diff --git a/examples/api/affine_image.py b/examples/api/affine_image.py
index 9ce1cff0563a..67606d932daa 100644
--- 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
index 9622cc015088..ddb0721c1203 100644
--- a/examples/frontpage/contour.py
+++ b/examples/frontpage/contour.py
@@ -8,7 +8,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
-from matplotlib import mlab, cm
+from matplotlib import cm
 
 extent = (-3, 3, -3, 3)
 
@@ -16,19 +16,19 @@
 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
index 95a63ebd690c..4689d3507f64 100644
--- 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
index 1fc1bbc95b05..3f0f007dcca4 100644
--- 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
index 3b1d5b3188cf..7140b9b13a44 100644
--- 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
index e719a2655d35..41523cfdf7c2 100644
--- 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
index 53d56d775cca..96ee55c51215 100644
--- a/examples/images_contours_and_fields/contourf_log.py
+++ b/examples/images_contours_and_fields/contourf_log.py
@@ -10,7 +10,6 @@
 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)
@@ -18,11 +17,12 @@
 
 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
deleted file mode 100644
index 540e5119256a..000000000000
--- a/examples/images_contours_and_fields/griddata_demo.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""
-=============
-Griddata Demo
-=============
-
-Example showing plotting of non uniform data points in the form of grid.
-"""
-from matplotlib.mlab import griddata
-import matplotlib.pyplot as plt
-import numpy as np
-
-# make up data.
-random_state = np.random.RandomState(19680801)
-
-npts = 200
-x = random_state.uniform(-2, 2, npts)
-y = random_state.uniform(-2, 2, npts)
-z = x*np.exp(-x**2 - y**2)
-# define grid.
-xi = np.linspace(-2.1, 2.1, 100)
-yi = np.linspace(-2.1, 2.1, 200)
-# grid the data.
-zi = griddata(x, y, z, xi, yi, interp='linear')
-# contour the gridded data, plotting dots at the nonuniform data points.
-CS = plt.contour(xi, yi, zi, 15, linewidths=0.5, colors='k')
-CS = plt.contourf(xi, yi, zi, 15,
-                  vmax=abs(zi).max(), vmin=-abs(zi).max())
-plt.colorbar()  # draw colorbar
-# plot data points.
-plt.scatter(x, y, marker='o', s=5, zorder=10)
-plt.xlim(-2, 2)
-plt.ylim(-2, 2)
-plt.title('griddata test (%d points)' % npts)
-plt.show()
diff --git a/examples/images_contours_and_fields/image_demo.py b/examples/images_contours_and_fields/image_demo.py
index 6400f6391109..b6d8eaed9ffe 100644
--- 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
index f94c5be1cb15..029201ef1a1b 100644
--- a/examples/images_contours_and_fields/image_masked.py
+++ b/examples/images_contours_and_fields/image_masked.py
@@ -13,7 +13,6 @@
 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
@@ -21,9 +20,9 @@
 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
index 5a055c1d9f33..37927f241318 100644
--- 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
deleted file mode 100644
index 42d1065005d0..000000000000
--- a/examples/images_contours_and_fields/tricontour_vs_griddata.py
+++ /dev/null
@@ -1,56 +0,0 @@
-"""
-======================
-Tricontour Vs Griddata
-======================
-
-Comparison of griddata and tricontour for an unstructured triangular grid.
-"""
-from __future__ import print_function
-import matplotlib.pyplot as plt
-import matplotlib.tri as tri
-import numpy as np
-import matplotlib.mlab as mlab
-import time
-
-np.random.seed(0)
-npts = 200
-ngridx = 100
-ngridy = 200
-x = np.random.uniform(-2, 2, npts)
-y = np.random.uniform(-2, 2, npts)
-z = x * np.exp(-x**2 - y**2)
-
-# griddata and contour.
-start = time.clock()
-plt.subplot(211)
-xi = np.linspace(-2.1, 2.1, ngridx)
-yi = np.linspace(-2.1, 2.1, ngridy)
-zi = mlab.griddata(x, y, z, xi, yi, interp='linear')
-plt.contour(xi, yi, zi, 15, linewidths=0.5, colors='k')
-plt.contourf(xi, yi, zi, 15,
-             norm=plt.Normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))
-plt.colorbar()  # draw colorbar
-plt.plot(x, y, 'ko', ms=3)
-plt.xlim(-2, 2)
-plt.ylim(-2, 2)
-plt.title('griddata and contour (%d points, %d grid points)' %
-          (npts, ngridx * ngridy))
-print('griddata and contour: %f seconds' % (time.clock() - start))
-
-# tricontour.
-start = time.clock()
-plt.subplot(212)
-triang = tri.Triangulation(x, y)
-plt.tricontour(x, y, z, 15, linewidths=0.5, colors='k')
-plt.tricontourf(x, y, z, 15,
-                norm=plt.Normalize(vmax=abs(zi).max(), vmin=-abs(zi).max()))
-plt.colorbar()
-plt.plot(x, y, 'ko', ms=3)
-plt.xlim(-2, 2)
-plt.ylim(-2, 2)
-plt.title('tricontour (%d points)' % npts)
-print('tricontour: %f seconds' % (time.clock() - start))
-
-plt.subplots_adjust(hspace=0.5)
-
-plt.show()
diff --git a/examples/lines_bars_and_markers/interp_demo.py b/examples/lines_bars_and_markers/interp_demo.py
index 4fcab49f4b94..ad85dfdead94 100644
--- 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
index b058e242e17d..1c00c92e4265 100644
--- 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',
diff --git a/examples/misc/hyperlinks_sgskip.py b/examples/misc/hyperlinks_sgskip.py
index 6f1b1cea0dab..5298d45cdc0e 100644
--- a/examples/misc/hyperlinks_sgskip.py
+++ b/examples/misc/hyperlinks_sgskip.py
@@ -12,7 +12,6 @@
 
 import numpy as np
 import matplotlib.cm as cm
-import matplotlib.mlab as mlab
 import matplotlib.pyplot as plt
 
 ###############################################################################
@@ -28,9 +27,9 @@
 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.gray,
                 origin='lower', extent=[-3, 3, -3, 3])
diff --git a/examples/misc/rec_groupby_demo.py b/examples/misc/rec_groupby_demo.py
deleted file mode 100644
index 9cf108a3acde..000000000000
--- a/examples/misc/rec_groupby_demo.py
+++ /dev/null
@@ -1,71 +0,0 @@
-"""
-================
-Rec Groupby Demo
-================
-
-"""
-from __future__ import print_function
-import numpy as np
-import matplotlib.mlab as mlab
-import matplotlib.cbook as cbook
-
-datafile = cbook.get_sample_data('msft.csv', asfileobj=False)
-print('loading', datafile)
-r = mlab.csv2rec(datafile)
-r.sort()
-
-
-def daily_return(prices):
-    'an array of daily returns from price array'
-    g = np.zeros_like(prices)
-    g[1:] = (prices[1:] - prices[:-1])/prices[:-1]
-    return g
-
-
-def volume_code(volume):
-    'code the continuous volume data categorically'
-    ind = np.searchsorted([1e5, 1e6, 5e6, 10e6, 1e7], volume)
-    return ind
-
-# a list of (dtype_name, summary_function, output_dtype_name).
-# rec_summarize will call on each function on the indicated recarray
-# attribute, and the result assigned to output name in the return
-# record array.
-summaryfuncs = (
-    ('date', lambda x: [thisdate.year for thisdate in x], 'years'),
-    ('date', lambda x: [thisdate.month for thisdate in x], 'months'),
-    ('date', lambda x: [thisdate.weekday() for thisdate in x], 'weekday'),
-    ('adj_close', daily_return, 'dreturn'),
-    ('volume', volume_code, 'volcode'),
-    )
-
-rsum = mlab.rec_summarize(r, summaryfuncs)
-
-# stats is a list of (dtype_name, function, output_dtype_name).
-# rec_groupby will summarize the attribute identified by the
-# dtype_name over the groups in the groupby list, and assign the
-# result to the output_dtype_name
-stats = (
-    ('dreturn', len, 'rcnt'),
-    ('dreturn', np.mean, 'rmean'),
-    ('dreturn', np.median, 'rmedian'),
-    ('dreturn', np.std, 'rsigma'),
-    )
-
-# you can summarize over a single variable, like years or months
-print('summary by years')
-ry = mlab.rec_groupby(rsum, ('years',), stats)
-print(mlab. rec2txt(ry))
-
-print('summary by months')
-rm = mlab.rec_groupby(rsum, ('months',), stats)
-print(mlab.rec2txt(rm))
-
-# or over multiple variables like years and months
-print('summary by year and month')
-rym = mlab.rec_groupby(rsum, ('years', 'months'), stats)
-print(mlab.rec2txt(rym))
-
-print('summary by volume')
-rv = mlab.rec_groupby(rsum, ('volcode',), stats)
-print(mlab.rec2txt(rv))
diff --git a/examples/pyplots/whats_new_98_4_fill_between.py b/examples/pyplots/whats_new_98_4_fill_between.py
index 6805d76dd2d1..ed4b7f4ac7d5 100644
--- a/examples/pyplots/whats_new_98_4_fill_between.py
+++ b/examples/pyplots/whats_new_98_4_fill_between.py
@@ -4,7 +4,6 @@
 =============================
 
