forked from astropy/astropy
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test_constraints.py
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test_constraints.py
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# Licensed under a 3-clause BSD style license - see LICENSE.rst
# pylint: disable=invalid-name
import platform
import types
import warnings
from contextlib import nullcontext
import numpy as np
import pytest
from numpy.random import default_rng
from numpy.testing import assert_allclose
from astropy.modeling import fitting, models
from astropy.modeling.core import Fittable1DModel
from astropy.modeling.parameters import Parameter
from astropy.utils.compat.optional_deps import HAS_SCIPY
from astropy.utils.exceptions import AstropyUserWarning
fitters = [
fitting.LevMarLSQFitter,
fitting.TRFLSQFitter,
fitting.LevMarLSQFitter,
fitting.DogBoxLSQFitter,
]
class TestNonLinearConstraints:
def setup_class(self):
self.g1 = models.Gaussian1D(10, 14.9, stddev=0.3)
self.g2 = models.Gaussian1D(10, 13, stddev=0.4)
self.x = np.arange(10, 20, 0.1)
self.y1 = self.g1(self.x)
self.y2 = self.g2(self.x)
rsn = default_rng(1234567890)
self.n = rsn.standard_normal(100)
self.ny1 = self.y1 + 2 * self.n
self.ny2 = self.y2 + 2 * self.n
@pytest.mark.skipif(not HAS_SCIPY, reason="requires scipy")
@pytest.mark.parametrize("fitter", fitters)
def test_fixed_par(self, fitter):
fitter = fitter()
g1 = models.Gaussian1D(10, mean=14.9, stddev=0.3, fixed={"amplitude": True})
model = fitter(g1, self.x, self.ny1)
assert model.amplitude.value == 10
@pytest.mark.skipif(not HAS_SCIPY, reason="requires scipy")
@pytest.mark.parametrize("fitter", fitters)
def test_tied_par(self, fitter):
fitter = fitter()
def tied(model):
mean = 50 * model.stddev
return mean
g1 = models.Gaussian1D(10, mean=14.9, stddev=0.3, tied={"mean": tied})
model = fitter(g1, self.x, self.ny1)
assert_allclose(model.mean.value, 50 * model.stddev, rtol=10 ** (-5))
@pytest.mark.skipif(not HAS_SCIPY, reason="requires scipy")
def test_joint_fitter(self):
from scipy import optimize
g1 = models.Gaussian1D(10, 14.9, stddev=0.3)
g2 = models.Gaussian1D(10, 13, stddev=0.4)
jf = fitting.JointFitter(
[g1, g2], {g1: ["amplitude"], g2: ["amplitude"]}, [9.8]
)
x = np.arange(10, 20, 0.1)
y1 = g1(x)
y2 = g2(x)
n = np.random.randn(100)
ny1 = y1 + 2 * n
ny2 = y2 + 2 * n
jf(x, ny1, x, ny2)
p1 = [14.9, 0.3]
p2 = [13, 0.4]
A = 9.8
p = np.r_[A, p1, p2]
def compmodel(A, p, x):
return A * np.exp(-0.5 / p[1] ** 2 * (x - p[0]) ** 2)
def errf(p, x1, y1, x2, y2):
return np.ravel(
np.r_[compmodel(p[0], p[1:3], x1) - y1, compmodel(p[0], p[3:], x2) - y2]
)
fitparams, _ = optimize.leastsq(errf, p, args=(x, ny1, x, ny2))
assert_allclose(jf.fitparams, fitparams, rtol=10 ** (-5))
assert_allclose(g1.amplitude.value, g2.amplitude.value)
@pytest.mark.skipif(not HAS_SCIPY, reason="requires scipy")
@pytest.mark.parametrize("fitter", fitters)
def test_no_constraints(self, fitter):
from scipy import optimize
fitter = fitter()
g1 = models.Gaussian1D(9.9, 14.5, stddev=0.3)
def func(p, x):
