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Commit de19281e authored by Fang Yuedong's avatar Fang Yuedong
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backup

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.gitignore 0 → 100644
View file @ de19281e
*.fits
*.cat
*.log
*.list
*.png
*.pyc
*.so
\ No newline at end of file
config/config_injection_20231203.yaml 0 → 100644
View file @ de19281e
---
###############################################
#
# Configuration file for CSST object injection
# Last modified: 2022/06/19
#
###############################################
# n_objects: 500
rotate_objs: NO
use_mpi: YES
run_name: "test_20231203"
project_cycle: 6
run_counter: 1
pos_sampling:
type: "HexGrid"
# type: "RectGrid"
# grid_spacing: 18.5 # arcsec (~250 pixels)
grid_spacing: 15 # arcsec (~500 pixels)
# type: "uniform"
# object_density: 37 # arcmin^-2
output_img_dir: "/share/home/fangyuedong/injection_pipeline/workspace"
input_img_list: "/share/home/fangyuedong/injection_pipeline/input_L1_IMG_20231203.list"
###############################################
# PSF setting
###############################################
psf_setting:
# Which PSF model to use:
# "Gauss": simple gaussian profile
# "Interp": Interpolated PSF from sampled ray-tracing data
psf_model: "Interp"
# PSF size [arcseconds]
# radius of 80% energy encircled
# NOTE: only valid for "Gauss" PSF
psf_rcont: 0.15
# path to PSF data
# NOTE: only valid for "Interp" PSF
psf_dir: "/share/simudata/CSSOSDataProductsSims/data/psfCube1"
###############################################
# Input path setting
# (NOTE) Used NGP Catalog for testing
###############################################
# Default path settings for NGP footprint simulation
data_dir: "/share/simudata/CSSOSDataProductsSims/data/"
input_path:
cat_dir: "OnOrbitCalibration/CTargets20211231"
star_cat: "CT-NGP_r1.8_G28.hdf5"
galaxy_cat: "galaxyCats_r_3.0_healpix_shift_192.859500_27.128300.hdf5"
SED_templates_path:
star_SED: "Catalog_20210126/SpecLib.hdf5"
galaxy_SED: "Templates/Galaxy/"
###############################################
# Instrumental effects setting
# (NOTE) Here only used to construct
# ObservationSim.Instrument.Chip object
# (TODO) Should readout from header
###############################################
ins_effects:
# switches
bright_fatter: ON # Whether to simulate Brighter-Fatter (also diffusion) effect
# values
# dark_exptime: 300 # Exposure time for dark current frames [seconds]
# flat_exptime: 150 # Exposure time for flat-fielding frames [seconds]
# readout_time: 40 # The read-out time for each channel [seconds]
# df_strength: 2.3 # Sillicon sensor diffusion strength
# bias_level: 500 # bias level [e-/pixel]
# gain: 1.1 # Gain
# full_well: 90000 # Full well depth [e-]
###############################################
# Random seeds
###############################################
random_seeds:
seed_Av: 121212 # Seed for generating random intrinsic extinction
###############################################
# Measurement setting
###############################################
measurement_setting:
input_img_list: "/share/home/fangyuedong/injection_pipeline/L1_INJECTED_20231203.list"
# input_img_list: "/share/home/fangyuedong/injection_pipeline/input_L1_img_MSC_0000000.list"
input_wht_list: "/share/home/fangyuedong/injection_pipeline/L1_WHT_20231203.list"
input_flg_list: "/share/home/fangyuedong/injection_pipeline/L1_FLG_20231203.list"
# input_psf_list: "/share/home/fangyuedong/injection_pipeline/psf_img_MSC_0000000.list"
sex_config: "/share/home/fangyuedong/injection_pipeline/config/default.config"
sex_param: "/share/home/fangyuedong/injection_pipeline/config/default.param"
n_jobs: 18
output_dir: "/share/home/fangyuedong/injection_pipeline/workspace"
\ No newline at end of file
evaluation/calculate_completeness_fraction.py
View file @ de19281e
...@@ -8,16 +8,30 @@ from ObservationSim.Instrument import Telescope, Filter, FilterParam ...@@ -8,16 +8,30 @@ from ObservationSim.Instrument import Telescope, Filter, FilterParam
VC_A = 2.99792458e+18 # speed of light: A/s VC_A = 2.99792458e+18 # speed of light: A/s
# def define_options():
# parser = argparse.ArgumentParser()
# parser.add_argument('--TU_catalog', dest='TU_catalog', type=str, required=True,
# help='path to the (injected) truth catalog')
# parser.add_argument('--source_catalog', dest='source_catalog', type=str, required=True,
# help='path to the (extracted) injected catalog')
# parser.add_argument('--orig_catalog', dest='orig_catalog', type=str, required=True,
# help='path to the (extracted) original catalog')
# parser.add_argument('--image', dest='image', type=str, required=True,
# help='path to the image, used to get the header info')
# parser.add_argument('--output_dir', dest='output_dir', type=str, required=False,
# default='./workspace', help='output path')
# return parser
def define_options(): def define_options():
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument('--TU_catalog', dest='TU_catalog', type=str, required=True, parser.add_argument('--TU_catalog_list', dest='TU_catalog_list', type=str, required=True,
help='path to the (injected) truth catalog') help='path to the list of (injected) truth catalog')
parser.add_argument('--source_catalog', dest='source_catalog', type=str, required=True, parser.add_argument('--source_catalog_list', dest='source_catalog_list', type=str, required=True,
help='path to the (extracted) injected catalog') help='path to the (extracted) injected catalog')
parser.add_argument('--orig_catalog', dest='orig_catalog', type=str, required=True, # parser.add_argument('--orig_catalog', dest='orig_catalog', type=str, required=True,
help='path to the (extracted) original catalog') # help='path to the list of (extracted) original catalog')
parser.add_argument('--image', dest='image', type=str, required=True, # parser.add_argument('--image', dest='image', type=str, required=True,
help='path to the image, used to get the header info') # help='path to the image, used to get the header info')
parser.add_argument('--output_dir', dest='output_dir', type=str, required=False, parser.add_argument('--output_dir', dest='output_dir', type=str, required=False,
default='./workspace', help='output path') default='./workspace', help='output path')
return parser return parser
...@@ -72,13 +86,24 @@ def convert_catalog(catname): ...@@ -72,13 +86,24 @@ def convert_catalog(catname):
fits_filename = os.path.join(data_dir, base_name + '.fits') fits_filename = os.path.join(data_dir, base_name + '.fits')
text_file.write(fits_filename, overwrite=True) text_file.write(fits_filename, overwrite=True)
