https://github.com/lmfit/lmfit-py
Tip revision: 8781a2dc33288b25fae6f3139595402155e4968b authored by Matthew Newville on 04 April 2024, 15:42:14 UTC
whatsnew for version 1.3.0
whatsnew for version 1.3.0
Tip revision: 8781a2d
test_model.py
"""Tests for the Model, CompositeModel, and ModelResult classes."""
import functools
import unittest
import warnings
import numpy as np
from numpy.testing import assert_allclose, assert_almost_equal
import pytest
from scipy import __version__ as scipy_version
import lmfit
from lmfit import Model, Parameters, models
from lmfit.lineshapes import gaussian, lorentzian, step, voigt
from lmfit.model import get_reducer, propagate_err
from lmfit.models import GaussianModel, PseudoVoigtModel
@pytest.fixture()
def gmodel():
"""Return a Gaussian model."""
return Model(gaussian)
def test_get_reducer_invalid_option():
"""Tests for ValueError when using an unsupported option."""
option = 'unknown'
msg = r'Invalid option'
with pytest.raises(ValueError, match=msg):
get_reducer(option)
test_data_get_reducer = [('real', [1.0, 1.0, 2.0, 2.0]),
('imag', [0.0, 10.0, 0.0, 20.0]),
('abs', [1.0, 10.04987562, 2.0, 20.09975124]),
('angle', [0.0, 1.47112767, 0.0, 1.471127670])]
@pytest.mark.parametrize('option, expected_array', test_data_get_reducer)
def test_get_reducer(option, expected_array):
"""Tests for ValueError when using an unsupported option."""
complex_array = np.array([1.0, 1.0+10j, 2.0, 2.0+20j], dtype='complex')
func = get_reducer(option)
real_array = func(complex_array)
assert np.all(np.isreal(real_array))
assert_allclose(real_array, expected_array)
# nothing should happen to an array that only contains real data
assert_allclose(func(real_array), real_array)
def test_propagate_err_invalid_option():
"""Tests for ValueError when using an unsupported option."""
z = np.array([0, 1, 2, 3, 4, 5])
dz = np.random.normal(size=z.size, scale=0.1)
option = 'unknown'
msg = r'Invalid option'
with pytest.raises(ValueError, match=msg):
propagate_err(z, dz, option)
def test_propagate_err_unequal_shape_z_dz():
"""Tests for ValueError when using unequal arrays for z and dz."""
z = np.array([0, 1, 2, 3, 4, 5])
dz = np.random.normal(size=z.size-1, scale=0.1)
msg = r'shape of z:'
with pytest.raises(ValueError, match=msg):
propagate_err(z, dz, option='abs')
@pytest.mark.parametrize('option', ['real', 'imag', 'abs', 'angle'])
def test_propagate_err(option):
"""Tests for ValueError when using an unsupported option."""
np.random.seed(2020)
z = np.array([1.0, 1.0+10j, 2.0, 2.0+20j], dtype='complex')
dz = np.random.normal(z.size, scale=0.1)*z
# if `z` is real, assume that `dz` is also real and return it as-is
err = propagate_err(np.real(z), np.real(dz), option)
assert_allclose(err, np.real(dz))
# if `z` is complex, but `dz` is real apply the err to both real/imag
err_complex_real = propagate_err(z, np.real(dz), option)
assert np.all(np.isreal(err_complex_real))
dz_used = np.real(dz)+1j*np.real(dz)
if option == 'real':
assert_allclose(err_complex_real, np.real(dz_used))
elif option == 'imag':
assert_allclose(err_complex_real, np.imag(dz_used))
elif option == 'abs':
assert_allclose(err_complex_real,
[3.823115, 3.823115, 7.646231, 7.646231],
rtol=1.0e-5)
elif option == 'angle':
assert_allclose(err_complex_real,
[3.823115, 0.380414, 3.823115, 0.380414],
rtol=1.0e-5)
# both `z` and `dz` are complex
err_complex_complex = propagate_err(z, dz, option)
assert np.all(np.isreal(err_complex_complex))
if option == 'real':
assert_allclose(err_complex_complex, np.real(dz))
elif option == 'imag':
assert_allclose(err_complex_complex, np.imag(dz))
elif option == 'abs':
assert_allclose(err_complex_complex,
[3.823115, 38.043322, 7.646231, 76.086645],
rtol=1.0e-5)
elif option == 'angle':
assert_allclose(err_complex_complex, [0., 0.535317, 0., 0.535317],
rtol=1.0e-5)
def test_initialize_Model_class_default_arguments(gmodel):
"""Test for Model class initialized with default arguments."""
assert gmodel.prefix == ''
assert gmodel._param_root_names == ['amplitude', 'center', 'sigma']
assert gmodel.param_names == ['amplitude', 'center', 'sigma']
assert gmodel.independent_vars == ['x']
assert gmodel.nan_policy == 'raise'
assert gmodel.name == 'Model(gaussian)'
assert gmodel.opts == {}
assert gmodel.def_vals == {'amplitude': 1.0, 'center': 0.0, 'sigma': 1.0}
def test_initialize_Model_class_independent_vars():
"""Test for Model class initialized with independent_vars."""
model = Model(gaussian, independent_vars=['amplitude'])
assert model._param_root_names == ['x', 'center', 'sigma']
assert model.param_names == ['x', 'center', 'sigma']
assert model.independent_vars == ['amplitude']
def test_initialize_Model_class_param_names():
"""Test for Model class initialized with param_names."""
model = Model(gaussian, param_names=['amplitude'])
assert model._param_root_names == ['amplitude']
assert model.param_names == ['amplitude']
@pytest.mark.parametrize("policy", ['raise', 'omit', 'propagate'])
def test_initialize_Model_class_nan_policy(policy):
"""Test for Model class initialized with nan_policy."""
model = Model(gaussian, nan_policy=policy)
assert model.nan_policy == policy
def test_initialize_Model_class_prefix():
"""Test for Model class initialized with prefix."""
model = Model(gaussian, prefix='test_')
assert model.prefix == 'test_'
assert model._param_root_names == ['amplitude', 'center', 'sigma']
assert model.param_names == ['test_amplitude', 'test_center', 'test_sigma']
assert model.name == "Model(gaussian, prefix='test_')"
model = Model(gaussian, prefix=None)
assert model.prefix == ''
def test_initialize_Model_name():
"""Test for Model class initialized with name."""
model = Model(gaussian, name='test_function')
assert model.name == 'Model(test_function)'
def test_initialize_Model_kws():
"""Test for Model class initialized with **kws."""
kws = {'amplitude': 10.0}
model = Model(gaussian,
independent_vars=['x', 'amplitude'], **kws)
assert model._param_root_names == ['center', 'sigma']
assert model.param_names == ['center', 'sigma']
assert model.independent_vars == ['x', 'amplitude']
assert model.opts == kws
test_reprstring_data = [(False, 'Model(gaussian)'),
(True, "Model(gaussian, amplitude='10.0')")]
@pytest.mark.parametrize("option, expected", test_reprstring_data)
def test_Model_reprstring(option, expected):
"""Test for Model class function _reprstring."""
