def effective_einstein_radius(self, kwargs_lens, center_x=None, center_y=None, model_bool_list=None, grid_num=200,
grid_spacing=0.05, get_precision=False, verbose=True):
"""
computes the radius with mean convergence=1
:param kwargs_lens: list of lens model keyword arguments
:param spacing: number of annular bins to compute the convergence (resolution of the Einstein radius estimate)
:param get_precision: If `True`, return the precision of estimated Einstein radius
:return: estimate of the Einstein radius
"""
center_x, center_y = analysis_util.profile_center(kwargs_lens, center_x, center_y)
x_grid, y_grid = util.make_grid(numPix=grid_num, deltapix=grid_spacing)
x_grid += center_x
y_grid += center_y
kappa = self._lens_model.kappa(x_grid, y_grid, kwargs_lens, k=model_bool_list)
if self._lens_model.lens_model_list[0] in ['INTERPOL', 'INTERPOL_SCALED']:
center_x = x_grid[kappa == np.max(kappa)][0]
center_y = y_grid[kappa == np.max(kappa)][0]
#kappa = util.array2image(kappa)
r_array = np.linspace(0, grid_num*grid_spacing/2., grid_num*2)
for r in r_array:
mask = np.array(1 - mask_util.mask_center_2d(center_x, center_y, r, x_grid, y_grid))
sum_mask = np.sum(mask)
if sum_mask > 0:
kappa_mean = np.sum(kappa*mask)/np.sum(mask)
if kappa_mean < 1:
if get_precision:
return r, r_array[1] - r_array[0]
else:
return r
if verbose:
Warning("No Einstein radius computed for the following model!", kwargs_lens)
return np.nan
def test_get_mask():
x = np.linspace(0, 10, 100)
y = np.linspace(0, 10, 100)
center_x = 5
center_y = 5
r = 1
mask = mask_util.mask_center_2d(center_x, center_y, r, x, y)
assert mask[0] == 1
assert mask[50] == 0
def effective_einstein_radius(self,
kwargs_lens,
center_x=None,
center_y=None,
model_bool_list=None,
grid_num=200,
grid_spacing=0.05,
get_precision=False,
verbose=True):
"""
computes the radius with mean convergence=1
:param kwargs_lens: list of lens model keyword arguments
:param center_x: position of the center (if not set, is attempting to find it from the parameters kwargs_lens)
:param center_y: position of the center (if not set, is attempting to find it from the parameters kwargs_lens)
:param model_bool_list: list of booleans indicating the addition (=True) of a model component in computing the
Einstein radius
:param grid_num: integer, number of grid points to numerically evaluate the convergence and estimate the
Einstein radius
:param grid_spacing: spacing in angular units of the grid
:param get_precision: If `True`, return the precision of estimated Einstein radius
:param verbose: boolean, if True prints warning if indication of insufficient result
:return: estimate of the Einstein radius
"""
center_x, center_y = analysis_util.profile_center(
kwargs_lens, center_x, center_y)
x_grid, y_grid = util.make_grid(numPix=grid_num, deltapix=grid_spacing)
x_grid += center_x
y_grid += center_y
kappa = self._lens_model.kappa(x_grid,
y_grid,
kwargs_lens,
k=model_bool_list)
if self._lens_model.lens_model_list[0] in [
'INTERPOL', 'INTERPOL_SCALED'
]:
center_x = x_grid[kappa == np.max(kappa)][0]
center_y = y_grid[kappa == np.max(kappa)][0]
#kappa = util.array2image(kappa)
r_array = np.linspace(0, grid_num * grid_spacing / 2., grid_num * 2)
for r in r_array:
mask = np.array(1 - mask_util.mask_center_2d(
center_x, center_y, r, x_grid, y_grid))
sum_mask = np.sum(mask)
if sum_mask > 0:
kappa_mean = np.sum(kappa * mask) / np.sum(mask)
if kappa_mean < 1:
if get_precision:
return r, r_array[1] - r_array[0]
else:
return r
if verbose:
Warning("No Einstein radius computed for the following model!",
kwargs_lens)
return np.nan
def get_masks(self):
"""
Create masks.