def validation_hist(val, idx, name="val", nbins=10, fig_name='detected_counts.png', output_dir='./'): def validation_hist(val, idx, name="val", nbins=10, bins=None, fig_name='detected_counts.png', output_dir='./', create_figure=True):
counts, bins = np.histogram(val, bins=nbins) if bins is None:
counts, bins = np.histogram(val, bins=nbins)
else:
counts, bins = np.histogram(val, bins=bins)
is_empty = np.full(len(val), False) is_empty = np.full(len(val), False)
for i in range(len(idx)): for i in range(len(idx)):
if idx[i].size == 0: if idx[i].size == 0:
is_empty[i] = True is_empty[i] = True
counts_detected, _ = np.histogram(val[~is_empty], bins=nbins) if bins is None:
counts_detected, _ = np.histogram(val[~is_empty], bins=nbins)
else:
counts_detected, _ = np.histogram(val[~is_empty], bins=bins)
if create_figure:
create_hist_figure(counts, counts_detected, bins, name, output_dir, fig_name)
return counts, counts_detected, bins
def create_hist_figure(counts, counts_detected, bins, name="val", output_dir='./', fig_name='detected_counts.png'):
plt.figure() plt.figure()
plt.stairs(counts, bins, color='r', label='TU objects') plt.stairs(counts, bins, color='r', label='TU objects')
plt.stairs(counts_detected, bins, color='g', label='Detected') plt.stairs(counts_detected, bins, color='g', label='Detected')
...@@ -87,22 +112,38 @@ def validation_hist(val, idx, name="val", nbins=10, fig_name='detected_counts.pn ...@@ -87,22 +112,38 @@ def validation_hist(val, idx, name="val", nbins=10, fig_name='detected_counts.pn
plt.legend(loc='upper right', fancybox=True) plt.legend(loc='upper right', fancybox=True)
fig_name = os.path.join(output_dir, fig_name) fig_name = os.path.join(output_dir, fig_name)
plt.savefig(fig_name) plt.savefig(fig_name)
return counts, bins
def hist_fraction(val, idx, name='val', nbins=10, normed=False, output_dir='./'): def hist_fraction(val, idx, name='val', nbins=10, bins=None, output_dir='./', fig_name="completeness_fraction.png"):
counts, bins = np.histogram(val, bins=nbins) if bins is None:
counts, bins = np.histogram(val, bins=nbins)
else:
counts, bins = np.histogram(val, bins=bins)
is_empty = np.full(len(val), False) is_empty = np.full(len(val), False)
for i in range(len(idx)): for i in range(len(idx)):
if idx[i].size == 0: if idx[i].size == 0:
is_empty[i] = True is_empty[i] = True
counts_detected, _ = np.histogram(val[~is_empty], bins=nbins, density=normed) if bins is None:
counts_detected, _ = np.histogram(val[~is_empty], bins=nbins)
else:
counts_detected, _ = np.histogram(val[~is_empty], bins=bins)
fraction = counts_detected / counts
fraction[np.where(np.isnan(fraction))[0]] = 0.
plt.figure()
plt.stairs(fraction, bins, color='r', label='completeness fraction')
plt.xlabel(name, size='x-large')
plt.title("Completeness Fraction")
fig_name = os.path.join(output_dir, fig_name)
plt.savefig(fig_name)
return fraction
def create_fraction_figure(counts, counts_detected, bins, name='val', output_dir='./', fig_name="completeness_fraction.png"):
fraction = counts_detected / counts fraction = counts_detected / counts
fraction[np.where(np.isnan(fraction))[0]] = 0. fraction[np.where(np.isnan(fraction))[0]] = 0.
plt.figure() plt.figure()
plt.stairs(fraction, bins, color='r', label='completeness fraction') plt.stairs(fraction, bins, color='r', label='completeness fraction')
plt.xlabel(name, size='x-large') plt.xlabel(name, size='x-large')
plt.title("Completeness Fraction") plt.title("Completeness Fraction")
fig_name = os.path.join(output_dir, "completeness_fraction_%s.png"%(name)) fig_name = os.path.join(output_dir, fig_name)
plt.savefig(fig_name) plt.savefig(fig_name)
return fraction return fraction
...@@ -116,6 +157,25 @@ def calculate_fraction(TU_catalog, source_catalog, output_dir, nbins=10): ...@@ -116,6 +157,25 @@ def calculate_fraction(TU_catalog, source_catalog, output_dir, nbins=10):
fraction = hist_fraction(val=mag_TU, idx=idx1, name="mag_injected", nbins=10, output_dir=output_dir) fraction = hist_fraction(val=mag_TU, idx=idx1, name="mag_injected", nbins=10, output_dir=output_dir)
return counts, bins, fraction return counts, bins, fraction
def calculate_fraction_multi_cats(TU_catalog_list, source_catalog_list, output_dir, nbins=10):
counts = np.zeros(nbins)
counts_detected = np.zeros(nbins)
bins = np.linspace(18, 26, num=(nbins+1))
for i in range(len(TU_catalog_list)):
TU_catalog = TU_catalog_list[i]
source_catalog = source_catalog_list[i]
convert_catalog(TU_catalog)
x_TU_temp, y_TU_temp, col_list = read_catalog(TU_catalog + '.fits', ext_num=1, ra_name="xImage", dec_name="yImage", col_list=["mag"])
mag_TU_temp = col_list[0]
x_source_temp, y_source_temp, _ = read_catalog(source_catalog, ext_num=1, ra_name="X_IMAGE", dec_name="Y_IMAGE")
idx1, _, = match_catalogs_img(x1=x_TU_temp, y1=y_TU_temp, x2=x_source_temp, y2=y_source_temp)
counts_temp, counts_detected_temp, _ = validation_hist(val=mag_TU_temp, idx=idx1, name="mag_injected", bins=bins, output_dir=output_dir, create_figure=False)
counts += counts_temp
counts_detected += counts_detected_temp
create_hist_figure(counts, counts_detected, bins, "mag_injected", output_dir)
fraction = create_fraction_figure(counts, counts_detected, bins, 'mag_injected', output_dir)
return counts, counts_detected, bins, fraction
def calculate_undetected_flux(orig_cat, mag_bins, fraction, mag_low=20.0, mag_high=26.0, image=None, output_dir='./'): def calculate_undetected_flux(orig_cat, mag_bins, fraction, mag_low=20.0, mag_high=26.0, image=None, output_dir='./'):
# Get info from original image # Get info from original image
hdu = fits.open(image) hdu = fits.open(image)
...@@ -166,19 +226,32 @@ def calculate_undetected_flux(orig_cat, mag_bins, fraction, mag_low=20.0, mag_hi ...@@ -166,19 +226,32 @@ def calculate_undetected_flux(orig_cat, mag_bins, fraction, mag_low=20.0, mag_hi
undetected_flux /= (float(nx_pix) * float(ny_pix)) undetected_flux /= (float(nx_pix) * float(ny_pix))
return undetected_flux return undetected_flux
# if __name__ == "__main__":
# args = define_options().parse_args()
# counts, bins, fraction = calculate_fraction(
# TU_catalog=args.TU_catalog,
# source_catalog=args.source_catalog,
# output_dir=args.output_dir,
# nbins=20
# )
# undetected_flux = calculate_undetected_flux(
# orig_cat=args.orig_catalog,
# mag_bins=bins,
# fraction=fraction,
# image=args.image,
# output_dir=args.output_dir,
# )
# print(undetected_flux)
if __name__ == "__main__": if __name__ == "__main__":
args = define_options().parse_args() args = define_options().parse_args()
counts, bins, fraction = calculate_fraction( with open(args.TU_catalog_list) as file:
TU_catalog=args.TU_catalog, TU_catalog_list = [line.rstrip() for line in file]
