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Commit 88aea0d6 authored by Fang Yuedong's avatar Fang Yuedong
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split sim steps into multiple files

parent 3d7404e7
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ObservationSim/ObservationSim.py
View file @ 88aea0d6
......@@ -14,7 +14,7 @@ from ObservationSim.Instrument.Chip import Effects
from ObservationSim.Instrument.Chip import ChipUtils as chip_utils
from ObservationSim._util import makeSubDir_PointingList
from ObservationSim.Astrometry.Astrometry_util import on_orbit_obs_position
from ObservationSim.SimSteps import SimSteps, SIM_STEP_TYPES
from ObservationSim.sim_steps import SimSteps, SIM_STEP_TYPES
class Observation(object):
def __init__(self, config, Catalog, work_dir=None, data_dir=None):
......
ObservationSim/SimSteps.py deleted 100644 → 0
View file @ 3d7404e7
import os
import galsim
import traceback
import gc
import psutil
import numpy as np
from astropy.io import fits
from datetime import datetime
from numpy.random import Generator, PCG64
from ObservationSim._util import get_shear_field
from ObservationSim.Straylight import calculateSkyMap_split_g
from ObservationSim.Config.Header import generatePrimaryHeader, generateExtensionHeader
from ObservationSim.PSF import PSFGauss, FieldDistortion, PSFInterp, PSFInterpSLS
from ObservationSim.Instrument.Chip import ChipUtils as chip_utils
from ObservationSim.Instrument.Chip import Effects
from ObservationSim.Instrument.Chip.libCTI.CTI_modeling import CTI_sim
from ObservationSim.MockObject import FlatLED
from ObservationSim.Instrument.FilterParam import FilterParam
class SimSteps:
def __init__(self, overall_config, chip_output, all_filters):
self.overall_config = overall_config
self.chip_output = chip_output
self.all_filters = all_filters
def prepare_headers(self, chip, pointing):
self.h_prim = generatePrimaryHeader(
xlen=chip.npix_x,
ylen=chip.npix_y,
pointNum = str(pointing.id),
ra=pointing.ra,
dec=pointing.dec,
pixel_scale=chip.pix_scale,
time_pt = pointing.timestamp,
exptime=pointing.exp_time,
im_type=pointing.pointing_type,
sat_pos=[pointing.sat_x, pointing.sat_y, pointing.sat_z],
sat_vel=[pointing.sat_vx, pointing.sat_vy, pointing.sat_vz],
project_cycle=self.overall_config["project_cycle"],
run_counter=self.overall_config["run_counter"],
chip_name=str(chip.chipID).rjust(2, '0'))
self.h_ext = generateExtensionHeader(
chip=chip,
xlen=chip.npix_x,
ylen=chip.npix_y,
ra=pointing.ra,
dec=pointing.dec,
pa=pointing.img_pa.deg,
gain=chip.gain,
readout=chip.read_noise,
dark=chip.dark_noise,
saturation=90000,
pixel_scale=chip.pix_scale,
pixel_size=chip.pix_size,
xcen=chip.x_cen,
ycen=chip.y_cen,
extName=pointing.pointing_type,
timestamp = pointing.timestamp,
exptime = pointing.exp_time,
readoutTime = chip.readout_time)
return self.h_prim, self.h_ext
def add_sky_flat_calibration(self, chip, filt, tel, pointing, catalog, obs_param):
if not hasattr(self, 'h_ext'):
_, _ = self.prepare_headers(chip=chip, pointing=pointing)
chip_wcs = galsim.FitsWCS(header = self.h_ext)
expTime = obs_param["exptime"]
skyback_level = obs_param["flat_level"]
filter_param = FilterParam()
sky_level_filt = obs_param["flat_level_filt"]
norm_scaler = skyback_level/expTime / filter_param.param[sky_level_filt][5]
flat_normal = np.ones_like(chip.img.array)
if obs_param["flat_fielding"] == True:
flat_normal = flat_normal * chip.flat_img.array / np.mean(chip.flat_img.array)
if obs_param["shutter_effect"] == True:
flat_normal = flat_normal * chip.shutter_img
flat_normal = np.array(flat_normal, dtype='float32')
if chip.survey_type == "photometric":
sky_map = flat_normal * np.ones_like(chip.img.array) * norm_scaler * filter_param.param[chip.filter_type][5] / tel.pupil_area * expTime
elif chip.survey_type == "spectroscopic":
# flat_normal = np.ones_like(chip.img.array)
if obs_param["flat_fielding"] == True:
flat_normal = flat_normal * chip.flat_img.array / np.mean(chip.flat_img.array)
if obs_param["shutter_effect"] == True:
flat_normal = flat_normal * chip.shutter_img
flat_normal = np.array(flat_normal, dtype='float32')
sky_map = calculateSkyMap_split_g(
skyMap=flat_normal,
blueLimit=filt.blue_limit,
redLimit=filt.red_limit,
conf=chip.sls_conf,
pixelSize=chip.pix_scale,
isAlongY=0,
flat_cube=chip.flat_cube)
sky_map = sky_map * norm_scaler * expTime
chip.img += sky_map
return chip, filt, tel, pointing
def add_sky_background(self, chip, filt, tel, pointing, catalog, obs_param):
if not hasattr(self, 'h_ext'):
_, _ = self.prepare_headers(chip=chip, pointing=pointing)
chip_wcs = galsim.FitsWCS(header = self.h_ext)
if "flat_level" not in obs_param or "flat_level_filt" not in obs_param:
chip, filt, tel, pointing = self.add_sky_background_sci(chip, filt, tel, pointing, catalog, obs_param)
else:
if obs_param.get('flat_level') is None or obs_param.get('flat_level_filt')is None:
chip, filt, tel, pointing = self.add_sky_background_sci(chip, filt, tel, pointing, catalog, obs_param)
else:
chip, filt, tel, pointing = self.add_sky_flat_calibration(chip, filt, tel, pointing, catalog, obs_param)
# chip, filt, tel, pointing = self.add_sky_background_sci(chip, filt, tel, pointing, catalog, obs_param)
return chip, filt, tel, pointing
def add_sky_background_sci(self, chip, filt, tel, pointing, catalog, obs_param):
flat_normal = np.ones_like(chip.img.array)
if obs_param["flat_fielding"] == True:
flat_normal = flat_normal * chip.flat_img.array / np.mean(chip.flat_img.array)
if obs_param["shutter_effect"] == True:
flat_normal = flat_normal * chip.shutter_img
flat_normal = np.array(flat_normal, dtype='float32')
if obs_param["enable_straylight_model"]:
# Filter.sky_background, Filter.zodical_spec will be updated
filt.setFilterStrayLightPixel(
jtime = pointing.jdt,
sat_pos = np.array([pointing.sat_x, pointing.sat_y, pointing.sat_z]),
pointing_radec = np.array([pointing.ra,pointing.dec]),
sun_pos = np.array([pointing.sun_x, pointing.sun_y, pointing.sun_z]))
self.chip_output.Log_info("================================================")
self.chip_output.Log_info("sky background + stray light pixel flux value: %.5f"%(filt.sky_background))
if chip.survey_type == "photometric":
sky_map = filt.getSkyNoise(exptime = obs_param["exptime"])
sky_map = sky_map * np.ones_like(chip.img.array) * flat_normal
sky_map = galsim.Image(array=sky_map)
else:
# chip.loadSLSFLATCUBE(flat_fn='flat_cube.fits')
sky_map = calculateSkyMap_split_g(
skyMap=flat_normal,
blueLimit=filt.blue_limit,
redLimit=filt.red_limit,
conf=chip.sls_conf,
pixelSize=chip.pix_scale,
isAlongY=0,
flat_cube=chip.flat_cube,
zoldial_spec = filt.zodical_spec)
sky_map = (sky_map + filt.sky_background)*obs_param["exptime"]
# sky_map = sky_map * tel.pupil_area * obs_param["exptime"]
chip.img += sky_map
return chip, filt, tel, pointing
def add_LED_Flat(self, chip, filt, tel, pointing, catalog, obs_param):
if not hasattr(self, 'h_ext'):
_, _ = self.prepare_headers(chip=chip, pointing=pointing)
chip_wcs = galsim.FitsWCS(header = self.h_ext)
pf_map = np.zeros_like(chip.img.array)
if obs_param["LED_TYPE"] is not None:
if len(obs_param["LED_TYPE"]) != 0:
print("LED OPEN--------")
led_obj = FlatLED(chip, filt)
led_flat = led_obj.drawObj_LEDFlat(led_type_list=obs_param["LED_TYPE"], exp_t_list=obs_param["LED_TIME"])
pf_map = led_flat
chip.img = chip.img + led_flat
return chip, filt, tel, pointing
def add_objects(self, chip, filt, tel, pointing, catalog, obs_param):
# Prepare output file(s) for this chip
self.chip_output.create_output_file()
# Prepare the PSF model
if self.overall_config["psf_setting"]["psf_model"] == "Gauss":
psf_model = PSFGauss(chip=chip, psfRa=self.overall_config["psf_setting"]["psf_rcont"])
elif self.overall_config["psf_setting"]["psf_model"] == "Interp":
if chip.survey_type == "spectroscopic":
psf_model = PSFInterpSLS(chip, filt, PSF_data_prefix=self.overall_config["psf_setting"]["psf_sls_dir"])
else:
psf_model = PSFInterp(chip=chip, npsf=chip.n_psf_samples, PSF_data_file=self.overall_config["psf_setting"]["psf_pho_dir"])
else:
self.chip_output.Log_error("unrecognized PSF model type!!", flush=True)
# Apply field distortion model
if obs_param["field_dist"] == True:
fd_model = FieldDistortion(chip=chip, img_rot=pointing.img_pa.deg)
else:
fd_model = None
# Update limiting magnitudes for all filters based on the exposure time
# Get the filter which will be used for magnitude cut
for ifilt in range(len(self.all_filters)):
temp_filter = self.all_filters[ifilt]
temp_filter.update_limit_saturation_mags(exptime=pointing.get_full_depth_exptime(temp_filter.filter_type), chip=chip)
if temp_filter.filter_type.lower() == self.overall_config["obs_setting"]["cut_in_band"].lower():
cut_filter = temp_filter
# Read in shear values from configuration file if the constant shear type is used
if self.overall_config["shear_setting"]["shear_type"] == "constant":
g1_field, g2_field, _ = get_shear_field(config=self.overall_config)
self.chip_output.Log_info("Use constant shear: g1=%.5f, g2=%.5f"%(g1_field, g2_field))
# Get chip WCS
if not hasattr(self, 'h_ext'):
_, _ = self.prepare_headers(chip=chip, pointing=pointing)
chip_wcs = galsim.FitsWCS(header = self.h_ext)
# Loop over objects
nobj = len(catalog.objs)
missed_obj = 0
bright_obj = 0
dim_obj = 0
for j in range(nobj):
# # [DEBUG] [TODO]
# if j >= 10:
# break
obj = catalog.objs[j]
# load and convert SED; also caculate object's magnitude in all CSST bands
try:
sed_data = catalog.load_sed(obj)
norm_filt = catalog.load_norm_filt(obj)
obj.sed, obj.param["mag_%s"%filt.filter_type.lower()], obj.param["flux_%s"%filt.filter_type.lower()] = catalog.convert_sed(
mag=obj.param["mag_use_normal"],
sed=sed_data,
target_filt=filt,
norm_filt=norm_filt,
)
_, obj.param["mag_%s"%cut_filter.filter_type.lower()], obj.param["flux_%s"%cut_filter.filter_type.lower()] = catalog.convert_sed(
mag=obj.param["mag_use_normal"],
sed=sed_data,
target_filt=cut_filter,
norm_filt=norm_filt,
)
except Exception as e:
traceback.print_exc()
self.chip_output.Log_error(e)
continue
# [TODO] Testing
# self.chip_output.Log_info("mag_%s = %.3f"%(filt.filter_type.lower(), obj.param["mag_%s"%filt.filter_type.lower()]))
# Exclude very bright/dim objects (for now)
if cut_filter.is_too_bright(
mag=obj.param["mag_%s"%self.overall_config["obs_setting"]["cut_in_band"].lower()],
margin=self.overall_config["obs_setting"]["mag_sat_margin"]):
self.chip_output.Log_info("obj %s too birght!! mag_%s = %.3f"%(obj.id, cut_filter.filter_type, obj.param["mag_%s"%self.overall_config["obs_setting"]["cut_in_band"].lower()]))
bright_obj += 1
obj.unload_SED()
continue
if filt.is_too_dim(
mag=obj.getMagFilter(filt),
margin=self.overall_config["obs_setting"]["mag_lim_margin"]):
self.chip_output.Log_info("obj %s too dim!! mag_%s = %.3f"%(obj.id, filt.filter_type, obj.getMagFilter(filt)))
dim_obj += 1
obj.unload_SED()
continue
# Get corresponding shear values
if self.overall_config["shear_setting"]["shear_type"] == "constant":
if obj.type == 'star':
obj.g1, obj.g2 = 0., 0.
