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CpicImgSim/camera.py 0 → 100644
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import os
import yaml
import numpy as np
import scipy.ndimage as nd
from astropy.io import fits
from .config import cpism_refdata, solar_spectrum, MAG_SYSTEM
from .utils import region_replace, random_seed_select
from .io import log
from .optics import filter_throughput
def sky_frame_maker(band, skybg, platescale, shape):
"""
generate a sky background frame.
Parameters
----------
band : str
The band of the sky background.
skybg : str
The sky background file name.
platescale : float
The platescale of the camera in arcsec/pixel.
shape : tuple
The shape of the output frame. (y, x)
Returns
-------
sky_bkg_frame : numpy.ndarray
The sky background frame.
"""
filter = filter_throughput(band)
sk_spec = solar_spectrum.renorm(skybg, MAG_SYSTEM, filter)
sky_bkg_frame = np.zeros(shape)
sky_bkg_frame += (sk_spec * filter).integrate() * platescale**2
return sky_bkg_frame
class CRobj(object):
"""
Cosmic ray object.
Attributes
----------
flux : float
The flux of the cosmic ray.
angle : float
The angle of the cosmic ray.
sigma : float
The width of the cosmic ray.
length : int
The length of the cosmic ray.
"""
def __init__(self, flux, angle, sigma, length) -> None:
self.flux = flux
self.angle = angle
self.sigma = sigma
self.length = length
class CosmicRayFrameMaker(object):
"""
Cosmic ray frame maker.
Parameters
----------
depth : float
The depth of the camera pixel in um.
pitch : float
The pitch of the camera pixel in um.
cr_rate : float
The cosmic ray rate per second per cm2.
"""
def __init__(self) -> None:
self.tmp_size = [7, 101]
self.freq_power = -0.9
self.trail_std = 0.1
self.depth = 10 # um
self.pitch = 13 # um
self.cr_rate = 1 # particle per s per cm2 from Miles et al. 2021
def make_CR(self, length, sigma, seed=-1):
"""
make a image of cosmic ray with given length and sigma.
Parameters
----------
length : int
The length of the cosmic ray in pixel.
sigma : float
The width of the cosmic ray in pixel.
Returns
-------
output : numpy.ndarray
The image of cosmic ray.
"""
h = self.tmp_size[0]
w = self.tmp_size[1]
freq = ((w-1)/2-np.abs(np.arange(w)-(w-1)/2)+1)**(self.freq_power)
x = np.arange(w) - (w-1)/2
hl = (length-1)/2
x_wing = np.exp(-(np.abs(x)-hl)**2/sigma/sigma/2)
x_wing[np.abs(x) < hl] = 1
cr = np.zeros([h, w])
center = (h-1)/2
for i in range(h):
phase = np.random.rand(w)*2*np.pi
trail0 = abs(np.fft.fft(freq*np.sin(phase) + 1j*x*np.cos(phase)))
# TODO maybe somthing wrong
trail_norm = (trail0 - trail0.mean())/trail0.std()
cr[i, :] = np.exp(-(i - center)**2/sigma/sigma/2) \
* (trail_norm * self.trail_std + 1) * x_wing
output = np.zeros([w, w])
d = (w-h)//2
output[d:d+h, :] = cr
return output
def _length_rand(self, N, seed=-1):
"""
randomly generate N cosmic ray length.
"""
len_out = []
seed = random_seed_select(seed=seed)
log.debug(f"cr length seed: {seed}")
for i in range(N):
x2y2 = 2
while x2y2 > 1:
lx = 1 - 2 * np.random.rand()
ly = 1 - 2 * np.random.rand()
x2y2 = lx * lx + ly * ly
z = 1 - 2 * x2y2
r = 2 * np.sqrt(x2y2 * (1 - x2y2))
length = abs(r / z * self.depth)
pitch = self.pitch
len_out.append(int(length/pitch))
return np.array(len_out)
def _number_rand(self, expt, pixsize, random=False, seed=-1):
"""
randomly generate the number of cosmic rays.
"""
area = (self.pitch / 1e4)**2 * pixsize[0] * pixsize[1]
ncr = area * expt * self.cr_rate
if random:
seed = random_seed_select(seed=seed)
log.debug(f"cr count: {seed}")
ncr = np.random.poisson(ncr)
else:
ncr = int(ncr)
self.ncr = ncr
return ncr
def _sigma_rand(self, N, seed=-1):
"""
randomly generate N cosmic ray sigma.
