mirror of
https://github.com/fjosw/pyerrors.git
synced 2025-03-15 14:50:25 +01:00
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
|
|
@ -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
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue