def like_me(self, **overrides):
if 'center' in overrides:
return super().like_me(**overrides)
for i, coord in enumerate(('x', 'y', 'z')):
if coord in overrides:
overrides['center[{}]'.format(i)] = overrides[coord]
del overrides[coord]
return self.from_parameters(updated(self.parameters, overrides))
def test_updated_keep_None(self):
input_dict = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
update_dict = {'c': 5, 'd': None, 'e': 6}
output_dict = updated(input_dict, update_dict, False)
self.assertEqual(input_dict, {'a': 1, 'b': 2, 'c': 3, 'd': 4})
self.assertEqual(output_dict, {
'a': 1,
'b': 2,
'c': 5,
'd': None,
'e': 6
})
def update_metadata(a,
medium_index=None,
illum_wavelen=None,
illum_polarization=None,
normals=None,
noise_sd=None):
"""Returns a copy of an image with updated metadata in its 'attrs' field.
Parameters
----------
a : xarray.DataArray
image to update.
medium_index : float
Updated refractive index of the medium in the image.
illum_wavelen : float
Updated wavelength of illumination in the image.
illum_polarization : list-like
Updated polarization of illumination in the image.
noise_sd : float
standard deviation of Gaussian noise in the image.
Returns
-------
b : xarray.DataArray
copy of input image with updated metadata.
"""
if normals is not None:
raise ValueError(NORMALS_DEPRECATION_MESSAGE)
attrlist = {
'medium_index': medium_index,
'illum_wavelen': dict_to_array(a, illum_wavelen),
'illum_polarization': dict_to_array(a, to_vector(illum_polarization)),
'noise_sd': dict_to_array(a, noise_sd)
}
b = a.copy()
b.attrs = updated(b.attrs, attrlist)
for attr in attrlist:
if not hasattr(b, attr):
b.attrs[attr] = None
return b
def detector_points(coords={}, x=None, y=None, z=None, r=None, theta=None,
phi=None, normals=None, name=None):
"""
Returns a one-dimensional set of detector coordinates at which scattering
calculations are to be done.
Parameters
----------
coords : dict, optional
Dictionary of detector coordinates. Default: empty dictionary.
Typical usage should not pass this argument, giving other parameters
(Cartesian `x`, `y`, and `z` or polar `r`, `theta`, and `phi`
coordinates) instead.
x, y : int or array_like, optional
Cartesian x and y coordinates of detectors.
z : int or array_like, optional
Cartesian z coordinates of detectors. If not specified, assume `z` = 0.
r : int or array_like, optional
Spherical polar radial coordinates of detectors. If not specified,
assume `r` = infinity (far-field).
theta : int or array_like, optional
Spherical polar coordinates (polar angle from z axis) of detectors.
phi : int or array_like, optional
Spherical polar azimuthal coodinates of detectors.
name : string
Returns
-------
grid : DataArray object
DataArray of zeros with calculated coordinates.
Notes
-----
Specify either the Cartesian or the polar coordinates of your detector.
This may be helpful for modeling static light scattering calculations.
Use detector_grid() to specify coordinates of a grid of pixels (e.g.,
a digital camera.)
"""
if normals is not None:
raise ValueError(NORMALS_DEPRECATION_MESSAGE)
updatelist = {'x': x, 'y': y, 'z': z, 'r': r, 'theta': theta, 'phi': phi}
coords = updated(coords, updatelist)
if 'x' in coords and 'y' in coords:
keys = ['x', 'y', 'z']
if coords.get('z') is None:
coords['z'] = 0
elif 'theta' in coords and 'phi' in coords:
keys = ['r', 'theta', 'phi']
if coords.get('r') is None:
coords['r'] = np.inf
else:
raise CoordSysError()
if name is None:
name = 'data'
coords = repeat_sing_dims(coords, keys)
coords = updated(coords, {key: ('point', coords[key]) for key in keys})
return xr.DataArray(np.zeros(len(coords[keys[0]][1])), dims=['point'],
coords=coords, name=name)
def test_kw_takes_priority(self):
input_dict = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
update_dict = {'c': 5, 'd': None, 'e': 6}
output_dict = updated(input_dict, update_dict, b=7, e=8)
self.assertEqual(input_dict, {'a': 1, 'b': 2, 'c': 3, 'd': 4})
self.assertEqual(output_dict, {'a': 1, 'b': 7, 'c': 5, 'd': 4, 'e': 8})
def test_updated_from_kw(self):
input_dict = {'a': 1, 'b': 2, 'c': 3, 'd': 4}
output_dict = updated(input_dict, b=7, c=None, e=8)
self.assertEqual(input_dict, {'a': 1, 'b': 2, 'c': 3, 'd': 4})
self.assertEqual(output_dict, {'a': 1, 'b': 7, 'c': 3, 'd': 4, 'e': 8})