kws = {'amplitude': 10.0}
model = Model(gaussian,
independent_vars=['x', 'amplitude'], **kws)
assert model._reprstring(option) == expected
def test_Model_get_state(gmodel):
"""Test for Model class function _get_state."""
out = gmodel._get_state()
assert isinstance(out, tuple)
assert out[1] == out[2] is None
sstate = out[0]
if isinstance(sstate, tuple): # state version 1
assert sstate[1] is not None
assert sstate[0] == 'gaussian'
assert sstate[2:] == ('gaussian', '', ['x'],
['amplitude', 'center', 'sigma'], {}, 'raise', {})
elif isinstance(sstate, dict) and 'version' in sstate: # state version 2 or higher
if sstate['version'] == '2':
assert sstate['funcname'] == 'gaussian'
assert sstate['name'] == 'gaussian'
assert sstate['independent_vars'] == ['x']
assert sstate['param_root_names'] == ['amplitude', 'center', 'sigma']
else:
assert sstate['version'] == 'unknown version!'
else:
assert sstate == 'unknown model state'
def test_Model_set_state(gmodel):
"""Test for Model class function _set_state.
This function is just calling `_buildmodel`, which will be tested
below together with the use of `funcdefs`.
"""
out = gmodel._get_state()
new_model = Model(lorentzian)
new_model = new_model._set_state(out)
assert new_model.prefix == gmodel.prefix
assert new_model._param_root_names == gmodel._param_root_names
assert new_model.param_names == gmodel.param_names
assert new_model.independent_vars == gmodel.independent_vars
assert new_model.nan_policy == gmodel.nan_policy
assert new_model.name == gmodel.name
assert new_model.opts == gmodel.opts
def test_Model_dumps_loads(gmodel):
"""Test for Model class functions dumps and loads.
These function are used when saving/loading the Model class and will be
tested more thoroughly in test_model_saveload.py.
"""
model_json = gmodel.dumps()
_ = gmodel.loads(model_json)
def test_Model_getter_setter_name(gmodel):
"""Test for Model class getter/setter functions for name."""
assert gmodel.name == 'Model(gaussian)'
gmodel.name = 'test_gaussian'
assert gmodel.name == 'Model(test_gaussian)'
def test_Model_getter_setter_prefix(gmodel):
"""Test for Model class getter/setter functions for prefix."""
assert gmodel.prefix == ''
assert gmodel.param_names == ['amplitude', 'center', 'sigma']
gmodel.prefix = 'g1_'
assert gmodel.prefix == 'g1_'
assert gmodel.param_names == ['g1_amplitude', 'g1_center', 'g1_sigma']
gmodel.prefix = ''
assert gmodel.prefix == ''
assert gmodel.param_names == ['amplitude', 'center', 'sigma']
def test_Model_getter_param_names(gmodel):
"""Test for Model class getter function for param_names."""
assert gmodel.param_names == ['amplitude', 'center', 'sigma']
def test_Model__repr__(gmodel):
"""Test for Model class __repr__ method."""
assert gmodel.__repr__() == '<lmfit.Model: Model(gaussian)>'
def test_Model_copy(gmodel):
"""Test for Model class copy method."""
msg = 'Model.copy does not work. Make a new Model'
with pytest.raises(NotImplementedError, match=msg):
gmodel.copy()
def test__parse_params_func_None():
"""Test for _parse_params function with func=None."""
mod = Model(None)
assert mod._prefix == ''
assert mod.func is None
assert mod._func_allargs == []
assert mod._func_haskeywords is False
assert mod.independent_vars == []
def test__parse_params_asteval_functions():
"""Test for _parse_params function with asteval functions."""
# TODO: cannot find a use-case for this....
pass
def test__parse_params_inspect_signature():
"""Test for _parse_params function using inspect.signature."""
# 1. function with a positional argument
def func_var_positional(a, *b):
pass
with pytest.raises(ValueError, match=r"varargs '\*b' is not supported"):
Model(func_var_positional)
# 2. function with a keyword argument
def func_keyword(a, b, **c):
pass
mod = Model(func_keyword)
assert mod._func_allargs == ['a', 'b']
assert mod._func_haskeywords is True
assert mod.independent_vars == ['a']
assert mod.def_vals == {}
# 3. function with keyword argument only
def func_keyword_only(**b):
pass
mod = Model(func_keyword_only)
assert mod._func_allargs == []
assert mod._func_haskeywords is True
assert mod.independent_vars == []
assert mod._param_root_names is None
# 4. function with default value
def func_default_value(a, b, c=10):
pass
mod = Model(func_default_value)
assert mod._func_allargs == ['a', 'b', 'c']
assert mod._func_haskeywords is False
assert mod.independent_vars == ['a']
assert isinstance(mod.def_vals, dict)
assert_allclose(mod.def_vals['c'], 10)
def test_make_params_withprefixs():
# tests Github Issue #893
gmod1 = GaussianModel(prefix='p1_')
gmod2 = GaussianModel(prefix='p2_')
model = gmod1 + gmod2
pars_1a = gmod1.make_params(p1_amplitude=10, p1_center=600, p1_sigma=3)
pars_1b = gmod1.make_params(amplitude=10, center=600, sigma=3)
pars_2a = gmod2.make_params(p2_amplitude=30, p2_center=730, p2_sigma=4)
pars_2b = gmod2.make_params(amplitude=30, center=730, sigma=4)
pars_a = Parameters()
pars_a.update(pars_1a)
pars_a.update(pars_2a)
pars_b = Parameters()
pars_b.update(pars_1b)
pars_b.update(pars_2b)
pars_c = model.make_params()
for pname in ('p1_amplitude', 'p1_center', 'p1_sigma',
'p2_amplitude', 'p2_center', 'p2_sigma'):
assert pname in pars_a
assert pname in pars_b
assert pname in pars_c
def test__parse_params_forbidden_variable_names():
"""Tests for _parse_params function using invalid variable names."""
def func_invalid_var(data, a):
pass
def func_invalid_par(a, weights):
pass
msg = r"Invalid independent variable name \('data'\) for function func_invalid_var"
with pytest.raises(ValueError, match=msg):
Model(func_invalid_var)
msg = r"Invalid parameter name \('weights'\) for function func_invalid_par"
with pytest.raises(ValueError, match=msg):
Model(func_invalid_par)
@pytest.mark.parametrize('input_dtype', (np.int16, np.int32, np.float32,
np.complex64, np.complex128, 'list',
'tuple', 'pandas-real',
'pandas-complex'))
def test_coercion_of_input_data(peakdata, input_dtype):
"""Test for coercion of 'data' and 'independent_vars'.
'data' and `independent_vars` should be coerced to 'float64' or 'complex128'
unless told not be coerced by setting ``coerce_farray=False``.
