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fix: flak8 & pytest
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3 changed files with 54 additions and 59 deletions
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examples/my_db.sqlite
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examples/my_db.sqlite
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@ -457,7 +457,6 @@ from .obs import *
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from .correlators import *
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from .fits import *
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from .misc import *
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from . import combined_fits
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from . import dirac
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from . import input
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from . import linalg
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112
pyerrors/fits.py
112
pyerrors/fits.py
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@ -70,13 +70,12 @@ class Fit_result(Sequence):
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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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```
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Parameters
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----------
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For an uncombined fit:
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x : list
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list of floats.
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y : list
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@ -100,12 +99,12 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
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```
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It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
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will not work.
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OR For a combined fit:
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Do not need to use ordered dictionaries: python version >= 3.7: Dictionary order is guaranteed to be insertion order.
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(https://docs.python.org/3/library/stdtypes.html#dict-views) Ensures that x, y and func values are mapped correctly.
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x : dict
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dict of lists.
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y : dict
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@ -117,16 +116,16 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
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import autograd.numpy as anp
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funcs = {"a": func_a,
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"b": func_b}
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def func_a(a, x):
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return a[1] * anp.exp(-a[0] * x)
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def func_b(a, x):
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return a[2] * anp.exp(-a[0] * x)
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It is important that all numpy functions refer to autograd.numpy, otherwise the differentiation
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will not work.
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priors : list, optional
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priors has to be a list with an entry for every parameter in the fit. The entries can either be
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Obs (e.g. results from a previous fit) or strings containing a value and an error formatted like
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@ -160,10 +159,10 @@ def least_squares(x, y, func, priors=None, silent=False, **kwargs):
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'''
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if priors is not None:
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return _prior_fit(x, y, func, priors, silent=silent, **kwargs)
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elif (type(x)==dict and type(y)==dict and type(func)==dict):
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elif (type(x) == dict and type(y) == dict and type(func) == dict):
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return _combined_fit(x, y, func, silent=silent, **kwargs)
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else:
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return _standard_fit(x, y, func, silent=silent, **kwargs)
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@ -688,39 +687,40 @@ def _standard_fit(x, y, func, silent=False, **kwargs):
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return output
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def _combined_fit(x,y,func,silent=False,**kwargs):
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def _combined_fit(x, y, func, silent=False, **kwargs):
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if kwargs.get('correlated_fit') is True:
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raise Exception("Correlated fit has not been implemented yet")
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output = Fit_result()
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output.fit_function = func
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if kwargs.get('num_grad') is True:
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jacobian = num_jacobian
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hessian = num_hessian
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else:
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jacobian = auto_jacobian
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hessian = auto_hessian
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x_all = []
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y_all = []
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for key in x.keys():
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x_all+=x[key]
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y_all+=y[key]
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x_all += x[key]
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y_all += y[key]
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x_all = np.asarray(x_all)
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if len(x_all.shape) > 2:
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raise Exception('Unknown format for x values')
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# number of fit parameters
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n_parms_ls = []
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for key in func.keys():
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if not callable(func[key]):
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raise TypeError('func (key='+ key + ') is not a function.')
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raise TypeError('func (key=' + key + ') is not a function.')
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if len(x[key]) != len(y[key]):
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raise Exception('x and y input (key='+ key + ') do not have the same length')
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raise Exception('x and y input (key=' + key + ') do not have the same length')
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for i in range(42):
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try:
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func[key](np.arange(i), x_all.T[0])
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@ -731,41 +731,41 @@ def _combined_fit(x,y,func,silent=False,**kwargs):
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else:
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break
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else:
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raise RuntimeError("Fit function (key="+ key + ") is not valid.")
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raise RuntimeError("Fit function (key=" + key + ") is not valid.")
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n_parms = i
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n_parms_ls.append(n_parms)
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n_parms = max(n_parms_ls)
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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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if 'initial_guess' in kwargs:
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x0 = kwargs.get('initial_guess')
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if len(x0) != n_parms:
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raise Exception('Initial guess does not have the correct length: %d vs. %d' % (len(x0), n_parms))
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else:
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x0 = [0.1] * n_parms
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output.method = kwargs.get('method', 'migrad')
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if not silent:
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print('Method:', output.method)
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def chisqfunc(p):
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chisq = 0.0
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for key in func.keys():
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x_array = np.asarray(x[key])
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model = anp.array(func[key](p,x_array))
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model = anp.array(func[key](p, x_array))
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y_obs = y[key]
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y_f = [o.value for o in y_obs]
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dy_f = [o.dvalue for o in y_obs]
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C_inv = np.diag(np.diag(np.ones((len(x_array),len(x_array)))))/dy_f/dy_f
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chisq += anp.sum((y_f - model)@ C_inv @(y_f - model))
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C_inv = np.diag(np.diag(np.ones((len(x_array), len(x_array))))) / dy_f / dy_f
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chisq += anp.sum((y_f - model) @ C_inv @ (y_f - model))
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return chisq
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if output.method == 'migrad':
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tolerance = 1e-4
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if 'tol' in kwargs:
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tolerance = kwargs.get('tol')
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fit_result = iminuit.minimize(chisqfunc, x0, tol=tolerance) # Stopping criterion 0.002 * tol * errordef
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fit_result = iminuit.minimize(chisqfunc, x0, tol=tolerance) # Stopping criterion 0.002 * tol * errordef
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output.iterations = fit_result.nfev
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else:
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tolerance = 1e-12
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@ -776,23 +776,23 @@ def _combined_fit(x,y,func,silent=False,**kwargs):
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chisquare = fit_result.fun
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output.message = fit_result.message
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if not fit_result.success:
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raise Exception('The minimization procedure did not converge.')