source_catalog=args.source_catalog, with open(args.source_catalog_list) as file:
source_catalog_list = [line.rstrip() for line in file]
counts, counts_detected, bins, fraction = calculate_fraction_multi_cats(
TU_catalog_list=TU_catalog_list,
source_catalog_list=source_catalog_list,
output_dir=args.output_dir, output_dir=args.output_dir,
nbins=20 nbins=20
) )
undetected_flux = calculate_undetected_flux( \ No newline at end of file
orig_cat=args.orig_catalog,
mag_bins=bins,
fraction=fraction,
image=args.image,
output_dir=args.output_dir,
)
print(undetected_flux)
\ No newline at end of file
evaluation/cross_match_catalogs.py
View file @ de19281e
import numpy as np import numpy as np
import astropy.units as u import astropy.units as u
import matplotlib.pyplot as plt
from astropy.coordinates import SkyCoord from astropy.coordinates import SkyCoord
from astropy.io import fits from astropy.io import fits
from astropy.io import ascii from astropy.io import ascii
from sklearn.neighbors import BallTree from sklearn.neighbors import BallTree
# TU_catalog = "test_RectGrid_20220628.cat"
# source_catalog = "extracted_test_RectGrid_20220628.fits"
TU_catalog = "injected_bkgsub_img.cat" TU_catalog = "injected_bkgsub_img.cat"
source_catalog = "extracted_injected_bkgsub_img.fits" source_catalog = "extracted_injected_bkgsub_img.fits"
...@@ -26,7 +23,6 @@ def read_catalog(catname, ext_num=1, ra_name='ra', dec_name='dec', col_list=[]): ...@@ -26,7 +23,6 @@ def read_catalog(catname, ext_num=1, ra_name='ra', dec_name='dec', col_list=[]):
if len(col_list) > 0: if len(col_list) > 0:
for col in col_list: for col in col_list:
col_other.append(data[col]) col_other.append(data[col])
# print(ra, dec)
return ra, dec, col_other return ra, dec, col_other
def match_catalogs_sky(ra1, dec1, ra2, dec2, max_dist=0.6, others1=[], others2=[], thresh=[]): def match_catalogs_sky(ra1, dec1, ra2, dec2, max_dist=0.6, others1=[], others2=[], thresh=[]):
...@@ -37,33 +33,24 @@ def match_catalogs_sky(ra1, dec1, ra2, dec2, max_dist=0.6, others1=[], others2=[ ...@@ -37,33 +33,24 @@ def match_catalogs_sky(ra1, dec1, ra2, dec2, max_dist=0.6, others1=[], others2=[
# print(idx2) # print(idx2)
# print(np.shape(idx1)) # print(np.shape(idx1))
# print(np.shape(idx2)) # print(np.shape(idx2))
# TODO
def match_catalogs_img(x1, y1, x2, y2, max_dist=0.5, others1=[], others2=[], thresh=[]): def match_catalogs_img(x1, y1, x2, y2, max_dist=2, others1=[], others2=[], thresh=[]):
cat1 = np.array([(x, y) for x,y in zip(x1, y1)]) cat1 = np.array([(x, y) for x,y in zip(x1, y1)])
cat2 = np.array([(x, y) for x,y in zip(x2, y2)]) cat2 = np.array([(x, y) for x,y in zip(x2, y2)])
# print(np.shape(cat1))
# print(np.shape(cat2))
tree = BallTree(cat2) tree = BallTree(cat2)
idx1 = tree.query_radius(cat1, r = max_dist) idx1 = tree.query_radius(cat1, r = max_dist)
tree = BallTree(cat1) tree = BallTree(cat1)
idx2 = tree.query_radius(cat2, r = max_dist) idx2 = tree.query_radius(cat2, r = max_dist)
# print(np.shape(idx1))
tot = 0 tot = 0
print(idx1)
for idx in idx1: for idx in idx1:
if len(idx) == 0: if len(idx) == 0:
continue continue
if len(idx) > 1: print(len(idx))
tot += 1 tot += 1
print(tot) print("number of matched sources = ", tot)
return idx1, idx2 return idx1, idx2
def validation_hist(val1, idx1, name="val1", nbins=10):
counts, bins = np.histogram(val1)
plt.stairs(counts, bins)
plt.xlabel(name, size='x-large')
plt.savefig("detection_completeness.png")
# plt.show()
if __name__=="__main__": if __name__=="__main__":
convert_catalog(TU_catalog) convert_catalog(TU_catalog)
ra_TU, dec_TU, _ = read_catalog('test_ascii_to_fits.fits', ext_num=1, ra_name="ra", dec_name="dec") ra_TU, dec_TU, _ = read_catalog('test_ascii_to_fits.fits', ext_num=1, ra_name="ra", dec_name="dec")
...@@ -73,5 +60,4 @@ if __name__=="__main__": ...@@ -73,5 +60,4 @@ if __name__=="__main__":
x_source, y_source, _ = read_catalog(source_catalog, ext_num=1, ra_name="X_IMAGE", dec_name="Y_IMAGE") x_source, y_source, _ = read_catalog(source_catalog, ext_num=1, ra_name="X_IMAGE", dec_name="Y_IMAGE")
# match_catalogs_sky(ra1=ra_TU, dec1=dec_TU, ra2=ra_source, dec2=dec_source) # match_catalogs_sky(ra1=ra_TU, dec1=dec_TU, ra2=ra_source, dec2=dec_source)
idx1, idx2, = match_catalogs_img(x1=x_TU, y1=y_TU, x2=x_source, y2=y_source) idx1, idx2, = match_catalogs_img(x1=x_TU, y1=y_TU, x2=x_source, y2=y_source)
# print(ra_TU, dec_TU) # print(ra_TU, dec_TU)
validation_hist(mag_TU, idx1, name="mag_injected") \ No newline at end of file
\ No newline at end of file
injection_pipeline/Catalog/C6_SimCat.py 0 → 100644
View file @ de19281e
import os
import galsim
import random
import numpy as np
import h5py as h5
import healpy as hp
import astropy.constants as cons
import traceback
from astropy.coordinates import spherical_to_cartesian
from astropy.table import Table
from scipy import interpolate
from datetime import datetime
from ObservationSim.MockObject import CatalogBase, Star, Galaxy, Quasar
from ObservationSim.MockObject._util import tag_sed, getObservedSED, getABMAG, integrate_sed_bandpass, comoving_dist
from ObservationSim.Astrometry.Astrometry_util import on_orbit_obs_position
# (TEST)
from astropy.cosmology import FlatLambdaCDM
from astropy import constants
from astropy import units as U
try:
import importlib.resources as pkg_resources
except ImportError:
# Try backported to PY<37 'importlib_resources'
import importlib_resources as pkg_resources
# CONSTANTS
NSIDE = 128
def get_bundleIndex(healpixID_ring, bundleOrder=4, healpixOrder=7):
assert NSIDE == 2**healpixOrder
shift = healpixOrder - bundleOrder
shift = 2*shift
nside_bundle = 2**bundleOrder
nside_healpix= 2**healpixOrder
healpixID_nest= hp.ring2nest(nside_healpix, healpixID_ring)
bundleID_nest = (healpixID_nest >> shift)
bundleID_ring = hp.nest2ring(nside_bundle, bundleID_nest)
return bundleID_ring
class SimCat(CatalogBase):
def __init__(self, config, chip, nobjects=None):
super().__init__()
self.cat_dir = os.path.join(config["data_dir"], config["catalog_options"]["input_path"]["cat_dir"])
self.seed_Av = config["catalog_options"]["seed_Av"]
self.cosmo = FlatLambdaCDM(H0=67.66, Om0=0.3111)
with pkg_resources.path('Catalog.data', 'SLOAN_SDSS.g.fits') as filter_path:
self.normF_star = Table.read(str(filter_path))
self.config = config
self.chip = chip
galaxy_dir = config["catalog_options"]["input_path"]["galaxy_cat"]
self.galaxy_path = os.path.join(self.cat_dir, galaxy_dir)
self.galaxy_SED_path = os.path.join(config["data_dir"], config["catalog_options"]["SED_templates_path"]["galaxy_SED"])
self._load_SED_lib_gals()
if "rotateEll" in config["catalog_options"]:
self.rotation = float(int(config["catalog_options"]["rotateEll"]/45.))