# - dtype for 'indepdendent_vars' is only changed when the input is a list,
# tuple, numpy.ndarray, or pandas.Series
"""
x, y = peakdata
def gaussian_lists(x, amplitude=1.0, center=0.0, sigma=1.0):
xarr = np.array(x, dtype=np.float64)
return ((amplitude/(max(1.e-15, np.sqrt(2*np.pi)*sigma)))
* np.exp(-(xarr-center)**2 / max(1.e-15, (2*sigma**2))))
for coerce_farray in True, False:
if (input_dtype in ('pandas-real', 'pandas-complex')
and not lmfit.minimizer.HAS_PANDAS):
return
if not coerce_farray and input_dtype in ('list', 'tuple'):
model = lmfit.Model(gaussian_lists)
else:
model = lmfit.Model(gaussian)
pars = model.make_params(amplitude=5, center=10, sigma=2)
if input_dtype == 'pandas-real':
result = model.fit(lmfit.model.Series(y, dtype=np.float32), pars,
x=lmfit.model.Series(x, dtype=np.float32),
coerce_farray=coerce_farray)
expected_dtype = np.float64 if coerce_farray else np.float32
elif input_dtype == 'pandas-complex':
result = model.fit(lmfit.model.Series(y, dtype=np.complex64), pars,
x=lmfit.model.Series(x, dtype=np.complex64),
coerce_farray=coerce_farray)
expected_dtype = np.complex128 if coerce_farray else np.complex64
elif input_dtype == 'list':
result = model.fit(y.tolist(), pars, x=x.tolist(),
coerce_farray=coerce_farray)
expected_dtype = np.float64 if coerce_farray else list
elif input_dtype == 'tuple':
result = model.fit(tuple(y), pars, x=tuple(x),
coerce_farray=coerce_farray)
expected_dtype = np.float64 if coerce_farray else tuple
else:
result = model.fit(np.asarray(y, dtype=input_dtype), pars,
x=np.asarray(x, dtype=input_dtype),
coerce_farray=coerce_farray)
expected_dtype = np.float64
if input_dtype in (np.complex64, np.complex128):
expected_dtype = np.complex128
expected_dtype = expected_dtype if coerce_farray else input_dtype
if not coerce_farray and input_dtype in ('list', 'tuple'):
assert isinstance(result.userkws['x'], (list, tuple))
assert isinstance(result.userargs[0], (list, tuple))
else:
assert result.userkws['x'].dtype == expected_dtype
assert result.userargs[0].dtype == expected_dtype
def test_figure_default_title(peakdata):
"""Test default figure title."""
pytest.importorskip('matplotlib')
x, y = peakdata
pvmodel = PseudoVoigtModel()
params = pvmodel.guess(y, x=x)
result = pvmodel.fit(y, params, x=x)
ax = result.plot_fit()
assert ax.axes.get_title() == 'Model(pvoigt)'
ax = result.plot_residuals()
assert ax.axes.get_title() == 'Model(pvoigt)'
fig = result.plot()
assert fig.axes[0].get_title() == 'Model(pvoigt)' # default model.name
assert fig.axes[1].get_title() == '' # no title for fit subplot
def test_figure_title_using_title_keyword_argument(peakdata):
"""Test setting figure title using title keyword argument."""
pytest.importorskip('matplotlib')
x, y = peakdata
pvmodel = PseudoVoigtModel()
params = pvmodel.guess(y, x=x)
result = pvmodel.fit(y, params, x=x)
ax = result.plot_fit(title='test')
assert ax.axes.get_title() == 'test'
ax = result.plot_residuals(title='test')
assert ax.axes.get_title() == 'test'
fig = result.plot(title='test')
assert fig.axes[0].get_title() == 'test'
assert fig.axes[1].get_title() == '' # no title for fit subplot
def test_figure_title_using_title_to_ax_kws(peakdata):
"""Test setting figure title by supplying ax_{fit,res}_kws."""
pytest.importorskip('matplotlib')
x, y = peakdata
pvmodel = PseudoVoigtModel()
params = pvmodel.guess(y, x=x)
result = pvmodel.fit(y, params, x=x)
ax = result.plot_fit(ax_kws={'title': 'ax_kws'})
assert ax.axes.get_title() == 'ax_kws'
ax = result.plot_residuals(ax_kws={'title': 'ax_kws'})
assert ax.axes.get_title() == 'ax_kws'
fig = result.plot(ax_res_kws={'title': 'ax_res_kws'})
assert fig.axes[0].get_title() == 'ax_res_kws'
assert fig.axes[1].get_title() == ''
fig = result.plot(ax_fit_kws={'title': 'ax_fit_kws'})
assert fig.axes[0].get_title() == 'Model(pvoigt)' # default model.name
assert fig.axes[1].get_title() == '' # no title for fit subplot
def test_priority_setting_figure_title(peakdata):
"""Test for setting figure title: title keyword argument has priority."""
pytest.importorskip('matplotlib')
x, y = peakdata
pvmodel = PseudoVoigtModel()
params = pvmodel.guess(y, x=x)
result = pvmodel.fit(y, params, x=x)
ax = result.plot_fit(ax_kws={'title': 'ax_kws'}, title='test')
assert ax.axes.get_title() == 'test'
ax = result.plot_residuals(ax_kws={'title': 'ax_kws'}, title='test')
assert ax.axes.get_title() == 'test'
fig = result.plot(ax_res_kws={'title': 'ax_res_kws'}, title='test')
assert fig.axes[0].get_title() == 'test'
assert fig.axes[1].get_title() == ''
fig = result.plot(ax_fit_kws={'title': 'ax_fit_kws'}, title='test')
assert fig.axes[0].get_title() == 'test'
assert fig.axes[1].get_title() == ''
def test_eval_with_kwargs():
# Check eval() with both params and kwargs, even when there are
# constraints
x = np.linspace(0, 30, 301)
np.random.seed(13)
y1 = (gaussian(x, amplitude=10, center=12.0, sigma=2.5) +
gaussian(x, amplitude=20, center=19.0, sigma=2.5))
y2 = (gaussian(x, amplitude=10, center=12.0, sigma=1.5) +
gaussian(x, amplitude=20, center=19.0, sigma=2.5))
model = Model(gaussian, prefix='g1_') + Model(gaussian, prefix='g2_')
params = model.make_params(g1_amplitude=10, g1_center=12.0, g1_sigma=1,
g2_amplitude=20, g2_center=19.0,
g2_sigma={'expr': 'g1_sigma'})
r1 = model.eval(params, g1_sigma=2.5, x=x)
assert_allclose(r1, y1, atol=1.e-3)
assert params['g2_sigma'].value == 1
assert params['g1_sigma'].value == 1
params['g1_sigma'].value = 1.5
params['g2_sigma'].expr = None
params['g2_sigma'].value = 2.5
r2 = model.eval(params, x=x)
assert_allclose(r2, y2, atol=1.e-3)
def test_guess_requires_x():
"""Test to make sure that ``guess()`` method requires the argument ``x``.
The ``guess`` method needs ``x`` values (i.e., the independent variable)
to estimate initial parameters, but this was not a required argument.
See GH #747.
