def effective_einstein_radius(self, kwargs_lens_list, k=None, spacing=1000):
"""
computes the radius with mean convergence=1
:param kwargs_lens:
:param spacing: number of annular bins to compute the convergence (resolution of the Einstein radius estimate)
:return:
"""
if 'center_x' in kwargs_lens_list[0]:
center_x = kwargs_lens_list[0]['center_x']
center_y = kwargs_lens_list[0]['center_y']
elif self._lensModel.lens_model_list[0] in ['INTERPOL', 'INTERPOL_SCALED']:
center_x, center_y = 0, 0
else:
center_x, center_y = 0, 0
numPix = 200
deltaPix = 0.05
x_grid, y_grid = util.make_grid(numPix=numPix, deltapix=deltaPix)
x_grid += center_x
y_grid += center_y
kappa = self._lensModel.kappa(x_grid, y_grid, kwargs_lens_list, k=k)
if self._lensModel.lens_model_list[0] in ['INTERPOL', 'INTERPOL_SCALED']:
center_x = x_grid[kappa == np.max(kappa)]
center_y = y_grid[kappa == np.max(kappa)]
kappa = util.array2image(kappa)
r_array = np.linspace(0.0001, numPix*deltaPix/2., spacing)
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:
return r
print(kwargs_lens_list, "Warning, no Einstein radius computed!")
return r_array[-1]
def effective_einstein_radius(self, kwargs_lens_list, k=None):
"""
computes the radius with mean convergence=1
:param kwargs_lens:
:return:
"""
center_x = kwargs_lens_list[0]['center_x']
center_y = kwargs_lens_list[0]['center_y']
numPix = 100
deltaPix = 0.05
x_grid, y_grid = util.make_grid(numPix=numPix, deltapix=deltaPix)
x_grid += center_x
y_grid += center_y
kappa = self.kappa(x_grid, y_grid, kwargs_lens_list, k=k)
kappa = util.array2image(kappa)
r_array = np.linspace(0.0001, numPix*deltaPix/2., 1000)
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:
return r
print(kwargs_lens_list, "Warning, no Einstein radius computed!")
return r_array[-1]
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][0] == 1
assert mask[5][5] == 0
def effective_einstein_radius(self,
kwargs_lens,
k=None,
spacing=1000,
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
"""
if 'center_x' in kwargs_lens[0]:
center_x = kwargs_lens[0]['center_x']
center_y = kwargs_lens[0]['center_y']
elif self._lensModel.lens_model_list[0] in [
'INTERPOL', 'INTERPOL_SCALED'
]:
center_x, center_y = 0, 0
else:
center_x, center_y = 0, 0
numPix = 200
deltaPix = 0.05
x_grid, y_grid = util.make_grid(numPix=numPix, deltapix=deltaPix)
x_grid += center_x
y_grid += center_y
kappa = self._lensModel.kappa(x_grid, y_grid, kwargs_lens, k=k)
if self._lensModel.lens_model_list[0] in [
'INTERPOL', 'INTERPOL_SCALED'
]:
center_x = x_grid[kappa == np.max(kappa)]
center_y = y_grid[kappa == np.max(kappa)]
kappa = util.array2image(kappa)
r_array = np.linspace(0.0001, numPix * deltaPix / 2., spacing)
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:
print(kwargs_lens, "Warning, no Einstein radius computed!")
return np.nan #r_array[-1]