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@ivalaginja
ivalaginja committed Nov 7, 2023
1 parent ba2e483 commit 4bbcd7a
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6 changes: 3 additions & 3 deletions hcipy/coronagraphy/fqpm.py
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Expand Up @@ -4,6 +4,6 @@
from ..field import Field

class FQPMCoronagraph(MultiScaleCoronagraph):
def __init__(self, input_grid, lyot_stop=None, q=1024, scaling_factor=4, window_size=32):
complex_mask = lambda grid: Field(np.sign(grid.x) * np.sign(grid.y), grid).astype('complex')
super().__init__(input_grid, complex_mask, lyot_stop, q, scaling_factor, window_size)
def __init__(self, input_grid, lyot_stop=None, q=1024, scaling_factor=4, window_size=32):
complex_mask = lambda grid: Field(np.sign(grid.x) * np.sign(grid.y), grid).astype('complex')
super().__init__(input_grid, complex_mask, lyot_stop, q, scaling_factor, window_size)
298 changes: 149 additions & 149 deletions hcipy/coronagraphy/multi_scale.py
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Expand Up @@ -7,152 +7,152 @@
from ..fourier import FastFourierTransform, MatrixFourierTransform, FourierFilter

class MultiScaleCoronagraph(OpticalElement):
'''A phase mask coronagraph.
This :class:`OpticalElement` simulates the propagation of light through
a phase mask in the focal plane. To resolve the singularity of the
phase plate, a multi-scale approach is made. Discretisation errors made at
a certain level are corrected by the next level with finer sampling.
Parameters
----------
input_grid : Grid
The grid on which the incoming wavefront is defined.
complex_mask : field_generator
The complex focal-plane phase mask.
lyot_stop : Field or OpticalElement
The Lyot stop for the coronagraph. If it's a Field, it is converted to an
OpticalElement for convenience. If this is None (default), then no Lyot stop is used.
q : scalar
The minimum number of pixels per lambda/D. The number of levels in the multi-scale
Fourier transforms will be chosen to reach at least this number of samples. The required
q for a high-accuracy vortex coronagraph for example depends on the charge of the vortex. For charge 2,
this can be as low as 32, but for charge 8 you need ~1024. Lower values give higher performance
as a smaller number of levels is needed, but increases the sampling errors near the singularity.
Charges not divisible by four require a much lower q. The default (q=1024) is conservative in
most cases.
scaling_factor : scalar
The fractional increase in spatial frequency sampling per level. Larger scaling factors
require a smaller number of levels, but each level requires a slower Fourier transform.
Factors of 2 or 4 usually perform the best.
window_size : integer
The size of the next level in the number of pixels on the current layer. Lowering this
increases performance in exchange for accuracy. Values smaller than 4-8 are not recommended.
'''
def __init__(self, input_grid, complex_mask, lyot_stop=None, q=1024, scaling_factor=4, window_size=32):
self.input_grid = input_grid
pupil_diameter = input_grid.shape * input_grid.delta

if hasattr(lyot_stop, 'forward') or lyot_stop is None:
self.lyot_stop = lyot_stop
else:
self.lyot_stop = Apodizer(lyot_stop)

levels = int(np.ceil(np.log(q / 2) / np.log(scaling_factor))) + 1
qs = [2 * scaling_factor**i for i in range(levels)]
num_airys = [input_grid.shape / 2]

focal_grids = []
self.focal_masks = []
self.props = []

for i in range(1, levels):
num_airys.append(num_airys[i - 1] * window_size / (2 * qs[i - 1] * num_airys[i - 1]))

for i in range(levels):
q = qs[i]
num_airy = num_airys[i]

focal_grid = make_focal_grid(q, num_airy, pupil_diameter=pupil_diameter, reference_wavelength=1, focal_length=1)
focal_mask = complex_mask(focal_grid)

if i != levels - 1:
wx = windows.tukey(window_size, 1, False)
wy = windows.tukey(window_size, 1, False)
w = np.outer(wy, wx)

w = np.pad(w, (focal_grid.shape - w.shape) // 2, 'constant').ravel()
focal_mask *= 1 - w

for j in range(i):
fft = FastFourierTransform(focal_grids[j])
mft = MatrixFourierTransform(focal_grid, fft.output_grid)

focal_mask -= mft.backward(fft.forward(self.focal_masks[j]))

if i == 0:
prop = FourierFilter(input_grid, focal_mask, q)
else:
prop = FraunhoferPropagator(input_grid, focal_grid)

focal_grids.append(focal_grid)
self.focal_masks.append(focal_mask)
self.props.append(prop)

def forward(self, wavefront):
'''Propagate a wavefront through the coronagraph.
Parameters
----------
wavefront : Wavefront
The wavefront to propagate. This wavefront is expected to be
in the pupil plane.
Returns
-------
Wavefront
The Lyot plane wavefront.
'''
wavelength = wavefront.wavelength
wavefront.wavelength = 1

for i, (mask, prop) in enumerate(zip(self.focal_masks, self.props)):
if i == 0:
lyot = Wavefront(prop.forward(wavefront.electric_field), input_stokes_vector=wavefront.input_stokes_vector)
else:
focal = prop(wavefront)
focal.electric_field *= mask
lyot.electric_field += prop.backward(focal).electric_field

lyot.wavelength = wavelength
wavefront.wavelength = wavelength

if self.lyot_stop is not None:
lyot = self.lyot_stop.forward(lyot)

return lyot

def backward(self, wavefront):
'''Propagate backwards through the coronagraph.
This essentially is a forward propagation through the same vortex
coronagraph, but in the case of a vortex with the sign of its charge flipped.
Parameters
----------
wavefront : Wavefront
The Lyot plane wavefront.
Returns
-------
Wavefront
The pupil-plane wavefront.
'''
if self.lyot_stop is not None:
wavefront = self.lyot_stop.backward(wavefront)

