matplotlib · tacaswell · Feb 5, 2018 · Sep 8, 2017 · Dec 24, 2017 · Dec 24, 2017
diff --git a/examples/api/affine_image.py b/examples/api/affine_image.py
@@ -9,7 +9,6 @@
 """
 
 import numpy as np
-import matplotlib.mlab as mlab
 import matplotlib.pyplot as plt
 import matplotlib.transforms as mtransforms
 
@@ -18,9 +17,9 @@ def get_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)
-    Z = Z2 - Z1  # difference of Gaussians
+    Z1 = np.exp(-X**2 - Y**2)
+    Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+    Z = (Z1 - Z2)
     return Z
 
 

diff --git a/examples/frontpage/contour.py b/examples/frontpage/contour.py
@@ -8,27 +8,27 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-from matplotlib import mlab, cm
+from matplotlib import cm
 
 extent = (-3, 3, -3, 3)
 
 delta = 0.5
 x = np.arange(-3.0, 4.001, delta)
 y = np.arange(-4.0, 3.001, delta)
 X, Y = np.meshgrid(x, y)
-Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, -0.5)
-Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
-Z = (Z1 - Z2) * 10
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = Z1 - Z2
 
-levels = np.linspace(-2.0, 1.601, 40)
 norm = cm.colors.Normalize(vmax=abs(Z).max(), vmin=-abs(Z).max())
 
 fig, ax = plt.subplots()
 cset1 = ax.contourf(
-    X, Y, Z, levels,
+    X, Y, Z, 40,
     norm=norm)
-ax.set_xlim(-3, 3)
-ax.set_ylim(-3, 3)
+ax.set_xlim(-2, 2)
+ax.set_ylim(-2, 2)
 ax.set_xticks([])
 ax.set_yticks([])
 fig.savefig("contour_frontpage.png", dpi=25)  # results in 160x120 px image
+plt.show()
diff --git a/examples/images_contours_and_fields/contour_demo.py b/examples/images_contours_and_fields/contour_demo.py
@@ -11,7 +11,6 @@
 import matplotlib
 import numpy as np
 import matplotlib.cm as cm
-import matplotlib.mlab as mlab
 import matplotlib.pyplot as plt
 
 matplotlib.rcParams['xtick.direction'] = 'out'
@@ -21,10 +20,9 @@
 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)
-# difference of Gaussians
-Z = 10.0 * (Z2 - Z1)
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 ###############################################################################
 # Create a simple contour plot with labels using default colors.  The

diff --git a/examples/images_contours_and_fields/contour_image.py b/examples/images_contours_and_fields/contour_image.py
@@ -13,7 +13,7 @@
 """
 import matplotlib.pyplot as plt
 import numpy as np
-from matplotlib import mlab, cm
+from matplotlib import cm
 
 # Default delta is large because that makes it fast, and it illustrates
 # the correct registration between image and contours.
@@ -24,9 +24,9 @@
 x = np.arange(-3.0, 4.001, delta)
 y = np.arange(-4.0, 3.001, 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)
-Z = (Z1 - Z2) * 10
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 # Boost the upper limit to avoid truncation errors.
 levels = np.arange(-2.0, 1.601, 0.4)

diff --git a/examples/images_contours_and_fields/contour_label_demo.py b/examples/images_contours_and_fields/contour_label_demo.py
@@ -11,7 +11,6 @@
 import matplotlib
 import numpy as np
 import matplotlib.cm as cm
-import matplotlib.mlab as mlab
 import matplotlib.ticker as ticker
 import matplotlib.pyplot as plt
 
@@ -25,10 +24,9 @@
 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)
-# difference of Gaussians
-Z = 10.0 * (Z2 - Z1)
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 ###############################################################################
 # Make contour labels using creative float classes

diff --git a/examples/images_contours_and_fields/contourf_demo.py b/examples/images_contours_and_fields/contourf_demo.py
@@ -14,9 +14,9 @@
 
 x = y = np.arange(-3.0, 3.01, delta)
 X, Y = np.meshgrid(x, y)
-Z1 = plt.mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
-Z2 = plt.mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
-Z = 10 * (Z1 - Z2)
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 nr, nc = Z.shape
 

diff --git a/examples/images_contours_and_fields/contourf_log.py b/examples/images_contours_and_fields/contourf_log.py
@@ -10,19 +10,19 @@
 import numpy as np
 from numpy import ma
 from matplotlib import ticker, cm
-from matplotlib.mlab import bivariate_normal
 
 N = 100
 x = np.linspace(-3.0, 3.0, N)
 y = np.linspace(-2.0, 2.0, N)
 
 X, Y = np.meshgrid(x, y)
 
-# A low hump with a spike coming out of the top right.
+# A low hump with a spike coming out.
 # Needs to have z/colour axis on a log scale so we see both hump and spike.
 # linear scale only shows the spike.
-z = (bivariate_normal(X, Y, 0.1, 0.2, 1.0, 1.0)
-     + 0.1 * bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0))
+Z1 = np.exp(-(X)**2 - (Y)**2)
+Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)
+z = Z1 + 50 * Z2
 
