Replace bivariate normal in testing methods

dstansby · dstansby · commit cabc4a3cf9fa · 2017-09-21T17:35:57.000+01:00
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
@@ -1873,6 +1873,7 @@ def derivs(x,t):
     return yout
 
 
+@cbook.deprecated('2.2')
 def bivariate_normal(X, Y, sigmax=1.0, sigmay=1.0,
                      mux=0.0, muy=0.0, sigmaxy=0.0):
     """
diff --git a/lib/matplotlib/tests/test_contour.py b/lib/matplotlib/tests/test_contour.py
@@ -4,7 +4,6 @@
 import datetime
 
 import numpy as np
-from matplotlib import mlab
 from matplotlib.testing.decorators import image_comparison
 from matplotlib import pyplot as plt
 from numpy.testing import assert_array_almost_equal
@@ -243,8 +242,10 @@ def test_labels():
     x = np.arange(-3.0, 3.0, delta)
     y = np.arange(-2.0, 2.0, delta)
     X, Y = np.meshgrid(x, y)
-    Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
-    Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+    Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi)
+    Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) /
+          (2 * np.pi * 0.5 * 1.5))
+
     # difference of Gaussians
     Z = 10.0 * (Z2 - Z1)
 
diff --git a/lib/matplotlib/tests/test_image.py b/lib/matplotlib/tests/test_image.py
@@ -608,8 +608,9 @@ def test_rotate_image():
     delta = 0.25
     x = y = np.arange(-3.0, 3.0, delta)
     X, Y = np.meshgrid(x, y)
-    Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
-    Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+    Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi)
+    Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) /
+          (2 * np.pi * 0.5 * 1.5))
     Z = Z2 - Z1  # difference of Gaussians
 
     fig, ax1 = plt.subplots(1, 1)
@@ -670,8 +671,9 @@ def test_mask_image_over_under():
     delta = 0.025
     x = y = np.arange(-3.0, 3.0, delta)
     X, Y = np.meshgrid(x, y)
-    Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
-    Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
+    Z1 = np.exp(-(X**2 + Y**2) / 2) / (2 * np.pi)
+    Z2 = (np.exp(-(((X - 1) / 1.5)**2 + ((Y - 1) / 0.5)**2) / 2) /
+          (2 * np.pi * 0.5 * 1.5))
     Z = 10*(Z2 - Z1)  # difference of Gaussians
 
     palette = copy(plt.cm.gray)
