def test_Trapezoid1D():
"""Regression test for https://github.com/astropy/astropy/issues/1721"""
model = models.Trapezoid1D(amplitude=4.2, x_0=2.0, width=1.0, slope=3)
xx = np.linspace(0, 4, 8)
yy = model(xx)
yy_ref = [0., 1.41428571, 3.12857143, 4.2, 4.2, 3.12857143, 1.41428571, 0.]
assert_allclose(yy, yy_ref, rtol=0, atol=1e-6)
def __init__(self, width, slope=1., **kwargs):
self._model = models.Trapezoid1D(1, 0, width, slope)
self._default_size = _round_up_to_odd_integer(width + 2. / slope)
super().__init__(**kwargs)
self.normalize()
astmodels.Ring2D(amplitude=10., x_0=0.5, y_0=1.5, r_in=5., width=10.),
astmodels.Sersic1D(amplitude=10., r_eff=1., n=4.),
astmodels.Sersic2D(amplitude=10.,
r_eff=1.,
n=4.,
x_0=0.5,
y_0=1.5,
ellip=0.0,
theta=0.0),
astmodels.Sine1D(amplitude=10., frequency=0.5, phase=1.),
astmodels.Cosine1D(amplitude=10., frequency=0.5, phase=1.),
astmodels.Tangent1D(amplitude=10., frequency=0.5, phase=1.),
astmodels.ArcSine1D(amplitude=10., frequency=0.5, phase=1.),
astmodels.ArcCosine1D(amplitude=10., frequency=0.5, phase=1.),
astmodels.ArcTangent1D(amplitude=10., frequency=0.5, phase=1.),
astmodels.Trapezoid1D(amplitude=10., x_0=0.5, width=5., slope=1.),
astmodels.TrapezoidDisk2D(amplitude=10.,
x_0=0.5,
y_0=1.5,
R_0=5.,
slope=1.),
astmodels.Voigt1D(x_0=0.55, amplitude_L=10., fwhm_L=0.5, fwhm_G=0.9),
astmodels.BlackBody(scale=10.0, temperature=6000. * u.K),
astmodels.Drude1D(amplitude=10.0, x_0=0.5, fwhm=2.5),
astmodels.Plummer1D(mass=10.0, r_plum=5.0),
astmodels.BrokenPowerLaw1D(amplitude=10,
x_break=0.5,
alpha_1=2.0,
alpha_2=3.5),
astmodels.ExponentialCutoffPowerLaw1D(10, 0.5, 2.0, 7.),
astmodels.LogParabola1D(
import astropy.modeling.models as models
from astropy.modeling import Parameter, Fittable1DModel
from astropy.modeling.polynomial import PolynomialModel
registry = {
'Gaussian1D':
models.Gaussian1D(1.0, 1.0, 1.0),
'GaussianAbsorption1D':
models.GaussianAbsorption1D(1.0, 1.0, 1.0),
'Lorentz1D':
models.Lorentz1D(1.0, 1.0, 1.0),
'MexicanHat1D':
models.MexicanHat1D(1.0, 1.0, 1.0),
'Trapezoid1D':
models.Trapezoid1D(1.0, 1.0, 1.0, 1.0),
'Moffat1D':
models.Moffat1D(1.0, 1.0, 1.0, 1.0),
'ExponentialCutoffPowerLaw1D':
models.ExponentialCutoffPowerLaw1D(1.0, 1.0, 1.0, 1.0),
'BrokenPowerLaw1D':
models.BrokenPowerLaw1D(1.0, 1.0, 1.0, 1.0),
'LogParabola1D':
models.LogParabola1D(1.0, 1.0, 1.0, 1.0),
'PowerLaw1D':
models.PowerLaw1D(1.0, 1.0, 1.0),
'Linear1D':
models.Linear1D(1.0, 0.0),
'Const1D':
models.Const1D(0.0),
'Redshift':
# 0.54952605, 0.3894018 ])
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling import models, fitting
# Generate fake data
np.random.seed(0)
x = np.linspace(-5., 5., 200)
y = 3 * np.exp(-0.5 * (x - 1.3)**2 / 0.8**2)
y += np.random.normal(0., 0.2, x.shape)
# Fit the data using a box model
t_init = models.Trapezoid1D(amplitude=1., x_0=0., width=1., slope=0.5)
fit_t = fitting.LevMarLSQFitter()
t = fit_t(t_init, x, y)
# Fit the data using a Gaussian
g_init = models.Gaussian1D(amplitude=1., mean=0, stddev=1.)
fit_g = fitting.LevMarLSQFitter()
g = fit_g(g_init, x, y)
# Plot the data with the best-fit model
plt.figure(figsize=(8,5))
plt.plot(x, y, 'ko')
plt.plot(x, t(x), label='Trapezoid')
plt.plot(x, g(x), label='Gaussian')
plt.xlabel('Position')
plt.ylabel('Flux')
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling import models, fitting
#Generate random data
np.random.seed(0)
x = np.linspace(-5., 5., 200)
y = 3 * np.exp(-0.5 * (x - 1.3)**2 / 0.8**2)
y += np.random.normal(0., 0.2, x.shape)
#Fit the data using a box model
t_init = models.Trapezoid1D(amplitude=1., x_0=0, width=1., slope=0.5,
bounds={"x_0": (-5., 5.)})
fit_t = fitting.LevMarLSQFitter()
t = fit_t(t_init, x, y)
#Fit the data using a gaussian
g_init = models.Gaussian1D(amplitude=1., mean=0, stddev=1.)
fit_g = fitting.LevMarLSQFitter()
g = fit_g(g_init, x, y)
#Plot data with the best fit model
plt.figure(figsize=(8,5))
plt.plot(x, y, 'ko')
plt.plot(x, t(x), label='Trapezoid')
plt.plot(x, g(x), label='Gaussian')
plt.xlabel('Position')
plt.ylabel('Flux')
plt.legend(loc=2)