wavelength = wavefront.wavelength
wavefront.wavelength = 1

for i, (mask, prop) in enumerate(zip(self.focal_masks, self.props)):
if i == 0:
pup = Wavefront(prop.backward(wavefront.electric_field), input_stokes_vector=wavefront.input_stokes_vector)
else:
focal = prop(wavefront)
focal.electric_field *= mask.conj()
pup.electric_field += prop.backward(focal).electric_field

pup.wavelength = wavelength
wavefront.wavelength = wavelength

return pup
'''A phase mask coronagraph.
This :class:`OpticalElement` simulates the propagation of light through
a phase mask in the focal plane. To resolve the singularity of the
phase plate, a multi-scale approach is made. Discretisation errors made at
a certain level are corrected by the next level with finer sampling.
Parameters
----------
input_grid : Grid
The grid on which the incoming wavefront is defined.
complex_mask : field_generator
The complex focal-plane phase mask.
lyot_stop : Field or OpticalElement
The Lyot stop for the coronagraph. If it's a Field, it is converted to an
OpticalElement for convenience. If this is None (default), then no Lyot stop is used.
q : scalar
The minimum number of pixels per lambda/D. The number of levels in the multi-scale
Fourier transforms will be chosen to reach at least this number of samples. The required
q for a high-accuracy vortex coronagraph for example depends on the charge of the vortex. For charge 2,
this can be as low as 32, but for charge 8 you need ~1024. Lower values give higher performance
as a smaller number of levels is needed, but increases the sampling errors near the singularity.
Charges not divisible by four require a much lower q. The default (q=1024) is conservative in
most cases.
scaling_factor : scalar
The fractional increase in spatial frequency sampling per level. Larger scaling factors
require a smaller number of levels, but each level requires a slower Fourier transform.
Factors of 2 or 4 usually perform the best.
window_size : integer
The size of the next level in the number of pixels on the current layer. Lowering this
increases performance in exchange for accuracy. Values smaller than 4-8 are not recommended.
'''
def __init__(self, input_grid, complex_mask, lyot_stop=None, q=1024, scaling_factor=4, window_size=32):
self.input_grid = input_grid
pupil_diameter = input_grid.shape * input_grid.delta

if hasattr(lyot_stop, 'forward') or lyot_stop is None:
self.lyot_stop = lyot_stop
else:
self.lyot_stop = Apodizer(lyot_stop)

levels = int(np.ceil(np.log(q / 2) / np.log(scaling_factor))) + 1
qs = [2 * scaling_factor**i for i in range(levels)]
num_airys = [input_grid.shape / 2]

focal_grids = []
self.focal_masks = []
self.props = []

for i in range(1, levels):
num_airys.append(num_airys[i - 1] * window_size / (2 * qs[i - 1] * num_airys[i - 1]))

for i in range(levels):
q = qs[i]
num_airy = num_airys[i]

focal_grid = make_focal_grid(q, num_airy, pupil_diameter=pupil_diameter, reference_wavelength=1, focal_length=1)
focal_mask = complex_mask(focal_grid)

if i != levels - 1:
wx = windows.tukey(window_size, 1, False)
wy = windows.tukey(window_size, 1, False)
w = np.outer(wy, wx)

w = np.pad(w, (focal_grid.shape - w.shape) // 2, 'constant').ravel()
focal_mask *= 1 - w

for j in range(i):
fft = FastFourierTransform(focal_grids[j])
mft = MatrixFourierTransform(focal_grid, fft.output_grid)

focal_mask -= mft.backward(fft.forward(self.focal_masks[j]))

if i == 0:
prop = FourierFilter(input_grid, focal_mask, q)
else:
prop = FraunhoferPropagator(input_grid, focal_grid)

focal_grids.append(focal_grid)
self.focal_masks.append(focal_mask)
self.props.append(prop)

def forward(self, wavefront):
'''Propagate a wavefront through the coronagraph.
Parameters
----------
wavefront : Wavefront
The wavefront to propagate. This wavefront is expected to be
in the pupil plane.
Returns
-------
Wavefront
The Lyot plane wavefront.
'''
wavelength = wavefront.wavelength
wavefront.wavelength = 1

for i, (mask, prop) in enumerate(zip(self.focal_masks, self.props)):
if i == 0:
lyot = Wavefront(prop.forward(wavefront.electric_field), input_stokes_vector=wavefront.input_stokes_vector)
else:
focal = prop(wavefront)
focal.electric_field *= mask
lyot.electric_field += prop.backward(focal).electric_field

lyot.wavelength = wavelength
wavefront.wavelength = wavelength

if self.lyot_stop is not None:
lyot = self.lyot_stop.forward(lyot)

return lyot

def backward(self, wavefront):
'''Propagate backwards through the coronagraph.
This essentially is a forward propagation through the same vortex
coronagraph, but in the case of a vortex with the sign of its charge flipped.
Parameters
----------
wavefront : Wavefront
The Lyot plane wavefront.
Returns
-------
Wavefront
The pupil-plane wavefront.
'''
if self.lyot_stop is not None:
wavefront = self.lyot_stop.backward(wavefront)

wavelength = wavefront.wavelength
wavefront.wavelength = 1

for i, (mask, prop) in enumerate(zip(self.focal_masks, self.props)):
if i == 0:
pup = Wavefront(prop.backward(wavefront.electric_field), input_stokes_vector=wavefront.input_stokes_vector)
else:
focal = prop(wavefront)
focal.electric_field *= mask.conj()
pup.electric_field += prop.backward(focal).electric_field

pup.wavelength = wavelength
wavefront.wavelength = wavelength

return pup

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