else:
# Figure out shear fields from overall configuration shear setting
obj.g1, obj.g2 = g1_field, g2_field
elif self.overall_config["shear_setting"]["shear_type"] == "catalog":
pass
else:
self.chip_output.Log_error("Unknown shear input")
raise ValueError("Unknown shear input")
# Get position of object on the focal plane
pos_img, _, _, _, fd_shear = obj.getPosImg_Offset_WCS(img=chip.img, fdmodel=fd_model, chip=chip, verbose=False, chip_wcs=chip_wcs, img_header=self.h_ext)
# [TODO] For now, only consider objects which their centers (after field distortion) are projected within the focal plane
# Otherwise they will be considered missed objects
# if pos_img.x == -1 or pos_img.y == -1 or (not chip.isContainObj(x_image=pos_img.x, y_image=pos_img.y, margin=0.)):
if pos_img.x == -1 or pos_img.y == -1:
self.chip_output.Log_info('obj_ra = %.6f, obj_dec = %.6f, obj_ra_orig = %.6f, obj_dec_orig = %.6f'%(obj.ra, obj.dec, obj.ra_orig, obj.dec_orig))
self.chip_output.Log_error("Objected missed: %s"%(obj.id))
missed_obj += 1
obj.unload_SED()
continue
# Draw object & update output catalog
try:
if self.overall_config["run_option"]["out_cat_only"]:
isUpdated = True
obj.real_pos = obj.getRealPos(chip.img, global_x=obj.posImg.x, global_y=obj.posImg.y, img_real_wcs=obj.chip_wcs)
pos_shear = 0.
elif chip.survey_type == "photometric" and not self.overall_config["run_option"]["out_cat_only"]:
isUpdated, pos_shear = obj.drawObj_multiband(
tel=tel,
pos_img=pos_img,
psf_model=psf_model,
bandpass_list=filt.bandpass_sub_list,
filt=filt,
chip=chip,
g1=obj.g1,
g2=obj.g2,
exptime=pointing.exp_time,
fd_shear=fd_shear)
elif chip.survey_type == "spectroscopic" and not self.overall_config["run_option"]["out_cat_only"]:
isUpdated, pos_shear = obj.drawObj_slitless(
tel=tel,
pos_img=pos_img,
psf_model=psf_model,
bandpass_list=filt.bandpass_sub_list,
filt=filt,
chip=chip,
g1=obj.g1,
g2=obj.g2,
exptime=pointing.exp_time,
normFilter=norm_filt,
fd_shear=fd_shear)
if isUpdated == 1:
# TODO: add up stats
self.chip_output.cat_add_obj(obj, pos_img, pos_shear)
pass
elif isUpdated == 0:
missed_obj += 1
self.chip_output.Log_error("Objected missed: %s"%(obj.id))
else:
self.chip_output.Log_error("Draw error, object omitted: %s"%(obj.id))
continue
except Exception as e:
traceback.print_exc()
self.chip_output.Log_error(e)
# Unload SED:
obj.unload_SED()
del obj
gc.collect()
del psf_model
gc.collect()
self.chip_output.Log_info("Running checkpoint #1 (Object rendering finished): pointing-%d chip-%d pid-%d memory-%6.2fGB"%(pointing.id, chip.chipID, os.getpid(), (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024) ))
self.chip_output.Log_info("# objects that are too bright %d out of %d"%(bright_obj, nobj))
self.chip_output.Log_info("# objects that are too dim %d out of %d"%(dim_obj, nobj))
self.chip_output.Log_info("# objects that are missed %d out of %d"%(missed_obj, nobj))
# Apply flat fielding (with shutter effects)
flat_normal = np.ones_like(chip.img.array)
if obs_param["flat_fielding"] == True:
flat_normal = flat_normal * chip.flat_img.array / np.mean(chip.flat_img.array)
if obs_param["shutter_effect"] == True:
flat_normal = flat_normal * chip.shutter_img
flat_normal = np.array(flat_normal, dtype='float32')
chip.img *= flat_normal
del flat_normal
return chip, filt, tel, pointing
def add_cosmic_rays(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info(" Adding Cosmic-Ray")
chip.img, crmap_gsimg, cr_event_num = chip_utils.add_cosmic_rays(
img=chip.img,
chip=chip,
exptime=pointing.exp_time,
seed=self.overall_config["random_seeds"]["seed_CR"]+pointing.id*30+chip.chipID)
# [TODO] output cosmic ray image
return chip, filt, tel, pointing
def apply_PRNU(self, chip, filt, tel, pointing, catalog, obs_param):
chip.img *= chip.prnu_img
return chip, filt, tel, pointing
def add_poisson_and_dark(self, chip, filt, tel, pointing, catalog, obs_param):
# Add dark current & Poisson noise
InputDark = False
if obs_param["add_dark"] == True:
if InputDark:
chip.img = chip_utils.add_inputdark(img=chip.img, chip=chip, exptime=pointing.exp_time)
else:
chip.img, _ = chip_utils.add_poisson(img=chip.img, chip=chip, exptime=pointing.exp_time, poisson_noise=chip.poisson_noise)
else:
chip.img, _ = chip_utils.add_poisson(img=chip.img, chip=self, exptime=pointing.exp_time, poisson_noise=chip.poisson_noise, dark_noise=0.)
