Resolve linting errors

.github/workflows/python-package.yml

@@ -48,8 +48,8 @@ jobs:
  source env/bin/activate
  # stop the build if there are Python syntax errors or undefined names
  flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude env/
- # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
- flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude env/
+ # exit-zero treats all errors as warnings
+ flake8 . --count --exit-zero --max-complexity=15 --max-line-length=120 --statistics --exclude env/
  - name: Test with pytest
  run: |
  source env/bin/activate

experiments/modularity_nonmonotonic_K/modularity_nonmonotonic_K.py

@@ -34,10 +34,10 @@
 
 def plot_and_exit(G, ranges):
  layout = ig.Graph.layout_fruchterman_reingold(G, niter=1000)
- print(f"plotting network...")
+ print("plotting network...")
  ig.plot(G, layout=layout).save("network.png")
 
- print(f"plotting CHAMP domains and gamma estimates...")
+ print("plotting CHAMP domains and gamma estimates...")
  gamma_estimates = ranges_to_gamma_estimates(G, ranges)
  plt.rc('text', usetex=True)
  plt.rc('font', family='serif')

tests/test_monolayer_parameter_estimation.py

@@ -1,4 +1,5 @@
-from .shared_testing_functions import assert_almost_equal_or_both_none_or_nan, generate_igraph_famous, generate_random_partition
+from .shared_testing_functions import assert_almost_equal_or_both_none_or_nan, generate_igraph_famous, \
+ generate_random_partition
 import igraph as ig
 from math import log
 from numpy import mean

utilities/parameter_estimation_utilities.py

@@ -162,7 +162,9 @@ def gamma_estimate(G, partition):
 
 
 def gamma_estimate_from_parameters(omega_in, omega_out):
- """Compute the "correct" value of gamma as in :meth:`~modularitypruning.parameter_estimation_utilities.gamma_estimate` from SBM parameters.
+ """
+ Compute the "correct" value of gamma as in
+ :meth:`~modularitypruning.parameter_estimation_utilities.gamma_estimate` from SBM parameters.
 
  :param omega_in: within-community edge propensity of a degree-corrected, planted partition SBM
  :type omega_in: float
@@ -361,11 +363,14 @@ def gamma_omega_estimate(G_intralayer, G_interlayer, layer_vec, membership, omeg
 
 
 def ranges_to_gamma_estimates(G, ranges):
- """Compute gamma estimates as in :meth:`~modularitypruning.parameter_estimation_utilities.gamma_estimate`, given domains of optimality from :meth:`~modularitypruning.champ_utilities.CHAMP_2D`.
+ """
+ Compute gamma estimates as in :meth:`~modularitypruning.parameter_estimation_utilities.gamma_estimate`, given
+ domains of optimality from :meth:`~modularitypruning.champ_utilities.CHAMP_2D`.
 
  :param G: graph of interest
  :type G: igraph.Graph
- :param ranges: list of ``(gamma_start, gamma_end, membership)`` tuples as returned from :meth:`~modularitypruning.champ_utilities.CHAMP_2D`
+ :param ranges: list of ``(gamma_start, gamma_end, membership)`` tuples as returned from
+ :meth:`~modularitypruning.champ_utilities.CHAMP_2D`
  :type ranges: list of tuple[float, float, tuple[int]]
  :return: a copy of input ranges with the corresponding gamma estimate appended to each tuple
  :rtype: list of tuple[float, float, tuple[int], float]
@@ -393,15 +398,19 @@ def gamma_estimates_to_stable_partitions(gamma_estimates):
 
 
 def domains_to_gamma_omega_estimates(G_intralayer, G_interlayer, layer_vec, domains, model='temporal'):
- """Compute (gamma, omega) estimates as in :meth:`~modularitypruning.parameter_estimation_utilities.gamma_omega_estimate`, given domains of optimality from :meth:`~modularitypruning.champ_utilities.CHAMP_3D`.
+ """
+ Compute (gamma, omega) estimates as in
+ :meth:`~modularitypruning.parameter_estimation_utilities.gamma_omega_estimate`, given domains of optimality from
+ :meth:`~modularitypruning.champ_utilities.CHAMP_3D`.
 
  :param G_intralayer: intralayer graph of interest
  :type G_intralayer: igraph.Graph
  :param G_interlayer: interlayer graph of interest
  :type G_interlayer: igraph.Graph
  :param layer_vec: list of each vertex's layer membership
  :type layer_vec: list[int]
- :param domains: list of ``(domain_vertices, membership)`` tuples as returned from :meth:`~modularitypruning.champ_utilities.CHAMP_3D`
+ :param domains: list of ``(domain_vertices, membership)`` tuples as returned from
+ :meth:`~modularitypruning.champ_utilities.CHAMP_3D`
  :type domains: list of tuple[list[float], tuple[int]]
  :param model: network layer topology (temporal, multilevel, multiplex)
  :type model: str