marker=" ")
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ax1.fill_between(x_samples, -1.0, 1.0, alpha=0.1, facecolor='k')
ax1.set_xlim([xstart, xstop])
ax1.set_ylabel('Residuals')
plt.subplots_adjust(wspace=None, hspace=None)
plt.draw()
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def error_band(x, func, beta):
"""Returns the error band for an array of sample values x, for given fit function func with optimized parameters beta."""
cov = covariance(beta)
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if np.any(np.abs(cov - cov.T) > 1000 * np.finfo(np.float64).eps):
warnings.warn("Covariance matrix is not symmetric within floating point precision", RuntimeWarning)
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deriv = []
for i, item in enumerate(x):
deriv.append(np.array(egrad(func)([o.value for o in beta], item)))
err = []
for i, item in enumerate(x):
err.append(np.sqrt(deriv[i] @ cov @ deriv[i]))
err = np.array(err)
return err
def ks_test(objects=None):
"""Performs a KolmogorovSmirnov test for the p-values of all fit object.
Parameters
----------
objects : list
List of fit results to include in the analysis (optional).
"""
if objects is None:
obs_list = []
for obj in gc.get_objects():
if isinstance(obj, Fit_result):
obs_list.append(obj)
else:
obs_list = objects
p_values = [o.p_value for o in obs_list]
bins = len(p_values)
x = np.arange(0, 1.001, 0.001)
plt.plot(x, x, 'k', zorder=1)
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xlabel('p-value')
plt.ylabel('Cumulative probability')
plt.title(str(bins) + ' p-values')
n = np.arange(1, bins + 1) / np.float64(bins)
Xs = np.sort(p_values)
plt.step(Xs, n)
diffs = n - Xs
loc_max_diff = np.argmax(np.abs(diffs))
loc = Xs[loc_max_diff]
plt.annotate('', xy=(loc, loc), xytext=(loc, loc + diffs[loc_max_diff]), arrowprops=dict(arrowstyle='<->', shrinkA=0, shrinkB=0))
plt.draw()
print(scipy.stats.kstest(p_values, 'uniform'))