"""
sig_sig = 0.5 # asuming the sigma of size of cosmic ray is 0.5
seed = random_seed_select(seed=seed)
log.debug(f"cr width seed: {seed}")
sig = abs(np.random.randn(N))*sig_sig + 1/np.sqrt(12) * 1.2
# assume sigma is 1.2 times of pictch sig
return sig
def _flux_rand(self, N, seed=-1):
"""
randomly generate N cosmic ray flux.
"""
seed = random_seed_select(seed=seed)
log.debug(f"cr flux seed: {seed}")
u = np.random.rand(N)
S0 = 800
lam = 0.57
S = (-np.log(1-u)/lam + S0**0.25)**4
return S
def random_CR_parameter(self, expt, pixsize):
"""
randomly generate cosmic ray parameters, including number, length, flux, sigma and angle.
Parameters
----------
expt : float
The exposure time in second.
pixsize : list
The size of the image in pixel.
Returns
-------
CRs : list
A list of cosmic ray objects.
"""
N = self._number_rand(expt, pixsize)
log.debug(f"cr count: {N}")
length = self._length_rand(N)
if N > 0:
log.debug(f"cr length, max: {length.max()}, min: {length.min()}")
flux = self._flux_rand(N)
log.debug(f"cr flux, max: {flux.max()}, min: {flux.min()}")
sig = self._sigma_rand(N)
log.debug(f"cr width, max: {sig.max()}, min: {sig.min()}")
seed = random_seed_select(seed=-1)
log.debug(f"cr angle seed: {seed}")
angle = np.random.rand(N) * 180
CRs = []
for i in range(N):
CRs.append(CRobj(flux[i], angle[i], sig[i], length[i]))
return CRs
def make_cr_frame(self, shape, expt, seed=-1):
"""
make a cosmic ray frame.
Parameters
----------
shape : list
The size of the image in pixel.
expt : float
The exposure time in second.
seed : int, optional
The random seed. The default is -1. If seed is -1, the seed will be randomly selected.
Returns
-------
image : numpy.ndarray
The cosmic ray frame.
"""
image = np.zeros(shape)
sz = shape
cr_array = self.random_CR_parameter(expt, shape)
cr_center = (self.tmp_size[1] - 1)/2
seed = random_seed_select(seed=seed)
log.debug(f"cr position seed: {seed}")
for i in range(len(cr_array)):
cr = cr_array[i]
x = np.random.rand() * sz[1]
y = np.random.rand() * sz[0]
cr_img = self.make_CR(cr.length, cr.sigma)
cr_img *= cr.flux
cr_img = abs(nd.rotate(cr_img, cr.angle, reshape=False))
if i == 0:
pdin = False
else:
pdin = True
if i == len(cr_array) - 1:
pdout = False
else:
pdout = True
image = region_replace(
image, cr_img,
[y-cr_center, x-cr_center],
padded_in=pdin,
padded_out=pdout, subpix=True
)
image = np.maximum(image, 0)
log.debug(f"cr image max: {image.max()}, min: {image.min()}")
return image
class EMCCD(object):
"""
EMCCD camera class
Parameters
----------
config_file : str
config file name
Attributes
----------
switch : dict
switch for each camera effects, including:
- 'flat': bool,
- 'dark': bool,
- 'stripe': bool,
- 'cic': bool,
- 'cte': bool,
- 'badcolumn': bool,
- 'nonlinear': bool,
- 'cosmicray': bool,
- 'blooming': bool,
"""
def __init__(self, config_file="emccd_config.yaml"):
self.plszx = 1024
self.plszy = 1024
self.pscan1 = 8
self.pscan2 = 0
self.oscan1 = 16
self.oscan2 = 18
self.udark = 6
self.bdark = 2
self.ldark = 16
self.rdark = 16
self.fullwell = 80_000
self.em_fullwell = 780_000
# if config file exists, load it, otherwise use default values
config_file = cpism_refdata + '/camera/' + config_file
log.debug(f"emccd config file: {config_file}")
if os.path.exists(config_file):
self.load_config(config_file)
else: # pragma: no cover
# set default values for EMCCD
# note: these values are default values, you can change them by load_config()
# ↓↓↓↓↓↓↓start default values setting↓↓↓↓↓↓
self.readout_noise = 40
self.ph_per_adu = 8
self.bias_level = 30
self.max_adu = 16_383
self.switch = {
'flat': True,
'dark': True,
'stripe': True,
'cic': False,
'cte': False,
'badcolumn': True,
'nonlinear': False,
'cosmicray': True,
'blooming': False,
}
self.dark_file = cpism_refdata + '/camera/emccd_dark_current.fits'
self.flat_file = cpism_refdata + '/camera/emccd_flat_field.fits'
self.cic_file = cpism_refdata + '/camera/emcid_cic.fits'
self.bad_col_file = cpism_refdata + '/camera/emccd_bad_columns.fits'
# ↑↑↑↑↑↑↑end default values setting↑↑↑↑↑↑
# note: these values are default values, you can change them by load_config()
self.flat_shape = [self.plszy, self.plszx]
darksz_x = self.plszx + self.rdark + self.ldark
darksz_y = self.plszy + self.udark + self.bdark
self.dark_shape = [darksz_y, darksz_x]
biassz_x = darksz_x + self.pscan1 + self.oscan1
biassz_y = darksz_y + self.pscan2 + self.oscan2
self.image_shape = [biassz_y, biassz_x]
self.flat = fits.getdata(self.flat_file)
self.cic = fits.getdata(self.cic_file)
self.dark = fits.getdata(self.dark_file)
self.bad_col = fits.getdata(self.bad_col_file)
def load_config(self, config_file):
"""
load config file. Only for internal use.
