Added permutations_array to Moran_Local class

esda/crand.py

@@ -69,6 +69,7 @@ def crand(
     w,
     observed,
     permutations,
+    permutations_array,
     keep,
     n_jobs,
     stat_func,
@@ -91,6 +92,8 @@ def crand(
         (N,) array with observed values
     permutations : int
         Number of permutations for conditional randomisation
+    permutations_array : ndarray
+        (permutations, ) array with indices of permuted
     keep : Boolean
         If True, store simulation; else do not return randomised statistics
     n_jobs : int
@@ -170,8 +173,21 @@ def crand(
     # self neighbor, since conditional randomization conditions on site i.
     cardinalities = np.array((adj_matrix != 0).sum(1)).flatten()
     max_card = cardinalities.max()
-    permuted_ids = vec_permutations(max_card, n, permutations, seed)
-
+
+    if permutations_array is None:
+        # Random permutation array
+        permuted_ids = vec_permutations(max_card, n, permutations, seed)
+    else:
+        # User defined permutation array
+        permuted_ids = permutations_array
+        if permuted_ids.shape[0] != permutations:
+            permutations = permuted_ids.shape[0]
+            warnings.warn(
+                f"Number of permutations has been adjusted to match the length of the "
+                f"permutations array. New value of 'permutations' is {permutations}.",
+                stacklevel=2,
+            )
+
     if n_jobs != 1:
         try:
             import joblib  # noqa: F401

esda/join_counts_local.py

@@ -138,6 +138,7 @@ def fit(self, y, n_jobs=1, permutations=999):
                 w=self.w,
                 observed=self.LJC,
                 permutations=permutations,
+                permutations_array=None,
                 keep=keep_simulations,
                 n_jobs=n_jobs,
                 stat_func=_ljc_uni,

esda/moran.py

@@ -894,6 +894,8 @@ class Moran_Local:  # noqa: N801
     permutations : int
         number of random permutations for calculation of pseudo
         p_values
+    permutations_array: array
+        user specified permutations
     geoda_quads : boolean
         (default=False)
         If True use GeoDa scheme: HH=1, LL=2, LH=3, HL=4
@@ -1027,6 +1029,7 @@ def __init__(
         w,
         transformation="r",
         permutations=PERMUTATIONS,
+        permutations_array=None,
         geoda_quads=False,
         n_jobs=1,
         keep_simulations=True,
@@ -1064,6 +1067,7 @@ def __init__(
                 w,
                 self.Is,
                 permutations,
+                permutations_array,
                 keep_simulations,
                 n_jobs=n_jobs,
                 stat_func=_moran_local_crand,
@@ -1406,6 +1410,7 @@ def __init__(
                 w,
                 self.Is,
                 permutations,
+                None,
                 keep_simulations,
                 n_jobs=n_jobs,
                 stat_func=_moran_local_bv_crand,
@@ -1650,6 +1655,7 @@ def __init__(
             w,
             transformation=transformation,
             permutations=permutations,
+            permutations_array=None,
             geoda_quads=geoda_quads,
             n_jobs=n_jobs,
             keep_simulations=keep_simulations,

esda/tests/test_moran.py

@@ -162,6 +162,7 @@ def setup_method(self):
         f = libpysal.io.open(libpysal.examples.get_path("desmith.txt"))
         self.y = np.array(f.by_col["z"])
 
+
     @parametrize_desmith
     def test_Moran_Local(self, w):
         lm = moran.Moran_Local(
@@ -174,6 +175,30 @@ def test_Moran_Local(self, w):
         )
         np.testing.assert_allclose(lm.z_sim[0], -0.6990291160835514)
         np.testing.assert_allclose(lm.p_z_sim[0], 0.24226691753791396)
+
+    @parametrize_desmith
+    def test_Moran_Local_custom_perms(self, w):
+        np.random.seed(SEED)
+        cardinalities = np.array((w.sparse != 0).sum(1)).flatten()
+        max_card = cardinalities.max()
+        original_array = np.arange(len(self.y))
+        permutations_array = np.zeros((99, max_card), dtype=np.int32)
+
+        for i in range(99):
+            random_number = np.random.randint(0, len(self.y) - max_card)
+            permutations_array[i] = original_array[random_number:random_number + max_card]
+
+        lm = moran.Moran_Local(
+            self.y,
+            w,
+            transformation="r",
+            permutations=99,
+            permutations_array=permutations_array,
+            keep_simulations=True,
+            seed=SEED,
+        )
+        np.testing.assert_allclose(lm.z_sim[0], -0.49229215590070813)
+        np.testing.assert_allclose(lm.p_z_sim[0], 0.311256412279757)
 
     @parametrize_sac
     def test_Moran_Local_labels(self, w):