return chip, filt, tel, pointing
def add_brighter_fatter(self, chip, filt, tel, pointing, catalog, obs_param):
chip.img = chip_utils.add_brighter_fatter(img=chip.img)
return chip, filt, tel, pointing
def add_detector_defects(self, chip, filt, tel, pointing, catalog, obs_param):
# Add Hot Pixels or/and Dead Pixels
rgbadpix = Generator(PCG64(int(self.overall_config["random_seeds"]["seed_defective"]+chip.chipID)))
badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
chip.img = Effects.DefectivePixels(
chip.img,
IfHotPix=obs_param["hot_pixels"],
IfDeadPix=obs_param["dead_pixels"],
fraction=badfraction,
seed=self.overall_config["random_seeds"]["seed_defective"]+chip.chipID, biaslevel=0)
# Apply Bad columns
if obs_param["bad_columns"] == True:
chip.img = Effects.BadColumns(chip.img, seed=self.overall_config["random_seeds"]["seed_badcolumns"], chipid=chip.chipID)
return chip, filt, tel, pointing
def add_nonlinearity(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info(" Applying Non-Linearity on the chip image")
chip.img = Effects.NonLinearity(GSImage=chip.img, beta1=5.e-7, beta2=0)
return chip, filt, tel, pointing
def add_blooming(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info(" Applying CCD Saturation & Blooming")
chip.img = Effects.SaturBloom(GSImage=chip.img, nsect_x=1, nsect_y=1, fullwell=int(chip.full_well))
return chip, filt, tel, pointing
def apply_CTE(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info(" Apply CTE Effect")
### 2*8 -> 1*16 img-layout
img = chip_utils.formatOutput(GSImage=chip.img)
chip.nsecy = 1
chip.nsecx = 16
img_arr = img.array
ny, nx = img_arr.shape
dx = int(nx/chip.nsecx)
dy = int(ny/chip.nsecy)
newimg = galsim.Image(nx, int(ny+chip.overscan_y), init_value=0)
for ichannel in range(16):
print('\n***add CTI effects: pointing-{:} chip-{:} channel-{:}***'.format(pointing.id, chip.chipID, ichannel+1))
noverscan, nsp, nmax = chip.overscan_y, 3, 10
beta, w, c = 0.478, 84700, 0
t = np.array([0.74, 7.7, 37],dtype=np.float32)
rho_trap = np.array([0.6, 1.6, 1.4],dtype=np.float32)
trap_seeds = np.array([0, 1000, 10000],dtype=np.int32) + ichannel + chip.chipID*16
release_seed = 50 + ichannel + pointing.id*30 + chip.chipID*16
newimg.array[:, 0+ichannel*dx:dx+ichannel*dx] = CTI_sim(img_arr[:, 0+ichannel*dx:dx+ichannel*dx],dx,dy,noverscan,nsp,nmax,beta,w,c,t,rho_trap,trap_seeds,release_seed)
newimg.wcs = img.wcs
del img
img = newimg
### 1*16 -> 2*8 img-layout
chip.img = chip_utils.formatRevert(GSImage=img)
chip.nsecy = 2
chip.nsecx = 8
# [TODO] make overscan_y == 0
chip.overscan_y = 0
return chip, filt, tel, pointing
def add_prescan_overscan(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info("Apply pre/over-scan")
chip.img = chip_utils.AddPreScan(GSImage=chip.img,
pre1=chip.prescan_x,
pre2=chip.prescan_y,
over1=chip.overscan_x,
over2=chip.overscan_y)
return chip, filt, tel, pointing
def add_bias(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info(" Adding Bias level and 16-channel non-uniformity")
if obs_param["bias_16channel"] == True:
chip.img = Effects.AddBiasNonUniform16(chip.img,
bias_level=float(chip.bias_level),
nsecy = chip.nsecy, nsecx=chip.nsecx,
seed=self.overall_config["random_seeds"]["seed_biasNonUniform"]+chip.chipID)
elif obs_param["bias_16channel"] == False:
chip.img += self.bias_level
return chip, filt, tel, pointing
def add_readout_noise(self, chip, filt, tel, pointing, catalog, obs_param):
seed = int(self.overall_config["random_seeds"]["seed_readout"]) + pointing.id*30 + chip.chipID
rng_readout = galsim.BaseDeviate(seed)
readout_noise = galsim.GaussianNoise(rng=rng_readout, sigma=chip.read_noise)
chip.img.addNoise(readout_noise)
return chip, filt, tel, pointing
def apply_gain(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info(" Applying Gain")
if obs_param["gain_16channel"] == True:
chip.img, chip.gain_channel = Effects.ApplyGainNonUniform16(
chip.img, gain=chip.gain,
nsecy = chip.nsecy, nsecx=chip.nsecx,
seed=self.overall_config["random_seeds"]["seed_gainNonUniform"]+chip.chipID)
elif obs_param["gain_16channel"] == False:
chip.img /= chip.gain
return chip, filt, tel, pointing
def quantization_and_output(self, chip, filt, tel, pointing, catalog, obs_param):
if obs_param["format_output"] == True:
self.chip_output.Log_info(" Apply 1*16 format")
chip.img = chip_utils.formatOutput(GSImage=chip.img)
chip.nsecy = 1
chip.nsecx = 16
chip.img.array[chip.img.array > 65535] = 65535
chip.img.replaceNegative(replace_value=0)
chip.img.quantize()
chip.img = galsim.Image(chip.img.array, dtype=np.uint16)
hdu1 = fits.PrimaryHDU(header=self.h_prim)
hdu1.add_checksum()
hdu1.header.comments['CHECKSUM'] = 'HDU checksum'
hdu1.header.comments['DATASUM'] = 'data unit checksum'
hdu2 = fits.ImageHDU(chip.img.array, header=self.h_ext)
hdu2.add_checksum()
hdu2.header.comments['XTENSION'] = 'extension type'
hdu2.header.comments['CHECKSUM'] = 'HDU checksum'
hdu2.header.comments['DATASUM'] = 'data unit checksum'
hdu2.header.comments["XTENSION"] = "image extension"
hdu1 = fits.HDUList([hdu1, hdu2])
fname = os.path.join(self.chip_output.subdir, self.h_prim['FILENAME'] + '.fits')
hdu1.writeto(fname, output_verify='ignore', overwrite=True)
return chip, filt, tel, pointing
SIM_STEP_TYPES = {
"scie_obs": "add_objects",
"sky_background": "add_sky_background",
"cosmic_rays": "add_cosmic_rays",
"PRNU_effect": "apply_PRNU",
"poisson_and_dark": "add_poisson_and_dark",
"bright_fatter": "add_brighter_fatter",
"detector_defects": "add_detector_defects",
"nonlinearity": "add_nonlinearity",
"blooming": "add_blooming",
"CTE_effect": "apply_CTE",
"prescan_overscan": "add_prescan_overscan",
"bias": "add_bias",
"readout_noise": "add_readout_noise",
"gain": "apply_gain",
"quantization_and_output": "quantization_and_output",
"led_calib_model":"add_LED_Flat",
# "sky_flatField":"add_sky_flat_calibration",
}
\ No newline at end of file
ObservationSim/sim_steps/__init__.py 0 → 100644
View file @ 88aea0d6
import os
class SimSteps:
def __init__(self, overall_config, chip_output, all_filters):
self.overall_config = overall_config
self.chip_output = chip_output
self.all_filters = all_filters