"""
mod = lmfit.model.Model(gaussian)
msg = r"guess\(\) missing 2 required positional arguments: 'data' and 'x'"
with pytest.raises(TypeError, match=msg):
mod.guess()
# Below is the content of the original test_model.py file. These tests still
# need to be checked and possibly updated to the pytest-style. They work fine
# though so leave them in for now.
def assert_results_close(actual, desired, rtol=1e-03, atol=1e-03, err_msg='',
verbose=True):
for param_name, value in desired.items():
assert_allclose(actual[param_name], value, rtol, atol, err_msg,
verbose)
def firstarg_ndarray(func):
"""a simple wrapper used for testing that wrapped
functions can be model functions"""
@functools.wraps(func)
def wrapper(x, *args, **kws):
x = np.asarray(x)
return func(x, *args, **kws)
return wrapper
@firstarg_ndarray
def linear_func(x, a, b):
"test wrapped model function"
return a*x+b
class CommonTests:
# to be subclassed for testing predefined models
def setUp(self):
np.random.seed(1)
self.noise = 0.0001*np.random.randn(self.x.size)
# Some Models need args (e.g., polynomial order), and others don't.
try:
args = self.args
except AttributeError:
self.model = self.model_constructor()
self.model_omit = self.model_constructor(nan_policy='omit')
self.model_raise = self.model_constructor(nan_policy='raise')
self.model_explicit_var = self.model_constructor(['x'])
func = self.model.func
else:
self.model = self.model_constructor(*args)
self.model_omit = self.model_constructor(*args, nan_policy='omit')
self.model_raise = self.model_constructor(*args, nan_policy='raise')
self.model_explicit_var = self.model_constructor(
*args, independent_vars=['x'])
func = self.model.func
self.data = func(x=self.x, **self.true_values()) + self.noise
@property
def x(self):
return np.linspace(1, 10, num=1000)
def test_fit(self):
model = self.model
# Pass Parameters object.
params = model.make_params(**self.guess())
result = model.fit(self.data, params, x=self.x)
assert_results_close(result.values, self.true_values())
# Pass individual Parameter objects as kwargs.
kwargs = dict(params.items())
result = self.model.fit(self.data, x=self.x, **kwargs)
assert_results_close(result.values, self.true_values())
# Pass guess values (not Parameter objects) as kwargs.
kwargs = {name: p.value for name, p in params.items()}
result = self.model.fit(self.data, x=self.x, **kwargs)
assert_results_close(result.values, self.true_values())
def test_explicit_independent_vars(self):
self.check_skip_independent_vars()
model = self.model_explicit_var
pars = model.make_params(**self.guess())
result = model.fit(self.data, pars, x=self.x)
assert_results_close(result.values, self.true_values())
def test_fit_with_weights(self):
model = self.model
# fit without weights
params = model.make_params(**self.guess())
out1 = model.fit(self.data, params, x=self.x)
# fit with weights
weights = 1.0/(0.5 + self.x**2)
out2 = model.fit(self.data, params, weights=weights, x=self.x)
max_diff = 0.0
for parname, val1 in out1.values.items():
val2 = out2.values[parname]
if max_diff < abs(val1-val2):
max_diff = abs(val1-val2)
assert max_diff > 1.e-8
def test_result_attributes(self):
pars = self.model.make_params(**self.guess())
result = self.model.fit(self.data, pars, x=self.x)
# result.init_values
assert_results_close(result.values, self.true_values())
self.assertEqual(result.init_values, self.guess())
# result.init_params
params = self.model.make_params()
for param_name, value in self.guess().items():
params[param_name].value = value
self.assertEqual(result.init_params, params)
# result.best_fit
assert_allclose(result.best_fit, self.data, atol=self.noise.max())
# result.init_fit
init_fit = self.model.func(x=self.x, **self.guess())
assert_allclose(result.init_fit, init_fit)
# result.model
self.assertTrue(result.model is self.model)
def test_result_eval(self):
# Check eval() output against init_fit and best_fit.
pars = self.model.make_params(**self.guess())
result = self.model.fit(self.data, pars, x=self.x)
assert_allclose(result.eval(x=self.x, **result.values),
result.best_fit)
assert_allclose(result.eval(x=self.x, **result.init_values),
result.init_fit)
def test_result_eval_custom_x(self):
self.check_skip_independent_vars()
pars = self.model.make_params(**self.guess())
result = self.model.fit(self.data, pars, x=self.x)
# Check that the independent variable is respected.
short_eval = result.eval(x=np.array([0, 1, 2]), **result.values)
if hasattr(short_eval, '__len__'):
self.assertEqual(len(short_eval), 3)
def test_result_report(self):
pars = self.model.make_params(**self.guess())
result = self.model.fit(self.data, pars, x=self.x)
report = result.fit_report()
assert "[[Model]]" in report
assert "[[Variables]]" in report
assert "[[Fit Statistics]]" in report
assert " # function evals =" in report
assert " Akaike " in report
assert " chi-square " in report
def test_data_alignment(self):
pytest.importorskip('pandas')
from pandas import Series
# Align data and indep var of different lengths using pandas index.
data = Series(self.data.copy()).iloc[10:-10]
x = Series(self.x.copy())
model = self.model
params = model.make_params(**self.guess())
result = model.fit(data, params, x=x)
result = model.fit(data, params, x=x)
assert_results_close(result.values, self.true_values())
# Skip over missing (NaN) values, aligning via pandas index.
data.iloc[500:510] = np.nan
result = self.model_omit.fit(data, params, x=x)
assert_results_close(result.values, self.true_values())
# Raise if any NaN values are present.
raises = lambda: self.model_raise.fit(data, params, x=x)
self.assertRaises(ValueError, raises)
def check_skip_independent_vars(self):
# to be overridden for models that do not accept indep vars
pass
def test_aic(self):
model = self.model
# Pass Parameters object.
params = model.make_params(**self.guess())
result = model.fit(self.data, params, x=self.x)
aic = result.aic
self.assertTrue(aic < 0) # aic must be negative
# Pass extra unused Parameter.
params.add("unused_param", value=1.0, vary=True)
result = model.fit(self.data, params, x=self.x)
aic_extra = result.aic
self.assertTrue(aic_extra < 0) # aic must be negative
self.assertTrue(aic < aic_extra) # extra param should lower the aic
def test_bic(self):
model = self.model
# Pass Parameters object.
params = model.make_params(**self.guess())
result = model.fit(self.data, params, x=self.x)
bic = result.bic
self.assertTrue(bic < 0) # aic must be negative
# Compare to AIC
aic = result.aic
self.assertTrue(aic < bic) # aic should be lower than bic
# Pass extra unused Parameter.
params.add("unused_param", value=1.0, vary=True)
result = model.fit(self.data, params, x=self.x)
bic_extra = result.bic
self.assertTrue(bic_extra < 0) # bic must be negative
self.assertTrue(bic < bic_extra) # extra param should lower the bic
class TestUserDefiniedModel(CommonTests, unittest.TestCase):
# mainly aimed at checking that the API does what it says it does
# and raises the right exceptions or warnings when things are not right
def setUp(self):
self.true_values = lambda: dict(amplitude=7.1, center=1.1, sigma=2.40)
self.guess = lambda: dict(amplitude=5, center=2, sigma=4)
# return a fresh copy
self.model_constructor = (
lambda *args, **kwargs: Model(gaussian, *args, **kwargs))
super().setUp()
@property
def x(self):
return np.linspace(-10, 10, num=1000)
def test_lists_become_arrays(self):
# smoke test
self.model.fit([1, 2, 3], x=[1, 2, 3], **self.guess())
pytest.raises(ValueError,
self.model.fit,
[1, 2, None, 3],
x=[1, 2, 3, 4],
**self.guess())
def test_missing_param_raises_error(self):
# using keyword argument parameters
guess_missing_sigma = self.guess()
del guess_missing_sigma['sigma']
# f = lambda: self.model.fit(self.data, x=self.x, **guess_missing_sigma)
# self.assertRaises(ValueError, f)
# using Parameters
params = self.model.make_params()
for param_name, value in guess_missing_sigma.items():
params[param_name].value = value
self.model.fit(self.data, params, x=self.x)
def test_extra_param_issues_warning(self):