return p[0] * np.exp(-0.5 / p[2] ** 2 * (x - p[1]) ** 2)
def errf(p, x, y):
return func(p, x) - y
p0 = [9.9, 14.5, 0.3]
y = g1(self.x)
n = np.random.randn(100)
ny = y + n
fitpar, s = optimize.leastsq(errf, p0, args=(self.x, ny))
model = fitter(g1, self.x, ny)
assert_allclose(model.parameters, fitpar, rtol=5 * 10 ** (-3))
@pytest.mark.skipif(not HAS_SCIPY, reason="requires scipy")
class TestBounds:
def setup_class(self):
A = -2.0
B = 0.5
self.x = np.linspace(-1.0, 1.0, 100)
self.y = A * self.x + B + np.random.normal(scale=0.1, size=100)
# fmt: off
data = np.array(
[
505.0, 556.0, 630.0, 595.0, 561.0, 553.0, 543.0, 496.0, 460.0, 469.0,
426.0, 518.0, 684.0, 798.0, 830.0, 794.0, 649.0, 706.0, 671.0, 545.0,
479.0, 454.0, 505.0, 700.0, 1058.0, 1231.0, 1325.0, 997.0, 1036.0, 884.0,
610.0, 487.0, 453.0, 527.0, 780.0, 1094.0, 1983.0, 1993.0, 1809.0, 1525.0,
1056.0, 895.0, 604.0, 466.0, 510.0, 678.0, 1130.0, 1986.0, 2670.0, 2535.0,
1878.0, 1450.0, 1200.0, 663.0, 511.0, 474.0, 569.0, 848.0, 1670.0, 2611.0,
3129.0, 2507.0, 1782.0, 1211.0, 723.0, 541.0, 511.0, 518.0, 597.0, 1137.0,
1993.0, 2925.0, 2438.0, 1910.0, 1230.0, 738.0, 506.0, 461.0, 486.0, 597.0,
733.0, 1262.0, 1896.0, 2342.0, 1792.0, 1180.0, 667.0, 482.0, 454.0, 482.0,
504.0, 566.0, 789.0, 1194.0, 1545.0, 1361.0, 933.0, 562.0, 418.0, 463.0,
435.0, 466.0, 528.0, 487.0, 664.0, 799.0, 746.0, 550.0, 478.0, 535.0,
443.0, 416.0, 439.0, 472.0, 472.0, 492.0, 523.0, 569.0, 487.0, 441.0,
428.0
]
)
# fmt: on
self.data = data.reshape(11, 11)
@pytest.mark.parametrize("fitter", fitters)
def test_bounds_lsq(self, fitter):
fitter = fitter()
guess_slope = 1.1
guess_intercept = 0.0
bounds = {"slope": (-1.5, 5.0), "intercept": (-1.0, 1.0)}
line_model = models.Linear1D(guess_slope, guess_intercept, bounds=bounds)
with pytest.warns(AstropyUserWarning, match=r"Model is linear in parameters"):
model = fitter(line_model, self.x, self.y)
slope = model.slope.value
intercept = model.intercept.value
assert slope + 10**-5 >= bounds["slope"][0]
assert slope - 10**-5 <= bounds["slope"][1]
assert intercept + 10**-5 >= bounds["intercept"][0]
assert intercept - 10**-5 <= bounds["intercept"][1]
def test_bounds_slsqp(self):
guess_slope = 1.1
guess_intercept = 0.0
bounds = {"slope": (-1.5, 5.0), "intercept": (-1.0, 1.0)}
line_model = models.Linear1D(guess_slope, guess_intercept, bounds=bounds)
fitter = fitting.SLSQPLSQFitter()
with pytest.warns(
AstropyUserWarning, match="consider using linear fitting methods"
):
model = fitter(line_model, self.x, self.y)
slope = model.slope.value
intercept = model.intercept.value
assert slope + 10**-5 >= bounds["slope"][0]
assert slope - 10**-5 <= bounds["slope"][1]
assert intercept + 10**-5 >= bounds["intercept"][0]
assert intercept - 10**-5 <= bounds["intercept"][1]
@pytest.mark.filterwarnings("ignore:The fit may be unsuccessful")
@pytest.mark.parametrize("fitter", fitters)
def test_bounds_gauss2d_lsq(self, fitter):
fitter = fitter()
X, Y = np.meshgrid(np.arange(11), np.arange(11))
bounds = {
"x_mean": [0.0, 11.0],
"y_mean": [0.0, 11.0],
"x_stddev": [1.0, 4],
"y_stddev": [1.0, 4],
}