else:
self.rotation = 0.
self._get_healpix_list()
self._load(nobjects=nobjects)
def _get_healpix_list(self):
self.sky_coverage = self.chip.getSkyCoverageEnlarged(self.chip.img.wcs, margin=0.2)
ra_min, ra_max, dec_min, dec_max = self.sky_coverage.xmin, self.sky_coverage.xmax, self.sky_coverage.ymin, self.sky_coverage.ymax
ra = np.deg2rad(np.array([ra_min, ra_max, ra_max, ra_min]))
dec = np.deg2rad(np.array([dec_max, dec_max, dec_min, dec_min]))
self.pix_list = hp.query_polygon(
NSIDE,
hp.ang2vec(np.radians(90.) - dec, ra),
inclusive=True
)
if self.logger is not None:
msg = str(("HEALPix List: ", self.pix_list))
self.logger.info(msg)
else:
print("HEALPix List: ", self.pix_list)
def load_norm_filt(self, obj):
if obj.type == "star":
return self.normF_star
elif obj.type == "galaxy" or obj.type == "quasar":
return None
else:
return None
def _load_SED_lib_gals(self):
pcs = h5.File(os.path.join(self.galaxy_SED_path, "pcs.h5"), "r")
lamb = h5.File(os.path.join(self.galaxy_SED_path, "lamb.h5"), "r")
self.lamb_gal = lamb['lamb'][()]
self.pcs = pcs['pcs'][()]
def _load_gals(self, gals, pix_id=None, cat_id=0, nobjects=1):
# Load how mnay objects?
max_ngals = len(gals['ra'])
remain = nobjects
for igals in range(max_ngals):
if remain == 0:
break
param = self.initialize_param()
param['ra'] = ra_arr[igals]
param['dec'] = dec_arr[igals]
param['ra_orig'] = gals['ra'][igals]
param['dec_orig'] = gals['dec'][igals]
# [TODO]
param['mag_use_normal'] = gals['mag_csst_%s'%(self.filt.filter_type)][igals]
# if self.filt.is_too_dim(mag=param['mag_use_normal'], margin=self.config["obs_setting"]["mag_lim_margin"]):
# continue
param['z'] = gals['redshift'][igals]
param['model_tag'] = 'None'
param['g1'] = gals['shear'][igals][0]
param['g2'] = gals['shear'][igals][1]
param['kappa'] = gals['kappa'][igals]
param['e1'] = gals['ellipticity_true'][igals][0]
param['e2'] = gals['ellipticity_true'][igals][1]
# For shape calculation
param['ell_total'] = np.sqrt(param['e1']**2 + param['e2']**2)
if param['ell_total'] > 0.9:
continue
remain -= 1
param['e1_disk'] = param['e1']
param['e2_disk'] = param['e2']
param['e1_bulge'] = param['e1']
param['e2_bulge'] = param['e2']
param['delta_ra'] = 0
param['delta_dec'] = 0
# Masses
param['bulgemass'] = gals['bulgemass'][igals]
param['diskmass'] = gals['diskmass'][igals]
param['size'] = gals['size'][igals]
if param['size'] > self.max_size:
self.max_size = param['size']
# Sersic index
param['disk_sersic_idx'] = 1.
param['bulge_sersic_idx'] = 4.
# Sizes
param['bfrac'] = param['bulgemass']/(param['bulgemass'] + param['diskmass'])
if param['bfrac'] >= 0.6:
param['hlr_bulge'] = param['size']
param['hlr_disk'] = param['size'] * (1. - param['bfrac'])
else:
param['hlr_disk'] = param['size']
param['hlr_bulge'] = param['size'] * param['bfrac']
# SED coefficients
param['coeff'] = gals['coeff'][igals]
param['detA'] = gals['detA'][igals]
# Others
param['galType'] = gals['type'][igals]
param['veldisp'] = gals['veldisp'][igals]
# TEST no redening and no extinction
param['av'] = 0.0
param['redden'] = 0
param['star'] = 0 # Galaxy
# TEMP
self.ids += 1
# param['id'] = self.ids
param['id'] = '%06d'%(int(pix_id)) + '%06d'%(cat_id) + '%08d'%(igals)
if param['star'] == 0:
obj = Galaxy(param, self.rotation, logger=self.logger)
self.objs.append(obj)
return remain
def _load(self, nobjects=1):
from itertools import cycle
self.objs = []
self.ids = 0
to_be_read_in = nobjects
pool = cycle(self.pix_list)
for pix in pool:
try:
if to_be_read_in == 0:
break
bundleID = get_bundleIndex(pix)
file_path = os.path.join(self.galaxy_path, "galaxies_C6_bundle{:06}.h5".format(bundleID))
gals_cat = h5.File(file_path, 'r')['galaxies']
gals = gals_cat[str(pix)]
to_be_read_in = self._load_gals(gals, pix_id=pix, cat_id=bundleID, n_objects=to_be_read_in)
del gals
except Exception as e:
traceback.print_exc()
print(e)
def load_sed(self, obj, **kwargs):
factor = 10**(-.4 * self.cosmo.distmod(obj.z).value)
flux = np.matmul(self.pcs, obj.coeff) * factor
# if np.any(flux < 0):
# raise ValueError("Glaxy %s: negative SED fluxes"%obj.id)
flux[flux < 0] = 0.