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if x_all.shape[-1] - n_parms > 0:
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output.chisquare = chisqfunc(fit_result.x)
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output.dof = x_all.shape[-1] - n_parms
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output.chisquare_by_dof = output.chisquare/output.dof
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output.chisquare_by_dof = output.chisquare / output.dof
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output.p_value = 1 - scipy.stats.chi2.cdf(output.chisquare, output.dof)
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else:
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output.chisquare_by_dof = float('nan')
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if not silent:
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print(fit_result.message)
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print('chisquare/d.o.f.:', output.chisquare_by_dof )
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print('fit parameters',fit_result.x)
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print('chisquare/d.o.f.:', output.chisquare_by_dof)
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print('fit parameters', fit_result.x)
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def chisqfunc_compact(d):
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chisq = 0.0
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list_tmp = []
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@ -800,38 +800,37 @@ def _combined_fit(x,y,func,silent=False,**kwargs):
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c2 = 0
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for key in func.keys():
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x_array = np.asarray(x[key])
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c2+=len(x_array)
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model = anp.array(func[key](d[:n_parms],x_array))
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c2 += len(x_array)
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model = anp.array(func[key](d[:n_parms], x_array))
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y_obs = y[key]
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y_f = [o.value for o in y_obs]
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dy_f = [o.dvalue for o in y_obs]
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C_inv = np.diag(np.diag(np.ones((len(x_array),len(x_array)))))/dy_f/dy_f
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list_tmp.append(anp.sum((d[n_parms+c1:n_parms+c2]- model)@ C_inv @(d[n_parms+c1:n_parms+c2]- model)))
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c1+=len(x_array)
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C_inv = np.diag(np.diag(np.ones((len(x_array), len(x_array))))) / dy_f / dy_f
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list_tmp.append(anp.sum((d[n_parms + c1:n_parms + c2] - model) @ C_inv @ (d[n_parms + c1:n_parms + c2] - model)))
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c1 += len(x_array)
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chisq = anp.sum(list_tmp)
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return chisq
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def prepare_hat_matrix():
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hat_vector = []
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for key in func.keys():
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x_array = np.asarray(x[key])
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if (len(x_array)!= 0):
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if (len(x_array) != 0):
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hat_vector.append(jacobian(func[key])(fit_result.x, x_array))
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hat_vector = [item for sublist in hat_vector for item in sublist]
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return hat_vector
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fitp = fit_result.x
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y_f = [o.value for o in y_all]
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y_f = [o.value for o in y_all]
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dy_f = [o.dvalue for o in y_all]
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if np.any(np.asarray(dy_f) <= 0.0):
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raise Exception('No y errors available, run the gamma method first.')
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try:
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hess = hessian(chisqfunc)(fitp)
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except TypeError:
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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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jac_jac_y = hessian(chisqfunc_compact)(np.concatenate((fitp, y_f)))
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# Compute hess^{-1} @ jac_jac_y[:n_parms + m, n_parms + m:] using LAPACK dgesv
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@ -839,29 +838,26 @@ def _combined_fit(x,y,func,silent=False,**kwargs):
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deriv_y = -scipy.linalg.solve(hess, jac_jac_y[:n_parms, n_parms:])
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except np.linalg.LinAlgError:
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raise Exception("Cannot invert hessian matrix.")
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if kwargs.get('expected_chisquare') is True:
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if kwargs.get('correlated_fit') is not True:
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W = np.diag(1 / np.asarray(dy_f))
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cov = covariance(y_all)
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hat_vector = prepare_hat_matrix()
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A = W @ hat_vector #hat_vector = 'jacobian(func)(fit_result.x, x)'
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A = W @ hat_vector # hat_vector = 'jacobian(func)(fit_result.x, x)'
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P_phi = A @ np.linalg.pinv(A.T @ A) @ A.T
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expected_chisquare = np.trace((np.identity(x_all.shape[-1]) - P_phi) @ W @ cov @ W)
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output.chisquare_by_expected_chisquare = chisquare / expected_chisquare
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if not silent:
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print('chisquare/expected_chisquare:', output.chisquare_by_expected_chisquare)
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result = []
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for i in range(n_parms):
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result.append(derived_observable(lambda x_all, **kwargs: (x_all[0] + np.finfo(np.float64).eps) / (y_all[0].value + np.finfo(np.float64).eps) * fitp[i], list(y_all), man_grad=list(deriv_y[i])))
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output.fit_parameters = result
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return output
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output.fit_parameters = result
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return output
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def fit_lin(x, y, **kwargs):
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