gauss = models.Gaussian2D(
amplitude=10.0,
x_mean=5.0,
y_mean=5.0,
x_stddev=4.0,
y_stddev=4.0,
theta=0.5,
bounds=bounds,
)
if isinstance(fitter, (fitting.LevMarLSQFitter, fitting.DogBoxLSQFitter)):
model = fitter(gauss, X, Y, self.data)
else:
if isinstance(fitter, fitting.TRFLSQFitter):
ctx = np.errstate(invalid="ignore", divide="ignore")
else:
ctx = nullcontext()
with ctx:
model = fitter(gauss, X, Y, self.data)
x_mean = model.x_mean.value
y_mean = model.y_mean.value
x_stddev = model.x_stddev.value
y_stddev = model.y_stddev.value
assert x_mean + 10**-5 >= bounds["x_mean"][0]
assert x_mean - 10**-5 <= bounds["x_mean"][1]
assert y_mean + 10**-5 >= bounds["y_mean"][0]
assert y_mean - 10**-5 <= bounds["y_mean"][1]
assert x_stddev + 10**-5 >= bounds["x_stddev"][0]
assert x_stddev - 10**-5 <= bounds["x_stddev"][1]
assert y_stddev + 10**-5 >= bounds["y_stddev"][0]
assert y_stddev - 10**-5 <= bounds["y_stddev"][1]
def test_bounds_gauss2d_slsqp(self):
X, Y = np.meshgrid(np.arange(11), np.arange(11))
bounds = {
"x_mean": [0.0, 11.0],
"y_mean": [0.0, 11.0],
"x_stddev": [1.0, 4],
"y_stddev": [1.0, 4],
}
gauss = models.Gaussian2D(
amplitude=10.0,
x_mean=5.0,
y_mean=5.0,
x_stddev=4.0,
y_stddev=4.0,
theta=0.5,
bounds=bounds,
)
gauss_fit = fitting.SLSQPLSQFitter()
# Warning does not appear in all the CI jobs.
# TODO: Rewrite the test for more consistent warning behavior.
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore",
message=r".*The fit may be unsuccessful.*",
category=AstropyUserWarning,
)
model = gauss_fit(gauss, X, Y, self.data)
x_mean = model.x_mean.value
y_mean = model.y_mean.value
x_stddev = model.x_stddev.value
y_stddev = model.y_stddev.value
assert x_mean + 10**-5 >= bounds["x_mean"][0]
assert x_mean - 10**-5 <= bounds["x_mean"][1]
assert y_mean + 10**-5 >= bounds["y_mean"][0]
assert y_mean - 10**-5 <= bounds["y_mean"][1]
assert x_stddev + 10**-5 >= bounds["x_stddev"][0]
assert x_stddev - 10**-5 <= bounds["x_stddev"][1]
assert y_stddev + 10**-5 >= bounds["y_stddev"][0]
assert y_stddev - 10**-5 <= bounds["y_stddev"][1]
class TestLinearConstraints:
def setup_class(self):
self.p1 = models.Polynomial1D(4)
self.p1.c0 = 0
self.p1.c1 = 0
self.p1.window = [0.0, 9.0]
self.x = np.arange(10)
self.y = self.p1(self.x)
rsn = default_rng(1234567890)
self.n = rsn.standard_normal(10)
self.ny = self.y + self.n
def test(self):
self.p1.c0.fixed = True
self.p1.c1.fixed = True
pfit = fitting.LinearLSQFitter()
model = pfit(self.p1, self.x, self.y)
assert_allclose(self.y, model(self.x))
# Test constraints as parameter properties
def test_set_fixed_1():
gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1)
gauss.mean.fixed = True
assert gauss.fixed == {"amplitude": False, "mean": True, "stddev": False}
def test_set_fixed_2():
gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1, fixed={"mean": True})
assert gauss.mean.fixed is True
def test_set_tied_1():
def tie_amplitude(model):
return 50 * model.stddev
gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1)
gauss.amplitude.tied = tie_amplitude
assert gauss.amplitude.tied is not False