"""
with open(config_file, 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
self.switch = config['switch']
self.readout_noise = config['readout_noise']
self.ph_per_adu = config['ph_per_adu']
self.bias_level = config['bias_level']
self.max_adu = config['max_adu']
self.dark_file = cpism_refdata + "/camera/" + config['dark_file']
self.flat_file = cpism_refdata + "/camera/" + config['flat_file']
self.cic_file = cpism_refdata + "/camera/" + config['cic_file']
self.bad_col_file = cpism_refdata + \
"/camera/" + config['bad_col_file']
def vertical_blooming(self, image):
"""
vertical blooming effect
"""
fullwell = self.fullwell
line = np.arange(image.shape[0])
yp, xp = np.where(image > fullwell)
n_saturated = len(xp)
log.debug(f"{len(xp)} pixels are saturated!")
if n_saturated > 5000:
log.warning(f"More than 5000({len(xp)}) pixels are saturated!")
img0 = image.copy()
for x, y in zip(xp, yp):
image[:, x] += np.exp(-(line-y)**2/20**2) * img0[y, x] * 0.2
return np.minimum(image, fullwell)
def nonlinear_effect(self, image):
"""
nonlinear effect
"""
fullwell = self.fullwell
nonlinear_coefficient = 0.1
log.debug(
f"nonlinear effect added with coefficient {nonlinear_coefficient}")
image += (image / fullwell)**2 * nonlinear_coefficient * fullwell
return image
def emregester_blooming(self, image, max_iteration=5):
"""
emregester blooming effect
"""
line = image.flatten().copy()
curve_x = np.arange(1300)+2
curve_y = np.exp(11*curve_x**(-0.19)-11)
curve_y[0] = 0
curve_y /= curve_y.sum()
over_limit_coe = 0.999
saturated = image > self.em_fullwell
n_saturated = saturated.sum()
if n_saturated > 0:
log.debug(f"{n_saturated} pixels are saturated during EM process.")
if n_saturated > 2000:
log.warning(
f"More than 2000 ({n_saturated}) pixels are saturated during EM process!")
for index in range(max_iteration):
over_limit = np.maximum(
line - self.em_fullwell * over_limit_coe, 0)
line = np.minimum(line, self.em_fullwell * over_limit_coe)
blooming = np.convolve(over_limit, curve_y, mode='full')[
:len(line)]
line = line + blooming
n_over = (line > self.em_fullwell).sum()
if n_over <= 0:
break
log.debug(
f'{index}/{max_iteration} loop: saturated pixel number: {n_over}')
return line.reshape(image.shape)
def cte(self, image):
"""
cte effect
"""
image = self.emregester_blooming(image, max_iteration=5)
return image
def readout(self, image_focal, emgain, expt, image_cosmic_ray=None):
"""
emccd readout. Generate a image with emccd readout effect.
Parameters
----------
image_focal : numpy.ndarray
image at focal plane. Unit: electron/second
emgain : float
emgain of emccd
expt : float
exposure time. Unit: second
image_cosmic_ray : numpy.ndarray, optional
cosmic ray image. Unit: electron/second, by default None
Returns
-------
numpy.ndarray
image with emccd readout effect. Unit: ADU
Notes
-----
1. effects include: dark, flat, cte, blooming, nonlinear, etc. Can be turned on/off by switch.
