'Refactored by Sourcery'

examples/merrill_1984_fig_2c_2d.py

@@ -41,6 +41,7 @@
 running as many simulations, however the Coombs results are consistently
 high.
 """
+
 import time
 from collections import Counter
 import numpy as np
@@ -94,7 +95,7 @@
                                         {'CW'})}
     start_time = time.monotonic()
 
-    for iteration in range(n):
+    for _ in range(n):
         for n_cands in n_cands_list:
             v, c = normal_electorate(n_voters, n_cands, dims=D, corr=corr,
                                      disp=disp)

examples/merrill_1984_table_1_fig_1.py

@@ -24,6 +24,7 @@
 | SU max    | 100.0 |  84.1 |  79.6 |  78.4 |  77.3 |  77.5 |
 | CW        | 100.0 |  91.7 |  83.1 |  75.6 |  64.3 |  52.9 |
 """
+
 import time
 from collections import Counter
 import numpy as np
@@ -51,7 +52,7 @@
 
 start_time = time.monotonic()
 
-for iteration in range(n):
+for _ in range(n):
     for n_cands in n_cands_list:
         utilities = random_utilities(n_voters, n_cands)
 
@@ -126,11 +127,12 @@
 
 # Likelihood that social utility maximizer is Condorcet Winner
 x, y = zip(*sorted(count['SU max'].items()))
-table.append(['SU max', *np.array(y)/y_cw*100])
-
-# Likelihood of Condorcet Winner (normalized by n iterations)
-table.append(['CW', *np.asarray(y_cw)/n*100])
-
+table.extend(
+    (
+        ['SU max', *np.array(y) / y_cw * 100],
+        ['CW', *np.asarray(y_cw) / n * 100],
+    )
+)
 print(tabulate(table, ["Method", *x], tablefmt="pipe", floatfmt='.1f'))
 
 plt.plot([], [], 'k:', lw=0.8, label='Merrill')  # Dummy plot for label

examples/wikipedia_condorcet_paradox_likelihood.py

@@ -53,15 +53,15 @@ def func():
     for n_voters in WP_table:
         # Reuse the same chunk of memory to save time
         election = np.empty((n_voters, n_cands), dtype=np.uint8)
-        for iteration in range(batch):
+        for _ in range(batch):
             election[:] = impartial_culture(n_voters, n_cands)
             CW = condorcet(election)
             if CW is None:
                 is_CP[n_voters] += 1
     return is_CP
 
 
-p = Parallel(n_jobs=-3, verbose=5)(delayed(func)() for i in range(n))
+p = Parallel(n_jobs=-3, verbose=5)(delayed(func)() for _ in range(n))
 is_CP = sum(p, Counter())
 
 x, y = zip(*WP_table.items())