assert isinstance(gauss.tied["amplitude"], types.FunctionType)
def test_set_tied_2():
def tie_amplitude(model):
return 50 * model.stddev
gauss = models.Gaussian1D(
amplitude=20, mean=2, stddev=1, tied={"amplitude": tie_amplitude}
)
assert gauss.amplitude.tied
def test_unset_fixed():
gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1, fixed={"mean": True})
gauss.mean.fixed = False
assert gauss.fixed == {"amplitude": False, "mean": False, "stddev": False}
def test_unset_tied():
def tie_amplitude(model):
return 50 * model.stddev
gauss = models.Gaussian1D(
amplitude=20, mean=2, stddev=1, tied={"amplitude": tie_amplitude}
)
gauss.amplitude.tied = False
assert gauss.tied == {"amplitude": False, "mean": False, "stddev": False}
def test_set_bounds_1():
gauss = models.Gaussian1D(
amplitude=20, mean=2, stddev=1, bounds={"stddev": (0, None)}
)
assert gauss.bounds == {
"amplitude": (None, None),
"mean": (None, None),
"stddev": (0.0, None),
}
def test_set_bounds_2():
gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1)
gauss.stddev.min = 0.0
assert gauss.bounds == {
"amplitude": (None, None),
"mean": (None, None),
"stddev": (0.0, None),
}
def test_unset_bounds():
gauss = models.Gaussian1D(amplitude=20, mean=2, stddev=1, bounds={"stddev": (0, 2)})
gauss.stddev.min = None
gauss.stddev.max = None
assert gauss.bounds == {
"amplitude": (None, None),
"mean": (None, None),
"stddev": (None, None),
}
def test_default_constraints():
"""Regression test for https://github.com/astropy/astropy/issues/2396
Ensure that default constraints defined on parameters are carried through
to instances of the models those parameters are defined for.
"""
class MyModel(Fittable1DModel):
a = Parameter(default=1)
b = Parameter(default=0, min=0, fixed=True)
@staticmethod
def evaluate(x, a, b):
return x * a + b
assert MyModel.a.default == 1
assert MyModel.b.default == 0
assert MyModel.b.min == 0
assert MyModel.b.bounds == (0, None)
assert MyModel.b.fixed is True
m = MyModel()
assert m.a.value == 1
assert m.b.value == 0
assert m.b.min == 0
assert m.b.bounds == (0, None)
assert m.b.fixed is True
assert m.bounds == {"a": (None, None), "b": (0, None)}
assert m.fixed == {"a": False, "b": True}
# Make a model instance that overrides the default constraints and values
m = MyModel(
3, 4, bounds={"a": (1, None), "b": (2, None)}, fixed={"a": True, "b": False}
)
assert m.a.value == 3
assert m.b.value == 4
assert m.a.min == 1
assert m.b.min == 2
assert m.a.bounds == (1, None)
assert m.b.bounds == (2, None)
assert m.a.fixed is True
assert m.b.fixed is False
assert m.bounds == {"a": (1, None), "b": (2, None)}
assert m.fixed == {"a": True, "b": False}
@pytest.mark.skipif(not HAS_SCIPY, reason="requires scipy")
@pytest.mark.filterwarnings(r"ignore:divide by zero encountered.*")
@pytest.mark.parametrize("fitter", fitters)
def test_fit_with_fixed_and_bound_constraints(fitter):
"""
Regression test for https://github.com/astropy/astropy/issues/2235
Currently doesn't test that the fit is any *good*--just that parameters
stay within their given constraints.
