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refactor!: fit_general deprecated and moved to tests
This commit is contained in:
Fabian Joswig
parent
5a8b6483c8
commit
87c50f54c0
2 changed files with 101 additions and 105 deletions
105
pyerrors/fits.py
105
pyerrors/fits.py
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@ -646,10 +646,10 @@ def fit_lin(x, y, **kwargs):
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return y
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if all(isinstance(n, Obs) for n in x):
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out = odr_fit(x, y, f, **kwargs)
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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):
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out = standard_fit(x, y, f, **kwargs)
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out = least_squares(x, y, f, **kwargs)
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return out.fit_parameters
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else:
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raise Exception('Unsupported types for x')
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@ -785,104 +785,3 @@ def ks_test(obs=None):
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plt.draw()
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print(scipy.stats.kstest(Qs, 'uniform'))
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def fit_general(x, y, func, silent=False, **kwargs):
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"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
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Plausibility of the results should be checked. To control the numerical differentiation
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the kwargs of numdifftools.step_generators.MaxStepGenerator can be used.
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func has to be of the form
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def func(a, x):
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y = a[0] + a[1] * x + a[2] * np.sinh(x)
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return y
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y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.
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x can either be a list of floats in which case no xerror is assumed, or
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a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
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Keyword arguments
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-----------------
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silent -- If true all output to the console is omitted (default False).
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initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear fits
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with many parameters.
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"""
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warnings.warn("New fit functions with exact error propagation are now available as alternative.", DeprecationWarning)
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if not callable(func):
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raise TypeError('func has to be a function.')
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for i in range(10):
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try:
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func(np.arange(i), 0)
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except:
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pass
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else:
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break
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n_parms = i
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if not silent:
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print('Fit with', n_parms, 'parameters')
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global print_output, beta0
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print_output = 1
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if 'initial_guess' in kwargs:
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beta0 = kwargs.get('initial_guess')
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if len(beta0) != n_parms:
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raise Exception('Initial guess does not have the correct length.')
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else:
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beta0 = np.arange(n_parms)
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if len(x) != len(y):
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raise Exception('x and y have to have the same length')
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if all(isinstance(n, Obs) for n in x):
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obs = x + y
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x_constants = None
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xerr = [o.dvalue for o in x]
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yerr = [o.dvalue for o in y]
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elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
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obs = y
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x_constants = x
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xerr = None
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yerr = [o.dvalue for o in y]
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else:
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raise Exception('Unsupported types for x')
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def do_the_fit(obs, **kwargs):
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global print_output, beta0
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func = kwargs.get('function')
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yerr = kwargs.get('yerr')
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length = len(yerr)
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xerr = kwargs.get('xerr')
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if length == len(obs):
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assert 'x_constants' in kwargs
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data = RealData(kwargs.get('x_constants'), obs, sy=yerr)
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fit_type = 2
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elif length == len(obs) // 2:
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data = RealData(obs[:length], obs[length:], sx=xerr, sy=yerr)
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fit_type = 0
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else:
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raise Exception('x and y do not fit together.')
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model = Model(func)
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odr = ODR(data, model, beta0, partol=np.finfo(np.float64).eps)
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odr.set_job(fit_type=fit_type, deriv=1)
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output = odr.run()
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if print_output and not silent:
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print(*output.stopreason)
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print('chisquare/d.o.f.:', output.res_var)
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print_output = 0
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beta0 = output.beta
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return output.beta[kwargs.get('n')]
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res = []
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for n in range(n_parms):
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res.append(derived_observable(do_the_fit, obs, function=func, xerr=xerr, yerr=yerr, x_constants=x_constants, num_grad=True, n=n, **kwargs))
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return res
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@ -232,8 +232,7 @@ def test_odr_derivatives():
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out = pe.total_least_squares(x, y, func)
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fit1 = out.fit_parameters
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with pytest.warns(DeprecationWarning):
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tfit = pe.fits.fit_general(x, y, func, base_step=0.1, step_ratio=1.1, num_steps=20)
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tfit = fit_general(x, y, func, base_step=0.1, step_ratio=1.1, num_steps=20)
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assert np.abs(np.max(np.array(list(fit1[1].deltas.values()))
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- np.array(list(tfit[1].deltas.values())))) < 10e-8
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@ -274,3 +273,101 @@ def test_r_value_persistence():
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assert np.isclose(fitp[1].value, fitp[1].r_values['a'])
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assert np.isclose(fitp[1].value, fitp[1].r_values['b'])
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def fit_general(x, y, func, silent=False, **kwargs):
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"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
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Plausibility of the results should be checked. To control the numerical differentiation
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the kwargs of numdifftools.step_generators.MaxStepGenerator can be used.
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func has to be of the form
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def func(a, x):
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y = a[0] + a[1] * x + a[2] * np.sinh(x)
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return y
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y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.
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x can either be a list of floats in which case no xerror is assumed, or
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a list of Obs, where the dvalues of the Obs are used as xerror for the fit.
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Keyword arguments
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-----------------
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silent -- If true all output to the console is omitted (default False).
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initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear fits
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with many parameters.
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"""
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if not callable(func):
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raise TypeError('func has to be a function.')
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for i in range(10):
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try:
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func(np.arange(i), 0)
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except:
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pass
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else:
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break
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n_parms = i
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if not silent:
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print('Fit with', n_parms, 'parameters')
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global print_output, beta0
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print_output = 1
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if 'initial_guess' in kwargs:
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beta0 = kwargs.get('initial_guess')
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if len(beta0) != n_parms:
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raise Exception('Initial guess does not have the correct length.')
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else:
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beta0 = np.arange(n_parms)
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if len(x) != len(y):
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raise Exception('x and y have to have the same length')
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if all(isinstance(n, pe.Obs) for n in x):
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obs = x + y
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x_constants = None
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xerr = [o.dvalue for o in x]
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yerr = [o.dvalue for o in y]
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elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
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obs = y
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x_constants = x
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xerr = None
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yerr = [o.dvalue for o in y]
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else:
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raise Exception('Unsupported types for x')
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def do_the_fit(obs, **kwargs):
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global print_output, beta0
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func = kwargs.get('function')
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yerr = kwargs.get('yerr')
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length = len(yerr)
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xerr = kwargs.get('xerr')
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if length == len(obs):
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assert 'x_constants' in kwargs
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data = RealData(kwargs.get('x_constants'), obs, sy=yerr)
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fit_type = 2
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elif length == len(obs) // 2:
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data = RealData(obs[:length], obs[length:], sx=xerr, sy=yerr)
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fit_type = 0
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else:
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raise Exception('x and y do not fit together.')
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model = Model(func)
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odr = ODR(data, model, beta0, partol=np.finfo(np.float64).eps)
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odr.set_job(fit_type=fit_type, deriv=1)
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output = odr.run()
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if print_output and not silent:
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print(*output.stopreason)
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print('chisquare/d.o.f.:', output.res_var)
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print_output = 0
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beta0 = output.beta
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return output.beta[kwargs.get('n')]
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res = []
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for n in range(n_parms):
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res.append(pe.derived_observable(do_the_fit, obs, function=func, xerr=xerr, yerr=yerr, x_constants=x_constants, num_grad=True, n=n, **kwargs))
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return res
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