# The function accepts extra params, Model will warn but not raise.
def flexible_func(x, amplitude, center, sigma, **kwargs):
return gaussian(x, amplitude, center, sigma)
flexible_model = Model(flexible_func)
pars = flexible_model.make_params(**self.guess())
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
flexible_model.fit(self.data, pars, x=self.x, extra=5)
self.assertTrue(len(w) == 1)
self.assertTrue(issubclass(w[-1].category, UserWarning))
def test_missing_independent_variable_raises_error(self):
pars = self.model.make_params(**self.guess())
f = lambda: self.model.fit(self.data, pars)
self.assertRaises(ValueError, f)
def test_independent_var_parsing(self):
"""test the parsing of independent variables for model functions
with keyword arguments
step: form='linear'
voigt: gamma=None, can become a variable!!
"""
stepmod = Model(step)
assert 'x' in stepmod.independent_vars
assert 'form' in stepmod.independent_vars
assert 'linear' == stepmod.independent_vars_defvals.get('form', None)
voigtmod = Model(voigt)
assert 'x' in voigtmod.independent_vars
assert 'gamma' in voigtmod.independent_vars
assert voigtmod.independent_vars_defvals['gamma'] is None
pars1 = voigtmod.make_params(amplitude=25, center=9.5, sigma=1)
assert 'sigma' in pars1
assert 'gamma' not in pars1
pars2 = voigtmod.make_params(amplitude=25, center=9.5, sigma=1, gamma=0.5)
assert 'sigma' in pars2
assert 'gamma' in pars2
def test_bounding(self):
true_values = self.true_values()
true_values['center'] = 1.3 # as close as it's allowed to get
pars = self.model.make_params(**self.guess())
pars['center'].set(value=2, min=1.3)
result = self.model.fit(self.data, pars, x=self.x)
assert_results_close(result.values, true_values, rtol=0.05)
def test_vary_false(self):
true_values = self.true_values()
true_values['center'] = 1.3
pars = self.model.make_params(**self.guess())
pars['center'].set(value=1.3, vary=False)
result = self.model.fit(self.data, pars, x=self.x)
assert_results_close(result.values, true_values, rtol=0.05)
# testing model addition...
def test_user_defined_gaussian_plus_constant(self):
data = self.data + 5.0
model = self.model + models.ConstantModel()
guess = self.guess()
pars = model.make_params(c=10.1, **guess)
true_values = self.true_values()
true_values['c'] = 5.0
result = model.fit(data, pars, x=self.x)
assert_results_close(result.values, true_values, rtol=0.01, atol=0.01)
def test_model_with_prefix(self):
# model with prefix of 'a' and 'b'
mod = models.GaussianModel(prefix='a')
vals = {'center': 2.45, 'sigma': 0.8, 'amplitude': 3.15}
data = gaussian(x=self.x, **vals) + self.noise/3.0
pars = mod.guess(data, x=self.x)
self.assertTrue('aamplitude' in pars)
self.assertTrue('asigma' in pars)
out = mod.fit(data, pars, x=self.x)
self.assertTrue(out.params['aamplitude'].value > 2.0)
self.assertTrue(out.params['acenter'].value > 2.0)
self.assertTrue(out.params['acenter'].value < 3.0)
mod = models.GaussianModel(prefix='b')
data = gaussian(x=self.x, **vals) + self.noise/3.0
pars = mod.guess(data, x=self.x)
self.assertTrue('bamplitude' in pars)
self.assertTrue('bsigma' in pars)
def test_change_prefix(self):
"should pass!"
mod = models.GaussianModel(prefix='b')
set_prefix_failed = None
try:
mod.prefix = 'c'
set_prefix_failed = False
except AttributeError:
set_prefix_failed = True
except Exception:
set_prefix_failed = None
self.assertFalse(set_prefix_failed)
new_expr = mod.param_hints['fwhm']['expr']
self.assertTrue('csigma' in new_expr)
self.assertFalse('bsigma' in new_expr)
def test_model_name(self):
# test setting the name for built-in models
mod = models.GaussianModel(name='user_name')
self.assertEqual(mod.name, "Model(user_name)")
def test_sum_of_two_gaussians(self):
# two user-defined gaussians
model1 = self.model
f2 = lambda x, amp, cen, sig: gaussian(x, amplitude=amp, center=cen,
sigma=sig)
model2 = Model(f2)
values1 = self.true_values()
values2 = {'cen': 2.45, 'sig': 0.8, 'amp': 3.15}
data = (gaussian(x=self.x, **values1) + f2(x=self.x, **values2) +
self.noise/3.0)
model = self.model + model2
pars = model.make_params()
pars['sigma'].set(value=2, min=0)
pars['center'].set(value=1, min=0.2, max=1.8)
pars['amplitude'].set(value=3, min=0)
pars['sig'].set(value=1, min=0)
pars['cen'].set(value=2.4, min=2, max=3.5)
pars['amp'].set(value=1, min=0)
true_values = dict(list(values1.items()) + list(values2.items()))
result = model.fit(data, pars, x=self.x)
assert_results_close(result.values, true_values, rtol=0.01, atol=0.01)
# user-defined models with common parameter names
# cannot be added, and should raise
f = lambda: model1 + model1
self.assertRaises(NameError, f)
# two predefined_gaussians, using suffix to differentiate
model1 = models.GaussianModel(prefix='g1_')
model2 = models.GaussianModel(prefix='g2_')
model = model1 + model2
true_values = {'g1_center': values1['center'],
'g1_amplitude': values1['amplitude'],
'g1_sigma': values1['sigma'],
'g2_center': values2['cen'],
'g2_amplitude': values2['amp'],
'g2_sigma': values2['sig']}
pars = model.make_params()
pars['g1_sigma'].set(2)
pars['g1_center'].set(1)
pars['g1_amplitude'].set(3)
pars['g2_sigma'].set(1)
pars['g2_center'].set(2.4)
pars['g2_amplitude'].set(1)
result = model.fit(data, pars, x=self.x)
assert_results_close(result.values, true_values, rtol=0.01, atol=0.01)
# without suffix, the names collide and Model should raise
model1 = models.GaussianModel()
model2 = models.GaussianModel()
f = lambda: model1 + model2
self.assertRaises(NameError, f)
def test_sum_composite_models(self):
# test components of composite model created adding composite model
model1 = models.GaussianModel(prefix='g1_')
model2 = models.GaussianModel(prefix='g2_')
model3 = models.GaussianModel(prefix='g3_')
model4 = models.GaussianModel(prefix='g4_')
model_total1 = (model1 + model2) + model3
for mod in [model1, model2, model3]:
self.assertTrue(mod in model_total1.components)
model_total2 = model1 + (model2 + model3)
for mod in [model1, model2, model3]:
self.assertTrue(mod in model_total2.components)
model_total3 = (model1 + model2) + (model3 + model4)
for mod in [model1, model2, model3, model4]:
self.assertTrue(mod in model_total3.components)
def test_eval_components(self):
model1 = models.GaussianModel(prefix='g1_')
model2 = models.GaussianModel(prefix='g2_')
model3 = models.ConstantModel(prefix='bkg_')
mod = model1 + model2 + model3
pars = mod.make_params()
values1 = dict(amplitude=7.10, center=1.1, sigma=2.40)
values2 = dict(amplitude=12.2, center=2.5, sigma=0.5)