from .prepare_headers import prepare_headers
from .add_sky_background import add_sky_background_sci, add_sky_flat_calibration, add_sky_background
from .add_objects import add_objects
from .add_cosmic_rays import add_cosmic_rays
from .add_pattern_noise import apply_PRNU, add_poisson_and_dark, add_detector_defects, add_nonlinearity, add_blooming, add_bias
from .add_brighter_fatter_CTE import add_brighter_fatter, apply_CTE
from .readout_output import add_prescan_overscan, add_readout_noise, apply_gain, quantization_and_output
SIM_STEP_TYPES = {
"scie_obs": "add_objects",
"sky_background": "add_sky_background",
"cosmic_rays": "add_cosmic_rays",
"PRNU_effect": "apply_PRNU",
"poisson_and_dark": "add_poisson_and_dark",
"bright_fatter": "add_brighter_fatter",
"detector_defects": "add_detector_defects",
"nonlinearity": "add_nonlinearity",
"blooming": "add_blooming",
"CTE_effect": "apply_CTE",
"prescan_overscan": "add_prescan_overscan",
"bias": "add_bias",
"readout_noise": "add_readout_noise",
"gain": "apply_gain",
"quantization_and_output": "quantization_and_output",
"led_calib_model":"add_LED_Flat",
"sky_flatField":"add_sky_flat_calibration",
}
\ No newline at end of file
ObservationSim/sim_steps/add_LED_flat.py 0 → 100644
View file @ 88aea0d6
import numpy as np
from ObservationSim.MockObject import FlatLED
def add_LED_Flat(self, chip, filt, tel, pointing, catalog, obs_param):
if not hasattr(self, 'h_ext'):
_, _ = self.prepare_headers(chip=chip, pointing=pointing)
chip_wcs = galsim.FitsWCS(header = self.h_ext)
pf_map = np.zeros_like(chip.img.array)
if obs_param["LED_TYPE"] is not None:
if len(obs_param["LED_TYPE"]) != 0:
print("LED OPEN--------")
led_obj = FlatLED(chip, filt)
led_flat = led_obj.drawObj_LEDFlat(led_type_list=obs_param["LED_TYPE"], exp_t_list=obs_param["LED_TIME"])
pf_map = led_flat
chip.img = chip.img + led_flat
return chip, filt, tel, pointing
\ No newline at end of file
ObservationSim/sim_steps/add_brighter_fatter_CTE.py 0 → 100644
View file @ 88aea0d6
import numpy as np
import galsim
from ObservationSim.Instrument.Chip import ChipUtils as chip_utils
from ObservationSim.Instrument.Chip.libCTI.CTI_modeling import CTI_sim
def add_brighter_fatter(self, chip, filt, tel, pointing, catalog, obs_param):
chip.img = chip_utils.add_brighter_fatter(img=chip.img)
return chip, filt, tel, pointing
def apply_CTE(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info(" Apply CTE Effect")
### 2*8 -> 1*16 img-layout
img = chip_utils.formatOutput(GSImage=chip.img)
chip.nsecy = 1
chip.nsecx = 16
img_arr = img.array
ny, nx = img_arr.shape
dx = int(nx/chip.nsecx)
dy = int(ny/chip.nsecy)
newimg = galsim.Image(nx, int(ny+chip.overscan_y), init_value=0)
for ichannel in range(16):
print('\n***add CTI effects: pointing-{:} chip-{:} channel-{:}***'.format(pointing.id, chip.chipID, ichannel+1))
noverscan, nsp, nmax = chip.overscan_y, 3, 10
beta, w, c = 0.478, 84700, 0
t = np.array([0.74, 7.7, 37],dtype=np.float32)
rho_trap = np.array([0.6, 1.6, 1.4],dtype=np.float32)
trap_seeds = np.array([0, 1000, 10000],dtype=np.int32) + ichannel + chip.chipID*16
release_seed = 50 + ichannel + pointing.id*30 + chip.chipID*16
newimg.array[:, 0+ichannel*dx:dx+ichannel*dx] = CTI_sim(img_arr[:, 0+ichannel*dx:dx+ichannel*dx],dx,dy,noverscan,nsp,nmax,beta,w,c,t,rho_trap,trap_seeds,release_seed)
newimg.wcs = img.wcs
del img
img = newimg
### 1*16 -> 2*8 img-layout
chip.img = chip_utils.formatRevert(GSImage=img)
chip.nsecy = 2
chip.nsecx = 8
# [TODO] make overscan_y == 0
chip.overscan_y = 0
return chip, filt, tel, pointing
\ No newline at end of file
ObservationSim/sim_steps/add_cosmic_rays.py 0 → 100644
View file @ 88aea0d6
from ObservationSim.Instrument.Chip import ChipUtils as chip_utils
def add_cosmic_rays(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info(" Adding Cosmic-Ray")
chip.img, crmap_gsimg, cr_event_num = chip_utils.add_cosmic_rays(
img=chip.img,
chip=chip,
exptime=pointing.exp_time,
seed=self.overall_config["random_seeds"]["seed_CR"]+pointing.id*30+chip.chipID)
# [TODO] output cosmic ray image
return chip, filt, tel, pointing
\ No newline at end of file
ObservationSim/sim_steps/add_objects.py 0 → 100644
View file @ 88aea0d6
import os
import gc
import psutil
import numpy as np
import galsim
from ObservationSim._util import get_shear_field
from ObservationSim.PSF import PSFGauss, FieldDistortion, PSFInterp, PSFInterpSLS
def add_objects(self, chip, filt, tel, pointing, catalog, obs_param):
# Prepare output file(s) for this chip
self.chip_output.create_output_file()
# Prepare the PSF model
if self.overall_config["psf_setting"]["psf_model"] == "Gauss":
psf_model = PSFGauss(chip=chip, psfRa=self.overall_config["psf_setting"]["psf_rcont"])
elif self.overall_config["psf_setting"]["psf_model"] == "Interp":
if chip.survey_type == "spectroscopic":
psf_model = PSFInterpSLS(chip, filt, PSF_data_prefix=self.overall_config["psf_setting"]["psf_sls_dir"])
else:
psf_model = PSFInterp(chip=chip, npsf=chip.n_psf_samples, PSF_data_file=self.overall_config["psf_setting"]["psf_pho_dir"])
else:
self.chip_output.Log_error("unrecognized PSF model type!!", flush=True)
# Apply field distortion model
if obs_param["field_dist"] == True:
fd_model = FieldDistortion(chip=chip, img_rot=pointing.img_pa.deg)
else:
fd_model = None
# Update limiting magnitudes for all filters based on the exposure time
# Get the filter which will be used for magnitude cut
for ifilt in range(len(self.all_filters)):
temp_filter = self.all_filters[ifilt]
temp_filter.update_limit_saturation_mags(exptime=pointing.get_full_depth_exptime(temp_filter.filter_type), chip=chip)
if temp_filter.filter_type.lower() == self.overall_config["obs_setting"]["cut_in_band"].lower():
cut_filter = temp_filter
# Read in shear values from configuration file if the constant shear type is used
if self.overall_config["shear_setting"]["shear_type"] == "constant":
g1_field, g2_field, _ = get_shear_field(config=self.overall_config)
self.chip_output.Log_info("Use constant shear: g1=%.5f, g2=%.5f"%(g1_field, g2_field))
# Get chip WCS
if not hasattr(self, 'h_ext'):
_, _ = self.prepare_headers(chip=chip, pointing=pointing)