 """
-import matplotlib.mlab as mlab
 import matplotlib.pyplot as plt
 import numpy as np
 
diff --git a/examples/pyplots/whats_new_99_axes_grid.py b/examples/pyplots/whats_new_99_axes_grid.py
index c6539f338e88..73c75daf5798 100644
--- a/examples/pyplots/whats_new_99_axes_grid.py
+++ b/examples/pyplots/whats_new_99_axes_grid.py
@@ -8,32 +8,31 @@
 import matplotlib.pyplot as plt
 from mpl_toolkits.axes_grid1.axes_rgb import RGBAxes
 
+
 def get_demo_image():
     # prepare image
     delta = 0.5
 
-    extent = (-3,4,-4,3)
+    extent = (-3, 4, -4, 3)
     x = np.arange(-3.0, 4.001, delta)
     y = np.arange(-4.0, 3.001, delta)
     X, Y = np.meshgrid(x, y)
-    import matplotlib.mlab as mlab
-    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
 
     return Z, extent
 
 
-
 def get_rgb():
     Z, extent = get_demo_image()
 
-    Z[Z<0] = 0.
-    Z = Z/Z.max()
+    Z[Z < 0] = 0.
+    Z = Z / Z.max()
 
-    R = Z[:13,:13]
-    G = Z[2:,2:]
-    B = Z[:13,2:]
+    R = Z[:13, :13]
+    G = Z[2:, 2:]
+    B = Z[:13, 2:]
 
     return R, G, B
 
diff --git a/examples/statistics/hexbin_demo.py b/examples/statistics/hexbin_demo.py
index 33446d686549..e04c68458bdd 100644
--- a/examples/statistics/hexbin_demo.py
+++ b/examples/statistics/hexbin_demo.py
@@ -7,13 +7,12 @@
 
 Hexbin is an axes method or pyplot function that is essentially
 a pcolor of a 2-D histogram with hexagonal cells.  It can be
-much more informative than a scatter plot. In the first subplot
+much more informative than a scatter plot. In the first plot
 below, try substituting 'scatter' for 'hexbin'.
 """
 
 import numpy as np
 import matplotlib.pyplot as plt
-import matplotlib.mlab as mlab
 
 # Fixing random state for reproducibility
 np.random.seed(19680801)
@@ -43,53 +42,3 @@
 cb.set_label('log10(N)')
 
 plt.show()
-
-
-###############################################################################
-# Below we'll simulate some 2-D probability distributions, and show how to
-# visualize them with Matplotlib.
-
-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
-
-x = X.ravel()
-y = Y.ravel()
-z = Z.ravel()
-
-# make some points 20 times more common than others, but same mean
-xcond = (-1 < x) & (x < 1)
-ycond = (-2 < y) & (y < 0)
-cond = xcond & ycond
-xnew = x[cond]
-ynew = y[cond]
-znew = z[cond]
-for i in range(20):
-    x = np.hstack((x, xnew))
-    y = np.hstack((y, ynew))
-    z = np.hstack((z, znew))
-
-xmin = x.min()
-xmax = x.max()
-ymin = y.min()
-ymax = y.max()
-
-gridsize = 30
-
-fig, (ax0, ax1) = plt.subplots(2, 1)
-
-c = ax0.hexbin(x, y, C=z, gridsize=gridsize, marginals=True, cmap=plt.cm.RdBu,
-               vmax=abs(z).max(), vmin=-abs(z).max())
-ax0.axis([xmin, xmax, ymin, ymax])
-cb = fig.colorbar(c, ax=ax0)
-cb.set_label('mean value')
-
-c = ax1.hexbin(x, y, gridsize=gridsize, cmap=plt.cm.Blues_r)
-ax1.axis([xmin, xmax, ymin, ymax])
-cb = fig.colorbar(c, ax=ax1)
-cb.set_label('N observations')
-
-plt.show()
diff --git a/examples/statistics/histogram_cumulative.py b/examples/statistics/histogram_cumulative.py
index 3f533ed63d22..b027cb3066da 100644
--- a/examples/statistics/histogram_cumulative.py
+++ b/examples/statistics/histogram_cumulative.py
@@ -5,8 +5,7 @@
 
 This shows how to plot a cumulative, normalized histogram as a
 step function in order to visualize the empirical cumulative
-distribution function (CDF) of a sample. We also use the ``mlab``
-module to show the theoretical CDF.
+distribution function (CDF) of a sample. We also show the theoretical CDF.
 
 A couple of other options to the ``hist`` function are demonstrated.
 Namely, we use the ``normed`` parameter to normalize the histogram and
@@ -37,7 +36,6 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib import mlab
 
 np.random.seed(19680801)
 
@@ -53,7 +51,9 @@
                            cumulative=True, label='Empirical')
 
 # Add a line showing the expected distribution.
-y = mlab.normpdf(bins, mu, sigma).cumsum()
+y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
+     np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
+y = y.cumsum()
 y /= y[-1]
 
 ax.plot(bins, y, 'k--', linewidth=1.5, label='Theoretical')
diff --git a/examples/statistics/histogram_features.py b/examples/statistics/histogram_features.py
index 8c056c49d17c..1199bdb6f74d 100644
--- a/examples/statistics/histogram_features.py
+++ b/examples/statistics/histogram_features.py
@@ -20,7 +20,6 @@
 """
 
 import numpy as np
-import matplotlib.mlab as mlab
 import matplotlib.pyplot as plt
 
 np.random.seed(19680801)
@@ -35,10 +34,11 @@
 fig, ax = plt.subplots()
 
 # the histogram of the data
-n, bins, patches = ax.hist(x, num_bins, normed=1)
+n, bins, patches = ax.hist(x, num_bins, density=1)
 
 # add a 'best fit' line
-y = mlab.normpdf(bins, mu, sigma)
+y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
+     np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
 ax.plot(bins, y, '--')
 ax.set_xlabel('Smarts')
 ax.set_ylabel('Probability density')
diff --git a/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py b/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py
index 9b42003527e0..1757a1245b5f 100644
--- a/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py
+++ b/examples/user_interfaces/histogram_demo_canvasagg_sgskip.py
@@ -14,7 +14,6 @@
 """
 from matplotlib.backends.backend_agg import FigureCanvasAgg
 from matplotlib.figure import Figure
-from matplotlib.mlab import normpdf
 import numpy as np
 
 fig = Figure(figsize=(5, 4), dpi=100)
@@ -29,7 +28,8 @@
 n, bins, patches = ax.hist(x, 50, density=True)
 
 # add a 'best fit' line
-y = normpdf(bins, mu, sigma)
+y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
+     np.exp(-0.5 * (1 / sigma * (bins - mu))**2))
 line, = ax.plot(bins, y, 'r--')
 line.set_linewidth(1)
 
diff --git a/examples/userdemo/colormap_normalizations.py b/examples/userdemo/colormap_normalizations.py
index c898a594c3a6..b13d7f213cf5 100644
--- a/examples/userdemo/colormap_normalizations.py
+++ b/examples/userdemo/colormap_normalizations.py
@@ -9,7 +9,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.colors as colors
-from matplotlib.mlab import bivariate_normal
 
 ###############################################################################
 # Lognorm: Instead of pcolor log10(Z1) you can have colorbars that have
@@ -18,21 +17,22 @@
 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.  Needs to have
+# A low hump with a spike coming out of the top.  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, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolor(X, Y, Z1,
-                   norm=colors.LogNorm(vmin=Z1.min(), vmax=Z1.max()),
+pcm = ax[0].pcolor(X, Y, Z,
+                   norm=colors.LogNorm(vmin=Z.min(), vmax=Z.max()),
                    cmap='PuBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='max')
 
-pcm = ax[1].pcolor(X, Y, Z1, cmap='PuBu_r')
+pcm = ax[1].pcolor(X, Y, Z, cmap='PuBu_r')
 fig.colorbar(pcm, ax=ax[1], extend='max')
 
 
@@ -45,7 +45,7 @@
 
 fig, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolormesh(X, Y, Z1, norm=colors.PowerNorm(gamma=1./2.),
+pcm = ax[0].pcolormesh(X, Y, Z1, norm=colors.PowerNorm(gamma=1. / 2.),
                        cmap='PuBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='max')
 
@@ -61,9 +61,9 @@
 # Note that colorbar labels do not come out looking very good.
 
 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
-Z1 = (bivariate_normal(X, Y, 1., 1., 1.0, 1.0))**2  \
-    - 0.4 * (bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0))**2
-Z1 = Z1/0.03
+Z1 = 5 * np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 fig, ax = plt.subplots(2, 1)
 
@@ -83,9 +83,9 @@
 # from the positive.
 