data = (1.01 + gaussian(x=self.x, **values1) +
gaussian(x=self.x, **values2) + 0.05*self.noise)
pars['g1_sigma'].set(2)
pars['g1_center'].set(1, max=1.5)
pars['g1_amplitude'].set(3)
pars['g2_sigma'].set(1)
pars['g2_center'].set(2.6, min=2.0)
pars['g2_amplitude'].set(1)
pars['bkg_c'].set(1.88)
result = mod.fit(data, params=pars, x=self.x)
self.assertTrue(abs(result.params['g1_amplitude'].value - 7.1) < 1.5)
self.assertTrue(abs(result.params['g2_amplitude'].value - 12.2) < 1.5)
self.assertTrue(abs(result.params['g1_center'].value - 1.1) < 0.2)
self.assertTrue(abs(result.params['g2_center'].value - 2.5) < 0.2)
self.assertTrue(abs(result.params['bkg_c'].value - 1.0) < 0.25)
comps = mod.eval_components(x=self.x)
assert 'bkg_' in comps
def test_composite_has_bestvalues(self):
# test that a composite model has non-empty best_values
model1 = models.GaussianModel(prefix='g1_')
model2 = models.GaussianModel(prefix='g2_')
mod = model1 + model2
pars = mod.make_params()
values1 = dict(amplitude=7.10, center=1.1, sigma=2.40)
values2 = dict(amplitude=12.2, center=2.5, sigma=0.5)
data = (gaussian(x=self.x, **values1) + gaussian(x=self.x, **values2)
+ 0.1*self.noise)
pars['g1_sigma'].set(value=2)
pars['g1_center'].set(value=1, max=1.5)
pars['g1_amplitude'].set(value=3)
pars['g2_sigma'].set(value=1)
pars['g2_center'].set(value=2.6, min=2.0)
pars['g2_amplitude'].set(value=1)
result = mod.fit(data, params=pars, x=self.x)
self.assertTrue(len(result.best_values) == 6)
self.assertTrue(abs(result.params['g1_amplitude'].value - 7.1) < 0.5)
self.assertTrue(abs(result.params['g2_amplitude'].value - 12.2) < 0.5)
self.assertTrue(abs(result.params['g1_center'].value - 1.1) < 0.2)
self.assertTrue(abs(result.params['g2_center'].value - 2.5) < 0.2)
for _, par in pars.items():
assert len(repr(par)) > 5
@pytest.mark.skipif(not lmfit.model._HAS_MATPLOTLIB,
reason="requires matplotlib.pyplot")
def test_composite_plotting(self):
# test that a composite model has non-empty best_values
import matplotlib
try:
matplotlib.pyplot.close('all')
except ValueError:
pass
matplotlib.use('Agg')
model1 = models.GaussianModel(prefix='g1_')
model2 = models.GaussianModel(prefix='g2_')
mod = model1 + model2
pars = mod.make_params()
values1 = dict(amplitude=7.10, center=1.1, sigma=2.40)
values2 = dict(amplitude=12.2, center=2.5, sigma=0.5)
data = (gaussian(x=self.x, **values1) + gaussian(x=self.x, **values2)
+ 0.1*self.noise)
pars['g1_sigma'].set(2)
pars['g1_center'].set(1, max=1.5)
pars['g1_amplitude'].set(3)
pars['g2_sigma'].set(1)
pars['g2_center'].set(2.6, min=2.0)
pars['g2_amplitude'].set(1)
result = mod.fit(data, params=pars, x=self.x)
fig = result.plot(show_init=True)
assert isinstance(fig, matplotlib.figure.Figure)
comps = result.eval_components(x=self.x)
assert len(comps) == 2
assert 'g1_' in comps
def test_hints_in_composite_models(self):
# test propagation of hints from base models to composite model
def func(x, amplitude):
pass
m1 = Model(func, prefix='p1_')
m2 = Model(func, prefix='p2_')
m1.set_param_hint('amplitude', value=1)
m2.set_param_hint('amplitude', value=2)
mx = (m1 + m2)
params = mx.make_params()
param_values = {name: p.value for name, p in params.items()}
self.assertEqual(param_values['p1_amplitude'], 1)
self.assertEqual(param_values['p2_amplitude'], 2)
def test_hints_for_peakmodels(self):
# test that height/fwhm do not cause asteval errors.
x = np.linspace(-10, 10, 101)
y = np.sin(x / 3) + x/100.
m1 = models.LinearModel(prefix='m1_')
params = m1.guess(y, x=x)
m2 = models.GaussianModel(prefix='m2_')
params.update(m2.make_params())
_m = m1 + m2 # noqa: F841
param_values = {name: p.value for name, p in params.items()}
assert_almost_equal(param_values['m1_intercept'], 0.)
self.assertEqual(param_values['m2_amplitude'], 1)
def test_weird_param_hints(self):
# tests Github Issue 312, a very weird way to access param_hints
def func(x, amp):
return amp*x
m = Model(func)
models = {}
for i in range(2):
m.set_param_hint('amp', value=1)
m.set_param_hint('amp', value=25)
models[i] = Model(func, prefix=f'mod{i}')
models[i].param_hints['amp'] = m.param_hints['amp']
self.assertEqual(models[0].param_hints['amp'],
models[1].param_hints['amp'])
def test_param_hint_explicit_value(self):
# tests Github Issue 384
pmod = PseudoVoigtModel()
params = pmod.make_params(sigma=2, fraction=0.77)
assert_allclose(params['fraction'].value, 0.77, rtol=0.01)
def test_symmetric_boundss(self):
# tests Github Issue 700
np.random.seed(0)
x = np.linspace(0, 20, 51)
y = gaussian(x, amplitude=8.0, center=13, sigma=2.5)
y += np.random.normal(size=len(x), scale=0.1)
mod = Model(gaussian)
params = mod.make_params(sigma=2.2, center=10, amplitude=10)
# carefully selected to have inexact floating-point representation
params['sigma'].min = 2.2 - 0.95
params['sigma'].max = 2.2 + 0.95
result = mod.fit(y, params, x=x)
print(result.fit_report())
self.assertTrue(result.params['sigma'].value > 2.3)
self.assertTrue(result.params['sigma'].value < 2.7)
self.assertTrue(result.params['sigma'].stderr is not None)
self.assertTrue(result.params['amplitude'].stderr is not None)
self.assertTrue(result.params['sigma'].stderr > 0.02)
self.assertTrue(result.params['sigma'].stderr < 0.50)
def test_unprefixed_name_collisions(self):
# tests Github Issue 710
np.random.seed(0)
x = np.linspace(0, 20, 201)
y = 6 + x * 0.55 + gaussian(x, 4.5, 8.5, 2.1) + np.random.normal(size=len(x), scale=0.03)
def myline(x, a, b):
return a + b * x
def mygauss(x, a, b, c):
return gaussian(x, a, b, c)
mod = Model(myline, prefix='line_') + Model(mygauss, prefix='peak_')
pars = mod.make_params(line_a=5, line_b=1, peak_a=10, peak_b=10, peak_c=5)
pars.add('a', expr='line_a + peak_a')
result = mod.fit(y, pars, x=x)
self.assertTrue(result.params['peak_a'].value > 4)
self.assertTrue(result.params['peak_a'].value < 5)
self.assertTrue(result.params['peak_b'].value > 8)
self.assertTrue(result.params['peak_b'].value < 9)
self.assertTrue(result.params['peak_c'].value > 1.5)
self.assertTrue(result.params['peak_c'].value < 2.5)
self.assertTrue(result.params['line_a'].value > 5.5)
self.assertTrue(result.params['line_a'].value < 6.5)
self.assertTrue(result.params['line_b'].value > 0.25)
self.assertTrue(result.params['line_b'].value < 0.75)
self.assertTrue(result.params['a'].value > 10)
self.assertTrue(result.params['a'].value < 11)
def test_composite_model_with_expr_constrains(self):
"""Smoke test for composite model fitting with expr constraints."""
