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Commit ddf8503d authored by Fang Yuedong's avatar Fang Yuedong
Browse files

made compatible with csst simultion v3.0

parent 568d0740
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config/config_injection.yaml
View file @ ddf8503d
......@@ -60,7 +60,7 @@ catalog_options:
###############################################
# Instrumental effects setting
# (NOTE) Here only used to construct
# ObservationSim.Instrument.Chip object
# observation_sim.instruments.chip object
# (TODO) Should readout from header
###############################################
ins_effects:
......
evaluation/calculate_completeness_fraction.py
View file @ ddf8503d
......@@ -4,7 +4,7 @@ import numpy as np
import matplotlib.pyplot as plt
from astropy.io import ascii, fits
from cross_match_catalogs import read_catalog, match_catalogs_img
from ObservationSim.Instrument import Telescope, Filter, FilterParam
from observation_sim.instruments import Telescope, Filter, FilterParam
VC_A = 2.99792458e+18 # speed of light: A/s
......@@ -22,6 +22,7 @@ VC_A = 2.99792458e+18 # speed of light: A/s
# default='./workspace', help='output path')
# return parser
def define_options():
parser = argparse.ArgumentParser()
parser.add_argument('--TU_catalog_list', dest='TU_catalog_list', type=str, required=True,
......@@ -36,29 +37,44 @@ def define_options():
default='./workspace', help='output path')
return parser
def getChipFilter(chipID, filter_layout=None):
"""Return the filter index and type for a given chip #(chipID)
"""
filter_type_list = ["nuv","u", "g", "r", "i","z","y","GU", "GV", "GI", "FGS"]
if filter_layout is not None:
return filter_layout[chipID][0], filter_layout[chipID][1]
# updated configurations
if chipID>42 or chipID<1: raise ValueError("!!! Chip ID: [1,42]")
if chipID in [6, 15, 16, 25]: filter_type = "y"
if chipID in [11, 20]: filter_type = "z"
if chipID in [7, 24]: filter_type = "i"
if chipID in [14, 17]: filter_type = "u"
if chipID in [9, 22]: filter_type = "r"
if chipID in [12, 13, 18, 19]: filter_type = "nuv"
if chipID in [8, 23]: filter_type = "g"
if chipID in [1, 10, 21, 30]: filter_type = "GI"
if chipID in [2, 5, 26, 29]: filter_type = "GV"
if chipID in [3, 4, 27, 28]: filter_type = "GU"
if chipID in range(31, 43): filter_type = 'FGS'
filter_id = filter_type_list.index(filter_type)
return filter_id, filter_type
"""Return the filter index and type for a given chip #(chipID)
"""
filter_type_list = ["nuv", "u", "g", "r",
"i", "z", "y", "GU", "GV", "GI", "FGS"]
if filter_layout is not None:
return filter_layout[chipID][0], filter_layout[chipID][1]
# updated configurations
if chipID > 42 or chipID < 1:
raise ValueError("!!! Chip ID: [1,42]")
if chipID in [6, 15, 16, 25]:
filter_type = "y"
if chipID in [11, 20]:
filter_type = "z"
if chipID in [7, 24]:
filter_type = "i"
if chipID in [14, 17]:
filter_type = "u"
if chipID in [9, 22]:
filter_type = "r"
if chipID in [12, 13, 18, 19]:
filter_type = "nuv"
if chipID in [8, 23]:
filter_type = "g"
if chipID in [1, 10, 21, 30]:
filter_type = "GI"
if chipID in [2, 5, 26, 29]:
filter_type = "GV"
if chipID in [3, 4, 27, 28]:
filter_type = "GU"
if chipID in range(31, 43):
filter_type = 'FGS'
filter_id = filter_type_list.index(filter_type)
return filter_id, filter_type
def magToFlux(mag):
"""
......@@ -73,12 +89,15 @@ def magToFlux(mag):
flux = 10**(-0.4*(mag+48.6))
return flux
def getElectronFluxFilt(mag, filt, tel, exptime=150.):
photonEnergy = filt.getPhotonE()
flux = magToFlux(mag)
factor = 1.0e4 * flux/photonEnergy * VC_A * (1.0/filt.blue_limit - 1.0/filt.red_limit)
factor = 1.0e4 * flux/photonEnergy * VC_A * \
(1.0/filt.blue_limit - 1.0/filt.red_limit)