chip_wcs = galsim.FitsWCS(header = self.h_ext)
# Loop over objects
nobj = len(catalog.objs)
missed_obj = 0
bright_obj = 0
dim_obj = 0
for j in range(nobj):
# # [DEBUG] [TODO]
# if j >= 10:
# break
obj = catalog.objs[j]
# load and convert SED; also caculate object's magnitude in all CSST bands
try:
sed_data = catalog.load_sed(obj)
norm_filt = catalog.load_norm_filt(obj)
obj.sed, obj.param["mag_%s"%filt.filter_type.lower()], obj.param["flux_%s"%filt.filter_type.lower()] = catalog.convert_sed(
mag=obj.param["mag_use_normal"],
sed=sed_data,
target_filt=filt,
norm_filt=norm_filt,
)
_, obj.param["mag_%s"%cut_filter.filter_type.lower()], obj.param["flux_%s"%cut_filter.filter_type.lower()] = catalog.convert_sed(
mag=obj.param["mag_use_normal"],
sed=sed_data,
target_filt=cut_filter,
norm_filt=norm_filt,
)
except Exception as e:
traceback.print_exc()
self.chip_output.Log_error(e)
continue
# [TODO] Testing
# self.chip_output.Log_info("mag_%s = %.3f"%(filt.filter_type.lower(), obj.param["mag_%s"%filt.filter_type.lower()]))
# Exclude very bright/dim objects (for now)
if cut_filter.is_too_bright(
mag=obj.param["mag_%s"%self.overall_config["obs_setting"]["cut_in_band"].lower()],
margin=self.overall_config["obs_setting"]["mag_sat_margin"]):
self.chip_output.Log_info("obj %s too birght!! mag_%s = %.3f"%(obj.id, cut_filter.filter_type, obj.param["mag_%s"%self.overall_config["obs_setting"]["cut_in_band"].lower()]))
bright_obj += 1
obj.unload_SED()
continue
if filt.is_too_dim(
mag=obj.getMagFilter(filt),
margin=self.overall_config["obs_setting"]["mag_lim_margin"]):
self.chip_output.Log_info("obj %s too dim!! mag_%s = %.3f"%(obj.id, filt.filter_type, obj.getMagFilter(filt)))
dim_obj += 1
obj.unload_SED()
continue
# Get corresponding shear values
if self.overall_config["shear_setting"]["shear_type"] == "constant":
if obj.type == 'star':
obj.g1, obj.g2 = 0., 0.
else:
# Figure out shear fields from overall configuration shear setting
obj.g1, obj.g2 = g1_field, g2_field
elif self.overall_config["shear_setting"]["shear_type"] == "catalog":
pass
else:
self.chip_output.Log_error("Unknown shear input")
raise ValueError("Unknown shear input")
# Get position of object on the focal plane
pos_img, _, _, _, fd_shear = obj.getPosImg_Offset_WCS(img=chip.img, fdmodel=fd_model, chip=chip, verbose=False, chip_wcs=chip_wcs, img_header=self.h_ext)
# [TODO] For now, only consider objects which their centers (after field distortion) are projected within the focal plane
# Otherwise they will be considered missed objects
# if pos_img.x == -1 or pos_img.y == -1 or (not chip.isContainObj(x_image=pos_img.x, y_image=pos_img.y, margin=0.)):
if pos_img.x == -1 or pos_img.y == -1:
self.chip_output.Log_info('obj_ra = %.6f, obj_dec = %.6f, obj_ra_orig = %.6f, obj_dec_orig = %.6f'%(obj.ra, obj.dec, obj.ra_orig, obj.dec_orig))
self.chip_output.Log_error("Objected missed: %s"%(obj.id))
missed_obj += 1
obj.unload_SED()
continue
# Draw object & update output catalog
try:
if self.overall_config["run_option"]["out_cat_only"]:
isUpdated = True
obj.real_pos = obj.getRealPos(chip.img, global_x=obj.posImg.x, global_y=obj.posImg.y, img_real_wcs=obj.chip_wcs)
pos_shear = 0.
elif chip.survey_type == "photometric" and not self.overall_config["run_option"]["out_cat_only"]:
isUpdated, pos_shear = obj.drawObj_multiband(
tel=tel,
pos_img=pos_img,
psf_model=psf_model,
bandpass_list=filt.bandpass_sub_list,
filt=filt,
chip=chip,
g1=obj.g1,
g2=obj.g2,
exptime=pointing.exp_time,
fd_shear=fd_shear)
elif chip.survey_type == "spectroscopic" and not self.overall_config["run_option"]["out_cat_only"]:
isUpdated, pos_shear = obj.drawObj_slitless(
tel=tel,
pos_img=pos_img,
psf_model=psf_model,
bandpass_list=filt.bandpass_sub_list,
filt=filt,
chip=chip,
g1=obj.g1,
g2=obj.g2,
exptime=pointing.exp_time,
normFilter=norm_filt,
fd_shear=fd_shear)
if isUpdated == 1:
# TODO: add up stats
self.chip_output.cat_add_obj(obj, pos_img, pos_shear)
pass
elif isUpdated == 0:
missed_obj += 1
self.chip_output.Log_error("Objected missed: %s"%(obj.id))
else:
self.chip_output.Log_error("Draw error, object omitted: %s"%(obj.id))
continue
except Exception as e:
traceback.print_exc()
self.chip_output.Log_error(e)
# Unload SED:
obj.unload_SED()
del obj
gc.collect()
del psf_model
gc.collect()
self.chip_output.Log_info("Running checkpoint #1 (Object rendering finished): pointing-%d chip-%d pid-%d memory-%6.2fGB"%(pointing.id, chip.chipID, os.getpid(), (psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 / 1024) ))
self.chip_output.Log_info("# objects that are too bright %d out of %d"%(bright_obj, nobj))
self.chip_output.Log_info("# objects that are too dim %d out of %d"%(dim_obj, nobj))
self.chip_output.Log_info("# objects that are missed %d out of %d"%(missed_obj, nobj))
# Apply flat fielding (with shutter effects)
flat_normal = np.ones_like(chip.img.array)
if obs_param["flat_fielding"] == True:
flat_normal = flat_normal * chip.flat_img.array / np.mean(chip.flat_img.array)
if obs_param["shutter_effect"] == True:
flat_normal = flat_normal * chip.shutter_img
flat_normal = np.array(flat_normal, dtype='float32')
chip.img *= flat_normal
del flat_normal
return chip, filt, tel, pointing
\ No newline at end of file
ObservationSim/sim_steps/add_pattern_noise.py 0 → 100644
View file @ 88aea0d6
from numpy.random import Generator, PCG64
from ObservationSim.Instrument.Chip import ChipUtils as chip_utils
from ObservationSim.Instrument.Chip import Effects
def apply_PRNU(self, chip, filt, tel, pointing, catalog, obs_param):
chip.img *= chip.prnu_img
return chip, filt, tel, pointing
def add_poisson_and_dark(self, chip, filt, tel, pointing, catalog, obs_param):
# Add dark current & Poisson noise
InputDark = False
if obs_param["add_dark"] == True:
if InputDark:
chip.img = chip_utils.add_inputdark(img=chip.img,
chip=chip,
exptime=pointing.exp_time)
else:
chip.img, _ = chip_utils.add_poisson(img=chip.img,
chip=chip,
exptime=pointing.exp_time,
poisson_noise=chip.poisson_noise)
else:
chip.img, _ = chip_utils.add_poisson(img=chip.img,
chip=self,
exptime=pointing.exp_time,
poisson_noise=chip.poisson_noise,
dark_noise=0.)
