refactor!: fit_general deprecated and moved to tests

This commit is contained in:
Fabian Joswig 2021-12-09 10:11:31 +00:00
parent 5a8b6483c8
commit 87c50f54c0
2 changed files with 101 additions and 105 deletions

View file

@ -646,10 +646,10 @@ def fit_lin(x, y, **kwargs):
return y
if all(isinstance(n, Obs) for n in x):
out = odr_fit(x, y, f, **kwargs)
out = total_least_squares(x, y, f, **kwargs)
return out.fit_parameters
elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
out = standard_fit(x, y, f, **kwargs)
out = least_squares(x, y, f, **kwargs)
return out.fit_parameters
else:
raise Exception('Unsupported types for x')
@ -785,104 +785,3 @@ def ks_test(obs=None):
plt.draw()
print(scipy.stats.kstest(Qs, 'uniform'))
def fit_general(x, y, func, silent=False, **kwargs):
"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
Plausibility of the results should be checked. To control the numerical differentiation
the kwargs of numdifftools.step_generators.MaxStepGenerator can be used.
func has to be of the form
def func(a, x):
y = a[0] + a[1] * x + a[2] * np.sinh(x)
return y
y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.
x 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.
Keyword arguments
-----------------
silent -- If true all output to the console is omitted (default False).
initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear fits
with many parameters.
"""
warnings.warn("New fit functions with exact error propagation are now available as alternative.", DeprecationWarning)
if not callable(func):
raise TypeError('func has to be a function.')
for i in range(10):
try:
func(np.arange(i), 0)
except:
pass
else:
break
n_parms = i
if not silent:
print('Fit with', n_parms, 'parameters')
global print_output, beta0
print_output = 1
if 'initial_guess' in kwargs:
beta0 = kwargs.get('initial_guess')
if len(beta0) != n_parms:
raise Exception('Initial guess does not have the correct length.')
else:
beta0 = np.arange(n_parms)
if len(x) != len(y):
raise Exception('x and y have to have the same length')
if all(isinstance(n, Obs) for n in x):
obs = x + y
x_constants = None
xerr = [o.dvalue for o in x]
yerr = [o.dvalue for o in y]
elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
obs = y
x_constants = x
xerr = None
yerr = [o.dvalue for o in y]
else:
raise Exception('Unsupported types for x')
def do_the_fit(obs, **kwargs):
global print_output, beta0
func = kwargs.get('function')
yerr = kwargs.get('yerr')
length = len(yerr)
xerr = kwargs.get('xerr')
if length == len(obs):
assert 'x_constants' in kwargs
data = RealData(kwargs.get('x_constants'), obs, sy=yerr)
fit_type = 2
elif length == len(obs) // 2:
data = RealData(obs[:length], obs[length:], sx=xerr, sy=yerr)
fit_type = 0
else:
raise Exception('x and y do not fit together.')
model = Model(func)
odr = ODR(data, model, beta0, partol=np.finfo(np.float64).eps)
odr.set_job(fit_type=fit_type, deriv=1)
output = odr.run()
if print_output and not silent:
print(*output.stopreason)
print('chisquare/d.o.f.:', output.res_var)
print_output = 0
beta0 = output.beta
return output.beta[kwargs.get('n')]
res = []
for n in range(n_parms):
res.append(derived_observable(do_the_fit, obs, function=func, xerr=xerr, yerr=yerr, x_constants=x_constants, num_grad=True, n=n, **kwargs))
return res

View file

@ -232,8 +232,7 @@ def test_odr_derivatives():
out = pe.total_least_squares(x, y, func)
fit1 = out.fit_parameters
with pytest.warns(DeprecationWarning):
tfit = pe.fits.fit_general(x, y, func, base_step=0.1, step_ratio=1.1, num_steps=20)
tfit = fit_general(x, y, func, base_step=0.1, step_ratio=1.1, num_steps=20)
assert np.abs(np.max(np.array(list(fit1[1].deltas.values()))
- np.array(list(tfit[1].deltas.values())))) < 10e-8
@ -274,3 +273,101 @@ def test_r_value_persistence():
assert np.isclose(fitp[1].value, fitp[1].r_values['a'])
assert np.isclose(fitp[1].value, fitp[1].r_values['b'])
def fit_general(x, y, func, silent=False, **kwargs):
"""Performs a non-linear fit to y = func(x) and returns a list of Obs corresponding to the fit parameters.
Plausibility of the results should be checked. To control the numerical differentiation
the kwargs of numdifftools.step_generators.MaxStepGenerator can be used.
func has to be of the form
def func(a, x):
y = a[0] + a[1] * x + a[2] * np.sinh(x)
return y
y has to be a list of Obs, the dvalues of the Obs are used as yerror for the fit.
x 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.
Keyword arguments
-----------------
silent -- If true all output to the console is omitted (default False).
initial_guess -- can provide an initial guess for the input parameters. Relevant for non-linear fits
with many parameters.
"""
if not callable(func):
raise TypeError('func has to be a function.')
for i in range(10):
try:
func(np.arange(i), 0)
except:
pass
else:
break
n_parms = i
if not silent:
print('Fit with', n_parms, 'parameters')
global print_output, beta0
print_output = 1
if 'initial_guess' in kwargs:
beta0 = kwargs.get('initial_guess')
if len(beta0) != n_parms:
raise Exception('Initial guess does not have the correct length.')
else:
beta0 = np.arange(n_parms)
if len(x) != len(y):
raise Exception('x and y have to have the same length')
if all(isinstance(n, pe.Obs) for n in x):
obs = x + y
x_constants = None
xerr = [o.dvalue for o in x]
yerr = [o.dvalue for o in y]
elif all(isinstance(n, float) or isinstance(n, int) for n in x) or isinstance(x, np.ndarray):
obs = y
x_constants = x
xerr = None
yerr = [o.dvalue for o in y]
else:
raise Exception('Unsupported types for x')
def do_the_fit(obs, **kwargs):
global print_output, beta0
func = kwargs.get('function')
yerr = kwargs.get('yerr')
length = len(yerr)
xerr = kwargs.get('xerr')
if length == len(obs):
assert 'x_constants' in kwargs
data = RealData(kwargs.get('x_constants'), obs, sy=yerr)
fit_type = 2
elif length == len(obs) // 2:
data = RealData(obs[:length], obs[length:], sx=xerr, sy=yerr)
fit_type = 0
else:
raise Exception('x and y do not fit together.')
model = Model(func)
odr = ODR(data, model, beta0, partol=np.finfo(np.float64).eps)
odr.set_job(fit_type=fit_type, deriv=1)
output = odr.run()
if print_output and not silent:
print(*output.stopreason)
print('chisquare/d.o.f.:', output.res_var)
print_output = 0
beta0 = output.beta
return output.beta[kwargs.get('n')]
res = []
for n in range(n_parms):
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))
return res