return factor * filt.efficiency * tel.pupil_area * exptime
def convert_catalog(catname):
data_dir = os.path.dirname(catname)
base_name = os.path.basename(catname)
......@@ -86,6 +105,7 @@ def convert_catalog(catname):
fits_filename = os.path.join(data_dir, base_name + '.fits')
text_file.write(fits_filename, overwrite=True)
def validation_hist(val, idx, name="val", nbins=10, bins=None, fig_name='detected_counts.png', output_dir='./', create_figure=True):
if bins is None:
counts, bins = np.histogram(val, bins=nbins)
......@@ -100,9 +120,11 @@ def validation_hist(val, idx, name="val", nbins=10, bins=None, fig_name='detecte
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)
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.stairs(counts, bins, color='r', label='TU objects')
......@@ -113,6 +135,7 @@ def create_hist_figure(counts, counts_detected, bins, name="val", output_dir='./
fig_name = os.path.join(output_dir, fig_name)
plt.savefig(fig_name)
def hist_fraction(val, idx, name='val', nbins=10, bins=None, output_dir='./', fig_name="completeness_fraction.png"):
if bins is None:
counts, bins = np.histogram(val, bins=nbins)
......@@ -136,6 +159,7 @@ def hist_fraction(val, idx, name='val', nbins=10, bins=None, output_dir='./', fi
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[np.where(np.isnan(fraction))[0]] = 0.
......@@ -147,16 +171,23 @@ def create_fraction_figure(counts, counts_detected, bins, name='val', output_dir
plt.savefig(fig_name)
return fraction
def calculate_fraction(TU_catalog, source_catalog, output_dir, nbins=10):
convert_catalog(TU_catalog)
x_TU, y_TU, col_list = read_catalog(TU_catalog + '.fits', ext_num=1, ra_name="xImage", dec_name="yImage", col_list=["mag"])
x_TU, y_TU, col_list = read_catalog(
TU_catalog + '.fits', ext_num=1, ra_name="xImage", dec_name="yImage", col_list=["mag"])
mag_TU = col_list[0]
x_source, y_source, _ = read_catalog(source_catalog, ext_num=1, ra_name="X_IMAGE", dec_name="Y_IMAGE")
idx1, idx2, = match_catalogs_img(x1=x_TU, y1=y_TU, x2=x_source, y2=y_source)
counts, bins = validation_hist(val=mag_TU, idx=idx1, name="mag_injected", output_dir=output_dir)
fraction = hist_fraction(val=mag_TU, idx=idx1, name="mag_injected", nbins=10, output_dir=output_dir)
x_source, y_source, _ = read_catalog(
source_catalog, ext_num=1, ra_name="X_IMAGE", dec_name="Y_IMAGE")
idx1, idx2, = match_catalogs_img(
x1=x_TU, y1=y_TU, x2=x_source, y2=y_source)
counts, bins = validation_hist(
val=mag_TU, idx=idx1, name="mag_injected", 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
def calculate_fraction_multi_cats(TU_catalog_list, source_catalog_list, output_dir, nbins=10):
counts = np.zeros(nbins)
counts_detected = np.zeros(nbins)
......@@ -165,17 +196,24 @@ def calculate_fraction_multi_cats(TU_catalog_list, source_catalog_list, output_d
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"])
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)
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)
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='./'):
# Get info from original image
hdu = fits.open(image)
......@@ -189,11 +227,12 @@ def calculate_undetected_flux(orig_cat, mag_bins, fraction, mag_low=20.0, mag_hi
hdu.close()
# Get info from original catalog
ra_orig, dec_orig, col_list_orig = read_catalog(orig_cat, ra_name='RA', dec_name='DEC', col_list=['Mag_Kron'])
ra_orig, dec_orig, col_list_orig = read_catalog(
orig_cat, ra_name='RA', dec_name='DEC', col_list=['Mag_Kron'])
mag_orig = col_list_orig[0]
nbins = len(mag_bins) - 1
counts, _ = np.histogram(mag_orig, bins=nbins)
mags = (mag_bins[:-1] + mag_bins[1:])/2.
counts_missing = (counts / fraction) - counts
counts_missing[np.where(np.isnan(counts_missing))[0]] = 0.