return chip, filt, tel, pointing
def add_detector_defects(self, chip, filt, tel, pointing, catalog, obs_param):
# Add Hot Pixels or/and Dead Pixels
rgbadpix = Generator(PCG64(int(self.overall_config["random_seeds"]["seed_defective"]+chip.chipID)))
badfraction = 5E-5*(rgbadpix.random()*0.5+0.7)
chip.img = Effects.DefectivePixels(
chip.img,
IfHotPix=obs_param["hot_pixels"],
IfDeadPix=obs_param["dead_pixels"],
fraction=badfraction,
seed=self.overall_config["random_seeds"]["seed_defective"]+chip.chipID, biaslevel=0)
# Apply Bad columns
if obs_param["bad_columns"] == True:
chip.img = Effects.BadColumns(chip.img,
seed=self.overall_config["random_seeds"]["seed_badcolumns"],
chipid=chip.chipID)
return chip, filt, tel, pointing
def add_nonlinearity(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info(" Applying Non-Linearity on the chip image")
chip.img = Effects.NonLinearity(GSImage=chip.img,
beta1=5.e-7,
beta2=0)
return chip, filt, tel, pointing
def add_blooming(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info(" Applying CCD Saturation & Blooming")
chip.img = Effects.SaturBloom(GSImage=chip.img,
nsect_x=1,
nsect_y=1,
fullwell=int(chip.full_well))
return chip, filt, tel, pointing
def add_bias(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info(" Adding Bias level and 16-channel non-uniformity")
if obs_param["bias_16channel"] == True:
chip.img = Effects.AddBiasNonUniform16(chip.img,
bias_level=float(chip.bias_level),
nsecy = chip.nsecy,
nsecx=chip.nsecx,
seed=self.overall_config["random_seeds"]["seed_biasNonUniform"]+chip.chipID)
elif obs_param["bias_16channel"] == False:
chip.img += self.bias_level
return chip, filt, tel, pointing
\ No newline at end of file
ObservationSim/sim_steps/add_sky_background.py 0 → 100644
View file @ 88aea0d6
import numpy as np
import galsim
from ObservationSim.Straylight import calculateSkyMap_split_g
def add_sky_background_sci(self, chip, filt, tel, pointing, catalog, obs_param):
flat_normal = np.ones_like(chip.img.array)
if obs_param["flat_fielding"] == True:
flat_normal = flat_normal * chip.flat_img.array / np.mean(chip.flat_img.array)
if obs_param["shutter_effect"] == True:
flat_normal = flat_normal * chip.shutter_img
flat_normal = np.array(flat_normal, dtype='float32')
if obs_param["enable_straylight_model"]:
# Filter.sky_background, Filter.zodical_spec will be updated
filt.setFilterStrayLightPixel(
jtime = pointing.jdt,
sat_pos = np.array([pointing.sat_x, pointing.sat_y, pointing.sat_z]),
pointing_radec = np.array([pointing.ra,pointing.dec]),
sun_pos = np.array([pointing.sun_x, pointing.sun_y, pointing.sun_z]))
self.chip_output.Log_info("================================================")
self.chip_output.Log_info("sky background + stray light pixel flux value: %.5f"%(filt.sky_background))
if chip.survey_type == "photometric":
sky_map = filt.getSkyNoise(exptime = obs_param["exptime"])
sky_map = sky_map * np.ones_like(chip.img.array) * flat_normal
sky_map = galsim.Image(array=sky_map)
else:
# chip.loadSLSFLATCUBE(flat_fn='flat_cube.fits')
sky_map = calculateSkyMap_split_g(
skyMap=flat_normal,
blueLimit=filt.blue_limit,
redLimit=filt.red_limit,
conf=chip.sls_conf,
pixelSize=chip.pix_scale,
isAlongY=0,
flat_cube=chip.flat_cube,
zoldial_spec = filt.zodical_spec)
sky_map = (sky_map + filt.sky_background)*obs_param["exptime"]
# sky_map = sky_map * tel.pupil_area * obs_param["exptime"]
chip.img += sky_map
return chip, filt, tel, pointing
def add_sky_flat_calibration(self, chip, filt, tel, pointing, catalog, obs_param):
if not hasattr(self, 'h_ext'):
_, _ = self.prepare_headers(chip=chip, pointing=pointing)
chip_wcs = galsim.FitsWCS(header = self.h_ext)
expTime = obs_param["exptime"]
skyback_level = obs_param["flat_level"]
filter_param = FilterParam()
sky_level_filt = obs_param["flat_level_filt"]
norm_scaler = skyback_level/expTime / filter_param.param[sky_level_filt][5]
flat_normal = np.ones_like(chip.img.array)
if obs_param["flat_fielding"] == True:
flat_normal = flat_normal * chip.flat_img.array / np.mean(chip.flat_img.array)
if obs_param["shutter_effect"] == True:
flat_normal = flat_normal * chip.shutter_img
flat_normal = np.array(flat_normal, dtype='float32')
if chip.survey_type == "photometric":
sky_map = flat_normal * np.ones_like(chip.img.array) * norm_scaler * filter_param.param[chip.filter_type][5] / tel.pupil_area * expTime
elif chip.survey_type == "spectroscopic":
# flat_normal = np.ones_like(chip.img.array)
if obs_param["flat_fielding"] == True:
flat_normal = flat_normal * chip.flat_img.array / np.mean(chip.flat_img.array)
if obs_param["shutter_effect"] == True:
flat_normal = flat_normal * chip.shutter_img
flat_normal = np.array(flat_normal, dtype='float32')
sky_map = calculateSkyMap_split_g(
