Reformat codebase for newer PEP8 imports guidance

Author: ragibson

tests/shared_testing_functions.py

@@ -1,8 +1,9 @@
-import igraph as ig
 from math import isnan
-from modularitypruning.partition_utilities import num_communities
 from random import randint, random, uniform
 
+import igraph as ig
+from modularitypruning.partition_utilities import num_communities
+
 
 def generate_connected_ER(n, m, directed):
     G = ig.Graph.Erdos_Renyi(n=n, m=m, directed=directed)

tests/test_champ_coefficients_2D.py

@@ -1,9 +1,11 @@
-from .shared_testing_functions import generate_connected_ER, generate_random_values, generate_random_partitions, \
-    generate_igraph_famous
+import unittest
+from random import seed
+
 from modularitypruning.champ_utilities import partition_coefficients_2D
 from modularitypruning.leiden_utilities import leiden_part_with_membership, repeated_leiden_from_gammas
-from random import seed
-import unittest
+
+from .shared_testing_functions import generate_connected_ER, generate_random_values, generate_random_partitions, \
+    generate_igraph_famous
 
 
 class TestCHAMPCoefficients2D(unittest.TestCase):

tests/test_champ_coefficients_3D.py

@@ -1,8 +1,10 @@
-from .shared_testing_functions import generate_connected_multilayer_ER, generate_random_partitions
+import unittest
+from random import seed
+
 from modularitypruning.champ_utilities import partition_coefficients_3D
 from modularitypruning.leiden_utilities import multilayer_leiden_part_with_membership, leiden_part_with_membership
-from random import seed
-import unittest
+
+from .shared_testing_functions import generate_connected_multilayer_ER, generate_random_partitions
 
 
 class TestCHAMPCoefficients3D(unittest.TestCase):

tests/test_champ_usage_2D.py

@@ -1,9 +1,11 @@
-from .shared_testing_functions import generate_connected_ER, generate_random_values, generate_random_partitions, \
-    generate_igraph_famous
+import unittest
+from random import seed
+
 from modularitypruning.champ_utilities import CHAMP_2D
 from modularitypruning.leiden_utilities import leiden_part_with_membership, repeated_leiden_from_gammas
-from random import seed
-import unittest
+
+from .shared_testing_functions import generate_connected_ER, generate_random_values, generate_random_partitions, \
+    generate_igraph_famous
 
 
 class TestCHAMP2D(unittest.TestCase):

tests/test_champ_usage_3D.py

@@ -1,10 +1,12 @@
-from .shared_testing_functions import generate_connected_multilayer_ER, generate_random_values, \
-    generate_random_partitions
+import unittest
+from random import seed
+
 from modularitypruning.champ_utilities import CHAMP_3D
 from modularitypruning.leiden_utilities import multilayer_leiden_part_with_membership
 from numpy import mean
-from random import seed
-import unittest
+
+from .shared_testing_functions import generate_connected_multilayer_ER, generate_random_values, \
+    generate_random_partitions
 
 
 def point_is_inside_champ_domain(gamma, omega, domain_vertices):

tests/test_deprecated_louvain_names.py

@@ -1,6 +1,7 @@
-import pytest
 import unittest
 
+import pytest
+
 
 class TestDeprecatedLouvainNames(unittest.TestCase):
     def test_deprecated_louvain_module(self):

tests/test_documentation_examples.py

@@ -3,6 +3,12 @@
 
 Sometimes this is simply checking that the code produces the intended output or runs without errors.
 """
+import unittest
+from random import seed, random
+
+import igraph as ig
+import matplotlib.pyplot as plt
+import numpy as np
 from modularitypruning import prune_to_stable_partitions, prune_to_multilayer_stable_partitions
 from modularitypruning.champ_utilities import CHAMP_2D, CHAMP_3D
 from modularitypruning.leiden_utilities import (repeated_parallel_leiden_from_gammas,
@@ -11,11 +17,6 @@
 from modularitypruning.partition_utilities import num_communities
 from modularitypruning.plotting import (plot_2d_domains_with_estimates, plot_2d_domains, plot_2d_domains_with_ami,
                                         plot_2d_domains_with_num_communities, plot_estimates, plot_multiplex_community)
-from random import seed, random
-import igraph as ig
-import matplotlib.pyplot as plt
-import numpy as np
-import unittest
 
