Remove soon to be deprecated nan/inf aliases

ksunden · ksunden · commit 2635e13df691 · 2023-08-04T13:47:29.000-05:00
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
@@ -1479,7 +1479,7 @@ def test_pcolorargs():
         ax.pcolormesh(x, y, Z[:-1, :-1], shading="gouraud")
     with pytest.raises(TypeError):
         ax.pcolormesh(X, Y, Z[:-1, :-1], shading="gouraud")
-    x[0] = np.NaN
+    x[0] = np.nan
     with pytest.raises(ValueError):
         ax.pcolormesh(x, y, Z[:-1, :-1])
     with np.errstate(invalid='ignore'):
@@ -7411,7 +7411,7 @@ def test_limits_after_scroll_zoom():
 def test_gettightbbox_ignore_nan():
     fig, ax = plt.subplots()
     remove_ticks_and_titles(fig)
-    ax.text(np.NaN, 1, 'Boo')
+    ax.text(np.nan, 1, 'Boo')
     renderer = fig.canvas.get_renderer()
     np.testing.assert_allclose(ax.get_tightbbox(renderer).width, 496)
 
@@ -8591,7 +8591,7 @@ def test_bar_leading_nan():
     barheights = np.array([0.5, 1.5, 2.0])
     barstarts = np.array([0.77]*3)
 
-    barx[0] = np.NaN
+    barx[0] = np.nan
 
     fig, ax = plt.subplots()
 
diff --git a/lib/matplotlib/tests/test_colors.py b/lib/matplotlib/tests/test_colors.py
@@ -339,7 +339,7 @@ def test_BoundaryNorm():
 
     # Masked arrays
     boundaries = [0, 1.1, 2.2]
-    vals = np.ma.masked_invalid([-1., np.NaN, 0, 1.4, 9])
+    vals = np.ma.masked_invalid([-1., np.nan, 0, 1.4, 9])
 
     # Without interpolation
     ncolors = len(boundaries) - 1
@@ -353,9 +353,9 @@ def test_BoundaryNorm():
     assert_array_equal(bn(vals), expected)
 
     # Non-trivial masked arrays
-    vals = np.ma.masked_invalid([np.Inf, np.NaN])
+    vals = np.ma.masked_invalid([np.inf, np.nan])
     assert np.all(bn(vals).mask)
-    vals = np.ma.masked_invalid([np.Inf])
+    vals = np.ma.masked_invalid([np.inf])
     assert np.all(bn(vals).mask)
 
     # Incompatible extend and clip
diff --git a/lib/matplotlib/tests/test_image.py b/lib/matplotlib/tests/test_image.py
@@ -1376,7 +1376,7 @@ def test_rgba_antialias():
     aa[:, int(N/2):] = a[:, int(N/2):]
 
     # set some over/unders and NaNs
-    aa[20:50, 20:50] = np.NaN
+    aa[20:50, 20:50] = np.nan
     aa[70:90, 70:90] = 1e6
     aa[70:90, 20:30] = -1e6
     aa[70:90, 195:215] = 1e6
