flake8

Author: rflamary

test/test_plot.py

@@ -9,17 +9,17 @@ def test_plot1D_mat():
 
     import ot
 
-    n = 100  # nb bins
+    n_bins = 100  # nb bins
 
     # bin positions
-    x = np.arange(n, dtype=np.float64)
+    x = np.arange(n_bins, dtype=np.float64)
 
     # Gaussian distributions
-    a = ot.datasets.get_1D_gauss(n, m=20, s=5)  # m= mean, s= std
-    b = ot.datasets.get_1D_gauss(n, m=60, s=10)
+    a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5)  # m= mean, s= std
+    b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10)
 
     # loss matrix
-    M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))
+    M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1)))
     M /= M.max()
 
     ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')
@@ -29,17 +29,17 @@ def test_plot2D_samples_mat():
 
     import ot
 
-    n = 50  # nb samples
+    n_bins = 50  # nb samples
 
     mu_s = np.array([0, 0])
     cov_s = np.array([[1, 0], [0, 1]])
 
     mu_t = np.array([4, 4])
     cov_t = np.array([[1, -.8], [-.8, 1]])
 
-    xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)
-    xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t)
+    xs = ot.datasets.get_2D_samples_gauss(n_bins, mu_s, cov_s)
+    xt = ot.datasets.get_2D_samples_gauss(n_bins, mu_t, cov_t)
 
-    G = 1.0 * (np.random.rand(n, n) < 0.01)
+    G = 1.0 * (np.random.rand(n_bins, n_bins) < 0.01)
 
     ot.plot.plot2D_samples_mat(xs, xt, G, thr=1e-5)

test/test_utils.py

@@ -3,9 +3,6 @@
 import ot
 import numpy as np
 
-# import pytest
-
-
 def test_parmap():
 
     n = 100