 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
-Z1 = (bivariate_normal(X, Y, 1., 1., 1.0, 1.0))**2  \
-    - 0.4 * (bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0))**2
-Z1 = Z1/0.03
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 # Example of making your own norm.  Also see matplotlib.colors.
 # From Joe Kington: This one gives two different linear ramps:
@@ -101,15 +101,17 @@ def __call__(self, value, clip=None):
         # simple example...
         x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
         return np.ma.masked_array(np.interp(value, x, y))
+
+
 #####
 fig, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolormesh(X, Y, Z1,
+pcm = ax[0].pcolormesh(X, Y, Z,
                        norm=MidpointNormalize(midpoint=0.),
                        cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='both')
 
-pcm = ax[1].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1))
+pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z))
 fig.colorbar(pcm, ax=ax[1], extend='both')
 
 ###############################################################################
@@ -122,7 +124,7 @@ def __call__(self, value, clip=None):
 # even bounds gives a contour-like effect
 bounds = np.linspace(-1, 1, 10)
 norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
-pcm = ax[0].pcolormesh(X, Y, Z1,
+pcm = ax[0].pcolormesh(X, Y, Z,
                        norm=norm,
                        cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='both', orientation='vertical')
@@ -130,10 +132,10 @@ def __call__(self, value, clip=None):
 # uneven bounds changes the colormapping:
 bounds = np.array([-0.25, -0.125, 0, 0.5, 1])
 norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
-pcm = ax[1].pcolormesh(X, Y, Z1, norm=norm, cmap='RdBu_r')
+pcm = ax[1].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[1], extend='both', orientation='vertical')
 
-pcm = ax[2].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1))
+pcm = ax[2].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z1))
 fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical')
 
 plt.show()
diff --git a/examples/userdemo/colormap_normalizations_bounds.py b/examples/userdemo/colormap_normalizations_bounds.py
index 9e977af020e9..d37113b9dd37 100644
--- a/examples/userdemo/colormap_normalizations_bounds.py
+++ b/examples/userdemo/colormap_normalizations_bounds.py
@@ -9,13 +9,12 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.colors as colors
-from matplotlib.mlab import bivariate_normal
 
 N = 100
 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
-Z1 = (bivariate_normal(X, Y, 1., 1., 1.0, 1.0))**2  \
-    - 0.4 * (bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0))**2
-Z1 = Z1/0.03
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 '''
 BoundaryNorm: For this one you provide the boundaries for your colors,
@@ -28,7 +27,7 @@
 # even bounds gives a contour-like effect
 bounds = np.linspace(-1, 1, 10)
 norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
-pcm = ax[0].pcolormesh(X, Y, Z1,
+pcm = ax[0].pcolormesh(X, Y, Z,
                        norm=norm,
                        cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='both', orientation='vertical')
@@ -36,10 +35,10 @@
 # uneven bounds changes the colormapping:
 bounds = np.array([-0.25, -0.125, 0, 0.5, 1])
 norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
-pcm = ax[1].pcolormesh(X, Y, Z1, norm=norm, cmap='RdBu_r')
+pcm = ax[1].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[1], extend='both', orientation='vertical')
 
-pcm = ax[2].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1))
+pcm = ax[2].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z))
 fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical')
 
 plt.show()
diff --git a/examples/userdemo/colormap_normalizations_custom.py b/examples/userdemo/colormap_normalizations_custom.py
index ef630a74df90..2eb042f1f4c5 100644
--- a/examples/userdemo/colormap_normalizations_custom.py
+++ b/examples/userdemo/colormap_normalizations_custom.py
@@ -9,7 +9,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.colors as colors
-from matplotlib.mlab import bivariate_normal
 
 N = 100
 '''
@@ -18,14 +17,13 @@
 from the positive.
 '''
 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
-Z1 = (bivariate_normal(X, Y, 1., 1., 1.0, 1.0))**2  \
-    - 0.4 * (bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0))**2
-Z1 = Z1/0.03
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
+
 
 # Example of making your own norm.  Also see matplotlib.colors.
 # From Joe Kington: This one gives two different linear ramps:
-
-
 class MidpointNormalize(colors.Normalize):
     def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
         self.midpoint = midpoint
@@ -36,15 +34,17 @@ def __call__(self, value, clip=None):
         # simple example...
         x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
         return np.ma.masked_array(np.interp(value, x, y))
+
+
 #####
 fig, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolormesh(X, Y, Z1,
+pcm = ax[0].pcolormesh(X, Y, Z,
                        norm=MidpointNormalize(midpoint=0.),
                        cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='both')
 
-pcm = ax[1].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1))
+pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z))
 fig.colorbar(pcm, ax=ax[1], extend='both')
 
 plt.show()
diff --git a/examples/userdemo/colormap_normalizations_lognorm.py b/examples/userdemo/colormap_normalizations_lognorm.py
index 9b3910866149..5b2f7c3668af 100644
--- a/examples/userdemo/colormap_normalizations_lognorm.py
+++ b/examples/userdemo/colormap_normalizations_lognorm.py
@@ -9,7 +9,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.colors as colors
-from matplotlib.mlab import bivariate_normal
 
 '''
 Lognorm: Instead of pcolor log10(Z1) you can have colorbars that have
@@ -21,17 +20,16 @@
 # A low hump with a spike coming out of the top right.  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)
+Z = np.exp(-X**2 - Y**2)
 
 fig, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolor(X, Y, Z1,
-                   norm=colors.LogNorm(vmin=Z1.min(), vmax=Z1.max()),
+pcm = ax[0].pcolor(X, Y, Z,
+                   norm=colors.LogNorm(vmin=Z.min(), vmax=Z.max()),
                    cmap='PuBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='max')
 
-pcm = ax[1].pcolor(X, Y, Z1, cmap='PuBu_r')
+pcm = ax[1].pcolor(X, Y, Z, cmap='PuBu_r')
 fig.colorbar(pcm, ax=ax[1], extend='max')
 
 plt.show()
diff --git a/examples/userdemo/colormap_normalizations_power.py b/examples/userdemo/colormap_normalizations_power.py
index 569c8a55cd69..77f052b28ac6 100644
--- a/examples/userdemo/colormap_normalizations_power.py
+++ b/examples/userdemo/colormap_normalizations_power.py
@@ -9,7 +9,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.colors as colors
-from matplotlib.mlab import bivariate_normal
 
 N = 100
 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
diff --git a/examples/userdemo/colormap_normalizations_symlognorm.py b/examples/userdemo/colormap_normalizations_symlognorm.py
index b038ca69e96a..780381e43da8 100644
--- a/examples/userdemo/colormap_normalizations_symlognorm.py
+++ b/examples/userdemo/colormap_normalizations_symlognorm.py
@@ -9,7 +9,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.colors as colors
-from matplotlib.mlab import bivariate_normal
 
 """
 SymLogNorm: two humps, one negative and one positive, The positive
@@ -22,19 +21,19 @@
 
 N = 100
 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
-Z1 = (bivariate_normal(X, Y, 1., 1., 1.0, 1.0)**2 -
-      0.4 * bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0)**2)
-Z1 = Z1 / 0.03
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 fig, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolormesh(X, Y, Z1,
+pcm = ax[0].pcolormesh(X, Y, Z,
                        norm=colors.SymLogNorm(linthresh=0.03, linscale=0.03,
                                               vmin=-1.0, vmax=1.0),
                        cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='both')
 
-pcm = ax[1].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1))
+pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z))
 fig.colorbar(pcm, ax=ax[1], extend='both')
 
 plt.show()
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
index 4dedcfc3872f..3abad6b80fef 100644
--- a/lib/matplotlib/axes/_axes.py
+++ b/lib/matplotlib/axes/_axes.py
@@ -5051,7 +5051,7 @@ def fill_between(self, x, y1, y2=0, where=None, interpolate=False,
         x, y1, y2 = np.broadcast_arrays(np.atleast_1d(x), y1, y2)
 
         polys = []
-        for ind0, ind1 in mlab.contiguous_regions(where):
+        for ind0, ind1 in cbook.contiguous_regions(where):
             xslice = x[ind0:ind1]
             y1slice = y1[ind0:ind1]
             y2slice = y2[ind0:ind1]
@@ -5232,7 +5232,7 @@ def fill_betweenx(self, y, x1, x2=0, where=None,
         y, x1, x2 = np.broadcast_arrays(np.atleast_1d(y), x1, x2)
 
         polys = []
-        for ind0, ind1 in mlab.contiguous_regions(where):
+        for ind0, ind1 in cbook.contiguous_regions(where):
             yslice = y[ind0:ind1]
             x1slice = x1[ind0:ind1]
             x2slice = x2[ind0:ind1]
diff --git a/lib/matplotlib/cbook/__init__.py b/lib/matplotlib/cbook/__init__.py
index 96c33fb3adb2..c90e42e9f641 100644
--- a/lib/matplotlib/cbook/__init__.py
+++ b/lib/matplotlib/cbook/__init__.py
@@ -1952,6 +1952,7 @@ def unmasked_index_ranges(mask, compressed=True):
 ls_mapper_r = {v: k for k, v in six.iteritems(ls_mapper)}
 
 
+@deprecated('2.2')
 def align_iterators(func, *iterables):
     """
     This generator takes a bunch of iterables that are ordered by func
@@ -1996,6 +1997,32 @@ def __call__(self, key):
             break
 
