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 __init__(self, width, **kwargs):
amplitude = 1.0 / (np.pi * width**4)
self._model = models.MexicanHat2D(amplitude, 0, 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_2D_MexicanHat(box,
center=None,
fixed_center=False,
deviation_center=None,
radius=None,
x_shift=0.0,
y_shift=0.0,
zoom_factor=1.0,
mask=None):
"""
This function ...
:param box:
:param center:
:param fixed_center:
:param deviation_center:
:param radius:
:param x_shift:
:param y_shift:
:param zoom_factor:
:param mask:
:return:
"""
# Get the dimensions of the box
box_ysize = box.shape[0]
box_xsize = box.shape[1]
# Set the initial guess for the center of the model (the one that is specified, otherwise the center of the box)
init_x0 = center[0] if center is not None else 0.5 * (box_xsize - 1)
init_y0 = center[1] if center is not None else 0.5 * (box_ysize - 1)
# Set the initial guess for the radius of the model (the one that is specified, otherwise one tenth of the width of the box)
init_sigma = radius if radius is not None else 0.1 * box_xsize
# Initialize an empty dictionary to specify fixed parameters
fixed_parameters = {}
if fixed_center:
fixed_parameters['x_0'] = True
fixed_parameters['y_0'] = True
# Initialize an empty dictionary to specify bounds
bounds = {}
if deviation_center is not None:
bounds['x_mean'] = [
init_x0 - deviation_center, init_x0 + deviation_center
]
bounds['y_mean'] = [
init_y0 - deviation_center, init_y0 + deviation_center
]
# Fit the data using astropy.modeling
mexicanhat_init = models.MexicanHat2D(amplitude=1.,
x_0=init_x0,
y_0=init_y0,
sigma=init_sigma,
fixed=fixed_parameters,
bounds=bounds)
fit_model = fitting.LevMarLSQFitter()
x_values = []
y_values = []
z_values = []
for x in range(box_xsize):
for y in range(box_ysize):
# If no mask is specified or the pixel is not masked, add the coordinates and value to the appropriate lists
if mask is None or not mask[y, x]:
x_values.append(x)
y_values.append(y)
z_values.append(box[y, x])
# Ignore model linearity warning from the fitter
with warnings.catch_warnings():
warnings.simplefilter('ignore')
mexicanhat = fit_model(
mexicanhat_init, x_values, y_values,
z_values) # What comes out is the model with the parameters set
# Adjust the position of the model to a different coordinate frame
if zoom_factor > 1.0:
mexicanhat.x_0.value = mexicanhat.x_0.value / zoom_factor + x_shift
mexicanhat.y_0.value = mexicanhat.y_0.value / zoom_factor + y_shift
mexicanhat.sigma.value /= zoom_factor
else:
mexicanhat.x_0.value += x_shift
mexicanhat.y_0.value += y_shift
# Return the fitted two-dimensional Mexican Hat model
return mexicanhat