 
 class TestDocumentationExamples(unittest.TestCase):

tests/test_monolayer_parameter_estimation.py

@@ -1,13 +1,15 @@
-from .shared_testing_functions import assert_almost_equal_or_both_none_or_nan, generate_igraph_famous, \
-    generate_random_partition
-import igraph as ig
+import unittest
 from math import log
-from numpy import mean
+from random import seed
+
+import igraph as ig
 from modularitypruning.parameter_estimation import iterative_monolayer_resolution_parameter_estimation
 from modularitypruning.parameter_estimation_utilities import gamma_estimate
 from modularitypruning.partition_utilities import all_degrees
-from random import seed
-import unittest
+from numpy import mean
+
+from .shared_testing_functions import assert_almost_equal_or_both_none_or_nan, generate_igraph_famous, \
+    generate_random_partition
 
 
 class TestMonolayerParameterEstimation(unittest.TestCase):

tests/test_multiplex_parameter_estimation.py

@@ -1,14 +1,16 @@
-from .shared_testing_functions import assert_almost_equal_or_both_none_or_nan, generate_random_partition, \
-    generate_multilayer_intralayer_SBM
-import igraph as ig
+import unittest
 from math import log
-from numpy import mean
+from random import seed
+
+import igraph as ig
 from modularitypruning.leiden_utilities import repeated_leiden_from_gammas_omegas
 from modularitypruning.parameter_estimation import iterative_multilayer_resolution_parameter_estimation
 from modularitypruning.parameter_estimation_utilities import gamma_omega_estimate
 from modularitypruning.partition_utilities import num_communities, all_degrees
-from random import seed
-import unittest
+from numpy import mean
+
+from .shared_testing_functions import assert_almost_equal_or_both_none_or_nan, generate_random_partition, \
+    generate_multilayer_intralayer_SBM
 
 
 class TestMultiplexParameterEstimation(unittest.TestCase):

tests/test_parallel_leiden_performance.py

@@ -1,17 +1,19 @@
-from .shared_testing_functions import generate_connected_ER, generate_multilayer_intralayer_SBM
-from modularitypruning.leiden_utilities import (repeated_leiden_from_gammas, repeated_parallel_leiden_from_gammas,
-                                                repeated_leiden_from_gammas_omegas,
-                                                repeated_parallel_leiden_from_gammas_omegas)
+import functools
+import unittest
+import warnings
 from multiprocessing import Pool, cpu_count
 from random import seed
-from time import time, sleep
-import functools
+from time import time
+
 import igraph as ig
 import numpy as np
 import psutil
 import pytest
-import unittest
-import warnings
+from modularitypruning.leiden_utilities import (repeated_leiden_from_gammas, repeated_parallel_leiden_from_gammas,
+                                                repeated_leiden_from_gammas_omegas,
+                                                repeated_parallel_leiden_from_gammas_omegas)
+
+from .shared_testing_functions import generate_connected_ER, generate_multilayer_intralayer_SBM
 
 # this set of tests ensures that we achieve >= 75% parallel performance compared to perfect scaling of
 # single-threaded jobs to multiple cores (with no memory contention). This threshold will be decreased in

tests/test_temporal_multilevel_parameter_estimation.py

@@ -1,14 +1,16 @@
-from .shared_testing_functions import assert_almost_equal_or_both_none_or_nan, generate_random_partition, \
-    generate_multilayer_intralayer_SBM
-import igraph as ig
+import unittest
 from math import log
-from numpy import mean
+from random import seed
+
+import igraph as ig
 from modularitypruning.leiden_utilities import repeated_leiden_from_gammas_omegas
 from modularitypruning.parameter_estimation import iterative_multilayer_resolution_parameter_estimation
 from modularitypruning.parameter_estimation_utilities import gamma_omega_estimate
 from modularitypruning.partition_utilities import num_communities, all_degrees
-from random import seed
-import unittest
+from numpy import mean
+
+from .shared_testing_functions import assert_almost_equal_or_both_none_or_nan, generate_random_partition, \
+    generate_multilayer_intralayer_SBM
 