 
+def contiguous_regions(mask):
+    """
+    Return a list of (ind0, ind1) such that mask[ind0:ind1].all() is
+    True and we cover all such regions
+    """
+    mask = np.asarray(mask, dtype=bool)
+
+    if not mask.size:
+        return []
+
+    # Find the indices of region changes, and correct offset
+    idx, = np.nonzero(mask[:-1] != mask[1:])
+    idx += 1
+
+    # List operations are faster for moderately sized arrays
+    idx = idx.tolist()
+
+    # Add first and/or last index if needed
+    if mask[0]:
+        idx = [0] + idx
+    if mask[-1]:
+        idx.append(len(mask))
+
+    return list(zip(idx[::2], idx[1::2]))
+
+
 def is_math_text(s):
     # Did we find an even number of non-escaped dollar signs?
     # If so, treat is as math text.
diff --git a/lib/matplotlib/collections.py b/lib/matplotlib/collections.py
index a22f1522957a..f56e6cc2e066 100644
--- a/lib/matplotlib/collections.py
+++ b/lib/matplotlib/collections.py
@@ -24,7 +24,7 @@
 import numpy as np
 import matplotlib as mpl
 from . import (_path, artist, cbook, cm, colors as mcolors, docstring,
-               lines as mlines, mlab, path as mpath, transforms)
+               lines as mlines, path as mpath, transforms)
 
 CIRCLE_AREA_FACTOR = 1.0 / np.sqrt(np.pi)
 
@@ -1040,7 +1040,7 @@ def span_where(x, ymin, ymax, where, **kwargs):
         passed on to the collection.
         """
         xranges = []
-        for ind0, ind1 in mlab.contiguous_regions(where):
+        for ind0, ind1 in cbook.contiguous_regions(where):
             xslice = x[ind0:ind1]
             if not len(xslice):
                 continue
diff --git a/lib/matplotlib/contour.py b/lib/matplotlib/contour.py
index 4c54786dd6a8..d09c3f273f5f 100644
--- a/lib/matplotlib/contour.py
+++ b/lib/matplotlib/contour.py
@@ -20,7 +20,6 @@
 import matplotlib.font_manager as font_manager
 import matplotlib.text as text
 import matplotlib.cbook as cbook
-import matplotlib.mlab as mlab
 import matplotlib.mathtext as mathtext
 import matplotlib.patches as mpatches
 import matplotlib.texmanager as texmanager
@@ -377,7 +376,7 @@ def calc_label_rot_and_inline(self, slc, ind, lw, lc=None, spacing=5):
         not empty (lc defaults to the empty list if None).  *spacing*
         is the space around the label in pixels to leave empty.
 
-        Do both of these tasks at once to avoid calling mlab.path_length
+        Do both of these tasks at once to avoid calculating path lengths
         multiple times, which is relatively costly.
 
         The method used here involves calculating the path length
@@ -392,7 +391,7 @@ def calc_label_rot_and_inline(self, slc, ind, lw, lc=None, spacing=5):
         hlw = lw / 2.0
 
         # Check if closed and, if so, rotate contour so label is at edge
-        closed = mlab.is_closed_polygon(slc)
+        closed = _is_closed_polygon(slc)
         if closed:
             slc = np.r_[slc[ind:-1], slc[:ind + 1]]
 
@@ -401,8 +400,10 @@ def calc_label_rot_and_inline(self, slc, ind, lw, lc=None, spacing=5):
 
             ind = 0
 
-        # Path length in pixel space
-        pl = mlab.path_length(slc)
+        # Calculate path lengths
+        pl = np.zeros(slc.shape[0], dtype=float)
+        dx = np.diff(slc, axis=0)
+        pl[1:] = np.cumsum(np.hypot(dx[:, 0], dx[:, 1]))
         pl = pl - pl[ind]
 
         # Use linear interpolation to get points around label
@@ -627,7 +628,7 @@ def labels(self, inline, inline_spacing):
                 # zero in print_label and locate_label.  Other than these
                 # functions, this is not necessary and should probably be
                 # eventually removed.
-                if mlab.is_closed_polygon(lc):
+                if _is_closed_polygon(lc):
                     slc = np.r_[slc0, slc0[1:2, :]]
                 else:
                     slc = slc0
@@ -688,6 +689,15 @@ def _find_closest_point_on_leg(p1, p2, p0):
     return d, pc
 
 
+def _is_closed_polygon(X):
+    """
+    Tests whether first and last object in a sequence are the same.  These are
+    presumably coordinates on a polygonal curve, in which case this function
+    tests if that curve is closed.
+    """
+    return np.all(X[0] == X[-1])
+
+
 def _find_closest_point_on_path(lc, point):
     """
     lc: coordinates of vertices
@@ -702,7 +712,7 @@ def _find_closest_point_on_path(lc, point):
     xcmin = None
     legmin = (None, None)
 
-    closed = mlab.is_closed_polygon(lc)
+    closed = _is_closed_polygon(lc)
 
     # build list of legs before and after this vertex
     legs = []
diff --git a/lib/matplotlib/mlab.py b/lib/matplotlib/mlab.py
index fe1811a12764..45731ee6b222 100644
--- a/lib/matplotlib/mlab.py
+++ b/lib/matplotlib/mlab.py
@@ -186,6 +186,7 @@
     long = int
 
 
+@cbook.deprecated("2.2", alternative='np.logspace, np.geomspace')
 def logspace(xmin, xmax, N):
     '''
     Return N values logarithmically spaced between xmin and xmax.
@@ -194,14 +195,6 @@ def logspace(xmin, xmax, N):
     return np.exp(np.linspace(np.log(xmin), np.log(xmax), N))
 
 
-def _norm(x):
-    '''
-    Return sqrt(x dot x).
-
-    '''
-    return np.sqrt(np.dot(x, x))
-
-
 def window_hanning(x):
     '''
     Return x times the hanning window of len(x).
@@ -1348,10 +1341,12 @@ def cohere(x, y, NFFT=256, Fs=2, detrend=detrend_none, window=window_hanning,
     return Cxy, f
 
 
+@cbook.deprecated('2.2')
 def donothing_callback(*args):
     pass
 
 
+@cbook.deprecated('2.2')
 def cohere_pairs(X, ij, NFFT=256, Fs=2, detrend=detrend_none,
                  window=window_hanning, noverlap=0,
                  preferSpeedOverMemory=True,
@@ -1509,6 +1504,7 @@ def cohere_pairs(X, ij, NFFT=256, Fs=2, detrend=detrend_none,
         return Cxy, Phase, freqs
 
 
+@cbook.deprecated('2.2')
 def entropy(y, bins):
     r"""
     Return the entropy of the data in *y* in units of nat.
@@ -1538,18 +1534,21 @@ def entropy(y, bins):
     return S
 
 
+@cbook.deprecated('2.2')
 def normpdf(x, *args):
     "Return the normal pdf evaluated at *x*; args provides *mu*, *sigma*"
     mu, sigma = args
     return 1./(np.sqrt(2*np.pi)*sigma)*np.exp(-0.5 * (1./sigma*(x - mu))**2)
 
 
+@cbook.deprecated('2.2')
 def find(condition):
     "Return the indices where ravel(condition) is true"
     res, = np.nonzero(np.ravel(condition))
     return res
 
 
+@cbook.deprecated('2.2')
 def longest_contiguous_ones(x):
     """
     Return the indices of the longest stretch of contiguous ones in *x*,
@@ -1577,11 +1576,13 @@ def longest_contiguous_ones(x):
     return ind
 
 
+@cbook.deprecated('2.2')
 def longest_ones(x):
     '''alias for longest_contiguous_ones'''
     return longest_contiguous_ones(x)
 
 
+@cbook.deprecated('2.2')
 class PCA(object):
     def __init__(self, a, standardize=True):
         """
@@ -1717,6 +1718,7 @@ def _get_colinear():
         return a
 
 
+@cbook.deprecated('2.2')
 def prctile(x, p=(0.0, 25.0, 50.0, 75.0, 100.0)):
     """
     Return the percentiles of *x*.  *p* can either be a sequence of
@@ -1756,6 +1758,7 @@ def _interpolate(a, b, fraction):
     return _interpolate(values[ai], values[bi], frac)
 
 
+@cbook.deprecated('2.2')
 def prctile_rank(x, p):
     """
     Return the rank for each element in *x*, return the rank
@@ -1781,6 +1784,7 @@ def prctile_rank(x, p):
     return np.searchsorted(ptiles, x)
 
 
+@cbook.deprecated('2.2')
 def center_matrix(M, dim=0):
     """
     Return the matrix *M* with each row having zero mean and unit std.
@@ -1797,6 +1801,7 @@ def center_matrix(M, dim=0):
     return M
 
 
+@cbook.deprecated('2.2')
 def rk4(derivs, y0, t):
     """
     Integrate 1D or ND system of ODEs using 4-th order Runge-Kutta.
@@ -1868,6 +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):
     """
@@ -1886,6 +1892,7 @@ def bivariate_normal(X, Y, sigmax=1.0, sigmay=1.0,
     return np.exp(-z/(2*(1-rho**2))) / denom
 