sedcat = np.vstack((self.lamb_gal, flux)).T
sed_data = getObservedSED(
sedCat=sedcat,
redshift=obj.z,
av=obj.param["av"],
redden=obj.param["redden"]
)
wave, flux = sed_data[0], sed_data[1]
speci = interpolate.interp1d(wave, flux)
lamb = np.arange(2000, 11001+0.5, 0.5)
y = speci(lamb)
# erg/s/cm2/A --> photon/s/m2/A
all_sed = y * lamb / (cons.h.value * cons.c.value) * 1e-13
sed = Table(np.array([lamb, all_sed]).T, names=('WAVELENGTH', 'FLUX'))
del wave
del flux
return sed
\ No newline at end of file
injection_pipeline/SingleEpochImage.py
View file @ de19281e
...@@ -57,23 +57,6 @@ class SingleEpochImage(object): ...@@ -57,23 +57,6 @@ class SingleEpochImage(object):
self.setup_image_for_injection() self.setup_image_for_injection()
# [TODO] Header should come from input image
# self.header_ext = generateExtensionHeader(
# chip=self.chip,
# xlen=self.chip.npix_x,
# ylen=self.chip.npix_y,
# ra=self.ra_cen,
# dec=self.dec_cen,
# pa=self.pos_ang,
# gain=self.chip.gain, # {TODO}
# readout=self.chip.read_noise,
# dark=self.chip.dark_noise,
# saturation=90000,
# pixel_scale=self.chip.pix_scale,
# pixel_size=self.chip.pix_size,
# xcen=self.chip.x_cen,
# ycen=self.chip.y_cen,
# extName = 'raw')
self.header_ext = self.header_img self.header_ext = self.header_img
# (TODO) specify sub-directory # (TODO) specify sub-directory
...@@ -95,10 +78,12 @@ class SingleEpochImage(object): ...@@ -95,10 +78,12 @@ class SingleEpochImage(object):
def setup_image_for_injection(self): def setup_image_for_injection(self):
self.ra_cen = self.wcs.wcs.crval[0] self.ra_cen = self.wcs.wcs.crval[0]
self.dec_cen = self.wcs.wcs.crval[1] self.dec_cen = self.wcs.wcs.crval[1]
self.wcs_fp = self.focal_plane.getTanWCS(self.ra_cen, self.dec_cen, self.pos_ang*galsim.degrees, self.pixel_scale) # self.wcs_fp = self.focal_plane.getTanWCS(self.ra_cen, self.dec_cen, self.pos_ang*galsim.degrees, self.pixel_scale)
self.chip.img = galsim.Image(self.image, copy=True) self.chip.img = galsim.Image(self.image, copy=True)
self.chip.img.setOrigin(self.chip.bound.xmin, self.chip.bound.ymin) self.chip.img.setOrigin(self.chip.bound.xmin, self.chip.bound.ymin)
self.chip.img.wcs = self.wcs_fp # self.chip.img.wcs = self.wcs_fp
# self.chip.img.setOrigin(0, 0)
self.chip.img.wcs = galsim.AstropyWCS(wcs=self.wcs)
def read_initial_image(self, filepath): def read_initial_image(self, filepath):
data_dir = os.path.dirname(filepath) data_dir = os.path.dirname(filepath)
...@@ -110,15 +95,20 @@ class SingleEpochImage(object): ...@@ -110,15 +95,20 @@ class SingleEpochImage(object):
hdu.close() hdu.close()
# Determine which CCD # Determine which CCD
self.chip_ID = int(self.header0['DETECTOR'][-2:]) # self.chip_ID = int(self.header0['DETECTOR'][-2:])
self.chip_ID = int(self.header_img["CHIPID"])
# Construnct Chip object # Construnct Chip object
self.chip = Chip(chipID=self.chip_ID, config=self.config) self.chip = Chip(chipID=self.chip_ID, config=self.config)
self.exp_time = float(self.header0 ['EXPTIME']) self.exp_time = float(self.header0 ['EXPTIME'])
self.chip.gain = float(self.header_img["GAIN1"]) # self.chip.gain = float(self.header_img["GAIN1"])
self.chip.gain = float(self.header_img["GAIN01"])
# Process L1 image # Process L1 image
# self.image = self.image * self.exp_time / self.chip.gain self.image = self.image * self.exp_time / self.chip.gain
self.image = self.image * self.chip.gain print(self.chip.gain, self.exp_time)
print(self.image)
print(np.sum(self.image < 0), np.sum(self.image > 0))
# self.image = self.image * self.chip.gain
# Process L1 SKY image # Process L1 SKY image
# [TODO] # [TODO]
...@@ -128,7 +118,8 @@ class SingleEpochImage(object): ...@@ -128,7 +118,8 @@ class SingleEpochImage(object):
# image -= sky_img # image -= sky_img
# Process L1 FLAG image # Process L1 FLAG image
flag_img_path = os.path.join(data_dir, img_name.replace("img_L1", "flg_L1")) # flag_img_path = os.path.join(data_dir, img_name.replace("img_L1", "flg_L1"))
flag_img_path = os.path.join(data_dir, img_name.replace("IMG", "FLG"))
flag_img = fits.getdata(flag_img_path) flag_img = fits.getdata(flag_img_path)
self.image[flag_img > 0] = 0. self.image[flag_img > 0] = 0.
...@@ -143,7 +134,7 @@ class SingleEpochImage(object): ...@@ -143,7 +134,7 @@ class SingleEpochImage(object):
# hdu1.writeto(fname, output_verify='ignore', overwrite=True) # hdu1.writeto(fname, output_verify='ignore', overwrite=True)
def _get_wcs(self, header): def _get_wcs(self, header):
self.pos_ang = float(header['POS_ANG']) # self.pos_ang = float(header['POS_ANG'])
self.wcs = wcs.WCS(header) self.wcs = wcs.WCS(header)
self.pixel_scale = 0.074 self.pixel_scale = 0.074
...@@ -186,7 +177,7 @@ class SingleEpochImage(object): ...@@ -186,7 +177,7 @@ class SingleEpochImage(object):
try: try:
sed_data = cat.load_sed(obj) sed_data = cat.load_sed(obj)
norm_filt = cat.load_norm_filt(obj) norm_filt = cat.load_norm_filt(obj)
obj.sed, obj.param["mag_%s"%self.filt.filter_type], obj.param["flux_%s"%self.filt.filter_type] = cat.convert_sed( obj.sed, obj.param["mag_%s"%self.filt.filter_type.lower()], obj.param["flux_%s"%self.filt.filter_type.lower()] = cat.convert_sed(
mag=obj.param["mag_use_normal"], mag=obj.param["mag_use_normal"],
sed=sed_data, sed=sed_data,
target_filt=self.filt, target_filt=self.filt,
...@@ -233,9 +224,9 @@ class SingleEpochImage(object): ...@@ -233,9 +224,9 @@ class SingleEpochImage(object):
self.chip.img = galsim.Image(self.chip.img.array, dtype=np.float32) self.chip.img = galsim.Image(self.chip.img.array, dtype=np.float32)
# [TODO] TEST # [TODO] TEST
# self.chip.img *= self.chip.gain self.chip.img *= self.chip.gain
# self.chip.img /= self.exp_time self.chip.img /= self.exp_time
self.chip.img /= self.chip.gain # self.chip.img /= self.chip.gain
hdu1 = fits.PrimaryHDU(header=self.header0) hdu1 = fits.PrimaryHDU(header=self.header0)
hdu2 = fits.ImageHDU(self.chip.img.array, header=self.header_img) hdu2 = fits.ImageHDU(self.chip.img.array, header=self.header_img)
......
measurement_pipeline/L1_pipeline/csst_msc_mbi_instrument.py 0 → 100644
View file @ de19281e
from datetime import datetime
from typing import Optional
import numpy as np
from itertools import product
from astropy.io import fits
from astropy.stats import sigma_clipped_stats
SATURATE = 50000
def core_msc_l1_mbi_instrument(
image_path: str = "/path/to/image",
bias_path: str = "/path/to/bias",
dark_path: str = "/path/to/dark",
flat_path: str = "/path/to/flat",
shutter_path: str = "/path/to/shutter",
image_output_path: str = "/path/to/image_output",
weight_output_path: str = "/path/to/weight_output",
flag_output_path: str = "/path/to/flag_output",
deepcr_model_path: Optional[str] = None,
config_ccd_info: Optional[str] = None,
config_bad_pixel: Optional[str] = None,
device: Optional[str] = "CPU",
):
"""
Make the instrument correction for one chip of CSST data.
