From eddd348f2b3e3893a19f6ccfea1de55a5f773894 Mon Sep 17 00:00:00 2001
From: dnjst <87722897+dnjst@users.noreply.github.com>
Date: Wed, 16 Apr 2025 13:06:33 +0200
Subject: [PATCH] Update core.py

replace np.int with int
---
 src/harmony/core.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/harmony/core.py b/src/harmony/core.py
index 2ba964f..de11ee1 100644
--- a/src/harmony/core.py
+++ b/src/harmony/core.py
@@ -56,7 +56,7 @@ def augmented_affinity_matrix(
     # dists, _ = nbrs.kneighbors(pca_projections.values)
     # adj = nbrs.kneighbors_graph(pca_projections.values, mode='distance')
     # # Scaling factors for affinity matrix construction
-    # ka = np.int(n_neighbors / 3)
+    # ka = int(n_neighbors / 3)
     # scaling_factors = pd.Series(dists[:, ka], index=cell_order)
     # # Affinity matrix
     # nn_aff = _convert_to_affinity(adj, scaling_factors, True)
@@ -189,7 +189,7 @@ def _construct_mnn(t1_cells, t2_cells, data_df, n_neighbors,device,n_jobs=-2):
 # Rewritten for speed improvements
 def _mnn_ka_distances(mnn, n_neighbors):
     # Function to find distance ka^th neighbor in the mutual nearest neighbor matrix
-    ka = np.int(n_neighbors / 3)
+    ka = int(n_neighbors / 3)
     ka_dists = np.repeat(None, mnn.shape[0])
     x, y, z = find(mnn)
     rows=pd.Series(x).value_counts()