 
+@cbook.deprecated('2.2')
 def get_xyz_where(Z, Cond):
     """
     *Z* and *Cond* are *M* x *N* matrices.  *Z* are data and *Cond* is
@@ -1898,6 +1905,7 @@ def get_xyz_where(Z, Cond):
     return X[Cond], Y[Cond], Z[Cond]
 
 
+@cbook.deprecated('2.2')
 def get_sparse_matrix(M, N, frac=0.1):
     """
     Return a *M* x *N* sparse matrix with *frac* elements randomly
@@ -1911,6 +1919,7 @@ def get_sparse_matrix(M, N, frac=0.1):
     return data
 
 
+@cbook.deprecated('2.2')
 def dist(x, y):
     """
     Return the distance between two points.
@@ -1919,6 +1928,7 @@ def dist(x, y):
     return np.sqrt(np.dot(d, d))
 
 
+@cbook.deprecated('2.2')
 def dist_point_to_segment(p, s0, s1):
     """
     Get the distance of a point to a segment.
@@ -1947,6 +1957,7 @@ def dist_point_to_segment(p, s0, s1):
     return dist(p, pb)
 
 
+@cbook.deprecated('2.2')
 def segments_intersect(s1, s2):
     """
     Return *True* if *s1* and *s2* intersect.
@@ -1973,6 +1984,7 @@ def segments_intersect(s1, s2):
     return 0.0 <= u1 <= 1.0 and 0.0 <= u2 <= 1.0
 
 
+@cbook.deprecated('2.2')
 def fftsurr(x, detrend=detrend_none, window=window_none):
     """
     Compute an FFT phase randomized surrogate of *x*.
@@ -1988,6 +2000,7 @@ def fftsurr(x, detrend=detrend_none, window=window_none):
     return np.fft.ifft(z).real
 
 
+@cbook.deprecated('2.2')
 def movavg(x, n):
     """
     Compute the len(*n*) moving average of *x*.
@@ -2048,6 +2061,7 @@ def movavg(x, n):
 exp_safe_MAX = 1.7976931348623157e+308
 
 
+@cbook.deprecated("2.2")
 def exp_safe(x):
     """
     Compute exponentials which safely underflow to zero.
@@ -2063,6 +2077,7 @@ def exp_safe(x):
         return math.exp(x)
 
 
+@cbook.deprecated("2.2", alternative='np.array(list(map(...)))')
 def amap(fn, *args):
     """
     amap(function, sequence[, sequence, ...]) -> array.
@@ -2073,6 +2088,7 @@ def amap(fn, *args):
     return np.array(list(map(fn, *args)))
 
 
+@cbook.deprecated("2.2")
 def rms_flat(a):
     """
     Return the root mean square of all the elements of *a*, flattened out.
@@ -2080,6 +2096,7 @@ def rms_flat(a):
     return np.sqrt(np.mean(np.abs(a) ** 2))
 
 
+@cbook.deprecated("2.2", alternative='np.linalg.norm(a, ord=1)')
 def l1norm(a):
     """
     Return the *l1* norm of *a*, flattened out.
@@ -2089,6 +2106,7 @@ def l1norm(a):
     return np.sum(np.abs(a))
 
 
+@cbook.deprecated("2.2", alternative='np.linalg.norm(a, ord=2)')
 def l2norm(a):
     """
     Return the *l2* norm of *a*, flattened out.
@@ -2098,6 +2116,7 @@ def l2norm(a):
     return np.sqrt(np.sum(np.abs(a) ** 2))
 
 
+@cbook.deprecated("2.2", alternative='np.linalg.norm(a.flat, ord=p)')
 def norm_flat(a, p=2):
     """
     norm(a,p=2) -> l-p norm of a.flat
@@ -2115,6 +2134,7 @@ def norm_flat(a, p=2):
         return np.sum(np.abs(a) ** p) ** (1 / p)
 
 
+@cbook.deprecated("2.2")
 def frange(xini, xfin=None, delta=None, **kw):
     """
     frange([start,] stop[, step, keywords]) -> array of floats
@@ -2182,6 +2202,7 @@ def frange(xini, xfin=None, delta=None, **kw):
 # end frange()
 
 
+@cbook.deprecated("2.2")
 def identity(n, rank=2, dtype='l', typecode=None):
     """
     Returns the identity matrix of shape (*n*, *n*, ..., *n*) (rank *r*).
@@ -2208,6 +2229,7 @@ def identity(n, rank=2, dtype='l', typecode=None):
     return iden
 
 
+@cbook.deprecated("2.2")
 def base_repr(number, base=2, padding=0):
     """
     Return the representation of a *number* in any given *base*.
@@ -2223,6 +2245,7 @@ def base_repr(number, base=2, padding=0):
                       max(padding - 1, max_exponent)))
 
 
+@cbook.deprecated("2.2")
 def binary_repr(number, max_length=1025):
     """
     Return the binary representation of the input *number* as a
@@ -2245,6 +2268,7 @@ def binary_repr(number, max_length=1025):
     return ''.join(map(repr, digits)).replace('L', '')
 
 
+@cbook.deprecated("2.2")
 def log2(x, ln2=math.log(2.0)):
     """
     Return the log(*x*) in base 2.
@@ -2263,6 +2287,7 @@ def log2(x, ln2=math.log(2.0)):
             return len(bin_n)
 
 
+@cbook.deprecated("2.2")
 def ispower2(n):
     """
     Returns the log base 2 of *n* if *n* is a power of 2, zero otherwise.
@@ -2277,6 +2302,7 @@ def ispower2(n):
         return len(bin_n)
 
 
+@cbook.deprecated("2.2")
 def isvector(X):
     """
     Like the MATLAB function with the same name, returns *True*
@@ -2293,7 +2319,7 @@ def isvector(X):
 
 
 # helpers for loading, saving, manipulating and viewing numpy record arrays
-
+@cbook.deprecated("2.2")
 def safe_isnan(x):
     ':func:`numpy.isnan` for arbitrary types'
     if isinstance(x, six.string_types):
@@ -2308,6 +2334,7 @@ def safe_isnan(x):
         return b
 
 
+@cbook.deprecated("2.2")
 def safe_isinf(x):
     ':func:`numpy.isinf` for arbitrary types'
     if isinstance(x, six.string_types):
@@ -2322,6 +2349,7 @@ def safe_isinf(x):
         return b
 
 
+@cbook.deprecated("2.2")
 def rec_append_fields(rec, names, arrs, dtypes=None):
     """
     Return a new record array with field names populated with data
@@ -2358,6 +2386,7 @@ def rec_append_fields(rec, names, arrs, dtypes=None):
     return newrec
 
 
+@cbook.deprecated("2.2")
 def rec_drop_fields(rec, names):
     """
     Return a new numpy record array with fields in *names* dropped.
@@ -2375,6 +2404,7 @@ def rec_drop_fields(rec, names):
     return newrec
 
 
+@cbook.deprecated("2.2")
 def rec_keep_fields(rec, names):
     """
     Return a new numpy record array with only fields listed in names
@@ -2390,6 +2420,7 @@ def rec_keep_fields(rec, names):
     return np.rec.fromarrays(arrays, names=names)
 
 
+@cbook.deprecated("2.2")
 def rec_groupby(r, groupby, stats):
     """
     *r* is a numpy record array
@@ -2433,6 +2464,7 @@ def rec_groupby(r, groupby, stats):
     return np.rec.fromrecords(rows, names=names)
 
 
+@cbook.deprecated("2.2")
 def rec_summarize(r, summaryfuncs):
     """
     *r* is a numpy record array
@@ -2455,6 +2487,7 @@ def rec_summarize(r, summaryfuncs):
     return np.rec.fromarrays(arrays, names=names)
 
 
+@cbook.deprecated("2.2")
 def rec_join(key, r1, r2, jointype='inner', defaults=None, r1postfix='1',
              r2postfix='2'):
     """
@@ -2598,6 +2631,7 @@ def mapped_r2field(name):
     return newrec
 
 
+@cbook.deprecated("2.2")
 def recs_join(key, name, recs, jointype='outer', missing=0., postfixes=None):
     """
     Join a sequence of record arrays on single column key.
@@ -2656,6 +2690,7 @@ def extract(r):
     return np.rec.fromrecords(results, names=names)
 
 
+@cbook.deprecated("2.2")
 def csv2rec(fname, comments='#', skiprows=0, checkrows=0, delimiter=',',
             converterd=None, names=None, missing='', missingd=None,
             use_mrecords=False, dayfirst=False, yearfirst=False):
@@ -2867,7 +2902,7 @@ def get_converters(reader, comments):
             break
 
         # remove these chars
-        delete = set(r"""~!@#$%^&*()-=+~\|]}[{';: /?.>,<""")
+        delete = set(r"""~!@#$%^&*()-=+~\|}[]{';: /?.>,<""")
         delete.add('"')
 
         names = []
@@ -2937,6 +2972,7 @@ def get_converters(reader, comments):
 
 
 # a series of classes for describing the format intentions of various rec views
+@cbook.deprecated("2.2")
 class FormatObj(object):
     def tostr(self, x):
         return self.toval(x)
@@ -2955,12 +2991,14 @@ def __hash__(self):
         return hash(self.__class__)
 
