np.in1d --> np.isin

Author: aphearin

halotools/empirical_models/phase_space_models/subhalo_based_models/subhalo_selection_kernel.py

@@ -119,7 +119,7 @@ def calculate_satellite_selection_mask(subhalo_hostids, satellite_occupations, h
     those rare subhalos with no matching host halo
     (this situation occurs in <0.1% for typical Rockstar catalogs).
 
-    >>> matched_mask = np.in1d(halocat.halo_table['halo_hostid'], halocat.halo_table['halo_id'])
+    >>> matched_mask = np.isin(halocat.halo_table['halo_hostid'], halocat.halo_table['halo_id'])
     >>> halos = halocat.halo_table[matched_mask]
 
     Now we will sort the catalog by the ``sorting_keys`` list.

halotools/utils/array_indexing_manipulations.py

@@ -337,7 +337,7 @@ def calculate_entry_multiplicity(sorted_repeated_hostids, unique_possible_hostid
 
     unique_appearances_of_hostid, unique_entry_multiplicity = (
         np.unique(sorted_repeated_hostids, return_counts=True))
-    hostid_has_match = np.in1d(unique_possible_hostids, unique_appearances_of_hostid,
+    hostid_has_match = np.isin(unique_possible_hostids, unique_appearances_of_hostid,
         assume_unique=True)
 
     entry_multiplicity = np.zeros_like(unique_possible_hostids)

halotools/utils/crossmatch.py

@@ -162,7 +162,7 @@ def crossmatch(x, y, skip_bounds_checking=False):
     unique_xvals, counts = np.unique(x_sorted, return_counts=True)
 
     # Determine which of the unique x values has a match in y
-    unique_xval_has_match = np.in1d(unique_xvals, y_sorted, assume_unique=True)
+    unique_xval_has_match = np.isin(unique_xvals, y_sorted, assume_unique=True)
 
     # Create a boolean array with True for each value in x with a match, otherwise False
     idx_x = np.repeat(unique_xval_has_match, counts)