......@@ -213,14 +252,15 @@ def calculate_undetected_flux(orig_cat, mag_bins, fraction, mag_low=20.0, mag_hi
filter_id, filter_type = getChipFilter(chipID=chipID)
filt = Filter(filter_id=filter_id,
filter_type=filter_type,
filter_param=filter_param)
filter_type=filter_type,
filter_param=filter_param)
undetected_flux = 0.
for i in range(len(mags)):
if mags[i] < mag_low or mags[i] > mag_high:
continue
flux_electrons = counts_missing[i] * getElectronFluxFilt(mag=mags[i], filt=filt, tel=tel)
flux_electrons = counts_missing[i] * \
getElectronFluxFilt(mag=mags[i], filt=filt, tel=tel)
undetected_flux += flux_electrons
undetected_flux /= (float(nx_pix) * float(ny_pix))
......@@ -243,6 +283,7 @@ def calculate_undetected_flux(orig_cat, mag_bins, fraction, mag_low=20.0, mag_hi
# )
# print(undetected_flux)
if __name__ == "__main__":
args = define_options().parse_args()
with open(args.TU_catalog_list) as file:
......@@ -254,4 +295,4 @@ if __name__ == "__main__":
source_catalog_list=source_catalog_list,
output_dir=args.output_dir,
nbins=20
)
\ No newline at end of file
)
evaluation/evaluation_utils.py 0 → 100644
View file @ ddf8503d
from astropy.wcs import WCS
from astropy.io import ascii, fits
from matplotlib.colors import LogNorm
from scipy.stats import binned_statistic
from astropy.visualization import ZScaleInterval
import os
import numpy as np
import matplotlib.pyplot as plt
from cross_match_catalogs import match_catalogs_img
def plot_injection_comparison(orig_img, injected_img, flg_img=None, save_fig_dir=None, fig_prefix="", figsize=(12, 8)):
z = ZScaleInterval()
plt.figure(figsize=figsize, dpi=100)
hdu_orig = fits.open(orig_img)[1]
data_orig = hdu_orig.data
if flg_img is not None:
flg_data = fits.getdata(flg_img)
data_orig[flg_data > 0] = 0.
wcs = WCS(hdu_orig.header)
plt.subplot(projection=wcs)
z1, z2 = z.get_limits(data_orig)
plt.imshow(data_orig, origin='lower', cmap='gray', vmin=z1, vmax=z2)
plt.grid(color='white', ls='solid')
if save_fig_dir is not None:
output_filename = fig_prefix + "original_img.png"
output_img_path = os.path.join(save_fig_dir, output_filename)
plt.savefig(output_img_path)
plt.show()
plt.figure(figsize=figsize, dpi=100)
hdu_inj = fits.open(injected_img)[1]
data_inj = hdu_inj.data
if flg_img is not None:
data_inj[flg_data > 0] = 0.
wcs = WCS(hdu_inj.header)
plt.subplot(projection=wcs)
z1, z2 = z.get_limits(data_inj)
plt.imshow(data_inj, origin='lower', cmap='gray', vmin=z1, vmax=z2)
plt.grid(color='white', ls='solid')
if save_fig_dir is not None:
output_filename = fig_prefix + "injected_img.png"
output_img_path = os.path.join(save_fig_dir, output_filename)
plt.savefig(output_img_path)
plt.show()
plt.figure(figsize=figsize, dpi=100)
plt.subplot(projection=wcs)
img_diff = data_inj - data_orig
z1 = 0.