 
+@cbook.deprecated("2.2")
 class FormatString(FormatObj):
     def tostr(self, x):
         val = repr(x)
         return val[1:-1]
 
 
+@cbook.deprecated("2.2")
 class FormatFormatStr(FormatObj):
     def __init__(self, fmt):
         self.fmt = fmt
@@ -2971,6 +3009,7 @@ def tostr(self, x):
         return self.fmt % self.toval(x)
 
 
+@cbook.deprecated("2.2")
 class FormatFloat(FormatFormatStr):
     def __init__(self, precision=4, scale=1.):
         FormatFormatStr.__init__(self, '%%1.%df' % precision)
@@ -2989,6 +3028,7 @@ def fromstr(self, s):
         return float(s)/self.scale
 
 
+@cbook.deprecated("2.2")
 class FormatInt(FormatObj):
 
     def tostr(self, x):
@@ -3001,6 +3041,7 @@ def fromstr(self, s):
         return int(s)
 
 
+@cbook.deprecated("2.2")
 class FormatBool(FormatObj):
     def toval(self, x):
         return str(x)
@@ -3009,21 +3050,25 @@ def fromstr(self, s):
         return bool(s)
 
 
+@cbook.deprecated("2.2")
 class FormatPercent(FormatFloat):
     def __init__(self, precision=4):
         FormatFloat.__init__(self, precision, scale=100.)
 
 
+@cbook.deprecated("2.2")
 class FormatThousands(FormatFloat):
     def __init__(self, precision=4):
         FormatFloat.__init__(self, precision, scale=1e-3)
 
 
+@cbook.deprecated("2.2")
 class FormatMillions(FormatFloat):
     def __init__(self, precision=4):
         FormatFloat.__init__(self, precision, scale=1e-6)
 
 
+@cbook.deprecated("2.2", alternative='date.strftime')
 class FormatDate(FormatObj):
     def __init__(self, fmt):
         self.fmt = fmt
@@ -3041,6 +3086,7 @@ def fromstr(self, x):
         return dateutil.parser.parse(x).date()
 
 
+@cbook.deprecated("2.2", alternative='datetime.strftime')
 class FormatDatetime(FormatDate):
     def __init__(self, fmt='%Y-%m-%d %H:%M:%S'):
         FormatDate.__init__(self, fmt)
@@ -3050,20 +3096,19 @@ def fromstr(self, x):
         return dateutil.parser.parse(x)
 
 
-defaultformatd = {
-    np.bool_:   FormatBool(),
-    np.int16:   FormatInt(),
-    np.int32:   FormatInt(),
-    np.int64:   FormatInt(),
-    np.float32: FormatFloat(),
-    np.float64: FormatFloat(),
-    np.object_: FormatObj(),
-    np.string_: FormatString(),
-    }
-
-
+@cbook.deprecated("2.2")
 def get_formatd(r, formatd=None):
     'build a formatd guaranteed to have a key for every dtype name'
+    defaultformatd = {
+        np.bool_: FormatBool(),
+        np.int16: FormatInt(),
+        np.int32: FormatInt(),
+        np.int64: FormatInt(),
+        np.float32: FormatFloat(),
+        np.float64: FormatFloat(),
+        np.object_: FormatObj(),
+        np.string_: FormatString()}
+
     if formatd is None:
         formatd = dict()
 
@@ -3076,6 +3121,7 @@ def get_formatd(r, formatd=None):
     return formatd
 
 
+@cbook.deprecated("2.2")
 def csvformat_factory(format):
     format = copy.deepcopy(format)
     if isinstance(format, FormatFloat):
@@ -3084,6 +3130,7 @@ def csvformat_factory(format):
     return format
 
 
+@cbook.deprecated("2.2", alternative='np.recarray.tofile')
 def rec2txt(r, header=None, padding=3, precision=3, fields=None):
     """
     Returns a textual representation of a record array.
@@ -3204,6 +3251,7 @@ def format(item, just_pad_prec_spacer):
     return text
 
 
+@cbook.deprecated("2.2", alternative='np.recarray.tofile')
 def rec2csv(r, fname, delimiter=',', formatd=None, missing='',
             missingd=None, withheader=True):
     """
@@ -3275,9 +3323,10 @@ def newfunc(val, mask, mval):
         fh.close()
 
 
+@cbook.deprecated('2.2')
 def griddata(x, y, z, xi, yi, interp='nn'):
-    """Interpolates from a nonuniformly spaced grid to some other
-    grid.
+    """
+    Interpolates from a nonuniformly spaced grid to some other grid.
 
     Fits a surface of the form z = f(`x`, `y`) to the data in the
     (usually) nonuniformly spaced vectors (`x`, `y`, `z`), then
@@ -3444,6 +3493,7 @@ def less_simple_linear_interpolation(x, y, xi, extrap=False):
     return yi
 
 
+@cbook.deprecated("2.2")
 def slopes(x, y):
     """
     :func:`slopes` calculates the slope *y*'(*x*)
@@ -3485,6 +3535,7 @@ def slopes(x, y):
     return yp
 
 
+@cbook.deprecated("2.2")
 def stineman_interp(xi, x, y, yp=None):
     """
     Given data vectors *x* and *y*, the slope vector *yp* and a new
@@ -3738,6 +3789,7 @@ def evaluate(self, points):
 ##################################################
 # Code related to things in and around polygons
 ##################################################
+@cbook.deprecated("2.2")
 def inside_poly(points, verts):
     """
     *points* is a sequence of *x*, *y* points.
@@ -3753,6 +3805,7 @@ def inside_poly(points, verts):
     return [idx for idx, p in enumerate(points) if poly.contains_point(p)]
 
 
+@cbook.deprecated("2.2")
 def poly_below(xmin, xs, ys):
     """
     Given a sequence of *xs* and *ys*, return the vertices of a
@@ -3783,6 +3836,7 @@ def poly_below(xmin, xs, ys):
     return x, y
 
 
+@cbook.deprecated("2.2")
 def poly_between(x, ylower, yupper):
     """
     Given a sequence of *x*, *ylower* and *yupper*, return the polygon
@@ -3810,6 +3864,7 @@ def poly_between(x, ylower, yupper):
     return x, y
 
 
+@cbook.deprecated('2.2')
 def is_closed_polygon(X):
     """
     Tests whether first and last object in a sequence are the same.  These are
@@ -3819,32 +3874,16 @@ def is_closed_polygon(X):
     return np.all(X[0] == X[-1])
 
 
+@cbook.deprecated("2.2", message='Moved to matplotlib.cbook')
 def contiguous_regions(mask):
     """
     return a list of (ind0, ind1) such that mask[ind0:ind1].all() is
     True and we cover all such regions
     """
-    mask = np.asarray(mask, dtype=bool)
-
-    if not mask.size:
-        return []
-
-    # Find the indices of region changes, and correct offset
-    idx, = np.nonzero(mask[:-1] != mask[1:])
-    idx += 1
-
-    # List operations are faster for moderately sized arrays
-    idx = idx.tolist()
-
-    # Add first and/or last index if needed
-    if mask[0]:
-        idx = [0] + idx
-    if mask[-1]:
-        idx.append(len(mask))
-
-    return list(zip(idx[::2], idx[1::2]))
+    return cbook.contiguous_regions(mask)
 
 
+@cbook.deprecated("2.2")
 def cross_from_below(x, threshold):
     """
     return the indices into *x* where *x* crosses some threshold from
@@ -3886,6 +3925,7 @@ def cross_from_below(x, threshold):
         return ind
 
 
+@cbook.deprecated("2.2")
 def cross_from_above(x, threshold):
     """
     return the indices into *x* where *x* crosses some threshold from
@@ -3909,6 +3949,7 @@ def cross_from_above(x, threshold):
 ##################################################
 # Vector and path length geometry calculations
 ##################################################
+@cbook.deprecated('2.2')
 def vector_lengths(X, P=2., axis=None):
     """
     Finds the length of a set of vectors in *n* dimensions.  This is
@@ -3923,6 +3964,7 @@ def vector_lengths(X, P=2., axis=None):
     return (np.sum(X**(P), axis=axis))**(1./P)
 
 
+@cbook.deprecated('2.2')
 def distances_along_curve(X):
     """
     Computes the distance between a set of successive points in *N* dimensions.
@@ -3935,6 +3977,7 @@ def distances_along_curve(X):
     return vector_lengths(X, axis=1)
 
 
+@cbook.deprecated('2.2')
 def path_length(X):
     """
     Computes the distance travelled along a polygonal curve in *N* dimensions.
@@ -3947,6 +3990,7 @@ def path_length(X):
     return np.concatenate((np.zeros(1), np.cumsum(X)))
 
 
+@cbook.deprecated('2.2')
 def quad2cubic(q0x, q0y, q1x, q1y, q2x, q2y):
     """
     Converts a quadratic Bezier curve to a cubic approximation.
@@ -3964,6 +4008,7 @@ def quad2cubic(q0x, q0y, q1x, q1y, q2x, q2y):
     return q0x, q0y, c1x, c1y, c2x, c2y, q2x, q2y
 
