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"""
Tests that relate to fitting models with quantity parameters
"""
import numpy as np
import pytest
from astropy.modeling import models
from astropy import units as u
from astropy.units import UnitsError
from astropy.tests.helper import assert_quantity_allclose
from astropy.utils import NumpyRNGContext
from astropy.modeling import fitting
try:
from scipy import optimize
HAS_SCIPY = True
except ImportError:
HAS_SCIPY = False
# Fitting should be as intuitive as possible to the user. Essentially, models
# and fitting should work without units, but if one has units, the other should
# have units too, and the resulting fitted parameters will also have units.
def _fake_gaussian_data():
# Generate fake data
with NumpyRNGContext(12345):
x = np.linspace(-5., 5., 2000)
y = 3 * np.exp(-0.5 * (x - 1.3)**2 / 0.8**2)
y += np.random.normal(0., 0.2, x.shape)
# Attach units to data
x = x * u.m
y = y * u.Jy
return x, y
compound_models_no_units = [models.Linear1D() + models.Gaussian1D() | models.Scale(),
models.Linear1D() + models.Gaussian1D() + models.Gaussian1D(),
models.Linear1D() + models.Gaussian1D() | models.Shift(),
]
@pytest.mark.skipif('not HAS_SCIPY')
def test_fitting_simple():
x, y = _fake_gaussian_data()
# Fit the data using a Gaussian with units
g_init = models.Gaussian1D()
fit_g = fitting.LevMarLSQFitter()
g = fit_g(g_init, x, y)
# TODO: update actual numerical results once implemented, but these should
# be close to the values below.
assert_quantity_allclose(g.amplitude, 3 * u.Jy, rtol=0.05)
assert_quantity_allclose(g.mean, 1.3 * u.m, rtol=0.05)
assert_quantity_allclose(g.stddev, 0.8 * u.m, rtol=0.05)
@pytest.mark.skipif('not HAS_SCIPY')
def test_fitting_with_initial_values():
x, y = _fake_gaussian_data()
# Fit the data using a Gaussian with units
g_init = models.Gaussian1D(amplitude=1. * u.mJy,
mean=3 * u.cm,
stddev=2 * u.mm)
fit_g = fitting.LevMarLSQFitter()
g = fit_g(g_init, x, y)
# TODO: update actual numerical results once implemented, but these should
# be close to the values below.
assert_quantity_allclose(g.amplitude, 3 * u.Jy, rtol=0.05)
assert_quantity_allclose(g.mean, 1.3 * u.m, rtol=0.05)
assert_quantity_allclose(g.stddev, 0.8 * u.m, rtol=0.05)
@pytest.mark.skipif('not HAS_SCIPY')
def test_fitting_missing_data_units():
"""
Raise an error if the model has units but the data doesn't
"""
g_init = models.Gaussian1D(amplitude=1. * u.mJy,
mean=3 * u.cm,
stddev=2 * u.mm)
fit_g = fitting.LevMarLSQFitter()
with pytest.raises(UnitsError) as exc:
fit_g(g_init, [1, 2, 3], [4, 5, 6])
assert exc.value.args[0] == ("'cm' (length) and '' (dimensionless) are not "
"convertible")
with pytest.raises(UnitsError) as exc:
fit_g(g_init, [1, 2, 3] * u.m, [4, 5, 6])
assert exc.value.args[0] == ("'mJy' (spectral flux density) and '' "
"(dimensionless) are not convertible")
@pytest.mark.skipif('not HAS_SCIPY')
def test_fitting_missing_model_units():
"""
Proceed if the data has units but the model doesn't
"""
x, y = _fake_gaussian_data()
g_init = models.Gaussian1D(amplitude=1., mean=3, stddev=2)
fit_g = fitting.LevMarLSQFitter()
g = fit_g(g_init, x, y)
assert_quantity_allclose(g.amplitude, 3 * u.Jy, rtol=0.05)
assert_quantity_allclose(g.mean, 1.3 * u.m, rtol=0.05)
assert_quantity_allclose(g.stddev, 0.8 * u.m, rtol=0.05)
g_init = models.Gaussian1D(amplitude=1., mean=3 * u.m, stddev=2 * u.m)
fit_g = fitting.LevMarLSQFitter()
g = fit_g(g_init, x, y)
assert_quantity_allclose(g.amplitude, 3 * u.Jy, rtol=0.05)
assert_quantity_allclose(g.mean, 1.3 * u.m, rtol=0.05)
assert_quantity_allclose(g.stddev, 0.8 * u.m, rtol=0.05)
@pytest.mark.skipif('not HAS_SCIPY')
def test_fitting_incompatible_units():
"""
Raise an error if the data and model have incompatible units
"""
g_init = models.Gaussian1D(amplitude=1. * u.Jy,
mean=3 * u.m,
stddev=2 * u.cm)
fit_g = fitting.LevMarLSQFitter()
with pytest.raises(UnitsError) as exc:
fit_g(g_init, [1, 2, 3] * u.Hz, [4, 5, 6] * u.Jy)
assert exc.value.args[0] == ("'Hz' (frequency) and 'm' (length) are not convertible")
@pytest.mark.skipif('not HAS_SCIPY')
@pytest.mark.parametrize('model', compound_models_no_units)
def test_compound_without_units(model):
x = np.linspace(-5, 5, 10) * u.Angstrom
with NumpyRNGContext(12345):
y = np.random.sample(10)
fitter = fitting.LevMarLSQFitter()
res_fit = fitter(model, x, y * u.Hz)
assert all([res_fit[i]._has_units for i in range(3)])
z = res_fit(x)
assert isinstance(z, u.Quantity)
res_fit = fitter(model, np.arange(10) * u.Unit('Angstrom'), y)
assert all([res_fit[i]._has_units for i in range(3)])
z = res_fit(x)
assert isinstance(z, np.ndarray)
@pytest.mark.skipif('not HAS_SCIPY')
def test_compound_fitting_with_units():
x = np.linspace(-5, 5, 15) * u.Angstrom
y = np.linspace(-5, 5, 15) * u.Angstrom
fitter = fitting.LevMarLSQFitter()
m = models.Gaussian2D(10*u.Hz,
3*u.Angstrom, 4*u.Angstrom,
1*u.Angstrom, 2*u.Angstrom)
p = models.Planar2D(3*u.Hz/u.Angstrom, 4*u.Hz/u.Angstrom, 1*u.Hz)
model = m + p
z = model(x, y)
res = fitter(model, x, y, z)
assert isinstance(res(x, y), np.ndarray)
assert all([res[i]._has_units for i in range(2)])
model = models.Gaussian2D() + models.Planar2D()
res = fitter(model, x, y, z)
assert isinstance(res(x, y), np.ndarray)
assert all([res[i]._has_units for i in range(2)])
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