def test_deprecated_hat_kernel():
# 'MexicanHat' was deprecated as a name for the models which are now
# 'RickerWavelet'. This test ensures that the models are correctly
# deprecated.
with pytest.warns(AstropyDeprecationWarning):
models.MexicanHat1D()
with pytest.warns(AstropyDeprecationWarning):
models.MexicanHat2D()
def fit_mex_hat_1d(data):
"""Fit a 1D Mexican Hat function to the data."""
ldata = len(data)
x = np.arange(ldata)
fixed_pars = {"x_0": True}
# Fit model to data
fit = fitting.LevMarLSQFitter()
# Mexican Hat 1D
model = models.MexicanHat1D(amplitude=np.max(data),
x_0=ldata/2, sigma=2., fixed=fixed_pars)
with warnings.catch_warnings():
# Ignore model linearity warning from the fitter
warnings.simplefilter('ignore')
results = fit(model, x, data)
# previous yield amp, ycenter, xcenter, sigma, offset
return results
# the astropy.modeling.Model registry. For now, we directly
# store hard-coded instances.
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':
def test_single_peak_estimate():
"""
Single Peak fit.
"""
# Create the spectrum
x_single, y_single = single_peak()
s_single = Spectrum1D(flux=y_single*u.Jy, spectral_axis=x_single*u.um)
#
# Estimate parameter Gaussian1D
#
g_init = estimate_line_parameters(s_single, models.Gaussian1D())
assert np.isclose(g_init.amplitude.value, 3.354169257846847)
assert np.isclose(g_init.mean.value, 6.218588636687762)
assert np.isclose(g_init.stddev.value, 1.6339001193853715)
assert g_init.amplitude.unit == u.Jy
assert g_init.mean.unit == u.um
assert g_init.stddev.unit == u.um
#
# Estimate parameter Lorentz1D
#
g_init = estimate_line_parameters(s_single, models.Lorentz1D())
assert np.isclose(g_init.amplitude.value, 3.354169257846847)
assert np.isclose(g_init.x_0.value, 6.218588636687762)
assert np.isclose(g_init.fwhm.value, 1.6339001193853715)
assert g_init.amplitude.unit == u.Jy
assert g_init.x_0.unit == u.um
assert g_init.fwhm.unit == u.um
#
# Estimate parameter Voigt1D
#
g_init = estimate_line_parameters(s_single, models.Voigt1D())
assert np.isclose(g_init.amplitude_L.value, 3.354169257846847)
assert np.isclose(g_init.x_0.value, 6.218588636687762)
assert np.isclose(g_init.fwhm_L.value, 1.1553418541989058)
assert np.isclose(g_init.fwhm_G.value, 1.1553418541989058)
assert g_init.amplitude_L.unit == u.Jy
assert g_init.x_0.unit == u.um
assert g_init.fwhm_L.unit == u.um
assert g_init.fwhm_G.unit == u.um
#
# Estimate parameter MexicanHat1D
#
mh = models.MexicanHat1D()
estimators = {
'amplitude': lambda s: max(s.flux),
'x_0': lambda s: centroid(s, region=None),
'stddev': lambda s: fwhm(s)
}
mh._constraints['parameter_estimator'] = estimators
g_init = estimate_line_parameters(s_single, mh)
assert np.isclose(g_init.amplitude.value, 3.354169257846847)
assert np.isclose(g_init.x_0.value, 6.218588636687762)
assert np.isclose(g_init.stddev.value, 1.6339001193853715)
assert g_init.amplitude.unit == u.Jy
assert g_init.x_0.unit == u.um
assert g_init.stddev.unit == u.um
def __init__(self, width, **kwargs):
amplitude = 1.0 / (np.sqrt(2 * np.pi) * width**3)
self._model = models.MexicanHat1D(amplitude, 0, width)
self._default_size = _round_up_to_odd_integer(8 * width)
super().__init__(**kwargs)
self._truncation = np.abs(self._array.sum() / self._array.size)
def fit_mex_hat_1d(data, sigma_factor=0, center_at=None, weighted=False):
"""Fit a 1D Mexican Hat function to the data.
Parameters
----------
data: 2D float array
should be a 2d array, the initial center is used to estimate
the fit center
sigma_factor: float (optional)
If sigma_factor > 0 then clipping will be performed
on the data during the model fit
center_at: float or None
None by default, set to value to use as center
weighted: bool
if weighted is True, then weight the values by basic
uncertainty hueristic
Returns
-------
The the fit model for mexican hat 1D function
"""
ldata = len(data)
if center_at:
x0 = 0
else:
x0 = int(ldata / 2.)
# assumes negligable background
if weighted:
z = np.nan_to_num(1. / np.sqrt(data)) # use as weight
x = np.arange(ldata)
fixed_pars = {"x_0": True}
# Initialize the fitter
fitter = fitting.LevMarLSQFitter()
if sigma_factor > 0:
fit = fitting.FittingWithOutlierRemoval(fitter,
sigma_clip,
niter=3,
sigma=sigma_factor)
else:
fit = fitter
# Mexican Hat 1D + constant
model = (models.MexicanHat1D(
amplitude=np.max(data), x_0=x0, sigma=2., fixed=fixed_pars) +
models.Polynomial1D(c0=data.min(), degree=0))
with warnings.catch_warnings():
# Ignore model linearity warning from the fitter
warnings.simplefilter('ignore')
if weighted:
results = fit(model, x, data, weights=z)
else:
results = fit(model, x, data)
# previous yield amp, ycenter, xcenter, sigma, offset
if sigma_factor > 0:
return results[1]
else:
return results