 
+@cbook.deprecated("2.2")
 def offset_line(y, yerr):
     """
     Offsets an array *y* by +/- an error and returns a tuple
diff --git a/lib/matplotlib/tests/test_contour.py b/lib/matplotlib/tests/test_contour.py
index c7b4732408f4..35d33b972532 100644
--- a/lib/matplotlib/tests/test_contour.py
+++ b/lib/matplotlib/tests/test_contour.py
@@ -3,7 +3,6 @@
 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
@@ -260,8 +259,10 @@ 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
index ff9b7d238792..569b7a3808b8 100644
--- a/lib/matplotlib/tests/test_image.py
+++ b/lib/matplotlib/tests/test_image.py
@@ -13,7 +13,7 @@
 from numpy.testing import assert_array_equal
 
 from matplotlib import (
-    colors, image as mimage, mlab, patches, pyplot as plt,
+    colors, image as mimage, patches, pyplot as plt,
     rc_context, rcParams)
 from matplotlib.image import (AxesImage, BboxImage, FigureImage,
                               NonUniformImage, PcolorImage)
@@ -644,8 +644,9 @@ 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)
@@ -706,8 +707,9 @@ 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)
diff --git a/lib/matplotlib/tests/test_mlab.py b/lib/matplotlib/tests/test_mlab.py
index a2909ac7db1b..cd432f713b6c 100644
--- a/lib/matplotlib/tests/test_mlab.py
+++ b/lib/matplotlib/tests/test_mlab.py
@@ -14,6 +14,7 @@
 
 import matplotlib.mlab as mlab
 import matplotlib.cbook as cbook
+from matplotlib.cbook.deprecation import MatplotlibDeprecationWarning
 
 
 try:
@@ -23,9 +24,18 @@
     HAS_NATGRID = False
 
 
+'''
+A lot of mlab.py has been deprecated in Matplotlib 2.2 and is scheduled for
+removal in the future. The tests that use deprecated methods have a block
+to catch the deprecation warning, and can be removed with the mlab code is
+removed.
+'''
+
+
 def test_colinear_pca():
-    a = mlab.PCA._get_colinear()
-    pca = mlab.PCA(a)
+    with pytest.warns(MatplotlibDeprecationWarning):
+        a = mlab.PCA._get_colinear()
+        pca = mlab.PCA(a)
 
     assert_allclose(pca.fracs[2:], 0., atol=1e-8)
     assert_allclose(pca.Y[:, 2:], 0., atol=1e-8)
@@ -52,17 +62,9 @@ def test_colinear_pca():
     [0, 75, 100],
 ])
 def test_prctile(input, percentile):
-    assert_allclose(mlab.prctile(input, percentile),
-                    np.percentile(input, percentile))
-
-
-def test_norm():
-    np.random.seed(0)
-    N = 1000
-    x = np.random.standard_normal(N)
-    targ = np.linalg.norm(x)
-    res = mlab._norm(x)
-    assert_almost_equal(targ, res)
+    with pytest.warns(MatplotlibDeprecationWarning):
+        assert_allclose(mlab.prctile(input, percentile),
+                        np.percentile(input, percentile))
 
 
 @pytest.mark.parametrize('xmin, xmax, N', [
@@ -77,7 +79,8 @@ def test_norm():
     'single',
 ])
 def test_logspace(xmin, xmax, N):
-    res = mlab.logspace(xmin, xmax, N)
+    with pytest.warns(MatplotlibDeprecationWarning):
+        res = mlab.logspace(xmin, xmax, N)
     targ = np.logspace(np.log10(xmin), np.log10(xmax), N)
     assert_allclose(targ, res)
     assert res.size == N
@@ -227,10 +230,10 @@ def test_recarray_csv_roundtrip(tempcsv):
     expected['x'][:] = np.linspace(-1e9, -1, 99)
     expected['y'][:] = np.linspace(1, 1e9, 99)
     expected['t'][:] = np.linspace(0, 0.01, 99)
-
-    mlab.rec2csv(expected, tempcsv)
-    tempcsv.seek(0)
-    actual = mlab.csv2rec(tempcsv)
+    with pytest.warns(MatplotlibDeprecationWarning):
+        mlab.rec2csv(expected, tempcsv)
+        tempcsv.seek(0)
+        actual = mlab.csv2rec(tempcsv)
 
     assert_allclose(expected['x'], actual['x'])
     assert_allclose(expected['y'], actual['y'])
@@ -242,14 +245,17 @@ def test_rec2csv_bad_shape_ValueError(tempcsv):
                                 (str('y'), float)])
 
     # the bad recarray should trigger a ValueError for having ndim > 1.
-    with pytest.raises(ValueError):
-        mlab.rec2csv(bad, tempcsv)
+    with pytest.warns(MatplotlibDeprecationWarning):
+        with pytest.raises(ValueError):
+            mlab.rec2csv(bad, tempcsv)
 
 
 def test_csv2rec_names_with_comments(tempcsv):
+
     tempcsv.write('# comment\n1,2,3\n4,5,6\n')
     tempcsv.seek(0)
-    array = mlab.csv2rec(tempcsv, names='a,b,c')
+    with pytest.warns(MatplotlibDeprecationWarning):
+        array = mlab.csv2rec(tempcsv, names='a,b,c')
     assert len(array) == 2
     assert len(array.dtype) == 3
 
@@ -282,7 +288,8 @@ def test_csv2rec_dates(tempcsv, input, kwargs):
                 datetime.datetime(2054, 6, 20, 14, 31, 45),
                 datetime.datetime(2000, 10, 31, 11, 50, 23)]
     tempcsv.seek(0)
-    array = mlab.csv2rec(tempcsv, names='a', **kwargs)
+    with pytest.warns(MatplotlibDeprecationWarning):
+        array = mlab.csv2rec(tempcsv, names='a', **kwargs)
     assert_array_equal(array['a'].tolist(), expected)
 
 
@@ -298,7 +305,8 @@ def test_rec2txt_basic():
     truth = ('       x   y   s     s2\n'
              '   1.000   2   foo   bing   \n'
              '   2.000   3   bar   blah   ').splitlines()
-    assert mlab.rec2txt(a).splitlines() == truth
+    with pytest.warns(MatplotlibDeprecationWarning):
+        assert mlab.rec2txt(a).splitlines() == truth
 
 
 class TestWindow(object):
@@ -2160,18 +2168,21 @@ def get_z(x, y):
     z = get_z(x, y)
     xi = [0.2, 0.4, 0.6, 0.8]
     yi = [0.1, 0.3, 0.7, 0.9]
-    zi = mlab.griddata(x, y, z, xi, yi, interp='linear')
+    with pytest.warns(MatplotlibDeprecationWarning):
+        zi = mlab.griddata(x, y, z, xi, yi, interp='linear')
     xi, yi = np.meshgrid(xi, yi)
     np.testing.assert_array_almost_equal(zi, get_z(xi, yi))
 
     # Passing 2D xi and yi arrays to griddata.
-    zi = mlab.griddata(x, y, z, xi, yi, interp='linear')
+    with pytest.warns(MatplotlibDeprecationWarning):
+        zi = mlab.griddata(x, y, z, xi, yi, interp='linear')
     np.testing.assert_array_almost_equal(zi, get_z(xi, yi))
 
     # Masking z array.
     z_masked = np.ma.array(z, mask=[False, False, False, True, False])
     correct_zi_masked = np.ma.masked_where(xi + yi > 1.0, get_z(xi, yi))
-    zi = mlab.griddata(x, y, z_masked, xi, yi, interp='linear')
+    with pytest.warns(MatplotlibDeprecationWarning):
+        zi = mlab.griddata(x, y, z_masked, xi, yi, interp='linear')
     matest.assert_array_almost_equal(zi, correct_zi_masked)
     np.testing.assert_array_equal(np.ma.getmask(zi),
                                   np.ma.getmask(correct_zi_masked))
@@ -2193,24 +2204,28 @@ def get_z(x, y):
                   [0.29999208, 0.8999978, 1.5000029, 2.1000059],
                   [-0.1000099, 0.4999943, 1.0999964, 1.6999979],
                   [-0.3000128, 0.2999894, 0.8999913, 1.4999933]]
-    zi = mlab.griddata(x, y, z, xi, yi, interp='nn')
+    with pytest.warns(MatplotlibDeprecationWarning):
+        zi = mlab.griddata(x, y, z, xi, yi, interp='nn')
     np.testing.assert_array_almost_equal(zi, correct_zi, 5)
 
-    # Decreasing xi or yi should raise ValueError.
-    with pytest.raises(ValueError):
-        mlab.griddata(x, y, z, xi[::-1], yi, interp='nn')
-    with pytest.raises(ValueError):
-        mlab.griddata(x, y, z, xi, yi[::-1], interp='nn')
+    with pytest.warns(MatplotlibDeprecationWarning):
+        # Decreasing xi or yi should raise ValueError.
+        with pytest.raises(ValueError):
+            mlab.griddata(x, y, z, xi[::-1], yi, interp='nn')
+        with pytest.raises(ValueError):
+            mlab.griddata(x, y, z, xi, yi[::-1], interp='nn')
 