This function corrects the instrument effect, such as bias, dark, flat, shutter, cosmicrays etc.
Parameters
----------
image_path : str
The sci file path.
bias_path : str
The bias file path.
dark_path : str
The dark file path.
flat_path : str
The flat file path.
shutter_path : str
The shutter file path.
image_output_path : str
The image output file path.
weight_output_path : str
The weight output file path.
flag_output_path : str
The flag output file path.
deepcr_model_path : str, optional
The deepcr model file path.
config_ccd_info : str, optional
The config for ccd file path.
config_bad_pixel : str, optional
The config for bad pixel file path.
device : str
The deepcr device input 'CPU' or 'GPU', by default 'CPU'.
Returns
-------
CsstResult
Result containing `status` and `files`.
"""
status_header = _set_status_header()
# CCD image processing
with fits.open(image_path) as raw:
hdu = fits.HDUList([
fits.PrimaryHDU(
header=raw[0].header.copy()),
fits.ImageHDU(
header=raw[1].header.copy(),
data=raw[1].data.copy())
])
# 创建wht[1].data hdulist
wht = fits.HDUList([
fits.PrimaryHDU(
header=raw[0].header.copy()),
fits.ImageHDU(
header=raw[1].header.copy(),
data=np.zeros_like(hdu[1].data, dtype=np.float32))
])
# 创建flg[1].data hdulist
flg = fits.HDUList([
fits.PrimaryHDU(
header=raw[0].header.copy()),
fits.ImageHDU(
header=raw[1].header.copy(),
data=np.zeros_like(hdu[1].data, dtype=np.uint32))])
img_err = np.zeros_like(hdu[1].data)
# shutter
shutter = fits.getdata(shutter_path, 1)
bias, bias_error = _read_ccds(bias_path)
dark, dark_error = _read_ccds(dark_path)
flat, flat_error = _read_ccds(flat_path)
hdu[1].data, img_err = subtract_bias(
hdu[1].data,
img_err,
bias,
bias_error,
)
status_header.set('STA_BIAS', 0)
hdu[1].data, img_err = subtract_dark(
hdu[1].data,
img_err,
dark,
dark_error,
hdu[0].header["EXPTIME"]
)
status_header.set('STA_DARK', 0)
hdu[1].data, img_err = _divide_flat(
hdu[1].data,
img_err,
flat,
flat_error,
)
status_header.set('STA_FLAT', 0)
# 非线性改正
hdu[1].data = _fix_nonlinear(hdu[1].data)
status_header.set('STA_NLIN', 0)
# 坏像素检测
flg[1].data = _check_badpix(
flg[1].data,
flat
)
# 热像素和暖像素检测
flg[1].data = _check_hot_and_warm_pix(
flg[1].data,
dark,
hdu[0].header["EXPTIME"],
hdu[1].header["RON01"]
)
# 过饱和检测
saturate = SATURATE
flg[1].data = _check_over_saturation(
flg[1].data,
hdu[1].data,
saturate
)
saturate = (saturate
* hdu[1].header["GAIN01"]
/ hdu[0].header["EXPTIME"])
status_header.set('SATURATE', saturate)
# 时间归一
hdu[1].data = hdu[1].data/hdu[0].header["EXPTIME"]
# 单位转换
hdu[1].data = _multiply_gain_map(hdu[1].data, hdu[1].header)
status_header.set('BUNIT', 'e/s')
# # 检测宇宙线
# hdu[1].data, flg[1].data, cr_count = remove_cr_deepcr(
# hdu[1].data,
# flg[1].data,
# device,
# hdu[1].header['CHIPID'],
# )
# status_header.set('STA_CRS', 0)
# status_header.set('CRCOUNT', cr_count)
# 记录BKG与RMS
_, bkg, rms = sigma_clipped_stats(
data=hdu[1].data,
mask=(flg[1].data == 16)
)
status_header.set('SKY_BKG', bkg)
status_header.set('SKY_RMS', rms)
# 检测卫星拖尾
flg[1].data = _check_satellitetrail(
flg[1].data,
hdu[1].data
)
status_header.set('STA_SAT', 0)
# 生成wht[1].data
wht[1].data = _create_weight(
hdu[1].data,
flg[1].data,
hdu[1].header["RON01"],
hdu[0].header["EXPTIME"]
)
# 数据类型
hdu[1].data = hdu[1].data.astype(np.float32)
# 快门改正
hdu[1].data = _fix_shutter(
hdu[1].data,
shutter,
hdu[0].header["EXPTIME"]
)
status_header.set('STA_SHUT', 0)
if all([status_header["STA_BIAS"] == 0,
status_header["STA_DARK"] == 0,
status_header["STA_FLAT"] == 0,
status_header["STA_CRS"] == 0,
status_header["STA_NLIN"] == 0,
# status_header["STA_CTE"] == 0, # 这个功能还未添加
status_header["STA_SAT"] == 0,
status_header["STA_SHUT"] == 0,
status_header["SKY_BKG"] != -9999,
status_header["SKY_RMS"] != -9999,
status_header["SATURATE"] != -9999,
status_header["CRCOUNT"] != -9999]):
status_header.set('STA_INST', 0)
# hdu[1].header.extend(status_header, bottom=True)
# 修正wht和flg数据类型
wht[1].header = hdu[1].header.copy()
wht[1].data = wht[1].data.astype(np.float32)
wht[1].header.remove('BUNIT')
flg[1].header = hdu[1].header.copy()
flg[1].data = flg[1].data.astype(np.int32)
flg[1].header.remove('BUNIT')
hdu.writeto(image_output_path, overwrite=True)
wht.writeto(weight_output_path, overwrite=True)
flg.writeto(flag_output_path, overwrite=True)
def _set_status_header():
status_header = fits.Header() # template Header
status_header.append(
('COMMENT', '=' * 66), bottom=True)
status_header.append(
('COMMENT', 'instrumental correction information'), bottom=True)
status_header.append(
('COMMENT', '=' * 66), bottom=True)
# status_header.append(
# ('VER_INST', csst_common.__version__, 'version of instrument'), bottom=True)
# status_header.append(
# ('STM_INST', csst_common.now(), 'time stamp of instrument processing'), bottom=True)
status_header.append(
('STA_INST', 1, '0=done 1=wrong'), bottom=True)
status_header.append(
('STA_BIAS', 1, 'status flag for bias frame correction'), bottom=True)
status_header.append(
('STA_DARK', 1, 'status flag for dark frame correction'), bottom=True)
status_header.append(
('STA_FLAT', 1, 'status flag for flat frame correction'), bottom=True)
status_header.append(