skyMap=flat_normal,
blueLimit=filt.blue_limit,
redLimit=filt.red_limit,
conf=chip.sls_conf,
pixelSize=chip.pix_scale,
isAlongY=0,
flat_cube=chip.flat_cube)
sky_map = sky_map * norm_scaler * expTime
chip.img += sky_map
return chip, filt, tel, pointing
def add_sky_background(self, chip, filt, tel, pointing, catalog, obs_param):
if not hasattr(self, 'h_ext'):
_, _ = self.prepare_headers(chip=chip, pointing=pointing)
chip_wcs = galsim.FitsWCS(header = self.h_ext)
if "flat_level" not in obs_param or "flat_level_filt" not in obs_param:
chip, filt, tel, pointing = self.add_sky_background_sci(chip, filt, tel, pointing, catalog, obs_param)
else:
if obs_param.get('flat_level') is None or obs_param.get('flat_level_filt')is None:
chip, filt, tel, pointing = self.add_sky_background_sci(chip, filt, tel, pointing, catalog, obs_param)
else:
chip, filt, tel, pointing = self.add_sky_flat_calibration(chip, filt, tel, pointing, catalog, obs_param)
return chip, filt, tel, pointing
\ No newline at end of file
ObservationSim/sim_steps/prepare_headers.py 0 → 100644
View file @ 88aea0d6
from ObservationSim.Config.Header import generatePrimaryHeader, generateExtensionHeader
def prepare_headers(self, chip, pointing):
self.h_prim = generatePrimaryHeader(
xlen=chip.npix_x,
ylen=chip.npix_y,
pointNum = str(pointing.id),
ra=pointing.ra,
dec=pointing.dec,
pixel_scale=chip.pix_scale,
time_pt = pointing.timestamp,
exptime=pointing.exp_time,
im_type=pointing.pointing_type,
sat_pos=[pointing.sat_x, pointing.sat_y, pointing.sat_z],
sat_vel=[pointing.sat_vx, pointing.sat_vy, pointing.sat_vz],
project_cycle=self.overall_config["project_cycle"],
run_counter=self.overall_config["run_counter"],
chip_name=str(chip.chipID).rjust(2, '0'))
self.h_ext = generateExtensionHeader(
chip=chip,
xlen=chip.npix_x,
ylen=chip.npix_y,
ra=pointing.ra,
dec=pointing.dec,
pa=pointing.img_pa.deg,
gain=chip.gain,
readout=chip.read_noise,
dark=chip.dark_noise,
saturation=90000,
pixel_scale=chip.pix_scale,
pixel_size=chip.pix_size,
xcen=chip.x_cen,
ycen=chip.y_cen,
extName=pointing.pointing_type,
timestamp = pointing.timestamp,
exptime = pointing.exp_time,
readoutTime = chip.readout_time)
return self.h_prim, self.h_ext
\ No newline at end of file
ObservationSim/sim_steps/readout_output.py 0 → 100644
View file @ 88aea0d6
import os
import galsim
import numpy as np
from astropy.io import fits
from ObservationSim.Instrument.Chip import ChipUtils as chip_utils
from ObservationSim.Instrument.Chip import Effects
def add_prescan_overscan(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info("Apply pre/over-scan")
chip.img = chip_utils.AddPreScan(GSImage=chip.img,
pre1=chip.prescan_x,
pre2=chip.prescan_y,
over1=chip.overscan_x,
over2=chip.overscan_y)
return chip, filt, tel, pointing
def add_readout_noise(self, chip, filt, tel, pointing, catalog, obs_param):
seed = int(self.overall_config["random_seeds"]["seed_readout"]) + pointing.id*30 + chip.chipID
rng_readout = galsim.BaseDeviate(seed)
readout_noise = galsim.GaussianNoise(rng=rng_readout, sigma=chip.read_noise)
chip.img.addNoise(readout_noise)
return chip, filt, tel, pointing
def apply_gain(self, chip, filt, tel, pointing, catalog, obs_param):
self.chip_output.Log_info(" Applying Gain")
if obs_param["gain_16channel"] == True:
chip.img, chip.gain_channel = Effects.ApplyGainNonUniform16(chip.img,
gain=chip.gain,
nsecy = chip.nsecy,
nsecx=chip.nsecx,
seed=self.overall_config["random_seeds"]["seed_gainNonUniform"]+chip.chipID)
elif obs_param["gain_16channel"] == False:
chip.img /= chip.gain
return chip, filt, tel, pointing
def quantization_and_output(self, chip, filt, tel, pointing, catalog, obs_param):
if obs_param["format_output"] == True:
self.chip_output.Log_info(" Apply 1*16 format")
chip.img = chip_utils.formatOutput(GSImage=chip.img)
chip.nsecy = 1
chip.nsecx = 16
chip.img.array[chip.img.array > 65535] = 65535
chip.img.replaceNegative(replace_value=0)
chip.img.quantize()
chip.img = galsim.Image(chip.img.array, dtype=np.uint16)
hdu1 = fits.PrimaryHDU(header=self.h_prim)
hdu1.add_checksum()
hdu1.header.comments['CHECKSUM'] = 'HDU checksum'
hdu1.header.comments['DATASUM'] = 'data unit checksum'
hdu2 = fits.ImageHDU(chip.img.array, header=self.h_ext)
hdu2.add_checksum()
hdu2.header.comments['XTENSION'] = 'extension type'
hdu2.header.comments['CHECKSUM'] = 'HDU checksum'
hdu2.header.comments['DATASUM'] = 'data unit checksum'
hdu2.header.comments["XTENSION"] = "image extension"
hdu1 = fits.HDUList([hdu1, hdu2])
fname = os.path.join(self.chip_output.subdir, self.h_prim['FILENAME'] + '.fits')
hdu1.writeto(fname, output_verify='ignore', overwrite=True)
return chip, filt, tel, pointing
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