z2 = 0.001
plt.imshow(img_diff, origin='lower', cmap='gray', vmin=z1, vmax=z2)
plt.grid(color='white', ls='solid')
if save_fig_dir is not None:
output_filename = fig_prefix + "diff_img.png"
output_img_path = os.path.join(save_fig_dir, output_filename)
plt.savefig(output_img_path)
plt.show()
def plot_ensemble_hist(cat_path_list, column_name="Mag_Kron", column_unit="mag", title="Total KRON MAG distribution", save_fig_dir=None, fig_prefix="",
nbins=50, low=16., high=28., density=False):
values = []
bins = np.linspace(low, high, nbins+1)
for cat_path in cat_path_list:
if cat_path.endswith(".fits"):
hdu = fits.open(cat_path)
value_temp = hdu[1].data[column_name]
elif cat_path.endswith(".cat"):
data = ascii.read(cat_path)
value_temp = data[column_name]
print("number of objects in %s: %d" %
(os.path.basename(cat_path), len(value_temp)))
values = np.append(values, value_temp)
plt.figure()
plt.hist(values, bins=bins, density=density)
plt.xlabel(column_name + '/' + column_unit, size='x-large')
if density is False:
plt.ylabel("Counts", size='x-large')
plt.title(title, size='x-large')
if save_fig_dir is not None:
output_filename = fig_prefix + "%s_ensemble_hist.png" % (column_name)
output_img_path = os.path.join(save_fig_dir, output_filename)
plt.savefig(output_img_path)
plt.show()
def create_hist_figure(counts, counts_detected, bins, name="val", output_dir='./', fig_name='detected_counts.png', save_figure=False, title=None):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_xlabel(name, size='x-large')
ax.set_ylabel("Counts", size='x-large')
if title is not None:
ax.set_title(title, size='x-large')
ax.stairs(counts, bins, color='r', label='TU objects')
ax.stairs(counts_detected, bins, color='g', label='Detected')
ax.legend(loc='upper right', fancybox=True)
if save_figure:
fig_name = os.path.join(output_dir, fig_name)
fig.savefig(fig_name)
return fig, ax
def create_fraction_figure(counts, counts_detected, bins, name='val', output_dir='./', fig_name="completeness_fraction.png",
save_figure=False, title=None, figure=None, color='r', label='patch_1', show_legend=False):
fraction = counts_detected / counts
fraction[np.where(np.isnan(fraction))[0]] = 0.
if figure is not None:
fig = figure
ax = fig.axes[0]
ax.stairs(fraction, bins, color=color, label=label)
if title is not None:
ax.set_title(title, size='x-large')
else:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.stairs(fraction, bins, color=color, label=label)
ax.set_xlabel(name, size='x-large')
if title is not None:
ax.set_title(title, size='x-large')
else:
ax.set_title("Completeness Fraction")
if show_legend:
ax.legend(loc='upper right', fancybox=True)
if save_figure:
fig_name = os.path.join(output_dir, fig_name)
fig.savefig(fig_name)
return fig, ax, fraction
def validation_hist(val, idx, name="val", nbins=10, bins=None, fig_name='detected_counts.png', output_dir='./', create_figure=True):
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)
for i in range(len(idx)):
if idx[i].size == 0:
is_empty[i] = True
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 plot_mag_comparison(truth_cat_list, measured_cat_root_dir, mag1_name="mag", mag2_name="Mag_Kron", save_fig_dir=None, fig_prefix="",
nbins=20, low=18., high=26., ylim=[-1., 1.], title=None):
diff_list = []
truth_list = []
bins = np.linspace(low, high, nbins+1)
