Replace deprecated np.in1d with np.isin

py/picca/bin/picca_export_co.py

@@ -209,7 +209,7 @@ def main(cmdargs):
                     sys.exit()
 
                 w = np.logical_not(
-                    np.in1d(data[type_corr1]['HEALPID'],
+                    np.isin(data[type_corr1]['HEALPID'],
                             data[type_corr2]['HEALPID']))
                 if w.sum() != 0:
                     userprint("WARNING: HEALPID are different by {} for {}:{} "

py/picca/bin/picca_export_cross_covariance.py

@@ -77,7 +77,7 @@ def main(cmdargs):
     # Add unshared healpix as empty data
     for key in sorted(list(data.keys())):
         key2 = (key + 1) % 2
-        w = np.logical_not(np.in1d(data[key2]['HEALPID'], data[key]['HEALPID']))
+        w = np.logical_not(np.isin(data[key2]['HEALPID'], data[key]['HEALPID']))
         if w.sum() > 0:
             new_healpix = data[key2]['HEALPID'][w]
             num_new_healpix = new_healpix.size

py/picca/bin/picca_reduce_spall.py

@@ -28,7 +28,7 @@
 qso_catalog = Table.read(args.qso_catalog)
 print(f'{len(qso_catalog)} entries found in QSO catalog')
 
-w = np.in1d(spall['THING_ID'], qso_catalog['THING_ID'])
+w = np.isin(spall['THING_ID'], qso_catalog['THING_ID'])
 spall_qso = spall[w]
 #-- Columns required for picca_deltas.py for spec, spplate formats and usage of multiple observations
 spall_qso.keep_columns(

py/picca/cf.py

@@ -1323,7 +1323,7 @@ def compute_xi_1d_cross(healpix):
         weights1 = delta1.weights[select1]
 
         thingids = [delta2.thingid for delta2 in data2[healpix]]
-        neighbours = data2[healpix][np.in1d(thingids, [delta1.thingid])]
+        neighbours = data2[healpix][np.isin(thingids, [delta1.thingid])]
         for delta2 in neighbours:
             select2 = delta2.log_lambda <= log_lambda_max
             select2 &= delta2.log_lambda >= log_lambda_min

py/picca/delta_extraction/quasar_catalogues/drq_catalogue.py

@@ -274,7 +274,7 @@ def read_spall(self, drq_catalogue):
                 f"message: {str(error)}"
             ) from error
 
-        w = np.in1d(catalogue["THING_ID"], drq_catalogue["THING_ID"])
+        w = np.isin(catalogue["THING_ID"], drq_catalogue["THING_ID"])
         self.logger.progress(f"Found {np.sum(w)} spectra with required THING_ID")
         w &= catalogue["PLATEQUALITY"] == "good"
         self.logger.progress(f"Found {np.sum(w)} spectra with 'good' plate")

py/picca/pk1d/compute_pk1d.py

@@ -326,7 +326,7 @@ def fill_masked_pixels(
     lambda_or_log_lambda_index += 0.5
     lambda_or_log_lambda_index = np.array(lambda_or_log_lambda_index, dtype=int)
     index_all = range(lambda_or_log_lambda_index[-1] + 1)
-    index_ok = np.in1d(index_all, lambda_or_log_lambda_index)
+    index_ok = np.isin(index_all, lambda_or_log_lambda_index)
 
     delta_new = np.zeros(len(index_all))
     delta_new[index_ok] = delta

py/picca/raw_io.py

@@ -73,7 +73,7 @@ def read_transmission_file(filename, num_bins, objs_thingid, tracer='F_LYA', lam
 
     hdul = fitsio.FITS(filename)
     thingid = hdul['METADATA']['MOCKID'][:]
-    if np.in1d(thingid, objs_thingid).sum() == 0:
+    if np.isin(thingid, objs_thingid).sum() == 0:
         hdul.close()
         return
     if 'RA' in hdul['METADATA'].get_colnames() and 'DEC' in hdul['METADATA'].get_colnames():
@@ -116,7 +116,7 @@ def read_transmission_file(filename, num_bins, objs_thingid, tracer='F_LYA', lam
                  (lambda_rest_frame < lambda_max_rest_frame)] = 1
     num_pixels = np.sum(valid_pixels, axis=1)
     w = num_pixels >= 50
-    w &= np.in1d(thingid, objs_thingid)
+    w &= np.isin(thingid, objs_thingid)
     if w.sum() == 0:
         hdul.close()
         return

py/picca/xcf.py

@@ -965,15 +965,15 @@ def compute_wick_terms(healpixs):
                 thingid4 = np.array([obj4.thingid for obj4 in neighbours])
 
                 if max_diagram == 5:
-                    w = np.in1d(delta1.neighbours, delta3.neighbours)
+                    w = np.isin(delta1.neighbours, delta3.neighbours)
                     if w.sum() == 0:
                         continue
                     aux_ang12 = ang12[w]
                     aux_r_comov2 = r_comov2[w]
                     aux_weights2 = weights2[w]
                     aux_thingid2 = thingid2[w]
 
-                    w = np.in1d(delta3.neighbours, delta1.neighbours)
+                    w = np.isin(delta3.neighbours, delta1.neighbours)
                     if w.sum() == 0:
                         continue
                     ang34 = ang34[w]

tutorials/picca_export_stacked_correlation.py

@@ -82,7 +82,7 @@
     ### Add unshared healpix as empty data
     for i in range(nbData):
         for j in range(nbData):
-            w = np.logical_not( np.in1d(data[j]['HEALPID'],data[i]['HEALPID']) )
+            w = np.logical_not( np.isin(data[j]['HEALPID'],data[i]['HEALPID']) )
             if w.sum()>0:
                 new_healpix = data[j]['HEALPID'][w]
                 nb_new_healpix = new_healpix.size