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import numpy as np
from scipy.signal import fftconvolve
from scipy.ndimage import rotate
from .config import config, S # S is synphot
from .utils import region_replace
from .io import log
from .psf_simulation import single_band_masked_psf, single_band_psf
FILTERS = {}
for key, value in config['bands'].items():
FILTERS[key] = S.FileBandpass(value)
default_band = config['default_band']
def filter_throughput(filter_name):
"""
Totally throughput of the each CPIC band.
Including the throughput of the filter, telescope, cpic, and camera QE.
If the filter_name is not supported, return the throughput of the default filter(f661).
Parameters
-----------
filter_name: str
The name of the filter.
One of ['f565', 'f661'(default), 'f743', 'f883', 'f940', 'f1265', 'f1425', 'f1542']
Returns
--------
synphot.Bandpass
The throughput of the filter.
"""
filter_name = filter_name.lower()
filter_name = default_band if filter_name == 'default' else filter_name
if filter_name not in FILTERS.keys():
log.warning(f"滤光片名称错误({filter_name})，返回默认滤光片({default_band})透过率")
filter_name = default_band
return FILTERS[filter_name]
def _rotate_and_shift(shift, rotation, init_shifts):
rotation_rad = rotation / 180 * np.pi
return np.array([
shift[0] * np.cos(rotation_rad) + shift[1] * np.sin(rotation_rad),
-shift[0] * np.sin(rotation_rad) + shift[1] * np.cos(rotation_rad)
]) + np.array(init_shifts)
def ideal_focus_image(
bandpass: S.spectrum.SpectralElement,
targets: list,
platescale,
platesize: list = [1024, 1024],
init_shifts: list = [0, 0],
rotation: float = 0) -> np.ndarray:
"""Ideal focus image of the targets.
Each star is a little point of 1pixel.
Parameters
-----------
bandpass: synphot.SpectralElement
The bandpass of the filter.
targets: list
The list of the targets. See the output of `spectrum_generator` for details.
platescale: float
The platescale of the camera. Unit: arcsec/pixel
platesize: list
The size of the image. Unit: pixel
init_shifts: list
The shifts of the targets to simulate the miss alignment. Unit: arcsec
rotation: float
The rotation of the image. Unit: degree
Returns
--------
np.ndarray
The ideal focus image.
"""
focal_image = np.zeros(platesize)
focal_shape = np.array(platesize)[::-1] # x, y
if not targets:
return focal_image
for target in targets:
sub_x, sub_y, sub_spectrum, sub_image = target
sub_shift = _rotate_and_shift([sub_x, sub_y], rotation, init_shifts) / platescale
sed = (sub_spectrum * bandpass).integrate()
if sub_image is None:
x = (focal_shape[0] - 1)/2 + sub_shift[0]
y = (focal_shape[1] - 1)/2 + sub_shift[1]
int_x = int(x)
int_y = int(y)
if int_x < 0 or int_x >= focal_shape[0] - 1 or int_y < 0 or int_y >= focal_shape[1] - 1:
continue
dx1 = x - int_x
dx0 = 1 - dx1
dy1 = y - int_y
dy0 = 1 - dy1
sub = np.array([
[dx0*dy0, dx1*dy0],
[dx0*dy1, dx1*dy1]]) * sed
focal_image[int_y: int_y+2, int_x: int_x+2] += sub
else:
# sub_image = sub_image
sub_image = np.abs(rotate(sub_image, rotation, reshape=False))
sub_image = sub_image / sub_image.sum()
sub_img_shape = np.array(sub_image.shape)[::-1]
sub_shift += (focal_shape-1)/2 - (sub_img_shape-1)/2
focal_image = region_replace(
focal_image,
sub_image * sed,
sub_shift,
subpix=True
)
return focal_image
def focal_convolve(
band: str,
targets: list,
init_shifts: list = [0, 0],
rotation: float = 0,
nsample: int = 5,
error: float = 0,
platesize: list = [1024, 1024]) -> np.ndarray :
"""PSF convolution of the ideal focus image.
Parameters
----------
band: str
The name of the band.
target: list
The list of thetargets. See the output of `spectrum_generator` for details.
init_shifts: list
The shifts of the targets to simulate the miss alignment. Unit: arcsec
rotation: float
The rotation of the image. Unit: degree
error: float
The error of the DM acceleration. Unit: nm
platesize: list
The size of the image. Unit: pixel
Returns
--------
np.ndarray
"""
# config = optics_config[which_focalplane(band)]
platescale = config['platescale']
# telescope_config = optics_config['telescope']
area = config['aperature_area']
filter = filter_throughput(band)
throughput = filter.throughput
wave = filter.wave
throughput_criterion = throughput.max() * 0.1
wave_criterion = wave[throughput > throughput_criterion]
min_wave = wave_criterion[0]
max_wave = wave_criterion[-1]
# print(min_wave, max_wave)
platescale = config['platescale']
iwa = config['mask_width'] / 2
if abs(init_shifts[0]) > 4 or abs(init_shifts[1]) > 4:
print('Input shifts are too large, and are set to zero')
init_shifts = [0, 0]
all_fp_image = []
if not targets:
return np.zeros((platesize[1], platesize[0]))
for i_wave in range(nsample):
d_wave = (max_wave - min_wave) / nsample
wave0 = min_wave + i_wave * d_wave
wave1 = min_wave + (i_wave + 1) * d_wave
center_wavelength = (wave0 + wave1) / 2 * 1e-10
i_throughput = throughput.copy()
i_throughput[(wave > wave1) | (wave < wave0)] = 0
i_band = S.ArrayBandpass(wave, i_throughput, waveunits='angstrom')
i_fp_image = ideal_focus_image(i_band, targets[1:], platescale, platesize, init_shifts, rotation)
psf = single_band_psf(center_wavelength, error=error)
_, _, cstar_sp, _ = targets[0]
cstar_flux = (cstar_sp * i_band).integrate()
cstar_psf = single_band_masked_psf(center_wavelength, error=error, shift=init_shifts)
c_fp_image = fftconvolve(i_fp_image, psf, mode='same')
c_fp_image = focal_mask(c_fp_image, iwa, platescale)
c_fp_image = c_fp_image + cstar_flux * cstar_psf
all_fp_image.append(c_fp_image * area) # trans to photon/second
return np.array(all_fp_image).sum(axis=0)
def focal_mask(image, iwa, platescale, throughtput=1e-6):
"""
Mask the image outside the inner working angle.
Parameters
-----------
image: np.ndarray
The image to be masked.
iwa: float
The inner working angle. Unit: arcsec.
platescale: float
The platescale of the image. Unit: arcsec/pixel.
throughtput: float
The throughtput of the mask. The default is 1e-6.
Returns
--------
np.ndarray
The masked image.
"""
xx, yy = np.mgrid[0:image.shape[0], 0:image.shape[1]]
center = np.array([(image.shape[0]-1)/2, (image.shape[1]-1)/2])
mask = (abs(xx - center[0]) < iwa /
platescale) | (abs(yy - center[1]) < iwa / platescale)
image_out = image.copy()
image_out[mask] *= throughtput
return image_out