 
 class TestTemporalAndMultilevelParameterEstimation(unittest.TestCase):

utilities/champ_utilities.py

@@ -1,14 +1,16 @@
-from .partition_utilities import all_degrees, in_degrees, out_degrees, membership_to_communities, \
-    membership_to_layered_communities
+import warnings
 from collections import defaultdict
-import numpy as np
-from numpy.random import choice
 from math import floor
 from multiprocessing import Pool, cpu_count
-from scipy.spatial import HalfspaceIntersection
+
+import numpy as np
+from numpy.random import choice
 from scipy.linalg import LinAlgWarning
 from scipy.optimize import linprog, OptimizeWarning
-import warnings
+from scipy.spatial import HalfspaceIntersection
+
+from .partition_utilities import all_degrees, in_degrees, out_degrees, membership_to_communities, \
+    membership_to_layered_communities
 
 
 def get_interior_point(halfspaces, initial_num_sampled=50, full_retry_limit=10):

utilities/leiden_utilities.py

@@ -1,11 +1,10 @@
 import functools
+from multiprocessing import Pool, cpu_count
+
 import igraph as ig
 import leidenalg
-from math import ceil
-from multiprocessing import Pool, cpu_count
-from tqdm import tqdm
 import numpy as np
-import psutil
+from tqdm import tqdm
 
 LOW_MEMORY_THRESHOLD = 1e9  # 1 GB
 

utilities/louvain_utilities.py

@@ -6,14 +6,16 @@
 in ``modularitypruning.leiden_utilities`` (though it still contains the legacy multi-layer functions since they can be
 faster in general -- leidenalg does not efficiently implement multilayer optimization).
 """
-from . import leiden_utilities
-from .leiden_utilities import sorted_tuple, LOW_MEMORY_THRESHOLD
-from .progress import Progress
+import warnings
 from math import ceil
 from multiprocessing import Pool, cpu_count
+
 import numpy as np
 import psutil
-import warnings
+
+from . import leiden_utilities
+from .leiden_utilities import sorted_tuple, LOW_MEMORY_THRESHOLD
+from .progress import Progress
 
 try:
     import louvain  # import louvain if possible

utilities/parameter_estimation.py

@@ -1,8 +1,9 @@
+import leidenalg
+
 from .leiden_utilities import singlelayer_leiden, multilayer_leiden
 from .parameter_estimation_utilities import leiden_part_with_membership, estimate_singlelayer_SBM_parameters, \
     gamma_estimate_from_parameters, omega_function_from_model, estimate_multilayer_SBM_parameters
 from .partition_utilities import in_degrees
-import leidenalg
 
 
 def iterative_monolayer_resolution_parameter_estimation(G, gamma=1.0, tol=1e-2, max_iter=25, verbose=False,

utilities/parameter_estimation_utilities.py

@@ -1,12 +1,14 @@
-from .leiden_utilities import leiden_part_with_membership, sorted_tuple
-from .champ_utilities import CHAMP_2D, CHAMP_3D
-from .partition_utilities import num_communities
+import warnings
+from math import log
+
 import igraph as ig
 import leidenalg
-from math import log
 import numpy as np
 from scipy.optimize import fsolve
-import warnings
+
+from .champ_utilities import CHAMP_2D, CHAMP_3D
+from .leiden_utilities import leiden_part_with_membership, sorted_tuple
+from .partition_utilities import num_communities
 
 
 def estimate_singlelayer_SBM_parameters(G, partition, m=None):

utilities/partition_utilities.py

@@ -1,4 +1,5 @@
 from collections import defaultdict
+
 from sklearn.metrics import adjusted_mutual_info_score, normalized_mutual_info_score
 
 

utilities/plotting.py

@@ -1,12 +1,14 @@
-from .partition_utilities import num_communities, ami
 from collections import defaultdict
 from random import sample, shuffle
-import numpy as np
+
 import matplotlib
-from matplotlib.patches import Polygon
-from matplotlib.collections import PatchCollection
 import matplotlib.pyplot as plt
+import numpy as np
 import seaborn as sbn
+from matplotlib.collections import PatchCollection
+from matplotlib.patches import Polygon
+
+from .partition_utilities import num_communities, ami
 
 
 def plot_adjacency(adj):