for cat_path_truth in truth_cat_list:
print("Injected truth catalog: ",
os.path.basename(cat_path_truth))
obs_id = cat_path_truth.split('/')[-2]
# Read truth catalog
data = ascii.read(cat_path_truth)
x_truth = data["xImage"]
y_truth = data["yImage"]
mag_truth = data[mag1_name]
# Read measured catalog
cat_path_measured = os.path.join(measured_cat_root_dir,
obs_id,
os.path.basename(cat_path_truth).replace("img", "cat").replace(".cat", ".fits"))
print("L1 processed photometry catalog: ",
os.path.basename(cat_path_truth))
hdu = fits.open(cat_path_measured)
x_measure = hdu[1].data["X"]
y_measure = hdu[1].data["Y"]
mag_measure = hdu[1].data["Mag_Kron"]
# Match measured objects vs truth
idx1, _, = match_catalogs_img(
x1=x_truth, y1=y_truth, x2=x_measure, y2=y_measure)
for i in range(len(idx1)):
if idx1[i].size == 0:
continue
else:
diff_list.append(mag_measure[idx1[i][0]] - mag_truth[i])
truth_list.append(mag_truth[i])
bin_means, bin_edges, binnumber = binned_statistic(truth_list, diff_list, 'mean',
bins=nbins, range=[low, high])
bin_median, bin_edges, binnumber = binned_statistic(truth_list, diff_list, 'median',
bins=nbins, range=[low, high])
bin_std, bin_edges, binnumber = binned_statistic(truth_list, diff_list, 'std',
bins=nbins, range=[low, high])
bin_width = (bin_edges[1] - bin_edges[0])
bin_centers = bin_edges[1:] - bin_width/2
plt.figure()
plt.plot(truth_list, diff_list, 'ro', alpha=0.1)
plt.axhline(y=0., color='k', alpha=0.6)
plt.plot(bin_centers, bin_median, '--',
label=r'$\rm{median}\ \Delta mag$', alpha=0.6)
plt.errorbar(bin_centers, bin_means, yerr=bin_std, fmt='bo', capsize=2,
label=r'$\rm{mean}\ \Delta mag$', alpha=0.6)
plt.xlim([low, high])
plt.ylim(ylim)
plt.xlabel("True mag", size='x-large')
plt.ylabel("Measured (Kron) - True mag", size='x-large')
plt.legend(loc='upper left', fancybox=True)
if title is not None:
plt.title(title, size='x-large')
if save_fig_dir is not None:
output_filename = fig_prefix + "measured-true_mag.png"
output_img_path = os.path.join(save_fig_dir, output_filename)
plt.savefig(output_img_path)
plt.show()
evaluation/photometry_evaluation.ipynb
View file @ ddf8503d
This source diff could not be displayed because it is too large. You can view the blob instead.
injection_pipeline/Catalog/C6_SimCat.py
View file @ ddf8503d
......@@ -12,9 +12,9 @@ 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
from observation_sim.mock_objects import CatalogBase, Star, Galaxy, Quasar
from observation_sim.mock_objects._util import tag_sed, getObservedSED, getABMAG, integrate_sed_bandpass, comoving_dist
from observation_sim.astrometry.Astrometry_util import on_orbit_obs_position
# (TEST)
from astropy.cosmology import FlatLambdaCDM
......@@ -110,9 +110,11 @@ class SimCat(CatalogBase):
# Load how mnay objects?
max_ngals = len(gals['ra'])
remain = nobjects
for igals in range(max_ngals):
for i in range(max_ngals):
if remain == 0:
break
igals = random.randint(0, len(gals['ra']))
param = self.initialize_param()
param['ra'] = gals['ra'][igals]
param['dec'] = gals['dec'][igals]
......@@ -135,8 +137,6 @@ class SimCat(CatalogBase):
param['e1'] = gals['ellipticity_true'][igals][0]
param['e2'] = gals['ellipticity_true'][igals][1]
# For shape calculation
# For shape calculation
param['e1'], param['e2'], param['ell_total'] = self.rotate_ellipticity(
e1=gals['ellipticity_true'][igals][0],
......