y = [0, 0, 4, 2, 1, 8, 21, 21, 23, 35, 50, 54, 46, 70, 77, 87, 98,
113, 148, 136, 185, 195, 194, 168, 170, 139, 155, 115, 132, 109,
102, 85, 69, 81, 82, 80, 71, 64, 79, 88, 111, 97, 97, 73, 72, 62,
41, 30, 13, 3, 9, 7, 0, 0, 0]
x = np.arange(-0.2, 1.2, 0.025)[:-1] + 0.5*0.025
def gauss(x, sigma, mu, A):
return A*np.exp(-(x-mu)**2/(2*sigma**2))
# Initial values
p1_mu = 0.2
p1_sigma = 0.1
p2_sigma = 0.1
peak1 = Model(gauss, prefix='p1_')
peak2 = Model(gauss, prefix='p2_')
model = peak1 + peak2
model.set_param_hint('p1_mu', value=p1_mu, min=-1, max=2)
model.set_param_hint('p1_sigma', value=p1_sigma, min=0.01, max=0.2)
model.set_param_hint('p2_sigma', value=p2_sigma, min=0.01, max=0.2)
model.set_param_hint('p1_A', value=100, min=0.01)
model.set_param_hint('p2_A', value=50, min=0.01)
# Constrains the distance between peaks to be > 0
model.set_param_hint('pos_delta', value=0.3, min=0)
model.set_param_hint('p2_mu', min=-1, expr='p1_mu + pos_delta')
# Test fitting
result = model.fit(y, x=x)
self.assertTrue(result.params['pos_delta'].value > 0)
def test_model_nan_policy(self):
"""Tests for nan_policy with NaN values in the input data."""
x = np.linspace(0, 10, 201)
np.random.seed(0)
y = gaussian(x, 10.0, 6.15, 0.8)
y += gaussian(x, 8.0, 6.35, 1.1)
y += gaussian(x, 0.25, 6.00, 7.5)
y += np.random.normal(size=len(x), scale=0.5)
# with NaN values in the input data
y[55] = y[91] = np.nan
mod = PseudoVoigtModel()
params = mod.make_params(amplitude=20, center=5.5,
sigma=1, fraction=0.25)
params['fraction'].vary = False
# with raise, should get a ValueError
result = lambda: mod.fit(y, params, x=x, nan_policy='raise')
msg = ('NaN values detected in your input data or the output of your '
'objective/model function - fitting algorithms cannot handle this!')
self.assertRaisesRegex(ValueError, msg, result)
# with propagate, should get no error, but bad results
result = mod.fit(y, params, x=x, nan_policy='propagate')
# for SciPy v1.10+ this results in an AbortFitException, even with
# `max_nfev=100000`:
# lmfit.minimizer.AbortFitException: fit aborted: too many function
# evaluations xxxxx
if int(scipy_version.split('.')[1]) < 10:
self.assertTrue(np.isnan(result.chisqr))
self.assertTrue(np.isnan(result.aic))
self.assertFalse(result.errorbars)
self.assertTrue(result.params['amplitude'].stderr is None)
self.assertTrue(abs(result.params['amplitude'].value - 20.0) < 0.001)
else:
pass
# with omit, should get good results
result = mod.fit(y, params, x=x, nan_policy='omit')
self.assertTrue(result.success)
self.assertTrue(result.chisqr > 2.0)
self.assertTrue(result.aic < -100)
self.assertTrue(result.errorbars)
self.assertTrue(result.params['amplitude'].stderr > 0.1)
self.assertTrue(abs(result.params['amplitude'].value - 20.0) < 5.0)
self.assertTrue(abs(result.params['center'].value - 6.0) < 0.5)
# with 'wrong_argument', should get a ValueError
err_msg = r"nan_policy must be 'propagate', 'omit', or 'raise'."
with pytest.raises(ValueError, match=err_msg):
mod.fit(y, params, x=x, nan_policy='wrong_argument')
def test_model_nan_policy_NaNs_by_model(self):
"""Test for nan_policy with NaN values generated by the model function."""
def double_exp(x, a1, t1, a2, t2):
return a1*np.exp(-x/t1) + a2*np.exp(-(x-0.1) / t2)
model = Model(double_exp)
truths = (3.0, 2.0, -5.0, 10.0)
x = np.linspace(1, 10, 250)
np.random.seed(0)
y = double_exp(x, *truths) + 0.1*np.random.randn(x.size)
p = model.make_params(a1=4, t1=3, a2=4, t2=3)
result = lambda: model.fit(data=y, params=p, x=x, method='Nelder',
nan_policy='raise')
msg = 'The model function generated NaN values and the fit aborted!'
self.assertRaisesRegex(ValueError, msg, result)
def test_wrapped_model_func(self):
x = np.linspace(-1, 1, 51)
y = 2.0*x + 3 + 0.0003 * x*x
y += np.random.normal(size=len(x), scale=0.025)
mod = Model(linear_func)
pars = mod.make_params(a=1.5, b=2.5)
tmp = mod.eval(pars, x=x)
self.assertTrue(tmp.max() > 3)
self.assertTrue(tmp.min() > -20)
result = mod.fit(y, pars, x=x)
self.assertTrue(result.chisqr < 0.05)
self.assertTrue(result.aic < -350)
self.assertTrue(result.errorbars)
self.assertTrue(abs(result.params['a'].value - 2.0) < 0.05)
self.assertTrue(abs(result.params['b'].value - 3.0) < 0.41)
def test_different_independent_vars_composite_modeld(self):
"""Regression test for different independent variables in CompositeModel.
