[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -30,8 +30,8 @@ repos:
       exclude: 'test_oauth.py'
     - id: trailing-whitespace
 
-- repo: https://github.com/ambv/black
-  rev: 19.3b0
+- repo: https://github.com/psf/black-pre-commit-mirror
+  rev: 25.12.0
   hooks:
     - id: black
       name: black

benchmarks/benchmarks/compute.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 import copy
 import timeit
 

benchmarks/benchmarks/emd.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 
 import os
 
@@ -16,10 +17,11 @@
 
 """
 
+
 def load_repertoire(name):
     """Load an array of repertoires in ./data/emd/."""
     root = os.path.abspath(os.path.dirname(__file__))
-    filename = os.path.join(root, 'data', 'emd', name)
+    filename = os.path.join(root, "data", "emd", name)
 
     return np.load(filename)
 
@@ -31,6 +33,7 @@ def generate_repertoire():
 
 class BenchmarkEmdRandom:
     """Benchmark EMD on random repertoires."""
+
     timeout = 100
 
     def setup(self):
@@ -50,11 +53,12 @@ class BenchmarkEmdRule152:
     The data is the first 100,000 repertoires encountered when computing
     ``big_phi`` for the network.
     """
+
     timeout = 100
 
     def setup(self):
-        self.d1 = load_repertoire('1.npy')
-        self.d2 = load_repertoire('2.npy')
+        self.d1 = load_repertoire("1.npy")
+        self.d2 = load_repertoire("2.npy")
 
     def time_effect_emd(self):
         for d1, d2 in zip(self.d1, self.d2):

benchmarks/benchmarks/subsystem.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 import copy
 
 from pyphi import Subsystem, compute, config, examples
@@ -49,7 +50,7 @@ def clear_network_caches(network):
             network.purview_cache = {}
 
 
-class BenchmarkSubsystem():
+class BenchmarkSubsystem:
 
     def setup(self):
         # 7-node network
@@ -104,7 +105,7 @@ def time_potential_purviews_with_cache(self):
 
 class BenchmarkEmdApproximation:
 
-    params = ['emd', 'l1']
+    params = ["emd", "l1"]
 
     number = 1
     repeat = 1
@@ -116,9 +117,9 @@ def setup(self, distance):
 
         self.default_config = copy.copy(config.__dict__)
 
-        if distance == 'emd':
+        if distance == "emd":
             config.L1_DISTANCE_APPROXIMATION = False
-        elif distance == 'l1':
+        elif distance == "l1":
             config.L1_DISTANCE_APPROXIMATION = True
         else:
             raise ValueError(distance)

benchmarks/benchmarks/utils.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 import numpy as np
 
 from pyphi import utils
@@ -7,7 +8,8 @@
     [1, 1, 0, 0, 0],
     [0, 0, 0, 1, 0],
     [0, 0, 1, 1, 1],
-    [1, 0, 0, 1, 1]]
+    [1, 0, 0, 1, 1],
+]
 
 cm1 = [
     [0, 1, 1, 0, 1, 0, 0],
@@ -16,7 +18,8 @@
     [0, 1, 0, 1, 1, 0, 0],
     [0, 0, 0, 1, 1, 0, 0],
     [0, 0, 0, 0, 0, 1, 1],
-    [0, 0, 0, 0, 0, 1, 1]]
+    [0, 0, 0, 0, 0, 1, 1],
+]
 
 cm2 = [
     [1, 1, 0, 0, 0, 0, 0, 1],
@@ -26,15 +29,16 @@
     [0, 0, 0, 1, 1, 1, 0, 0],
     [0, 0, 0, 0, 1, 1, 1, 0],
     [0, 0, 0, 0, 0, 1, 1, 1],
-    [1, 0, 0, 0, 0, 0, 1, 1]]
+    [1, 0, 0, 0, 0, 0, 1, 1],
+]
 
 cm3 = [[1] * 16] * 16
 
 
 matrices = [cm0, cm1, cm2, cm3]
 
 
-class BenchmarkBlockCm():
+class BenchmarkBlockCm:
 
     params = [0, 1, 2, 3]
 
@@ -45,19 +49,18 @@ def time_block_cm(self, m):
         utils.block_cm(self.cm)
 
 
-class BenchmarkReducibility():
+class BenchmarkReducibility:
 
-    params = [[0, 1, 2, 3], ['block', 'full']]
-    param_names = ['cm', 'type']
+    params = [[0, 1, 2, 3], ["block", "full"]]
+    param_names = ["cm", "type"]
 
     def setup(self, m, _type):
         self.cm = np.array(matrices[m])
 
     def time_reducibility(self, m, _type):
-        idxs = (tuple(range(self.cm.shape[0])),
-                tuple(range(self.cm.shape[1])))
+        idxs = (tuple(range(self.cm.shape[0])), tuple(range(self.cm.shape[1])))
 
-        if _type == 'block':
+        if _type == "block":
             utils.block_reducible(self.cm, *idxs)
-        elif _type == 'full':
+        elif _type == "full":
             utils.fully_connected(self.cm, *idxs)