injection_pipeline/SingleEpochImage.py
View file @ ddf8503d
......@@ -7,11 +7,10 @@ from astropy import wcs
from astropy.io import fits
from astropy.time import Time
# from ObservationSim.Config.Header import generatePrimaryHeader, generateExtensionHeader
from ObservationSim.Instrument import Chip, Filter, FilterParam, FocalPlane, Telescope
from ObservationSim.PSF import PSFGauss, PSFInterp, FieldDistortion
from ObservationSim.Config import ChipOutput
from ObservationSim.Config import Pointing
from observation_sim.instruments import Chip, Filter, FilterParam, FocalPlane, Telescope
from observation_sim.psf import PSFGauss, PSFInterp, FieldDistortion
from observation_sim.config import ChipOutput
from observation_sim.config import Pointing
class SingleEpochImage(object):
......
injection_pipeline/injection_pipeline.py
View file @ ddf8503d
......@@ -110,13 +110,14 @@ class InjectionPipeline(object):
if __name__ == "__main__":
input_dir = "/public/home/fangyuedong/project/50sqDeg_L1_outputs"
pointing_label_list = ["MSC_0000000", "MSC_0000001",
"MSC_0000002", "MSC_0000003", "MSC_0000004", "MSC_0000005"]
input_dir = "/public/home/fangyuedong/project/50sqDeg_L1_outputs/50sqDeg_Photo_W2/"
# pointing_label_list = ["MSC_0000000", "MSC_0000001",
# "MSC_0000002", "MSC_0000003", "MSC_0000004", "MSC_0000005", "MSC_0000006", "MSC_0000007", "MSC_0000008", "MSC_0000009"]
chip_label_list = None
# pointing_label_list = ["MSC_0000000"]
# chip_label_list = ["08"]
output_dir = "/public/home/fangyuedong/project/injected_50sqDeg_L1_outputs"
pointing_label_list = ["MSC_0000000"]
chip_label_list = ["07"]
# output_dir = "/public/home/fangyuedong/project/injected_50sqDeg_L1_outputs/50sqDeg_Photo_W3/"
output_dir = "/public/home/fangyuedong/project/test_photometry/50sqDeg_Photo_W2/"
config_file = "/public/home/fangyuedong/project/injection_pipeline/config/config_injection.yaml"
pipeline = InjectionPipeline(config_file=config_file)
......
injection_pipeline/submit_source_injection.sh
View file @ ddf8503d
......@@ -3,7 +3,7 @@
#SBATCH -J INJECT
#SBATCH -N 1
#SBATCH --ntasks-per-node=36
#SBATCH -p batch
#SBATCH -p debug
#SBATCH --mem=240G
module load mpi/openmpi/4.0.2/gcc-7.3.1
......
measurement_pipeline/L1_pipeline/csst_msc_instrument/csst_msc_mbi_instrument.py
View file @ ddf8503d
......@@ -200,7 +200,7 @@ def core_msc_l1_mbi_instrument(
status_header["S_BIAS"] == 0,
status_header["S_DARK"] == 0,
status_header["S_FLAT"] == 0,
# status_header["S_CRS"] == 0,
status_header["S_CRS"] == 0,
status_header["S_NLIN"] == 0,
# status_header["S_CTE"] == 0, # 这个功能还未添加
status_header["S_SAT"] == 0,
......@@ -208,7 +208,7 @@ def core_msc_l1_mbi_instrument(
status_header["SKY_BKG"] != -9999,
status_header["SKY_RMS"] != -9999,
status_header["SATURATE"] != -9999,
# status_header["CRCOUNT"] != -9999,
status_header["CRCOUNT"] != -9999,
]
):
status_header.set("S_INST", 0)
......