('SKY_BKG', -9999, 'estimated sky background (e-/s per pixel)'), bottom=True)
status_header.append(
('SKY_RMS', -9999, 'standard dev of frame background (ADU)-> e-/s'), bottom=True)
status_header.append(
('SATURATE', -9999, 'the flux limit of saturated pixel (e-/s)'), bottom=True)
status_header.append(
('STA_CTE', 1, 'status flag for CTE correction'), bottom=True)
status_header.append(
('STA_SAT', 1, 'status flag for satellite correction'), bottom=True)
status_header.append(
('STA_CRS', 1, 'status flag for cosmic rays mask'), bottom=True)
status_header.append(
('CRCOUNT', -9999, 'cosmic rays pixel counts'), bottom=True)
status_header.append(
('STA_NLIN', 1, 'status flag for non-linear correction'), bottom=True)
status_header.append(
('STA_SHUT', 1, 'status flag for shutter effect correction'), bottom=True)
return status_header
def _read_ccds(
ref_path: str,
) -> tuple[np.array, np.array]:
ref = fits.getdata(ref_path)
return ref, np.zeros_like(ref)
def _divide_flat(
image: np.ndarray,
image_error: np.ndarray,
flat: np.ndarray,
flat_error: np.ndarray,
) -> tuple[np.ndarray, np.ndarray]:
def divide(a, b):
return np.divide(a, b, out=np.zeros_like(a, float), where=(b != 0))
result = divide(image, flat)
result_error = np.abs(result) * ((divide(image_error, image)) ** 2 +
(divide(flat_error, flat)) ** 2)**0.5
return result, result_error
def _fix_nonlinear(
image: np.ndarray,
beta1: float = 5e-7
) -> np.ndarray:
# 计算非线性系数
beta = 5e-7
f1 = [1]
fnlin0 = 1+beta
fnlin = [fnlin0]
for i in range(1000, 65000, 1000):
f1.append(i)
fnlin.append(1+beta*i)
# 插值函数
from scipy.interpolate import PchipInterpolator as PCHIP
# PCHIP stands for Piecewise Cubic Hermite Interpolating Polynomial
interp_func = PCHIP(f1, fnlin)
# 非线性系数矩阵
imgnlin = interp_func(image)
# 图像改正
image = imgnlin*image
return image
def _check_badpix(
flag: np.ndarray,
flat: np.ndarray,
) -> np.ndarray:
med = np.median(flat)
_flag = (flat < 0.5 * med) | (1.5 * med < flat)
flag = flag | (_flag * 1)
return flag
def _check_hot_and_warm_pix(
flag: np.ndarray,
dark: np.ndarray,
exptime: float,
rdnoise: float,
) -> np.ndarray:
_dark = dark * exptime
_dark[_dark < 0] = 0
_flag = 1 * rdnoise ** 2 <= _dark # 不确定是否包含 暂定包含
flag = flag | (_flag * 2)
_flag = (0.5 * rdnoise ** 2 < _dark) & (_dark < 1 * rdnoise ** 2)
flag = flag | (_flag * 4)
return flag
def _check_over_saturation(
flag: np.ndarray,
image: np.ndarray,
saturate: int = 65535,
iterations: int = 0,
flag_value: int = 8
) -> np.ndarray:
_flag = image >= saturate
flag_dilated = _dilation(_flag, iterations=iterations)
flag = flag | (flag_dilated * flag_value)
return flag
def _dilation(array_orig: np.ndarray, iterations: int = 1):
"""
Make a dilation for array
This function makes a dilation for the 2D mask array (saturated pixels)
Parameters
----------
array_orig : np.ndarray
The mask array without dilation.
structure : int
The number of dilations performed on the structure with itself.
Returns
-------
array_out : np.ndarray
The mask array after dilation.
"""
from scipy import ndimage
struct1 = ndimage.generate_binary_structure(
2, 1) # give a saturate structure
struct_ext = ndimage.iterate_structure(
struct1, iterations) # make a dilation
if iterations == 0:
array_out = array_orig
else:
array_out = ndimage.binary_dilation(
array_orig, structure=struct_ext, iterations=1).astype(array_orig.dtype)
return array_out
def _check_satellitetrail(
flag: np.ndarray,
image: np.ndarray,
) -> np.ndarray:
from skimage import exposure, transform
from skimage.draw import line
from skimage.feature import canny
# 调整图像
p1, p2 = np.percentile(image, (0.1, 99.9))
image = exposure.rescale_intensity(image, in_range=(p1, p2))
# 边界识别，转化为二值图像
immax = np.max(image)
# canny边界
edge = canny(image=image,
sigma=2.0,
low_threshold=immax * 0.1,
high_threshold=immax * 0.5)
# 概率霍夫变换
angle = np.radians(np.arange(2, 178, 0.5, dtype=float))
# 直线端点
result = transform.probabilistic_hough_line(image=edge,
threshold=210,
line_length=400,
line_gap=75,
theta=angle)
result = np.asarray(result)
# 绘制mask
mask = np.zeros(image.shape)
# 遍历每一条线段
for (x1, y1), (x2, y2) in result:
xx, yy = line(x1, y1, x2, y2)
mask[yy, xx] = 1
mask = mask.astype(np.int32)
flag = flag | (mask * 32)
return flag
def _create_weight(
img: np.ndarray,
flg: np.ndarray,
rdnoise: float,
exptime: float,
) -> np.ndarray:
data = img.copy()
data[img < 0] = 0
var_raw = data * exptime + rdnoise ** 2
var_bias = 0.0
weight = 1. / (var_raw + var_bias) * exptime ** 2
weight[flg > 0] = 0
wht = weight
return wht
def _fix_shutter(
img: np.ndarray,
shutter: np.ndarray,
exptime: float = 150.
) -> np.ndarray:
img_cor = np.float32(img * exptime*1000. / (exptime*1000. + shutter))
return img_cor
def _multiply_gain_map(
image: np.ndarray,
header: fits.header,
) -> np.ndarray:
gain_map = np.zeros_like(image)
h = header['NAXIS1'] // 8
w = header['NAXIS2'] // 2
for y, x in product(range(8), (0, 1)):
y = y if x == 0 else 7-y
gain = header['GAIN' + str(x*8+y+1).zfill(2)]
gain_map[w*x:w*(x+1), h*y:h*(y+1)] = gain
return image * gain_map
def subtract_bias(image: np.ndarray,
image_err: np.ndarray,
bias: np.ndarray,
bias_err: np.ndarray,
) -> tuple[np.ndarray, np.ndarray]:
"""
Subtract bias.
Subtract bias.
