more fixes

Author: nwlandry

xgi/drawing/draw.py

@@ -24,7 +24,7 @@
 from ..convert import to_bipartite_edgelist
 from ..core import DiHypergraph, Hypergraph, SimplicialComplex
 from ..exception import XGIError
-from ..utils import subfaces, crest_r
+from ..utils import crest_r, subfaces
 from .draw_utils import (
     _CCW_sort,
     _draw_arg_to_arr,

xgi/utils/tensor.py

@@ -1,13 +1,9 @@
 ## Tensor times same vector in all but one (TTSV1) and all but two (TTSV2)
+import math
 from collections import defaultdict
 from itertools import combinations
-from math import factorial
 
 import numpy as np
-from numpy import prod
-from scipy.signal import convolve
-from scipy.sparse import coo_array
-from scipy.special import binom as binomial
 
 __all__ = [
     "pairwise_incidence",
@@ -78,9 +74,10 @@ def banerjee_coeff(size, max_size):
     Sinan Aksoy, Ilya Amburg, Stephen Young,
     https://doi.org/10.1137/23M1584472
     """
+    from scipy.special import binom
+
     return sum(
-        ((-1) ** j) * binomial(size, j) * (size - j) ** max_size
-        for j in range(size + 1)
+        ((-1) ** j) * binom(size, j) * (size - j) ** max_size for j in range(size + 1)
     )
 
 
@@ -119,15 +116,15 @@ def ttsv1(node_dict, edge_dict, r, a):
     """
     n = len(node_dict)
     s = np.zeros(n)
-    r_minus_1_factorial = factorial(r - 1)
+    r_minus_1_factorial = math.factorial(r - 1)
     for node, edges in node_dict.items():
         c = 0
         for e in edges:
             l = len(edge_dict[e])
             alpha = banerjee_coeff(l, r)
             edge_without_node = [v for v in edge_dict[e] if v != node]
             if l == r:
-                gen_fun_coef = prod(a[edge_without_node])
+                gen_fun_coef = np.prod(a[edge_without_node])
             elif 2 ** (l - 1) < r * (l - 1):
                 gen_fun_coef = _get_gen_coef_subset_expansion(
                     a[edge_without_node], a[node], r - 1
@@ -175,6 +172,9 @@ def ttsv2(pair_dict, edge_dict, r, a, n):
     Sinan Aksoy, Ilya Amburg, Stephen Young,
     https://doi.org/10.1137/23M1584472
     """
+    from scipy.signal import convolve
+    from scipy.sparse import coo_array
+
     s = {}
     r_minus_2_factorial = factorial(r - 2)
     for (v1, v2), edges in pair_dict.items():
@@ -317,6 +317,8 @@ def _get_gen_coef_fft_fast_array(edge_without_node, a, node, l, r):
     Sinan Aksoy, Ilya Amburg, Stephen Young,
     https://doi.org/10.1137/23M1584472
     """
+    from scipy.signal import convolve
+
     coefs = [1]
     for i in range(1, r):
         coefs.append(coefs[-1] * a[node] / i)

xgi/utils/utilities.py

@@ -31,6 +31,7 @@
     "crest_r",
 ]
 
+
 class IDDict(dict):
     """A dict that holds (node or edge) IDs.
 
@@ -593,5 +594,9 @@ def geometric(p):
 
 
 def crest_r():
-    palette = [(0.17363177, 0.19076859, 0.44549087), (0.20350004, 0.5231837, 0.55370601), (0.6468274, 0.80289262, 0.56592265)]
-    return LinearSegmentedColormap.from_list("crest_r", palette)
+    palette = [
+        (0.17363177, 0.19076859, 0.44549087),
+        (0.20350004, 0.5231837, 0.55370601),
+        (0.6468274, 0.80289262, 0.56592265),
+    ]
+    return LinearSegmentedColormap.from_list("crest_r", palette)