 # Put in some negative values (lower left corner) to cause trouble with logs:
 z[:5, :5] = -1
@@ -39,10 +39,10 @@
 
 # Alternatively, you can manually set the levels
 # and the norm:
-#lev_exp = np.arange(np.floor(np.log10(z.min())-1),
+# lev_exp = np.arange(np.floor(np.log10(z.min())-1),
 #                    np.ceil(np.log10(z.max())+1))
-#levs = np.power(10, lev_exp)
-#cs = P.contourf(X, Y, z, levs, norm=colors.LogNorm())
+# levs = np.power(10, lev_exp)
+# cs = P.contourf(X, Y, z, levs, norm=colors.LogNorm())
 
 # The 'extend' kwarg does not work yet with a log scale.
 

diff --git a/examples/images_contours_and_fields/griddata_demo.py b/examples/images_contours_and_fields/griddata_demo.py
diff --git a/examples/images_contours_and_fields/image_demo.py b/examples/images_contours_and_fields/image_demo.py
@@ -14,7 +14,6 @@
 
 import numpy as np
 import matplotlib.cm as cm
-import matplotlib.mlab as mlab
 import matplotlib.pyplot as plt
 import matplotlib.cbook as cbook
 from matplotlib.path import Path
@@ -26,9 +25,9 @@
 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)
-Z = Z2 - Z1  # difference of Gaussians
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 im = plt.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn,
                 origin='lower', extent=[-3, 3, -3, 3],
@@ -156,9 +155,9 @@
 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)
-Z = Z2 - Z1  # difference of Gaussians
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 path = Path([[0, 1], [1, 0], [0, -1], [-1, 0], [0, 1]])
 patch = PathPatch(path, facecolor='none')

diff --git a/examples/images_contours_and_fields/image_masked.py b/examples/images_contours_and_fields/image_masked.py
@@ -13,17 +13,16 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.colors as colors
-import matplotlib.mlab as mlab
 
 # compute some interesting data
 x0, x1 = -5, 5
 y0, y1 = -3, 3
 x = np.linspace(x0, x1, 500)
 y = np.linspace(y0, y1, 500)
 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)
-Z = 10*(Z2 - Z1)  # difference of Gaussians
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 # Set up a colormap:
 # use copy so that we do not mutate the global colormap instance

diff --git a/examples/images_contours_and_fields/pcolor_demo.py b/examples/images_contours_and_fields/pcolor_demo.py
@@ -11,8 +11,6 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.colors import LogNorm
-from matplotlib.mlab import bivariate_normal
-
 
 ###############################################################################
 # A simple pcolor demo
@@ -91,19 +89,20 @@
 N = 100
 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
 
-# A low hump with a spike coming out of the top right.
+# A low hump with a spike coming out.
 # Needs to have z/colour axis on a log scale so we see both hump and spike.
 # linear scale only shows the spike.
-Z1 = (bivariate_normal(X, Y, 0.1, 0.2, 1.0, 1.0) +
-      0.1 * bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0))
+Z1 = np.exp(-(X)**2 - (Y)**2)
+Z2 = np.exp(-(X * 10)**2 - (Y * 10)**2)
+Z = Z1 + 50 * Z2
 
 fig, (ax0, ax1) = plt.subplots(2, 1)
 
-c = ax0.pcolor(X, Y, Z1,
-               norm=LogNorm(vmin=Z1.min(), vmax=Z1.max()), cmap='PuBu_r')
+c = ax0.pcolor(X, Y, Z,
+               norm=LogNorm(vmin=Z.min(), vmax=Z.max()), cmap='PuBu_r')
 fig.colorbar(c, ax=ax0)
 
-c = ax1.pcolor(X, Y, Z1, cmap='PuBu_r')
+c = ax1.pcolor(X, Y, Z, cmap='PuBu_r')
 fig.colorbar(c, ax=ax1)
 
 plt.show()
diff --git a/examples/images_contours_and_fields/tricontour_vs_griddata.py b/examples/images_contours_and_fields/tricontour_vs_griddata.py
diff --git a/examples/lines_bars_and_markers/interp_demo.py b/examples/lines_bars_and_markers/interp_demo.py
@@ -6,13 +6,12 @@
 """
 import matplotlib.pyplot as plt
 import numpy as np
-from matplotlib.mlab import stineman_interp
 
-x = np.linspace(0, 2*np.pi, 20)
+x = np.linspace(0, 2 * np.pi, 20)
 y = np.sin(x)
 yp = None
 xi = np.linspace(x[0], x[-1], 100)
-yi = stineman_interp(xi, x, y, yp)
+yi = np.interp(xi, x, y, yp)
 
 fig, ax = plt.subplots()
 ax.plot(x, y, 'o', xi, yi, '.')

diff --git a/examples/misc/demo_agg_filter.py b/examples/misc/demo_agg_filter.py
@@ -8,7 +8,6 @@
 
 import numpy as np
 import matplotlib.cm as cm
-import matplotlib.mlab as mlab
 import matplotlib.transforms as mtransforms
 from matplotlib.colors import LightSource
 from matplotlib.artist import Artist
@@ -187,10 +186,9 @@ def filtered_text(ax):
     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)
-    # difference of Gaussians
-    Z = 10.0 * (Z2 - Z1)
+    Z1 = np.exp(-X**2 - Y**2)
+    Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+    Z = (Z1 - Z2) * 2
 
     # draw
     im = ax.imshow(Z, interpolation='bilinear', origin='lower',