"""
# DogBoxLSQFitter causes failure on s390x, aremel possibly others (not x86_64 or arm64)
if fitter == fitting.DogBoxLSQFitter and (
platform.machine() not in ("x86_64", "arm64")
):
pytest.xfail(
"DogBoxLSQFitter can to be unstable on non-standard platforms leading to "
"random test failures"
)
fitter = fitter()
m = models.Gaussian1D(
amplitude=3,
mean=4,
stddev=1,
bounds={"mean": (4, 5)},
fixed={"amplitude": True},
)
x = np.linspace(0, 10, 10)
y = np.exp(-(x**2) / 2)
if isinstance(fitter, fitting.TRFLSQFitter):
ctx = np.errstate(invalid="ignore", divide="ignore")
else:
ctx = nullcontext()
with ctx:
fitted_1 = fitter(m, x, y)
assert fitted_1.mean >= 4
assert fitted_1.mean <= 5
assert fitted_1.amplitude == 3.0
m.amplitude.fixed = False
# Cannot enter np.errstate twice, so we need to indent everything in between.
_ = fitter(m, x, y)
# It doesn't matter anymore what the amplitude ends up as so long as the
# bounds constraint was still obeyed
assert fitted_1.mean >= 4
assert fitted_1.mean <= 5
@pytest.mark.skipif(not HAS_SCIPY, reason="requires scipy")
@pytest.mark.parametrize("fitter", fitters)
def test_fit_with_bound_constraints_estimate_jacobian(fitter):
"""
Regression test for https://github.com/astropy/astropy/issues/2400
Checks that bounds constraints are obeyed on a custom model that does not
define fit_deriv (and thus its Jacobian must be estimated for non-linear
fitting).
"""
fitter = fitter()
class MyModel(Fittable1DModel):
a = Parameter(default=1)
b = Parameter(default=2)
@staticmethod
def evaluate(x, a, b):
return a * x + b
m_real = MyModel(a=1.5, b=-3)
x = np.arange(100)
y = m_real(x)
m = MyModel()
fitted_1 = fitter(m, x, y)
# This fit should be trivial so even without constraints on the bounds it
# should be right
assert np.allclose(fitted_1.a, 1.5)
assert np.allclose(fitted_1.b, -3)
m2 = MyModel()
m2.a.bounds = (-2, 2)
_ = fitter(m2, x, y)
assert np.allclose(fitted_1.a, 1.5)
assert np.allclose(fitted_1.b, -3)
# Check that the estimated Jacobian was computed (it doesn't matter what
# the values are so long as they're not all zero.
if fitter == fitting.LevMarLSQFitter:
assert np.any(fitter.fit_info["fjac"] != 0)
# https://github.com/astropy/astropy/issues/6014
@pytest.mark.skipif(not HAS_SCIPY, reason="requires scipy")
@pytest.mark.parametrize("fitter", fitters)
def test_gaussian2d_positive_stddev(fitter):
# This is 2D Gaussian with noise to be fitted, as provided by @ysBach
fitter = fitter()
# fmt: off
test = [