measurement_pipeline/run_csst_msc_instrument.py
View file @ ddf8503d
......@@ -124,11 +124,13 @@ if __name__ == "__main__":
# pointing_label_list = ["MSC_0000000"]
# chip_label_list = ["08"]
# output_dir = "/public/home/fangyuedong/project/test_deepcr"
input_dir = "/public/share/yangxuliu/CSSOSDataProductsSims/outputs_50sqDeg/50sqDeg_Photo_W2/"
# input_dir = "/public/share/yangxuliu/CSSOSDataProductsSims/outputs_50sqDeg/50sqDeg_Photo_W2/"
# output_dir = "/public/home/fangyuedong/project/50sqDeg_L1_outputs/50sqDeg_Photo_W2/"
input_dir = "/public/share/yangxuliu/CSSOSDataProductsSims/outputs_50sqDeg/50sqDeg_Photo_W3/"
output_dir = "/public/home/fangyuedong/project/50sqDeg_L1_outputs/50sqDeg_Photo_W3/"
pointing_label_list = ["MSC_0000000", "MSC_0000001",
"MSC_0000002", "MSC_0000003", "MSC_0000004", "MSC_0000005", "MSC_0000006", "MSC_0000007", "MSC_0000008", "MSC_0000009"]
chip_label_list = None
output_dir = "/public/home/fangyuedong/project/50sqDeg_L1_outputs/50sqDeg_Photo_W2/"
run_pointing_list(input_dir=input_dir,
pointing_label_list=pointing_label_list,
......
measurement_pipeline/run_csst_msc_mbi_photometry.py
View file @ ddf8503d
......@@ -133,13 +133,17 @@ if __name__ == "__main__":
# pointing_label_list = ["MSC_0000000", "MSC_0000001",
# "MSC_0000002", "MSC_0000003", "MSC_0000004", "MSC_0000005"]
# chip_label_list = None
output_dir = "/public/home/fangyuedong/project/processed_injected_50sqDeg_L1_outputs"
flag_weight_dir = "/public/home/fangyuedong/project/50sqDeg_L1_outputs"
input_dir = "/public/home/fangyuedong/project/50sqDeg_L1_outputs"
pointing_label_list = ["MSC_0000000"]
chip_label_list = ["08"]
# output_dir = "/public/home/fangyuedong/project/test_photometry"
# output_dir = "/public/home/fangyuedong/project/processed_injected_50sqDeg_L1_outputs"
# flag_weight_dir = "/public/home/fangyuedong/project/50sqDeg_L1_outputs/50sqDeg_Photo_W2/"
flag_weight_dir = "/public/home/fangyuedong/project/50sqDeg_L1_outputs/50sqDeg_Photo_W3/"
# input_dir = "/public/home/fangyuedong/project/50sqDeg_L1_outputs/50sqDeg_Photo_W3/"
input_dir = "/public/home/fangyuedong/project/injected_50sqDeg_L1_outputs/50sqDeg_Photo_W3/"
pointing_label_list = ["MSC_0000000", "MSC_0000001",
"MSC_0000002", "MSC_0000003", "MSC_0000004", "MSC_0000005", "MSC_0000006", "MSC_0000007", "MSC_0000008", "MSC_0000009"]
chip_label_list = None
# output_dir = "/public/home/fangyuedong/project/50sqDeg_L1_outputs/50sqDeg_Photo_W3/"
output_dir = "/public/home/fangyuedong/project/processed_injected_50sqDeg_L1_outputs/50sqDeg_Photo_W3/"
run_pointing_list(input_dir=input_dir,
flag_weight_dir=flag_weight_dir,
pointing_label_list=pointing_label_list,
......
measurement_pipeline/submit_L1_instrument.sh
View file @ ddf8503d
......@@ -2,7 +2,7 @@
#SBATCH -J L1_INST
#SBATCH -N 1
#SBATCH --ntasks-per-node=24
#SBATCH --ntasks-per-node=36
#SBATCH -p batch
#SBATCH --mem=240G
......@@ -10,4 +10,4 @@ module load mpi/openmpi/4.0.2/gcc-7.3.1
date
srun hostname -s | sort -n | awk -F"-" '{print $2}' | uniq > pnodes
mpirun -mca pml ucx -x UCX_NET_DEVICES=mlx5_0:1 -machinefile pnodes -np 24 --map-by node python run_csst_msc_instrument.py
\ No newline at end of file
mpirun -mca pml ucx -x UCX_NET_DEVICES=mlx5_0:1 -machinefile pnodes -np 36 --map-by node python run_csst_msc_instrument.py
\ No newline at end of file
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