:return:
:rtype:
"""
if 'mask' in self.settings:
if self.settings['mask'] is not None:
if 'provided' in self.settings['mask'] \
and self.settings['mask']['provided'] is not None:
return self.settings['mask']['provided']
else:
masks = []
mask_options = deepcopy(self.settings['mask'])
for n in range(self.band_number):
ra_at_xy_0 = mask_options['ra_at_xy_0'][n]
dec_at_xy_0 = mask_options['dec_at_xy_0'][n]
transform_pix2angle = np.array(
mask_options['transform_matrix'][n]
)
num_pixel = mask_options['size'][n]
radius = mask_options['radius'][n]
offset = mask_options['centroid_offset'][n]
coords = Coordinates(transform_pix2angle,
ra_at_xy_0, dec_at_xy_0)
x_coords, y_coords = coords.coordinate_grid(num_pixel,
num_pixel)
mask_outer = mask_util.mask_center_2d(
self.deflector_center_ra + offset[0],
self.deflector_center_dec + offset[1],
radius,
util.image2array(x_coords),
util.image2array(y_coords)
)
extra_masked_regions = []
try:
self.settings['mask']['extra_regions']
except (NameError, KeyError):
pass
else:
if self.settings['mask']['extra_regions'] is \
not None:
for reg in self.settings['mask'][
'extra_regions'][n]:
extra_masked_regions.append(
mask_util.mask_center_2d(
self.deflector_center_ra + reg[0],
self.deflector_center_dec + reg[1],
reg[2],
util.image2array(x_coords),
util.image2array(y_coords)
)
)
mask = 1. - mask_outer
for extra_region in extra_masked_regions:
mask *= extra_region
# sanity check
mask[mask >= 1.] = 1.
mask[mask <= 0.] = 0.
masks.append(util.array2image(mask))
return masks
return None
def get_masks(self):
"""
Create masks.
:return:
:rtype:
"""
if 'mask' in self.settings:
if self.settings['mask'] is not None:
if 'provided' in self.settings['mask'] \
and self.settings['mask']['provided'] is not None:
return self.settings['mask']['provided']
else:
masks = []
mask_options = deepcopy(self.settings['mask'])
for n in range(self.band_number):
ra_at_xy_0 = mask_options['ra_at_xy_0'][n]
dec_at_xy_0 = mask_options['dec_at_xy_0'][n]
transform_pix2angle = np.array(
mask_options['transform_matrix'][n]
)
num_pixel = mask_options['size'][n]
radius = mask_options['radius'][n]
offset = mask_options['centroid_offset'][n]
coords = Coordinates(transform_pix2angle,
ra_at_xy_0, dec_at_xy_0)
x_coords, y_coords = coords.coordinate_grid(num_pixel,
num_pixel)
mask_outer = mask_util.mask_center_2d(
self.deflector_center_ra + offset[0],
self.deflector_center_dec + offset[1],
radius,
util.image2array(x_coords),
util.image2array(y_coords)
)
extra_masked_regions = []
try:
self.settings['mask']['extra_regions']
except (NameError, KeyError):
pass
else:
if self.settings['mask']['extra_regions'] is \
not None:
for reg in self.settings['mask'][
'extra_regions'][n]:
extra_masked_regions.append(
mask_util.mask_center_2d(
self.deflector_center_ra + reg[0],
self.deflector_center_dec + reg[1],
reg[2],
util.image2array(x_coords),
util.image2array(y_coords)
)
)
mask = 1. - mask_outer
for extra_region in extra_masked_regions:
mask *= extra_region
# Mask Edge Pixels
try:
self.settings['mask']['mask_edge_pixels']
except (NameError, KeyError):
pass
else:
border_length = \
self.settings['mask']['mask_edge_pixels'][n]
if border_length > 0:
edge_mask = 0 * np.ones(
(num_pixel, num_pixel), dtype=int)
edge_mask[border_length:-border_length,
border_length:-border_length] = 1
edge_mask = (edge_mask.flatten()).tolist()
elif border_length == 0:
edge_mask = 1 * np.ones(
(num_pixel, num_pixel), dtype=int)
edge_mask = (edge_mask.flatten()).tolist()
mask *= edge_mask
# Add custom Mask
try:
self.settings['mask']['custom_mask']
except (NameError, KeyError):
pass
else:
if self.settings['mask']['custom_mask'][n]\
is not None:
provided_mask = \
self.settings['mask']['custom_mask'][n]
provided_mask = np.array(provided_mask)
# make sure that mask consist of only 0 and 1
provided_mask[provided_mask > 0.] = 1.
provided_mask[provided_mask <= 0.] = 0.
mask *= provided_mask
# sanity check
mask[mask >= 1.] = 1.
mask[mask <= 0.] = 0.
masks.append(util.array2image(mask))
return masks
return None