[-54.33, 13.81, -34.55, 8.95, -143.71, -0.81, 59.25, -14.78, -204.9,
-30.87, -124.39, 123.53, 70.81, -109.48, -106.77, 35.64, 18.29],
[-126.19, -89.13, 63.13, 50.74, 61.83, 19.06, 65.7, 77.94, 117.14,
139.37, 52.57, 236.04, 100.56, 242.28, -180.62, 154.02, -8.03],
[91.43, 96.45, -118.59, -174.58, -116.49, 80.11, -86.81, 14.62, 79.26,
7.56, 54.99, 260.13, -136.42, -20.77, -77.55, 174.52, 134.41],
[33.88, 7.63, 43.54, 70.99, 69.87, 33.97, 273.75, 176.66, 201.94,
336.34, 340.54, 163.77, -156.22, 21.49, -148.41, 94.88, 42.55],
[82.28, 177.67, 26.81, 17.66, 47.81, -31.18, 353.23, 589.11, 553.27,
242.35, 444.12, 186.02, 140.73, 75.2, -87.98, -18.23, 166.74],
[113.09, -37.01, 134.23, 71.89, 107.88, 198.69, 273.88, 626.63, 551.8,
547.61, 580.35, 337.8, 139.8, 157.64, -1.67, -26.99, 37.35],
[106.47, 31.97, 84.99, -125.79, 195.0, 493.65, 861.89, 908.31, 803.9,
781.01, 532.59, 404.67, 115.18, 111.11, 28.08, 122.05, -58.36],
[183.62, 45.22, 40.89, 111.58, 425.81, 321.53, 545.09, 866.02, 784.78,
731.35, 609.01, 405.41, -19.65, 71.2, -140.5, 144.07, 25.24],
[137.13, -86.95, 15.39, 180.14, 353.23, 699.01, 1033.8, 1014.49,
814.11, 647.68, 461.03, 249.76, 94.8, 41.17, -1.16, 183.76, 188.19],
[35.39, 26.92, 198.53, -37.78, 638.93, 624.41, 816.04, 867.28, 697.0,
491.56, 378.21, -18.46, -65.76, 98.1, 12.41, -102.18, 119.05],
[190.73, 125.82, 311.45, 369.34, 554.39, 454.37, 755.7, 736.61, 542.43,
188.24, 214.86, 217.91, 7.91, 27.46, -172.14, -82.36, -80.31],
[-55.39, 80.18, 267.19, 274.2, 169.53, 327.04, 488.15, 437.53, 225.38,
220.94, 4.01, -92.07, 39.68, 57.22, 144.66, 100.06, 34.96],
[130.47, -4.23, 46.3, 101.49, 115.01, 217.38, 249.83, 115.9, 87.36,
105.81, -47.86, -9.94, -82.28, 144.45, 83.44, 23.49, 183.9],
[-110.38, -115.98, 245.46, 103.51, 255.43, 163.47, 56.52, 33.82,
-33.26, -111.29, 88.08, 193.2, -100.68, 15.44, 86.32, -26.44, -194.1],
[109.36, 96.01, -124.89, -16.4, 84.37, 114.87, -65.65, -58.52, -23.22,
42.61, 144.91, -209.84, 110.29, 66.37, -117.85, -147.73, -122.51],
[10.94, 45.98, 118.12, -46.53, -72.14, -74.22, 21.22, 0.39, 86.03,
23.97, -45.42, 12.05, -168.61, 27.79, 61.81, 84.07, 28.79],
[46.61, -104.11, 56.71, -90.85, -16.51, -66.45, -141.34, 0.96, 58.08,
285.29, -61.41, -9.01, -323.38, 58.35, 80.14, -101.22, 145.65]
]
# fmt: on
g_init = models.Gaussian2D(x_mean=8, y_mean=8)
if isinstance(fitter, (fitting.TRFLSQFitter, fitting.DogBoxLSQFitter)):
pytest.xfail("TRFLSQFitter seems to be broken for this test.")
y, x = np.mgrid[:17, :17]
g_fit = fitter(g_init, x, y, test)