See: https://github.com/lmfit/lmfit-py/discussions/787
"""
def two_independent_vars(y, z, a):
return a * y + z
BackgroundModel = Model(two_independent_vars,
independent_vars=["y", "z"], prefix="yz_")
PeakModel = Model(gaussian, independent_vars=["x"], prefix="x_")
CompModel = BackgroundModel + PeakModel
assert CompModel.independent_vars == ['x', 'y', 'z']
class TestLinear(CommonTests, unittest.TestCase):
def setUp(self):
self.true_values = lambda: dict(slope=5, intercept=2)
self.guess = lambda: dict(slope=10, intercept=6)
self.model_constructor = models.LinearModel
super().setUp()
class TestParabolic(CommonTests, unittest.TestCase):
def setUp(self):
self.true_values = lambda: dict(a=5, b=2, c=8)
self.guess = lambda: dict(a=1, b=6, c=3)
self.model_constructor = models.ParabolicModel
super().setUp()
class TestPolynomialOrder2(CommonTests, unittest.TestCase):
# class Polynomial constructed with order=2
def setUp(self):
self.true_values = lambda: dict(c2=5, c1=2, c0=8)
self.guess = lambda: dict(c1=1, c2=6, c0=3)
self.model_constructor = models.PolynomialModel
self.args = (2,)
super().setUp()
class TestPolynomialOrder3(CommonTests, unittest.TestCase):
# class Polynomial constructed with order=3
def setUp(self):
self.true_values = lambda: dict(c3=2, c2=5, c1=2, c0=8)
self.guess = lambda: dict(c3=1, c1=1, c2=6, c0=3)
self.model_constructor = models.PolynomialModel
self.args = (3,)
super().setUp()
class TestConstant(CommonTests, unittest.TestCase):
def setUp(self):
self.true_values = lambda: dict(c=5)
self.guess = lambda: dict(c=2)
self.model_constructor = models.ConstantModel
super().setUp()
def check_skip_independent_vars(self):
raise pytest.skip("ConstantModel has not independent_vars.")
class TestPowerlaw(CommonTests, unittest.TestCase):
def setUp(self):
self.true_values = lambda: dict(amplitude=5, exponent=3)
self.guess = lambda: dict(amplitude=2, exponent=8)
self.model_constructor = models.PowerLawModel
super().setUp()
class TestExponential(CommonTests, unittest.TestCase):
def setUp(self):
self.true_values = lambda: dict(amplitude=5, decay=3)
self.guess = lambda: dict(amplitude=2, decay=8)
self.model_constructor = models.ExponentialModel
super().setUp()
class TestComplexConstant(CommonTests, unittest.TestCase):
def setUp(self):
self.true_values = lambda: dict(re=5, im=5)
self.guess = lambda: dict(re=2, im=2)
self.model_constructor = models.ComplexConstantModel
super().setUp()
class TestExpression(CommonTests, unittest.TestCase):
def setUp(self):
self.true_values = lambda: dict(off_c=0.25, amp_c=1.0, x0=2.0)
self.guess = lambda: dict(off_c=0.20, amp_c=1.5, x0=2.5)
self.expression = "off_c + amp_c * exp(-x/x0)"
self.model_constructor = (
lambda *args, **kwargs: models.ExpressionModel(self.expression, *args, **kwargs))
super().setUp()
def test_composite_with_expression(self):
expression_model = models.ExpressionModel("exp(-x/x0)", name='exp')
amp_model = models.ConstantModel(prefix='amp_')
off_model = models.ConstantModel(prefix='off_', name="off")
comp_model = off_model + amp_model * expression_model
x = self.x
true_values = self.true_values()
data = comp_model.eval(x=x, **true_values) + self.noise
# data = 0.25 + 1 * np.exp(-x / 2.)
params = comp_model.make_params(**self.guess())
result = comp_model.fit(data, x=x, params=params)
assert_results_close(result.values, true_values, rtol=0.01, atol=0.01)
data_components = comp_model.eval_components(x=x)
self.assertIn('exp', data_components)
def test_make_params_valuetypes():
mod = lmfit.models.SineModel()
pars = mod.make_params(amplitude=1, frequency=1, shift=-0.2)
pars = mod.make_params(amplitude={'value': 0.9, 'min': 0},
frequency=1.03,
shift={'value': -0.2, 'vary': False})
val_i32 = np.arange(10, dtype=np.int32)
val_i64 = np.arange(10, dtype=np.int64)
# np.longdouble equals to np.float128 on Linux and macOS, np.float64 on Windows
val_ld = np.arange(10, dtype=np.longdouble)/3.0
val_c128 = np.arange(10, dtype=np.complex128)/3.0
pars = mod.make_params(amplitude=val_i64[2],
frequency=val_i32[3],
shift=-val_ld[4])
pars = mod.make_params(amplitude=val_c128[2],
frequency=val_i32[3],
shift=-val_ld[4])
assert pars is not None
with pytest.raises(ValueError):
pars = mod.make_params(amplitude='a string', frequency=2, shift=7)
with pytest.raises(TypeError):
pars = mod.make_params(amplitude={'v': 3}, frequency=2, shift=7)
with pytest.raises(TypeError):
pars = mod.make_params(amplitude={}, frequency=2, shift=7)
def test_complex_model_eval_uncertainty():
"""Github #900"""
def cmplx(f, omega, areal, aimag, off, sigma):
return (areal*np.cos(f*omega + off) + 1j*aimag*np.sin(f*omega + off))*np.exp(-f/sigma)
f = np.linspace(0, 10, 501)
dat = cmplx(f, 4, 10, 5, 0.2, 4.5) + (0.1 + 0.2j)*np.random.normal(scale=0.25, size=len(f))
mod = Model(cmplx)
params = mod.make_params(omega=5, areal=5, aimag=5,
off={'value': 0.5, 'min': -2, 'max': 2},
sigma={'value': 3, 'min': 1.e-5, 'max': 1000})
result = mod.fit(dat, params=params, f=f)
dfit = result.eval_uncertainty()
assert len(dfit) == len(f)
assert dfit.dtype == 'complex128'
def test_compositemodel_returning_list():
"""Github #875"""
def lin1(x, k):
return [k*x1 for x1 in x]
def lin2(x, k):
return [k*x1 for x1 in x]
y = np.linspace(0, 100, 100)
x = np.linspace(0, 100, 100)
Model1 = Model(lin1, independent_vars=["x"], prefix="m1_")
Model2 = Model(lin2, independent_vars=["x"], prefix="m2_")
ModelSum = Model1 + Model2
pars = Parameters()
pars.add('m1_k', value=0.5)
pars.add('m2_k', value=0.5)
result = ModelSum.fit(y, pars, x=x)
assert len(result.best_fit) == len(x)
def test_rsquared_with_weights():
"""Github #921"""
def func(x, k=1, b=0):
return k*x+b
x = np.array([1, 2, 3, 4])
y = np.array([1.1, 1.9, 3.05, 3.95])
yerr = np.array([0.03, 0.04, 0.01, 0.02])
mod = Model(func)
params = mod.make_params()
result = mod.fit(y, params, x=x, weights=1.0/yerr)
assert result.rsquared < 1.00
assert result.rsquared > 0.95
