made compatible with csst simultion v3.0

config/config_injection.yaml

@@ -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

@@ -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,
@@ -36,30 +37,45 @@ 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"]
+    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'
+    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):
     """
     flux of a given AB magnitude
@@ -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)
@@ -189,7 +227,8 @@ 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)
@@ -220,7 +259,8 @@ def calculate_undetected_flux(orig_cat, mag_bins, fraction, mag_low=20.0, mag_hi
     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:

evaluation/evaluation_utils.py

+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()

injection_pipeline/Catalog/C6_SimCat.py

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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