# Compare with @ysBach original result:
# - x_stddev was negative, so its abs value is used for comparison here.
# - theta is beyond (-90, 90) deg, which doesn't make sense, so ignored.
assert_allclose(
[g_fit.amplitude.value, g_fit.y_stddev.value],
[984.7694929790363, 3.1840618351417307],
rtol=1.5e-6,
)
assert_allclose(g_fit.x_mean.value, 7.198391516587464)
assert_allclose(g_fit.y_mean.value, 7.49720660088511, rtol=5e-7)
assert_allclose(g_fit.x_stddev.value, 1.9840185107597297, rtol=2e-6)
@pytest.mark.skipif(not HAS_SCIPY, reason="requires scipy")
@pytest.mark.filterwarnings(r"ignore:Model is linear in parameters.*")
@pytest.mark.parametrize("fitter", fitters)
def test_2d_model(fitter):
"""Issue #6403"""
from astropy.utils import NumpyRNGContext
fitter = fitter()
# 2D model with LevMarLSQFitter
gauss2d = models.Gaussian2D(10.2, 4.3, 5, 2, 1.2, 1.4)
X = np.linspace(-1, 7, 200)
Y = np.linspace(-1, 7, 200)
x, y = np.meshgrid(X, Y)
z = gauss2d(x, y)
w = np.ones(x.size)
w.shape = x.shape
with NumpyRNGContext(1234567890):
n = np.random.randn(x.size)
n.shape = x.shape
m = fitter(gauss2d, x, y, z + 2 * n, weights=w)
assert_allclose(m.parameters, gauss2d.parameters, rtol=0.05)
m = fitter(gauss2d, x, y, z + 2 * n, weights=None)
assert_allclose(m.parameters, gauss2d.parameters, rtol=0.05)
# 2D model with LevMarLSQFitter, fixed constraint
gauss2d.x_stddev.fixed = True
m = fitter(gauss2d, x, y, z + 2 * n, weights=w)
assert_allclose(m.parameters, gauss2d.parameters, rtol=0.05)
m = fitter(gauss2d, x, y, z + 2 * n, weights=None)
assert_allclose(m.parameters, gauss2d.parameters, rtol=0.05)
# Polynomial2D, col_fit_deriv=False
p2 = models.Polynomial2D(1, c0_0=1, c1_0=1.2, c0_1=3.2)
z = p2(x, y)
m = fitter(p2, x, y, z + 2 * n, weights=None)
assert_allclose(m.parameters, p2.parameters, rtol=0.05)
m = fitter(p2, x, y, z + 2 * n, weights=w)
assert_allclose(m.parameters, p2.parameters, rtol=0.05)
# Polynomial2D, col_fit_deriv=False, fixed constraint
p2.c1_0.fixed = True
m = fitter(p2, x, y, z + 2 * n, weights=w)
assert_allclose(m.parameters, p2.parameters, rtol=0.05)
m = fitter(p2, x, y, z + 2 * n, weights=None)
assert_allclose(m.parameters, p2.parameters, rtol=0.05)
def test_set_prior_posterior():
model = models.Polynomial1D(1)
model.c0.prior = models.Gaussian1D(2.3, 2, 0.1)
assert model.c0.prior(2) == 2.3
model.c0.posterior = models.Linear1D(1, 0.2)
assert model.c0.posterior(1) == 1.2
def test_set_constraints():
g = models.Gaussian1D()
p = models.Polynomial1D(1)
# Set bounds before model combination
g.stddev.bounds = (0, 3)
m = g + p
assert m.bounds == {
"amplitude_0": (None, None),
"mean_0": (None, None),
"stddev_0": (0.0, 3.0),
"c0_1": (None, None),
"c1_1": (None, None),
}
# Set bounds on the compound model
m.stddev_0.bounds = (1, 3)
assert m.bounds == {
"amplitude_0": (None, None),
"mean_0": (None, None),
"stddev_0": (1.0, 3.0),
"c0_1": (None, None),
"c1_1": (None, None),
}
# Set the bounds of a Parameter directly in the bounds dict
m.bounds["stddev_0"] = (4, 5)
assert m.bounds == {
"amplitude_0": (None, None),
"mean_0": (None, None),
"stddev_0": (4, 5),
"c0_1": (None, None),
"c1_1": (None, None),
}
# Set the bounds of a Parameter on the child model bounds dict
g.bounds["stddev"] = (1, 5)
m = g + p
assert m.bounds == {
"amplitude_0": (None, None),
"mean_0": (None, None),
"stddev_0": (1, 5),
"c0_1": (None, None),
"c1_1": (None, None),
}