2. size of input image_focal must be 1024x1024
3. size of output image is 1080x1056 (including overscan and dark reference region)
4. Q.E is not included in this function. It should be included in image_focal. See optics.py for details.
"""
log.debug(
fr"EMCCD readout: {emgain=:}, {expt=:}, image_comic_ray:{'None' if image_cosmic_ray is None else 'Not None'}")
log.debug(
f"camera effects switch={self.switch}"
)
image = image_focal * expt
if self.switch['flat']:
image = image * self.flat
if self.switch['nonlinear']:
image = self.nonlinear_effect(image)
darksz_x = self.plszx + self.rdark + self.ldark
darksz_y = self.plszy + self.udark + self.bdark
img_dark = np.zeros((darksz_y, darksz_x))
img_dark[
self.bdark:self.plszy+self.bdark,
self.ldark:self.ldark+self.plszx
] = image
image = img_dark
if self.switch['dark']:
image += self.dark * expt
if self.switch['cic']:
image += self.cic
if image_cosmic_ray is not None and self.switch['cosmicray']:
image += image_cosmic_ray
if self.switch['blooming']:
image = self.vertical_blooming(image)
if self.switch['badcolumn']:
for i in range(self.bad_col.shape[1]):
deadpix_x = self.bad_col[0, i]
deadpix_y = self.bad_col[1, i]
image[deadpix_y:, deadpix_x] = 0
biassz_x = darksz_x + self.pscan1 + self.oscan1
biassz_y = darksz_y + self.pscan2 + self.oscan2
img_bias = np.zeros((biassz_y, biassz_x), dtype=int)
seed = random_seed_select()
log.debug(f"photon noise seed: {seed}")
img_bias[
self.pscan2:self.pscan2+darksz_y,
self.pscan1:self.pscan1+darksz_x
] = np.random.poisson(image)
image = img_bias
if self.switch['cte']:
image = self.cte(image * emgain) / emgain
seed = random_seed_select()
log.debug(f"gamma noise seed: {seed}")
if emgain != 1:
image = np.random.gamma(image, emgain)
image = np.minimum(image, self.em_fullwell)
seed = random_seed_select()
log.debug(f"readout noise seed: {seed}")
image += np.random.randn(biassz_y, biassz_x) * self.readout_noise
image = image / self.ph_per_adu + self.bias_level
if self.switch['stripe']:
image += self.add_stripe_effect(image)
image = np.minimum(image, self.max_adu)
image = np.maximum(image, 0)
return image.astype(np.uint16)
def add_stripe_effect(self, image):
"""
add stripe effect
"""
shape = image.shape
v_width = 1
v_amplitude = 30
v_limit = 0.01
v_base = 10
h_width = 20
h_amplitude = 10
h_limit = 0.9
h_base = 20
index = np.linspace(0, np.pi, shape[0] * shape[1])
def stripe(width, limit, amplitude, base, axis=0):
seed = random_seed_select()
log.debug(f"stripe noise seed: {seed}")
dim_axis = shape[axis]
dim_other = shape[0] * shape[1] // shape[axis]
value = np.sin(index / width * dim_axis + np.pi *
dim_axis / width * np.random.randint(1024))
value = np.maximum(value, -limit)
value = np.minimum(value, limit)
value = (value / limit + limit) / 2 * amplitude + base
value = value.reshape(dim_axis, dim_other)
if axis == 1:
value = value.T
return value
output = stripe(v_width, v_limit, v_amplitude, v_base, axis=1)
output += stripe(h_width, h_limit, h_amplitude, h_base, axis=0)
return output
# # plt.plot(horizontal_index, horizontal_value)
# # # plt.xlim([0, 6.28])
# # plt.show()
# fits.writeto('horizontal_value.fits', output, overwrite=True)
# if __name__ == '__main__':
# import matplotlib.pyplot as plt
# emccd = EMCCD()
# image_focal = np.zeros((emccd.plszy, emccd.plszx)) + 1000
# image_focal[100:105, 100:105] = 10_000_000
# after_cte = emccd.emregester_blooming(image_focal, max_iteration=100)
# print(after_cte.sum(), image_focal.sum())
# fits.writeto('after_cte.fits', after_cte, overwrite=True)
# # darksz_x = emccd.plszx + emccd.rdark + emccd.ldark
# # darksz_y = emccd.plszy + emccd.udark + emccd.bdark
# # iamge_cosmic_ray = np.zeros((darksz_y, darksz_x))
# # emgain = 10
# # expt = 10
# # image = emccd.readout(image_focal, emgain, expt, iamge_cosmic_ray)
# # fits.writeto('test.fits', image, overwrite=True)
# image = np.zeros((1000, 1000))
# make_cosmic_ray_frame = CosmicRayFrameMaker()
# crimage = make_cosmic_ray_frame(image.shape, 3000)
# fits.writeto('crimage.fits', crimage, overwrite=True)
# # emccd.add_stripe_effect(image)
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