     # Passing 2D xi and yi arrays to griddata.
     xi, yi = np.meshgrid(xi, yi)
-    zi = mlab.griddata(x, y, z, xi, yi, interp='nn')
+    with pytest.warns(MatplotlibDeprecationWarning):
+        zi = mlab.griddata(x, y, z, xi, yi, interp='nn')
     np.testing.assert_array_almost_equal(zi, correct_zi, 5)
 
     # Masking z array.
     z_masked = np.ma.array(z, mask=[False, False, False, True, False])
     correct_zi_masked = np.ma.masked_where(xi + yi > 1.0, correct_zi)
-    zi = mlab.griddata(x, y, z_masked, xi, yi, interp='nn')
+    with pytest.warns(MatplotlibDeprecationWarning):
+        zi = mlab.griddata(x, y, z_masked, xi, yi, interp='nn')
     np.testing.assert_array_almost_equal(zi, correct_zi_masked, 5)
     np.testing.assert_array_equal(np.ma.getmask(zi),
                                   np.ma.getmask(correct_zi_masked))
@@ -2414,22 +2429,26 @@ def test_contiguous_regions():
     # Starts and ends with True
     mask = [True]*a + [False]*b + [True]*c
     expected = [(0, a), (a+b, a+b+c)]
-    assert mlab.contiguous_regions(mask) == expected
+    with pytest.warns(MatplotlibDeprecationWarning):
+        assert mlab.contiguous_regions(mask) == expected
     d, e = 6, 7
     # Starts with True ends with False
     mask = mask + [False]*e
-    assert mlab.contiguous_regions(mask) == expected
+    with pytest.warns(MatplotlibDeprecationWarning):
+        assert mlab.contiguous_regions(mask) == expected
     # Starts with False ends with True
     mask = [False]*d + mask[:-e]
     expected = [(d, d+a), (d+a+b, d+a+b+c)]
-    assert mlab.contiguous_regions(mask) == expected
+    with pytest.warns(MatplotlibDeprecationWarning):
+        assert mlab.contiguous_regions(mask) == expected
     # Starts and ends with False
     mask = mask + [False]*e
-    assert mlab.contiguous_regions(mask) == expected
-    # No True in mask
-    assert mlab.contiguous_regions([False]*5) == []
-    # Empty mask
-    assert mlab.contiguous_regions([]) == []
+    with pytest.warns(MatplotlibDeprecationWarning):
+        assert mlab.contiguous_regions(mask) == expected
+        # No True in mask
+        assert mlab.contiguous_regions([False]*5) == []
+        # Empty mask
+        assert mlab.contiguous_regions([]) == []
 
 
 def test_psd_onesided_norm():
diff --git a/lib/matplotlib/widgets.py b/lib/matplotlib/widgets.py
index 5a177dc34690..ac6198ff8587 100644
--- a/lib/matplotlib/widgets.py
+++ b/lib/matplotlib/widgets.py
@@ -19,7 +19,6 @@
 import numpy as np
 from matplotlib import rcParams
 
-from .mlab import dist
 from .patches import Circle, Rectangle, Ellipse
 from .lines import Line2D
 from .transforms import blended_transform_factory
@@ -1032,7 +1031,8 @@ def _clicked(self, event):
 
         def inside(p):
             pcirc = np.array([p.center[0], p.center[1]])
-            return dist(pclicked, pcirc) < p.radius
+            d = pclicked - pcirc
+            return np.sqrt(np.dot(d, d)) < p.radius
 
         for i, (p, t) in enumerate(zip(self.circles, self.labels)):
             if t.get_window_extent().contains(event.x, event.y) or inside(p):
diff --git a/lib/mpl_toolkits/mplot3d/axes3d.py b/lib/mpl_toolkits/mplot3d/axes3d.py
index a7f97bef6cdc..66de8d84574e 100644
--- a/lib/mpl_toolkits/mplot3d/axes3d.py
+++ b/lib/mpl_toolkits/mplot3d/axes3d.py
@@ -2952,13 +2952,12 @@ def get_test_data(delta=0.05):
     '''
     Return a tuple X, Y, Z with a test data set.
     '''
-
-    from matplotlib.mlab import  bivariate_normal
     x = y = np.arange(-3.0, 3.0, delta)
     X, Y = np.meshgrid(x, y)
 
-    Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
-    Z2 = 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
 
     X = X * 10
@@ -2967,7 +2966,6 @@ def get_test_data(delta=0.05):
     return X, Y, Z
 
 
-
 ########################################################
 # Register Axes3D as a 'projection' object available
 # for use just like any other axes
diff --git a/tutorials/colors/colormapnorms.py b/tutorials/colors/colormapnorms.py
index eb344f0164fe..3e60e383d2a1 100644
--- a/tutorials/colors/colormapnorms.py
+++ b/tutorials/colors/colormapnorms.py
@@ -46,7 +46,6 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.colors as colors
-from matplotlib.mlab import bivariate_normal
 
 N = 100
 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
@@ -54,17 +53,18 @@
 # A low hump with a spike coming out of the top right.  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, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolor(X, Y, Z1,
-                   norm=colors.LogNorm(vmin=Z1.min(), vmax=Z1.max()),
+pcm = ax[0].pcolor(X, Y, Z,
+                   norm=colors.LogNorm(vmin=Z.min(), vmax=Z.max()),
                    cmap='PuBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='max')
 
-pcm = ax[1].pcolor(X, Y, Z1, cmap='PuBu_r')
+pcm = ax[1].pcolor(X, Y, Z, cmap='PuBu_r')
 fig.colorbar(pcm, ax=ax[1], extend='max')
 fig.show()
 
@@ -89,19 +89,19 @@
 
 N = 100
 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
-Z1 = (bivariate_normal(X, Y, 1., 1., 1.0, 1.0))**2  \
-    - 0.4 * (bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0))**2
-Z1 = Z1/0.03
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 fig, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolormesh(X, Y, Z1,
+pcm = ax[0].pcolormesh(X, Y, Z,
                        norm=colors.SymLogNorm(linthresh=0.03, linscale=0.03,
                                               vmin=-1.0, vmax=1.0),
                        cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='both')
 
-pcm = ax[1].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1))
+pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z))
 fig.colorbar(pcm, ax=ax[1], extend='both')
 fig.show()
 
@@ -129,7 +129,7 @@
 
 fig, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolormesh(X, Y, Z1, norm=colors.PowerNorm(gamma=1./2.),
+pcm = ax[0].pcolormesh(X, Y, Z1, norm=colors.PowerNorm(gamma=0.5),
                        cmap='PuBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='max')
 
@@ -162,16 +162,16 @@
 
 N = 100
 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
-Z1 = (bivariate_normal(X, Y, 1., 1., 1.0, 1.0))**2  \
-    - 0.4 * (bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0))**2
-Z1 = Z1/0.03
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 fig, ax = plt.subplots(3, 1, figsize=(8, 8))
 ax = ax.flatten()
 # even bounds gives a contour-like effect
 bounds = np.linspace(-1, 1, 10)
 norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
-pcm = ax[0].pcolormesh(X, Y, Z1,
+pcm = ax[0].pcolormesh(X, Y, Z,
                        norm=norm,
                        cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='both', orientation='vertical')
@@ -179,10 +179,10 @@
 # uneven bounds changes the colormapping:
 bounds = np.array([-0.25, -0.125, 0, 0.5, 1])
 norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
-pcm = ax[1].pcolormesh(X, Y, Z1, norm=norm, cmap='RdBu_r')
+pcm = ax[1].pcolormesh(X, Y, Z, norm=norm, cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[1], extend='both', orientation='vertical')
 
-pcm = ax[2].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1))
+pcm = ax[2].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z))
 fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical')
 fig.show()
 
@@ -207,9 +207,9 @@
 
 N = 100
 X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
-Z1 = (bivariate_normal(X, Y, 1., 1., 1.0, 1.0))**2  \
-    - 0.4 * (bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0))**2
-Z1 = Z1/0.03
+Z1 = np.exp(-X**2 - Y**2)
+Z2 = np.exp(-(X - 1)**2 - (Y - 1)**2)
+Z = (Z1 - Z2) * 2
 
 
 class MidpointNormalize(colors.Normalize):
@@ -223,13 +223,14 @@ def __call__(self, value, clip=None):
         x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
         return np.ma.masked_array(np.interp(value, x, y))
 
+
 fig, ax = plt.subplots(2, 1)
 
-pcm = ax[0].pcolormesh(X, Y, Z1,
+pcm = ax[0].pcolormesh(X, Y, Z,
                        norm=MidpointNormalize(midpoint=0.),
                        cmap='RdBu_r')
 fig.colorbar(pcm, ax=ax[0], extend='both')
 
-pcm = ax[1].pcolormesh(X, Y, Z1, cmap='RdBu_r', vmin=-np.max(Z1))
+pcm = ax[1].pcolormesh(X, Y, Z, cmap='RdBu_r', vmin=-np.max(Z))
 fig.colorbar(pcm, ax=ax[1], extend='both')
 fig.show()