Parameters
----------
image : numpy.ndarray
The input image to be corrected.
image_err : numpy.ndarray
The uncertainty of input image.
bias : numpy.ndarray
The input bias to be subtracted.
bias_err : numpy.ndarray
The uncertainty of input bias.
Returns
-------
output : np.ndarray
Output corrected image.
output_err : np.ndarray
Output uncertainty map.
"""
output = image - bias
output_err = np.sqrt(image_err ** 2 + bias_err ** 2)
return output, output_err
def subtract_dark(image: np.ndarray,
image_err: np.ndarray,
dark: np.ndarray,
dark_err: np.ndarray,
tdark_image: float = 1.0,
) -> tuple[np.ndarray, np.ndarray]:
"""
Subtract dark current.
Subtract dark current.
Parameters
----------
image : numpy.ndarray
The input image to be corrected.
image_err : numpy.ndarray
The uncertainty of input image.
dark : numpy.ndarray
The input dark current image to be subtracted.
dark_err : numpy.ndarray
The uncertainty of input dark current.
tdark_image : float, optional
The effective dark current cumulation time of input image. Default value is 1.0.
Returns
-------
output : np.ndarray
Output corrected image.
output_err : np.ndarray
Output uncertainty map.
"""
output = image - dark * tdark_image
output_err = np.sqrt(image_err ** 2 + (dark_err * tdark_image) ** 2)
return output, output_err
measurement_pipeline/run_measurement.py
View file @ de19281e
...@@ -36,7 +36,7 @@ class MeasurementPipeline(object): ...@@ -36,7 +36,7 @@ class MeasurementPipeline(object):
self.wht_list = [line.rstrip() for line in input_list] self.wht_list = [line.rstrip() for line in input_list]
with open(self.config["measurement_setting"]["input_flg_list"]) as input_list: with open(self.config["measurement_setting"]["input_flg_list"]) as input_list:
self.flg_list = [line.rstrip() for line in input_list] self.flg_list = [line.rstrip() for line in input_list]
if self.config["measurement_setting"]["input_psf_list"]: if "input_psf_list" in self.config["measurement_setting"] and self.config["measurement_setting"]["input_psf_list"]:
with open(self.config["measurement_setting"]["input_psf_list"]) as input_list: with open(self.config["measurement_setting"]["input_psf_list"]) as input_list:
self.psf_list = [line.rstrip() for line in input_list] self.psf_list = [line.rstrip() for line in input_list]
else: else:
......
run_calculate_completeness_fraction.sh
View file @ de19281e
#!/bin/bash #!/bin/bash
python /share/home/fangyuedong/injection_pipeline/evaluation/calculate_completeness_fraction.py \ python /share/home/fangyuedong/injection_pipeline/evaluation/calculate_completeness_fraction.py \
--TU_catalog /share/home/fangyuedong/injection_pipeline/workspace/test_20230517/CSST_MSC_MS_SCI_20240817060512_20240817060742_100000000_08_img_L1_injected.cat \ --TU_catalog_list /share/home/fangyuedong/injection_pipeline/cat_INJECTED_20231203.list \
--source_catalog /share/home/fangyuedong/injection_pipeline/workspace/test_20230517/CSST_MSC_MS_SCI_20240817060512_20240817060742_100000000_08_img_L1_injected_extracted.cat \ --source_catalog_list /share/home/fangyuedong/injection_pipeline/cat_EXTRACTED_20231203.list \
--orig_catalog /share/L1Result/C6_2sq_sheared_photometry/MSC_0000000/CSST_MSC_MS_SCI_20240817060512_20240817060742_100000000_08_cat.fits \ --output_dir /share/home/fangyuedong/injection_pipeline/workspace/test_20231203
--image /share/L1Result/C6_2sq_sheared_photometry/MSC_0000000/CSST_MSC_MS_SCI_20240817060512_20240817060742_100000000_08_img_L1.fits \ # --TU_catalog /share/home/fangyuedong/injection_pipeline/workspace/test_20230517/CSST_MSC_MS_SCI_20240817060512_20240817060742_100000000_08_img_L1_injected.cat \
--output_dir /share/home/fangyuedong/injection_pipeline/workspace/test_20230517 # --source_catalog /share/home/fangyuedong/injection_pipeline/workspace/test_20230517/CSST_MSC_MS_SCI_20240817060512_20240817060742_100000000_08_img_L1_injected_extracted.cat \
\ No newline at end of file # --orig_catalog /share/L1Result/C6_2sq_sheared_photometry/MSC_0000000/CSST_MSC_MS_SCI_20240817060512_20240817060742_100000000_08_cat.fits \
# --image /share/L1Result/C6_2sq_sheared_photometry/MSC_0000000/CSST_MSC_MS_SCI_20240817060512_20240817060742_100000000_08_img_L1.fits \
# --output_dir /share/home/fangyuedong/injection_pipeline/workspace/test_20230517
\ No newline at end of file
run_injection.sh
View file @ de19281e
#!/bin/bash #!/bin/bash
python /share/home/fangyuedong/injection_pipeline/injection_pipeline/injection_pipeline.py config_injection.yaml -c /share/home/fangyuedong/injection_pipeline/config # python /share/home/fangyuedong/injection_pipeline/injection_pipeline/injection_pipeline.py config_injection.yaml -c /share/home/fangyuedong/injection_pipeline/config
\ No newline at end of file python /share/home/fangyuedong/injection_pipeline/injection_pipeline/injection_pipeline.py config_injection_20231203.yaml -c /share/home/fangyuedong/injection_pipeline/config
\ No newline at end of file
run_measurement.sh
View file @ de19281e
#!/bin/bash #!/bin/bash
python /share/home/fangyuedong/injection_pipeline/measurement_pipeline/run_measurement.py config_injection.yaml -c /share/home/fangyuedong/injection_pipeline/config # python /share/home/fangyuedong/injection_pipeline/measurement_pipeline/run_measurement.py config_injection.yaml -c /share/home/fangyuedong/injection_pipeline/config
\ No newline at end of file python /share/home/fangyuedong/injection_pipeline/measurement_pipeline/run_measurement.py config_injection_20231203.yaml -c /share/home/fangyuedong/injection_pipeline/config
\ No newline at end of file
run_injection.pbs → submit_injection.pbs
View file @ de19281e
...@@ -10,4 +10,5 @@ cd $PBS_O_WORKDIR ...@@ -10,4 +10,5 @@ cd $PBS_O_WORKDIR
NP=20 NP=20
date date
mpirun -np $NP python /share/home/fangyuedong/injection_pipeline/injection_pipeline/injection_pipeline.py config_injection.yaml -c /share/home/fangyuedong/injection_pipeline/config # mpirun -np $NP python /share/home/fangyuedong/injection_pipeline/injection_pipeline/injection_pipeline.py config_injection.yaml -c /share/home/fangyuedong/injection_pipeline/config
\ No newline at end of file mpirun -np $NP python /share/home/fangyuedong/injection_pipeline/injection_pipeline/injection_pipeline.py config_injection_20231203.yaml -c /share/home/fangyuedong/injection_pipeline/config
\ No newline at end of file
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