Added option for zmin and zmax

py/desisim/scripts/gen_qso_catalog.py

@@ -24,6 +24,12 @@ def main():
 
     parser.add_argument("--exptime", type=float, default=None, required=False,
                         help='Exposure time to assign to all targets in the mock catalog')
+
+    parser.add_argument("--zmin", type=float, default=1.7, required=False,
+                        help='Minimum redshift')
+
+    parser.add_argument("--zmax", type=float, default=10.0, required=False,
+                        help='Maximum redshift')
 
     parser.add_argument("--release", type=str, default='jura', choices=['iron','jura','Y5'], required=False,
                         help='DESI survey release to reproduce')
@@ -47,7 +53,7 @@ def main():
     # Note: For Y1 and Y3 mocks (and probably Y5 too) the target selection redshift distribution
     # from Chaussidon et al. 2022 works better to match QSO targets Iron catalog.
     # The option distribution='from_data' should be a better option once I finish implementing it.
-    survey.apply_redshift_dist(distribution='target_selection', zmin=1.8)
+    survey.apply_redshift_dist(distribution='target_selection', zmin=args.zmin, zmax=args.zmax)
 
     # Apply NPASS geometry either from a release or a custom tiles file.
     survey.apply_data_geometry(release=args.release, tilefile=args.tiles_file)

py/desisim/survey_release.py

@@ -296,7 +296,6 @@ def assign_exposures(self,exptime=None):
                 log.warning('Effective exposure time TSNR2_LRG column not found in observed data catalog.') 
                 log.warning('Will compute effective exposure time from alternative templates.')
                 if 'TSNR2_LYA' in self.data.colnames:
-                    log.warning('TSNR2_LRG column not found in observed data catalog.')
                     log.info('Getting effective exposure time in data catalog by 11.8*TSNR2_LYA.')
                     exptime_data = 11.8*self.data['TSNR2_LYA']
                 elif 'TSNR2_QSO' in self.data.colnames:
@@ -418,7 +417,7 @@ def get_catalog_dndzdm(cat,zbins=np.linspace(0,10,100+1), rmagbins=np.linspace(1
     return dn_dzdm, zbins, rmagbins
 
 def generate_random_fluxes(dist, z, zcenters, rmagcenters,invert=False):
-    cdf=np.sum(dist,axis=1)
+    cdf=np.cumsum(dist,axis=1)
     cdf = np.divide(cdf,cdf[:,-1][:,None],where=cdf[:,-1][:,None]>0,out=np.zeros_like(cdf))
     dz = zcenters[1]-zcenters[0]
     mags=np.zeros(len(z))